Updated genetic score based on 34 confirmed type 2 diabetes Loci is associated with diabetes incidence and regression to normoglycemia in the diabetes prevention program

Increased Genetic Risk for β-Cell Failure Is Associated With β-Cell Function Decline in People With Prediabetes

Partitioned polygenic scores (pPS) have been developed to capture pathophysiologic processes underlying type 2 diabetes (T2D). We investigated the association of T2D pPS with diabetes-related traits and T2D incidence in the Diabetes Prevention Program. We generated five T2D pPS (β-cell, proinsulin, liver/lipid, obesity, lipodystrophy) in 2,647 participants randomized to intensive lifestyle, metformin, or placebo arms. Associations were tested with general linear models and Cox regression with adjustment for age, sex, and principal components. Sensitivity analyses included adjustment for BMI. Higher β-cell pPS was associated with lower insulinogenic index and corrected insulin response at 1-year follow-up with adjustment for baseline measures (effect per pPS SD -0.04, P = 9.6 × 10-7, and -8.45 μU/mg, P = 5.6 × 10-6, respectively) and with increased diabetes incidence with adjustment for BMI at nominal significance (hazard ratio 1.10 per SD, P = 0.035). The liver/lipid pPS was associated with reduced 1-year baseline-adjusted triglyceride levels (effect per SD -4.37, P = 0.001). There was no significant interaction between T2D pPS and randomized groups. The remaining pPS were associated with baseline measures only. We conclude that despite interventions for diabetes prevention, participants with a high genetic burden of the β-cell cluster pPS had worsening in measures of β-cell function.

ARTICLE HIGHLIGHTS

Perspectives on genetic studies of type 2 diabetes from the genome-wide association studies era to precision medicine

Genome-wide association studies (GWAS) have facilitated a substantial and rapid increase in the number of confirmed genetic susceptibility variants for complex diseases. Approximately 700 variants predisposing individuals to the risk for type 2 diabetes have been identified through GWAS until 2023. From 2018 to 2022, hundreds of type 2 diabetes susceptibility loci with smaller effect sizes were identified through large-scale GWAS with sample sizes of 200,000 to >1 million. The clinical translation of genetic information for type 2 diabetes includes the development of novel therapeutics and risk predictions. Although drug discovery based on loci identified in GWAS remains challenging owing to the difficulty of functional annotation, global efforts have been made to identify novel biological mechanisms and therapeutic targets by applying multi-omics approaches or searching for disease-associated coding variants in isolated founder populations. Polygenic risk scores (PRSs), comprising up to millions of associated variants, can identify individuals with higher disease risk than those in the general population. In populations of European descent, PRSs constructed from base GWAS data with a sample size of approximately 450,000 have predicted the onset of diseases well. However, European GWAS-derived PRSs have limited predictive performance in non-European populations. The predictive accuracy of a PRS largely depends on the sample size of the base GWAS data. The results of GWAS meta-analyses for multi-ethnic groups as base GWAS data and cross-population polygenic prediction methodology have been applied to establish a universal PRS applicable to small isolated ethnic populations.

Multi-omic prediction of incident type 2 diabetes

AIMS/HYPOTHESIS

The identification of people who are at high risk of developing type 2 diabetes is a key part of population-level prevention strategies. Previous studies have evaluated the predictive utility of omics measurements, such as metabolites, proteins or polygenic scores, but have considered these separately. The improvement that combined omics biomarkers can provide over and above current clinical standard models is unclear. The aim of this study was to test the predictive performance of genome, proteome, metabolome and clinical biomarkers when added to established clinical prediction models for type 2 diabetes.

METHODS

We developed sparse interpretable prediction models in a prospective, nested type 2 diabetes case-cohort study (N=1105, incident type 2 diabetes cases=375) with 10,792 person-years of follow-up, selecting from 5759 features across the genome, proteome, metabolome and clinical biomarkers using least absolute shrinkage and selection operator (LASSO) regression. We compared the predictive performance of omics-derived predictors with a clinical model including the variables from the Cambridge Diabetes Risk Score and HbA1c.

RESULTS

Among single omics prediction models that did not include clinical risk factors, the top ten proteins alone achieved the highest performance (concordance index [C index]=0.82 [95% CI 0.75, 0.88]), suggesting the proteome as the most informative single omic layer in the absence of clinical information. However, the largest improvement in prediction of type 2 diabetes incidence over and above the clinical model was achieved by the top ten features across several omic layers (C index=0.87 [95% CI 0.82, 0.92], Δ C index=0.05, p=0.045). This improvement by the top ten omic features was also evident in individuals with HbA1c <42 mmol/mol (6.0%), the threshold for prediabetes (C index=0.84 [95% CI 0.77, 0.90], Δ C index=0.07, p=0.03), the group in whom prediction would be most useful since they are not targeted for preventative interventions by current clinical guidelines. In this subgroup, the type 2 diabetes polygenic risk score was the major contributor to the improvement in prediction, and achieved a comparable improvement in performance when added onto the clinical model alone (C index=0.83 [95% CI 0.75, 0.90], Δ C index=0.06, p=0.002). However, compared with those with prediabetes, individuals at high polygenic risk in this group had only around half the absolute risk for type 2 diabetes over a 20 year period.

CONCLUSIONS/INTERPRETATION

Omic approaches provided marginal improvements in prediction of incident type 2 diabetes. However, while a polygenic risk score does improve prediction in people with an HbA1c in the normoglycaemic range, the group in whom prediction would be most useful, even individuals with a high polygenic burden in that subgroup had a low absolute type 2 diabetes risk. This suggests a limited feasibility of implementing targeted population-based genetic screening for preventative interventions.

Impact of individual and environmental factors on dietary or lifestyle interventions to prevent type 2 diabetes development: a systematic review

BACKGROUND

The variability in the effectiveness of type 2 diabetes (T2D) preventive interventions highlights the potential to identify the factors that determine treatment responses and those that would benefit the most from a given intervention. We conducted a systematic review to synthesize the evidence to support whether sociodemographic, clinical, behavioral, and molecular factors modify the efficacy of dietary or lifestyle interventions to prevent T2D.

METHODS

We searched MEDLINE, Embase, and Cochrane databases for studies reporting on the effect of a lifestyle, dietary pattern, or dietary supplement interventions on the incidence of T2D and reporting the results stratified by any effect modifier. We extracted relevant statistical findings and qualitatively synthesized the evidence for each modifier based on the direction of findings reported in available studies. We used the Diabetes Canada Clinical Practice Scale to assess the certainty of the evidence for a given effect modifier.

RESULTS

The 81 publications that met our criteria for inclusion are from 33 unique trials. The evidence is low to very low to attribute variability in intervention effectiveness to individual characteristics such as age, sex, BMI, race/ethnicity, socioeconomic status, baseline behavioral factors, or genetic predisposition.

CONCLUSIONS

We report evidence, albeit low certainty, that those with poorer health status, particularly those with prediabetes at baseline, tend to benefit more from T2D prevention strategies compared to healthier counterparts. Our synthesis highlights the need for purposefully designed clinical trials to inform whether individual factors influence the success of T2D prevention strategies.

Assessment of multi-population polygenic risk scores for lipid traits in African Americans

Polygenic risk scores (PRS) based on genome-wide discoveries are promising predictors or classifiers of disease development, severity, and/or progression for common clinical outcomes. A major limitation of most risk scores is the paucity of genome-wide discoveries in diverse populations, prompting an emphasis to generate these needed data for trans-population and population-specific PRS construction. Given diverse genome-wide discoveries are just now being completed, there has been little opportunity for PRS to be evaluated in diverse populations independent from the discovery efforts. To fill this gap, we leverage here summary data from a recent genome-wide discovery study of lipid traits (HDL-C, LDL-C, triglycerides, and total cholesterol) conducted in diverse populations represented by African Americans, Hispanics, Asians, Native Hawaiians, Native Americans, and others by the Population Architecture using Genomics and Epidemiology (PAGE) Study. We constructed lipid trait PRS using PAGE Study published genetic variants and weights in an independent African American adult patient population linked to de-identified electronic health records and genotypes from the Illumina Metabochip (n = 3,254). Using multi-population lipid trait PRS, we assessed levels of association for their respective lipid traits, clinical outcomes (cardiovascular disease and type 2 diabetes), and common clinical labs. While none of the multi-population PRS were strongly associated with the tested trait or outcome, PRS_LDL-Cwas nominally associated with cardiovascular disease. These data demonstrate the complexity in applying PRS to real-world clinical data even when data from multiple populations are available.

