Diabetes Mellitus

Diabetes Mellitus

CHAPTER 86 Diabetes Mellitus Leslie J Raffel Medical Genetics Institute, Cedars-Sinai Medical Center, Pacific Theatres, Los Angeles, CA, USA Mark O...

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CHAPTER

86

Diabetes Mellitus Leslie J Raffel Medical Genetics Institute, Cedars-Sinai Medical Center, Pacific Theatres, Los Angeles, CA, USA

Mark O Goodarzi Division of Endocrinology, Diabetes and Metabolism, Cedars-Sinai Medical Center, Los Angeles, CA, USA

This article is a revision of the previous edition article by Leslie J Raffel, Mark O Goodarzi and Jerome I Rotter, volume 2, pp 1980–2022, © 2007, Elsevier Ltd.

86.1 INTRODUCTION Diabetes mellitus is a diagnostic term for a group of disorders characterized by abnormal glucose homeostasis resulting in elevated blood sugar. There is variability in its manifestations, wherein some individuals have only asymptomatic glucose intolerance, while others present acutely with diabetic ketoacidosis, and still others develop chronic complications such as nephropathy, neuropathy, retinopathy, or accelerated atherosclerosis. It is among the most common of chronic disorders, affecting up to 5–10% of the adult population of the Western world. Its prevalence varies over the globe, with certain populations, including some American Indian tribes and the inhabitants of Micronesia and Polynesia, having extremely high rates of diabetes (1,2). The prevalence of diabetes is increasing dramatically and it has been estimated that the worldwide prevalence will increase by more than 50% between the years 2000 and 2030 (3). It is clearly established that diabetes mellitus is not a single disease but a genetically heterogeneous group of disorders that share glucose intolerance in common (4– 7). The concept of genetic heterogeneity (i.e. that different genetic and/or environmental etiologic factors can result in similar phenotypes) has significantly altered the genetic analysis of this common disorder. Diabetes and glucose intolerance are not diagnostic terms, but, like anemia, simply describe symptoms and/or laboratory abnormalities that can have a number of distinct etiologies.

86.2 DIFFICULTIES IN GENETIC STUDIES OF DIABETES The geneticist is confronted with a number of obstacles in attempting to unravel the genetics of diabetes. These include differences in the definition of affected

individuals, modification of the expression of the diabetic genotype by environmental factors, and variability in the age of onset of the disease. One of the major sources of confusion in the study of diabetes mellitus has been the definition of an “affected” individual. Some investigators have called an individual diabetic only if they have clinical symptoms of the disease, whereas others have accepted a mildly abnormal glucose tolerance test. There is marked clinical variability in diabetes. The phenotypic expression of the diabetes genotype (or genotypes) appears to be modified by a variety of environmental factors, including diet, obesity, infection, and physical activity, as well as sex and parity. Obese individuals with type 2 diabetes may lose all signs of the disorder, clinical as well as chemical, if their weight returns to normal. Because of the marked variability in the age of onset of the disease, at any given time only a fraction of those individuals possessing the diabetic genotype may be recognized. Therefore, it may be impossible to say at any specific time whether a clinically unaffected individual carries the diabetic genotype. Thus, longitudinal studies are required to detect those genetically affected family members who will eventually manifest clinical disease. The high prevalence of diabetes in the population presents additional difficulties for the geneticist. Are relatives affected because they have the same genotype, because they share the same environment, or because they have a chance occurrence of a common disorder? Furthermore, the diabetic syndromes are sufficiently common that two genetically different forms may occasionally occur in the same family. The most important impediment to genetic analysis has been a lack of knowledge concerning the basic defect(s) in each of the disorders leading to diabetes. Because of this, there is no certain method for detecting all individuals with disease-predisposing genotypes

© 2013, Elsevier Ltd. All rights reserved.

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CHAPTER 86  Diabetes Mellitus

before their clinical manifestations, that is, individuals who possess the diabetic genotypes but have no signs of abnormal carbohydrate metabolism. Despite all these obstacles, major strides have been made in delineating the genetic basis of the diabetic syndromes. The main force behind these advances has been genome-wide association studies (GWAS) conducted in thousands to tens of thousands of subjects, typically in cross-sectional case/control cohorts, targeting either type 1 or type 2 diabetes. Such large sample sizes have been able to overcome clinical heterogeneity and have yielded dozens of susceptibility loci for both forms of diabetes.

86.3 DIABETES IN FAMILIES AND TWINS 86.3.1 Familial Aggregation of Diabetes Many authors have shown that diabetics have an “increased family history” of the disease (4). In most reports, the frequency of diabetics with positive family histories of the disease ranges from 25% to 50%. Since the frequency of a positive family history of diabetes in nondiabetic individuals has usually been found to be below 15%, this family history information has been used to support the hypothesis that diabetes mellitus is a hereditary disorder. These types of data, however, are not very powerful. A more accurate method of assessing familial aggregation is to compare diabetes prevalence in specific relatives of affected individuals with that found among similar relatives of nondiabetic controls. Pincus and White (8) were the first to use this method in the study of diabetes, when they statistically established the increased prevalence of the disease among the relatives of diabetics. These findings have since been confirmed by many other investigators (4). Using more sensitive markers of the diabetic genotype, such as oral, intravenous, and cortisone-induced glucose tolerance tests, it has been found that the prevalence of affected individuals among the relatives of diabetics is even higher (usually ranging between 10% and 30% of the parents, sibs, or close relatives, as compared to a prevalence of 1–6% of the relatives of nondiabetic individuals). Thus the prevalence of both clinical diabetes and abnormal glucose tolerance is significantly greater among the close relatives of diabetics than among similar relatives of nondiabetic individuals.

86.3.2 Early Twin Studies Familial aggregation of a trait may be caused by either genetic or environmental factors and twin studies have been used to confirm the importance of genetic factors in the etiology of diabetes. Using clinical diabetes as the criteria for affected, most investigators have reported a concordance rate for monozygotic (MZ) twins between 45% and 96% and for dizygotic twins between 3% and 37%. When type 2 diabetes is considered separately

and glucose tolerance tests are performed in the “nondiabetic” MZ cotwins, the concordance rate is usually above 70%. Thus the concordance of diabetes mellitus in MZ twins is significantly greater than in dizygotic twins, suggesting an important genetic component to disease etiology. The available data suggest that dizygotic twin risk appears to be approximately equivalent to that of siblings, arguing that whatever environmental factors contribute are present in the majority of a given population and suggesting that there is not a large contribution from unique family environments. As will be discussed later, the MZ concordance rates are very different for type 1 and type 2 diabetes.

86.4 GENETIC HETEROGENEITY IN DIABETES Although the evidence from studies of familial aggregation and twins leaves no doubt as to the importance of genetic factors in the etiology of diabetes, for many years there was little agreement as to the nature of the genetic factors involved. This confusion can, in large part, be explained by the genetic heterogeneity that is now known to exist in diabetes. In 1967, the hypothesis of genetic heterogeneity was proposed based on several lines of evidence. Indirect evidence included (1) the existence of distinct, mostly rare genetic disorders (now numbering over 80) that have glucose intolerance as one of their features; (2) genetic heterogeneity in diabetic animal models; (3) ethnic variability in prevalence and clinical features; (4) clinical variability between the thin, ketosis-prone, insulindependent juvenile onset diabetic (type 1) and the obese, nonketotic insulin-resistant adult-onset diabetic (type 2); and (5) physiological variability (i.e. the demonstration of decreased plasma insulin in juvenile versus the relative hyperinsulinism of maturity-onset diabetics). In addition, some direct evidence for heterogeneity came from clinical genetic studies that suggested that juvenile and adultonset diabetes differed genetically within families (9).

86.4.1 Rare Syndrome Associations There are more than 80 distinct genetic disorders associated with glucose intolerance and, in some cases, clinical diabetes (Table 86-1). Although individually rare, these syndromes demonstrate that mutations at many different loci can produce glucose intolerance. Furthermore, they illustrate the wide variety of pathogenetic mechanisms that can result in glucose intolerance. These mechanisms range from absolute insulin deficiency due to pancreatic degeneration (in such disorders as hereditary relapsing pancreatitis, cystic fibrosis, and polyendocrine deficiency disease) to relative insulinopenia (in the growth hormone deficiency syndromes), to inhibition of insulin secretion (in the hereditary pheochromocytoma syndromes associated with elevated catecholamines), to various deficits in the interaction of insulin and its receptor (in the

TAB L E 8 6 - 1    Genetic Syndromes Associated with Glucose Intolerance and Diabetes Mellitus Syndromes

Types of DM

Syndromes associated with pancreatic degeneration Congenital absence of the pancreas Type 1 (congenital) Immunodysregulation, Polyendocrinopathy, and enteropathy, X-linked (IPEX) Congenital pancreatic hypoplasia Permanent neonatal diabetes mellitus with pancreatic and cerebellar agenesis

Type 1 (congenital) Type 1 (infancy) Type 1 (infancy) IGT, Type 1

Cystic fibrosis Polyendocrine deficiency disease (Schmidt syndrome)

IGT, Type 1 Type 1

IgA deficiency, malabsorption and diabetes Hemochromatosis (includes juvenile hemochromatosis (HFE2), HFE3 and HFE4

Type 1 Type 2

Thalassemia α-I-antitrypsin deficiency Cystinosis, Nephropathic

IGT→Type 2 IGT Type 1

Tropical calcific pancreatitis

Type 1

McKusick No.

Genes

AD, ?AR

260370

PDX1

XR

304790

FOXP3

IUGR, pancreatic exocrine deficiency IUGR, cerebellar hypoplasia/agenesis, optic nerve hypoplasia, beaked nose, dysplastic ears, ­triangular face, decreased subcutaneous fat Abdominal pain, chronic pancreatitis, portal and splenic vein thrombosis

?AR AR

260370, 600001 609069

PDX1 PTF1A

AD

167800

Malabsorption, chronic respiratory disease Autoimmune endocrine disease, hypothyroidism, hypoadrenalism, female predominance, usually onset in middle age IgA deficiency, malabsorption Hepatic, pancreatic, skin, cardiac, and endocrine complications of iron storage

AR ?AR, AD

219700 269200

PRSS1 SPINK1 CFTR CFTR

?AD AR

Anemia, iron overload Emphysema, cirrhosis Failure to thrive, Fanconi syndrome and renal failure, photophobia and decreased visual acuity because of corneal crystals, rickets, hepatosplenomegaly, hypopigmentation, primary hypothyroidism Juvenile-onset pancreatitis, insulin-dependent but ketosis-resistant diabetes, pancreatic cancer

AR AR AR

137100 235200 602390 604250 606069 141900 107400 219800

HFE HJV, HAMP TRF2 SLC40A1 Beta Globin SERPINA1 CTNS

AR (may require additional mutations in other genes as well)

608189

SPINK1

AR AD or AR AD Imprinting (hypomethlation) abnormalities of chr. 6q24

601410 610374 610582

ZPF57 ABCC8 KCNJ11

AR AD AD AD or AR

606176

GCK KCNJ11 INS ABCC8

IUGR, poor adipose and muscle, malabsorption, dehydration IUGR, dehydration, ±fatal secretory diarrhea

Disorders causing neonatal diabetes without pancreatic degeneration Transient neonatal diabetes Neonatal→Type 2 Transient neonatal diabetes that resolves at a median age of 3 months, many develop type 2 diabetes later in life, IUGR, macroglossia, abdominal wall defects, brain anomalies, congenital heart disease depending on the gene involved and whether hypomethylation of other genes occurs Permanent neonatal diabetes Neonatal Permanent diabetes beginning in early infancy, IUGR, DEND (developmental delay, epilepsy and neonatal diabetes) associated with KCNJ11 mutations

CHAPTER 86  Diabetes Mellitus

Hereditary pancreatitis

Pattern of Inheritance

Associated Clinical Findings

3 Continued

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TAB L E 8 6 - 1    Genetic Syndromes Associated with Glucose Intolerance and Diabetes Mellitus—cont’d Types of DM Neonatal

Associated Clinical Findings Congenital hypothyroidism and diabetes

Pattern of Inheritance AR

McKusick No. 610199

Genes GLIS3

Neonatal

Cerebellar agenesis and diabetes, dysmorphic facies

AR

609069

PTF1A

Proportionate dwarfism

AD AR AR XR AR

173100 262400 262600 312000 243800

GH1 GH1 PROP1 SOX3 UBR1

AR AR AD

GHR ABCC8 KCNJ11 GCK HADH INSR GLUD1 SLC16A1 VHL RET SDHD SDHB TMEM127 KIF1B MEN1

Hereditary endocrine disorders with glucose intolerance Isolated growth hormone deficiency Type 2 Hereditary panhypopituitary dwarfism

Type 2

Johanson-Blizzard syndrome

Type 1

Laron dwarfism Hyperinsulinemic hypoglycemia, familial

Type 2 Type 2

Pheochromocytoma

IGT

Hypertension, tremor, paroxysmal sweating

AD

262500 256450 601820 602485 609975 609968 606762 610021 171300

Multiple endocrine neoplasia Type 1

IGT

Pituitary (acromegaly), parathyroid (renal stones), pancreatic adenomas (peptic ulcer)

AD

131100

Mental retardation, microcephaly, IUGR, dwarfism, enamel hypoplasia, high blood pyruvate, lactate and alanine Early-onset obesity, malabsorption, diarrhea, ­intestinal villous atrophy, reactive ­hypoglycemia, hypocortisolemia, hypogonadotropic ­hypogonadism, and primary amenorrhea Megaloblastic anemia responsive only to thiamine, sideroblasts, sensorineural ringed syndrome ­deafness, hoarseness, progressive optic ­atrophy, situs inversus, septal defects, generalized ­puffiness, aminoaciduria Blepharospasm, retinal degeneration, ataxia, chorea, torticollis, progressive dementia, mild anemia; onset between age 30–50 years

?AR

202900

AR

600955

PCSK1

AR

249270

SLC19A2

AR

604290

CP

Inborn errors of metabolism with glucose intolerance Alaninuria Type 1 (infancy) (Stimmler syndrome) Proprotein convertase 1 deficiency

Type 2

Thiamine-responsive megaloblastic anemia

Type 2

Aceruloplasminemia

Type 1

Proportionate dwarfism hypogonadism ± TSH and ACTH deficiency Hypoplastic nasal alae, deafness, ­hypothyroidism, growth retardation, mental retardation, ­malabsorption Proportionate dwarfism Hyperinsulinism, hypoglycemia in infancy can evolve into late glucose intolerance

CHAPTER 86  Diabetes Mellitus

Syndromes Neonatal diabetes with congenital ­hypothyroidism Neonatal diabetes with cerebellar agenesis

Leprechaunism (point mutations in insulin receptor gene)

Insulin-resistant

Seemanova syndrome

Insulin-resistant

SHORT syndrome

Insulin-resistant

Rabson-Mendenhall syndrome

Insulin-resistant

Acanthosis nigricans insulin-resistant diabetes syndromes Type A

Insulin-resistant decreased ­receptors Insulin-resistant (postreceptor defect) Insulin-resistant

Type A with acral hypertrophy and cramps Type A with brachydactyly and dental ­anomalies Type A with muscle cramps and coarse facies Type B

Insulin-resistant (postreceptor defect) Insulin-resistant (circulating inhibitor)

IUGR and growth retardation, large hands, feet and genitals, acanthosis nigricans, decreased ­subcutaneous fat, hirsutism Obesity, MR, delayed puberty, macroordchidism, acanthosis nigricans, curly hair Short, hyperextensibility, ocular depression, Rieger anomaly, delayed teething, lipodystrophy Unusual facies, enlarged genitals, precocious puberty, acanthosis nigricans, hirsutism, pineal hyperplasia Acanthosis nigricans, ovarian dysfunction, hirsutism, accelerated growth

AR

246200

AD

100600

AR

269880

AR

262190

INSR

AD

610549

INSR

Large hands, acanthosis muscle cramps, enlarged kidneys, polycystic ovaries

?AR

Acanthosis nigricans, bitemporal narrowing, acral hypertrophy, decreased body fat, brachydactyly, dental anomalies Coarse facies, muscular women, acanthosis nigricans, headaches, facies muscle cramps, hyperprolactinemia, no ovarian dysfunction Acanthosis nigricans, immunological disease

?AR

KAL1 PROKR2 FGFR1 CHD7 PROK2 FGF8

AR

308700 244200 147950 612370 610628 600483 229850

AD AR XR AR

158900 253600 310200 255310

AD AD AD AD

143100 109150 172500 160900 602668 222300 604928 598500

D4Z4 CAPN3 DMD ACTA1 SEPN1 TPM3 HTT ATXN3

Hereditary neuromuscular disorders associated with glucose intolerance Anosmia-hypogonadism syndrome (Kallmann IGT or Type 1 Anosmia, hypogonadotropic, hypogonadism, hearing syndrome) loss, ±cleft lip and palate

Muscular dystrophies

IGT→Type 2

MR, craniofacial dysmorphism, hypogonadism, seizures Muscular dystrophy

Congenital Myopathy with Fiber-Type ­Disproportion

IGT→Type 2

Hypotonia, weakness, joint contractures

Huntington disease Machado-Joseph disease Herrmann syndrome Myotonic dystrophy

IGT→Type 2 Type 2 Type 2 Type 2

Diabetes mellitus—optic atrophy, diabetes insipidus—deafness syndrome (Wolfram, DIDMOAD syndrome)

Type 1

Chorea, dementia Ataxia Photomyoclonus, deafness, nephropathy, dementia Myotonia, muscular dystrophy, cataracts, ­hypogonadism, frontal balding, and ECG changes Optic atrophy, diabetes insipidus, deafness, ­neurologic symptoms

?

