Polygenic scores for longitudinal prediction of incident type 2 diabetes in an ancestrally and medically diverse primary care physician network: a patient cohort study.

BACKGROUND: The clinical utility of genetic information for type 2 diabetes (T2D) prediction with polygenic scores (PGS) in ancestrally diverse, real-world US healthcare systems is unclear, especially for those at low clinical phenotypic risk for T2D. METHODS: We tested the association of PGS with T2D incidence in patients followed within a primary care practice network over 16 years in four hypothetical scenarios that varied by clinical data availability (N = 14,712): (1) age and sex; (2) age, sex, body mass index (BMI), systolic blood pressure, and family history of T2D; (3) all variables in (2) and random glucose; and (4) all variables in (3), HDL, total cholesterol, and triglycerides, combined in a clinical risk score (CRS). To determine whether genetic effects differed by baseline clinical risk, we tested for interaction with the CRS. RESULTS: PGS was associated with incident T2D in all models. Adjusting for age and sex only, the Hazard Ratio (HR) per PGS standard deviation (SD) was 1.76 (95% CI 1.68, 1.84) and the HR of top 5% of PGS vs interquartile range (IQR) was 2.80 (2.39, 3.28). Adjusting for the CRS, the HR per SD was 1.48 (1.40, 1.57) and HR of the top 5% of PGS vs IQR was 2.09 (1.72, 2.55). Genetic effects differed by baseline clinical risk ((PGS-CRS interaction p = 0.05; CRS below the median: HR 1.60 (1.43, 1.79); CRS above the median: HR 1.45 (1.35, 1.55)). CONCLUSIONS: Genetic information can help identify high-risk patients even among those perceived to be low risk in a clinical evaluation.

Predicting post-stroke cognitive impairment using electronic health record data.

BACKGROUND: Secondary prevention interventions to reduce post-stroke cognitive impairment (PSCI) can be aided by the early identification of high-risk individuals who would benefit from risk factor modification. AIMS: To develop and evaluate a predictive model to identify patients at increased risk of PSCI over 5 years using data easily accessible from electronic health records. METHODS: Cohort study that included primary care patients from two academic medical centers. Patients were aged 45 years or older, without prior stroke or prevalent cognitive impairment, with primary care visits and an incident ischemic stroke between 2003 and 2016 (development/internal validation cohort) or 2010 and 2022 (external validation cohort). Predictors of PSCI were ascertained from the electronic health record. The outcome was incident dementia/cognitive impairment within 5 years and beginning 3 months following stroke, ascertained using International Classification of Diseases, Ninth/Tenth Revision (ICD-9/10) codes. For model variable selection, we considered potential predictors of PSCI and constructed 400 bootstrap samples with two-thirds of the model derivation sample. We ran 10-fold cross-validated Cox proportional hazards models using a least absolute shrinkage and selection operator (LASSO) penalty. Variables selected in >25% of samples were included. RESULTS: The analysis included 332 incident diagnoses of PSCI in the development cohort (n = 3741), and 161 and 128 incident diagnoses in the internal (n = 1925) and external (n = 2237) validation cohorts, respectively. The C-statistic for predicting PSCI was 0.731 (95% confidence interval (CI): 0.694-0.768) in the internal validation cohort, and 0.724 (95% CI: 0.681-0.766) in the external validation cohort. A risk score based on the beta coefficients of predictors from the development cohort stratified patients into low (0-7 points), intermediate (8-11 points), and high (12-23 points) risk groups. The hazard ratios (HRs) for incident PSCI were significantly different by risk categories in internal (high, HR: 6.2, 95% CI: 4.1-9.3; Intermediate, HR: 2.7, 95% CI: 1.8-4.1) and external (high, HR: 6.1, 95% CI: 3.9-9.6; Intermediate, HR: 2.8, 95% CI: 1.9-4.3) validation cohorts. CONCLUSION: Five-year risk of PSCI can be accurately predicted using routinely collected data. Model output can be used to risk stratify and identify individuals at increased risk for PSCI for preventive efforts. DATA ACCESS STATEMENT: Mass General Brigham data contain protected health information and cannot be shared publicly. The data processing scripts used to perform analyses will be made available to interested researchers upon reasonable request to the corresponding author.

Predicting post-stroke cognitive impairment using electronic health record data.

