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OBJECTIVE Maturity-onset diabetes of the young (MODY) is frequently misdiagnosed as type 1 or type 2 diabetes. Correct diagnosis may result in a change in clinical treatment and impacts prediction of complications and familial risk. In this study, we aimed to assess the prevalence of MODY in multiethnic youth under age 20 years with a clinical diagnosis of type 2 diabetes. RESEARCH DESIGN AND METHODS We evaluated whole-exome sequence data of youth with a clinical diagnosis of type 2 diabetes. We considered participants to have MODY if they carried a MODY gene variant classified as likely pathogenic (LP) or pathogenic (P) according to current guidelines. RESULTS Of 3,333 participants, 93 (2.8%) carried an LP/P variant in HNF4A (16 participants), GCK (23), HNF1A (44), PDX1 (5), INS (4), and CEL (1). Compared with those with no LP/P variants, youth with MODY had a younger age at diagnosis (12.9 ± 2.5 vs. 13.6 ± 2.3 years, P = 0.002) and lower fasting C-peptide levels (3.0 ± 1.7 vs. 4.7 ± 3.5 ng/mL, P < 0.0001). Youth with MODY were less likely to have hypertension (6.9% vs. 19.5%, P = 0.007) and had higher HDL cholesterol (43.8 vs. 39.7 mg/dL, P = 0.006). CONCLUSIONS By comprehensively sequencing the coding regions of all MODY genes, we identified MODY in 2.8% of youth with clinically diagnosed type 2 diabetes; importantly, in 89% (n = 83) the specific diagnosis would have changed clinical management. No clinical criterion reliably separated the two groups. New tools are needed to find ideal criteria for selection of individuals for genetic testing.

The convergence of advances in medical science, human biology, data science, and technology has enabled the generation of new insights into the phenotype known as “diabetes.” Increased knowledge of this condition has emerged from populations around the world, illuminating the differences in how diabetes presents, its variable prevalence, and how best practice in treatment varies between populations. In parallel, focus has been placed on the development of tools for the application of precision medicine to numerous conditions. This Consensus Report presents the American Diabetes Association (ADA) Precision Medicine in Diabetes Initiative in partnership with the European Association for the Study of Diabetes (EASD), including its mission, the current state of the field, and prospects for the future. Expert opinions are presented on areas of precision diagnostics and precision therapeutics (including prevention and treatment), and key barriers to and opportunities for implementation of precision diabetes medicine, with better care and outcomes around the globe, are highlighted. Cases where precision diagnosis is already feasible and effective (i.e., monogenic forms of diabetes) are presented, while the major hurdles to the global implementation of precision diagnosis of complex forms of diabetes are discussed. The situation is similar for precision therapeutics, in which the appropriate therapy will often change over time owing to the manner in which diabetes evolves within individual patients. This Consensus Report describes a foundation for precision diabetes medicine, while highlighting what remains to be done to realize its potential. This, combined with a subsequent, detailed evidence-based review (due 2022), will provide a roadmap for precision medicine in diabetes that helps improve the quality of life for all those with diabetes.

OBJECTIVE To assess whether an ethnic-specific variant (p.E508K) in the maturity-onset diabetes of the young (MODY) gene hepatocyte nuclear factor-1α (HNF1A) found in Mexicans is associated with higher sensitivity to sulfonylureas, as documented in patients with MODY3. RESEARCH DESIGN AND METHODS We recruited 96 participants (46 variant carriers and 50 age- and sex-matched noncarriers). Response to glipizide (one 2.5–5.0-mg dose), metformin (four 500-mg doses), and an oral glucose challenge was evaluated using a previously validated protocol. Glucose and insulin levels and their areas under the curve (AUCs) were compared between groups. RESULTS Carriers of the p.E508K variant had a lower maximum insulin peak during the glipizide challenge as compared with noncarriers with diabetes (P < 0.05). Also, carriers had a lower insulin response after the oral glucose challenge. Following an oral glucose tolerance test in the presence of metformin, carriers of the p.E508K variant with diabetes had a lower maximum insulin peak and total and incremental insulin AUC value as compared with noncarriers with diabetes (P < 0.05). A similar but nonsignificant trend was seen in participants without type 2 diabetes. CONCLUSIONS Carriers of variant p.E508K in HNF1A have a reduced insulin response rather than the increased sensitivity to sulfonylureas seen in patients with MODY3.