XR ?AR ?AD

AR ?Mitochondrial

DMPK ZNF9 WFS1 CISD2

5

Type 1

AD

CHAPTER 86  Diabetes Mellitus

Fryns syndrome

INSR

Continued

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TAB L E 8 6 - 1    Genetic Syndromes Associated with Glucose Intolerance and Diabetes Mellitus—cont’d McKusick No. 229300

Genes FXN

AR

208900

ATM

AR

266270

AD ?AD/multifactorial (most sporadic)

158500 184850

AD

604484

AD

180800

PMP22 MPZ

Progeroid syndromes associated with glucose intolerance Cockayne syndrome IGT Dwarfism, progeria, MR, deafness, blindness

AR

ERCC8 ERCC6

Acrogeria, Grotton type (Metageria)

Type 2

?AR

Werner syndrome

Type 2

Early atherosclerosis, tall and thin, birdlike facies and aged appearance, normal sexual ­development, atrophic mottled skin, telangiectasia, little ­subcutaneous fat Premature aging, cataracts, arteriosclerosis

216400 133540 201200

RECQL2

Mulvihill-Smith syndrome

Type 1

Premature aging, pigmented nevi, lack of facial subcutaneous fat, microcephaly, short stature, sensorineural hearing loss, mental retardation, immunodeficiency, tumors, severe insomnia, and cognitive decline

AD

277700, 604611 176690

Mitochondrial syndromes Ballanger-Wallace syndrome

Type 1/Type 2

Deafness, cardiomyopathy, retinopathy

520000

MELAS syndrome

Type 1/Type 2

Myopathy, encephalopathy, lactic acidosis, strokelike episodes

540000

MTTL1 MTTE MTTK MTTL1 MTTQ MTTH MTTK MTTS1 MTND1 MTND5 MTND6 MTTS2

Types of DM Type 1 or Type 2

Ataxia telangiectasia

Type 2

Ramon syndrome

Type 1

Pseudo-Refsum syndrome Stiff Person syndrome

Type 2 Type 1

Hereditary motor and sensory neuropathy, Okinawa type

Type 2

Roussy-Levy syndrome

Type 2

Associated Clinical Findings Spinocerebellar degeneration, visual field defects, hypertrophic cardiomayopathy Conjunctival and cutaneous telangiectasia, ­nystagmus, hypogonadism, cerebellar ataxia, recurrent infection, malignancies Gingival fibromatosis, cherubism, MR, epilepsy, JRA, vascular skin lesions Muscle atrophy, ataxia, retinitis pigmentosa Fluctuating muscle rigidity with painful spasm, ­characteristic EMG, autoimmune disease of nervous and endocrine system Neurogenic atrophy, sensory involvement, painful muscle cramps, fasciculations, areflexia, elevated CK levels, hyperlipidemia Ataxia, areflexia with amyotrophy

AR

CHAPTER 86  Diabetes Mellitus

Pattern of Inheritance AR

Syndromes Friedrich ataxia

Kearns-Sayre syndrome

Type 1/Type 2

Rotig syndrome

Type 1/Type 2

Pearson Marrow-pancreas syndrome

Type 1

Mitochondrial myopathy, lipid type

Type 1/Type 2

Short stature, microcephaly, sensorineural hearing loss, progressive external, ophthalmoplegia, retinopathy muscle weakness, cerebellar ataxia, dementia Proximal tubulopathy, cerebellar ataxia, myopathy, skin abnormalities Sideroblastic anemia, exocrine pancreatic ­dysfunction, failure to thrive, metabolic acidosis Myopathy, cerebellar ataxia

Mitochondrial deletions

530000

560000 Mitochondrial deletions

557000 500002

MTTE FGFR3 There are now 15 genes including ARL6, MKKS, TTC8, TRIM32, MKS1, CEP290, C2ORF86 ALMS1

AD AR (but may require more than one gene)

100800 209900, 600151, 600374

Alstrom syndrome

IGT→Type 2

AR

203800

Hyperostosis frontalis interna

Type 2

AD? (most sporadic, majority female)

144800

Prader-Willi syndrome

Type 2

15q abnormal (deletion or altered imprinting

176270

XR

309620

Contiguous gene ­deletion

194050

AR

226980

AD

151800

Features similar to Bardet-Biedl syndrome but no polydactyly Hyperostosis frontalis interna, obesity, hypertrichosis, galactorrhea, hyperprolactinemia, menstrual irregularity, and hyperphosphatasemia Obesity, short stature, acromicria, MR, ­disproportionate dwarfism

Miscellaneous syndromes associated with glucose intolerance Christian syndrome IGT, Type 2 Short stature, ridged metopic suture, mental ­retardation, fusion of cervical vertebrae thoracic hernivertebrae, scoliosis, sacral hypoplasia, abducens palsy, carrier females may have type 2 DM or IGT Williams syndrome IGT→Type 2 Short stature, elfin facies, stellate iris, hypercalcemia, supravalvular aortic stenosis, mental retardation, hoarse voice, anxiety Epiphyseal dysplasia and infantile onset diaType 1 (congenital) Epiphyseal dysplasia, tooth and skin defects betes mellitus (Wolcoff-Rallison syndrome) Symmetric lipomatosis IGT→Type 2 Diffuse symmetric lipomas of neck and trunk, stiff skin, muscle cramps, decreased sensation, ­heating loss, urolithiasis, hypertension, peptic ulcers

Contiguous gene deletion or imprinting error including SNRPN and necdin

Multiple genes deleted, including ELN EIF2AK3

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Continued

CHAPTER 86  Diabetes Mellitus

Syndromes with glucose intolerance secondary to obesity Achondroplasia IGT Disproportionate dwarfism, relative obesity Bardet-Biedl syndrome IGT→Type 2 Mental retardation, pigmentary hypogonadism, and obesity

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TAB L E 8 6 - 1    Genetic Syndromes Associated with Glucose Intolerance and Diabetes Mellitus—cont’d Types of DM Type 2

Woodhouse-Sakati syndrome

Type 2

Bloom syndrome

Type 2

AREDYLD

Type 2 (Lipoatrophic) Type 2

Dunnigan-Type familial partial lipodystrophy Mandibuloacral dysplasia with type A ­lipodystrophy

Type 2

Lipodystrophy, familial partial, type 3 Berardinelli-Seip syndrome

Insulin-resistant type 2

Lipodystrophy, familial partial, type 1

Type 2

Mandibuloacral dysplasia with type B

Type 2

Congenital malabsorptive diarrhea, type 4

Type 1

Associated Clinical Findings Cortisol-responsive pyogenic arthritis, pyoderma gangrenosum, and acne severe cystic acne, proteinuria Unusual facies, hypogonadism, absent breast tissue, sparse hair, mental retardation, sensineural ­deafness and ECG abnormalities Prenatal onset growth retardation, ­microcephaly, facial telangiectasia, spotty hypo- and ­hyperpigmentation, photosensitivity, ­hypertrichosis, learning disability and/or mild mental retardation, life-threatening infections, neoplasia Prognathism, peculiar shape of nose, pronounced antitragal incisura, hypotrichosis Loss of subcutaneous fat from limbs, hypertension, dyslipidemia, premature CAD Partial lipodystrophy of extremities, mandibular hypoplasia, acroosteolysis, skin atrophy, alopecia in males, hyperlipidemia Acanthosis nigricans, insulin resistance, Congenital lipodystrophy, advanced bone age, overgrowth, hepatosplenomegaly, mild MR, lytic cystic lesions in appendicular bones, hypertriglyceridemia Loss of subcutaneous adipose tissue in limbs, increase on trunk, xanthomata, CAD, hypertension, pancreatitis Mandibular hypoplasia, bird- like facies, generalized lipodystrophy lipodystrophy including face and neck, acroosteolysis, skin atrophy and sparse hair Congenital diarrhea

Cytogenetic disorders associated with glucose intolerance Down syndrome IGT MR, short stature, typical facies Klinefelter syndrome IGT→Type 2 Hypogonadism, tall stature, MR Turner syndrome IGT→Type 2 Short stature, gonadal dysgenesis, web neck

Pattern of Inheritance AD

McKusick No. 604416

Genes PSTPIP1

AR

241080

C2ORF37

AR

210900

RECQL3

AR

207780

AD

151660

LMNA

AR (Allelic to ­Dunnigan-type ­familial partial ­lipodystrophy) AD AR

248370

LMNA

604367 269700

PPARG BSCL2

AD or X-linked ­dominant?—only females reported AR

608600 604367

PPARG

608612

ZMPSTE24

AR

610370

NEUROG3

Trisomy 21 47, XXY 45, XO

AD, autosomal dominant; AR, autosomal recessive; Type 1, Type 1 insulin-dependent diabetes mellitus; IGT, impaired glucose tolerance; IUGR, intrauterine growth retardation; JRA, juvenile rheumatoid arthritis; MR, mental retardation; Type 2, Type 2 non-insulin-dependent diabetes mellitus.

CHAPTER 86  Diabetes Mellitus

Syndromes Pyogenic sterile arthritis, pyoderma ­gangrenosum

CHAPTER 86  Diabetes Mellitus nonketotic insulin-resistant states, such as myotonic dystrophy and the lipoatrophic diabetes syndromes), to relative insulin resistance (in the hereditary syndromes associated with obesity). Even within these individual categories, further division can be made, either by mechanism or by genetic criteria. For example, the lipoatrophic syndromes—characterized by the total or partial absence of adipose tissue, hyperlipidemia, insulin resistance, nonketotic diabetes mellitus, increased basal metabolic rate, and hepatomegaly—can be further subdivided into a recessive, several dominant, and nongenetic forms (10) (MIM#’s 604367, 608594, 608600, 269700, 207780). Even within a more restricted phenotype, such as that of type 1 diabetes, these disorders demonstrate formal linkage heterogeneity. Thus neither the diabetes insipidus optic atrophy (Wolfram) syndrome, nor Friedreich ataxia, both of which clearly cause an insulin-dependent form of diabetes, are linked to the HLA region on chromosome 6 (11–13). In fact, frataxin, the gene for Friedreich ataxia, is located on the long arm of chromosome 9 (14), and the genes (WFS1 and CISD2 [WFS2]) causing Wolfram syndrome are located on the short and long arms of chromosome 4, respectively (15,16). There are a variety of syndromes that are characterized by marked insulin resistance. The pathophysiology of the resistance of many of these disorders has been defined by studies of the insulin receptor and its interactions, with some disorders characterized by decreased receptor number, others by decreased receptor affinity, and still others by humoral antagonists to the receptor (17,18). A number of distinct molecular defects in the insulin receptor gene have been described in leprechaunism (Donohue syndrome), Rabson Mendenhall syndrome, and type A acanthosis nigricans syndrome (see MIM ID *147670 for a description of the various mutations; (19, 20)). It is of interest that these affected individuals are often homozygous for a mutant allele or are compound heterozygotes. Relatives who are heterozygous for these defects have been found to have hyperinsulinemia without hyperglycemia (21). Even within what is felt to be one genetic entity, multiple endocrine neoplasia syndrome type 1, an autosomal dominant disorder characterized by pituitary, parathyroid, and pancreatic adenomas, a variety of different hormonal mechanisms can result in insulin antagonism (e.g. eosinophilic adenomas of the pituitary may secrete growth hormone, adenomas of the adrenal gland can secrete cortisol, and non-beta islet cells of the pancreas can produce glucagon) (22). Individually, each of the hormones is an insulin antagonist and their excess can lead to marked glucose intolerance. Thus, each of these many different genetic diseases are capable of resulting in carbohydrate intolerance through a variety of different pathogenetic mechanisms. More recently, a distinct form of diabetes, presenting in very young infants, has also been found to be genetically heterogeneous (23). In one form of neonatal diabetes, transient diabetes develops at birth or shortly

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thereafter but resolves after a few months. Such transient neonatal diabetes can result from mutations in at least three genes (ZFP57, ABCC8, KCNJ11) or from altered imprinting of a region of chromosome 6q24. Depending on the specific gene and mutation, the diabetes can be accompanied by intrauterine growth retardation, macroglossia, abdominal wall defects, congenital heart disease, and/or brain malformations. In addition, mutations in ABCC8, KCNJ11, INS, GCK, GLIS3, and PTF1A can produce permanent forms of neonatal diabetes, with or without additional abnormalities. Identifying the specific genetic mutation becomes critical in these conditions, as this will determine the most appropriate way to treat the diabetes. These rare syndromes suggest that a similar degree of heterogeneity, both genetic and pathogenetic, may exist in “idiopathic” diabetes mellitus.

86.4.2 Heterogeneity Between Type 1 and Type 2 Diabetes As summarized in Table 86-2, a number of lines of clinical and genetic evidence have led to the eventual separation of type 1 and type 2 diabetes as clearly distinct groups of disorders. Clinical differences that tended to run true in families provided some of the first evidence (24–28). In addition, the extensive MZ twin studies by Pyke and his coworkers in England strongly supported the separation of juvenile insulin-dependent and maturity non-insulin-dependent diabetes (29). Among 200 pairs of MZ (identical) twins, concordance for diabetes was shown to be less than 50% for type 1 diabetes, but close to 100% for type 2 diabetes. This suggested that there are a large group of individuals with type 1 diabetes in whom nongenetic as well as genetic factors may play a role in the development of clinical disease. Physiological studies further supported the separation of type 1 and 2 diabetes. The absolute insulinopenic response of juvenile-onset diabetics versus the relative hyperinsulinemic response of maturity-onset diabetes parallels the therapeutic observation of the absolute insulin requirement of the juvenile (type 1) diabetic, which contrasts with the ability to manage most adult cases with oral hypoglycemics and/or diet (type 2 diabetes). Immunologic studies pinpointed the importance of immune mechanisms in the etiology of type 1 but not type 2 diabetes. Direct evidence for an autoimmune role in the pathogenesis of type 1 diabetes came from the discovery of organ-specific cell-mediated immunity to pancreatic islets, and then the successful demonstration of antibodies to the islet cells of the pancreas (30). While these antibodies were first detected only in insulin-dependent diabetics with coexistent autoimmune endocrine disease, it soon became apparent that they were common ­(60–80%) in most newly diagnosed juvenile diabetics. Studies on islet cell antibodies (ICA) supported the differentiation of insulin-dependent from

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CHAPTER 86  Diabetes Mellitus

TA B L E 8 6 - 2    Separation of Type 1 from Type 2 Diabetes Other Nomenclature

Type 1 IDDM (Juvenile-Onset Type)

Type 2 NIDDM (Maturity-Onset Type)

(1) Clinical

Obese Ketosis resistant Often treatable by diet or drugs Onset predominantly after 40

(2) Family studies (3) Twin studies

Thin Ketosis prone Insulin required for survival Onset predominantly in childhood and early adulthood Increased prevalence of juvenile or type 1 <50% concordance in monozygotic twins

(4) Insulin response to a glucose load (5) Associated with other autoimmune (6) Islet cell antibodies and pancreatic (7) HLA associations and linkage

Flat Yes Yes Yes

non-insulin-dependent diabetes, as autoantibodies were present in 30–40% of the former group (even after onset) as opposed to 5–8% of the latter. Many (probably the majority) of the non-insulin-dependent, yet antibody positive, patients appear to become insulin dependent with time. They have flat insulin responses to a glucose load, and they also have the HLA-associated DR3 and DR4 antigens (31). This has suggested that etiologically, these cases belong to the insulin-dependent category (i.e. they are just in a transitional state on the way to eventual insulin dependence and share the same underlying pathogenetic mechanisms as type 1 diabetes). Thus immunologic studies have served both to separate disorders (juvenile versus adult) and combine others (­ insulin dependent and non-insulin dependent, yet antibody positive). Finally, the clear and consistent findings of HLA associations with juvenile insulin-dependent, but not maturity-onset non-insulin-dependent diabetes, became a major argument for etiologic differences between these two disorders; approximately 95% of type 1 diabetes patients have DR3 or DR4 or both (reviewed in (4,32)). Based to a large extent on the evidence reviewed previously, the American Diabetes Association’s current classification divides diabetes mellitus into four major subcategories: type 1 diabetes (insulin-dependent diabetes), type 2 diabetes (non-insulin-dependent diabetes), gestational diabetes, and other specific types of diabetes (33). The major defining characteristics of each of these categories are summarized in Table 86-3. There is, however, some evidence that families of either type 1 or type 2 diabetes have more of the other type of diabetes than do families in the general population (34). Part of this overlap may be attributed to the insulin-independent phase of the insulin-dependent type (the frequency of which is still being defined), but it may also be the result of even further etiologic heterogeneity (see later discussion). In addition, the age of onset and other clinical differences can be important. For example, a distinct form of non-insulin-dependent diabetes that has been termed “maturity-onset diabetes of the young” (MODY) has been described (35) (discussed later).

Increased prevalence of maturity or type 2 Close to 100% concordance in monozygotic twins Variable No endocrine diseases and antibodies No cell-mediated immunity No

The delineation of this entity clearly demonstrated that age of onset is a useful clinical criterion for classification purposes. Similarly, there is evidence that the age of onset may still be helpful as an additional classification criterion in type 1 diabetes, in that those individuals with both DR3 and DR4 alleles have the youngest age of onset. It has also been recognized that adults may develop type 1 diabetes, often with a less dramatic presentation than in children; this has been termed latent autoimmune diabetes of adults (LADA).

86.4.3 Type 1 (Insulin-dependent) Diabetes Mellitus Type 1 diabetes mellitus is characterized by low levels or absence of endogenous insulin production (see Table 86-3). This is secondary to destruction of the insulin-producing beta cells of the pancreas and is the single characteristic that most decisively separates type 1 and type 2 diabetes. It is estimated that 5–10% of all US patients with diabetes have type 1 diabetes, and that the US incidence in children aged 0–14 years is in the range of 12 per 20/100,000 (36). The incidence appears to vary dramatically worldwide, from an estimated low of 1 per 100,000 children in parts of Asia and South America to greater than 40 per 100,000 in some regions in Scandinavia (36– 39). In most regions of the world, the incidence appears to be increasing (38,40–43). The increasing incidence suggests that some environmental risk factor(s) for type 1 diabetes are becoming more prevalent, but the nature of these environmental risks is still under investigation.

86.4.4 Natural History and Pathophysiology of Type 1 Diabetes Patients with a new diagnosis of type 1 diabetes typically present acutely ill, with severe dehydration, ketoacidosis, and marked hyperglycemia. The history is generally that the individual was well until perhaps a week or two before presentation, when increased thirst and urination were noted. Occasionally an account

CHAPTER 86  Diabetes Mellitus

11

TA B L E 8 6 - 3    Etiologic Classification of Diabetes Mellitus Class Name Type 1

diabetesa

Former Terminology

Characteristics

Insulin-dependent diabetes (IDDM)

β-cell destruction, usually leading to absolute insulin deficiency Dependent on injected insulin to prevent ketosis and sustain life

Immune mediated

Juvenile diabetes

Idiopathic

Juvenile-onset diabetes (JOD) Ketosis-prone diabetes Brittle diabetes

Type 2 diabetesa

Non–insulin-dependent diabetes (NIDDM)

Adult-onset diabetes Maturity-onset diabetes (MOD) Ketosis-resistant diabetes

Stable diabetes Gestational diabetes (GDM)

Gestational diabetes

Other specific types includes: Genetic defects of β-cell function

Secondary diabetes

Onset predominantly in youth but can occur at any age Associated with HLA DR3 and DR4 Islet cell antibodies are frequently present before and at diagnosis May range from predominantly insulin resistance with relative insulin deficiency to a predominantly secretory defect with insulin resistance Insulin levels may be normal, elevated, or depressed Not insulin-dependent or ketosis-prone under normal circumstances, but may use insulin for treatment of hyperglycemia or during stress conditions Onset predominantly after age 40, but can occur at any age Approximately 60% of patients are obese Hyperinsulinemia and insulin resistance characterize some patients Glucose intolerance that has its onset during pregnancy; (GDM) virtually all patients return to normal glucose tolerance following parturition Conveys increased risk for progression to diabetes In addition to the presence of the specific condition, hyperglycemia at a level diagnostic of diabetes is also present

Genetic defects in insulin action Diseases of the exocrine pancreas Endocrinopathies Drug- or chemical-induced infections Uncommon forms of immune- mediated diabetes Other genetic syndromes sometimes associated with diabetes aPatients

with any form of diabetes may require insulin treatment at some stage of their disease. Such use of insulin does not, of itself, classify the patient. Data from National Diabetes Data Group, 1979 (485), Expert Committee on Classification, American Diabetes Association, 2003 (33).

is given of a viral upper respiratory tract infection or other mild infectious illness shortly before the increased thirst and urination began. As a result, for many years, type 1 ­diabetes was thought to be an acute onset disorder. However, with the appreciation of the autoimmune nature of the beta cell destruction, this assumption began to be questioned. Several studies followed nondiabetic identical cotwins and triplets of type 1 diabetic probands with serial testing for the presence of circulating autoantibodies (44,45) and, not surprisingly, many of these twins and triplets ultimately developed type 1 diabetes. What was

not anticipated, however, was that anti-ICA were often detectable months to many years before the time when these cotwins became overtly diabetic. These studies demonstrate that the development of type 1 diabetes actually occurs gradually. A variety of ­abnormalities in immune function and insulin release precede the “abrupt” development of the diabetic syndrome in patients genetically predisposed to type 1 diabetes (46). Eisenbarth (47) proposed dividing the development of type 1 diabetes into six stages: (1) genetic susceptibility; (2) triggering events; (3) active autoimmunity; (4) gradual loss of glucose-stimulated insulin secretion;

12

CHAPTER 86  Diabetes Mellitus

(5) appearance of overt diabetes, with some residual insulin secretion; and (6) complete beta cell destruction. The autoimmune destruction of pancreatic beta cells progresses slowly over time, and it is not until the majority of these cells have been destroyed that clinically apparent diabetes occurs. At the onset of type 1 diabetes, as little as 10% of the beta cells remain and, within several years, essentially all beta cells are destroyed (46). Maclaren (48) also pointed out that the pace of these events may well relate to the age of onset and the underlying genetic heterogeneity. Nonobese diabetes (NOD) mice and biobreeding (BB) rats are excellent models of the autoimmune form of type 1 diabetes (see recent reviews in (49–51)). Data from these animal models indicate that T lymphocytes are important in the pathogenesis of islet T-cell destruction, as activated T lymphocytes from acute-diabetic BB rats can transfer diabetes to other animals (52). Similar evidence for the importance of T lymphocytes in human diabetes comes from the studies of pancreatic transplantation between identical twins (53,54). When pancreata were transplanted from nondiabetic twins to their diabetic MZ cotwins without immunosuppression, islet cell destruction with massive T-cell infiltration and relapse of the diabetes occurred within weeks. Thus, the basic defect in type 1 diabetes appears to be extrinsic to the pancreas and related to the activation of T lymphocytes, which then mediates the destruction of the islets (46). The mechanisms resulting in the autoimmune destruction of the islets are complex. Although both T cells and the cytokines they excrete are directly involved in the destruction, it has become increasingly clear that it is not just autoreactive T cells that are important. Macrophages, natural killer cells, dendritic cells and altered Treg cell function have also been implicated (55). In light of the complex processes involved, it has been difficult to identify what the initiating abnormality is that triggers the autoimmune process. Given the many immunerelated genes that are now implicated in type 1 diabetes, it is likely that the combination of a number of alterations in immune function is necessary to initiate beta cell destruction. Because it is estimated that at diagnosis of type 1 diabetes about 10% of beta cells are still alive and capable of producing insulin, some patients go through a transient “honeymoon” phase in the early months after diagnosis, during which their requirement for exogenous insulin may decrease or even disappear. These remaining beta cells are lost within the first months to years following diagnosis, however, and all patients ultimately will require lifelong treatment with insulin. With the recognition that there were functional beta cells still present at the time of diagnosis, treatment with immunosuppressants was attempted to see if type 1 diabetes could be reversed. Although it was possible to produce a remission in some patients who were

treated with cyclosporine within the first few months after diagnosis, the effect was short lived in most cases and overt diabetes returned as soon as cyclosporine was discontinued (56,57). More recent attempts to produce remission have utilized more narrowly targeted agents, such as humanized anti-CD3 or anti-CD20 monoclonal antibodies. While none of the subjects treated with these agents went into remission, there was evidence of preservation of C-peptide levels and in some cases a reduced insulin requirement was observed (58–60). The observation that these effects may persist for several years after short-term treatment is encouraging (60,61). At present, while research continues, there is no proof that it will be possible to cure diabetes once beta cell destruction has progressed to the point of overt diabetes. Therefore, attention has also been focused on attempts to intervene in high-risk individuals who have not yet progressed to clinical diabetes (see later discussion).