Importance: Secondary prevention interventions to reduce post-stroke cognitive impairment (PSCI) can be aided by the early identification of high-risk individuals who would benefit from risk factor modification. Objective: To develop and evaluate a predictive model to identify patients at increased risk of PSCI over 5 years using data easily accessible from electronic health records. Design: Cohort study with patients enrolled between 2003-2016 with follow-up through 2022. Setting: Primary care practices affiliated with two academic medical centers. Participants: Individuals 45 years or older, without prior stroke or prevalent cognitive impairment, with primary care visits and an incident ischemic stroke between 2003-2016 (development/internal validation cohort) or 2010-2022 (external validation cohort). Exposures: Predictors of PSCI were ascertained from the electronic health record. Main Outcome: The outcome was incident dementia/cognitive impairment within 5 years and beginning 3 months following stroke, ascertained using ICD-9/10 codes. For model variable selection, we considered potential predictors of PSCI and constructed 400 bootstrap samples with two-thirds of the model derivation sample. We ran 10-fold cross-validated Cox proportional hazards models using a least absolute shrinkage and selection operator (LASSO) penalty. Variables selected in >25% of samples were included. Results: The analysis included 332 incident diagnoses of PSCI in the development cohort (n=3,741), and 161 and 128 incident diagnoses in the internal (n=1,925) and external (n=2,237) validation cohorts. The c-statistic for predicting PSCI was 0.731 (95% CI: 0.694-0.768) in the internal validation cohort, and 0.724 (95% CI: 0.681-0.766) in the external validation cohort. A risk score based on the beta coefficients of predictors from the development cohort stratified patients into low (0-7 points), intermediate (8-11 points), and high (12-35 points) risk groups. The hazard ratios for incident PSCI were significantly different by risk categories in internal (High, HR: 6.2, 95% CI 4.1-9.3; Intermediate, HR 2.7, 95% CI: 1.8-4.1) and external (High, HR: 6.1, 95% CI: 3.9-9.6; Intermediate, HR 2.8, 95% CI: 1.9-4.3) validation cohorts. Conclusions and Relevance: Five-year risk of PSCI can be accurately predicted using routinely collected data. Model output can be used to risk stratify and identify individuals at increased risk for PSCI for preventive efforts.

Rare variant association analysis in 51,256 type 2 diabetes cases and 370,487 controls informs the spectrum of pathogenicity of monogenic diabetes genes.

We meta-analyzed array data imputed with the TOPMed reference panel and whole-genome sequence (WGS) datasets and performed the largest, rare variant (minor allele frequency as low as 5×10-5) GWAS meta-analysis of type 2 diabetes (T2D) comprising 51,256 cases and 370,487 controls. We identified 52 novel variants at genome-wide significance (p<5 × 10-8), including 8 novel variants that were either rare or ancestry-specific. Among them, we identified a rare missense variant in HNF4A p.Arg114Trp (OR=8.2, 95% confidence interval [CI]=4.6-14.0, p = 1.08×10-13), previously reported as a variant implicated in Maturity Onset Diabetes of the Young (MODY) with incomplete penetrance. We demonstrated that the diabetes risk in carriers of this variant was modulated by a T2D common variant polygenic risk score (cvPRS) (carriers in the top PRS tertile [OR=18.3, 95%CI=7.2-46.9, p=1.2×10-9] vs carriers in the bottom PRS tertile [OR=2.6, 95% CI=0.97-7.09, p = 0.06]. Association results identified eight variants of intermediate penetrance (OR>5) in monogenic diabetes (MD), which in aggregate as a rare variant PRS were associated with T2D in an independent WGS dataset (OR=4.7, 95% CI=1.86-11.77], p = 0.001). Our data also provided support evidence for 21% of the variants reported in ClinVar in these MD genes as benign based on lack of association with T2D. Our work provides a framework for using rare variant imputation and WGS analyses in large-scale population-based association studies to identify large-effect rare variants and provide evidence for informing variant pathogenicity.

A Type 1 Diabetes Polygenic Score Is Not Associated With Prevalent Type 2 Diabetes in Large Population Studies.