OBJECTIVE The rs7903146 T allele in transcription factor 7 like 2 (TCF7L2) is strongly associated with type 2 diabetes (T2D), but the mechanisms for increased risk remain unclear. We evaluated the physiologic and hormonal effects of TCF7L2 genotype before and after interventions that influence glucose physiology. RESEARCH DESIGN AND METHODS We genotyped rs7903146 in 608 individuals without diabetes and recorded biochemical data before and after 1) one dose of glipizide (5 mg) on visit 1 and 2) a 75-g oral glucose tolerance test (OGTT) performed after administration of metformin 500 mg twice daily over 2 days. Incretin levels were measured in 150 of the 608 participants. RESULTS TT risk-allele homozygotes had 1.6 mg/dL higher baseline fasting glucose levels and 2.5 pg/mL lower glucagon levels per T allele than carriers of other genotypes at baseline. In a subset of participants, the T allele was associated with higher basal glucagon-like peptide 1 (GLP-1) levels at visit 1 (β = 1.52, P = 0.02 and β = 0.96, P = 0.002 for total and active GLP-1, respectively), and across all points of the OGTT after metformin administration. Regarding drug response, the T allele was associated with a shorter time (β = −7.00, P = 0.03) and a steeper slope (β = 0.23, P = 0.04) to trough glucose levels after glipizide administration, and lower visit 2 fasting glucose level adjusted for visit 1 fasting glucose level (β = −1.02, P = 0.04) and a greater decline in glucose level between visits (β = −1.61, P = 0.047) after metformin administration. CONCLUSIONS Our findings demonstrate that common variation at TCF7L2 influences acute responses to both glipizide and metformin in people without diabetes and highlight altered incretin signaling as a potential mechanism by which TCF7L2 variation increases T2D risk.

OBJECTIVE To characterize physiologic subtypes of gestational diabetes mellitus (GDM). RESEARCH DESIGN AND METHODS Insulin sensitivity and secretion were estimated in 809 women at 24–30 weeks' gestation, using oral glucose tolerance test–based indices. In women with GDM (8.3%), defects in insulin sensitivity or secretion were defined below the 25th percentile in women with normal glucose tolerance (NGT). GDM subtypes were defined based on the defect(s) present. RESULTS Relative to women with NGT, women with predominant insulin sensitivity defects (51% of GDM) had higher BMI and fasting glucose, larger infants (birth weight z score 0.57 [−0.01 to 1.37] vs. 0.03 [−0.53 to 0.52], P = 0.001), and greater risk of GDM-associated adverse outcomes (57.6 vs. 28.2%, P = 0.003); differences were independent of BMI. Women with predominant insulin secretion defects (30% of GDM) had BMI, fasting glucose, infant birth weights, and risk of adverse outcomes similar to those in women with NGT. CONCLUSIONS Heterogeneity of physiologic processes underlying hyperglycemia exists among women with GDM. GDM with impaired insulin sensitivity confers a greater risk of adverse outcomes.

OBJECTIVE Type 2 diabetes (T2D) is associated with increased mortality in ethnically diverse populations, although the extent to which this association is genetically determined is unknown. We sought to determine whether T2D-related genetic variants predicted all-cause mortality, even after accounting for BMI, in the Third National Health and Nutrition Examination Survey. RESEARCH DESIGN AND METHODS We modeled mortality risk using a genetic risk score (GRS) from a weighted sum of risk alleles at 38 T2D-related single nucleotide polymorphisms. In age-, sex-, and BMI-adjusted logistic regression models, accounting for the complex survey design, we tested the association with mortality in 6,501 participants. We repeated the analysis within ethnicities (2,528 non-Hispanic white [NHW], 1,979 non-Hispanic black [NHB], and 1,994 Mexican American [MA]) and within BMI categories ( RESULTS Over 17 years, 1,556 participants died. GRS was associated with mortality risk (OR 1.04 [95% CI 1.00–1.07] per T2D-associated risk allele, P = 0.05). Within ethnicities, GRS was positively associated with mortality risk in NHW and NHB, but not in MA (0.95 [0.90–1.01], P = 0.07). The negative trend in MA was largely driven by those with BMI CONCLUSIONS In the U.S., a higher T2D genetic risk was associated with increased mortality risk, especially among obese NHW. The underlying genetic basis for mortality likely involves complex interactions with factors related to ethnicity, T2D, and body weight.