86.4.5 Penetrance of Type 1 Diabetes When the mode of inheritance is unknown, the only estimate we have for penetrance comes from identical twin concordance data. The largest twin data set (the British diabetic twin study) reported concordance for type 1 diabetes of some 50% of cases (29,62). However, it is clear that this sample is an unrepresentative one, with only a fraction of the twins in the British Isles identified, and thus there is a presumed bias toward concordant pairs (63). Studies of less-biased, but much smaller, samples reported concordances of approximately 20% (64). Finally, in 1988, a prospective study of twins from the British group yielded a concordance estimate of about 36%, an estimate similar to that in a recent (2009) study from Sweden (65,66). While the best estimate is that perhaps only one-third of all persons with the genes for type 1 diabetes actually develop clinical disease, one recent study has suggested that the concordance may be higher if twins are followed up long term (67). Whichever estimate is used, however, it is clear that MZ twin concordance is substantially less than 100%. This reduced penetrance indicates that what is inherited in type 1 diabetes is disease susceptibility; other factors, presumably environmental, are required to convert genetic susceptibility into clinical disease. This view is supported both by the observation of the time of onset of type 1 diabetes clusters in families and twin pairs (68,69) and the epidemiological, experimental, animal, and clinical evidence for viral infections as a supervening factor in at least some cases (70–72) (see later discussion). However, environmental influence is not necessarily the only explanation, as the somatic recombination that occurs within the immune system is also a potential explanation for the reduced penetrance (see later discussion).

CHAPTER 86  Diabetes Mellitus

86.4.6 The HLA Region and Type 1 Diabetes With the discovery of HLA antigen associations with type 1 diabetes, the genetic region that provides the major (but by no means only) genetic susceptibility to type 1 diabetes was located. The earliest studies implicating the HLA region in type 1 diabetes susceptibility demonstrated an increased frequency of the class I antigens B8 and B15 in white type 1 diabetes subjects (73–75). Subsequently, a large number of studies found an increased frequency of the class II HLA antigens DR3 and DR4 among white type 1 diabetes patients (76,77). Because of the linkage disequilibrium within the HLA region, the associations of B8 and B15 were thought to result from these alleles occurring with a high degree of frequency on DR3-containing (in the case of B8) and DR4-containing (in the case of B15) haplotypes. The type 1 diabetes association is unusual among HLA disease associations, because it involves two antigens, HLA-DR3 and DR4. In addition, the relative risk for type 1 diabetes in individuals who carry both DR3 and DR4 (compound heterozygotes) is greater than those homozygous for either DR3 or DR4 (76–78). This finding of the increased risk of the DR3/ DR4 heterozygote was the first suggestion that more than one gene may predispose to type 1 diabetes. Approximately 95% of all type 1 diabetes (in white populations) have HLA-DR3, DR4, or both, compared with about 50% of individuals in the nondiabetic population (32). There are also more subtle relative increases in HLA-DR1 (especially among those who have only one copy of DR3 or DR4); conversely, DR2 and DR5 are decreased in individuals with type 1 diabetes (79,80). HLA-DR3 and DR4 (as defined serologically) are not pathognomonic of type 1 diabetes; nearly half the US population has either DR3 or DR4 (only 1–3% have both), yet only a small percentage (about 0.5%) of these individuals will develop type 1 diabetes. However, if one’s sibling has type 1 diabetes, the chance of a DR3 or DR4 individual developing type 1 diabetes rises sharply (12–24%). These observations suggested that DR3 and DR4 as defined serologically could not adequately explain the risk for type 1 diabetes present in the HLA region. Either the serologic DR typing was not sensitive enough to detect the type 1 diabetes-specific forms of DR3 and DR4 or other genes were responsible for at least some of the type 1 diabetes susceptibility associated with the HLA region. With the advent of molecular HLA typing, it became clear that both these possible explanations are indeed important in understanding the role of the HLA region in type 1 diabetes susceptibility. Initially, when the complexity of the HLA class II region was uncovered, attention focused on the hypothesis that another locus in tight linkage disequilibrium with DR was actually responsible for type 1 diabetes susceptibility. Studies demonstrated that the HLA class II region consists of at least three

13

genetic loci: DR, DQ, and DP, each of which codes for a slightly different glycoprotein consisting of two peptide chains, alpha and beta. Because of this variability, there are differences at the DNA level between diabetics and nondiabetics, even when they share the same serologic DR type. Given the extensive linkage disequilibrium, identifying which class II locus was primarily responsible for type 1 diabetes susceptibility was challenging. Studies in white populations initially implied that the primary locus for type 1 diabetes susceptibility was the DQ beta gene, with the DQB1*0302 allele being highest risk. However, subsequent studies in other ethnic groups led to the realization that other genes within the class II region clearly are of importance as well. Thus, for example, in ­Mexican American type 1 diabetes patients, DRB1*0402DQB1*0302 and DRB1*0405-DQB1*0302 (European white haplotypes) are strongly associated with risk, whereas DRB1*0408-DQB1*0302 and DRB1*0411DQB1*0302 (Native American haplotypes) are actually protective against type 1 diabetes, even though they all contain the DQB1*0302 allele (81). In most, but not all populations that have been studied, the HLA haplotypes that are commonly associated with risk for type 1 diabetes are DRB1*0301-DQA1*0\-DQB1*0201 and DRB1*0401-DQA1*0301-DQB1*0302, whereas other haplotypes such as DRB1*1501-DQA1*0102DQB1*0602 are strongly protective against type 1 diabetes (82,83). Such data suggest that at a minimum, the DRB1, DQA1, and DQB1 loci are implicated in diabetes susceptibility. Additionally, the DPB1 locus has also been shown to play a role in type 1 diabetes susceptibility in both white and Mexican American populations (84,85). It thus now appears that many of the class II loci are involved in type 1 diabetes susceptibility, with associations reported with the DQ beta region (DQB1*0302), DQ alpha region (DQA1*0301) and the DP beta region (DPB1*0301), as well as with DR itself (84,86,87). The HLA region is even more complex, however, extending across approximately 7.6 Mb and containing more than 250 genes (88). Because of the extensive linkage disequilibrium across the HLA region, it has proven to be very difficult to determine the loci that actually contribute to type 1 diabetes risk versus those loci that demonstrate associations only as a result of linkage disequilibrium. It is now clear that the HLA class II region is not the only portion of the HLA region conferring risk to type 1 diabetes. Data have been reported suggesting that genes in many other HLA regions may also be involved in disease susceptibility (86,89–96). There is quite strong evidence implicating the HLA-A and B class I genes (87,97). Most recently, a conditional ­meta-analysis was performed using data from the Welcome Trust Case-Control Consortium and International Type 1 Diabetes Consortium and it implicated the genes TCF19, POU5F1, CCHCR1, and PSORS1C1 within the class I region (98).

14

CHAPTER 86  Diabetes Mellitus

While it is still unclear which of these putative associations will ultimately be confirmed, the observed increased risk for type 1 diabetes in DR3/4 heterozygotes compared with either DR3/3 or DR4/4 homozygotes means that the susceptility to type 1 diabetes associated with DR3 and DR4 are different. This is reinforced by the observation that familial aggregation of type 1 diabetes suggests that DR3 susceptibility acts in a recessive manner, with most DR3-carrying type 1 diabetes patients also having a second high-risk HLA haplotype (containing either DR3 or DR4). DR4-related type 1 diabetes susceptibility, on the other hand, appears to act in a dominant manner, as demonstrated by the observation of many DR4-carrying type 1 diabetes patients who do not carry a second highrisk haplotype (99). There is also evidence for phenotypic heterogeneity between type 1 diabetes associated with HLA-DR3 and that associated with HLA-DR4 (Table 86-4) (reviewed in (4)). The DR3 form of the disease is characterized by a greater persistence of pancreatic ICA and antipancreatic cell-mediated immunity, but a relative lack of antibody response to exogenous insulin. This form apparently has onset throughout life and probably accounts for a significant fraction of older-onset type 1 diabetes. In the older age groups, this form of type 1 diabetes may present as latent autoimmune diabetes (LADA) and may be treated without insulin for a significant period, but the presence of ICA presages eventual insulin dependence (100). Type 1 diabetes associated with DR4 is not as strongly associated with autoimmune disease or ICA, but this form is accompanied by an increased antibody response to exogenous insulin (101,102). The relation between HLA-DR4 and insulin immunogenicity also can be seen before the initiation of exogenous insulin therapy, with the occurrence of insulin antibodies before disease onset (103–105). DR4-associated type 1 diabetes also appears to have an earlier age of onset, exhibits seasonality, and may be related to viral infections.

With multiple genes participating, it is clear that the mechanisms by which the HLA region produces susceptibility must be complex. Based on our current understanding of the functioning of the various HLA genes, the alleles associated with increased risk of diabetes likely alter the way that the class I and class II receptors bind peptide antigens and interact with antigen-presenting cells (APCs), CD4+ T-cells and CD8+ T cells (106). However, a more complete understanding of these mechanisms must await further clarification of both the number and identity of the HLA-linked genes that account for type 1 diabetes risk.

86.4.7 Non–HLA Region Genes and Type 1 Diabetes Estimates of the proportion of genetic susceptibility to type 1 diabetes for which the HLA region accounts vary, but even the highest estimates are in the range of 60– 70%, clearly indicating that other, non-HLA loci must also exist that play a role in type 1 diabetes (99,107,108). Over the past several decades, numerous other regions have been implicated in type 1 diabetes susceptibility, but until the advent of the GWAS era, the data in support of most loci remained fairly tentative. 86.4.7.1 Candidate Genes and Type 1 Diabetes.  Before GWAS, the best substantiated locus was IDDM2, near the insulin gene on the short arm of chromosome 11. This region was first implicated in the early 1980s, when a polymorphic region 5′ to the insulin gene (INS) was discovered (109). The polymorphism results from the presence of a variable number tandem repeat (VNTR) region near the 5′ regulatory region of INS. A number of population studies reported an association between type 1 diabetes and class I alleles, which have smaller numbers of tandem repeats in this region as compared to the class III alleles (110,111). Although it is now appreciated that classical linkage methods are

TA B L E 8 6 - 4    Heterogeneity within Type 1 Diabetes Evidence

DR3

DR4

Combined Form (DR3/DR4)

Linkage disequilibrium

A1, B8

Penetrance in MZ twins

Insulin antibodies

Nonresponder (low antibody titers) Persistent Less frequent Increased

B15, DQβ1*0302 Risk to siblings High responder (high antibody titers) Transient Increased frequency Not increased

Yes Yes

Less frequent Type 1 DM No endocrine diseases

Increased Any age Lesser frequency Preserved longer

Not increased Type 1 DM Younger age Greater frequency clinical onset Absent after shorter duration

Islet cell antibodies Insulin autoantibodies Antipancreatic cell-mediated immunity Thyroid autoimmunity in Associated with other ­autoimmune IgA deficiency in Age of onset Ketoacidosis at Levels of C-peptide

Occurrence in familial cases Highest titers

Youngest Lowest

CHAPTER 86  Diabetes Mellitus rarely informative when analyzing loci contributing a comparatively small amount to disease susceptibility, when these association studies were followed by family studies that failed to demonstrate linkage, there was some controversy as to whether the association was real or not. With the advent of family-based association methodologies, a role for IDDM2 was better substantiated and this locus became the second (after HLA) well accepted in type 1 diabetes susceptibility locus (110–112). For many years, however, questions remained about how this region results in diabetes susceptibility, until it was shown that it affects expression of insulin mRNA in the fetal thymus and thus influences the development of tolerance to antigenic determinants of insulin (113,114). Class I alleles, which contain smaller numbers of repeats and are associated with increased risk for diabetes, have been observed to result in lower insulin gene mRNA expression in the thymus. The other common allele size, known as Class III alleles, has greater numbers of repeats within the VNTR and has been associated with decreased diabetes risk and increased thymic mRNA levels. Recently Cai et al. (115) showed that the mechanism for this altered expression involves the autoimmune regulator (AIRE) protein, which interacts with the INS VNTR to regulate insulin expression. The correlation between VNTR length and thymic expression is not absolute, however. Vafiadis and coworkers (116) observed that in a minority of subjects, silencing of the Class III allele occurs, resulting in thymic expression from the other insulin allele. Such silenced Class III alleles predispose to type 1 diabetes, presumably because they lead to overall reduced mRNA levels. At the level of the VNTR, the mechanism responsible for silencing has not yet been identified but is hypothesized to result from altered imprinting. CTLA4, cytotoxic T-lymphocyte-associated protein 4, is a member of the immunoglobulin superfamily, and was tested as a candidate gene within the IDDM12 linkage region on chromosome 2q33, which was homologous to the Idd5 locus in the mouse. Association with type 1 diabetes was demonstrated in several ethnic groups, as well as associations with several other autoimmune disorders, especially thyroid disease (117–126). CTLA4 is a negative immunoregulatory molecule involved in T-cell activation and expansion that appears to be important in normal Treg function (127,128). PTPN22 encodes the lymphoid-specific protein tyrosine phoshatase (LYP), which is another inhibitor of T-cell activation (83,129,130). The 1858 T allele, which replaces arginine with a tryptophan residue at codon 620, was originally reported to be associated with type 1 diabetes by Bottini et  al. (131). Like CTLA4, it has also been implicated in other autoimmune diseases, including rheumatoid arthritis, autoimmune thyroid disease, lupus, celiac disease, and autoimmune vasculitis (122,132–135). The associated allele results in a gain of

15

function (136), suggesting that inhibitors of LYP could be of therapeutic benefit. IL2RA, interleukin 2 receptor A gene (also known as CD25), was first reported to be associated with type 1 diabetes by Vella et al. (137) and subsequently replicated (138–140). Like CTLA4 and PTPN22, IL2RA is associated with risk for other autoimmune diseases including Graves’ disease, multiple sclerosis, and juvenile idiopathic arthritis (141–143). The mechanism by which IL2RA results in risk for autoimmune disease is still under study, but the allele associated with protection against type 1 diabetes results in higher CD25 levels on CD4+ memory cells and such cells are likely more responsive to IL-2 and to TCR-mediated activation (144).

86.4.8 Identifying Type 1 Diabetes Genes by Genome-Wide Linkage Scans With the application of systematic linkage mapping to type 1 diabetes, a variety of other candidate gene regions were mapped. Some of the linkage regions corresponded to previously known loci, including HLA and INS. Studies of candidate genes within other regions, including CTLA4 (located within the IDDM 12 region on chromosome 2q and SUMO4 in the IDDM5 region on chromosome 6q), appear to explain the observed linkage peaks (118,121,145–150). Data suggest that SUMO4 contributes to type 1 diabetes susceptibility in Asians, but it is less clear whether this gene is important in Caucasian populations (140,149–151). Data for other regions have been equivocal and many may ultimately turn out to have been false positives.

86.4.9 Type 1 Diabetes Genes Identified by Genome-Wide Association Studies GWAS have resulted in the identification of a substantial number of genes for type 1 diabetes (Table 86-5). This has been facilitated by cooperation between multiple centers, both in the acquisition of large numbers of cases and controls and in data sharing to confirm association signals. The first genome-wide association study for type 1 diabetes was published in 2006 reporting an association with the innate immunity viral RNA receptor gene region, IFIH1 (152). This was followed rapidly by a number of other GWAS (153–156) that increased the number of loci demonstrating association. Additional loci have been identified by GWAS meta-analyses using very large combined study populations (one with more than 3500 cases and 4500 controls (157) and a second with more than 7500 cases and 9000 controls (158)). Many of the GWAS and meta-analysis findings have also been confirmed in independent samples, and as of early 2011, there are more than 50 loci with quite convincing evidence for association with type 1 diabetes (Table 86-5; see also http://www.t1dbase.org (159)).

16

TAB L E 8 6 - 5    Susceptibility Genes for Type 1 Diabetes Other GenomeWide Associated Disorder(s)

Candidate gene study in North American Caucasians

2.05

Crohn’s, Graves’, RA, SLE, Vitiligo

Candidate gene study ­following up a Celiac GWAS hit ­European Caucasian GWAS meta-analysis in European & North American Caucasians GWAS meta-analysis in European & North American Caucasians GWAS analysis in European Caucasians

0.89

Celiac, MS

0.84

Crohn’s, SLE, UC

1.11

JIA, RA

0.81–0.86

Graves’, SLE

Candidate gene study following up a RA & SLE GWAS/linkage hits European Caucasians Candidate gene study

1.1–1.11

RA, SLE, SSc, pSS

0.82–0.88

Celiac, RA

Candidate gene study in ­European Caucasians; ­multiple prior small studies with equivocal results GWAS meta-analysis in European & North American Caucasians GWAS meta-analysis in European & North American Caucasians

0.85

Celiac

Chromosomal Location

Marker(s)

Variant Type(s)

Source of Initial Discovery

PTPN22

1p13.2

rs2476601

Arg620Trp

RGS1

1q31.2

rs2816316

8 kb upstream

CD55, IL10

1q32.1

rs3024505

AFF3

2q11.2

IFIH1

2q24.2

rs9653442 rs1160542 rs1990760

1 kb downstream of IL10 66 kb upstream, 73 kb upstream Ala946Thr

STAT4

2q32.2

rs7574865

Intronic

CTLA4

2q33.2

rs6752770 rs3087243

CCR5

3p21.31

rs11711054 rs333

66 kb upstream, 32-bp insertion– deletion variant

4p15.2

rs10517086

Intergenic

IL2

4q27

rs2069762, rs2069763, rs4505848

Immediately upstream, Leu38Leu, 240 kb downstream

HLA-DQB1, HLA-B, HLA-DRB1, HLA-C BACH2

MHC

Immediately ­downstream

6q15

rs11755527

Intronic

6q22.32

rs9388489

Intergenic

Protein Protein tyrosine phosphatase, nonreceptor type 22 (lymphoid) Regulator of G-­protein ­signaling 1 CD55 antigen interleukin 10 AF4/FMR2 family, member 3 Interferon induced with helicase C domain 1; receptor for dsRNA from viral infections Signal transducer and activator of transcription 4 Cytotoxic T-­lymphocyteassociated protein 4 Human C–C chemokine receptor gene

1.09 0.89 1.13

Candidate gene studies in ­multiple populations

3.05

Candidate SNP follow-up of ­suggestive GWAS associations in European & North American Caucasians GWAS meta-analysis in European & North American Caucasians

1.13

1.17

UC

Interleukin2

CHAPTER 86  Diabetes Mellitus

Diabetes Effect: OR (95% CI)

Locus: Nearest Gene(s)

