| 1. |
- Nettleton, Jennifer A., et al.
(author)
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Meta-Analysis Investigating Associations Between Healthy Diet and Fasting Glucose and Insulin Levels and Modification by Loci Associated With Glucose Homeostasis in Data From 15 Cohorts
- 2013
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In: American Journal of Epidemiology. - : Oxford University Press (OUP). - 0002-9262 .- 1476-6256. ; 177:2, s. 103-115
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Research review (peer-reviewed)abstract
- Whether loci that influence fasting glucose (FG) and fasting insulin (FI) levels, as identified by genome-wide association studies, modify associations of diet with FG or FI is unknown. We utilized data from 15 US and European cohort studies comprising 51,289 persons without diabetes to test whether genotype and diet interact to influence FG or FI concentration. We constructed a diet score using study-specific quartile rankings for intakes of whole grains, fish, fruits, vegetables, and nuts/seeds (favorable) and red/processed meats, sweets, sugared beverages, and fried potatoes (unfavorable). We used linear regression within studies, followed by inverse-variance-weighted meta-analysis, to quantify 1) associations of diet score with FG and FI levels and 2) interactions of diet score with 16 FG-associated loci and 2 FI-associated loci. Diet score (per unit increase) was inversely associated with FG ( 0.004 mmol/L, 95 confidence interval: 0.005, 0.003) and FI ( 0.008 ln-pmol/L, 95 confidence interval: 0.009, 0.007) levels after adjustment for demographic factors, lifestyle, and body mass index. Genotype variation at the studied loci did not modify these associations. Healthier diets were associated with lower FG and FI concentrations regardless of genotype at previously replicated FG- and FI-associated loci. Studies focusing on genomic regions that do not yield highly statistically significant associations from main-effect genome-wide association studies may be more fruitful in identifying diet-gene interactions.
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| 2. |
- Bray, Molly S, et al.
(author)
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NIH working group report-using genomic information to guide weight management: From universal to precision treatment.
- 2016
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In: Obesity. - : Wiley. - 1930-739X .- 1930-7381. ; 24:1, s. 14-22
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Research review (peer-reviewed)abstract
- Precision medicine utilizes genomic and other data to optimize and personalize treatment. Although more than 2,500 genetic tests are currently available, largely for extreme and/or rare phenotypes, the question remains whether this approach can be used for the treatment of common, complex conditions like obesity, inflammation, and insulin resistance, which underlie a host of metabolic diseases.
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| 3. |
- Chung, Wendy K., et al.
(author)
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Precision Medicine in Diabetes : A Consensus Report From the American Diabetes Association (ADA) and the European Association for the Study of Diabetes (EASD)
- 2020
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In: Diabetes Care. - : American Diabetes Association. - 1935-5548 .- 0149-5992. ; 43:7, s. 1617-1635
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Research review (peer-reviewed)abstract
- 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.
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| 4. |
- Cooper, A. J., et al.
(author)
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Fruit and vegetable intake and type 2 diabetes : EPIC-InterAct prospective study and meta-analysis
- 2012
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In: European Journal of Clinical Nutrition. - London : Nature Publishing Group. - 0954-3007 .- 1476-5640. ; 66:10, s. 1082-1092
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Research review (peer-reviewed)abstract
- Fruit and vegetable intake (FVI) may reduce the risk of type 2 diabetes (T2D), but the epidemiological evidence is inconclusive. The aim of this study is to examine the prospective association of FVI with T2D and conduct an updated meta-analysis. In the European Prospective Investigation into Cancer-InterAct (EPIC-InterAct) prospective case-cohort study nested within eight European countries, a representative sample of 16 154 participants and 12 403 incident cases of T2D were identified from 340 234 individuals with 3.99 million person-years of follow-up. For the meta-analysis we identified prospective studies on FVI and T2D risk by systematic searches of MEDLINE and EMBASE until April 2011. In EPIC-InterAct, estimated FVI by dietary questionnaires varied more than twofold between countries. In adjusted analyses the hazard ratio (95% confidence interval) comparing the highest with lowest quartile of reported intake was 0.90 (0.80-1.01) for FVI; 0.89 (0.76-1.04) for fruit and 0.94 (0.84-1.05) for vegetables. Among FV subtypes, only root vegetables were inversely associated with diabetes 0.87 (0.77-0.99). In meta-analysis using pooled data from five studies including EPIC-InterAct, comparing the highest with lowest category for FVI was associated with a lower relative risk of diabetes (0.93 (0.87-1.00)). Fruit or vegetables separately were not associated with diabetes. Among FV subtypes, only green leafy vegetable (GLV) intake (relative risk: 0.84 (0.74-0.94)) was inversely associated with diabetes. Subtypes of vegetables, such as root vegetables or GLVs may be beneficial for the prevention of diabetes, while total FVI may exert a weaker overall effect.