Role of sociodemographic, clinical, behavioral, and molecular factors in precision prevention of type 2 diabetes: a systematic review

The variability in the effectiveness of type 2 diabetes (T2D) preventive interventions highlights the potential to identify the factors that determine treatment responses and those that would benefit the most from a given intervention. We conducted a systematic review to synthesize the evidence to support whether sociodemographic, clinical, behavioral, and molecular characteristics modify the efficacy of dietary or lifestyle interventions to prevent T2D. Among the 80 publications that met our criteria for inclusion, the evidence was low to very low to attribute variability in intervention effectiveness to individual characteristics such as age, sex, BMI, race/ethnicity, socioeconomic status, baseline behavioral factors, or genetic predisposition. We found evidence, albeit low certainty, to support conclusions that those with poorer health status, particularly those with prediabetes at baseline, tend to benefit more from T2D prevention strategies compared to healthier counterparts. Our synthesis highlights the need for purposefully designed clinical trials to inform whether individual factors influence the success of T2D prevention strategies.

The utility of a type 2 diabetes polygenic score in addition to clinical variables for prediction of type 2 diabetes incidence in birth, youth and adult cohorts in an Indigenous study population

AIMS/HYPOTHESIS

There is limited information on how polygenic scores (PSs), based on variants from genome-wide association studies (GWASs) of type 2 diabetes, add to clinical variables in predicting type 2 diabetes incidence, particularly in non-European-ancestry populations.

METHODS

For participants in a longitudinal study in an Indigenous population from the Southwestern USA with high type 2 diabetes prevalence, we analysed ten constructions of PS using publicly available GWAS summary statistics. Type 2 diabetes incidence was examined in three cohorts of individuals without diabetes at baseline. The adult cohort, 2333 participants followed from age ≥20 years, had 640 type 2 diabetes cases. The youth cohort included 2229 participants followed from age 5-19 years (228 cases). The birth cohort included 2894 participants followed from birth (438 cases). We assessed contributions of PSs and clinical variables in predicting type 2 diabetes incidence.

RESULTS

Of the ten PS constructions, a PS using 293 genome-wide significant variants from a large type 2 diabetes GWAS meta-analysis in European-ancestry populations performed best. In the adult cohort, the AUC of the receiver operating characteristic curve for clinical variables for prediction of incident type 2 diabetes was 0.728; with the PS, 0.735. The PS's HR was 1.27 per SD (p=1.6 × 10-8; 95% CI 1.17, 1.38). In youth, corresponding AUCs were 0.805 and 0.812, with HR 1.49 (p=4.3 × 10-8; 95% CI 1.29, 1.72). In the birth cohort, AUCs were 0.614 and 0.685, with HR 1.48 (p=2.8 × 10-16; 95% CI 1.35, 1.63). To further assess the potential impact of including PS for assessing individual risk, net reclassification improvement (NRI) was calculated: NRI for the PS was 0.270, 0.268 and 0.362 for adult, youth and birth cohorts, respectively. For comparison, NRI for HbA1c was 0.267 and 0.173 for adult and youth cohorts, respectively. In decision curve analyses across all cohorts, the net benefit of including the PS in addition to clinical variables was most pronounced at moderately stringent threshold probability values for instituting a preventive intervention.

CONCLUSIONS/INTERPRETATION

This study demonstrates that a European-derived PS contributes significantly to prediction of type 2 diabetes incidence in addition to information provided by clinical variables in this Indigenous study population. Discriminatory power of the PS was similar to that of other commonly measured clinical variables (e.g. HbA1c). Including type 2 diabetes PS in addition to clinical variables may be clinically beneficial for identifying individuals at higher risk for the disease, especially at younger ages.

An update on diagnosis and therapeutics for type-2 diabetes mellitus

Type-2 Diabetes mellitus is a common metabolic disorder. It is combined with co-morbidities, such as obesity, hyperlipidemia, hypertension and cardiovascular disease which taken together, comprise the 'Metabolic Syndrome'. This disease causes crucial morbidity and mortality at considerable expense to patients, their families and society. Different categories of drugs such as insulin secretagogues, insulin sensitizers, alpha-glucosidase inhibitors, GLP-1 agonists, DPP4 inhibitors, dual PPAR agonists and others are used for its management. Therefore, it is of interest to highlight the recent advances in diagnosis and therapeutics used in the treatment of type-2 diabetes mellitus. The classical and online-literature were used to compile data for this study. This includes the electronic search engine such as Scopus, Google Scholar, Sci Finder, PubMed and Web of Science. Data shows that there are different families of oral and injectable drugs at hand for the treatment of T2DM. Hence, we need to develop a novel, safety and effective agents that will improve the quality of life of T2DM patients, considering effectiveness and durability of lowering blood Glucose, risk of hypoglycemia and diabetes complications.

MicroRNAs Associated with Incident Diabetes in the Diabetes Prevention Program

OBJECTIVE

MicroRNAs (miRs) are short (i.e., 18-26 nucleotide) regulatory elements of messenger RNA translation to amino acids. The purpose of this study was to assess whether miRs are predictive of incident T2D in the Diabetes Prevention Program (DPP) trial.

RESEARCH DESIGN AND METHODS

This was a secondary analysis (n = 1,000) of a subset of the DPP cohort that leveraged banked biospecimens to measure miRs. We used random survival forest and Lasso to identify the optimal miR predictors and cox proportional hazards to model time to T2D overall and within intervention arms.

RESULTS

We identified five miRs (miR-144, miR-186, miR-203a, miR-205, miR-206) that constituted the optimal predictors of incident T2D after adjustment for covariates (hazards ratio 2.81 (95% confidence interval (CI) 2.05, 3.87); p < 0.001). Predictive risk scores following cross-validation showed the HR for the highest quartile risk group compared to the lowest quartile risk group was 5.91 (95% CI (2.02, 17.3); p < 0.001). There was significant interaction between the intensive lifestyle (HR 3.60, 95% CI (2.50, 5.18); p < 0.001) and the metformin (HR 2.72; 95% CI (1.47, 5.00); p = 0.001) groups compared to placebo. Of the five miRs identified, one targets a gene with prior known associations with risk for T2D.

DISCUSSION

We identified five miRs that are optimal predictors of incident T2D in the DPP cohort. Future directions include validation of this finding in an independent sample in order to determine whether this risk score may have potential clinical utility for risk stratification of individuals with prediabetes, and functional analysis of the potential genes and pathways targeted by the miRs that were included in the risk score.

Personalised prevention of type 2 diabetes

It is well established from clinical trials that behavioural interventions can halve the risk of progression from prediabetes to type 2 diabetes but translating this evidence of efficacy into effective real-world interventions at scale is an ongoing challenge. A common suggestion is that future preventive interventions need to be more personalised in order to enhance effectiveness. This review evaluates the degree to which existing interventions are already personalised and outlines how greater personalisation could be achieved through better identification of those at high risk, division of type 2 diabetes into specific subgroups and, above all, more individualisation of the behavioural targets for preventive action. Approaches using more dynamic real-time data are in their scientific infancy. Although these approaches are promising they need longer-term evaluation against clinical outcomes. Whatever personalised preventive approaches for type 2 diabetes are developed in the future, they will need to be complementary to existing individual-level interventions that are being rolled out and that are demonstrably effective. They will also need to ideally synergise with, and at the very least not detract attention from, efforts to develop and implement strategies that impact on type 2 diabetes risk at the societal level.

Do gene-environment interactions have implications for the precision prevention of type 2 diabetes?

The past decades have seen a rapid global rise in the incidence of type 2 diabetes. This surge has been driven by diabetogenic environmental changes that may act together with a genetic predisposition to type 2 diabetes. It is possible that there is a synergistic gene-environment interaction, where the effects of the diabetogenic environment depend on the genetic predisposition to type 2 diabetes. Randomised trials have shown that it is possible to delay, or even prevent the development of type 2 diabetes in individuals at elevated risk through behavioural modification, focusing on weight loss, physical activity and diet. There is wide heterogeneity between individuals regarding the effectiveness of these interventions, which could, in part, be due to genetic differences. However, the studies of gene-environment interactions performed thus far suggest that behavioural modifications appear equally effective in reducing the incidence of type 2 diabetes from the stage of impaired glucose tolerance, regardless of the known underlying genetic predisposition. Recent studies suggest that there may be several subtypes of type 2 diabetes, which give new opportunities for gaining insight into gene-environment interactions. At present, the role of gene-environment interactions in the development of type 2 diabetes remains unclear. With many puzzle pieces missing in the general picture of type 2 diabetes development, the available evidence of gene-environment interactions is not ready for translation to individualised type 2 diabetes prevention based on genetic profiling.