TNFAIP3

6q23.3

rs10499194, rs2327832, rs6920220

188 kb upstream, 216 kb upstream, 182 kb upstream 5′ untranslated region

TAGAP

6q25.3

rs1738074

SKAP2

7p15.2

rs7804356

Intronic

IKZF1

7p12.2

rs10272724

4 kb downstream

7p12.1

rs4948088

Intergenic

GLIS3

9p24.2

rs7020673

Intronic

IL2RA

10p15.1

rs11594656, rs12722495

18 kb upstream, intronic

PRKCQ

10p15.1

ZMIZ1

10q22.3

Arg616Arg, 79 kb downstream Intronic

RNLS

10q23.31

rs11258747, rs947474 rs1250550, rs1250558 rs10509540

INS

11p15.5

rs689

CD69

12p13.31

rs4763879

5′ untranslated region Intronic

CYP27B1

12q13.3

rs10877012

Immediately upstream of gene

ERBB3

12q13.2

rs2292239

Intronic

GWAS meta-analysis in European & North American Caucasians

1.31

SH2B3

12q24.12

rs3184504,

Trp262Arg

GWAS in European Caucasians

1.28

11 kb downstream

Candidate gene study following up an RA GWAS in North American Caucasians

0.9 1.09

Celiac, RA, SLE, UC

Candidate gene study following up a Celiac GWAS in European Caucasians GWAS in North American ­Caucasians Candidate gene study following up an acute leukemia GWAS & borderline T1D GWAS in European Caucasians GWAS meta-analysis in European & North American Caucasians GWAS meta-analysis in European & North American Caucasians Candidate gene study in European & North American Caucasians GWAS meta-analysis in European & North American Caucasians GWAS meta-analysis in European & North American Caucasians GWAS meta-analysis in European & North American Caucasians

0.92

Celiac

0.87

0.77 0.88 0.84–0.87 0.62–0.63

MS, RA, Vitiligo

0.69 0.88–0.91

GLIS family zinc finger 3 Interleukin 2 receptor, alpha chain

0.42

Protein kinase C, theta Zinc finger, MIZ-type containing 1 Renalase, FADdependent amine oxidase Insulin

1.09

CD69 antigen

IBD 0.75

1.22

MS

Celiac, MS

Cytochrome P450, family 27, subfamily B, polypeptide 1 v-erb-b2 erythroblastic leukemia viral oncogene homolog 3 SH2B adaptor protein 3

CHAPTER 86  Diabetes Mellitus

Candidate gene studies in ­multiple populations GWAS meta-analysis in European & North American Caucasians Candidate gene study in ­European Caucasian families

0.88

T-cell activation Rho GTPase-activating protein src family associated phosphoprotein 2 IKAROS family zinc finger 1

Continued

17

18

Locus: Nearest Gene(s) GPR183

Chromosomal Location 13q32.3

Marker(s) rs9585056

Variant Type(s) 122 kb upstream

14q24.1

rs1465788

Intergenic

14q32.2

rs4900384

Intergenic

DLK1

14q32.2

rs941576

105 kb downstream

RASGRP1

15q14

rs17574546, rs7171171

45 kb upstream, 50 kb upstream

CTSH

15q25.1

rs3825932

Intronic

CLEC16A

16p13.13

rs12708716

Intergenic

IL27

16p13.13 16p11.2

rs12927773 rs4788084

Intergenic 22 kb upstream

16q23.1

rs7202877

Intergenic

17q12

rs2290400

Intron of GSDMB

17q21.2

rs7221109

Intergenic

GSDMB, ORMDL3

Source of Initial Discovery Candidate gene study in ­European Caucasians ­following up rat expression data & borderline T1D GWAS in European Caucasians GWAS meta-analysis in European & North American Caucasians GWAS meta-analysis in European & North American Caucasians GWAS meta-analysis in European & North American Caucasians Family-based GWAS ­meta-analysis in North ­American Caucasians GWAS meta-analysis in European & North American Caucasians GWAS meta-analysis in European & North American Caucasians GWAS in European Caucasians GWAS meta-analysis in European & North American Caucasians GWAS meta-analysis in European & North American Caucasians GWAS meta-analysis in European & North American Caucasians GWAS meta-analysis in European & North American Caucasians

Diabetes Effect: OR (95% CI) 1.15

Other GenomeWide Associated Disorder(s)

Protein G-protein-coupled receptor 183

0.86 1.09 0.90

Delta-like 1 homolog

1.21

RAS guanyl releasing protein 1

0.86

Cathepsin H

0.81

MS, SLE

0.81 0.86

Celiac Crohn’s, IBD

C-type lectin domain family 16, ­member A Interleukin 27

1.28 0.87 0.95

Crohn’s, UC

Gasdermin B ORM1-like 3

CHAPTER 86  Diabetes Mellitus

TAB L E 8 6 - 5    Susceptibility Genes for Type 1 Diabetes—cont’d

PTPN2

18p11.21

rs45450798, rs478582

Intronic

GWAS in European Caucasians

1.28 0.83

Celiac, Crohn’s

CD226

18q22.2

rs763361

Ser307Gly

1.16

MS

TYK2

19p13.2

rs2304256

Val362Phe

PRKD2

19q13.32

rs425105

Intronic

FUT2

19q13.4

rs602662

Gly258Ser

SIRPG

20p13

rs2281808

Intronic

UBASH3A

21q22.3

rs3788013

Intronic

GWAS meta-analysis in European & North American Caucasians GWAS meta-analysis in European & North American Caucasians GWAS meta-analysis in European & North American Caucasians GWAS meta-analysis in European & North American Caucasians GWAS meta-analysis in European & North American Caucasians Family-based GWAS in North American Caucasians

AIRE

21q22.3

rs760426

Intronic

Candidate gene causing APECED

1.12

22q12.2

rs5753037,

Intergenic

1.10

IL2RB

22q12.3

rs3218253

Intronic

C1QTNF6

22q13.1

rs229527, rs229541

Gly21Val, 7 kb upstream

GWAS meta-analysis in European & North American Caucasians GWAS meta-analysis in European & North American Caucasians GWAS meta-analysis in European & North American Caucasians

1.11–1.12

Vitiligo

TLR7, TLR8

Xp22.2

rs5979785

Intergenic

0.86

Celiac

GAB3

Xq28

rs2664170

Intronic

GWAS meta-analysis in European & North American Caucasians GWAS meta-analysis in European & North American Caucasians

0.86

Tyrosine kinase 2

0.86

Protein kinase D2 Crohn’s

0.90 1.13

Vitiligo

Signal-regulatory protein gamma Ubiquitin associated and SH3 domain containing, A Autoimmune ­regulator

Crohn’s, IBD RA

1.16

Fucosyltransferase 2

Interleukin 2 receptor, beta chain C1q and tumor ­necrosis factor related protein 6 Toll-like receptors 7, 8 GRB2-associated binding protein 3

CHAPTER 86  Diabetes Mellitus

Adapted from http://t1dbase.org/ accessed 3/20/2011.

Protein tyrosine phosphatase, ­non-receptor type 2 CD226 antigen

19

20

CHAPTER 86  Diabetes Mellitus

GWAS identify single nucleotide polymorphisms (SNPs) that demonstrate association with a disorder or trait of interest. Occasionally, the associated SNP results in a functional change and is directly responsible for the observed association. More commonly, however, it is unlikely that the associated SNP is actually causing disease risk. In such situations, the SNP association directs research to a specific chromosomal segment, but identifying which genes within the associated regions and which variants within specific genes are responsible for type 1 diabetes susceptibility continues to be a challenge. Many of the GWAS loci contain genes that are involved in immune function, which are good candidates. In addition, a number of the same regions have been identified in GWAS performed for other autoimmune diseases, including rheumatoid arthritis, systemic lupus erythematosis, multiple sclerosis, celiac disease, Graves’ disease, Crohn’s disease, and vitiligo. Based on such observations, it is likely that some loci are associated with a generalized risk for autoimmunity, whereas other loci may provide the specificity that determines exactly which autoimmune disease will present clinically. Understanding the ways in which the various loci interact to produce type 1 diabetes will only be possible after the actual susceptibility genes and functional variants have been definitely identified. What also remains to be clarified is how many genes are involved in type 1 diabetes susceptibility. As is true with all complex genetic disorders, the genes and loci known from candidate gene studies and GWAS still do not account for all the observed heritability of the disease. One possible explanation for this “missing heritability” is that rare variants that are not detectable using current analytic methods and sample sizes may contribute to disease. On the assumption that such rare variants will only be identifiable from sequencing, exome and whole-genome sequencing projects are now underway. While it is expected that these approaches will increase the proportion of heritability that can be explained, other factors, including gene–gene interactions, epigenetic factors, copy number variations, noncoding RNAs that affect gene expression, and gene–environment interactions, may also be implicated.

86.4.10 The Role of Environmental Factors in Type 1 Diabetes The possibility that there are important environmental components to the etiology of type 1 diabetes is raised by the MZ twin data, which show a type 1 diabetes concordance of approximately 20–40%. As discussed previously, the lack of 100% concordance in MZ twins does not absolutely require the involvement of environmental factors; immunologic gene rearrangements could also provide an explanation for such a reduced penetrance (160–162). Yet the possibility of environmental factors having a significant role must be thoroughly investigated,

especially as regards the implications for preventive strategies. Environmental effects on diabetes development have been postulated going back to the 1920s, based on the observation of seasonal variations in the diagnosis of diabetes in children and young adults (163). As the pathogenetic processes that lead to type 1 diabetes appear to be complex and may take years from initiation to completion, environmental agents could function as initiating factors (i.e. factors that begin the etiologic processes that eventually terminate in type 1 diabetes), or, alternatively they could act mainly as precipitating factors (i.e. factors that convert preclinical diabetes into clinical disease). In either role (or both), what is clear is that environmental factors must act on genetically susceptible individuals for type 1 diabetes to occur. Several classes of environmental agents have been implicated in the etiology of type 1 diabetes. 86.4.10.1 Infectious Agents.  A viral etiology for diabetes has been suggested for many years, with case reports of diabetes following an episode of an infectious disease dating back to the 1800s (164,165). The current evidence for a role of viral agents comes from several sources, including case reports, epidemiological studies, clinical studies, and evidence from animal and human models. Anecdotally, a “viral-like illness” is known to precede the onset of many cases of type 1 diabetes (166). Several lines of epidemiological evidence are also consistent with an infectious etiology. For example, it has been noted that trends in age at onset of diabetes are consistent with a viral etiology (69). The total number of infections during the preceding year has been shown to correlate with type 1 diabetes risk (167). Another suggestion that environmental agents play a role in the etiology of type 1 diabetes comes from studies of time of clinical disease onset in pairs of siblings with type 1 diabetes. At least one study suggested that sibling pairs are more likely to have their onset of diabetes within a year of one another than would be expected by chance (69). Similarly, the period of discordance for type 1 diabetes in MZ twins where both ultimately develop type 1 diabetes has been reported to be less than 3 years in 60% (168). There is also limited evidence for an infectious agent’s role (e.g. mumps, coxsackie) from seroepidemiological studies (i.e. studies that compare viral and bacterial antibody titers in type 1 diabetics and nondiabetic controls) (71,72,166). While some studies have suggested an association, there have also been many negative studies. Green and colleagues (169) performed a systematic review of all case-control studies examining Coxsakie viruses and found a range of odds ratios from 0.2 to 22.3 for type 1 diabetes mellitus in serology-positive vs. ­serology-negative subjects; 7 of 13 studies had point estimates significantly greater than 1.0 (P < 0.05). Additionally, studies have utilized molecular techniques to determine the prevalence of viral DNA. For example,

CHAPTER 86  Diabetes Mellitus Nairn and colleagues (170) reported detection of enterovirus RNA in 27% of children with new-onset type 1 diabetes as compared to only 4.9% of controls and a recent meta-analysis of multiple studies has provided further support (171). The effect of childhood immunization on type 1 diabetes susceptibility has also been controversial. Although a study of the incidence of type 1 diabetes in relationship to the introduction of the measles-mumps-rubella vaccination and the subsequent disappearance of mumps in Finland suggested that elimination of natural mumps infection has decreased the incidence of type 1 diabetes (172) and measles vaccination by itself correlated with a lower risk of type 1 diabetes (167), the observation that an increasing incidence of type 1 diabetes has paralleled the expansion of childhood immunization has resulted in postulation that one or more of these vaccines may increase diabetes risk. While a number of large-scale studies appear to have disproven this hypothesis (173– 175), it remains a concern for many, particularly among parents’ groups. If indeed viral infections do play a role in the development of overt diabetes, the question that must be asked is what mechanisms are involved. The insulitis that has been noted in early type 1 diabetes could be consistent with viral infection of the pancreas, and autopsy studies have clearly documented pancreatic beta cell damage in children dying from overwhelming viral infections (176). Coxsackie B-specific antigens have specifically been found in the islets of Langerhans, and the coxsackie B4 virus itself has been isolated from the pancreas of a child dying of acute-onset type 1 diabetes (177) as well as from pancreata removed from diabetic patients undergoing organ transplantation (178). Several types of viruses are known to be capable of infecting human pancreatic beta cells in vitro, and data suggest that coxsackie virus B groups, rubella virus, and possibly cytomegalovirus are capable of producing pathologic beta cell changes in vivo. Molecular mimicry has also been postulated to be a mechanism by which viral infection can impact the development of type 1 diabetes (179). There are regions of sequence homology between glutamic acid decarboxylase (GAD) and the p2C protein of coxsackie B virus, and between regions of both GAD and tyrosine phosphatase IA-2 and the VP7 protein of rotavirus (180,181). IA-2 also demonstrates significant sequence homologies with a variety of other viral genomes, including dengue, CMV, measles, and hepatitis C (182). Such observations raise the possibility that viral infection can trigger an anti-beta cell autoimmune response by such molecular mimicry. This possibility is further supported by the observation that the homologous peptides from VP7 and IA-2 both bind to HLA-DR4 (*0401), whereas peptides from p2C and GAD both bind to DR3 (182,183). Antibodies to GAD species and to the p2C protein have also been shown to cross-react

21

in some, but not all, studies. Some authors have argued that molecular mimicry with viral proteins may actually play more of a role in the activation of preexisting autoreactive T cells than in de novo stimulation of an autoimmune response (184). In this case, a viral infection could act to precipitate the development of diabetes by acutely exacerbating a previously existing low-grade autoimmune process. The best human models of infectious agents in type 1 diabetes come from studies of individuals with the congenital rubella syndrome and from serial studies of children with viral infections who subsequently develop type 1 diabetes. The incidence of type 1 diabetes and other autoimmune diseases among children and young adults with the congenital rubella syndrome is markedly increased over that in the general population and may be as high as 15–40%. Those cases of congenital rubella with type 1 diabetes have an increased frequency of HLA-DR3 and DR4 and a decreased frequency of HLA-DR2, much as in non-rubella type 1 diabetes cases (185). A significant proportion of patients with congenital rubella syndrome have T-cell subset abnormalities, and a variety of autoimmune antibodies, including anti-thyroid microsomal, anti-thyroglobulin, and antiislet cell and islet-cell surface antibodies, suggesting an autoimmune etiology for their type 1 diabetes (185,186). Rubella virus has been isolated from the pancreas of several cases with congenital rubella syndrome (187), and at least one case is known of insulitis and beta cell destruction in an infant with congenital rubella infection who died of acute diabetes (188). This evidence suggests that rubella can indeed infect and damage the beta cell, and that the diabetes seen in congenital rubella syndrome could be due either to initiation of an immune process by the rubella virus, or directly to persistent pancreatic rubella infection. 86.4.10.2 Dietary Agents and Molecular Mimicry.  A continuing area of interest regarding environmental triggers for type 1 diabetes has been dietary exposure. In the 1990s several studies reported an association of the introduction of cow’s milk and cessation of breast feeding with type 1 diabetes risk (189–193). Elevated antibodies to several cow’s milk proteins have also been found in children with type 1 diabetes (193,194). When it was reported that one epitope of bovine serum albumin, a 17-amino-acid residue peptide (ABBOS) cross-reacts with p69, a beta cell surface protein, the hypothesis was raised that early introduction of cow’s milk into the infant diet could result in initiation of autoimmune injury to the beta cell via this molecular mimicry (195,196). In addition to bovine albumin, the hypothesis has been put forward that bovine insulin in cow’s milk could trigger development of anti-insulin antibodies (197). However, several subsequent large studies of infant nutrition have not supported the association of autoimmunity to cow’s milk and type 1 diabetes risk and the likelihood that cow’s milk has a significant effect on risk now appears

22

CHAPTER 86  Diabetes Mellitus

small (198–200). Despite these studies, the possible role for dietary exposure in diabetes risk remains a topic of study. Dietary exposures may, for example, influence the development of type 1 diabetes in individuals who are genetically at high risk (201,202). To date, however, the data appear to argue more strongly for infectious triggers than for dietary ones.

86.4.11 Genetic Counseling in Type 1 Diabetes Because the mode of inheritance in type 1 diabetes is not straightforward, most genetic counseling for type 1 diabetes is based on empirical risk estimates, which have been developed from both population-based and family-based epidemiological studies (Table 86-6). These recurrence risks are frequently reassuring to families, as the risks are often less than the family has feared, particularly for siblings of the type 1 diabetes patient. The empirical risk of recurrence for type 1 diabetes is dependent on the relationship of the individual in question to the affected family member. For siblings the empirical risk is approximately 5–10%. If the father is affected, the risk to his offspring is 4–6%, compared with 2–3% if the mother is affected (203,204). For further refinement of sibling risks, HLA testing and autoantibodies can be used to determine haplotype sharing with the diabetic sib (205). If two HLA haplotypes are shared, the risk increases to 16–17%, and is 20–25% if the haplotypes contain both DR3 and DR4. Siblings who share one haplotype have a risk in the range of 5–7%, whereas the risk is approximately 1–2% if no haplotypes are shared (4). It is important to realize that

TA B L E 8 6 - 6    Risks for Type 1 Diabetes Population risks Overall HLA-DR related No high risk allele One high risk allele (i.e. DR3/x or DR4/x) HLA-DR4 subset defined by molecular techniques HLA-DR3/3 or DR4/4 HLA-DR3/4 Risks in relatives Siblings Overall HLA haplotypes shared with diabetic sibling 0 haplotypes shared 1 haplotypes shared 2 haplotypes shared 3 haplotypes shared and DR3/4 Monozygotic Twin of Diabetic Offspring Overall Offspring of affected female Offspring of affected male

1/500 1/5000 1/400 1/300 1/150 1/40 1/14 1/100 1/20 1/6 1/5–1/4 1/3 1/25 1/50–1/40 1/20

the sibling of an individual with type 1 diabetes still has a risk for type 1 diabetes that is increased above that of the general population, even when the sib shares no HLA haplotypes in common with the diabetic in the family. There is some question regarding the benefit of performing genotyping or autoantibody testing for the siblings of an individual with type 1 diabetes when at present it will not lead to any alteration in management. While testing may be performed as part of research studies examining possible prevention strategies, at present there are no proven therapies for individuals who are found to be at high risk. Particular attention must be paid to the potential negative effects of stigmatization and the risks of the child being treated as though he is ill. The concept that HLA testing at best identifies someone more susceptible to developing type 1 diabetes but in no way guarantees that she will become diabetic must be stressed in discussions with the family. The potential for other negative effects, such as possibly being ruled ineligible for health, life, and/or disability insurance because of the presence of a “preexisting” condition must also be discussed whenever a family is contemplating more refined testing. Such risks have been reduced in the US with the enactment of the Genetic Information Nondiscrimination Act of 2008 (GINA), but still remain of concern (206). What is clear is that at present genotyping is not appropriate as a clinically applied screening tool for the general population. Approximately 50% of the nondiabetic population has the same HLA-DR types as patients with type 1 diabetes. Thus, at least 98% of the people with DR3 or DR4 will never develop type 1 diabetes. For every 1000 persons with HLA-DR3 or DR4 in the population, only two to four will develop type 1 diabetes in their lifetimes (see Table 86-6). While GWAS have identified many more genetic loci that contribute to risk, the overall ability to predict individuals who will go on to develop type 1 diabetes remains far from ideal (207). In order for risk prediction to become appropriate, a mechanism for intervening to delay or prevent diabetes onset is needed. A few decades ago, there was great hope that such interventions were close to reality. A variety of clinical trials were undertaken to test various methods of intervention to prevent or delay the onset of type 1 diabetes, including the Diabetes Prevention Trial (DPT-1) (USA), Type 1 Diabetes Prediction and Prevention Project (DIPP) (Finland), European Nicotinamide Diabetes Intervention Trial (ENDIT), European Paediatric Prediabetes Subcutaneous Insulin Trial (EPP-SCIT), and the Finnish Trial to Reduce IDDM in the Genetically at Risk Study (TRIGR) (208–212). Unfortunately, many of these efforts have been proved to be ineffective and the future of type 1 diabetes prevention is less hopeful. A few prevention studies continue, however, including ones involving oral insulin (NCT00419562; NCT00336674) and teplizumab (NCT01030861) (http://www.clinicaltrials.