Context: Both type 1 diabetes (T1D) and type 2 diabetes (T2D) have significant genetic contributions to risk and understanding their overlap can offer clinical insight. Objective: We examined whether a T1D polygenic score (PS) was associated with a diagnosis of T2D in the Cohorts for Heart and Aging Research in Genomic Epidemiology (CHARGE) consortium. Methods: We constructed a T1D PS using 79 known single nucleotide polymorphisms associated with T1D risk. We analyzed 13 792 T2D cases and 14 169 controls from CHARGE cohorts to determine the association between the T1D PS and T2D prevalence. We validated findings in an independent sample of 2256 T2D cases and 27 052 controls from the Mass General Brigham Biobank (MGB Biobank). As secondary analyses in 5228 T2D cases from CHARGE, we used multivariable regression models to assess the association of the T1D PS with clinical outcomes associated with T1D. Results: The T1D PS was not associated with T2D both in CHARGE (P = .15) and in the MGB Biobank (P = .87). The partitioned human leukocyte antigens only PS was associated with T2D in CHARGE (OR 1.02 per 1 SD increase in PS, 95% CI 1.01-1.03, P = .006) but not in the MGB Biobank. The T1D PS was weakly associated with insulin use (OR 1.007, 95% CI 1.001-1.012, P = .03) in CHARGE T2D cases but not with other outcomes. Conclusion: In large biobank samples, a common variant PS for T1D was not consistently associated with prevalent T2D. However, possible heterogeneity in T2D cannot be ruled out and future studies are needed do subphenotyping.

Polygenic Scores for Longitudinal Prediction of Incident Type 2 Diabetes in an Ancestrally and Medically Diverse Primary Care Network.

OBJECTIVE: The clinical utility of genetic information for type 2 diabetes (T2D) prediction with polygenic score (PGS) in ancestrally diverse, real-world US healthcare systems is unclear, especially for those at low clinical phenotypic risk for T2D. RESEARCH DESIGN AND METHODS: We tested the association of PGS with T2D incidence in patients followed within a primary care practice network over 16 years in four hypothetical scenarios that varied by clinical data availability (N = 14,712): 1) age and sex, 2) age, sex, BMI, systolic blood pressure, and family history of diabetes; 3) all variables in (2) and random glucose; 4) all variables in (3), HDL, total cholesterol, and triglycerides, combined in a clinical risk score (CRS). To determine whether genetic effects differed by baseline clinical risk, we tested for interaction with the CRS. RESULTS: PGS was associated with incident diabetes in all models. Adjusting for age and sex only, the Hazard Ratio (HR) per PGS standard deviation (SD) was 1.76 (95% CI 1.68, 1.84) and the HR of top 5% of PGS vs interquartile range (IQR) was 2.80 (2.39, 3.28). Adjusting for the CRS, the HR per SD was 1.48 (1.40, 1.57) and HR of top 5% of PGS vs IQR was 2.09 (1.72, 2.55). Genetic effects differed by baseline clinical risk [(PGS-CRS interaction p =0.05; CRS below the median: HR 1.60 (1.43, 1.79); CRS above the median: HR 1.45 (1.35, 1.55)]. CONCLUSIONS: Genetic information can help identify high-risk patients even among those perceived to be low risk in a clinical evaluation.

Algorithms for the identification of prevalent diabetes in the All of Us Research Program validated using polygenic scores - a new resource for diabetes precision medicine.

OBJECTIVE: The study aimed to develop and validate algorithms for identifying people with type 1 and type 2 diabetes in the All of Us Research Program (AoU) cohort, using electronic health record (EHR) and survey data. RESEARCH DESIGN AND METHODS: Two sets of algorithms were developed, one using only EHR data (EHR), and the other using a combination of EHR and survey data (EHR+). Their performance was evaluated by testing their association with polygenic scores for both type 1 and type 2 diabetes. RESULTS: For type 1 diabetes, the EHR-only algorithm showed a stronger association with T1D polygenic score (p=3×10-5) than the EHR+. For type 2 diabetes, the EHR+ algorithm outperformed both the EHR-only and the existing AoU definition, identifying additional cases (25.79% and 22.57% more, respectively) and showing stronger association with T2D polygenic score (DeLong p=0.03 and 1×10-4, respectively). CONCLUSIONS: We provide new validated definitions of type 1 and type 2 diabetes in AoU, and make them available for researchers. These algorithms, by ensuring consistent diabetes definitions, pave the way for high-quality diabetes research and future clinical discoveries.

The power of TOPMed imputation for the discovery of Latino-enriched rare variants associated with type 2 diabetes.