Observational studies show that higher hemoglobin ATo examine the association of A1C with CAD, we selected 50 A1C-associated variants (logGenetically increased A1C was associated with higher CAD risk (odds ratio [OR] 1.61 [95% CI 1.40, 1.84] per %-unit,Genetic evidence supports a causal link between higher A1C and higher CAD risk. This relationship is driven not only by glycemic but also by erythrocytic, glycemia-independent factors.

OBJECTIVE Glycated hemoglobin (HbA1c), a standard measure of chronic glycemia for managing diabetes, has been proposed to diagnose diabetes and identify people at risk. The Diabetes Prevention Program (DPP) was a 3.2-year randomized clinical trial of preventing type 2 diabetes with a 10-year follow-up study, the DPP Outcomes Study (DPPOS). We evaluated baseline HbA1c as a predictor of diabetes and determined the effects of treatments on diabetes defined by an HbA1c ≥6.5% (48 mmol/mol). RESEARCH DESIGN AND METHODS We randomized 3,234 nondiabetic adults at high risk of diabetes to placebo, metformin, or intensive lifestyle intervention and followed them for the development of diabetes as diagnosed by fasting plasma glucose (FPG) and 2-h postload glucose (2hPG) concentrations (1997 American Diabetes Association [ADA] criteria). HbA1c was measured but not used for study eligibility or outcomes. We now evaluate treatment effects in the 2,765 participants who did not have diabetes at baseline according to FPG, 2hPG, or HbA1c (2010 ADA criteria). RESULTS Baseline HbA1c predicted incident diabetes in all treatment groups. Diabetes incidence defined by HbA1c ≥6.5% was reduced by 44% by metformin and 49% by lifestyle during the DPP and by 38% by metformin and 29% by lifestyle throughout follow-up. Unlike the primary DPP and DPPOS findings based on glucose criteria, metformin and lifestyle were similarly effective in preventing diabetes defined by HbA1c. CONCLUSIONS HbA1c predicted incident diabetes. In contrast to the superiority of the lifestyle intervention on glucose-defined diabetes, metformin and lifestyle interventions had similar effects in preventing HbA1c-defined diabetes. The long-term implications for other health outcomes remain to be determined.

OBJECTIVE This study examined specific measures of weight loss in relation to incident diabetes and improvement in cardiometabolic risk factors. RESEARCH DESIGN AND METHODS This prospective, observational study analyzed nine weight measures, characterizing baseline weight, short- versus long-term weight loss, short- versus long-term weight regain, and weight cycling, within the Diabetes Prevention Program (DPP) lifestyle intervention arm (n = 1,000) for predictors of incident diabetes and improvement in cardiometabolic risk factors over 2 years. RESULTS Although weight loss in the first 6 months was protective of diabetes (hazard ratio [HR] 0.94 per kg, 95% CI 0.90, 0.98; P < 0.01) and cardiometabolic risk factors (P < 0.01), weight loss from 0 to 2 years was the strongest predictor of reduced diabetes incidence (HR 0.90 per kg, 95% CI 0.87, 0.93; P < 0.01) and cardiometabolic risk factor improvement (e.g., fasting glucose: β = −0.57 mg/dL per kg, 95% CI −0.66, −0.48; P < 0.01). Weight cycling (defined as number of 5-lb [2.25-kg] weight cycles) ranged 0–6 times per participant and was positively associated with incident diabetes (HR 1.33, 95% CI 1.12, 1.58; P < 0.01), fasting glucose (β = 0.91 mg/dL per cycle; P = 0.02), HOMA-IR (β = 0.25 units per cycle; P = 0.04), and systolic blood pressure (β = 0.94 mmHg per cycle; P = 0.01). After adjustment for baseline weight, the effect of weight cycling remained statistically significant for diabetes risk (HR 1.22, 95% CI 1.02, 1.47; P = 0.03) but not for cardiometabolic traits. CONCLUSIONS Two-year weight loss was the strongest predictor of reduced diabetes risk and improvements in cardiometabolic traits.