CHAPTER 86  Diabetes Mellitus gov/ct2/home). More trials are underway in newly diagnosed diabetic subjects (reviewed in (213)). 86.4.11.1 Screening for Other Autoimmune Disorders.  Diabetes is not the only autoimmune disorder for which relatives of an individual with type 1 diabetes are at risk. Family members, as well as the diabetic patient, are at an increased risk for autoimmune thyroid disease (Hashimoto thyroiditis, Graves’ disease), pernicious anemia secondary to autoimmune gastritis, autoimmune adrenal disease (Addison’s disease), myasthenia gravis, vitiligo, and celiac disease (214,215). A study looking at individuals with type 1 diabetes and their relatives found that 21% of the diabetics and 22% of their first-degree relatives had evidence of autoimmune disease (216). Of patients with persistent ICA, 57% had other autoimmune conditions compared with 15% of those not found to have persistant ICA (216). About 75% of the autoimmune disease in relatives occurred in families in which there was a proband with autoimmune disease, indicating that there may be increased genetic susceptibility to other autoimmune disorders in certain type 1 diabetes families. The most common form of autoimmune disease in families with type 1 diabetes is thyroid disease (216– 218). Although the proportion of type 1 diabetes patients with clinical or subclinical thyroid disease has been reported to be as high as 35%, the actual proportion is thought to be closer to 15–20% (216,219). In contrast, the prevalence of autoimmune thyroid disease in nondiabetic whites is thought to be 4.5% (218). The prevalence of clinical or subclinical autoimmune thyroid disease in first-degree relatives of individuals with type 1 diabetes is estimated to be 15–25% (216,219). As is true with autoimmune thyroid disease in the general population, female family members have higher rates of thyroid and gastric autoimmunity than do males. Other autoimmune disorders are also seen with increased frequency in type 1 diabetic individuals and their relatives. Autoimmune gastritis, as evidenced by the detection of gastric parietal cell autoantibodies or pernicious anemia, is seen in 5–12% of individuals with type 1 diabetes and 2.5–6% of their first-degree relatives (216,219–221). The prevalence of adrenal autoantibodies is 1–3% in individuals with type 1 diabetes compared with up to 0.6% in nondiabetics (216,218). It is particularly important for the relatives of patients with type 1 diabetes to be made aware of this increased risk for autoimmune disease, since approximately 40% of all families that include an individual with type 1 diabetes will have at least one other family member with latent or clinical autoimmune disease (216). Although most physicians know of the association of type 1 diabetes with other autoimmune disease, the fact that close relatives are also at risk is not as well appreciated in the medical community. Since many of these autoimmune disorders can have relatively insidious onsets, with fairly nonspecific symptoms, making the relatives and their

23

physicians aware of the increased risk may lead to earlier diagnosis and treatment. Given this increased risk for autoimmune diseases, periodic screening of individuals with type 1 diabetes and all their first-degree relatives is warranted, particularly for thyroid dysfunction (via standard tests such as obtaining T4 and TSH levels) and for vitamin B12 deficiency, which if untreated, leads to pernicious anemia. 86.4.11.2 Pregnancy and Type 1 Diabetes.  There is a markedly increased risk of congenital anomalies in the offspring of women with type 1 diabetes (222,223). In the general population, the risk to have a child with a birth defect is 2–3%, whereas for women with type 1 diabetes, the risk is increased threefold i.e. 6–10% (224–226). The malformations seen in infants born to diabetic women tend to be more severe than those seen in infants of nondiabetic women and include abnormalities of the skeletal, renal, cardiac, and central nervous systems (Table 86-7) (225,227–231). Virtually all anomalies occur with increased frequency in infants of diabetic mothers but those that have the highest relative risks are caudal regression, renal agenesis, transposition of the great vessels, ventricular septal defects, atrial septal defects, situs inversus, focal femoral hypoplasia/ unusual facies, and neural tube defects (anencephaly and meningomyelocele). Although these malformations are generally not specific for diabetes, caudal regression is seen much more often in infants of diabetic mothers than in the general population. The relative risk for caudal regression in the offspring of a diabetic woman has been estimated to be as high as 200 (228). The relative risks for the other defects are not as high, due in large part to their higher incidence in the general population (227). The disruption of embryogenesis leading to the abnormalities occurs before the eighth week of pregnancy (i.e. often before a woman realizes that she is pregnant) (232). There is evidence to suggest that elevated glycosylated hemoglobin (HbA1c) levels are associated with a high risk for malformations, and vigorous control of

TABLE 86-7     Congenital Malformations in Infants of Diabetic Mothers Malformation Caudal regression Spina bifida, hydrocephalus, and other CNS defects Cardiac defects (including transposition of the great vessels, ventricular septal defects, atrial septal defects) Anal/rectal atresia Agenesis Cystic kidney Duplicated ureter Situs inversus aIn

diabetic vs nondiabetic pregnancies. Data from Mills JL (486) and Soler et al. (230).

Ratio of Incidencesa 200–600 2 4 3 6 4 23 84

24

CHAPTER 86  Diabetes Mellitus

blood glucose levels before conception has been shown to significantly reduce the incidence of congenital malformations (233–236). Although it is beneficial to optimize diabetes control even in women who present when they are already pregnant, postconceptional intervention is less likely to reduce the malformation risk (232,237). Beginning in early adolescence, diabetic women of childbearing age should be made aware of the risk of congenital malformations and counseled that planning their pregnancies is essential so that optimal metabolic control of their disease can be achieved before conception and continued throughout their pregnancy. Because of the increased risk for major structural malformations, prenatal diagnostic tests should be recommended for all pregnant women who have type 1 diabetes. These should be performed during the second trimester (usually between 16 and 20 weeks gestation), providing women with abnormal results the opportunity to obtain genetic counseling regarding the anomaly (i.e. prognosis and treatment options) and to make informed decisions regarding pregnancy options. For women who have normal results, the information obtained via prenatal diagnosis can be very reassuring and help alleviate anxiety for the remainder of the pregnancy. Ultrasonography can be used to evaluate fetal growth and to rule out major fetal structural anomalies such as renal agenesis, neural tube defects, and caudal regression. Fetal echocardiography, performed at 16–22 weeks following the first day of the last menstrual period, enables prenatal diagnosis of major structural cardiac malformations. Elevations of maternal serum alpha-fetoprotein (MSAFP) are associated with open neural tube defects such as anencephaly and meningomyelocele (238,239); thus, MSAFP, ideally incorporated into triple marker screening (with β-HCG and estriol), is recommended for all pregnant diabetics. Because MSAFP levels are altered in pregnant diabetics compared to nondiabetics, tables specific for diabetic women must be used when calculating their MSAFP values and it is therefore important that the laboratory performing the assay be made aware that the patient is diabetic (240–242). There is good evidence from the general population that folic acid supplementation, begun before conception, is helpful in decreasing the risk for neural tube defects (243–246). Although studies looking specifically at infants of diabetic mothers have not been reported, folic acid supplementation before conception should be strongly considered, as the potential benefits (i.e. reducing the risk of neural tube defects and possibly cardiac defects as well) outweigh any known risks (247).

86.5 TYPE 2 DIABETES MELLITUS 86.5.1 Introduction Type 2 diabetes is characterized by a relative disparity between endogenous insulin production and insulin requirements, leading to an elevated blood glucose. In

contrast to type 1 diabetes, there is always some endogenous insulin production in type 2 diabetes; many type 2 patients have normal or even elevated blood insulin levels. The disease usually occurs in persons over the age of 40 years, although occurrence in childhood is increasingly recognized, and the onset may be insidious, or even clinically inapparent. The hyperglycemia of type 2 diabetes can often be controlled by diet or oral hypoglycemic agents, although exogenous insulin may be required. Type 2 diabetes continues to increase in frequency and is considered a rising epidemic worldwide (248). It has been estimated that one in every three individuals (one in two minorities) born in the United States in the year 2000 will develop diabetes in their lifetime (249), mainly due to the epidemic of obesity. The primary pathogenetic lesion in type 2 diabetes has yet to be discovered. Primary insulin resistance of the peripheral tissues has been suggested by many as the initial event. Similarly, insulin secretion abnormalities have been argued as the primary defect in type 2 diabetes. It is likely that both phenomena are important in the development of type 2 diabetes, and genetic defects predisposing to both are likely to be important contributors to the disease process. In the most commonly accepted pathogenetic model, insulin resistance initially develops and induces compensatory insulin hypersecretion to maintain normoglycemia. Diabetes develops only when the pancreatic beta cells become, over time, unable to secrete enough insulin to overcome peripheral insulin resistance. Type 2 diabetes is a progressive disorder in that beta cell function declines with increasing duration of diabetes. GWAS in recent years appear to suggest that failure of insulin secretion is the key event that initiates type 2 diabetes; most diabetes loci discovered by GWAS appear to compromise insulin secretion or pancreatic beta cell development. 86.5.1.1 Evidence of a Genetic Contribution to Type 2 Diabetes.  Several lines of evidence suggest the importance of genetic susceptibilities underlying the development of type 2 diabetes (250). Genetic epidemiological studies provide convincing descriptive data including population and ethnic differences, studies of familial aggregation, familial transmission patterns, and comparisons of twin concordance rates. Animal models of type 2 diabetes and studies of specific genetic syndromes that feature glucose intolerance provide further data supporting the etiologic role of genetic factors in the pathogenesis of type 2 diabetes. Finally, the genetic etiologies for type 2 diabetes have also been supported by association and linkage studies of genetic markers in populations and families. 86.5.1.2 Evidence from Animal Models.  Relevant animal models provide the opportunity to study genes and pathophysiological mechanisms that may have application to human diabetes. Variability in blood glucose levels occurs between different strains of inbred mice and rats (251). Among the more intensively studied

CHAPTER 86  Diabetes Mellitus mouse models of type 2 diabetes are the ob/ob (obesity and hyperglycemia) and db/db (diabetic obese) mice (252,253). The diabetes and obesity seen in conjunction with these two mutations are modified by the genetic background of the strain of mouse in which they occur (254,255). In the ob/ob mouse, the obesity and diabetes phenotype results from a nonsense mutation that generates a stop codon in the gene for the hormone leptin. The db/db mouse model has resistance to the ob gene product because of mutation in the leptin receptor (256,257). As with leptin, mutations in the leptin receptor are extremely rare in humans (258). Several other animal models have been proposed as being more relevant to the human condition. These include the C57BL/6 J mouse strain (without leptin or leptin receptor mutations) (251,259–261), the NSY mouse (262), the TSOD mouse (263), which manifests both obesity and diabetes, and the SHR/N-cp rat (264,265). Multiple genes appear to be involved in causing diabetes in these models, similar to the apparent multigenic etiology of type 2 diabetes in humans. Investigation in these rodent models has only very rarely implicated loci that may affect type 2 diabetes in humans (266). 86.5.1.3 Evidence from Population Studies.  Population-based studies of the distribution of a phenotypic trait can be helpful as a first step in evaluating whether the trait is likely to be controlled by a “major gene” or by multiple factors (either genetic or environmental). Several studies suggest that in populations with a high prevalence of type 2 diabetes, the distribution of glucose tolerance may be bimodal; that is, fasting glucose levels appear to be distributed around two distinct mean values. For example, in the Pima Indians, the Oklahoma Seminoles and several South Pacific populations, the distribution of glucose tolerance values in adults is consistent with an underlying bimodal distribution (259,267–269). This is usually interpreted as suggesting that there is a major gene that influences glucose tolerance, although these data are also consistent with more than one major gene. However, in most populations, blood glucose values in the population appear to be distributed unimodally. This is likely due to the heterogeneous nature of most other populations under study. 86.5.1.4 Evidence from Twin Studies and Family Studies.  Studies in twins and families have long suggested a “genetic,” or at least a strong familial component, to the susceptibility to type 2 diabetes. MZ twin studies demonstrate very high concordance for type 2 diabetes in the twin pairs (62), yet the overall familial aggregation of clinical disease or glucose levels is not consistent with a single, simple mode of inheritance (270). Genetic heterogeneity would seem the most likely explanation and is supported by recent GWAS findings. In addition, exposure to environmental factors is known to be important as well. The identical twin data, with 70–90% concordance in MZ twins, suggest that, in the urbanized Western world, the environment is sufficiently

25

constant (and diabetogenic) such that genetic susceptibility is the primary determinant for the development of type 2 diabetes. In studying a specific phenotype hypothesized to be related to type 2 diabetes development, Tremblay and coworkers (271) found tentative evidence for genetic factors influencing sensitivity of insulin levels with physical training in response to short-term exercise in male MZ twin pairs. 86.5.1.5 Difficulties in Studying the Genetics of Type 2 Diabetes.  Type 2 diabetes and other common chronic diseases present a number of difficult analytic challenges to the geneticist. The late and variable age of onset of type 2 diabetes, probably resulting from interactions of both genetic and environmental factors, can result in an underestimation of the number of individuals who are genetically susceptible to type 2 diabetes. This is a particularly vexing problem for family studies, in which linkage of type 2 diabetes with genetic markers is often the goal. While there is typically no confusion about the status of an affected living individual, unaffected individuals who carry the requisite gene(s), but who have not yet lived long enough to express diabetes, will not be recognizable. In addition, at the time a family is studied, many affected members in the older generations will be deceased and may have had their diabetes diagnosed (or not diagnosed) years ago, using perhaps less-than-­optimal diagnostic criteria. The late age of onset also means that some individuals who are genetically “affected” will die of other causes before developing diabetes. Another difficulty in studying the genetics of type 2 diabetes is the strong environmental component involved in many forms of diabetes. In industrialized or “westernized” countries, high MZ twin concordance rates suggest that the environment is sufficiently uniform (and diabetogenic) such that most individuals with the genetic predisposition will develop diabetes. On the other hand, in nonwesternized countries, studies of the genetics of type 2 diabetes are far more difficult to carry out. Many people with the requisite genes will simply never have the opportunity to manifest clinical disease under existing environmental conditions. Studies in migrant populations that have had a rapid change in diet and/or exercise levels give some indication of the strength of the environmental component in the etiology of type 2 diabetes. For example, among the Nauruans of the South Pacific, documented prevalence of diabetes has increased from low rates to more than 50% of the adult population in a time period of about 30 years (272). Similar increases in prevalence with westernization have been noted in other populations as diverse as the natives of Australia, Africa, and Near Eastern immigrants to Israel, Japanese immigrants to the United States, and certain Native American populations (273–276). Perhaps the most problematic aspect of studying the genetics of type 2 diabetes is the likely extensive etiologic

26

CHAPTER 86  Diabetes Mellitus

heterogeneity that underlies this disease. Genetic defects could (and probably do) influence any of the many steps involved in glucose regulation. Each of these defects, either alone or in concert with other defects, could result in type 2 diabetes. While such etiologic complexity by no means precludes genetic investigations, extensive etiologic heterogeneity implies that to understand particular pathogenetic mechanisms, one must be able to measure physiological “defects” at a more specific level than the gross phenotype of glucose intolerance.

86.5.2 Genetics of Intermediate Phenotypes for Type 2 Diabetes Mellitus The study of physiological traits associated with type 2 diabetes within families can be useful in several levels, including dissection of disease heterogeneity. First, it may allow characterization of early stages of, and variability in, the natural history of the disease. It also allows for comparison between families, which may be helpful in separating etiologic subtypes. Finally, it can lead to better studies of mode of inheritance and linkage to genetic markers, as more of the genetically “affected” individuals in the pedigree will be identified. Another advantage of the study of intermediate phenotypes is that they may more closely reflect underlying genetic defects that predispose to disease. The first physiological studies in families with type 2 diabetes were conducted using glucose tolerance as the phenotype. Even with this relatively crude measure of glucose metabolism, there was evidence that, in normal healthy subjects, glucose and insulin responses have an appreciable genetic component (277). In their studies of large pedigrees with type 2 diabetes, Beaty and Fajans (278) also assessed the role of genetic determinants of fasting blood sugar levels. Their data were consistent with a role for additive genetic factors, although a large proportion of the intrafamilial variability could not be explained by genetic factors. Familial studies of liability for hyperglycemia in Pacific Nauruans have also been interpreted as consistent with the effect of a major gene (279). However, in studies of Japanese ­Americans, ­Williams and colleagues (280) concluded that heritability of fasting blood glucose within families was low, and they could find no evidence for a major gene. Similar results were reported from a study of families in ­Jerusalem (281). In contrast, a study among healthy female twin pairs found a high heritability (proportion of total trait variance that is due to genetic factors) of 0.75 for fasting glucose (282). In studies of Danish twins, the heritability of abnormal glucose tolerance was found to be 0.61; the heritabilities for women and men of fasting glucose were 0.12 and 0.38 and of 2-h postload glucose were 0.38 and 0.43, respectively (283,284). Insulin sensitivity/resistance is known to be heritable, evidenced by the observation of reduced insulin sensitivity in nondiabetic relatives of subjects with type 2

diabetes (285,286). Fasting insulin level is a simple surrogate measure of insulin sensitivity/resistance. Studies in the Mexican American population, a high-risk population with a high prevalence of type 2 diabetes, have demonstrated a genetic “dosage effect” on fasting insulin levels (287). An increase in fasting insulin levels was a function of whether an individual had 0, 1, or 2 diabetic parents, suggesting that insulin resistance is familial. Reported estimates for the heritability of fasting insulin range from 0.17 (288) to 0.38 (289) to 0.53 (290,291). In addition to genetic differences between populations studied, this wide range may reflect the fact that fasting insulin is determined not only by insulin sensitivity but also by insulin secretion and insulin clearance, each of which appears to be genetically determined (289). Insulin sensitivity may also be more directly quantified by physiological phenotyping procedures used in research settings, such as the euglycemic hyperinsulinemic clamp, the frequently sampled intravenous glucose tolerance test, or the insulin suppression test (292). In an elegant study in Pima Indians (another high risk group), Lillioja and associates (293) demonstrated using the euglycemic clamp that in vivo insulin action has a familial component. Glucose uptake at maximally stimulating insulin concentrations showed a high degree of familiality that was independent of age, sex, or degree of obesity. To control for familial correlations in dietary intake, subjects were placed on a standard diet for at least 7 days. Thus, the “familial” component, which was estimated to explain 38% of the variance in insulin action, appeared to be due to genetic rather than environmental similarities. Other studies utilizing the euglycemic clamp, widely considered to be the “gold standard” in quantification of insulin sensitivity, found heritabilities of 0.37–0.55 (289,294–296). The heritability of insulin sensitivity, quantified by the frequently sampled intravenous glucose tolerance test ranged from 0.28 to 0.44 (278,297,298). Besides insulin resistance, an abnormality in insulin secretion is present in people with type 2 diabetes. Genetic/environmental influences on the insulin response to glucose were studied at the Karolinska Hospital in Sweden (299,300). In studying insulin release after a glucose infusion in family members, as well as fasting and stimulated glucose and insulin levels, these researchers first concluded that their data showed considerable intrafamilial correlation and was consistent with a major recessive gene common in the Swedish population (with a gene frequency perhaps as high as 20%) (299). More recent studies of insulin release and sensitivity in these families still suggest that these variables are genetically regulated, although the evidence for a major gene is no longer as convincing (300). The studies of Haffner and colleagues (301) in both Mexican American and nonHispanic whites have also provided evidence that insulin secretion is likely genetically influenced, as a family history of diabetes was associated with decreased insulin secretion in response to an oral glucose load, as well as

CHAPTER 86  Diabetes Mellitus with fasting insulin levels. Other studies have also looked at physiological abnormalities of insulin secretion in relatives of diabetics (302). For example, O’Rahilly and colleagues (303) studied the normal pulsatile release of insulin in first-degree relatives of type 2 diabetes subjects. Compared to controls, the first-degree relatives lacked the normal oscillations in insulin secretion following an intravenous glucose challenge. Since these relatives had mild glucose intolerance and high normal fasting glucose levels, this lack of pulsatile insulin release may be the first expression of type 2 diabetes in these high-risk relatives (304). Heritability estimates for insulin secretion range from 0.25 to 0.57 as determined from insulin measurements during oral glucose tolerance testing (283,284,305) and 0.23 to 0.84 as determined from intravenous glucose tolerance tests (277,296,297,306–308). The genetic basis of the previously mentioned intermediate traits is supported by the fact that linkage signals for these traits have been detected in genome-wide scans. The Insulin Resistance Atherosclerosis (IRAS) Family Study found linkage of both fasting glucose and fasting insulin with a locus on chromosome 17p (309). The same study found linkage peaks for insulin secretion on chromosomes 4q, 11q, and 12q; insulin sensitivity index was linked to chromosome 15p (310). The insulin secretion linkage to 4q was replicated by the HERITAGE Family Study, which also identified another locus for insulin secretion on 10p (311). The Finland-United States Investigation of Non-insulin Dependent Diabetes Mellitus Genetics (FUSION) study also found linkage of insulin secretion to 10p, as well as 9p (312).

