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1.
Arthritis Res Ther ; 23(1): 75, 2021 03 04.
Artigo em Inglês | MEDLINE | ID: mdl-33663556

RESUMO

BACKGROUND: Prevention of hyperuricaemia (HU) is critical to the prevention of gout. Understanding causal relationships and relative contributions of various risk factors to hyperuricemia is therefore important in the prevention of gout. Here, we use attributable fraction to compare the relative contribution of genetic, dietary, urate-lowering therapy (ULT) and other exposures to HU. We use Mendelian randomisation to test for the causality of diet in urate levels. METHODS: Four European-ancestry sample sets, three from the general population (n = 419,060) and one of people with gout (n = 6781) were derived from the Database of Genotypes and Phenotypes (ARIC, FHS, CARDIA, CHS) and UK Biobank. Dichotomised exposures to diet, genetic risk variants, BMI, alcohol, diuretic treatment, sex and age were used to calculate adjusted population and average attributable fractions (PAF/AAF) for HU (≥0.42 mmol/L [≥7 mg/dL]). Exposure to ULT was also assessed in the gout cohort. Two sample Mendelian randomisation was done in the UK Biobank using dietary pattern-associated genetic variants as exposure and serum urate levels as outcome. RESULTS: Adherence to dietary recommendations, BMI (< 25 kg/m2), and absence of the SLC2A9 rs12498742 urate-raising allele produced PAFs for HU of 20 to 24%, 59 to 69%, and 57 to 64%, respectively, in the three non-gout cohorts. In the gout cohort, diet, BMI, SLC2A9 rs12498742 and ULT PAFs for HU were 12%, 49%, 48%, and 63%, respectively. Mendelian randomisation demonstrated weak causal effects of four dietary habits on serum urate levels (e.g. preferentially drinking skim milk increased urate, ß = 0.047 mmol/L, P = 3.78 × 10-8). These effects were mediated by BMI, and they were not significant (P ≥ 0.06) in multivariable models assessing the BMI-independent effect of diet on urate. CONCLUSIONS: Diet has a relatively minor role in determining serum urate levels and HU. In gout, the use of ULT was the largest attributable fraction tested for HU.

2.
PLoS Med ; 18(3): e1003553, 2021 03.
Artigo em Inglês | MEDLINE | ID: mdl-33661905

RESUMO

BACKGROUND: Epidemiological studies report associations of diverse cardiometabolic conditions including obesity with COVID-19 illness, but causality has not been established. We sought to evaluate the associations of 17 cardiometabolic traits with COVID-19 susceptibility and severity using 2-sample Mendelian randomization (MR) analyses. METHODS AND FINDINGS: We selected genetic variants associated with each exposure, including body mass index (BMI), at p < 5 × 10-8 from genome-wide association studies (GWASs). We then calculated inverse-variance-weighted averages of variant-specific estimates using summary statistics for susceptibility and severity from the COVID-19 Host Genetics Initiative GWAS meta-analyses of population-based cohorts and hospital registries comprising individuals with self-reported or genetically inferred European ancestry. Susceptibility was defined as testing positive for COVID-19 and severity was defined as hospitalization with COVID-19 versus population controls (anyone not a case in contributing cohorts). We repeated the analysis for BMI with effect estimates from the UK Biobank and performed pairwise multivariable MR to estimate the direct effects and indirect effects of BMI through obesity-related cardiometabolic diseases. Using p < 0.05/34 tests = 0.0015 to declare statistical significance, we found a nonsignificant association of genetically higher BMI with testing positive for COVID-19 (14,134 COVID-19 cases/1,284,876 controls, p = 0.002; UK Biobank: odds ratio 1.06 [95% CI 1.02, 1.10] per kg/m2; p = 0.004]) and a statistically significant association with higher risk of COVID-19 hospitalization (6,406 hospitalized COVID-19 cases/902,088 controls, p = 4.3 × 10-5; UK Biobank: odds ratio 1.14 [95% CI 1.07, 1.21] per kg/m2, p = 2.1 × 10-5). The implied direct effect of BMI was abolished upon conditioning on the effect on type 2 diabetes, coronary artery disease, stroke, and chronic kidney disease. No other cardiometabolic exposures tested were associated with a higher risk of poorer COVID-19 outcomes. Small study samples and weak genetic instruments could have limited the detection of modest associations, and pleiotropy may have biased effect estimates away from the null. CONCLUSIONS: In this study, we found genetic evidence to support higher BMI as a causal risk factor for COVID-19 susceptibility and severity. These results raise the possibility that obesity could amplify COVID-19 disease burden independently or through its cardiometabolic consequences and suggest that targeting obesity may be a strategy to reduce the risk of severe COVID-19 outcomes.