Phenotypic and genetic classification of diabetes

The historical subclassification of diabetes into predominantly types 1 and 2 is well appreciated to inadequately capture the heterogeneity seen in patient presentations, disease course, response to therapy and disease complications. This review summarises proposed data-driven approaches to further refine diabetes subtypes using clinical phenotypes and/or genetic information. We highlight the benefits as well as the limitations of these subclassification schemas, including practical barriers to their implementation that would need to be overcome before incorporation into clinical practice.

On the Verge of Precision Medicine in Diabetes

The epidemic of type 2 diabetes (T2D) is a significant global public health challenge and a major cause of morbidity and mortality. Despite the recent proliferation of pharmacological agents for the treatment of T2D, current therapies simply treat the symptom, i.e. hyperglycemia, and do not directly address the underlying disease process or modify the disease course. This article summarizes how genomic discovery has contributed to unraveling the heterogeneity in T2D, reviews relevant discoveries in the pharmacogenetics of five commonly prescribed glucose-lowering agents, presents evidence supporting how pharmacogenetics can be leveraged to advance precision medicine, and calls attention to important research gaps to its implementation to guide treatment choices.

Polygenic Risk Scores for Cardiovascular Disease: A Scientific Statement From the American Heart Association

Cardiovascular disease is the leading contributor to years lost due to disability or premature death among adults. Current efforts focus on risk prediction and risk factor mitigation' which have been recognized for the past half-century. However, despite advances, risk prediction remains imprecise with persistently high rates of incident cardiovascular disease. Genetic characterization has been proposed as an approach to enable earlier and potentially tailored prevention. Rare mendelian pathogenic variants predisposing to cardiometabolic conditions have long been known to contribute to disease risk in some families. However, twin and familial aggregation studies imply that diverse cardiovascular conditions are heritable in the general population. Significant technological and methodological advances since the Human Genome Project are facilitating population-based comprehensive genetic profiling at decreasing costs. Genome-wide association studies from such endeavors continue to elucidate causal mechanisms for cardiovascular diseases. Systematic cataloging for cardiovascular risk alleles also enabled the development of polygenic risk scores. Genetic profiling is becoming widespread in large-scale research, including in health care-associated biobanks, randomized controlled trials, and direct-to-consumer profiling in tens of millions of people. Thus, individuals and their physicians are increasingly presented with polygenic risk scores for cardiovascular conditions in clinical encounters. In this scientific statement, we review the contemporary science, clinical considerations, and future challenges for polygenic risk scores for cardiovascular diseases. We selected 5 cardiometabolic diseases (coronary artery disease, hypercholesterolemia, type 2 diabetes, atrial fibrillation, and venous thromboembolic disease) and response to drug therapy and offer provisional guidance to health care professionals, researchers, policymakers, and patients.

Development and validation of a trans-ancestry polygenic risk score for type 2 diabetes in diverse populations

BACKGROUND

Type 2 diabetes (T2D) is a worldwide scourge caused by both genetic and environmental risk factors that disproportionately afflicts communities of color. Leveraging existing large-scale genome-wide association studies (GWAS), polygenic risk scores (PRS) have shown promise to complement established clinical risk factors and intervention paradigms, and improve early diagnosis and prevention of T2D. However, to date, T2D PRS have been most widely developed and validated in individuals of European descent. Comprehensive assessment of T2D PRS in non-European populations is critical for equitable deployment of PRS to clinical practice that benefits global populations.

METHODS

We integrated T2D GWAS in European, African, and East Asian populations to construct a trans-ancestry T2D PRS using a newly developed Bayesian polygenic modeling method, and assessed the prediction accuracy of the PRS in the multi-ethnic Electronic Medical Records and Genomics (eMERGE) study (11,945 cases; 57,694 controls), four Black cohorts (5137 cases; 9657 controls), and the Taiwan Biobank (4570 cases; 84,996 controls). We additionally evaluated a post hoc ancestry adjustment method that can express the polygenic risk on the same scale across ancestrally diverse individuals and facilitate the clinical implementation of the PRS in prospective cohorts.

RESULTS

The trans-ancestry PRS was significantly associated with T2D status across the ancestral groups examined. The top 2% of the PRS distribution can identify individuals with an approximately 2.5-4.5-fold of increase in T2D risk, which corresponds to the increased risk of T2D for first-degree relatives. The post hoc ancestry adjustment method eliminated major distributional differences in the PRS across ancestries without compromising its predictive performance.

CONCLUSIONS

By integrating T2D GWAS from multiple populations, we developed and validated a trans-ancestry PRS, and demonstrated its potential as a meaningful index of risk among diverse patients in clinical settings. Our efforts represent the first step towards the implementation of the T2D PRS into routine healthcare.

Metformin for diabetes prevention: update of the evidence base

We have conducted a narrative review based on a structured search strategy, focusing on the effects of metformin on the progression of non-diabetic hyperglycemia to clinical type 2 diabetes mellitus. The principal trials that demonstrated a significantly lower incidence of diabetes in at-risk populations randomized to metformin (mostly with impaired glucose tolerance [IGT]) were published mainly from 1999 to 2012. Metformin reduced the 3-year risk of diabetes by -31% in the randomized phase of the Diabetes Prevention Program (DPP), vs. -58% for intensive lifestyle intervention (ILI). Metformin was most effective in younger, heavier subjects. Diminishing but still significant reductions in diabetes risk for subjects originally randomized to these groups were present in the trial's epidemiological follow-up, the DPP Outcomes Study (DPPOS) at 10 years (-18 and -34%, respectively), 15 years (-18 and -27%), and 22 years (-18 and -25%). Long-term weight loss was also seen in both groups, with better maintenance under metformin. Subgroup analyses from the DPP/DPPOS have shed important light on the actions of metformin, including a greater effect in women with prior gestational diabetes, and a reduction in coronary artery calcium in men that might suggest a cardioprotective effect. Improvements in long-term clinical outcomes with metformin in people with non-diabetic hyperglycemia ("prediabetes") have yet to be demonstrated, but cardiovascular and microvascular benefits were seen for those in the DPPOS who did not vs. did develop diabetes. Multiple health economic analyses suggest that either metformin or ILI is cost-effective in a community setting. Long-term diabetes prevention with metformin is feasible and is supported in influential guidelines for selected groups of subjects. Future research will demonstrate whether intervention with metformin in people with non-diabetic hyperglycemia will improve long-term clinical outcomes.

Genetic susceptibility, obesity and lifetime risk of type 2 diabetes: The ARIC study and Rotterdam Study

AIMS

Both lifestyle factors and genetic background contribute to the development of type 2 diabetes. Estimation of the lifetime risk of diabetes based on genetic information has not been presented, and the extent to which a normal body weight can offset a high lifetime genetic risk is unknown.

METHODS

We used data from 15,671 diabetes-free participants of European ancestry aged 45 years and older from the prospective population-based ARIC study and Rotterdam Study (RS). We quantified the remaining lifetime risk of diabetes stratified by genetic risk and quantified the effect of normal weight in terms of relative and lifetime risks in low, intermediate and high genetic risk.

RESULTS

At age 45 years, the lifetime risk of type 2 diabetes in ARIC in the low, intermediate and high genetic risk category was 33.2%, 41.3% and 47.2%, and in RS 22.8%, 30.6% and 35.5% respectively. The absolute lifetime risk for individuals with normal weight compared to individuals with obesity was 24% lower in ARIC and 8.6% lower in RS in the low genetic risk group, 36.3% lower in ARIC and 31.3% lower in RS in the intermediate genetic risk group, and 25.0% lower in ARIC and 29.4% lower in RS in the high genetic risk group.

CONCLUSIONS

Genetic variants for type 2 diabetes have value in estimating the lifetime risk of type 2 diabetes. Normal weight mitigates partly the deleterious effect of high genetic risk.

