Publications by authors named "Jose C Florez"

258 Publications

Genetic analysis of dietary intake identifies new loci and functional links with metabolic traits.

Nat Hum Behav 2021 Aug 23. Epub 2021 Aug 23.

Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA.

Dietary intake is a major contributor to the global obesity epidemic and represents a complex behavioural phenotype that is partially affected by innate biological differences. Here, we present a multivariate genome-wide association analysis of overall variation in dietary intake to account for the correlation between dietary carbohydrate, fat and protein in 282,271 participants of European ancestry from the UK Biobank (n = 191,157) and Cohorts for Heart and Aging Research in Genomic Epidemiology Consortium (n = 91,114), and identify 26 distinct genome-wide significant loci. Dietary intake signals map exclusively to specific brain regions and are enriched for genes expressed in specialized subtypes of GABAergic, dopaminergic and glutamatergic neurons. We identified two main clusters of genetic variants for overall variation in dietary intake that were differently associated with obesity and coronary artery disease. These results enhance the biological understanding of interindividual differences in dietary intake by highlighting neural mechanisms, supporting functional follow-up experiments and possibly providing new avenues for the prevention and treatment of prevalent complex metabolic diseases.
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http://dx.doi.org/10.1038/s41562-021-01182-wDOI Listing
August 2021

Sequencing Cell-free Fetal DNA in Pregnant Women With GCK-MODY.

J Clin Endocrinol Metab 2021 Aug;106(9):2678-2689

Department of Pediatrics, Seoul National University Children's Hospital, Seoul 03080, Korea.

Context: Individuals with monogenic diabetes due to inactivating glucokinase (GCK) variants typically do not require treatment, except potentially during pregnancy. In pregnancy, fetal GCK genotype determines whether treatment is indicated, but noninvasive methods are not clinically available.

Objective: This work aims to develop a method to determine fetal GCK genotype noninvasively using maternal cell-free fetal DNA.

Methods: This was a proof-of-concept study involving 3 pregnant women with a causal GCK variant that used information from 1) massive parallel sequencing of maternal plasma cell-free DNA, 2) direct haplotype sequences of maternal genomic DNA, and 3) the paternal genotypes to estimate relative haplotype dosage of the pathogenic variant-linked haplotype. Statistical testing of variant inheritance was performed using a sequential probability ratio test (SPRT).

Results: In each of the 3 cases, plasma cell-free DNA was extracted once between gestational weeks 24 and 36. The fetal fraction of cell-free DNA ranged from 21.8% to 23.0%. Paternal homozygous alleles that were identical to the maternal GCK variant-linked allele were not overrepresented in the cell-free DNA. Paternal homozygous alleles that were identical to the maternal wild-type-linked allele were significantly overrepresented. Based on the SPRT, we predicted that all 3 cases did not inherit the GCK variant. Postnatal infant genotyping confirmed our prediction in each case.

Conclusion: We have successfully implemented a noninvasive method to predict fetal GCK genotype using cell-free DNA in 3 pregnant women carrying an inactivating GCK variant. This method could guide tailoring of hyperglycemia treatment in pregnancies of women with GCK monogenic diabetes.
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http://dx.doi.org/10.1210/clinem/dgab265DOI Listing
August 2021

Monogenic Diabetes in Youth With Presumed Type 2 Diabetes: Results From the Progress in Diabetes Genetics in Youth (ProDiGY) Collaboration.

Diabetes Care 2021 Aug 6. Epub 2021 Aug 6.

Division of Endocrinology, Diabetes, and Nutrition, Department of Medicine, University of Maryland School of Medicine, Baltimore, MD

Objective: Maturity-onset diabetes of the young (MODY) is frequently misdiagnosed as type 1 or type 2 diabetes. Correct diagnosis may result in a change in clinical treatment and impacts prediction of complications and familial risk. In this study, we aimed to assess the prevalence of MODY in multiethnic youth under age 20 years with a clinical diagnosis of type 2 diabetes.

Research Design And Methods: We evaluated whole-exome sequence data of youth with a clinical diagnosis of type 2 diabetes. We considered participants to have MODY if they carried a MODY gene variant classified as likely pathogenic (LP) or pathogenic (P) according to current guidelines.