AIMS/HYPOTHESIS: The Latino population has been systematically underrepresented in large-scale genetic analyses, and previous studies have relied on the imputation of ungenotyped variants based on the 1000 Genomes (1000G) imputation panel, which results in suboptimal capture of low-frequency or Latino-enriched variants. The National Heart, Lung, and Blood Institute (NHLBI) Trans-Omics for Precision Medicine (TOPMed) released the largest multi-ancestry genotype reference panel representing a unique opportunity to analyse rare genetic variations in the Latino population. We hypothesise that a more comprehensive analysis of low/rare variation using the TOPMed panel would improve our knowledge of the genetics of type 2 diabetes in the Latino population. METHODS: We evaluated the TOPMed imputation performance using genotyping array and whole-exome sequence data in six Latino cohorts. To evaluate the ability of TOPMed imputation to increase the number of identified loci, we performed a Latino type 2 diabetes genome-wide association study (GWAS) meta-analysis in 8150 individuals with type 2 diabetes and 10,735 control individuals and replicated the results in six additional cohorts including whole-genome sequence data from the All of Us cohort. RESULTS: Compared with imputation with 1000G, the TOPMed panel improved the identification of rare and low-frequency variants. We identified 26 genome-wide significant signals including a novel variant (minor allele frequency 1.7%; OR 1.37, p=3.4 × 10-9). A Latino-tailored polygenic score constructed from our data and GWAS data from East Asian and European populations improved the prediction accuracy in a Latino target dataset, explaining up to 7.6% of the type 2 diabetes risk variance. CONCLUSIONS/INTERPRETATION: Our results demonstrate the utility of TOPMed imputation for identifying low-frequency variants in understudied populations, leading to the discovery of novel disease associations and the improvement of polygenic scores. DATA AVAILABILITY: Full summary statistics are available through the Common Metabolic Diseases Knowledge Portal ( https://t2d.hugeamp.org/downloads.html ) and through the GWAS catalog ( https://www.ebi.ac.uk/gwas/ , accession ID: GCST90255648). Polygenic score (PS) weights for each ancestry are available via the PGS catalog ( https://www.pgscatalog.org , publication ID: PGP000445, scores IDs: PGS003443, PGS003444 and PGS003445).

Genetic predisposition to macronutrient preference and workplace food choices.

Prior research identified genetic variants influencing macronutrient preference, but whether genetic differences underlying nutrient preference affect long-term food choices is unknown. Here we examined the associations of polygenic scores for carbohydrate, fat, and protein preference with 12 months' workplace food purchases among 397 hospital employees from the ChooseWell 365 study. Food purchases were obtained retrospectively from the hospital's cafeteria sales data for the 12 months before participants were enrolled in the ChooseWell 365 study. Traffic light labels, visible to employees when making purchases, measured the quality of workplace purchases. During the 12-month study period, there were 215,692 cafeteria purchases. Each SD increase in the polygenic score for carbohydrate preference was associated with 2.3 additional purchases/month (95%CI, 0.2 to 4.3; p = 0.03) and a higher number of green-labeled purchases (ß = 1.9, 95%CI, 0.5-3.3; p = 0.01). These associations were consistent in subgroup and sensitivity analyses accounting for additional sources of bias. There was no evidence of associations between fat and protein polygenic scores and cafeteria purchases. Findings from this study suggest that genetic differences in carbohydrate preference could influence long-term workplace food purchases and may inform follow-up experiments to enhance our understanding of the molecular mechanisms underlying food choice behavior.

A food bank program to help food pantries improve healthy food choices: mixed methods evaluation of The Greater Boston Food Bank's Healthy Pantry Program.

BACKGROUND: The Greater Boston Food Bank's (GBFB) Healthy Pantry Program (HPP) is an online training that teaches food pantry staff to implement behavioral nudges (e.g., traffic-light nutrition labels, choice architecture) to promote healthier client choices. This study assessed if HPP was associated with healthier food bank orders by food pantries and identified implementation facilitators and barriers. METHODS: This mixed methods study collected quantitative data from a matched cohort of 10 HPP food pantries and 99 matched control food pantries in eastern Massachusetts that allow clients to choose their own food, and qualitative data from structured individual interviews with 8 HPP pantry staff. A difference-in-differences analysis compared changes in percentage of pantries' food bank orders (by weight) of foods labeled green/yellow (healthier choices) and fresh produce from baseline to 6 and 10 months between HPP and control pantries. Interviews were coded for implementation facilitators and barriers. RESULTS: Before starting HPP, green-yellow ordering was 92.0% (SD 4.9) in control and 87.4% (SD 5.4) in HPP pantries. Participation in HPP was not associated with changes in green-yellow or fresh produce ordering at 6 or 10 months. HPP implementation facilitators included HPP training being accessible (sub-themes: customizable, motivating) and compatible with client-choice values. Barriers included resource limitations (sub-themes: staff shortage, limited space) and concerns about stigmatizing client food choices with use of labels for unhealthy foods. CONCLUSIONS: An online program to help pantries promote healthier client choices was not associated with changes in how much healthy food pantries ordered from the food bank, suggesting it did not substantially change client choices. Implementation challenges and high baseline healthy ordering may have influenced HPP's effectiveness.