The β Cell in Diabetes: Integrating Biomarkers With Functional Measures

The pathogenesis of hyperglycemia observed in most forms of diabetes is intimately tied to the islet β cell. Impairments in propeptide processing and secretory function, along with the loss of these vital cells, is demonstrable not only in those in whom the diagnosis is established but typically also in individuals who are at increased risk of developing the disease. Biomarkers are used to inform on the state of a biological process, pathological condition, or response to an intervention and are increasingly being used for predicting, diagnosing, and prognosticating disease. They are also proving to be of use in the different forms of diabetes in both research and clinical settings. This review focuses on the β cell, addressing the potential utility of genetic markers, circulating molecules, immune cell phenotyping, and imaging approaches as biomarkers of cellular function and loss of this critical cell. Further, we consider how these biomarkers complement the more long-established, dynamic, and often complex measurements of β-cell secretory function that themselves could be considered biomarkers.

Anticipation of Precision Diabetes and Promise of Integrative Multi-Omics

Precision diabetes is a concept of customizing delivery of health practices based on variability of diabetes. The authors reviewed recent research on type 2 diabetes heterogeneity and -omic biomarkers, including genomic, epigenomic, and metabolomic markers associated with type 2 diabetes. The emerging multiomics approach integrates complementary and interconnected molecular layers to provide systems level understanding of disease mechanisms and subtypes. Although the multiomic approach is not currently ready for routine clinical applications, future studies in the context of precision diabetes, particular in populations from diverse ethnic and demographic groups, may lead to improved diagnosis, treatment, and management of diabetes and diabetic complications.

Exercise and health: historical perspectives and new insights

Since ancient times, the health benefits of regular physical activity/exercise have been recognized and the classic studies of Morris and Paffenbarger provided the epidemiological evidence in support of such an association. Cardiorespiratory fitness, often measured by maximal oxygen uptake, and habitual physical activity levels are inversely related to mortality. Thus, studies exploring the biological bases of the health benefits of exercise have largely focused on the cardiovascular system and skeletal muscle (mass and metabolism), although there is increasing evidence that multiple tissues and organ systems are influenced by regular exercise. Communication between contracting skeletal muscle and multiple organs has been implicated in exercise benefits, as indeed has other interorgan "cross-talk." The application of molecular biology techniques and "omics" approaches to questions in exercise biology has opened new lines of investigation to better understand the beneficial effects of exercise and, in so doing, inform the optimization of exercise regimens and the identification of novel therapeutic strategies to enhance health and well-being.

Interaction Between Physical Activity and Polygenic Score on Type 2 Diabetes Mellitus in Older Black and White Participants From the Health and Retirement Study

This study investigated the association of lifestyle factors and polygenic risk scores (PGS), and their interaction, on type 2 diabetes mellitus (T2D). We examined data from the U.S. Health and Retirement Study, a prospective longitudinal cohort of adults aged 50 years and older, containing nationally representative samples of Black and White Americans with precalculated PGS for T2D (N = 14 001). Predicted prevalence and incidence of T2D were calculated with logistic regression models. We calculated differences in T2D prevalence and incidence by PGS percentiles and for interaction variables using nonparametric bootstrap method. Black participants had approximately twice the prevalence of Whites (26.2% vs 14.2%), with a larger difference between the 90th and 10th PGS percentile from age 50 to 80 years. Significant interaction (pinteraction = .0096) was detected between PGS and physical activity among Whites. Among Whites in the 90th PGS percentile, T2D prevalence for moderate physical activity was 17.0% (95% CI: 14.8, 19.6), 6.8% lower compared to no/some physical activity (23.8%; 95% CI: 20.4, 27.5). T2D prevalence was similar (~10%) for both groups in the 10th PGS percentile. Incident T2D in Whites followed a similar pattern (pinteraction = .0325). No significant interactions with PGS were detected among Black participants. Interaction of different genetic risk profiles with lifestyle factors may inform understanding of varying inventions' efficacy for different groups of people, potentially improving clinical and prevention interventions.

Interaction of diabetes genetic risk and successful lifestyle modification in the Diabetes Prevention Programme

AIM

To test whether diabetes genetic risk modifies the association of successful lifestyle changes with incident diabetes.

MATERIALS AND METHODS

We studied 823 individuals randomized to the intensive lifestyle intervention (ILS) arm of the Diabetes Prevention Programme who were diabetes-free 1 year after enrolment. We tested additive and multiplicative interactions of a 67-variant diabetes genetic risk score (GRS) with achievement of three ILS goals at 1 year (≥7% weight loss, ≥150 min/wk of moderate leisure-time physical activity, and/or a goal for self-reported total fat intake) on the primary outcome of incident diabetes over 3 years of follow-up.

RESULTS

A lower GRS and achieving each or all three ILS goals were each associated with lower incidence of diabetes (all P < 0.05). Additive interactions were significant between the GRS and achievement of the weight loss goal (P < 0.001), physical activity goal (P = 0.02), and all three ILS goals (P < 0.001) for diabetes risk. Achievement of all three ILS goals was associated with 1.8 (95% CI 0.3, 3.4), 3.1 (95% CI 1.5, 4.7), and 3.9 (95% CI 1.6, 6.2) fewer diabetes cases/100-person-years in the first, second and third GRS tertiles (P < 0.001 for trend). Multiplicative interactions between the GRS and ILS goal achievement were significant for the diet goal (P < 0.001), but not for weight loss (P = 0.18) or physical activity (P = 0.62) goals.

CONCLUSIONS

Genetic risk may identify high-risk subgroups for whom successful lifestyle modification is associated with greater absolute reduction in the risk of incident diabetes.

A Genetic Risk Score Improves the Prediction of Type 2 Diabetes Mellitus in Mexican Youths but Has Lower Predictive Utility Compared With Non-Genetic Factors

Background

Type 2 diabetes (T2D) is a multifactorial disease caused by a complex interplay between environmental risk factors and genetic predisposition. To date, a total of 10 single nucleotide polymorphism (SNPs) have been associated with pediatric-onset T2D in Mexicans, with a small individual effect size. A genetic risk score (GRS) that combines these SNPs could serve as a predictor of the risk for pediatric-onset T2D.

Objective

To assess the clinical utility of a GRS that combines 10 SNPs to improve risk prediction of pediatric-onset T2D in Mexicans.

Methods

This case-control study included 97 individuals with pediatric-onset T2D and 84 controls below 18 years old without T2D. Information regarding family history of T2D, demographics, perinatal risk factors, anthropometric measurements, biochemical variables, lifestyle, and fitness scores were then obtained. Moreover, 10 single nucleotide polymorphisms (SNPs) previously associated with pediatric-onset T2D in Mexicans were genotyped. The GRS was calculated by summing the 10 risk alleles. Pediatric-onset T2D risk variance was assessed using multivariable logistic regression models and the area under the receiver operating characteristic curve (AUC).

Results

The body mass index Z-score (Z-BMI) [odds ratio (OR) = 1.7; p = 0.009] and maternal history of T2D (OR = 7.1; p < 0.001) were found to be independently associated with pediatric-onset T2D. No association with other clinical risk factors was observed. The GRS also showed a significant association with pediatric-onset T2D (OR = 1.3 per risk allele; p = 0.006). The GRS, clinical risk factors, and GRS plus clinical risk factors had an AUC of 0.66 (95% CI 0.56-0.75), 0.72 (95% CI 0.62-0.81), and 0.78 (95% CI 0.70-0.87), respectively (p < 0.01).

Conclusion

The GRS based on 10 SNPs was associated with pediatric-onset T2D in Mexicans and improved its prediction with modest significance. However, clinical factors, such the Z-BMI and family history of T2D, continue to have the highest predictive utility in this population.

Trajectories of Body Weight and Fat Mass in Relation to Incident Prediabetes in a Biracial Cohort of Free-Living Adults

Objectives

Obesity is a risk factor for type 2 diabetes (T2D), but prospective data relating adiposity measures to incident prediabetes are scant.

Methods

The Pathobiology of Prediabetes in A Biracial Cohort study followed normoglycemic African Americans (AA) and European Americans (EA) with parental history of T2D for the primary outcome of incident prediabetes (impaired fasting glucose and/or impaired glucose tolerance) for 5.5 years. Serial assessments included anthropometry and body fat composition. We analyzed weight, body mass index (BMI), waist, total, and abdominal fat mass in relation to incident prediabetes risk.