Results: Of 3,333 participants, 93 (2.8%) carried an LP/P variant in (16 participants), (23), (44), (5), (4), and (1). Compared with those with no LP/P variants, youth with MODY had a younger age at diagnosis (12.9 ± 2.5 vs. 13.6 ± 2.3 years, = 0.002) and lower fasting C-peptide levels (3.0 ± 1.7 vs. 4.7 ± 3.5 ng/mL, < 0.0001). Youth with MODY were less likely to have hypertension (6.9% vs. 19.5%, = 0.007) and had higher HDL cholesterol (43.8 vs. 39.7 mg/dL, = 0.006).

Conclusions: By comprehensively sequencing the coding regions of all MODY genes, we identified MODY in 2.8% of youth with clinically diagnosed type 2 diabetes; importantly, in 89% ( = 83) the specific diagnosis would have changed clinical management. No clinical criterion reliably separated the two groups. New tools are needed to find ideal criteria for selection of individuals for genetic testing.
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http://dx.doi.org/10.2337/dc21-0491DOI Listing
August 2021

Determinants of penetrance and variable expressivity in monogenic metabolic conditions across 77,184 exomes.

Nat Commun 2021 06 9;12(1):3505. Epub 2021 Jun 9.

Department of Medicine and Therapeutics, The Chinese University of Hong Kong, Hong Kong, China.

Hundreds of thousands of genetic variants have been reported to cause severe monogenic diseases, but the probability that a variant carrier develops the disease (termed penetrance) is unknown for virtually all of them. Additionally, the clinical utility of common polygenetic variation remains uncertain. Using exome sequencing from 77,184 adult individuals (38,618 multi-ancestral individuals from a type 2 diabetes case-control study and 38,566 participants from the UK Biobank, for whom genotype array data were also available), we apply clinical standard-of-care gene variant curation for eight monogenic metabolic conditions. Rare variants causing monogenic diabetes and dyslipidemias display effect sizes significantly larger than the top 1% of the corresponding polygenic scores. Nevertheless, penetrance estimates for monogenic variant carriers average 60% or lower for most conditions. We assess epidemiologic and genetic factors contributing to risk prediction in monogenic variant carriers, demonstrating that inclusion of polygenic variation significantly improves biomarker estimation for two monogenic dyslipidemias.
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http://dx.doi.org/10.1038/s41467-021-23556-4DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8190084PMC
June 2021

The trans-ancestral genomic architecture of glycemic traits.

Nat Genet 2021 06 31;53(6):840-860. Epub 2021 May 31.

Department of Epidemiology, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands.

Glycemic traits are used to diagnose and monitor type 2 diabetes and cardiometabolic health. To date, most genetic studies of glycemic traits have focused on individuals of European ancestry. Here we aggregated genome-wide association studies comprising up to 281,416 individuals without diabetes (30% non-European ancestry) for whom fasting glucose, 2-h glucose after an oral glucose challenge, glycated hemoglobin and fasting insulin data were available. Trans-ancestry and single-ancestry meta-analyses identified 242 loci (99 novel; P < 5 × 10), 80% of which had no significant evidence of between-ancestry heterogeneity. Analyses restricted to individuals of European ancestry with equivalent sample size would have led to 24 fewer new loci. Compared with single-ancestry analyses, equivalent-sized trans-ancestry fine-mapping reduced the number of estimated variants in 99% credible sets by a median of 37.5%. Genomic-feature, gene-expression and gene-set analyses revealed distinct biological signatures for each trait, highlighting different underlying biological pathways. Our results increase our understanding of diabetes pathophysiology by using trans-ancestry studies for improved power and resolution.
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http://dx.doi.org/10.1038/s41588-021-00852-9DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7610958PMC
June 2021

Estimating heritability and its enrichment in tissue-specific gene sets in admixed populations.

Hum Mol Genet 2021 07;30(16):1521-1534

Center for Data Sciences, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.