Genetics determine only a small proportion of common disease risk, but the potential to motivate health behavior change remains a proposed benefit of genetic susceptibility testing. Evidence supporting this benefit is scant (1), and studies of long-term clinical outcomes are absent. Type 2 diabetes is heritable yet preventable by behavior change. Although genetic scores predict incident diabetes (2), we demonstrated in the Genetic Counseling/Lifestyle Change (GC/LC) Study that learning one’s genetic risk did not impact short-term weight loss or behavior change motivation among participants in a diabetes prevention program (3). Here, we examine 6-year diabetes incidence among participants. The GC/LC Study was a randomized trial of diabetes genetic risk counseling among primary care patients (3). Eligible participants did not have diabetes but were at high phenotypic risk for diabetes in that they were overweight and met at least one other metabolic syndrome criterion. Participants were randomized to undergo genotyping for a diabetes genetic risk score (tested) versus no genotyping (control). Tested participants in …

Type 2 diabetes has rapidly emerged as a global health crisis. Because population-level genetic changes take many generations to occur, this epidemic is almost certainly primarily a consequence of recent environmental changes; nonetheless, diabetes does appear to occur preferentially in genetically predisposed populations, which suggests that the effects of pre-existing susceptibility genes have been triggered by recent shifts in nongenetic factors. Predisposition is influenced by the level of certain environmental exposures, personal factors, access to good-quality primary care, and by genotype. Interactions between genetic and nongenetic risk factors are hypothesized to raise diabetes risk in a synergistic manner; reciprocally, health-enhancing changes in behavior, body composition, or medication may reduce the risk of disease conveyed by genetic factors. Defining the nature of these interactions and identifying ways through which reliable observations of gene-environment interactions (GEIs) can be translated into the public health setting might help 1 ) optimize targeting of health interventions to persons most likely to respond well to them, 2 ) improve cost- and health-effectiveness of existing preventive and treatment paradigms; 3 ) reduce unnecessary adverse consequences of interventions; 4 ) increase patient adherence to health practitioners’ recommendations; and 5 ) identify novel interventions that are beneficial only in a defined genetic subgroup of the population. In this Perspective, we describe the rationale and evidence relating to the existence of gene-environment and gene-treatment interactions in type 2 diabetes. We discuss the tried, tested, and often-failed approaches to investigating gene-lifestyle interactions in type 2 diabetes; we discuss some recent developments in gene-treatment interactions (pharmacogenetics); and we look forward to the strategies that are likely to dominate these fields of research in the future. We conclude with a discussion of the requirements for translating findings from these future studies into a form where they can be used to help predict, prevent, or treat diabetes. Here we …

OBJECTIVE To examine whether diabetes genetic risk testing and counseling can improve diabetes prevention behaviors. RESEARCH DESIGN AND METHODS We conducted a randomized trial of diabetes genetic risk counseling among overweight patients at increased phenotypic risk for type 2 diabetes. Participants were randomly allocated to genetic testing versus no testing. Genetic risk was calculated by summing 36 single nucleotide polymorphisms associated with type 2 diabetes. Participants in the top and bottom score quartiles received individual genetic counseling before being enrolled with untested control participants in a 12-week, validated, diabetes prevention program. Middle-risk quartile participants were not studied further. We examined the effect of this genetic counseling intervention on patient self-reported attitudes, program attendance, and weight loss, separately comparing higher-risk and lower-risk result recipients with control participants. RESULTS The 108 participants enrolled in the diabetes prevention program included 42 participants at higher diabetes genetic risk, 32 at lower diabetes genetic risk, and 34 untested control subjects. Mean age was 57.9 ± 10.6 years, 61% were men, and average BMI was 34.8 kg/m2, with no differences among randomization groups. Participants attended 6.8 ± 4.3 group sessions and lost 8.5 ± 10.1 pounds, with 33 of 108 (30.6%) losing ≥5% body weight. There were few statistically significant differences in self-reported motivation, program attendance, or mean weight loss when higher-risk recipients and lower-risk recipients were compared with control subjects (P > 0.05 for all but one comparison). CONCLUSIONS Diabetes genetic risk counseling with currently available variants does not significantly alter self-reported motivation or prevention program adherence for overweight individuals at risk for diabetes.