86.5.3 Evidence Supporting Heterogeneity in Type 2 Diabetes When surveys of glucose tolerance have been performed in populations of European ancestry, the number of individuals found to have latent (subclinical) diabetes has been approximately equal to that with known diabetes. Among the Eskimos, however, clinical diabetes is extremely rare, but abnormal glucose tolerance tests have been found to be very common (313). Thus, abnormal glucose tolerance in the Eskimo appears to be a biochemical trait that rarely leads to clinical diabetes. The maximum plasma insulin response to an oral glucose challenge in healthy Navajo and Pima Indians was over three times as great as that observed in Western Europeans (314,315). In addition, the insulin output of type 2 diabetics was also clearly different in the American Indians than in the Europeans. Physiological studies of Asian Indians with type 2 diabetes suggest that they are more insulin resistant than are whites with type 2 diabetes, even when the degree of obesity is comparable (316). These data raise the possibility that there may be distinct subtypes of type 2 diabetes in different populations or ethnic groups and that it is possible that these differences are genetically determined.

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Even early clinical genetic studies suggested heterogeneity within type 2 diabetes. When Kobberling (317) divided his adult-onset probands into low, moderate, and markedly overweight categories, he found a significantly higher frequency of affected siblings in the light-proband category (38%) and a significantly lower frequency in the heavy-proband category (10%). Irvine and colleagues (318) also suggested a difference between nonobese and obese insulin-dependent propositi. They observed a different clinical range of diabetes in the relatives of the nonobese and obese propositi. Fajans and coworkers (319) have demonstrated metabolic heterogeneity in nonobese latent diabetes. These investigators were able to divide their latent diabetic patients into two broad groups: those with an insulinopenic form of glucose intolerance and those with high levels of plasma immunoreactive insulin. The high responders and low responders remained consistent and distinct over many years of follow-up, suggesting that they represented different metabolic disorders. There is remarkable variability in the physiological abnormalities seen in patients with type 2 diabetes, ranging from structural and numeric abnormalities of pancreatic alpha and beta cells to abnormalities in pancreatic insulin secretion and decreased insulin sensitivity in the pancreas and peripheral tissues. There is considerable evidence for even further physiological heterogeneity in type 2 diabetes (320). For example, among patients with mild type 2 diabetes or impaired glucose tolerance are individuals with early insulin responses that range from supernormal to subnormal. Similar variability has been documented for the late insulin response in such patients (320). Because so much variability is seen in individuals with presumably early states of diabetes, it strongly suggests that type 2 diabetes is not caused by a single defect. Genetic studies, as discussed later, support this notion.

86.5.4 Genetic Approaches in Type 2 Diabetes In dealing with heterogeneity in type 2 diabetes, there are several possible research strategies that can potentially be employed. In general, there are three options: (1) start with observable physiological differences and then work backward to determine if these differences can be explained by different genetic defects (working from the phenotype down); (2) start with a candidate gene or allele proposed to be related to diabetes, establish a genetic relationship, and work forward (working from the genotype up) to determine if specific physiological traits are associated with this gene or gene defect; and (3) use genome-wide linkage or GWAS approaches to identify chromosomal regions likely to contain diabetesrelated traits or clinical diabetes itself and then assess if there are detectable physiological differences between those individuals (or families) displaying linkage and/or linkage disequilibrium and those individuals or families

28

CHAPTER 86  Diabetes Mellitus

that do not appear to be linked to the particular genomic locus. In actuality, research often involves the sequential application of all these approaches, as is well demonstrated by the investigations of MODY.

86.5.5 Maturity Onset Diabetes of the Young MODY was originally described in 1964 (321) and was clearly identified as an autosomal dominant subtype of type 2 diabetes in the 1970s (322). In addition to the criteria for the diagnosis of diabetes, the MODY diabetic must meet the following additional criteria: (1) age of onset for at least one family member under 25 years; (2) correction of fasting hyperglycemia for at least 2 years without insulin; and (3) nonketotic diabetes (322). Using these criteria, many families with clearly dominant inheritance have been identified. However, there is considerable clinical heterogeneity within MODY, which is now appreciated to be due in large measure to genetic heterogeneity. In the French population, using rather stringent criteria, it is estimated that MODY may account for as much as 10–15% of familial diabetes cases, but less of general or later-onset type 2 diabetes (323). Although MODY is a relatively rare disorder, accounting for approximately 2–5% of all type 2 diabetes cases, it has taken on great importance in the past decade because of the lessons it has taught about the loci involved in type 2 diabetes and genetic heterogeneity. The first MODY locus was identified by Bell and colleagues (324), with the demonstration of linkage of MODY with the adenosine deaminase (ADA) locus on the long arm of chromosome 20 in one large MODY family (the RW pedigree). Interestingly, clear delineation of linkage was only possible after exclusion of certain branches of the RW pedigree from analysis, following appreciation of the fact that non-MODY type 2 diabetes was occurring in these branches. The responsible gene is HNF-4α (325); to date only a handful of MODY families have mutations in HNF-4α, suggesting that this locus (MODY1) accounts for a minority of MODY subjects. Not long after linkage to chromosome 20 was reported, linkage in other MODY families was reported with the glucokinase gene (GCK) on chromosome 7p (326,327). Unlike the ADA locus on chromosome 20, which was tested simply as a polymorphic marker in a systematic mapping approach, GCK was tested as a candidate gene because of its role in glucose homeostasis (323,328,329). Most MODY patients have a decreased insulin response to glucose, suggesting a primary pancreatic beta cell defect (330); thus the GCK was an excellent candidate for genetic investigations. Following the demonstration of linkage, actual mutations within the coding region were identified (331–333). Mutations in GCK account for a major portion of MODY pedigrees; as high as 60% of French MODY families have GCK mutations (334).

To date, mutations in six genes have been identified that produce a MODY phenotype, with another five being proposed (Table 86-8). Mutations in HNF-1α (MODY3) are the most common form of MODY. While in most forms of MODY diabetes is the only identified abnormality, mutations in HNF-1β are also associated with serious renal defects and sometimes also with genital anomalies (Mullerian aplasia) (335–338). Heterozygous mutations in IPF1 cause MODY, whereas homozygous mutations produce pancreatic aplasia or hypoplasia and neonatal diabetes requiring insulin treatment (339,340). The severity of diabetes varies depending on which gene is altered; defects in the hepatocyte nuclear factor genes result in much more serious, often insulin-requiring, diabetes than is seen with GCK mutations. Unlike classical type 2 diabetes, the predominant abnormality leading to diabetes resulting from MODY gene mutations lies in insulin secretion, with insulin resistance not being a significant factor. Heterozygous GCK mutations cause a relatively mild form of diabetes or only impaired glucose tolerance in most affected individuals. The prevalence of frank diabetes compared with impaired glucose tolerance is less than 50% (341). They rarely require insulin and usually do not develop vascular complications (342). Mutations in GCK are thought to alter the set point of the beta cell so that a higher circulating glucose level is necessary to trigger insulin secretion (343). The much rarer homozygous GCK mutations result in permanent neonatal diabetes requiring insulin therapy (344). Even with the identification of six (and perhaps eleven) genetically distinct forms of MODY, there may be additional MODY loci to be found. Approximately 15–20% of MODY in England and France, and an even higher proportion in Japan, does not result from mutations in any of the known MODY genes (345). Given that several genes are able to cause MODY, it is not surprising that even greater genetic heterogeneity has been observed within “classical” type 2 diabetes. Just as different MODY defects appear to cause varying degrees of diabetes severity and complications, the clinical and physiological differences among type 2 diabetes patients may well result from genetically separate forms of diabetes.

86.5.6 Candidate Genes and Type 2 Diabetes A large number of candidate genes have been tested for possible roles in the etiology of type 2 diabetes with mostly negative results. Many associations initially reported were not reproduced in other studies; this may be a result of false-positive studies, false-negative studies, or true variability in association among different populations, wherein a variant may alter diabetes risk differently depending on the genetic and/or environmental characteristics of the particular population (120). Those genes with the strongest evidence of association

TAB L E 8 6 - 8    Genes that Can Cause Maturity-Onset Type Diabetes Locus

Chr

Gene

Function

References

MODY 1

20

Hepatocyte nuclear factor-4α (HNF4, TCF14)

(487–489)

MODY 2

7

Glucokinase (GCK)

MODY 3

12

Hepatocyte nuclear factor 1α (HNF 1A; TCF1)

MODY 4

13

Insulin promoter factor (IPF1)

MODY 5

17

Hepatocyte nuclear factor-1β (HNF1B; TCF2)

MODY 6

2

MODY 7 MODY 8

2 9

Neurogenic differentiation 1 or beta cell E-box trans-activator 2 (NEUROD1; BETA2) Kruppel-like factor 11 Carboxyl-ester lipase

Expressed in liver, kidney, intestine, and pancreatic islets Member steroid/thyroid hormone receptor superfamily A key regulator of pancreatic gene expression, it is an upstream activator of ­HNF-1α expression First step in glycolysis; phosphorylates glucose/glucose-6-P; functions as glucose sensor in beta cells by controlling glucose entry to glycolysis; expressed in beta cells and liver Transcription factor in liver, kidney and β-cell Weak activator of transcription of the rat insulin gene; impaired dimerization may be the cause of beta cell dysfunction; associated with a more severe insulin secretory defect than seen in MODY2 Key regulator of islet peptide hormone expression and also responsible for development of the pancreas, likely by determining maturation and differentiation of pancreatic precursor cells in the developing gut. Heterozygous mutations cause MODY; homozygous mutations cause pancreatic agenesis Transcription factor in liver, kidney and β-cell Associated with renal abnormalities (nephron agenesis, hypoplastic glomerulocystic disease, severe nephropathy leading to renal failure) and genital anomalies Transcription factor required for normal development of pancreatic beta islets; Transcriptional activator of insulin gene

MODY 9 MODY 10

7 11

Paired box gene 4 Insulin

MODY 11

8

B-lymphocyte specific tyrosine kinase

(325,492)

(339,340)

(335–338)

(493,494) (495,496) (497) (498) (499,500) (501,502)

CHAPTER 86  Diabetes Mellitus

Transcription factor that activates the insulin gene Major component of pancreatic juice. Mutation leads to diabetes and pancreatic exocrine dysfunction, suggesting a link between exocrine and endocrine pancreatic function Transcription factor involved in β-cell development Rare missense mutations in the insulin gene have led to MODY in a few families. Some affected members had mild diabetes initially controlled by oral agents, followed by insulin Expressed in β-cells, where it colocalizes with insulin. Overexpression in islets enhanced insulin secretion in high glcuose conditions

(331,334,490,491) (332,333)

MODY, maturity-onset diabetes of the young.

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CHAPTER 86  Diabetes Mellitus

are peroxisome-proliferator-activated receptor gamma (PPARG), the pancreatic beta cell inwardly rectifying potassium channel Kir 6.2 (KCNJ11), transcription factor 2, hepatic (TCF2, also known as HNF1B), and wolfram syndrome 1 (WFS1). Of note, rare, severe mutations in all four of these genes lead to syndromic diabetes, and two of these genes (PPARG, KCNJ11) code for proteins that are targets of oral antidiabetic medications. PPARG codes for the nuclear hormone receptor transcription factor PPAR-γ, which is highly expressed in adipose tissue and plays a role in adipocyte differentiation and insulin sensitivity. A common variant, Pro12Ala, is associated with diabetes, with increased risk conferred by the more frequent proline allele (odds ratio 1.2). While the effect on diabetes is modest, the frequency of the proline allele may translate to a large population-attributable risk, which has been estimated as high as 25% (346,347). PPARG is the target of the thiazolidinedione class of oral antidiabetic agents used in type 2 diabetes. The pancreatic beta cell potassium channel encoded by KCNJ11 codes for Kir6.2, which complexes with the product of ABCC8 (also known as SUR1, sulfonylurea receptor-1) to form the pancreatic beta cell potassium channel whose function governs insulin secretion. This channel is closed in response to the ATP generated when glucose enters glycolysis in pancreatic beta cells; the closure of this channel leads to exocytosis of insulin. Sulfonylureas bind to and close these channels, triggering insulin release. Rare mutations in KCNJ11 cause monogenic syndromes; activating mutations can cause transient or permanent neonatal diabetes, and inactivating mutations can cause persistent hyperinsulinemic hypoglycemia of infancy. In terms of common type 2 diabetes, the lysine allele of the common Glu23Lys variant in KCNJ11 has been found to confer an odds ratio of 1.4 for diabetes (348). This allele has also been associated with impaired insulin response during oral glucose tolerance testing, supporting its effect on insulin secretion. The most recently identified candidate genes for type 2 diabetes were previously known for their role in monogenic diabetes syndromes, TCF2 with MODY 5 and WFS1 with Wolfram syndrome (also known as DIDMOAD, diabetes insipidus, diabetes mellitus, optic atrophy, and deafness). While rare missense variants in these genes cause these syndromes, noncoding variants in or near these genes contribute to risk of common type 2 diabetes (349–351), most likely via an effect on beta cell development or survival (352). TCF2 is a transcription factor that may play a role in pancreatic beta cell development. Expressed in the brain and pancreas, WFS1 codes for an integral membrane glycoprotein that localizes primarily in the endoplasmic reticulum, where it regulates calcium fluxes. Of note, the odds of diabetes in carriers of particular variants in these genes ranges from 1.1 to 1.2 (348,352,353); such moderate effects are detectable only

by sufficiently powered studies. This likely contributed to the limited success of the candidate gene approach.

86.5.7 Identifying Type 2 Diabetes Genes by Genome-Wide Linkage Scans Given that candidate gene studies did not appear to explain the majority of genetic susceptibility to type 2 diabetes, there was great hope that the advent of genomewide linkage analyses would finally allow the major loci involved in type 2 diabetes to be identified. While some potential loci have come from these approaches, the yield has been much less than was initially anticipated. To date, dozens of genome-wide linkage scans have been performed to identify loci for type 2 diabetes and have led to the identification of only two diabetes genes. Many other loci for type 2 diabetes have been suggested, with the strongest evidence across studies for loci on chromosomes 1q and 20q. The first locus reported from a genome scan was NIDDM1 on chromosome 2 (354). This study was performed in Mexican American sib pairs and subsequent searches in non-Hispanic populations were unable to confirm this locus (355–358). However, Horikawa and coworkers (359) reported the identification of calpain 10 (CAPN10) as the gene on distal chromosome 2 responsible for the NIDDM1 linkage previously reported. Their studies suggested that a particular haplogenotype (termed 112/121, composed of three intronic variants) accounts for type 2 diabetes susceptibility attributable to this locus. Since the original publication, a large number of reports examining association of CAPN10 variants with type 2 diabetes and related insulin and glucose traits have been published, with many, but not all, suggesting a role for CAPN10 in diabetes pathogenesis. Notably, comprehensive meta-analyses support association of particular intronic variants with type 2 diabetes (360,361). Whether these intronic variants are functional or are linked to functional variation elsewhere in the gene is unknown. No CAPN10 variants have been identified as associated with type 2 diabetes by GWAS. In 2006, TCF7L2 (transcription factor 7 like 2), the locus with the strongest effect on the risk of type 2 diabetes, was discovered by investigators who were following up a linkage signal found on chromosome 10 in Icelandic individuals (362). Each risk allele at this locus confers a 1.4 odds of type 2 diabetes in Europeans; the 7% of Europeans with two risk alleles have double the diabetes risk compared to the 55% of individuals with no risk alleles (363). A large number of subsequent studies reproduced association of TCF7L2 variation with type 2 diabetes, not only in Europeans but also in other racial/ethnic groups (364). The actual variants that influence diabetes may differ by racial groups (365,366). TCF7L2 diabetes alleles are associated with reduced insulin secretory response to oral or intravenous glucose in nondiabetic individuals (367–370). TCF7L2,

CHAPTER 86  Diabetes Mellitus a nuclear receptor for beta-catenin, is a component of the Wnt signaling pathway; as such, it may regulate cell proliferation, motility, cancer, myogenesis and/or adipogenesis (371). TCF7L2 transactivates the insulin and proglucagon genes, the latter codes for both glucagon and the incretin hormone glucagon-like ­peptide-1 (GLP-1), which potentiates ­glucose-stimulated insulin secretion. Impairment of these systems is a likely mechanism whereby variants in TCF7L2 reduce insulin secretion (372–374). Another locus of interest from the genome scans is located on the long arm of chromosome 1. This locus is near a “diabesity” locus mapped in the Pima (375). Linkage of 1q with type 2 diabetes and related phenotypes has been demonstrated in numerous populations, including whites and African Americans, French whites, United Kingdom whites, Old Order Amish, Chinese, Hispanics, Pima Indians, and Utah whites (311,356,375–381). Notably, the region of the rat genome that is syntenic to human 1q has been linked with glucose tolerance and fasting insulin level in genome scans in the Goko Kakizaki diabetic rats (382,383). The fact that this is an often-replicated locus for type 2 diabetes led to the formation of the International 1q Consortium, whose goal is to identify the responsible gene(s). Recently, the Consortium fine-mapped a 23-Mb region of 1q, finding signals in the NOS1AP.and ASH1L/PKLR regions that subsequently did not replicate association with type 2 diabetes (384). A weak effect of variation in NOS1AP was subsequently found in a Chinese study (385). Linkage has also been reported between type 2 diabetes and the MODY1 region on chromosome 20 in a number of white populations (386–389), as well as Japanese (390). Linkage of 20q with diabetes was not demonstrated in other studies (391). Linkage of this region of 20q with fasting insulin was also reported in a Chinese population (392). While Hani and coworkers (393) reported an HNF-4 alpha mutation in one family with late-onset type 2 diabetes, other HNF-4 alpha mutations have only been found in classical MODY pedigrees. It thus appears that the type 2 diabetes locus on chromosome 20 is likely separate from HNF-4 alpha. Fine mapping of the 20q region in Japanese cohorts also did not implicate HNF-4 alpha in type 2 diabetes (394,395). The genome-wide linkage scans performed to date in type 2 diabetes and other multifactorial disorders have yielded few susceptibility genes. Even when linkage to a chromosomal region is firmly established, determining which gene and which sequence variant within or nearby that gene are responsible for diabetes susceptibility has been challenging. The advent and application of GWAS has been much more productive. Virtually none of the type 2 diabetes loci discovered by GWAS have corresponded to linkage signals (396), suggesting that linkage might be explained by rare variants of high effect (or other types of genetic variation), rather than common variation typically covered by GWAS.