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| 5. |
- Estampador, Angela C., et al.
(author)
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Precision Medicine in Obesity and Type 2 Diabetes : The Relevance of Early-Life Exposures
- 2018
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In: Clinical Chemistry. - : Oxford University Press (OUP). - 0009-9147 .- 1530-8561. ; 64:1, s. 130-141
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Research review (peer-reviewed)abstract
- BACKGROUND: Type 2 diabetes is highly prevalent and devastating. Obesity is a diabetogenic factor, driving insulin resistance and a compensatory demand for increased insulin secretion from the pancreatic β cells; a failure to address this demand results in diabetes. Accordingly, primary and secondary prevention of obesity are at the core of diabetes prevention programs. The development of obesity and declining β-cell function often span many years or decades before diabetes is clinically manifest. Thus, characterizing the early-life process and risk factors that set disease trajectories may yield novel targets for early intervention and help improve the accuracy of prediction algorithms, factors germane to the emerging field of precision medicine.CONTENT: Here, we overview the concepts of precision medicine and fetal programming. We discuss the barriers to preventing obesity and type 2 diabetes in adulthood and present the rationale for considering early-life events in this context. In so doing, we discuss proof-of-concept studies and cutting-edge technological developments that are likely to transform current thinking on the etiology and pathogenesis of obesity and type 2 diabetes. We also review the factors hampering progress, including the success and failures of pregnancy intervention trials.SUMMARY: Obesity and type 2 diabetes are among the major health and economic burdens of our time. Defeating these diseases is likely to require life-course approaches, which may include aggressive interventions informed by biomarker profiling undertaken during early life.
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| 6. |
- Estampador, Angela, et al.
(author)
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Genetic and epigenetic catalysts in early-life programming of adult cardiometabolic disorders
- 2014
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In: Diabetes, Metabolic Syndrome and Obesity. - : Dove Medical Press. - 1178-7007. ; 7, s. 575-586
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Research review (peer-reviewed)abstract
- Evidence has emerged across the past few decades that the lifetime risk of developing morbidities like type 2 diabetes, obesity, and cardiovascular disease may be influenced by exposures that occur in utero and in childhood. Developmental abnormalities are known to occur at various stages in fetal growth. Epidemiological and mechanistic studies have sought to delineate developmental processes and plausible risk factors influencing pregnancy outcomes and later health. Whether these observations reflect causal processes or are confounded by genetic and social factors remains unclear, although animal (and some human) studies suggest that epigenetic programming events may be involved. Regardless of the causal basis to observations of early-life risk factors and later disease risk, the fact that such associations exist and that they are of a fairly large magnitude justifies further research around this topic. Furthermore, additional information is needed to substantiate public health guidelines on lifestyle behaviors during pregnancy to improve infant health outcomes. Indeed, lifestyle intervention clinical trials in pregnancy are now coming online, where materials and data are being collected that should facilitate understanding of the causal nature of intrauterine exposures related with gestational weight gain, such as elevated maternal blood glucose concentrations. In this review, we provide an overview of these concepts.
