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.

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).

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.

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.

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.

Impact of Rare and Common Genetic Variants on Diabetes Diagnosis by Hemoglobin A1c in Multi-Ancestry Cohorts: The Trans-Omics for Precision Medicine Program.

Hemoglobin A1c (HbA1c) is widely used to diagnose diabetes and assess glycemic control in individuals with diabetes. However, nonglycemic determinants, including genetic variation, may influence how accurately HbA1c reflects underlying glycemia. Analyzing the NHLBI Trans-Omics for Precision Medicine (TOPMed) sequence data in 10,338 individuals from five studies and four ancestries (6,158 Europeans, 3,123 African-Americans, 650 Hispanics, and 407 East Asians), we confirmed five regions associated with HbA1c (GCK in Europeans and African-Americans, HK1 in Europeans and Hispanics, FN3K and/or FN3KRP in Europeans, and G6PD in African-Americans and Hispanics) and we identified an African-ancestry-specific low-frequency variant (rs1039215 in HBG2 and HBE1, minor allele frequency (MAF) = 0.03). The most associated G6PD variant (rs1050828-T, p.Val98Met, MAF = 12% in African-Americans, MAF = 2% in Hispanics) lowered HbA1c (-0.88% in hemizygous males, -0.34% in heterozygous females) and explained 23% of HbA1c variance in African-Americans and 4% in Hispanics. Additionally, we identified a rare distinct G6PD coding variant (rs76723693, p.Leu353Pro, MAF = 0.5%; -0.98% in hemizygous males, -0.46% in heterozygous females) and detected significant association with HbA1c when aggregating rare missense variants in G6PD. We observed similar magnitude and direction of effects for rs1039215 (HBG2) and rs76723693 (G6PD) in the two largest TOPMed African American cohorts, and we replicated the rs76723693 association in the UK Biobank African-ancestry participants. These variants in G6PD and HBG2 were monomorphic in the European and Asian samples. African or Hispanic ancestry individuals carrying G6PD variants may be underdiagnosed for diabetes when screened with HbA1c. Thus, assessment of these variants should be considered for incorporation into precision medicine approaches for diabetes diagnosis.

Effectiveness of Lifestyle Intervention for Type 2 Diabetes in Primary Care: the REAL HEALTH-Diabetes Randomized Clinical Trial.

BACKGROUND: Intensive lifestyle interventions (LI) improve outcomes in obesity and type 2 diabetes but are not currently available in usual care. OBJECTIVE: To compare the effectiveness and costs of two group LI programs, in-person LI and telephone conference call (telephone LI), to medical nutrition therapy (MNT) on weight loss in primary care patients with type 2 diabetes. DESIGN: A randomized, assessor-blinded, practice-based clinical trial in three community health centers and one hospital-based practice affiliated with a single health system. PARTICIPANTS: A total of 208 primary care patients with type 2 diabetes, HbA1c 6.5 to < 11.5, and BMI > 25 kg/m2 (> 23 kg/m2 in Asians). INTERVENTIONS: Dietitian-delivered in-person or telephone group LI programs with medication management or MNT referral. MAIN MEASURES: Primary outcome: mean percent weight change. SECONDARY OUTCOMES: 5% and 10% weight loss, change in HbA1c, and cost per kilogram lost. KEY RESULTS: Participants' mean age was 62 (SD 10) years, 45% were male, and 77% were White, with BMI 35 (SD 5) kg/m2 and HbA1c 7.7 (SD 1.2). Seventy were assigned to in-person LI, 72 to telephone LI, and 69 to MNT. The mean percent weight loss (95% CI) at 6 and 12 months was 5.6% (4.4-6.8%) and 4.6% (3.1-6.1%) for in-person LI, 4.6% (3.3-6.0%) and 4.8% (3.3-6.2%) for telephone LI, and 1.1% (0.2-2.0%) and 2.0% (0.9-3.0%) for MNT, with statistically significant differences between each LI arm and MNT (P < 0.001) but not between LI arms (P = 0.63). HbA1c improved in all participants. Compared with MNT, the incremental cost per kilogram lost was $789 for in-person LI and $1223 for telephone LI. CONCLUSIONS: In-person LI or telephone group LI can achieve good weight loss outcomes in primary care type 2 diabetes patients at a reasonable cost. CLINICAL TRIAL REGISTRATION: ClinicalTrials.gov Identifier: NCT02320253.