Assuntos
Índice de Massa Corporal , Doença da Artéria Coronariana , Diabetes Mellitus Tipo 2 , Suscetibilidade a Doenças , Obesidade , Insuficiência Renal Crônica , Acidente Vascular Cerebral , /diagnóstico , /genética , Causalidade , Doença da Artéria Coronariana/epidemiologia , Doença da Artéria Coronariana/genética , Diabetes Mellitus Tipo 2/epidemiologia , Diabetes Mellitus Tipo 2/genética , Variação Genética , Estudo de Associação Genômica Ampla/estatística & dados numéricos , Humanos , Análise da Randomização Mendeliana , Metanálise como Assunto , Obesidade/diagnóstico , Obesidade/epidemiologia , Obesidade/metabolismo , Insuficiência Renal Crônica/epidemiologia , Insuficiência Renal Crônica/genética , Índice de Gravidade de Doença , Acidente Vascular Cerebral/epidemiologia , Acidente Vascular Cerebral/genética
3.
Diabetes ; 70(4): 996-1005, 2021 Apr.
Artigo em Inglês | MEDLINE | ID: mdl-33479058

RESUMO

The prevalence of type 2 diabetes in youth has increased substantially, yet the genetic underpinnings remain largely unexplored. To identify genetic variants predisposing to youth-onset type 2 diabetes, we formed ProDiGY, a multiethnic collaboration of three studies (TODAY, SEARCH, and T2D-GENES) with 3,006 youth case subjects with type 2 diabetes (mean age 15.1 ± 2.9 years) and 6,061 diabetes-free adult control subjects (mean age 54.2 ± 12.4 years). After stratifying by principal component-clustered ethnicity, we performed association analyses on ∼10 million imputed variants using a generalized linear mixed model incorporating a genetic relationship matrix to account for population structure and adjusting for sex. We identified seven genome-wide significant loci, including the novel locus rs10992863 in PHF2 (P = 3.2 × 10-8; odds ratio [OR] = 1.23). Known loci identified in our analysis include rs7903146 in TCF7L2 (P = 8.0 × 10-20; OR 1.58), rs72982988 near MC4R (P = 4.4 × 10-14; OR 1.53), rs200893788 in CDC123 (P = 1.1 × 10-12; OR 1.32), rs2237892 in KCNQ1 (P = 4.8 × 10-11; OR 1.59), rs937589119 in IGF2BP2 (P = 3.1 × 10-9; OR 1.34), and rs113748381 in SLC16A11 (P = 4.1 × 10-8; OR 1.04). Secondary analysis with 856 diabetes-free youth control subjects uncovered an additional locus in CPEB2 (P = 3.2 × 10-8; OR 2.1) and consistent direction of effect for diabetes risk. In conclusion, we identified both known and novel loci in the first genome-wide association study of youth-onset type 2 diabetes.

4.
Diabetes Obes Metab ; 23(4): 1030-1040, 2021 Apr.
Artigo em Inglês | MEDLINE | ID: mdl-33394545

RESUMO

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.