Results

Of the 376 subjects enrolled (217 AA, 159 EA; mean age 44.2 years, BMI 31.4 kg/m²), 343 (192 AA, 151 EA) had evaluable follow-up data. A total of 101 (52 AA, 49 EA) developed prediabetes during follow-up. Progressors to prediabetes had a mean baseline weight of 90.0 ± 20.4 kg versus 82.9 ± 21.7 kg among nonprogressors (P = 0.0036). During 5.5 (mean 2.62) years of follow-up, the weight change among nonprogressors was 0.63 ± 6.11 kg compared with 2.54 ± 6.91 kg among progressors (ANOVA P = 0.0072). Progressors also showed greater increases in total fat (P = 0.0015) and trunk fat (P = 0.0005) mass than nonprogressors. Adjusted for age and sex, the significant predictors of incident prediabetes were BMI (P = 0.0013), waist (P < 0.0001), total fat (P = 0.0025), and trunk fat (P < 0.0001) mass.

Conclusions

Among obese free-living offspring of parents with T2D, long-term normoglycemic status was associated with a weight gain of ~0.2 kg/y, whereas progression to prediabetes was associated with a weight gain of ~1 kg/y.

A Polygenic Score for Type 2 Diabetes Risk Is Associated With Both the Acute and Sustained Response to Sulfonylureas

There is a limited understanding of how genetic loci associated with glycemic traits and type 2 diabetes (T2D) influence the response to antidiabetic medications. Polygenic scores provide increasing power to detect patterns of disease predisposition that might benefit from a targeted pharmacologic intervention. In the Study to Understand the Genetics of the Acute Response to Metformin and Glipizide in Humans (SUGAR-MGH), we constructed weighted polygenic scores using known genome-wide significant associations for T2D, fasting glucose, and fasting insulin, comprising 65, 43, and 13 single nucleotide polymorphisms, respectively. Multiple linear regression tested for associations between scores and glycemic traits as well as pharmacodynamic end points, adjusting for age, sex, race, and BMI. A higher T2D score was nominally associated with a shorter time to insulin peak, greater glucose area over the curve, shorter time to glucose trough, and steeper slope to glucose trough after glipizide. In replication, a higher T2D score was associated with a greater 1-year hemoglobin A1c reduction to sulfonylureas in the Genetics of Diabetes Audit and Research in Tayside Scotland (GoDARTS) study (P = 0.02). Our findings suggest that individuals with a higher genetic burden for T2D experience a greater acute and sustained response to sulfonylureas.

Precision medicine in diabetes: a Consensus Report from the American Diabetes Association (ADA) and the European Association for the Study of Diabetes (EASD)

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 realise 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.

Precision Medicine in Diabetes: A Consensus Report From the American Diabetes Association (ADA) and the European Association for the Study of Diabetes (EASD)

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.

Additive and Multiplicative Interactions Between Genetic Risk Score and Family History and Lifestyle in Relation to Risk of Type 2 Diabetes

We examined interactions between lifestyle factors and genetic risk of type 2 diabetes (T2D-GR), captured by genetic risk score (GRS) and family history (FH). Our initial study cohort included 20,524 European-ancestry participants, of whom 1,897 developed incident T2D, in the Nurses' Health Study (1984-2016), Nurses' Health Study II (1989-2016), and Health Professionals Follow-up Study (1986-2016). The analyses were replicated in 19,183 European-ancestry controls and 2,850 incident T2D cases in the Women's Genome Health Study (1992-2016). We defined 2 categories of T2D-GR: high GRS (upper one-third) with FH and low GRS or without FH. Compared with participants with the healthiest lifestyle and low T2D-GR, the relative risk of T2D for participants with the healthiest lifestyle and high T2D-GR was 2.24 (95% confidence interval (CI): 1.76, 2.86); for participants with the least healthy lifestyle and low T2D-GR, it was 4.05 (95% CI: 3.56, 4.62); and for participants with the least healthy lifestyle and high T2D-GR, it was 8.72 (95% CI: 7.46, 10.19). We found a significant departure from an additive risk difference model in both the initial and replication cohorts, suggesting that adherence to a healthy lifestyle could lead to greater absolute risk reduction among those with high T2D-GR. The public health implication is that a healthy lifestyle is important for diabetes prevention, especially for individuals with high GRS and FH of T2D.

Five-Year Glycemic Trajectories Among Healthy African-American and European-American Offspring of Parents With Type 2 Diabetes

BACKGROUND

Cross-sectional surveys report a higher prevalence of diagnosed type 2 diabetes mellitus (T2DM) in African Americans (AA) than European Americans (EA). We studied 5-year glycemic excursions among AA and EA in the Pathobiology of Prediabetes in A Biracial Cohort study, to assess ethnic disparities.

MATERIALS AND METHODS

Pathobiology of Prediabetes in A Biracial Cohort followed normoglycemic offspring of parents with T2DM for 5 years, with serial assessments of oral glucose tolerance test , anthropometry, body fat, insulin sensitivity and beta-cell function. The primary outcome was progression to prediabetes (impaired fasting glucose and/or impaired glucose tolerance). We further analyzed 5-year changes in fasting (FPG) and 2-hour plasma glucose (2hrPG).

RESULTS

One hundred and one (52 AA, 49 EA) out of 343 subjects developed prediabetes during follow-up. The change in FPG ranged from -24 mg/dl to +38 mg/dl. The FPG remained stable (± 5 mg/dl from baseline) in 50% of EA and 46.8% of AA and the 2hrPG remained stable (± 25 mg/dl from baseline) in 73.7% of EA and 71.0 % of AA during follow-up. The proportions with change in FPG of 5mg/dl to >25 mg/dl and 2hrPG of 25 mg/dl to >50 mg/dl were similar in EA and AA offspring, as were the 10th - 90th percentiles of the distribution of 5-year changes in FPG and 2hrPG.

CONCLUSIONS

During 5 years of follow-up, black and white offspring of parents with T2DM exhibited remarkable phenotypic concordance of glycemic trajectories. Thus, parental history of T2DM may be a stronger factor than race/ethnicity in the prediction of longitudinal glycemic trends.

Epigenetic Regulation of Circadian Rhythm and Its Possible Role in Diabetes Mellitus

This review aims to summarize the knowledge about the relationship between circadian rhythms and their influence on the development of type 2 diabetes mellitus (T2DM) and metabolic syndrome. Circadian rhythms are controlled by internal molecular feedback loops that synchronize the organism with the external environment. These loops are affected by genetic and epigenetic factors. Genetic factors include polymorphisms and mutations of circadian genes. The expression of circadian genes is regulated by epigenetic mechanisms that change from prenatal development to old age. Epigenetic modifications are influenced by the external environment. Most of these modifications are affected by our own life style. Irregular circadian rhythm and low quality of sleep have been shown to increase the risk of developing T2DM and other metabolic disorders. Here, we attempt to provide a wide description of mutual relationships between epigenetic regulation, circadian rhythm, aging process and highlight new evidences that show possible therapeutic advance in the field of chrono-medicine which will be more important in the upcoming years.

Genetic Risk, a Healthy Lifestyle, and Type 2 Diabetes: the Dongfeng-Tongji Cohort Study

OBJECTIVE

The objective of this study is to examine whether healthy lifestyle could reduce diabetes risk among individuals with different genetic profiles.

DESIGN

A prospective cohort study with a median follow-up of 4.6 years from the Dongfeng-Tongji cohort was performed.

PARTICIPANTS

A total of 19 005 individuals without diabetes at baseline participated in the study.

MAIN VARIABLE MEASURE

A healthy lifestyle was determined based on 6 factors: nonsmoker, nondrinker, healthy diet, body mass index of 18.5 to 23.9 kg/m2, waist circumference less than 85 cm for men and less than 80 cm for women, and higher level of physical activity. Associations of combined lifestyle factors and incident diabetes were estimated using Cox proportional hazard regression. A polygenic risk score of 88 single-nucleotide polymorphisms previously associated with diabetes was constructed to test for association with diabetes risk among 7344 individuals, using logistic regression.

RESULTS

A total of 1555 incident diabetes were ascertained. Per SD increment of simple and weighted genetic risk score was associated with a 1.39- and 1.34-fold higher diabetes risk, respectively. Compared with poor lifestyle, intermediate and ideal lifestyle were reduced to a 23% and 46% risk of incident diabetes, respectively. Association of lifestyle with diabetes risk was independent of genetic risk. Even among individuals with high genetic risk, intermediate and ideal lifestyle were separately associated with a 29% and 49% lower risk of diabetes.

CONCLUSION

Genetic and combined lifestyle factors were independently associated with diabetes risk. A healthy lifestyle could lower diabetes risk across different genetic risk categories, emphasizing the benefit of entire populations adhering to a healthy lifestyle.