More appendicular lean mass relative to body mass index is associated with lower incident diabetes in middle-aged adults in the CARDIA study.

BACKGROUND AND AIMS: Although lower lean mass is associated with greater diabetes prevalence in cross-sectional studies, prospective data specifically in middle-aged Black and White adults are lacking. Relative appendicular lean mass (ALM), such as ALM adjusted for body mass index (BMI), is important to consider since muscle mass is associated with overall body size. We investigated whether ALM/BMI is associated with incident type 2 diabetes in the Coronary Artery Risk Development in Young Adults study. METHODS AND RESULTS: 1893 middle-aged adults (55% women) were included. ALM was measured by DXA in 2005-06. Incident type 2 diabetes was defined in 2010-11 or 2015-16 as fasting glucose ≥7 mmol/L (126 mg/dL), 2-h glucose on OGTT ≥11.1 mmol/L (200 mg/dL) (2010-11 only), HbA1C ≥48 mmol/mol (6.5%) (2010-11 only), or glucose-lowering medications. Cox regression models with sex stratification were performed. In men and women, ALM/BMI was 1.07 ± 0.14 (mean ± SD) and 0.73 ± 0.12, respectively. Seventy men (8.2%) and 71 women (6.8%) developed type 2 diabetes. Per sex-specific SD higher ALM/BMI, unadjusted diabetes risk was lower by 21% in men [HR 0.79 (0.62-0.99), p = 0.04] and 29% in women [HR 0.71 (0.55-0.91), p = 0.008]. After adjusting for age, race, smoking, education, physical activity, and waist circumference, the association was no longer significant. Adjustment for waist circumference accounted for the attenuation in men. CONCLUSION: Although more appendicular lean mass relative to BMI is associated with lower incident type 2 diabetes in middle-aged men and women over 10 years, its effect may be through other metabolic risk factors such as waist circumference, which is a correlate of abdominal fat mass.

Results of a 2-year lifestyle intervention for type 2 diabetes: the Reach Ahead for Lifestyle and Health-Diabetes randomized controlled trial.

OBJECTIVE: The Reach Ahead for Lifestyle and Health (REAL HEALTH)-Diabetes study assessed the comparative effectiveness of two Look AHEAD (Action for Health in Diabetes)-adapted lifestyle intervention (LI) arms targeting weight loss in type 2 diabetes compared with medical nutrition therapy (MNT) referral. At 1 year, LI had greater weight loss than MNT. This study reports outcomes at 24 (end of LI) and 36 months. METHODS: Participants (N = 211) with type 2 diabetes and BMI > 25 kg/m2 were randomly assigned to in-person LI, telephone conference call LI, or MNT. The primary outcome was percentage weight change. Secondary outcomes included 5% and 10% weight loss, glycated hemoglobin (HbA1c), and patient-reported outcomes. RESULTS: Participants were 61.7 (SD 10.2) years old; 55% were female; 77% were non-Hispanic White; and had mean (SD) weight of 98 (18.9) kg and mean (SD) HbA1c of 7.7% (1.2%). Mean (SD) weight change at 24 and 36 months was -4.4% (5.9%) and -4.4% (5.4%) in in-person LI, -4.0% (5.8%) and -5.3% (6.4%) in telephone LI, and -3.1% (5.2%) and -5.8% (7.1%) in MNT, with no statistically significant difference in weight or HbA1c at 24 and 36 months. Compared with MNT, LI arms had favorable changes in patient-reported outcomes related to learned dietary skills. CONCLUSIONS: There were no differences in weight outcomes among LI participants compared with referral to MNT at the end of intervention or 1 year after its conclusion.