5.
J Endocr Soc ; 4(11): bvaa121, 2020 Nov 01.
Artigo em Inglês | MEDLINE | ID: mdl-33150273

RESUMO

Glucocorticoids have multiple therapeutic benefits and are used both for immunosuppression and treatment purposes. Notwithstanding their benefits, glucocorticoid use often leads to hyperglycemia. Owing to the pathophysiologic overlap in glucocorticoid-induced hyperglycemia (GIH) and type 2 diabetes (T2D), we hypothesized that genetic variation in glucocorticoid pathways contributes to T2D risk. To determine the genetic contribution of glucocorticoid action on T2D risk, we conducted multiple genetic studies. First, we performed gene-set enrichment analyses on 3 collated glucocorticoid-related gene sets using publicly available genome-wide association and whole-exome data and demonstrated that genetic variants in glucocorticoid-related genes are associated with T2D and related glycemic traits. To identify which genes are driving this association, we performed gene burden tests using whole-exome sequence data. We identified 20 genes within the glucocorticoid-related gene sets that are nominally enriched for T2D-associated protein-coding variants. The most significant association was found in coding variants in coiled-coil α-helical rod protein 1 (CCHCR1) in the HLA region (P = .001). Further analyses revealed that noncoding variants near CCHCR1 are also associated with T2D at genome-wide significance (P = 7.70 × 10-14), independent of type 1 diabetes HLA risk. Finally, gene expression and colocalization analyses demonstrate that variants associated with increased T2D risk are also associated with decreased expression of CCHCR1 in multiple tissues, implicating this gene as a potential effector transcript at this locus. Our discovery of a genetic link between glucocorticoids and T2D findings support the hypothesis that T2D and GIH may have shared underlying mechanisms.

6.
Diabetes ; 2020 Oct 13.
Artigo em Inglês | MEDLINE | ID: mdl-33051273

RESUMO

Hundreds of common genetic variants acting through distinguishable physiologic pathways influence the risk of type 2 diabetes (T2D). It is unknown to what extent the physiology underlying gestational diabetes (GDM) is distinct from that underlying T2D. In this study of over 5,000 pregnant women from three cohorts, we aimed to identify physiologically related groups of maternal variants associated with GDM using two complementary approaches based on Bayesian non-negative matrix factorization (bNMF) clustering. First, we tested five bNMF clusters of maternal T2D-associated variants grouped based on physiology outside of pregnancy for association with GDM. We found that cluster polygenic scores representing genetic determinants of reduced beta-cell function and abnormal hepatic lipid metabolism were associated with GDM; these clusters were not associated with infant birthweight. Second, we derived bNMF clusters of maternal variants based on pregnancy physiology and tested these clusters for association with GDM. We identified a cluster which was strongly associated with GDM and also associated with higher infant birthweight. The effect size for this cluster's association with GDM appeared greater than that for T2D. Our findings imply that the genetic and physiologic pathways that lead to GDM differ, at least in part, from those that lead to T2D.

8.
Diabetes ; 2020 Oct 26.
Artigo em Inglês | MEDLINE | ID: mdl-33106254

RESUMO

There is a limited understanding of how genetic loci associated with glycemic traits and type 2 diabetes (T2D) influence the response to anti-diabetes 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 (FG), and fasting insulin (FI), comprised of 65, 43, and 13 single nucleotide polymorphisms, respectively. Multiple linear regression tested for associations between scores and glycemic traits as well as pharmacodynamic endpoints, adjusting for age, sex, race, and body mass index (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 HbA1c reduction to sulfonylureas in the Genetics of Diabetes Audit and Research, Tayside and 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.