Trans-ethnic Meta-analysis and Functional Annotation Illuminates the Genetic Architecture of Fasting Glucose and Insulin.

Knowledge of the genetic basis of the type 2 diabetes (T2D)-related quantitative traits fasting glucose (FG) and insulin (FI) in African ancestry (AA) individuals has been limited. In non-diabetic subjects of AA (n = 20,209) and European ancestry (EA; n = 57,292), we performed trans-ethnic (AA+EA) fine-mapping of 54 established EA FG or FI loci with detailed functional annotation, assessed their relevance in AA individuals, and sought previously undescribed loci through trans-ethnic (AA+EA) meta-analysis. We narrowed credible sets of variants driving association signals for 22/54 EA-associated loci; 18/22 credible sets overlapped with active islet-specific enhancers or transcription factor (TF) binding sites, and 21/22 contained at least one TF motif. Of the 54 EA-associated loci, 23 were shared between EA and AA. Replication with an additional 10,096 AA individuals identified two previously undescribed FI loci, chrX FAM133A (rs213676) and chr5 PELO (rs6450057). Trans-ethnic analyses with regulatory annotation illuminate the genetic architecture of glycemic traits and suggest gene regulation as a target to advance precision medicine for T2D. Our approach to utilize state-of-the-art functional annotation and implement trans-ethnic association analysis for discovery and fine-mapping offers a framework for further follow-up and characterization of GWAS signals of complex trait loci.

Metabolomics insights into early type 2 diabetes pathogenesis and detection in individuals with normal fasting glucose.

AIMS/HYPOTHESIS: Identifying the metabolite profile of individuals with normal fasting glucose (NFG [<5.55 mmol/l]) who progressed to type 2 diabetes may give novel insights into early type 2 diabetes disease interception and detection. METHODS: We conducted a population-based prospective study among 1150 Framingham Heart Study Offspring cohort participants, age 40-65 years, with NFG. Plasma metabolites were profiled by LC-MS/MS. Penalised regression models were used to select measured metabolites for type 2 diabetes incidence classification (training dataset) and to internally validate the discriminatory capability of selected metabolites beyond conventional type 2 diabetes risk factors (testing dataset). RESULTS: Over a follow-up period of 20 years, 95 individuals with NFG developed type 2 diabetes. Nineteen metabolites were selected repeatedly in the training dataset for type 2 diabetes incidence classification and were found to improve type 2 diabetes risk prediction beyond conventional type 2 diabetes risk factors (AUC was 0.81 for risk factors vs 0.90 for risk factors + metabolites, p = 1.1 × 10-4). Using pathway enrichment analysis, the nitrogen metabolism pathway, which includes three prioritised metabolites (glycine, taurine and phenylalanine), was significantly enriched for association with type 2 diabetes risk at the false discovery rate of 5% (p = 0.047). In adjusted Cox proportional hazard models, the type 2 diabetes risk per 1 SD increase in glycine, taurine and phenylalanine was 0.65 (95% CI 0.54, 0.78), 0.73 (95% CI 0.59, 0.9) and 1.35 (95% CI 1.11, 1.65), respectively. Mendelian randomisation demonstrated a similar relationship for type 2 diabetes risk per 1 SD genetically increased glycine (OR 0.89 [95% CI 0.8, 0.99]) and phenylalanine (OR 1.6 [95% CI 1.08, 2.4]). CONCLUSIONS/INTERPRETATION: In individuals with NFG, information from a discrete set of 19 metabolites improved prediction of type 2 diabetes beyond conventional risk factors. In addition, the nitrogen metabolism pathway and its components emerged as a potential effector of earliest stages of type 2 diabetes pathophysiology.

The impact of active mentorship: results from a survey of faculty in the Department of Medicine at Massachusetts General Hospital.