Diabetes is a heterogenous disease. Its very definition, anchored by thresholds for hyperglycemia, rests on the final common event on which disparate pathological processes converge. Although the traditional classification into type 1 and type 2 diabetes has proven useful in differentiating distinct pathophysiological mechanisms with clear therapeutic implications, it remains insufficient in explaining the wide variety of clinical manifestations of this disease. For example, we see lean members of specific ethnic groups with antibody-negative, nonketotic diabetes; we treat patients with childhood-onset, antibody-positive diabetes who become insulin resistant as they age; we do not fully understand why some patients progress rapidly to microvascular and/or macrovascular complications or require aggressive escalation of therapy; and we cannot predict the rate of β-cell failure, the degree of weight loss required to normalize glycemia, or the type of medication best suited for a given patient. Attempts at capturing greater granularity have resulted in the creation of new entities, such as latent autoimmune diabetes in the adult to describe autoimmune diabetes with onset after age 30 (1), and ketosis-prone diabetes to describe antibody-negative diabetes with a ketotic onset but only transient insulin requirements (2,3); however, absent a full understanding of the mechanism and its implications, these subtypes emerge mired in controversy or fail to penetrate clinical care (4,5). Evidently, despite extensive epidemiological and physiological characterization, we have fallen short in cataloging risk factors, identifying triggering events, elucidating pathophysiological pathways, outlining prognostic course, selecting effective therapies, and predicting complications. Specific patients continue to represent diagnostic challenges, and our approach to therapy continues to be based on population averages. Recent thoughtful attempts at tailoring type 2 diabetes therapy to individual patients have focused on personal, social, and economic considerations rather than on a lucid molecular understanding of the underlying disease process (6). …

OBJECTIVE The C allele at the rs11212617 polymorphism in the ataxia-telangiectasia–mutated (ATM) gene has been associated with greater clinical response to metformin in people with type 2 diabetes. We tested whether this variant modified the effect of metformin in the Diabetes Prevention Program (DPP), in which metformin reduced diabetes incidence by 31% in volunteers with impaired glucose tolerance. RESEARCH DESIGN AND METHODS We genotyped rs11212617 in 2,994 DPP participants and analyzed its effects on diabetes incidence and related traits. RESULTS Contrary to expectations, C carriers enjoyed no preventive advantage on metformin; their hazard ratio, compared with A carriers, was 1.17 ([95% CI 0.96–1.42], P = 0.13) under metformin. There were no significant differences by genotype in metformin’s effects on insulin sensitivity, fasting glucose, glycated hemoglobin, or disposition index. CONCLUSIONS The reported association of rs11212617 with metformin response was not confirmed for diabetes prevention or for effects on relevant physiologic parameters in the DPP.

OBJECTIVE We tested genetic associations with weight loss and weight regain in the Diabetes Prevention Program, a randomized controlled trial of weight loss–inducing interventions (lifestyle and metformin) versus placebo. RESEARCH DESIGN AND METHODS Sixteen obesity-predisposing single nucleotide polymorphisms (SNPs) were tested for association with short-term (baseline to 6 months) and long-term (baseline to 2 years) weight loss and weight regain (6 months to study end). RESULTS Irrespective of treatment, the Ala12 allele at PPARG associated with short- and long-term weight loss (−0.63 and −0.93 kg/allele, P ≤ 0.005, respectively). Gene–treatment interactions were observed for short-term (LYPLAL1 rs2605100, Plifestyle*SNP = 0.032; GNPDA2 rs10938397, Plifestyle*SNP = 0.016; MTCH2 rs10838738, Plifestyle*SNP = 0.022) and long-term (NEGR1 rs2815752, Pmetformin*SNP = 0.028; FTO rs9939609, Plifestyle*SNP = 0.044) weight loss. Three of 16 SNPs were associated with weight regain (NEGR1 rs2815752, BDNF rs6265, PPARG rs1801282), irrespective of treatment. TMEM18 rs6548238 and KTCD15 rs29941 showed treatment-specific effects (Plifestyle*SNP < 0.05). CONCLUSIONS Genetic information may help identify people who require additional support to maintain reduced weight after clinical intervention.

OBJECTIVE The Diabetes Prevention Program (DPP) trial investigated rates of progression to diabetes among adults with prediabetes randomized to treatment with placebo, metformin, or intensive lifestyle intervention. Among women in the DPP, diabetes risk reduction with metformin was greater in women with prior gestational diabetes mellitus (GDM) compared with women without GDM but with one or more previous live births. RESEARCH DESIGN AND METHODS We asked if genetic variability could account for these differences by comparing β-cell function and genetic risk scores (GRS), calculated from 34 diabetes-associated loci, between women with and without histories of GDM. RESULTS β-Cell function was reduced in women with GDM. The GRS was positively associated with a history of GDM; however, the GRS did not predict progression to diabetes or modulate response to intervention. CONCLUSIONS These data suggest that a diabetes-associated GRS is associated with development of GDM and may characterize women at risk for development of diabetes due to β-cell dysfunction.