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86.5.8 Type 2 Diabetes Genes Identified by Genome-Wide Association Studies GWAS have resulted in the discovery of a dramatic number of genes for type 2 diabetes (Table 86-9). This was facilitated by cooperation between multiple centers, both in the acquisition of large numbers of cases and controls and in data sharing to confirm association signals. The first wave of GWAS in type 2 diabetes consisted of five studies published in 2007, all of which were conducted in European-origin subjects (Finnish, French, British, Swedish, Icelandic) (397–401). The loci identified included polymorphisms in or near HHEX/ IDE, SLC30A8, CDKAL1, CDKN2A/2B (2 independent signals), and IGF2BP2. FTO was identified as a locus for body mass index (BMI) and type 2 diabetes via the GWAS that did not match diabetes cases and controls for BMI (398,401) and confirmed in other GWAS (402,403). In 2008, three of the groups behind the initial studies, the Wellcome Trust Case Control Consortium (401), the Diabetes Genetics Initiative (397), and the Finland-US Investigation of NIDDM (FUSION) study (398), collaborated to conduct a GWAS meta-analysis as the Diabetes Genetics Replication and Meta-analysis (DIAGRAM) study, which discovered an additional six loci, JAZF1, CDC123/CAMK1D, TSPAN8/LGR5, THADA, ADAMTS9, NOTCH2 (404). This year also saw the first type 2 diabetes GWAS conducted in Asian populations (Japanese, Korean, Chinese, Singaporean), which identified KCNQ1, subsequently found to be associated with diabetes in Europeans as well (405,406). In Asians, KCNQ1 is the locus with the strongest effect size, comparable to TCF7L2 in Europeans. In 2009, a multistage GWAS in French, Danish, and Finnish subjects identified a variant near the IRS1 gene as associated with type 2 diabetes (407). Multiple studies following up GWAS signals for fasting glucose identified six additional type 2 diabetes loci, ADCY5, DGKB, GCK, GCKR, MTNR1B, and PROX1 (408–411). In 2010, a GWAS in Taiwanese subjects added two more genes, SRR and PTPRD (412), while two additional European meta-analyses identified 13 additional loci, RBMS1/ ITGB6, BCL11A, DUSP9, KLF14, CENTD2, HMGA2, HNF1A, ZBED3, CHCHD9, ZFAND6, PRC1, and a second signal in KCNQ1 (413,414). A GWAS in Japanese subjects identified C2CD4A/4B and UBE2E2 as additional diabetes loci; only the latter was also associated with diabetes in Europeans (415). A different SNP downstream of C2CD4B had previously been associated with fasting glucose (408), and the diabetogenic SNP was later found to affect glucose-stimulated insulin secretion (416). A GWAS from the Asian Consortium of Diabetes discovered a new locus near SPRY2 and identified two new signals in the CDC123/CAMK1D and C2CD4A/4B regions (417). It is important to note that for most of these loci, the causal gene and variant have not been established; the

32

TAB L E 8 6 - 9    Susceptibility Genes for Type 2 Diabetes Source of Initial Discovery

Genome-Wide Diabetes Associated Effect: OR Trait(s) (95% CI)

Chromosomal Location

Marker(s)

Variant Type(s)

ADAMSTS9

3p14.3-p14.2

rs4607103

38 kb upstream

European GWAS meta-analysis

T2DM

ADCY5

3q13.2-q21

rs2877716, rs11708067

Intronic

FG, HOMA-B, 2-h 1.12 (1.09–1.15) G, T2DM, birth weight

BCL11A

2p16.1

rs243021

99 kb downstream

European fasting measures GWAS meta-analysis, 2-h measures GWAS meta-analysis European GWAS meta-analysis

T2DM

1.08 (1.06–1.10)

C2CD4A/4B

15q22.2

rs7172432

Intergenic

Japanese GWAS

T2DM

1.13 (1.09–1.18)

CDC123/ CAMK1D

10p13

rs12779790

Intergenic

European GWAS meta-analysis

T2DM

1.11 (1.07–1.14)

CDKAL1

6p22.3

rs7754840, rs10946398

Intronic

Multiple European GWAS

T2DM

1.12 (1.08–1.16)

CDKN2A/2B

9p21

rs10811661

125 kb upstream

Multiple European GWAS

T2DM

1.20 (1.14–1.25)

CENTD2

11q13.4

rs1552224

5′ UTR

T2DM

1.14 (1.11–1.17)

DGKB/ TMEM195

7p21.2

rs2191349

Intergenic

European GWAS meta-analysis European fasting measures GWAS meta-analysis

FG, T2DM

1.06 (1.04–1.08)

DUSP9

Xq28

rs5945326

8 kb upstream

T2DM

1.27 (1.18–1.37)

FTO

16q12.2

rs8050136, rs9939609

Intronic

BMI, T2DM

1.15 (1.09–1.22)

European GWAS meta-analysis Multiple European GWAS

1.09 (1.06–1.12)

Protein ADAM metallopeptidase with thrombospondin type 1 motif, 9 Adenylate cyclase 5

B-cell CLL/lymphoma 11A (zinc finger protein) C2 calcium-­ dependent domain ­containing 4B Cell division cycle 123 homolog (Saccharomyces cerevisiae)/Calcium/­ calmodulindependent protein kinase type 1D CDK5 regulatory subunit ­associated protein 1-like 1 Cyclin-dependent kinase inhibitor 2A/2B Centaurin, delta 2 Diacylglycerol kinase, beta 90 kDa/ Transmembrane protein 195 Dual specificity ­phosphatase 9 Fat mass and obesity-­ associated protein

Apparent Effect of Risk Allele(s) Insulin resistance

Unknown

β-cell dysfunction Unknown

β-cell dysfunction

β-cell dysfunction β-cell dysfunction β-cell dysfunction β-cell dysfunction

Unknown BMI-dependent insulin ­resistance

CHAPTER 86  Diabetes Mellitus

Locus: Nearest Gene(s)

FG, HbA1c, T2DM

1.07 (1.05–1.10)

Glucokinase ­(Hexokinase 4)

β-cell dysfunction

FG, FI, 2-hr G, HOMA-IR, T2DM

1.06 (1.04–1.08)

Glucokinase ­(hexokinase 4) regulator

Hepatic insulin resistance

T2DM

1.13 (1.08–1.17)

β-cell dysfunction

T2DM

1.10 (1.07–1.14)

T2DM

1.07 (1.05–1.10)

Hematopoietically expressed homeobox/ Insulin-degrading enzyme High mobility group AT-hook 2 HNF1 homeobox A

T2DM

1.12 (1.07–1.18)

β-cell dysfunction

Multiple European GWAS

T2DM

1.17 (1.10–1.25)

502 kb upstream

French, Danish GWAS

T2DM

1.19 (1.13–1.25)

rs864745

Intronic

European GWAS meta-analysis

T2DM

1.10 (1.07–1.13)

11p15.1

rs5219

Glu23Lys in KCNJ11

Candidate gene

T2DM

1.15 (1.09–1.21)

KCNQ1

11p15.5

rs2237892

Intronic

Japanese, Korean, Chinese GWAS

T2DM

1.40 (1.34–1.47)

KCNQ1

11p15.5

rs231362

Intronic

European GWAS meta-analysis

T2DM

1.08 (1.06–1.10)

KLF14

7q32.3

rs972283

47 kb upstream

European GWAS meta-analysis

T2DM

1.07 (1.05–1.10)

Hepatocyte nuclear factor 1-beta Insulin-like growth factor 2 mRNA binding protein 2 Insulin receptor substrate 1 Juxtaposed with another zinc finger protein 1 K inwardly-rectifying channel, subfamily J, member 11/ ATP-binding cassette, sub-family C (CFTR/MRP), member 8 K voltage-gated channel, ­KQT-like subfamily, ­member 1 K voltage-gated channel, ­KQT-like subfamily, ­member 1 Kruppel-like factor 14

7p15.3-p15.1

rs4607517, rs1799884 (-30G>A) rs780094, rs1260326

36 kb upstream, promoter

GCKR

2p23

HHEX/IDE

10q23/10q23-q25

rs1111875

7.7 kb downstream

HMGA2

12q15

rs1531343

43 kb upstream

HNF1A

12q24.2

rs7957197

20 kb downstream

TCF2 (HNF1B)

17cen-q21.3

Intronic

IGF2BP2

3q27.2

rs757210, rs4430796 rs4402960

Intronic

IRS1

2q36

rs2943641

JAZF1

7p15.2-p15.1

KCNJ11/ ABCC8

Leu446Pro, intronic

European fasting measures GWAS meta-analysis European fasting measures GWAS meta-analysis, 2-hr measures GWAS metaanalysis French GWAS

European GWAS meta-analysis European GWAS meta-analysis Candidate gene

Unknown β-cell dysfunction

β-cell dysfunction Insulin resistance β-cell dysfunction β-cell dysfunction

β-cell dysfunction, decreased incretin secretion Unknown

CHAPTER 86  Diabetes Mellitus

GCK

Insulin resistance 33

Continued

34

Genome-Wide Associated Trait(s) FG, HOMA-B, HbA1c, T2DM

Diabetes Effect: OR (95% CI) Protein 1.09 (1.06–1.12) Melatonin ­receptor 1B

European GWAS meta-analysis

T2DM

1.13 (1.08–1.17)

Pro12Ala

Candidate gene

T2DM

1.14 (1.08–1.20)

rs8042680

Intronic

T2DM

1.07 (1.05–1.09)

1q32.2–q32.3

rs340874

2 kb upstream

FG, T2DM

1.07 (1.05–1.09)

SLC30A8

8q24.11

rs13266634

Arg325Trp

European GWAS meta-analysis European fasting measures GWAS meta-analysis French GWAS

T2DM, FG, HbA1c

1.12 (1.07–1.16)

TCF7L2

10q25.3

rs7903146, rs7901695

Intronic

Icelandic linkage region

T2DM, FG, HbA1c

1.37 (1.28–1.47)

THADA

2p21

rs7578597

Thr1187Ala

T2DM

1.15 (1.10–1.20)

TLE4 (CHCHD9)

9q21.31

rs13292136

234 kb upstream

European GWAS meta-analysis European GWAS meta-analysis

T2DM

1.11 (1.07–1.15)

Locus: Nearest Gene(s) MTNR1B

Chromosomal Location 11q21-q22

Marker(s) rs10830963

Variant Type(s) Intronic

NOTCH2

1p13-p11

rs10923931

Intronic

PPARG

3p25

rs1801282

PRC1

15q26.1

PROX1

Source of Initial Discovery European GWAS meta-analysis

Neurogenic locus notch homolog protein 2 ­(Drosophila) Peroxisome proliferator-­ activated receptor-γ Protein regulator of cytokinesis 1 Prospero homeobox 1 Solute carrier family 30 (zinc transporter), member 8 Transcription factor 7-like 2 (T-cell-specific, HMG-box) Thyroid adenoma associated Transducin-like enhancer of split 4 (E(sp1) ­homolog, ­Drosophila)

Apparent Effect of Risk Allele(s) Increased ­melatonin inhibition of insulin ­secretion Unknown

Insulin resistance

Unknown β-cell dysfunction β-cell dysfunction

β-cell dysfunction, decreased incretin-­ stimulated insulin ­secretion β-cell dysfunction Unknown

CHAPTER 86  Diabetes Mellitus

TAB L E 8 6 - 9    Susceptibility Genes for Type 2 Diabetes—cont’d

TP53INP1

8q22

rs896854

Intronic

European GWAS meta-analysis

T2DM

1.06 (1.04–1.09)

TSPAN8/ LGR5

12q14.1q21.1/12q22q23

rs7961581

Intergenic

European GWAS meta-analysis

T2DM

1.09 (1.06–1.12)

WFS1

4p16

Intronic

Candidate gene

T2DM

1.13 (1.07–1.18)

ZBED3

5q13.3

rs1801214, rs10010131 rs4457053

41 kb upstream

T2DM

1.08 (1.06–1.11)

ZFAND6

15q25.1

rs11634397

1.5 kb downstream

T2DM

1.06 (1.04–1.08)

SPRY2

13q13.1

rs1359790

T2DM

1.15 (1.10–1.20)

SRR PTPRD

17p13 9p23–p24.3

rs391300 rs17584499

193 kb downstream Intronic Intronic

European GWAS meta-analysis European GWAS meta-analysis Asian GWAS Taiwanese GWAS Taiwanese GWAS

T2DM T2DM

1.28 (1.18–1.39) 1.57 (1.36–1.82)

UBE2E2

3p24.2

rs7612463

Intronic

Japanese GWAS

T2DM

1.19 (1.12–1.26)

RBMS1/ITGB6

2q24.2

rs7593730

Intron 3 of RBMS1

European GWAS meta-analysis

T2DM

0.90 (0.86–0.93)

Tumor protein p53 inducible nuclear protein 1 Tetraspanin 8/Leucine-rich repeatcontaining G protein-­coupled receptor 5 Wolfram syndrome 1 (wolframin) Zinc finger, BED-type containing 3 Zinc finger, AN1-type domain 6 Sprouty homolog 2 (Drosophila) Serine racemase Protein tyrosine phosphatase, receptor type, D Ubiquitin-conjugating enzyme E2E 2 (UBC4/5 homolog, yeast) RNA binding motif, single stranded interacting protein 1/integrin, beta 6

Unknown β-cell dysfunction

β-cell dysfunction Unknown Unknown Unknown Unknown Unknown β-cell dysfunction

Possibly improved insulin sensitivity

CHAPTER 86  Diabetes Mellitus

FG, fasting glucose; T2DM, type 2 diabetes mellitus; HbA1c, hemoglobin A1c; HOMA-IR, homeostasis model assessment of insulin resistance; HOMA-B, homeostasis model assessment of beta cell function; 2-h G, 2-h glucose level on oral glucose tolerance test.

35

36

CHAPTER 86  Diabetes Mellitus

associated variants may be in linkage disequilibrium with functional variants in the nearest gene (typically used to label the locus) or elsewhere. As displayed in Table 86-9, in most cases the associated variants are in introns or intergenic regions; only a handful of functional missense variants have been discovered. Even in the latter cases, complex scenarios are possible, for example, the Glu23Lys variant in KCNJ11 is strongly linked to the Ser1369Ala variant in ABCC8, with both genes coding for components of the ATP-sensitive potassium channel that is critical to glucose-stimulated insulin secretion (418). Evidence suggests that the presence of both variants may lead to deficient insulin secretion (419). Also, intronic variants may prove functional, as suggested by the association of the TCF7L2 SNP rs7903146 with open chromatin and greater enhancer activity (420). GWAS for fasting glucose and 2-h glucose identified associations with genes previously or later found to also be associated with type 2 diabetes (e.g. TCF7L2, ADCY5, GCKR, see Figure 86-1); however, they also identified several genes associated with glucose only, suggesting that some genes influence glucose in the physiological range without contributing to diabetes risk (408,421). Genes with similar effect on fasting glucose, such as ADCY5 and MADD, each of which has an effect of approximately 0.5 mg/dL per risk allele, may have very different effects on diabetes risk (only ADCY5 is associated with diabetes) (408). On the other hand, a gene with a small effect on diabetes (e.g. MTNR1B, odds ratio 1.2 per allele) may have a large effect on fasting glucose (1.2 mg/dL per allele), whereas a gene with a strong effect on diabetes (e.g. TCF7L2, odds ratio 1.4 per allele) may have a smaller effect on fasting glucose (0.4 mg/dL per allele) (408). These facts point to the complexity of glucose regulation and how genes that affect glucose may or may not influence the risk of diabetes. A similar situation is found in GWAS of hemogloblin A1c (HbA1c), which reflects average glycemia over a

2- to 3-month period. Hemoglobin A1c, used to monitor treatment response in diabetes for many years, has been recently advocated as a diagnostic tool (422). A GWAS for HbA1c found association with loci previously implicated in fasting glucose (G6PC2) and fasting glucose and type 2 diabetes (SLC30A8, GCK) (423). This GWAS also identified a novel locus HK1 (hexokinase 1 expressed mainly in red blood cells), which is not associated with fasting or 2-h glucose or type 2 diabetes (423,424). Subsequent meta-analyses added TCF7L2 and MTNR1B as diabetes loci that were associated with HbA1c (425,426), however, several other loci were identified that appear to influence HbA1c via nonglycemic pathways, such as anemia and iron storage (424,426). Notably, loci associated with HbA1c in studies of type 1 diabetes were not associated with HbA1c in cohorts with type 2 diabetes (427). Type 2 diabetes is thought to arise from concurrent insulin resistance and deficient insulin secretion. Several studies, typically in nondiabetic subjects, have examined the type 2 diabetes loci for association with indexes of insulin resistance and insulin secretion. Most studies derived these traits from fasting insulin and glucose measures, via homeostatic model assessment (HOMA) indexes of insulin resistance (HOMA-IR) and beta-cell function (HOMA-%B) or via indexes derived from oral glucose tolerance testing. A minority of studies employed more sophisticated methods of quantifying insulin-related traits, such as euglycemic or hyperglycemic clamps or frequently sampled intravenous glucose tolerance tests. The overwhelming finding of these studies is that the majority of type 2 diabetes loci are associated with impaired beta-cell function, thought to act via compromised development of beta cells or defective insulin responses to glucose or to the incretin hormones (Figure 86-1). Only a few genes appear to act via insulin resistance. This was unexpected, as insulin resistance was known to be a heritable trait, and may reflect a different genetic architecture of insulin resistance (e.g. more

T2DM

KCNJ11/ABCC8* THADA* HNF1A* HHEX/IDE* IGF2BP2* UBE2E2* GCK*† CDC123/CAMKD* BCL11A* TCF2* SLC30A8*† TSPAN8/LGR5* CDKN2A/B* DGKB/TMEM195 JAZF1* CDKAL1* TLE4 MTNR1B* † WFS1* KCNQ1* ZBED3 NOTCH2 PROX1* CENTD2* PRC1 C2CD4A/4B TCF7L2*† SRR ADCY5 DUSP9 PPARG PTPRD HMGA2 FTO GCKR TP53INP1 KLF14 ZFAND6 RBMS1/ITGB6 SPRY2 ADAMTS9

Fasting glucose G6PC2*† C2CD4B* GLIS3* MADD* ADRA2A* FADS1* SLC2A2 CRY2

IRS1

IGF1

Insulin resistance

GIPR* VPS13C

2-h glucose

FIGURE 86-1  Established loci for type 2 diabetes, fasting glucose, 2-h glucose, and insulin resistance. Those loci also associated with insulin secretion (apparent effects on beta cell development/mass or glucose/incretin-stimulated insulin release) and hemoglobin A1c are indicated with * and †, respectively.

CHAPTER 86  Diabetes Mellitus influenced by rare variants). On the other hand, insulin resistance may not be well reflected by the quantitative traits commonly examined (428). Alternatively, insulin resistance may be more influenced by environmental and lifestyle factors, such as diet and weight gain. The predominance of diabetes genes associating with beta cell function suggests a model wherein genetically determined robustness of the beta cell’s ability to maintain compensatory insulin secretion in the face of insulin resistance is the main gateway by which subjects develop type 2 diabetes. Those individuals who maintain sufficient insulin secretion do not develop diabetes, even if substantial insulin resistance is present. Consistent with this is the fact that obesity and insulin resistance are more prevalent in the population than type 2 diabetes. The explosion of GWAS publications had led some to question their value. As of early 2011, the over 40 genes described as associated with type 2 diabetes appear to explain only 10% of the inheritance of this disease. The unexplained approximately 90% has been called the “missing heritability.” A number of factors may ultimately account for the missing heritability. First, GWAS chips capture (via linkage disequilibrium), mainly common (frequency >5%) variation. Depending on the racial group studied, they capture 70–80% of such variation. Missed common variation notwithstanding, those loci that are identified may be in linkage disequilibrium with functional variants elsewhere; identifying those variants may explain more of the heritability. Many believe that a substantial portion of the missing heritability will ultimately be explained by rare variation with large effect sizes. Already, rare variants are being examined in diabetes genes such as TCF2, with some manifesting association with type 2 diabetes (429). Current whole-exome sequencing efforts (1000 Genomes Project, www.1000genomes.org/page.php) are anticipated to capture rare variation. Other factors that may account for missing heritability include the effects of gene–gene interactions, gene–environment interactions, epigenetic factors (e.g. DNA methylation and histone modification), prenatal programming, and non-SNP variation (e.g. copy number variation). Noncoding RNAs that affect gene expression may be responsible for observed associations, as suggested in the CDKN2A/2B locus (430). It is clear that GWAS, while increasing the number of diabetes genes 20-fold, are only the beginning of a complete understanding of diabetes genetics. The diabetes genes discovered by GWAS have yet to translate to applications in clinical care. Several studies evaluating the utility of genetics to predict who may develop diabetes in the future, typically utilizing a risk score based on the total number of risk alleles, have found little incremental predictive value when added to traditional risk factors (e.g. age, BMI, gender, family history) (431–434). There is some evidence that genetic information in the prediction of type 2 diabetes may be more effective in younger adults (435).

37

Ultimately, the major contribution of GWAS to diabetes care may be in the development of new therapies. GWAS have led to novel insights in the pathophysiology of diabetes, and may have already identified key pharmacological targets. Two of the earliest diabetes genes, PPARG and KCNJ11, encode drug targets. It is highly likely that the many additional genes harbor one or more potential targets. Assisted by advances in technology, GWAS have discovered diabetes loci much more quickly than molecular, physiological, and pharmaceutical research are able to develop these into new drug therapies. Within the next decade, these arenas should be able to utilize genetic information to produce new treatments for diabetes. Another frontier that is likely to advance as a result of the many new genes is pharmacogenetics: use of genetic information to predict treatment response and adverse effects. Several studies have already documented effects of diabetes alleles on treatment response (436–439). In the future, once larger numbers of diseaseresponse-predicting alleles have been elucidated, genetic testing before starting therapy may become common practice.