Association between muscle mass and diabetes prevalence independent of body fat distribution in adults under 50 years old.

BACKGROUND/OBJECTIVES: Although relatively less muscle mass has been associated with greater diabetes prevalence, whether there is an association between muscle mass and diabetes prevalence independent of body fat distribution is unknown. The objective was to determine whether less skeletal muscle mass is associated with greater diabetes prevalence in young men and women independent of body fat distribution. SUBJECTS/METHODS: One thousand seven hundred and sixty-four adults, aged 20-49 years old, from the United States National Health and Nutrition Examination Survey (2005-2006). Body composition, including appendicular lean mass (ALM), was measured by dual-energy x-ray absorptiometry. Diabetes was defined as fasting blood glucose ≥7 mmol/l, 2-h blood glucose ≥11.1 mmol/l on 75 g OGTT, HbA1c ≥ 48 mmol/mol (6.5%), use of diabetes medications, or self-reported diagnosis of diabetes. RESULTS: The odds of diabetes were 1.31 times higher in men [OR 1.31 (1.18-1.45), p = 0.0001], and 1.24 times higher in women [OR 1.24 (1.05-1.46), p = 0.01], per percent decrease in ALM/weight after controlling for age, race, height, smoking, and education. After additionally controlling for android/gynoid fat, the odds of diabetes were 1.20 times higher per percent decrease in ALM/weight in men [OR 1.20 (1.04-1.37), p = 0.01]; an inverse association was also observed in women, albeit was not statistically significant [OR 1.08 (0.90-1.30), p = 0.42]. CONCLUSIONS: Less muscle mass was associated with greater diabetes prevalence independent of body fat distribution in young men. The association was not statistically significant in women after controlling for android and gynoid adiposity. Low muscle mass could be a causal factor in the development of type 2 diabetes or a correlated marker of higher metabolic risk.

Health Literacy, Numeracy, and Health Promotion: A Secondary Analysis of the Choosewell 365 Workplace Trial.

INTRODUCTION: Health literacy and numeracy are linked to obesity and dietary behaviors. This study investigates whether the effect of a workplace behavioral intervention to prevent weight gain and improve diet differed by employee health literacy and numeracy. METHODS: ChooseWell 365 was an RCT of hospital employees testing a 12-month intervention using nudges and feedback to promote healthier choices, building on existing cafeteria traffic light labels (e.g., green=healthy, red=unhealthy). Health literacy and numeracy were measured with the Newest Vital Sign (range=0-6) and General Numeracy Scale (range=0-3). Mixed-effects linear models examined if intervention effects on cafeteria purchases, diet quality (Healthy Eating Index 2015, range=0-100), and weight change over 24 months differed by higher versus lower health literacy or numeracy. Data were collected in 2016-2020 and analyzed in 2020-2021. RESULTS: In 12 months, 510 participants completed the Newest Vital Sign and General Numerancy Scale; 36.7% had Newest Vital Sign<6 (lower health literacy) and 31.6% had General Numerancy Scale<2 (lower numeracy). Intervention participants increased healthy purchases over 24 months compared with controls in both higher and lower health literacy and numeracy groups. At 12 months, Healthy Eating Index 2015 scores increased in intervention versus control participants with lower health literacy (5.5 points, 95% CI=1.51, 9.54) but not in those with higher health literacy (p-interaction=0.040). BMI did not differ by health literacy or numeracy. CONCLUSIONS: A behavioral intervention improved cafeteria food choices of hospital employees of varying health literacy and numeracy levels and improved diet quality among employees with lower health literacy, suggesting this group also improved food choices outside of work.

Host and gut microbial tryptophan metabolism and type 2 diabetes: an integrative analysis of host genetics, diet, gut microbiome and circulating metabolites in cohort studies.