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| 7. |
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| 8. |
- Franks, Paul W., et al.
(author)
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Exposing the exposures responsible for type 2 diabetes and obesity
- 2016
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In: Science. - : American Association for the Advancement of Science (AAAS). - 0036-8075 .- 1095-9203. ; 354:6308, s. 69-73
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Research review (peer-reviewed)abstract
- The rising prevalences of type 2 diabetes and obesity constitutemajor threats to human health globally. Powerful social and economic factors influence the distribution of these diseases among and within populations. These factors act on a substrate of individual predisposition derived from the composite effects of inherited DNA variation and a range of environmental exposures experienced throughout the life course. Although "Western" lifestyle represents a convenient catch-all culprit for such exposures, effective treatment and prevention will be informed by characterization of the most critical, causal environmental factors. In this Review, we examine how burgeoning understanding of the genetic basis of type 2 diabetes and obesity can highlight nongenetic exposures that drive development of these conditions.
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| 9. |
- Franks, Paul W., et al.
(author)
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Gene-lifestyle interplay in type 2 diabetes
- 2018
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In: Current Opinion in Genetics and Development. - : CURRENT BIOLOGY LTD. - 0959-437X .- 1879-0380. ; 50, s. 35-40
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Research review (peer-reviewed)abstract
- Type 2 diabetes (T2D) is widespread, affecting the health of hundreds of millions worldwide. The disease results from the complex interplay of lifestyle factors acting on a backdrop of inherited DNA risk variants. Detecting and understanding biomarkers, whether genotypes or other downstream biological features that dictate a person's phenotypic response to different lifestyle exposures, may have tremendous utility in the prevention of T2D. Here, we explore (i) evidence of how human genetic adaptation to diverse local environments might interact with lifestyle factors in T2D, (ii) the key challenges facing the research area of gene x lifestyle interactions in T2D, and (iii) the solutions that might be pursued in future studies. Overall, many preliminary examples of such interactions exist, but none is sufficient to have a major impact on clinical decision making. Future studies, integrating genetics and other biological markers into regulatory networks, are likely to be necessary to facilitate the integration of genomics into lifestyle medicine in T2D.
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| 10. |
- Franks, Paul W., et al.
(author)
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Next-generation epidemiology : the role of high-resolution molecular phenotyping in diabetes research
- 2020
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In: Diabetologia. - : Springer Science and Business Media LLC. - 0012-186X .- 1432-0428. ; 63:12, s. 2521-2532
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Research review (peer-reviewed)abstract
- Epidemiologists have for many decades reported on the patterns and distributions of diabetes within and between populations and have helped to elucidate the aetiology of the disease. This has helped raise awareness of the tremendous burden the disease places on individuals and societies; it has also identified key risk factors that have become the focus of diabetes prevention trials and helped shape public health recommendations. Recent developments in affordable high-throughput genetic and molecular phenotyping technologies have driven the emergence of a new type of epidemiology with a more mechanistic focus than ever before. Studies employing these technologies have identified gene variants or causal loci, and linked these to other omics data that help define the molecular processes mediating the effects of genetic variation in the expression of clinical phenotypes. The scale of these epidemiological studies is rapidly growing; a trend that is set to continue as the public and private sectors invest heavily in omics data generation. Many are banking on this massive volume of diverse molecular data for breakthroughs in drug discovery and predicting sensitivity to risk factors, response to therapies and susceptibility to diabetes complications, as well as the development of disease-monitoring tools and surrogate outcomes. To realise these possibilities, it is essential that omics technologies are applied to well-designed epidemiological studies and that the emerging data are carefully analysed and interpreted. One might view this as next-generation epidemiology, where complex high-dimensionality data analysis approaches will need to be blended with many of the core principles of epidemiological research. In this article, we review the literature on omics in diabetes epidemiology and discuss how this field is evolving. [Figure not available: see fulltext.]
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