OBJECTIVE To test if knowledge of type 2 diabetes genetic variants improves disease prediction. RESEARCH DESIGN AND METHODS We tested 40 single nucleotide polymorphisms (SNPs) associated with diabetes in 3,471 Framingham Offspring Study subjects followed over 34 years using pooled logistic regression models stratified by age ( RESULTS In people CONCLUSIONS Knowledge of common genetic variation appropriately reclassifies younger people for type 2 diabetes risk beyond clinical risk factors but not older people.

OBJECTIVE Whole-grain foods are touted for multiple health benefits, including enhancing insulin sensitivity and reducing type 2 diabetes risk. Recent genome-wide association studies (GWAS) have identified several single nucleotide polymorphisms (SNPs) associated with fasting glucose and insulin concentrations in individuals free of diabetes. We tested the hypothesis that whole-grain food intake and genetic variation interact to influence concentrations of fasting glucose and insulin. RESEARCH DESIGN AND METHODS Via meta-analysis of data from 14 cohorts comprising ∼48,000 participants of European descent, we studied interactions of whole-grain intake with loci previously associated in GWAS with fasting glucose (16 loci) and/or insulin (2 loci) concentrations. For tests of interaction, we considered a P value RESULTS Greater whole-grain food intake was associated with lower fasting glucose and insulin concentrations independent of demographics, other dietary and lifestyle factors, and BMI (β [95% CI] per 1-serving-greater whole-grain intake: −0.009 mmol/l glucose [−0.013 to −0.005], P < 0.0001 and −0.011 pmol/l [ln] insulin [−0.015 to −0.007], P = 0.0003). No interactions met our multiple testing–adjusted statistical significance threshold. The strongest SNP interaction with whole-grain intake was rs780094 (GCKR) for fasting insulin (P = 0.006), where greater whole-grain intake was associated with a smaller reduction in fasting insulin concentrations in those with the insulin-raising allele. CONCLUSIONS Our results support the favorable association of whole-grain intake with fasting glucose and insulin and suggest a potential interaction between variation in GCKR and whole-grain intake in influencing fasting insulin concentrations.

With the exception of rare monogenic disorders, most type 2 diabetes results from the interaction of genetic variation at multiple different chromosomal sites with environmentalexposures experienced throughout thelifespan (1). This complex genetic architecture has important consequences for understanding the pathophysiology of type 2 diabetes, both for researchers seeking mechanistic insight into disease progression and for clinicians hoping to translate this new genetic information into more effective patient management. With nearly two dozen genes associated with type 2 diabetes, including some genetic variants that appear to modify responses to commonly prescribed diabetes medications and lifestyle interventions, we may be on the verge of a new era in which a patient's individual genetic profile can add useful information to clinical care. Indeed, commercial companies are already offering genome-wide genetic profiling that includes information related to diabetes risk (2). Further advances in type 2 diabetes genetic discovery hold the promise, as yet unrealized, of enabling clinicians to individualize care for their patients by basing their clinical decisions on patient risk for disease progression, propensity to develop specific complications, and likely response to different medication classes. At present it is unknown whether individual genetic information may also serve to effectively motivate patient behavior change, a cornerstone of diabetes and pre-diabetes management. In this review of polygenic type 2 diabetes, we focus on recent discoveries made via linkage analyses, candidate gene association studies, and genome-wide association (GWA) scans and highlight potential clinical applications of new genetic knowledge to risk prediction, pharmacologic management, and patient behavior. Monogenic diabetes has recently been reviewed elsewhere (3). ### Progress in gene discovery #### Linkage studies and candidate genes. In contrast to monogenic disorders, where results from single mutations lead to predictable phenotypes, the complex genetic architecture of susceptible and protective alleles in polygenic type 2 diabetes is more difficult to discern. Indeed, accumulating data suggest that type 2 diabetes …