It is important to study the genetics of complex traits in diverse populations. Here, we introduce covariate-adjusted linkage disequilibrium (LD) score regression (cov-LDSC), a method to estimate SNP-heritability (${\boldsymbol{h}}_{\boldsymbol{g}}^{\mathbf{2}})$ and its enrichment in homogenous and admixed populations with summary statistics and in-sample LD estimates. In-sample LD can be estimated from a subset of the genome-wide association studies samples, allowing our method to be applied efficiently to very large cohorts. In simulations, we show that unadjusted LDSC underestimates ${\boldsymbol{h}}_{\boldsymbol{g}}^{\mathbf{2}}$ by 10-60% in admixed populations; in contrast, cov-LDSC is robustly accurate. We apply cov-LDSC to genotyping data from 8124 individuals, mostly of admixed ancestry, from the Slim Initiative in Genomic Medicine for the Americas study, and to approximately 161 000 Latino-ancestry individuals, 47 000 African American-ancestry individuals and 135 000 European-ancestry individuals, as classified by 23andMe. We estimate ${\boldsymbol{h}}_{\boldsymbol{g}}^{\mathbf{2}}$ and detect heritability enrichment in three quantitative and five dichotomous phenotypes, making this, to our knowledge, the most comprehensive heritability-based analysis of admixed individuals to date. Most traits have high concordance of ${\boldsymbol{h}}_{\boldsymbol{g}}^{\mathbf{2}}$ and consistent tissue-specific heritability enrichment among different populations. However, for age at menarche, we observe population-specific heritability estimates of ${\boldsymbol{h}}_{\boldsymbol{g}}^{\mathbf{2}}$. We observe consistent patterns of tissue-specific heritability enrichment across populations; for example, in the limbic system for BMI, the per-standardized-annotation effect size $ \tau $* is 0.16 ± 0.04, 0.28 ± 0.11 and 0.18 ± 0.03 in the Latino-, African American- and European-ancestry populations, respectively. Our approach is a powerful way to analyze genetic data for complex traits from admixed populations.
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http://dx.doi.org/10.1093/hmg/ddab130DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8330913PMC
July 2021

The impact of non-additive genetic associations on age-related complex diseases.

Nat Commun 2021 04 23;12(1):2436. Epub 2021 Apr 23.

Barcelona Supercomputing Center (BSC), Barcelona, Spain.

Genome-wide association studies (GWAS) are not fully comprehensive, as current strategies typically test only the additive model, exclude the X chromosome, and use only one reference panel for genotype imputation. We implement an extensive GWAS strategy, GUIDANCE, which improves genotype imputation by using multiple reference panels and includes the analysis of the X chromosome and non-additive models to test for association. We apply this methodology to 62,281 subjects across 22 age-related diseases and identify 94 genome-wide associated loci, including 26 previously unreported. Moreover, we observe that 27.7% of the 94 loci are missed if we use standard imputation strategies with a single reference panel, such as HRC, and only test the additive model. Among the new findings, we identify three novel low-frequency recessive variants with odds ratios larger than 4, which need at least a three-fold larger sample size to be detected under the additive model. This study highlights the benefits of applying innovative strategies to better uncover the genetic architecture of complex diseases.
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http://dx.doi.org/10.1038/s41467-021-21952-4DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8065056PMC
April 2021

Sequencing cell-free fetal DNA in pregnant women with GCK-MODY: a proof-of-concept study.

J Clin Endocrinol Metab 2021 Apr 20. Epub 2021 Apr 20.

Department of Pediatrics, Seoul National University Children's Hospital, Seoul, Korea.

Context: Persons with monogenic diabetes due to inactivating glucokinase (GCK) variants typically do not require treatment, except potentially during pregnancy. In pregnancy, fetal GCK genotype determines whether treatment is indicated, but non-invasive methods are not clinically available.

Objective: To develop a method to determine fetal GCK genotype non-invasively using maternal cell-free fetal DNA.

Design, And Main Outcome Measure: This was a proof-of-concept study which used information from 1) massive parallel sequencing of maternal plasma cell-free DNA, 2) direct haplotype sequences of maternal genomic DNA, and 3) paternal genotype to estimate relative haplotype dosage of the pathogenic variant-linked haplotype. Statistical testing of variant inheritance was performed using a sequential probability ratio test (SPRT).

Patients And Setting: Three pregnant women with causal GCK variant.