Interaction Between Type 2 Diabetes Prevention Strategies and Genetic Determinants of Coronary Artery Disease on Cardiometabolic Risk Factors

Coronary artery disease (CAD) is more frequent among individuals with dysglycemia. Preventive interventions for diabetes can improve cardiometabolic risk factors (CRFs), but it is unclear whether the benefits on CRFs are similar for individuals at different genetic risk for CAD. We built a 201-variant polygenic risk score (PRS) for CAD and tested for interaction with diabetes prevention strategies on 1-year changes in CRFs in 2,658 Diabetes Prevention Program (DPP) participants. We also examined whether separate lifestyle behaviors interact with PRS and affect changes in CRFs in each intervention group. Participants in both the lifestyle and metformin interventions had greater improvement in the majority of recognized CRFs compared with placebo (P < 0.001) irrespective of CAD genetic risk (P interaction > 0.05). We detected nominal significant interactions between PRS and dietary quality and physical activity on 1-year change in BMI, fasting glucose, triglycerides, and HDL cholesterol in individuals randomized to metformin or placebo, but none of them achieved the multiple-testing correction for significance. This study confirms that diabetes preventive interventions improve CRFs regardless of CAD genetic risk and delivers hypothesis-generating data on the varying benefit of increasing physical activity and improving diet on intermediate cardiovascular risk factors depending on individual CAD genetic risk profile.

Genetic factors and risk of type 2 diabetes among women with a history of gestational diabetes: findings from two independent populations

OBJECTIVE

Women with a history of gestational diabetes mellitus (GDM) have an exceptionally high risk for type 2 diabetes (T2D). Yet, little is known about genetic determinants for T2D in this population. We examined the association of a genetic risk score (GRS) with risk of T2D in two independent populations of women with a history of GDM and how this association might be modified by non-genetic determinants for T2D.

RESEARCH DESIGN AND METHODS

This cohort study included 2434 white women with a history of GDM from the Nurses' Health Study II (NHSII, n=1884) and the Danish National Birth Cohort (DNBC, n=550). A GRS for T2D was calculated using 59 candidate single nucleotide polymorphisms for T2D identified from genome-wide association studies in European populations. An alternate healthy eating index (AHEI) score was derived to reflect dietary quality after the pregnancy affected by GDM.

RESULTS

Women on average were followed for 21 years in NHSII and 13 years in DNBC, during which 446 (23.7%) and 155 (28.2%) developed T2D, respectively. The GRS was generally positively associated with T2D risk in both cohorts. In the pooled analysis, the relative risks (RRs) for increasing quartiles of GRS were 1.00, 0.97, 1.25 and 1.19 (p trend=0.02). In both cohorts, the association appeared to be stronger among women with poorer (AHEI <median) than better dietary quality (AHEI ≥median), although the interaction was not significant. For example, in NHSII, the RRs across increasing quartiles of GRS were 1.00, 0.99, 1.51 and 1.29 (p trend=0.06) among women with poorer dietary quality and 1.00, 0.83, 0.81 and 0.94 (p trend=0.79) among women with better dietary quality (p interaction=0.11).

CONCLUSIONS

Among white women with a history of GDM, higher GRS for T2D was associated with an increased risk of T2D.

Genetic Risk Scores for Diabetes Diagnosis and Precision Medicine

During the last decade, there have been substantial advances in the identification and characterization of DNA sequence variants associated with individual predisposition to type 1 and type 2 diabetes. As well as providing insights into the molecular, cellular, and physiological mechanisms involved in disease pathogenesis, these risk variants, when combined into a polygenic score, capture information on individual patterns of disease predisposition that have the potential to influence clinical management. In this review, we describe the various opportunities that polygenic scores provide: to predict diabetes risk, to support differential diagnosis, and to understand phenotypic and clinical heterogeneity. We also describe the challenges that will need to be overcome if this potential is to be fully realized.

Precision behavioral medicine: Implications of genetic and genomic discoveries for behavioral weight loss treatment

This article reviews the concept of precision behavioral medicine and the progress toward applying genetics and genomics as tools to optimize weight management intervention. We discuss genetic, epigenetic, and genomic markers, as well as interactions between genetics and the environment as they relate to obesity and behavioral weight loss to date. Recommendations for the conditions under which genetics and genomics could be incorporated to support clinical decision-making in behavioral weight loss are outlined and illustrative scenarios of how this approach could improve clinical outcomes are provided. It is concluded that there is not yet sufficient evidence to leverage genetics or genomics to aid the treatment of obesity but the foundations are being laid. (PsycINFO Database Record (c) 2018 APA, all rights reserved).

Gene-lifestyle interaction on risk of type 2 diabetes: A systematic review

The pathophysiological influence of gene-lifestyle interactions on the risk to develop type 2 diabetes (T2D) is currently under intensive research. This systematic review summarizes the evidence for gene-lifestyle interactions regarding T2D incidence. MEDLINE, EMBASE, and Web of Science were systematically searched until 31 January 2019 to identify publication with (a) prospective study design; (b) T2D incidence; (c) gene-diet, gene-physical activity, and gene-weight loss intervention interaction; and (d) population who are healthy or prediabetic. Of 66 eligible publications, 28 reported significant interactions. A variety of different genetic variants and dietary factors were studied. Variants at TCF7L2 were most frequently investigated and showed interactions with fiber and whole grain on T2D incidence. Further gene-diet interactions were reported for, eg, a western dietary pattern with a T2D-GRS, fat and carbohydrate with IRS1 rs2943641, and heme iron with variants of HFE. Physical activity showed interaction with HNF1B, IRS1, PPARγ, ADRA2B, SLC2A2, and ABCC8 variants and weight loss interventions with ENPP1, PPARγ, ADIPOR2, ADRA2B, TNFα, and LIPC variants. However, most findings represent single study findings obtained in European ethnicities. Although some interactions have been reported, their conclusiveness is still low, as most findings were not yet replicated across multiple study populations.

No significant association of type 2 diabetes-related genetic risk scores with glycated haemoglobin levels after initiating metformin or sulphonylurea derivatives

AIM

To explore the added value of diabetes-related genetic risk scores (GRSs) to readily available clinical variables in the prediction of glycated haemoglobin (HbA1c) levels after initiation of glucose-regulating drugs.

MATERIALS AND METHODS

We conducted a cohort study in people with type 2 diabetes (T2DM) from the Groningen Initiative to Analyse Type 2 Diabetes Treatment (GIANTT) database who initiated metformin (MET) or sulphonylurea derivatives (SUs) and for whom blood samples were genotyped. The primary outcome was HbA1c level at 6 months, adjusted for baseline HbA1c. GRSs were based on single nucleotide polymorphisms linked to insulin sensitivity, β-cell activity, and T2DM risk in general. Associations were analysed using multiple linear regression to assess whether adding the GRSs increased the explained variance in a prediction model that included age, gender, diabetes duration and cardio-metabolic biomarkers.

RESULTS

We included 282 patients initiating MET and 89 patients initiating SUs. In the MET prediction model, diabetes duration of >3 months when starting MET was associated with 2.7-mmol/mol higher HbA1c level. For SUs, no significant clinical predictors were identified. Addition of the GRS linked to insulin sensitivity (for MET), β-cell activity (for SUs) and T2DM risk (for both) to the models did not improve the explained variance significantly (22% without vs. 22% with GRS) for the MET and (14% without vs. 14% with GRS) for the SUs model, respectively.

CONCLUSION

This study did not indicate a significant effect of GRS related to T2DM in general or to the drugs' mechanism of action for prediction of inter-individual HbA1c variability in the short term after initiation of MET or SU therapy.

Kahn S, Boyko EJ, Bray GA, Horton ES, Hivert MF, Goldberg R, Chen L, Mercader J, Harden M, Florez JC. A Polygenic Lipodystrophy Genetic Risk Score Characterizes Risk Independent of BMI in the Diabetes Prevention Program

CONTEXT

There is substantial heterogeneity in insulin sensitivity, and genetics may suggest possible mechanisms by which common variants influence this trait.

OBJECTIVES

We aimed to evaluate an 11-variant polygenic lipodystrophy genetic risk score (GRS) for association with anthropometric, glycemic and metabolic traits in the Diabetes Prevention Program (DPP). In secondary analyses, we tested the association of the GRS with cardiovascular risk factors in the DPP.

DESIGN

In 2713 DPP participants, we evaluated a validated GRS of 11 common variants associated with fasting insulin-based measures of insulin sensitivity discovered through genome-wide association studies that cluster with a metabolic profile of lipodystrophy, conferring high metabolic risk despite low body mass index (BMI).