BACKGROUND: To assess mentorship experiences among the faculty of a large academic department of medicine and to examine how those experiences relate to academic advancement and job satisfaction. METHODS: Among faculty members in the Massachusetts General Hospital Department of Medicine, we assessed personal and professional characteristics as well as job satisfaction and examined their relationship with two mentorship dimensions: (1) currently have a mentor and (2) role as a mentor. We also developed a mentorship quality score and examined the relationship of each mentorship variable to academic advancement and job satisfaction. RESULTS: 553/988 (56.0%) of eligible participants responded. 64.9% reported currently having a mentor, of whom 21.3% provided their mentor a low quality score; 66.6% reported serving as a mentor to others. Faculty with a current mentor had a 3.50-fold increased odds of serving as a mentor to others (OR 3.50, 95% CI 1.84-6.67, p < 0.001). Faculty who reported their mentorship as high quality had a decreased likelihood of being stalled in rank (OR 0.28, 95% CI: 0.10-0.78, p = 0.02) and an increased likelihood of high job satisfaction (OR 3.91, 95% CI 1.77-8.63, p < 0.001) compared with those who reported their mentorship of low quality; further, having a low mentorship score had a similar relationship to job satisfaction as not having a mentor. CONCLUSIONS: A majority of faculty survey respondents had mentorship, though not all of it of high caliber. Because quality mentorship significantly and substantially impacts both academic progress and job satisfaction, efforts devoted to improve the adoption and the quality of mentorship should be prioritized.

Impact of common genetic determinants of Hemoglobin A1c on type 2 diabetes risk and diagnosis in ancestrally diverse populations: A transethnic genome-wide meta-analysis.

BACKGROUND: Glycated hemoglobin (HbA1c) is used to diagnose type 2 diabetes (T2D) and assess glycemic control in patients with diabetes. Previous genome-wide association studies (GWAS) have identified 18 HbA1c-associated genetic variants. These variants proved to be classifiable by their likely biological action as erythrocytic (also associated with erythrocyte traits) or glycemic (associated with other glucose-related traits). In this study, we tested the hypotheses that, in a very large scale GWAS, we would identify more genetic variants associated with HbA1c and that HbA1c variants implicated in erythrocytic biology would affect the diagnostic accuracy of HbA1c. We therefore expanded the number of HbA1c-associated loci and tested the effect of genetic risk-scores comprised of erythrocytic or glycemic variants on incident diabetes prediction and on prevalent diabetes screening performance. Throughout this multiancestry study, we kept a focus on interancestry differences in HbA1c genetics performance that might influence race-ancestry differences in health outcomes. METHODS & FINDINGS: Using genome-wide association meta-analyses in up to 159,940 individuals from 82 cohorts of European, African, East Asian, and South Asian ancestry, we identified 60 common genetic variants associated with HbA1c. We classified variants as implicated in glycemic, erythrocytic, or unclassified biology and tested whether additive genetic scores of erythrocytic variants (GS-E) or glycemic variants (GS-G) were associated with higher T2D incidence in multiethnic longitudinal cohorts (N = 33,241). Nineteen glycemic and 22 erythrocytic variants were associated with HbA1c at genome-wide significance. GS-G was associated with higher T2D risk (incidence OR = 1.05, 95% CI 1.04-1.06, per HbA1c-raising allele, p = 3 × 10-29); whereas GS-E was not (OR = 1.00, 95% CI 0.99-1.01, p = 0.60). In Europeans and Asians, erythrocytic variants in aggregate had only modest effects on the diagnostic accuracy of HbA1c. Yet, in African Americans, the X-linked G6PD G202A variant (T-allele frequency 11%) was associated with an absolute decrease in HbA1c of 0.81%-units (95% CI 0.66-0.96) per allele in hemizygous men, and 0.68%-units (95% CI 0.38-0.97) in homozygous women. The G6PD variant may cause approximately 2% (N = 0.65 million, 95% CI 0.55-0.74) of African American adults with T2D to remain undiagnosed when screened with HbA1c. Limitations include the smaller sample sizes for non-European ancestries and the inability to classify approximately one-third of the variants. Further studies in large multiethnic cohorts with HbA1c, glycemic, and erythrocytic traits are required to better determine the biological action of the unclassified variants. CONCLUSIONS: As G6PD deficiency can be clinically silent until illness strikes, we recommend investigation of the possible benefits of screening for the G6PD genotype along with using HbA1c to diagnose T2D in populations of African ancestry or groups where G6PD deficiency is common. Screening with direct glucose measurements, or genetically-informed HbA1c diagnostic thresholds in people with G6PD deficiency, may be required to avoid missed or delayed diagnoses.