Results: In each of the three cases, plasma cell-free DNA was extracted once between gestational weeks 24 and 36. The fetal fraction of cell-free DNA ranged between 21.8 - 23.0%. Paternal homozygous alleles that were identical to the maternal GCK variant-linked allele were not overrepresented in the cell-free DNA. Paternal homozygous alleles that were identical to maternal wild-type-linked allele were significantly overrepresented. Based on the SPRT, we predicted that all three cases did not inherit the GCK variant. Postnatal infant genotyping confirmed our prediction in each case.

Conclusions: We have successfully implemented a non-invasive method to predict fetal GCK genotype using cell-free DNA in three pregnant women carrying an inactivating GCK variant. This method could guide tailoring of hyperglycemia treatment in pregnancies of women with GCK monogenic diabetes.
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http://dx.doi.org/10.1210/clinem/dgab265DOI Listing
April 2021

Genome-wide gene-diet interaction analysis in the UK Biobank identifies novel effects on hemoglobin A1c.

Hum Mol Genet 2021 Aug;30(18):1773-1783

Programs in Metabolism and Medical & Population Genetics, Broad Institute of Harvard and MIT, Cambridge, MA 02142, USA.

Diet is a significant modifiable risk factor for type 2 diabetes (T2D), and its effect on disease risk is under partial genetic control. Identification of specific gene-diet interactions (GDIs) influencing risk biomarkers such as glycated hemoglobin (HbA1c) is a critical step towards precision nutrition for T2D prevention, but progress has been slow due to limitations in sample size and accuracy of dietary exposure measurement. We leveraged the large UK Biobank (UKB) cohort and a diverse group of dietary exposures, including 30 individual dietary traits and 8 empirical dietary patterns, to conduct genome-wide interaction studies in ~340 000 European-ancestry participants to identify novel GDIs influencing HbA1c. We identified five variant-dietary trait pairs reaching genome-wide significance (P < 5 × 10-8): two involved dietary patterns (meat pattern with rs147678157 and a fruit & vegetable-based pattern with rs3010439) and three involved individual dietary traits (bread consumption with rs62218803, dried fruit consumption with rs140270534 and milk type [dairy vs. other] with 4:131148078_TAGAA_T). These were affected minimally by adjustment for geographical and lifestyle-related confounders, and four of the five variants lacked genetic main effects that would have allowed their detection in a traditional genome-wide association study for HbA1c. Notably, multiple loci near transient receptor potential subfamily M genes (TRPM2 and TRPM3) interacted with carbohydrate-containing food groups. These interactions were further characterized using non-European UKB subsets and alternative measures of glycaemia (fasting glucose and follow-up HbA1c measurements). Our results highlight GDIs influencing HbA1c for future investigation, while reinforcing known challenges in detecting and replicating GDIs.
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http://dx.doi.org/10.1093/hmg/ddab109DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8411984PMC
August 2021

The comparative effect of exposure to various risk factors on the risk of hyperuricaemia: diet has a weak causal effect.

Arthritis Res Ther 2021 03 4;23(1):75. Epub 2021 Mar 4.

Department of Biochemistry, University of Otago, Dunedin, New Zealand.

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/m), 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, β = 0.047 mmol/L, P = 3.78 × 10). 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.
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http://dx.doi.org/10.1186/s13075-021-02444-8DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7931603PMC
March 2021

Cardiometabolic risk factors for COVID-19 susceptibility and severity: A Mendelian randomization analysis.

PLoS Med 2021 03 4;18(3):e1003553. Epub 2021 Mar 4.

Department of Medicine, Harvard Medical School, Boston, Massachusetts, United States of America.

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.
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http://dx.doi.org/10.1371/journal.pmed.1003553DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7971850PMC
March 2021

The First Genome-Wide Association Study for Type 2 Diabetes in Youth: The Progress in Diabetes Genetics in Youth (ProDiGY) Consortium.

Diabetes 2021 04 21;70(4):996-1005. Epub 2021 Jan 21.

Department of Epidemiology, University of Colorado School of Public Health, Aurora, CO.