9.
medRxiv ; 2020 Sep 01.
Artigo em Inglês | MEDLINE | ID: mdl-32909013

RESUMO

IMPORTANCE: Early epidemiological studies report associations of diverse cardiometabolic conditions especially body mass index (BMI), with COVID-19 susceptibility and severity, but causality has not been established. Identifying causal risk factors is critical to inform preventive strategies aimed at modifying disease risk. OBJECTIVE: We sought to evaluate the causal associations of cardiometabolic conditions with COVID-19 susceptibility and severity. DESIGN: Two-sample Mendelian Randomization (MR) Study. SETTING: Population-based cohorts that contributed to the genome-wide association study (GWAS) meta-analysis by the COVID-19 Host Genetics Initiative. PARTICIPANTS: Patients hospitalized with COVID-19 diagnosed by RNA PCR, serologic testing, or clinician diagnosis. Population controls defined as anyone who was not a case in the cohorts. Exposures: Selected genetic variants associated with 17 cardiometabolic diseases, including diabetes, coronary artery disease, stroke, chronic kidney disease, and BMI, at p<5 x 10-8 from published largescale GWAS. MAIN OUTCOMES: We performed an inverse-variance weighted averages of variant-specific causal estimates for susceptibility, defined as people who tested positive for COVID-19 vs. population controls, and severity, defined as patients hospitalized with COVID-19 vs. population controls, and repeated the analysis for BMI using effect estimates from UKBB. To estimate direct and indirect causal effects of BMI through obesity-related cardiometabolic diseases, we performed pairwise multivariable MR. We used p<0.05/17 exposure/2 outcomes=0.0015 to declare statistical significance. RESULTS: Genetically increased BMI was causally associated with testing positive for COVID-19 [6,696 cases / 1,073,072 controls; p=6.7 x 10-4, odds ratio and 95% confidence interval 1.08 (1.03, 1.13) per kg/m2] and a higher risk of COVID-19 hospitalization [3,199 cases/897,488 controls; p=8.7 x 10-4, 1.12 (1.04, 1.21) per kg/m2]. In the multivariable MR, the direct effect of BMI was abolished upon conditioning on the effect on type 2 diabetes but persisted when conditioning on the effects on coronary artery disease, stroke, chronic kidney disease, and c-reactive protein. No other cardiometabolic exposures tested were associated with a higher risk of poorer COVID-19 outcomes. CONCLUSIONS AND RELEVANCE: Genetic evidence supports BMI as a causal risk factor for COVID-19 susceptibility and severity. This relationship may be mediated via type 2 diabetes. Obesity may have amplified the disease burden of the COVID-19 pandemic either single-handedly or through its metabolic consequences.

10.
Diabetes Care ; 43(7): 1617-1635, 2020 Jul.
Artigo em Inglês | MEDLINE | ID: mdl-32561617

RESUMO

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.

11.
Diabetologia ; 63(9): 1671-1693, 2020 09.
Artigo em Inglês | MEDLINE | ID: mdl-32556613

RESUMO

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.

12.
PLoS One ; 15(5): e0230815, 2020.
Artigo em Inglês | MEDLINE | ID: mdl-32379818

RESUMO

Smoking is a potentially causal behavioral risk factor for type 2 diabetes (T2D), but not all smokers develop T2D. It is unknown whether genetic factors partially explain this variation. We performed genome-environment-wide interaction studies to identify loci exhibiting potential interaction with baseline smoking status (ever vs. never) on incident T2D and fasting glucose (FG). Analyses were performed in participants of European (EA) and African ancestry (AA) separately. Discovery analyses were conducted using genotype data from the 50,000-single-nucleotide polymorphism (SNP) ITMAT-Broad-CARe (IBC) array in 5 cohorts from from the Candidate Gene Association Resource Consortium (n = 23,189). Replication was performed in up to 16 studies from the Cohorts for Heart Aging Research in Genomic Epidemiology Consortium (n = 74,584). In meta-analysis of discovery and replication estimates, 5 SNPs met at least one criterion for potential interaction with smoking on incident T2D at p<1x10-7 (adjusted for multiple hypothesis-testing with the IBC array). Two SNPs had significant joint effects in the overall model and significant main effects only in one smoking stratum: rs140637 (FBN1) in AA individuals had a significant main effect only among smokers, and rs1444261 (closest gene C2orf63) in EA individuals had a significant main effect only among nonsmokers. Three additional SNPs were identified as having potential interaction by exhibiting a significant main effects only in smokers: rs1801232 (CUBN) in AA individuals, rs12243326 (TCF7L2) in EA individuals, and rs4132670 (TCF7L2) in EA individuals. No SNP met significance for potential interaction with smoking on baseline FG. The identification of these loci provides evidence for genetic interactions with smoking exposure that may explain some of the heterogeneity in the association between smoking and T2D.