OBJECTIVE: Tryptophan can be catabolised to various metabolites through host kynurenine and microbial indole pathways. We aimed to examine relationships of host and microbial tryptophan metabolites with incident type 2 diabetes (T2D), host genetics, diet and gut microbiota. METHOD: We analysed associations between circulating levels of 11 tryptophan metabolites and incident T2D in 9180 participants of diverse racial/ethnic backgrounds from five cohorts. We examined host genome-wide variants, dietary intake and gut microbiome associated with these metabolites. RESULTS: Tryptophan, four kynurenine-pathway metabolites (kynurenine, kynurenate, xanthurenate and quinolinate) and indolelactate were positively associated with T2D risk, while indolepropionate was inversely associated with T2D risk. We identified multiple host genetic variants, dietary factors, gut bacteria and their potential interplay associated with these T2D-relaetd metabolites. Intakes of fibre-rich foods, but not protein/tryptophan-rich foods, were the dietary factors most strongly associated with tryptophan metabolites. The fibre-indolepropionate association was partially explained by indolepropionate-associated gut bacteria, mostly fibre-using Firmicutes. We identified a novel association between a host functional LCT variant (determining lactase persistence) and serum indolepropionate, which might be related to a host gene-diet interaction on gut Bifidobacterium, a probiotic bacterium significantly associated with indolepropionate independent of other fibre-related bacteria. Higher milk intake was associated with higher levels of gut Bifidobacterium and serum indolepropionate only among genetically lactase non-persistent individuals. CONCLUSION: Higher milk intake among lactase non-persistent individuals, and higher fibre intake were associated with a favourable profile of circulating tryptophan metabolites for T2D, potentially through the host-microbial cross-talk shifting tryptophan metabolism toward gut microbial indolepropionate production.

Obesity Partially Mediates the Diabetogenic Effect of Lowering LDL Cholesterol.

OBJECTIVE: LDL cholesterol (LDLc)-lowering drugs modestly increase body weight and type 2 diabetes risk, but the extent to which the diabetogenic effect of lowering LDLc is mediated through increased BMI is unknown. RESEARCH DESIGN AND METHODS: We conducted summary-level univariable and multivariable Mendelian randomization (MR) analyses in 921,908 participants to investigate the effect of lowering LDLc on type 2 diabetes risk and the proportion of this effect mediated through BMI. We used data from 92,532 participants from 14 observational studies to replicate findings in individual-level MR analyses. RESULTS: A 1-SD decrease in genetically predicted LDLc was associated with increased type 2 diabetes odds (odds ratio [OR] 1.12 [95% CI 1.01, 1.24]) and BMI (ß = 0.07 SD units [95% CI 0.02, 0.12]) in univariable MR analyses. The multivariable MR analysis showed evidence of an indirect effect of lowering LDLc on type 2 diabetes through BMI (OR 1.04 [95% CI 1.01, 1.08]) with a proportion mediated of 38% of the total effect (P = 0.03). Total and indirect effect estimates were similar across a number of sensitivity analyses. Individual-level MR analyses confirmed the indirect effect of lowering LDLc on type 2 diabetes through BMI with an estimated proportion mediated of 8% (P = 0.04). CONCLUSIONS: These findings suggest that the diabetogenic effect attributed to lowering LDLc is partially mediated through increased BMI. Our results could help advance understanding of adipose tissue and lipids in type 2 diabetes pathophysiology and inform strategies to reduce diabetes risk among individuals taking LDLc-lowering medications.

Automated Behavioral Workplace Intervention to Prevent Weight Gain and Improve Diet: The ChooseWell 365 Randomized Clinical Trial.