OBJECTIVE To examine whether diabetes genetic risk testing and counseling can improve diabetes prevention behaviors. RESEARCH DESIGN AND METHODS We conducted a randomized trial of diabetes genetic risk counseling among overweight patients at increased phenotypic risk for type 2 diabetes. Participants were randomly allocated to genetic testing versus no testing. Genetic risk was calculated by summing 36 single nucleotide polymorphisms associated with type 2 diabetes. Participants in the top and bottom score quartiles received individual genetic counseling before being enrolled with untested control participants in a 12-week, validated, diabetes prevention program. Middle-risk quartile participants were not studied further. We examined the effect of this genetic counseling intervention on patient self-reported attitudes, program attendance, and weight loss, separately comparing higher-risk and lower-risk result recipients with control participants. RESULTS The 108 participants enrolled in the diabetes prevention program included 42 participants at higher diabetes genetic risk, 32 at lower diabetes genetic risk, and 34 untested control subjects. Mean age was 57.9 ± 10.6 years, 61% were men, and average BMI was 34.8 kg/m2, with no differences among randomization groups. Participants attended 6.8 ± 4.3 group sessions and lost 8.5 ± 10.1 pounds, with 33 of 108 (30.6%) losing ≥5% body weight. There were few statistically significant differences in self-reported motivation, program attendance, or mean weight loss when higher-risk recipients and lower-risk recipients were compared with control subjects (P > 0.05 for all but one comparison). CONCLUSIONS Diabetes genetic risk counseling with currently available variants does not significantly alter self-reported motivation or prevention program adherence for overweight individuals at risk for diabetes.

OBJECTIVE To estimate allele frequencies and the marginal and combined effects of novel fasting glucose (FG)-associated single nucleotide polymorphisms (SNPs) on FG levels and on risk of impaired FG (IFG) among non-Hispanic white, non-Hispanic black, and Mexican Americans. RESEARCH DESIGN AND METHODS DNA samples from 3,024 adult fasting participants in the National Health and Nutrition Examination Survey (NHANES) III (1991–1994) were genotyped for 16 novel FG-associated SNPs in multiple genes. We determined the allele frequencies and influence of these SNPs alone and in a weighted genetic risk score on FG, homeostasis model assessment of β-cell function (HOMA-B), and IFG by race/ethnicity, while adjusting for age and sex. RESULTS All allele frequencies varied significantly by race/ethnicity. A weighted genetic risk score, based on 16 SNPs, was associated with a 0.022 mmol/l (95% CI 0.009–0.035), 0.036 mmol/l (0.019–0.052), and 0.033 mmol/l (0.020–0.046) increase in FG levels per risk allele among non-Hispanic whites, non-Hispanic blacks, and Mexican Americans, respectively. Adjusted odds ratios for IFG were 1.78 for non-Hispanic whites (95% CI 1.00–3.17), 2.40 for non-Hispanic blacks (1.07–5.37), and 2.39 for Mexican Americans (1.37–4.14) when we compared the highest with the lowest quintiles of genetic risk score (P = 0.365 for testing heterogeneity of effect across race/ethnicity). CONCLUSIONS We conclude that allele frequencies of 16 novel FG-associated SNPs vary significantly by race/ethnicity, but the influence of these SNPs on FG levels, HOMA-B, and IFG were generally consistent across all racial/ethnic groups.

OBJECTIVE This study tested the hypothesis that genetically raised hyperglycemia increases coronary artery disease (CAD) risk separately from the risk conferred by type 2 diabetes as a whole. RESEARCH DESIGN AND METHODS We conducted a Mendelian randomization (MR) analysis using summary-level statistics from the largest published meta-analyses of genome-wide association studies (GWAS) for fasting glucose (FG) (n = 133,010 participants free of diabetes) and CAD (n = 63,746 case subjects and 130,681 control subjects) of predominantly European ancestry. FG-increasing variants associated with type 2 diabetes from the largest GWAS for type 2 diabetes were excluded. Variants with pleiotropic effects on other CAD risk factors (blood lipids, blood pressure, and obesity) were excluded using summary-level data from the largest published GWAS. Data from the Framingham Heart Study were used to validate the MR instrument and to build an FG genetic risk score (GRS). RESULTS In an instrumental variable analysis comprising 12 FG-raising variants, a 1 mmol/L increase in FG revealed an effect-size estimate of 1.43 CAD odds (95% CI 1.14–1.79). The association was preserved after excluding variants for heterogeneity and pleiotropic effects on other CAD risk factors (odds ratio [OR] 1.33 [95% CI 1.02–1.73]). The 12 FG-increasing variants did not significantly increase type 2 diabetes risk (OR 1.05 [95% CI 0.91–1.23]), and its prevalence was constant across FG GRS quintiles (P = 0.72). CONCLUSIONS Our data support that genetic predisposition to hyperglycemia raises the odds of CAD separately from type 2 diabetes and other CAD risk factors. These findings suggest that modulating glycemia may provide cardiovascular benefit.