RESULTS

At baseline, a higher polygenic lipodystrophy GRS was associated with lower weight, BMI, and waist circumference measurements, but with worse insulin sensitivity index (ISI) values. Despite starting at a lower weight and BMI, a higher GRS was associated with less weight and BMI reduction at one year and less improvement in ISI after adjusting for baseline values but was not associated with diabetes incidence. A higher GRS was also associated with more atherogenic low-density lipoprotein peak-particle-density at baseline but was not associated with coronary artery calcium scores in the Diabetes Prevention Program Outcomes Study.

CONCLUSIONS

In the DPP, a higher polygenic lipodystrophy GRS for insulin resistance with lower BMI was associated with diminished improvement in insulin sensitivity and potential higher cardiovascular disease risk. This GRS helps characterize insulin resistance in a cohort of individuals at high risk for diabetes, independent of adiposity.

Primary Prevention of ASCVD and T2DM in Patients at Metabolic Risk: An Endocrine Society* Clinical Practice Guideline

OBJECTIVE

To develop clinical practice guidelines for the primary prevention of atherosclerotic cardiovascular disease (ASCVD) and type 2 diabetes mellitus (T2DM) in individuals at metabolic risk for developing these conditions.

CONCLUSIONS

Health care providers should incorporate regular screening and identification of individuals at metabolic risk (at higher risk for ASCVD and T2DM) with measurement of blood pressure, waist circumference, fasting lipid profile, and blood glucose. Individuals identified at metabolic risk should undergo 10-year global risk assessment for ASCVD or coronary heart disease to determine targets of therapy for reduction of apolipoprotein B-containing lipoproteins. Hypertension should be treated to targets outlined in this guideline. Individuals with prediabetes should be tested at least annually for progression to diabetes and referred to intensive diet and physical activity behavioral counseling programs. For the primary prevention of ASCVD and T2DM, the Writing Committee recommends lifestyle management be the first priority. Behavioral programs should include a heart-healthy dietary pattern and sodium restriction, as well as an active lifestyle with daily walking, limited sedentary time, and a structured program of physical activity, if appropriate. Individuals with excess weight should aim for loss of ≥5% of initial body weight in the first year. Behavior changes should be supported by a comprehensive program led by trained interventionists and reinforced by primary care providers. Pharmacological and medical therapy can be used in addition to lifestyle modification when recommended goals are not achieved.

Quality of dietary fat and genetic risk of type 2 diabetes: individual participant data meta-analysis

OBJECTIVE

To investigate whether the genetic burden of type 2 diabetes modifies the association between the quality of dietary fat and the incidence of type 2 diabetes.

DESIGN

Individual participant data meta-analysis.

DATA SOURCES

Eligible prospective cohort studies were systematically sourced from studies published between January 1970 and February 2017 through electronic searches in major medical databases (Medline, Embase, and Scopus) and discussion with investigators.

REVIEW METHODS

Data from cohort studies or multicohort consortia with available genome-wide genetic data and information about the quality of dietary fat and the incidence of type 2 diabetes in participants of European descent was sought. Prospective cohorts that had accrued five or more years of follow-up were included. The type 2 diabetes genetic risk profile was characterized by a 68-variant polygenic risk score weighted by published effect sizes. Diet was recorded by using validated cohort-specific dietary assessment tools. Outcome measures were summary adjusted hazard ratios of incident type 2 diabetes for polygenic risk score, isocaloric replacement of carbohydrate (refined starch and sugars) with types of fat, and the interaction of types of fat with polygenic risk score.

RESULTS

Of 102 305 participants from 15 prospective cohort studies, 20 015 type 2 diabetes cases were documented after a median follow-up of 12 years (interquartile range 9.4-14.2). The hazard ratio of type 2 diabetes per increment of 10 risk alleles in the polygenic risk score was 1.64 (95% confidence interval 1.54 to 1.75, I²=7.1%, τ²=0.003). The increase of polyunsaturated fat and total omega 6 polyunsaturated fat intake in place of carbohydrate was associated with a lower risk of type 2 diabetes, with hazard ratios of 0.90 (0.82 to 0.98, I²=18.0%, τ²=0.006; per 5% of energy) and 0.99 (0.97 to 1.00, I²=58.8%, τ²=0.001; per increment of 1 g/d), respectively. Increasing monounsaturated fat in place of carbohydrate was associated with a higher risk of type 2 diabetes (hazard ratio 1.10, 95% confidence interval 1.01 to 1.19, I²=25.9%, τ²=0.006; per 5% of energy). Evidence of small study effects was detected for the overall association of polyunsaturated fat with the risk of type 2 diabetes, but not for the omega 6 polyunsaturated fat and monounsaturated fat associations. Significant interactions between dietary fat and polygenic risk score on the risk of type 2 diabetes (P>0.05 for interaction) were not observed.

CONCLUSIONS

These data indicate that genetic burden and the quality of dietary fat are each associated with the incidence of type 2 diabetes. The findings do not support tailoring recommendations on the quality of dietary fat to individual type 2 diabetes genetic risk profiles for the primary prevention of type 2 diabetes, and suggest that dietary fat is associated with the risk of type 2 diabetes across the spectrum of type 2 diabetes genetic risk.

Recent advances and perspectives in next generation sequencing application to the genetic research of type 2 diabetes

Type 2 diabetes (T2D) mellitus is a common complex disease that currently affects more than 400 million people worldwide and has become a global health problem. High-throughput sequencing technologies such as whole-genome and whole-exome sequencing approaches have provided numerous new insights into the molecular bases of T2D. Recent advances in the application of sequencing technologies to T2D research include, but are not limited to: (1) Fine mapping of causal rare and common genetic variants; (2) Identification of confident gene-level associations; (3) Identification of novel candidate genes by specific scoring approaches; (4) Interrogation of disease-relevant genes and pathways by transcriptional profiling and epigenome mapping techniques; and (5) Investigation of microbial community alterations in patients with T2D. In this work we review these advances in application of next-generation sequencing methods for elucidation of T2D pathogenesis, as well as progress and challenges in implementation of this new knowledge about T2D genetics in diagnosis, prevention, and treatment of the disease.

The Interaction of a Diabetes Gene Risk Score With 3 Different Antihypertensive Medications for Incident Glucose-level Elevation

BACKGROUND

Elevations of fasting glucose (FG) levels are frequently encountered in people treated with thiazide diuretics. The risk is lower in people treated with ACE inhibitors (ACEi). To determine if genetic factors play a role in FG elevation, we examined the interaction of a diabetes gene risk score (GRS) with the use of 3 different antihypertensive medications.

METHODS

We examined 376 nondiabetic hypertensive individuals with baseline FG <100 mg/dl who were genotyped for 24 genes associated with risk of elevated glucose levels. All participants had ≥1 follow-up FG level over 6 years of follow-up. Participants were randomized to treatment with a thiazide-like diuretic (chlorthalidone), a calcium channel blocker (CCB; amlodipine), or an ACEi (lisinopril). Outcomes were an FG increase of ≥13 or ≥27 mg/dl, the upper 75% and 90% FG increase in the parent cohort from which the present cohort was obtained. Odds ratios were adjusted for factors that increase FG levels.

RESULTS

For every 1 allele increase in GRS, the adjusted odds ratios (ORs) were 1.06 (95% confidence interval (CI): 0.99, 1.14; P = 0.06) and 1.09 (95% CI: 0.99, 1.20; P = 0.08). When results were examined by randomized medications, participants randomized to amlodipine had statistically significant odds for either outcome (OR: 1.23; 95% CI: 1.03, 1.48; P = 0.01 and OR: 1.31; 95% CI: 1.06, 1.62; P = 0.01). No such risk increase was found in participants randomized to the other 2 medications.

CONCLUSIONS

A diabetes GRS predicts FG elevation in people treated with a CCB, but not with an ACEi or diuretic. These findings require confirmation.

CLINICAL TRIALS REGISTRATION

Trial number NCT00000542.

The Continuing Evolution of Precision Health in Type 2 Diabetes: Achievements and Challenges

PURPOSE OF REVIEW

The purpose of this review was to summarize recent advances in the genomics of type 2 diabetes (T2D) and to highlight current initiatives to advance precision health.