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 ( = 3.2 × 10; odds ratio [OR] = 1.23). Known loci identified in our analysis include rs7903146 in ( = 8.0 × 10; OR 1.58), rs72982988 near ( = 4.4 × 10; OR 1.53), rs200893788 in ( = 1.1 × 10; OR 1.32), rs2237892 in ( = 4.8 × 10; OR 1.59), rs937589119 in ( = 3.1 × 10; OR 1.34), and rs113748381 in ( = 4.1 × 10; OR 1.04). Secondary analysis with 856 diabetes-free youth control subjects uncovered an additional locus in ( = 3.2 × 10; 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.
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http://dx.doi.org/10.2337/db20-0443DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7980197PMC
April 2021

Sex-dimorphic genetic effects and novel loci for fasting glucose and insulin variability.

Nat Commun 2021 01 5;12(1):24. Epub 2021 Jan 5.

Department of Biostatistics and Data Science, Division of Public Health Sciences, Wake Forest University School of Medicine, Winston-Salem, NC, USA.

Differences between sexes contribute to variation in the levels of fasting glucose and insulin. Epidemiological studies established a higher prevalence of impaired fasting glucose in men and impaired glucose tolerance in women, however, the genetic component underlying this phenomenon is not established. We assess sex-dimorphic (73,089/50,404 women and 67,506/47,806 men) and sex-combined (151,188/105,056 individuals) fasting glucose/fasting insulin genetic effects via genome-wide association study meta-analyses in individuals of European descent without diabetes. Here we report sex dimorphism in allelic effects on fasting insulin at IRS1 and ZNF12 loci, the latter showing higher RNA expression in whole blood in women compared to men. We also observe sex-homogeneous effects on fasting glucose at seven novel loci. Fasting insulin in women shows stronger genetic correlations than in men with waist-to-hip ratio and anorexia nervosa. Furthermore, waist-to-hip ratio is causally related to insulin resistance in women, but not in men. These results position dissection of metabolic and glycemic health sex dimorphism as a steppingstone for understanding differences in genetic effects between women and men in related phenotypes.
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http://dx.doi.org/10.1038/s41467-020-19366-9DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7785747PMC
January 2021

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

Diabetes Obes Metab 2021 04 29;23(4):1030-1040. Epub 2021 Jan 29.

Centre for Lifecourse Epidemiology of Adiposity and Diabetes, Colorado School of Public Health, Aurora, Colorado, USA.

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.
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http://dx.doi.org/10.1111/dom.14309DOI Listing
April 2021

Analysis of Glucocorticoid-Related Genes Reveal as a New Candidate Gene for Type 2 Diabetes.

J Endocr Soc 2020 Nov 24;4(11):bvaa121. Epub 2020 Aug 24.

Center for Genomic Medicine, Massachusetts General Hospital, Boston, Massachusetts.

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 () in the HLA region ( = .001). Further analyses revealed that noncoding variants near are also associated with T2D at genome-wide significance ( = 7.70 × 10), 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 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.
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http://dx.doi.org/10.1210/jendso/bvaa121DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7594651PMC
November 2020

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

Diabetes 2021 01 26;70(1):293-300. Epub 2020 Oct 26.

Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA

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 A reduction to sulfonylureas in the Genetics of Diabetes Audit and Research in Tayside Scotland (GoDARTS) study ( = 0.02). Our findings suggest that individuals with a higher genetic burden for T2D experience a greater acute and sustained response to sulfonylureas.
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http://dx.doi.org/10.2337/db20-0530DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7881853PMC
January 2021

Genetic Loci and Physiologic Pathways Involved in Gestational Diabetes Mellitus Implicated Through Clustering.

Diabetes 2021 01 13;70(1):268-281. Epub 2020 Oct 13.

Diabetes Unit, Endocrine Division, Massachusetts General Hospital, Boston, MA.