Assuntos
Glicemia/análise , Fumar Cigarros/genética , Diabetes Mellitus Tipo 2/epidemiologia , Diabetes Mellitus Tipo 2/genética , Jejum/sangue , Genótipo , Adulto , Grupo com Ancestrais do Continente Africano/genética , Idoso , Fumar Cigarros/etnologia , Estudos de Coortes , Diabetes Mellitus Tipo 2/sangue , Diabetes Mellitus Tipo 2/etnologia , Grupo com Ancestrais do Continente Europeu/genética , Estudos de Viabilidade , Feminino , Loci Gênicos , Estudo de Associação Genômica Ampla , Humanos , Incidência , Masculino , Pessoa de Meia-Idade , Polimorfismo de Nucleotídeo Único , Risco
13.
Nat Rev Nephrol ; 16(7): 377-390, 2020 07.
Artigo em Inglês | MEDLINE | ID: mdl-32398868

RESUMO

Diabetes is one of the fastest growing diseases worldwide, projected to affect 693 million adults by 2045. Devastating macrovascular complications (cardiovascular disease) and microvascular complications (such as diabetic kidney disease, diabetic retinopathy and neuropathy) lead to increased mortality, blindness, kidney failure and an overall decreased quality of life in individuals with diabetes. Clinical risk factors and glycaemic control alone cannot predict the development of vascular complications; numerous genetic studies have demonstrated a clear genetic component to both diabetes and its complications. Early research aimed at identifying genetic determinants of diabetes complications relied on familial linkage analysis suited to strong-effect loci, candidate gene studies prone to false positives, and underpowered genome-wide association studies limited by sample size. The explosion of new genomic datasets, both in terms of biobanks and aggregation of worldwide cohorts, has more than doubled the number of genetic discoveries for both diabetes and diabetes complications. We focus herein on genetic discoveries for diabetes and diabetes complications, empowered primarily through genome-wide association studies, and emphasize the gaps in research for taking genomic discovery to the next level.


Assuntos
Complicações do Diabetes/genética , Diabetes Mellitus Tipo 2/genética , Doenças Cardiovasculares/etiologia , Doenças Cardiovasculares/genética , Complicações do Diabetes/etiologia , Diabetes Mellitus Tipo 2/complicações , Nefropatias Diabéticas/etiologia , Nefropatias Diabéticas/genética , Neuropatias Diabéticas/etiologia , Neuropatias Diabéticas/genética , Retinopatia Diabética/etiologia , Retinopatia Diabética/genética , Humanos
14.
Nat Commun ; 11(1): 1467, 2020 03 19.
Artigo em Inglês | MEDLINE | ID: mdl-32193382

RESUMO

Unhealthful dietary habits are leading risk factors for life-altering diseases and mortality. Large-scale biobanks now enable genetic analysis of traits with modest heritability, such as diet. We perform a genomewide association on 85 single food intake and 85 principal component-derived dietary patterns from food frequency questionnaires in UK Biobank. We identify 814 associated loci, including olfactory receptor associations with fruit and tea intake; 136 associations are only identified using dietary patterns. Mendelian randomization suggests our top healthful dietary pattern driven by wholemeal vs. white bread consumption is causally influenced by factors correlated with education but is not strongly causal for coronary artery disease or type 2 diabetes. Overall, we demonstrate the value in complementary phenotyping approaches to complex dietary datasets, and the utility of genomic analysis to understand the relationships between diet and human health.


Assuntos
Bancos de Espécimes Biológicos , Comportamento Alimentar , Estudos de Associação Genética , Genômica , Ingestão de Alimentos , Loci Gênicos , Estudo de Associação Genômica Ampla , Humanos , Padrões de Herança/genética , Análise da Randomização Mendeliana , Polimorfismo de Nucleotídeo Único/genética , Análise de Componente Principal , Receptores Odorantes/metabolismo , Fatores de Risco , Reino Unido
15.
J Diabetes Sci Technol ; 14(6): 1122-1128, 2020 Nov.
Artigo em Inglês | MEDLINE | ID: mdl-31903769