RECENT FINDINGS

Generation of multi-omic data to measure each of the "biologic layers," developments in describing genomic function and annotation in T2D relevant tissue, along with the increasing recognition that T2D is a heterogeneous disease, and large-scale collaborations have all contributed to advancing our understanding of the molecular basis of T2D. Substantial advances have been made in understanding the molecular basis of T2D pathogenesis, such that precision health diabetes is increasingly becoming a reality. For precision diabetes to become a routine in clinical and public health, additional large-scale multi-omic initiatives are needed along with better assessment of our environment to delineate an individual's diabetes subtype for improved detection and management.

Review of Metformin Use for Type 2 Diabetes Prevention

CONTEXT

Prediabetes is prevalent and significantly increases lifetime risk of progression to type 2 diabetes. This review summarizes the evidence surrounding metformin use for type 2 diabetes prevention.

EVIDENCE ACQUISITION

Articles published between 1998 and 2017 examining metformin use for the primary indication of diabetes prevention available on MEDLINE.

EVIDENCE SYNTHESIS

Forty articles met inclusion criteria and were summarized into four general categories: (1) RCTs of metformin use for diabetes prevention (n=7 and n=2 follow-up analyses); (2) observational analyses examining metformin use in heterogeneous subgroups of patients with prediabetes (n=9 from the Diabetes Prevention Program, n=1 from the biguanides and the prevention of the risk of obesity [BIGPRO] trial); (3) observational analyses examining cost effectiveness of metformin use for diabetes prevention (n=11 from the Diabetes Prevention Program, n=1 from the Indian Diabetes Prevention Program); and (4) real-world assessments of metformin eligibility or use for diabetes prevention (n=9). Metformin was associated with reduced relative risk of incident diabetes, with the strongest evidence for use in those at highest risk (i.e., aged <60 years, BMI ≥35, and women with histories of gestational diabetes). Metformin was also deemed cost effective in 11 economic analyses. Recent studies highlighted low rates of metformin use for diabetes prevention in real-world settings.

CONCLUSIONS

Two decades of evidence support metformin use for diabetes prevention among higher-risk patients. However, metformin is not widely used in real-world practice, and enhancing the translation of this evidence to real-world practice has important implications for patients, providers, and payers.

Precision medicine in diabetes prevention, classification and management

Diabetes has become a major burden of healthcare expenditure. Diabetes management following a uniform treatment algorithm is often associated with progressive treatment failure and development of diabetic complications. Recent advances in our understanding of the genomic architecture of diabetes and its complications have provided the framework for development of precision medicine to personalize diabetes prevention and management. In the present review, we summarized recent advances in the understanding of the genetic basis of diabetes and its complications. From a clinician's perspective, we attempted to provide a balanced perspective on the utility of genomic medicine in the field of diabetes. Using genetic information to guide management of monogenic forms of diabetes represents the best-known examples of genomic medicine for diabetes. Although major strides have been made in genetic research for diabetes, its complications and pharmacogenetics, ongoing efforts are required to translate these findings into practice by incorporating genetic information into a risk prediction model for prioritization of treatment strategies, as well as using multi-omic analyses to discover novel drug targets with companion diagnostics. Further research is also required to ensure the appropriate use of this information to empower individuals and healthcare professionals to make personalized decisions for achieving the optimal outcome.

Genetic variation of the transcription factor GATA3, not STAT4, is associated with the risk of type 2 diabetes in the Bangladeshi population

Type 2 diabetes mellitus is a multifactorial metabolic disorder caused by environmental factors and has a strong association with hereditary issues. These hereditary issues result in an imbalance in CD4⁺T cells and a decreased level of naïve CD4⁺T cells, which may be critical in the pathogenesis of type 2 diabetes. Transcription factors GATA3 and STAT4 mediate the cytokine-induced development of naïve T cells into Th1 or Th2 types. In the present study, genetic analyses of GATA3 SNP rs3824662 and STAT4 SNP rs10181656 were performed to investigate the association of allelic and genotypic variations with the risk of T2D in the Bangladeshi population. A total of 297 unrelated Bangladeshi patients with type 2 diabetes and 247 healthy individuals were included in the study. The allelic and genotypic frequencies of rs10181656 located in the STAT4 gene were not found to be associated with risk of type 2 diabetes. The GATA3 rs3824662 T allele and mutant TT genotype had a significant association with the risk of T2D [OR: 1.52 (1.15–2.02), X² = 8.66, p = 0.003 and OR: 2.98 (1.36–6.55), X² = 7.98, p = 0.04, respectively]. Thus, the present study postulates that the genetic variation of the transcription factor GATA3, not STAT4, is associated with the risk of type 2 diabetes in the Bangladeshi population.

Assessing the Associations of Blood Metabolites With Osteoporosis: A Mendelian Randomization Study

Context

Osteoporosis is a metabolic bone disease. The effect of blood metabolites on the development of osteoporosis remains elusive.

Objective

To explore the relationship between blood metabolites and osteoporosis.

Design and Methods

We used 2286 unrelated white subjects for the discovery samples and 3143 unrelated white subjects from the Framingham Heart Study (FHS) for the replication samples. The bone mineral density (BMD) was measured using dual-energy X-ray absorptiometry. Genome-wide single nucleotide polymorphism (SNP) genotyping was performed using Affymetrix Human SNP Array 6.0 (for discovery samples) and Affymetrix SNP 500K and 50K array (for FHS replication samples). The SNP sets significantly associated with blood metabolites were obtained from a reported whole-genome sequencing study. For each subject, the genetic risk score of the metabolite was calculated from the genotype data of the metabolite-associated SNP sets. Pearson correlation analysis was conducted to evaluate the potential effect of blood metabolites on the variations in bone phenotypes; 10,000 permutations were conducted to calculate the empirical P value and false discovery rate.

Results

We analyzed 481 blood metabolites. We identified multiple blood metabolites associated with hip BMD, such as 1,5-anhydroglucitol (Pdiscovery < 0.0001; Preplication = 0.0361), inosine (Pdiscovery = 0.0018; Preplication = 0.0256), theophylline (Pdiscovery = 0.0048; Preplication = 0.0433, gamma-glutamyl methionine (Pdiscovery = 0.0047; Preplication = 0.0471), 1-linoleoyl-2-arachidonoyl-GPC (18:2/20:4n6; Pdiscovery = 0.0018; Preplication = 0.0390), and X-12127 (Pdiscovery = 0.0002; Preplication = 0.0249).

Conclusions

Our results suggest a modest effect of blood metabolites on the variations of BMD and identified several candidate blood metabolites for osteoporosis.

Global aetiology and epidemiology of type 2 diabetes mellitus and its complications

Globally, the number of people with diabetes mellitus has quadrupled in the past three decades, and diabetes mellitus is the ninth major cause of death. About 1 in 11 adults worldwide now have diabetes mellitus, 90% of whom have type 2 diabetes mellitus (T2DM). Asia is a major area of the rapidly emerging T2DM global epidemic, with China and India the top two epicentres. Although genetic predisposition partly determines individual susceptibility to T2DM, an unhealthy diet and a sedentary lifestyle are important drivers of the current global epidemic; early developmental factors (such as intrauterine exposures) also have a role in susceptibility to T2DM later in life. Many cases of T2DM could be prevented with lifestyle changes, including maintaining a healthy body weight, consuming a healthy diet, staying physically active, not smoking and drinking alcohol in moderation. Most patients with T2DM have at least one complication, and cardiovascular complications are the leading cause of morbidity and mortality in these patients. This Review provides an updated view of the global epidemiology of T2DM, as well as dietary, lifestyle and other risk factors for T2DM and its complications.

Type 2 Diabetes Genetic Risk Scores Are Associated With Increased Type 2 Diabetes Risk Among African Americans by Cardiometabolic Status

The relationship between genetic risk variants associated with glucose homeostasis and type 2 diabetes risk has yet to be fully explored in African American populations. We pooled data from 4 prospective studies including 4622 African Americans to assess whether β-cell dysfunction (BCD) and/or insulin resistance (IR) genetic variants were associated with increased type 2 diabetes risk. The BCD genetic risk score (GRS) and combined BCD/IR GRS were significantly associated with increased type 2 diabetes risk. In cardiometabolic-stratified models, the BCD and IR GRS were associated with increased type 2 diabetes risk among 5 cardiometabolic strata: 3 clinically healthy strata and 2 clinically unhealthy strata. Genetic risk scores related to BCD and IR were associated with increased risk of type 2 diabetes in African Americans. Notably, the GRSs were significant predictors of type 2 diabetes among individuals in clinically normal ranges of cardiometabolic traits.