Incident Type 2 Diabetes Risk is Influenced by Obesity and Diabetes in Social Contacts: a Social Network Analysis.

BACKGROUND: Obesity and diabetes family history are the two strongest risk factors for type 2 diabetes (T2D). Prior work shows that an individual's obesity risk is associated with obesity in social contacts, but whether T2D risk follows similar patterns is unknown. OBJECTIVE: We aimed to estimate the relationship between obesity or diabetes in an individual's social contacts and his/her T2D risk. We hypothesized that obesity and diabetes in social contacts would increase an individual's T2D risk. DESIGN: This was a retrospective analysis of the community-based Framingham Offspring Study (FOS). PARTICIPANTS: FOS participants with T2D status, height and weight, and at least one social contact were eligible for this study (n = 4797 at Exam 1). Participants' interpersonal ties, cardiometabolic and demographic variables were available at eight exams from 1971 to 2008, and a T2D additive polygenic risk score was measured at the fifth exam. MAIN MEASURES: Primary exposures were T2D (fasting glucose ≥ 7 mmol/L or taking diabetes medications) and obesity status (BMI ≥ 30 kg/m(2)) of social contacts at a prior exam. Primary outcome was incident T2D in participants. KEY RESULTS: Incident T2D was associated with having a social contact with diabetes (OR 1.32, p = 0.004) or with obesity (OR 1.21, p = 0.004). In stratified analyses, incident T2D was associated with diabetes in siblings (OR 1.64, p = 0.001) and obesity in spouses (OR 1.54, p = 0.0004). The associations between diabetes and obesity in social contacts and an individual's incident diabetes risk were stronger in individuals with a high diabetes genetic risk score. CONCLUSIONS: T2D and obesity in social contacts, particularly siblings and spouses, were associated with an individual's risk of incident diabetes even after accounting for parental T2D history. Assessing risk factors in an individual's siblings and spouses can inform T2D risk; furthermore, social network based lifestyle interventions involving spouses and siblings might be a novel T2D prevention approach.

Metabolic factors and genetic risk mediate familial type 2 diabetes risk in the Framingham Heart Study.

AIMS/HYPOTHESIS: Type 2 diabetes mellitus in parents is a strong determinant of diabetes risk in their offspring. We hypothesise that offspring diabetes risk associated with parental diabetes is mediated by metabolic risk factors. METHODS: We studied initially non-diabetic participants of the Framingham Offspring Study. Metabolic risk was estimated using beta cell corrected insulin response (CIR), HOMA-IR or a count of metabolic syndrome components (metabolic syndrome score [MSS]). Dietary risk and physical activity were estimated using questionnaire responses. Genetic risk score (GRS) was estimated as the count of 62 type 2 diabetes risk alleles. The outcome of incident diabetes in offspring was examined across levels of parental diabetes exposure, accounting for sibling correlation and adjusting for age, sex and putative mediators. The proportion mediated was estimated by comparing regression coefficients for parental diabetes with (ß adj) and without (ß unadj) adjustments for CIR, HOMA-IR, MSS and GRS (percentage mediated = 1 - ß adj / ß unadj). RESULTS: Metabolic factors mediated 11% of offspring diabetes risk associated with parental diabetes, corresponding to a reduction in OR per diabetic parent from 2.13 to 1.96. GRS mediated 9% of risk, corresponding to a reduction in OR per diabetic parent from 2.13 to 1.99. CONCLUSIONS/INTERPRETATION: Metabolic risk factors partially mediated offspring type 2 diabetes risk conferred by parental diabetes to a similar magnitude as genetic risk. However, a substantial proportion of offspring diabetes risk associated with parental diabetes remains unexplained by metabolic factors, genetic risk, diet and physical activity, suggesting that important familial influences on diabetes risk remain undiscovered.

Tobacco-, alcohol-, and drug-attributable deaths and their contribution to mortality disparities in a cohort of homeless adults in Boston.