RESUMO

Precision medicine refers to the tailoring of medical treatment for an individual based on large amounts of biologic and extrinsic data. The fast advancing fields of molecular biology, gene sequencing, machine learning, and other technologies enable precision medicine to utilize this detailed information to enhance clinical management decision-making for an individual in the real time of the disease course. Traditional clinical decision making is based on reacting to a relatively limited number of phenotypes that are determined by history, physical examination, and conventional lab tests. Precision medicine depends on highly detailed profiling of the patient's genetic, morphologic, and metabolic makeup. The precision medicine approach can be applied to individuals with diabetes to select treatments most likely to offer benefit and least likely to cause side effects, offering prospects of improved clinical outcomes and economic costs savings over current empiric practices. As genetic, metabolomic, immunologic, and other sophisticated testing becomes less expensive and more widespread in the medical record, it is expected that precision medicine will become increasingly applied to diabetes care.

16.
Ann Neurol ; 87(4): 516-524, 2020 04.
Artigo em Inglês | MEDLINE | ID: mdl-31975536

RESUMO

OBJECTIVE: To systematically investigate causal relationships between obesity and cerebrovascular disease and the extent to which hypertension and hyperglycemia mediate the effect of obesity on cerebrovascular disease. METHODS: We used summary statistics from genome-wide association studies for body mass index (BMI), waist-to-hip ratio (WHR), and multiple cerebrovascular disease phenotypes. We explored causal associations with 2-sample Mendelian randomization (MR) accounting for genetic covariation between BMI and WHR, and we assessed what proportion of the association between obesity and cerebrovascular disease was mediated by systolic blood pressure (SBP) and blood glucose levels, respectively. RESULTS: Genetic predisposition to higher BMI did not increase the risk of cerebrovascular disease. In contrast, for each 10% increase in WHR there was a 75% increase (95% confidence interval [CI] = 44-113%) in risk for large artery ischemic stroke, a 57% (95% CI = 29-91%) increase in risk for small vessel ischemic stroke, a 197% increase (95% CI = 59-457%) in risk of intracerebral hemorrhage, and an increase in white matter hyperintensity volume (ß = 0.11, 95% CI = 0.01-0.21). These WHR associations persisted after adjusting for genetic determinants of BMI. Approximately one-tenth of the observed effect of WHR was mediated by SBP for ischemic stroke (proportion mediated: 12%, 95% CI = 4-20%), but no evidence of mediation was found for average blood glucose. INTERPRETATION: Abdominal adiposity may trigger causal pathological processes, partially independent from blood pressure and totally independent from glucose levels, that lead to cerebrovascular disease. Potential targets of these pathological processes could represent novel therapeutic opportunities for stroke. ANN NEUROL 2020;87:516-524.


Assuntos
Transtornos Cerebrovasculares/epidemiologia , Obesidade/epidemiologia , Glicemia/genética , Pressão Sanguínea/genética , Índice de Massa Corporal , Hemorragia Cerebral/epidemiologia , Hemorragia Cerebral/genética , Doenças de Pequenos Vasos Cerebrais/epidemiologia , Doenças de Pequenos Vasos Cerebrais/genética , Transtornos Cerebrovasculares/genética , Humanos , Análise da Randomização Mendeliana , Obesidade/genética , Acidente Vascular Cerebral/epidemiologia , Acidente Vascular Cerebral/genética , Relação Cintura-Quadril , Substância Branca/diagnóstico por imagem
17.
Trends Endocrinol Metab ; 31(3): 192-204, 2020 03.
Artigo em Inglês | MEDLINE | ID: mdl-31901302

RESUMO

The past decade has witnessed a revival of interest in the hormone melatonin, partly attributable to the discovery that genetic variation in MTNR1B - the melatonin receptor gene - is a risk factor for impaired fasting glucose and type 2 diabetes (T2D). Despite intensive investigation, there is considerable confusion and seemingly conflicting data on the metabolic effects of melatonin and MTNR1B variation, and disagreement on whether melatonin is metabolically beneficial or deleterious, a crucial issue for melatonin agonist/antagonist drug development and dosing time. We provide a conceptual framework - anchored in the dimension of 'time' - to reconcile paradoxical findings in the literature. We propose that the relative timing between elevated melatonin concentrations and glycemic challenge should be considered to better understand the mechanisms and therapeutic opportunities of melatonin signaling in glycemic health and disease.