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 mellitus (GDM) is distinct from that underlying T2D. In this study of >5,000 pregnant women from three cohorts, we aimed to identify physiologically related groups of maternal variants associated with GDM using two complementary approaches that were based on Bayesian nonnegative matrix factorization (bNMF) clustering. First, we tested five bNMF clusters of maternal T2D-associated variants grouped on the basis of physiology outside of pregnancy for association with GDM. We found that cluster polygenic scores representing genetic determinants of reduced β-cell function and abnormal hepatic lipid metabolism were associated with GDM; these clusters were not associated with infant birth weight. Second, we derived bNMF clusters of maternal variants on the basis of pregnancy physiology and tested these clusters for association with GDM. We identified a cluster that was strongly associated with GDM as well as associated with higher infant birth weight. 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.
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http://dx.doi.org/10.2337/db20-0772DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7876560PMC
January 2021

Cardiometabolic Risk Factors for COVID-19 Susceptibility and Severity: A Mendelian Randomization Analysis.

medRxiv 2020 Sep 1. Epub 2020 Sep 1.

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.
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http://dx.doi.org/10.1101/2020.08.26.20182709DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7480065PMC
September 2020

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

Diabetes Care 2020 07;43(7):1617-1635

Genetic and Molecular Epidemiology Unit, Lund University Diabetes Centre, Lund University, Malmo, Sweden

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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http://dx.doi.org/10.2337/dci20-0022DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7305007PMC
July 2020

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

Diabetologia 2020 09;63(9):1671-1693

Genetic and Molecular Epidemiology Unit, Lund University Diabetes Centre, Lund University, CRC, Skåne University Hospital - Malmö, Building 91, Level 12, Jan Waldenströms gata 35, SE-205 02, Malmö, Sweden.

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.
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http://dx.doi.org/10.1007/s00125-020-05181-wDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8185455PMC
September 2020

Genetics of diabetes mellitus and diabetes complications.

Nat Rev Nephrol 2020 07 12;16(7):377-390. Epub 2020 May 12.

Programs in Metabolism and Medical & Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA.

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.
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http://dx.doi.org/10.1038/s41581-020-0278-5DOI Listing
July 2020

Smoking-by-genotype interaction in type 2 diabetes risk and fasting glucose.

PLoS One 2020 7;15(5):e0230815. Epub 2020 May 7.

Department of Epidemiology, School of Public Health, University of Michigan, Ann Arbor, MI, United States of America.

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.
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http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0230815PLOS
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7205201PMC
August 2020

Comprehensive genomic analysis of dietary habits in UK Biobank identifies hundreds of genetic associations.

Nat Commun 2020 03 19;11(1):1467. Epub 2020 Mar 19.

Programs in Metabolism and Medical and Population Genetics, The Broad Institute of MIT and Harvard, Cambridge, MA, USA.

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.
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http://dx.doi.org/10.1038/s41467-020-15193-0DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7081342PMC
March 2020

Mendelian Randomization Study of Obesity and Cerebrovascular Disease.

Ann Neurol 2020 04 19;87(4):516-524. Epub 2020 Feb 19.

Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA.

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 (β = 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.
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http://dx.doi.org/10.1002/ana.25686DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7392199PMC
April 2020

The Need for Precision Medicine to be Applied to Diabetes.

J Diabetes Sci Technol 2020 11 6;14(6):1122-1128. Epub 2020 Jan 6.

Kinexum, Harpers Ferry, WV, USA.

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.
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http://dx.doi.org/10.1177/1932296819894295DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7645141PMC
November 2020

Melatonin Effects on Glucose Metabolism: Time To Unlock the Controversy.

Trends Endocrinol Metab 2020 03 1;31(3):192-204. Epub 2020 Jan 1.

Medical Chronobiology Program, Division of Sleep and Circadian Disorders, Departments of Medicine and Neurology, Brigham and Women's Hospital, Boston, MA, USA; Department of Medicine, Harvard Medical School, Boston, MA, USA; Broad Institute of Massachusetts Institute of Technology (MIT) and Harvard, Cambridge, MA, USA. Electronic address:

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.
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http://dx.doi.org/10.1016/j.tem.2019.11.011DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7349733PMC
March 2020

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

Diabetes 2020 01 21;69(1):112-120. Epub 2019 Oct 21.

Diabetes Epidemiology and Clinical Research Section, National Institute of Diabetes and Digestive and Kidney Diseases, Phoenix, AZ.

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 ( < 0.001) irrespective of CAD genetic risk ( > 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.
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http://dx.doi.org/10.2337/db19-0097DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6925585PMC
January 2020
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