Importance: Personalized interventions that leverage workplace data and environments could improve effectiveness, sustainability, and scalability of employee wellness programs. Objective: To test an automated behavioral intervention to prevent weight gain and improve diet using employee cafeteria purchasing data. Design, Setting, and Participants: This individual-level randomized clinical trial of a 12-month intervention with 12 months of follow-up was conducted among employees of a hospital in Boston, Massachusetts, who purchased food at on-site cafeterias that used traffic-light labels (ie, green indicates healthy; yellow, less healthy; red, unhealthy). Participants were enrolled September 2016 to February 2018. Data were analyzed from May to September 2020. Interventions: For 12 months, participants in the intervention group received 2 emails per week with feedback on previous cafeteria purchases and personalized health and lifestyle tips and 1 letter per month with peer comparisons and financial incentives for healthier purchases. Emails and letters were automatically generated using survey, health, and cafeteria data. Control group participants received 1 letter per month with general healthy lifestyle information. Main Outcomes and Measures: The main outcome was change in weight from baseline to 12 months and 24 months of follow-up. Secondary outcomes included changes in cafeteria purchases, including proportion of green- and red-labeled purchases and calories purchased per day, from baseline (12 months preintervention) to the intervention (months 1-12) and follow-up (months 13-24) periods. Baseline Healthy Eating Index-15 (HEI-15) scores were compared to HEI-15 scores at 6, 12, and 24 months. Results: Among 602 employees enrolled (mean [SD] age, 43.6 [12.2] years; 478 [79.4%] women), 299 were randomized to the intervention group and 303 were randomized to the control group. Baseline mean (SD) body mass index (BMI; calculated as weight in kilograms divided by height in meters squared) was 28.3 (6.6) and HEI-15 score was 60.4 (12.4). There were no between-group differences in weight change at 12 (0.2 [95% CI, -0.6 to 1.0] kg) or 24 (0.6 [95% CI, -0.3 to 1.4] kg) months. Compared with baseline, the intervention group increased green-labeled purchases by 7.3% (95% CI, 5.4% to 9.3%) and decreased red-labeled purchases by 3.9% (95% CI, -5.0% to -2.7%) and calories purchased per day by 49.5 (95% CI, -75.2 to -23.9) kcal more than the control group during the intervention period. In the intervention group, differences in changes in green (4.8% [95% CI, 2.9% to 6.8%]) and red purchases (-3.1% [95% CI, -4.3% to -2.0%]) were sustained at the 24-month follow-up. Differences in changes in HEI-15 scores were not significantly different in the intervention compared with the control group at 6 (2.2 [95% CI, 0 to 4.4]), 12 (1.8 [95% CI, -0.6 to 4.1]), and 24 (1.6, 95% CI, -0.7 to 3.8]) months. Conclusions and Relevance: The findings of this randomized clinical trial suggest that an automated behavioral intervention using workplace cafeteria data improved employees' food choices but did not prevent weight gain over 2 years. Trial Registration: ClinicalTrials.gov Identifier: NCT02660086.

Social connections and the healthfulness of food choices in an employee population.

Unhealthy food choice is an important driver of obesity, but research examining the relationship of food choices and social influence has been limited. We sought to assess associations in the healthfulness of workplace food choices among a large population of diverse employees whose food-related social connections were identified using passively collected data in a validated model. Data were drawn from 3 million encounters where pairs of employees made purchases together in 2015-2016. The healthfulness of food items was defined by 'traffic light' labels. Cross-sectional simultaneously autoregressive models revealed that proportions of both healthy and unhealthy items purchased were positively associated between connected employees. Longitudinal generalized estimating equation models also found positive associations between an employee's current food purchase and the most recent previous food purchase a coworker made together with the employee. These data indicate that workplace interventions to promote healthy eating and reduce obesity should test peer-based strategies.

Massachusetts general hospital Covid-19 registry reveals two distinct populations of hospitalized patients by race and ethnicity.

OBJECTIVE: To evaluate differences by race/ethnicity in clinical characteristics and outcomes among hospitalized patients with Covid-19 at Massachusetts General Hospital (MGH). METHODS: The MGH Covid-19 Registry includes confirmed SARS-CoV-2-infected patients hospitalized at MGH and is based on manual chart reviews and data extraction from electronic health records (EHRs). We evaluated differences between White/Non-Hispanic and Hispanic patients in demographics, complications and 14-day outcomes among the N = 866 patients hospitalized with Covid-19 from March 11, 2020-May 4, 2020. RESULTS: Overall, 43% of patients hospitalized with Covid-19 were women, median age was 60.4 [IQR = (48.2, 75)], 11.3% were Black/non-Hispanic and 35.2% were Hispanic. Hispanic patients, representing 35.2% of patients, were younger than White/non-Hispanic patients [median age 51y; IQR = (40.6, 61.6) versus 72y; (58.0, 81.7) (p<0.001)]. Hispanic patients were symptomatic longer before presenting to care (median 5 vs 3d, p = 0.039) but were more likely to be sent home with self-quarantine than be admitted to hospital (29% vs 16%, p<0.001). Hispanic patients had fewer comorbidities yet comparable rates of ICU or death (34% vs 36%). Nonetheless, a greater proportion of Hispanic patients recovered by 14 days after presentation (62% vs 45%, p<0.001; OR = 1.99, p = 0.011 in multivariable adjusted model) and fewer died (2% versus 18%, p<0.001). CONCLUSIONS: Hospitalized Hispanic patients were younger and had fewer comorbidities compared to White/non-Hispanic patients; despite comparable rates of ICU care or death, a greater proportion recovered. These results have implications for public health policy and the design and conduct of clinical trials.