18.
Diabetes ; 69(1): 112-120, 2020 01.
Artigo em Inglês | MEDLINE | ID: mdl-31636172

RESUMO

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.


Assuntos
Doenças Cardiovasculares/genética , Doença da Artéria Coronariana/genética , Diabetes Mellitus Tipo 2/prevenção & controle , Interação Gene-Ambiente , Síndrome Metabólica/genética , Estado Pré-Diabético , Serviços Preventivos de Saúde , Adulto , Doenças Cardiovasculares/terapia , Doença da Artéria Coronariana/terapia , Diabetes Mellitus Tipo 2/genética , Exercício Físico , Terapia por Exercício , Feminino , Predisposição Genética para Doença , Humanos , Estilo de Vida , Masculino , Síndrome Metabólica/terapia , Metformina/uso terapêutico , Pessoa de Meia-Idade , Estado Pré-Diabético/genética , Estado Pré-Diabético/terapia , Fatores de Risco , Estados Unidos/epidemiologia
19.
Cell Rep ; 29(3): 778-780, 2019 10 15.
Artigo em Inglês | MEDLINE | ID: mdl-31618643

RESUMO

Human genetic variants in SLC16A11 are associated with increased risk of type 2 diabetes (T2D). We previously identified two distinct mechanisms through which co-inherited T2D-risk coding and non-coding variants disrupt SLC16A11 expression and activity, thus implicating reduced SLC16A11 function as the disease-relevant direction of effect. In a recent publication, Zhao et al. (2019a) argue that human SLC16A11 coding variants confer gain of function, basing their conclusions on phenotypic changes observed following overexpression of mutant murine Slc16a11. However, data necessary to demonstrate gain-of-function activity are not reported. Furthermore, several fundamental flaws in their experimental system-including inaccurate modeling of the human variant haplotype and expression conditions that are not physiologically relevant-prevent conclusions about T2D-risk variant effects on human physiology. This Matters Arising paper is in response to Zhao et al. (2019a), published in Cell Reports. See also the response by Zhao et al. (2019b) in this issue of Cell Reports.


Assuntos
Diabetes Mellitus Tipo 2 , Animais , Mutação com Ganho de Função , Haplótipos , Humanos , Camundongos , Transportadores de Ácidos Monocarboxílicos/genética
20.
Diabetes ; 68(12): 2337-2349, 2019 12.
Artigo em Inglês | MEDLINE | ID: mdl-31582408

RESUMO

Novel biomarkers of type 2 diabetes (T2D) and response to preventative treatment in individuals with similar clinical risk may highlight metabolic pathways that are important in disease development. We profiled 331 metabolites in 2,015 baseline plasma samples from the Diabetes Prevention Program (DPP). Cox models were used to determine associations between metabolites and incident T2D, as well as whether associations differed by treatment group (i.e., lifestyle [ILS], metformin [MET], or placebo [PLA]), over an average of 3.2 years of follow-up. We found 69 metabolites associated with incident T2D regardless of treatment randomization. In particular, cytosine was novel and associated with the lowest risk. In an exploratory analysis, 35 baseline metabolite associations with incident T2D differed across the treatment groups. Stratification by baseline levels of several of these metabolites, including specific phospholipids and AMP, modified the effect that ILS or MET had on diabetes development. Our findings highlight novel markers of diabetes risk and preventative treatment effect in individuals who are clinically at high risk and motivate further studies to validate these interactions.


Assuntos
Citosina/sangue , Diabetes Mellitus Tipo 2/sangue , Adulto , Idoso , Biomarcadores/sangue , Diabetes Mellitus Tipo 2/epidemiologia , Feminino , Humanos , Incidência , Estilo de Vida , Masculino , Metaboloma , Pessoa de Meia-Idade , Fatores de Risco
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