86.5.9 Mitochondrial Mutations and Maternal Transmission In early studies of type 2 diabetes and MODY-like families, Dorner and colleagues (440,441) reported evidence that diabetes occurred more frequently on the maternal than on the paternal side of families ascertained through a diabetic proband. A study of French white type 2 diabetes patients demonstrated a significant excess of diabetic mothers and maternal relatives (aunts and uncles) compared with fathers and paternal relatives (442). Pettitt and coworkers, studying the inheritance of diabetes in Pima Indian type 2 diabetes families, also have evidence that supports the importance of maternal diabetes in determining the risk for diabetes in the offspring (443). In these studies, 45% of the offspring of women diabetic before pregnancy were themselves diabetic by age 20–24 years, compared with 1.4% and 8.6% of the offspring of nondiabetic and “prediabetic” women (women who became diabetic later), respectively. The paternal diabetic status appeared to contribute little additional risk to the offspring, after correcting for maternal diabetes and other risk factors (443). Significant excess maternal transmission of diabetes and an insulin-resistant phenotype characterized by heart disease, stroke, and hypertension, was also described in a Mexican-American type 2 diabetes population (444), although these findings were not observed in a similar population (445). Finally, Lin and coworkers (446) have described a maternal excess of diabetes inheritance in a Taiwanese population, with an odds ratio for reporting maternal diabetes of 2.64 (95% CI: 1.12–5.71) in diabetic patients compared with nondiabetic subjects.

38

CHAPTER 86  Diabetes Mellitus

Mitochondrial mutations could explain this excess maternal transmission. Indeed, inheritance of mitochondrial gene mutations leading to defects in glucose tolerance has been identified. The first mitochondrial mutation causing diabetes was shown to be caused by inheritance of deletions or duplications at a common breakpoint in the mitochondrial genome; however, this was shown in only one pedigree (447,448). A more widespread mutation at nucleotide 3243, a conserved position in the mitochondrial gene for tRNA Leu(UUR), has been reported by multiple groups in diabetes with maternal transmission history and associated hearing loss. This mutation alters the dihydrouridine loop in this tRNA, leading to impairment of mitochondrial transcription termination. This may cause defects in mitochondrial translation and protein synthesis. This same mutation is also responsible for the more dramatic MELAS syndrome (mitochondrial myopathy, encephalopathy, lactic acidosis, and strokelike episodes) (448,449). Individuals with this mutation have been characterized as having insulin secretory defects along with an increased prevalence of sensorineural deafness or maternally inherited diabetes and deafness (MIDD) (450,451,452). Additionally, some of these individuals with the nucleotide 3243 mutation have been islet cell antibody positive type 2 diabetes patients who went on to require exogenous insulin therapy, due to beta-cell destruction and/or failure (453). The reason for the marked phenotypic variability seen in association with the 3243 mutation is still unclear. Although it has been proposed that the degree of heteroplasmy may explain the phenotypic variation, this appears to be an inadequate explanation. It is possible the mitochondrial–nuclear gene interactions are important (454). Like MODY, mitochondrial mutations are likely to account for only a small proportion of type 2 diabetes cases; Smith and colleagues (455) estimated that the 3243A-G mutation accounts for 0.5–2.8% of diabetes. Variants in nuclear-encoded mitochondrial genes also do not appear to play a significant role in typical type 2 diabetes (456). A number of other rare mutations in the mitochondrial genome have also been shown to cause diabetes, often but not always with associated hearing loss (457–460). Several other hypotheses have been generated to explain this excess maternal transmission. These include: (1) a metabolic (teratogenic) effect of a diabetic or subclinical diabetic (“prediabetic”) environment during pregnancy; (2) the involvement of an imprinted gene that is only expressed when it is passed through a female meiosis; and (3) reporting bias. One of the GWAS-­discovered loci for type 2 diabetes, KLF14, exhibits imprinted expression from the maternal allele, with maternal expression of the risk allele responsible for the effect on diabetes risk (413). Parent-of-origin effects have also been observed at the KCNQ1 locus (461). Reporting bias seems an unlikely explanation, however, as this phenomenon has also been observed in rats. Results from crosses between Goto-Kakizaki rats, which exhibit spontaneous type 2

diabetes, and outbred nondiabetic Wistar rats have demonstrated an effect of maternal inheritance on diabetes in offspring of the first generation (383). These hypotheses are not exclusive and one or more may be interacting to cause the observed excess maternal transmission of disease. Several reports have identified low birthweight as a risk factor for type 2 diabetes and insulin resistance, suggesting a mechanism that may support maternal transmission (462–465). Decreased numbers of beta cells and impaired beta cell function have been associated with low birthweight, but a lack of correlation between low birthweight and the subsequent development of diabetes suggests that additional genetic or environmental factors are necessary for the development of type 2 diabetes (466). Furthermore, there were no differences in the birthweights of individuals with type 2 diabetes, impaired glucose tolerance, or normoglycemic subjects when the offspring of hyperglycemic and normoglycemic mothers were considered separately (466). Thus, these data do not provide conclusive evidence for a ­fuel-mediated teratogenic mechanism since diabetic mothers were no more likely than nondiabetic mothers to have babies of low birthweight. Further research examining mitochondrial genes, sexinfluenced autosomal loci, and environmental factors is warranted.

86.5.10 Genetic Counseling for Type 2 Diabetes For the most part, we must depend on empirical recurrence risks for genetic counseling. For relatives of an individual with type 2 diabetes, the empirical recurrence risk to first-degree relatives is of the order of 10–15% for clinical diabetes and 20–30% for impaired glucose tolerance. In general, this increased risk appears to be for type 2 diabetes, not for type 1 diabetes, although as discussed previously, in some studies there is a somewhat increased risk for both forms of diabetes (467). For MODY diabetics, in whom diabetes is an autosomal dominant disorder, the risk to siblings and offspring is 50%. 86.5.10.1 Screening and Prevention for Type 2 Diabetes.  Screening of first-degree relatives of type 2 diabetics can be accomplished by periodic glucose tolerance testing. In scenarios where this is not feasible, easily obtained clinical parameters can be entered into prediction tools to calculate the current (468) or future (469) risk of diabetes. Those relatives with impaired glucose tolerance should be advised to attain ideal body weight through diet and exercise, as this will improve glucose tolerance. This should be strongly encouraged, with the goals being to delay or prevent progression to frank diabetes and minimize the cardiovascular risks associated with impaired glucose intolerance. Indeed, lifestyle modification in subjects with impaired glucose tolerance has been demonstrated to reduce the incidence of type 2

CHAPTER 86  Diabetes Mellitus diabetes by approximately 60% (470,471). Screening and intervention for other risk factors for cardiovascular disease (e.g. hypertension and hyperlipidemias) is also important, as a family history of diabetes is a significant risk factor for CAD. Screening and implementation of appropriate life style modifications must begin early in life. While type 2 diabetes (with the exception of MODY) is traditionally thought of as an adult-onset disorder, there is a rapidly growing epidemic of adolescent and childhood onset of type 2 diabetes (472), particularly in children of Mexican American and African American descent (472,473). While the etiology is still under investigation, it appears that overeating and a sedentary lifestyle, in the context of a family history of type 2 diabetes, markedly increase risk. Further refinement of genetic risk currently is only possible in MODY families and in those rare forms of type 2 diabetes due to mutant insulin, insulin receptor variants, or mitochondrial mutations. In such families, individuals at risk can potentially be identified at any age at which DNA can be obtained (e.g. even in childhood or prenatally). The issues that must be considered before going to DNA studies are complex, however. Particularly with forms of diabetes that have a later (e.g. adult) onset, the benefit of childhood carrier detection is not clear, and there is the risk of stigmatization as well as the risk of adversely impacting insurability. Therefore the pros and cons of DNA analysis should be discussed carefully before any testing. As discussed above, it is currently premature to utilize genes for typical type 2 diabetes as predictive tools, although private companies have already made this available to the public. 86.5.10.2 Type 2 Diabetes and Pregnancy.  As detailed previously in the section discussing pregnancy and type 1 diabetes, it is well known that women with diabetes have a higher rate of pregnancy complications than do nondiabetics. Although attention is usually focused on the pregnant patient with type 1 diabetes, women with type 2 diabetes are also at increased risk for complications during pregnancy. In general, the more severe the diabetes, the poorer the pregnancy outcome. Women with type 2 diabetes have a significantly increased risk of delivering a child with congenital malformations (226,227,474,475), but the risk is probably less than that for women with type 1 diabetes. Becerra and coworkers (474) reported that the relative risk for major malformations for women with type 1 diabetes was 7.9 (95% CI: 1.9–33.5) as compared to nondiabetic women, whereas the relative risk for women with type 2 diabetes who required insulin treatment during pregnancy was 3.4 (95% CI: 1.0–11.7). Towner and colleagues (475) reported a major malformation frequency of 8.9% in women with type 2 diabetes. The spectrum of malformations was similar to that seen in association with type 1 diabetes. Of importance is that the risk of malformations was correlated with the degree of maternal glycemic control, suggesting

39

that preconceptual optimization of metabolic control is as important in women with type 2 diabetes as in those with type 1 diabetes. With the trend toward delaying pregnancy into the 30s and 40s, coupled with the increasing proportion of the US population that is of Hispanic ethnic background, type 2 diabetes in pregnancy will be seen with increasing prevalence and may, without appropriate intervention, become an increasingly important cause of congenital anomalies (475,476). Women with type 2 diabetes should be counseled about the risks associated with diabetes in pregnancy and encouraged to maximize diabetes control before conception. Prenatal diagnostic tests should also be recommended for all pregnant women who have type 2 diabetes, similar to those recommended for type 1 diabetes. These tests include ultrasonography to evaluate fetal growth and to rule out major fetal structural anomalies such as renal agenesis, neural tube defects, and caudal regression; fetal echocardiography (at 18–22 weeks’ gestation) to identify major structural cardiac malformations; and MSAFP screening to screen for neural tube defects. Since there is evidence that in the general population folic acid supplementation begun before conception is helpful in decreasing the risk for neural tube defects (243–246), folic acid supplementation before conception should be strongly considered as the potential benefits (i.e. possibly reducing the risk for neural tube defects) outweigh any known risks.

86.6 FINAL CONSIDERATIONS AND SPECULATIONS 86.6.1 Evolutionary Aspects Heterogeneity within both the insulin-dependent and non-insulin-dependent types of diabetes appears extensive. An important question arises from the population genetic viewpoint as to why these genes should be so prevalent. These diabetic disorders, whose susceptibility appears to be primarily genetically determined, are deleterious, and thus reproductive fitness should be impaired. As regards type 2 diabetes, a possible explanation is the concept of a “thrifty” genotype, as first proposed by Neel (477). He proposed that the diabetic genotype somehow allowed more efficient utilization of foodstuffs by the body in periods of famine to which primitive humans were often exposed. Such a thrifty gene would therefore have a selective survival advantage and would tend to increase in frequency. However, in the modern western world, with its continuous abundance of calories, such a gene would lead to diabetes and obesity. Neel’s hypothesis has received support by observations in both humans and animals. The extremely high frequency of diabetes and obesity in populations such as the Pima Indians (478) and Pacific Islanders (479,480), and their apparent increase with modernization and urbanization, are

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CHAPTER 86  Diabetes Mellitus

entirely consistent with the thrifty genotype hypothesis. Direct support comes from studies that have shown that heterozygotes for rodent diabetes–obesity genes exhibit a much better ability to survive fasting than normal rodents (480). What might be the selective advantage of the genes that predispose to type 1 diabetes? Since type 1 diabetes is a disorder in which autoimmunity and immune response genes are implicated, a possible role in the resistance to infectious agents has been proposed. However, one should realize that the problem of the selective advantage of type 1 diabetes is much greater than for type 2 diabetes. Before the onset of insulin therapy, type 1 diabetes was usually a lethal disorder, at least in genetic terms (i.e. failure to reproduce). Since susceptibility seems to be provided even by single alleles of HLAlinked genes, this negative selection is much greater than that for recessive genetic disorders such as sickle cell anemia or Tay-Sachs disease, where negative selection operates only on those homozygous for the disease genes. Thus, one would suppose that the positive selective advantage would of necessity be dramatic and that the positive selection should have continued into modern human history. Otherwise the incidence of the disorder would have been decreasing dramatically before the advent of insulin therapy. Yet no such positive selective advantage has been discerned, at least postnatally. Evidence has been developed that indeed suggests a potential selective advantage mechanism for type 1 diabetes in utero. What has been observed in some studies is preferential transmission of diabetogenic HLA haplotypes, not only to affected offspring but also to unaffected offspring (481,482). Furthermore, the available evidence suggests that this possibly occurs via in utero selection (482,483). These data may thus provide an explanation for the maintenance of the high population frequency for this previously frequent genetically lethal disease. In addition, the suggestion that this prenatal selection could occur via immunologically mediated events raises the theoretical possibility that an additional consequence of these events, in fetuses that survive, might be immune changes that presage the eventual development of type 1 diabetes (270,484).

86.6.2 Counseling Summary Given these recent advances in our knowledge of the genetics and heterogeneity of diabetes, what is the genetic counseling we can provide at this time to our diabetic patients? First, as in all genetic counseling, an accurate diagnosis must be made. On clinical grounds one can distinguish between type 1 typically juvenile-onset insulin-dependent type diabetes, type 2 maturity-onset non-insulin-dependent type diabetes, and MODY type diabetes. In distinguishing among these phenotypes, one already has important counseling information. As discussed previously, in a given family the increased risk for

diabetes over the general population is in general only for the specific type of diabetes that has already occurred in the family, not for all diabetes. Thus, if the index case presenting for counseling is a juvenile insulin-dependent diabetic, the increased risk for that patient’s relatives is for type 1 diabetes. If the index case is a non-insulindependent diabetic, the increased risk for the patient’s relatives is, for the most part, for type 2 diabetes only. Associated abnormalities or diseases may suggest one of the rare genetic syndromes that include diabetes, where the risk of recurrence is dependent on the specific diagnosis. Once we have accurately characterized the clinical phenotype of the patient, how do we then proceed? At this stage, we must fall back for the most part on observed empirical recurrence risks (i.e. data concerning the actually observed recurrence of these disorders in a large number of families). Even these empirical recurrence risks have limitations, since for the most part they have been reported only from white populations. Even with the reservation that these empirical risks can be safely applied only to the populations from which they were derived, the most reassuring aspect of the data is the overall low absolute risk for the development of clinical diabetes in first-degree relatives, especially for ­insulin-dependent diabetes. The heterogeneity that has so far been discovered among typical diabetes mellitus probably represents just the tip of the iceberg. But even this currently demonstrable heterogeneity has immediate relevance to current research efforts into the pathogenesis and therapy of the diabetic state. The susceptibility to a given environmental agent may very well depend on the heterogeneity elucidated by these studies. There may also be heterogeneity in the diabetic complications associated with genetically distinct forms of diabetes, having implications for disease management. Only when each of the many disorders resulting in diabetes mellitus and/or glucose intolerance are delineated will specific prognostication and therapy be possible for all diabetic patients.

CROSS REFERENCES Analysis of Genetic Linkage, Rita M. Cantor; Multifactoral Inheritance and Complex Diseases, Hemant Tiwari; Genetic Epidemiology, Neil Risch; Genetic Evaluation for Common Diseases of Adulthood, Maren Scheuner and Shannon Rhodes; Autoimmunity: Genetics and Immunological Mechanisms, Nancy Reinsmoen.

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CHAPTER 86  Diabetes Mellitus 499. Edghill, E. L.; Flanagan, S. E.; Patch, A. M.; Boustred, C.; Parrish, A.; Shields, B.; Shepherd, M. H.; Hussain, K.; Kapoor, R. R.; Malecki, M., et al. Insulin Mutation Screening in 1,044 Patients with Diabetes: Mutations in the INS Gene Are a Common Cause of Neonatal Diabetes but a Rare Cause of Diabetes Diagnosed in Childhood or Adulthood. Diabetes 2008, 57 (4)), 1034–1042. 500. Molven, A.; Ringdal, M.; Nordbo, A. M.; Raeder, H.; Stoy, J.; Lipkind, G. M.; Steiner, D. F.; Philipson, L. H.; Bergmann, I.; Aarskog, D., et al. Mutations in the Insulin Gene can Cause MODY and Autoantibody-Negative Type 1 Diabetes. Diabetes 2008, 57 (4)), 1131–1135. 501. Kim, S. H.; Ma, X.; Weremowicz, S.; Ercolino, T.; Powers, C.; Mlynarski, W.; Bashan, K. A.; Warram, J. H.; Mychaleckyj, J.; Rich, S. S., et  al. Identification of a Locus for ­Maturity-Onset Diabetes of the Young on Chromosome 8p23. Diabetes 2004, 53 (5), 1375–1384. 502. Borowiec, M.; Liew, C. W.; Thompson, R.; Boonyasrisawat, W.; Hu, J.; Mlynarski, W. M.; El Khattabi, I.; Kim, S. H.; Marselli, L.; Rich, S. S., et al. Mutations at the BLK Locus Linked to Maturity Onset Diabetes of the Young and BetaCell Dysfunction. Proc. Natl. Acad. Sci. U.S.A. 2009, 106 (34), 14460–14465.

FURTHER READING Barrett, J. C.; Clayton, D. G.; Concannon, P.; Akolkar, B.; Cooper, J. D.; Erlich, H. A.; Julier, C.; Morahan, G.; Nerup, J.; Nierras, C., et al. Genome-Wide Association Study and Meta-Analysis Find that Over 40 Loci Affect Risk of Type 1 Diabetes. Nat. Genet. 2009, 41 (6), 703–707.

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Cooper, J. D.; Smyth, D. J.; Smiles, A. M.; Plagnol, V.; Walker, N. M.; Allen, J. E.; Downes, K.; Barrett, J. C.; Healy, B. C.; Mychaleckyj, J. C., et  al. Meta-Analysis of Genome-Wide Association Study Data Identifies Additional Type 1 Diabetes Risk Loci. Nat. Genet. 2008, 40 (12), 1399–1401. Dupuis, J.; Langenberg, C.; Prokopenko, I.; Saxena, R.; Soranzo, N.; Jackson, A. U.; Wheeler, E.; Glazer, N. L.; Bouatia-Naji, N.; Gloyn, A. L., et al. New Genetic Loci Implicated in Fasting Glucose Homeostasis and Their Impact on Type 2 Diabetes Risk. Nat. Genet. 2010, 42 (2), 105–116. Zeggini, E.; Scott, L. J.; Saxena, R.; Voight, B. F.; Marchini, J. L.; Hu, T.; de Bakker, P. I.; Abecasis, G. R.; Almgren, P.; Andersen, G., et  al. Meta-Analysis of Genome-Wide Association Data and Large-Scale Replication Identifies Additional Susceptibility Loci for Type 2 Diabetes. Nat. Genet. 2008, 40 (5), 638–645. McCarthy, M. I. Genomics, Type 2 Diabetes, and Obesity. N. Engl. J. Med. 2010, 363 (24), 2339–2350. Voight, B. F.; Scott, L. J.; Steinthorsdottir, V.; Morris, A. P.; Dina, C.; Welch, R. P.; Zeggini, E.; Huth, C.; Aulchenko, Y. S.; Thorleifsson, G., et  al. Twelve Type 2 Diabetes Susceptibility Loci Identified Through Large-Scale Association Analysis. Nat. Genet. 2010, 42 (7), 579–589.

RELEVANT WEB PAGES T1DBase http://t1dbase.org/. American Diabetes Association http://www.diabetes.org/. Juvenile Diabetes Research Foundation International http://www. jdrf.org/.

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CHAPTER 86  Diabetes Mellitus Biographies Leslie J Raffel, MD is the Associate Director of the Common Disease Genetics Program in the Medical Genetics Institute at Cedars-Sinai Medical Center. She is also Associate Director of the UCLA Clinical and Translational Science Institute. She is a Professor of Pediatrics at CedarsSinai and Professor of Pediatrics and Assistant Dean for Clinical and Translational Sciences at the David Geffen School of Medicine at the University of California, Los Angeles. Her research interests are in the genetics of cardiovascular disease, diabetes and insulin resistance.

Mark O Goodarzi, MD, PhD is the Director of the Division of Endocrinology, Diabetes and Metabolism at Cedars-Sinai Medical Center (CSMC). He is also Associate Professor of Medicine at CSMC and the David Geffen School of Medicine at the University of California, Los Angeles (UCLA). He is the Site Director of the CSMC/VA Greater Los Angeles Healthcare System Endocrinology Fellowship Training Program. Dr. Goodarzi’s research interests are in the genetic epidemiology of insulin resistance and related disorders such as diabetes mellitus, cardiovascular disease, and PCOS. He recently received the Endocrine Society’s Richard E. Weitzman Memorial Award.