OBJECTIVES: We quantified tobacco-, alcohol-, and drug-attributable deaths and their contribution to mortality disparities among homeless adults. METHODS: We ascertained causes of death among 28 033 adults seen at the Boston Health Care for the Homeless Program in 2003 to 2008. We calculated population-attributable fractions to estimate the proportion of deaths attributable to tobacco, alcohol, or drug use. We compared attributable mortality rates with those for Massachusetts adults using rate ratios and differences. RESULTS: Of 1302 deaths, 236 were tobacco-attributable, 215 were alcohol-attributable, and 286 were drug-attributable. Fifty-two percent of deaths were attributable to any of these substances. In comparison with Massachusetts adults, tobacco-attributable mortality rates were 3 to 5 times higher, alcohol-attributable mortality rates were 6 to 10 times higher, and drug-attributable mortality rates were 8 to 17 times higher. Disparities in substance-attributable deaths accounted for 57% of the all-cause mortality gap between the homeless cohort and Massachusetts adults. CONCLUSIONS: In this clinic-based cohort of homeless adults, over half of all deaths were substance-attributable, but this did not fully explain the mortality disparity with the general population. Interventions should address both addiction and non-addiction sources of excess mortality.

Association of African genetic ancestry with fasting glucose and HbA1c levels in non-diabetic individuals: the Boston Area Community Health (BACH) Prediabetes Study.

AIMS/HYPOTHESIS: To test among diabetes-free urban community-dwelling adults the hypothesis that the proportion of African genetic ancestry is positively associated with glycaemia, after accounting for other continental ancestry proportions, BMI and socioeconomic status (SES). METHODS: The Boston Area Community Health cohort is a multi-stage 1:1:1 stratified random sample of self-identified African-American, Hispanic and white adults from three Boston inner city areas. We measured 62 ancestry informative markers, fasting glucose (FG), HbA1c, BMI and SES (income, education, occupation and insurance status) and analysed 1,387 eligible individuals (379 African-American, 411 Hispanic, 597 white) without clinical or biochemical evidence of diabetes. We used three-heritage multinomial linear regression models to test the association of FG or HbA1c with genetic ancestry proportion adjusted for: (1) age and sex; (2) age, sex and BMI; and (3) age, sex, BMI and SES. RESULTS: Mean age- and sex-adjusted FG levels were 5.73 and 5.54 mmol/l among those with 100% African or European ancestry, respectively. Using per cent European ancestry as the referent, each 1% increase in African ancestry proportion was associated with an age- and sex-adjusted FG increase of 0.0019 mmol/l (p = 0.01). In the BMI- and SES-adjusted model the slope was 0.0019 (p = 0.02). Analysis of HbA1c gave similar results. CONCLUSIONS/INTERPRETATION: A greater proportion of African genetic ancestry is independently associated with higher FG levels in a non-diabetic community-based cohort, even accounting for other ancestry proportions, obesity and SES. The results suggest that differences between African-Americans and whites in type 2 diabetes risk may include genetically mediated differences in glucose homeostasis.

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.

Association of smoking cessation and weight change with cardiovascular disease among adults with and without diabetes.

IMPORTANCE: Smoking cessation reduces the risks of cardiovascular disease (CVD), but weight gain that follows quitting smoking may weaken the CVD benefit of quitting. OBJECTIVE: To test the hypothesis that weight gain following smoking cessation does not attenuate the benefits of smoking cessation among adults with and without diabetes. DESIGN, SETTING, AND PARTICIPANTS: Prospective community-based cohort study using data from the Framingham Offspring Study collected from 1984 through 2011. At each 4-year examination, self-reported smoking status was assessed and categorized as smoker, recent quitter (≤ 4 years), long-term quitter (>4 years), and nonsmoker. Pooled Cox proportional hazards models were used to estimate the association between quitting smoking and 6-year CVD events and to test whether 4-year change in weight following smoking cessation modified the association between smoking cessation and CVD events. MAIN OUTCOME MEASURE: Incidence over 6 years of total CVD events, comprising coronary heart disease, cerebrovascular events, peripheral artery disease, and congestive heart failure. RESULTS: After a mean follow-up of 25 (SD, 9.6) years, 631 CVD events occurred among 3251 participants. Median 4-year weight gain was greater for recent quitters without diabetes (2.7 kg [interquartile range {IQR}, -0.5 to 6.4]) and with diabetes (3.6 kg [IQR, -1.4 to 8.2]) than for long-term quitters (0.9 kg [IQR, -1.4 to 3.2] and 0.0 kg [IQR, -3.2 to 3.2], respectively, P < .001). Among participants without diabetes, age- and sex-adjusted incidence rate of CVD was 5.9 per 100 person-examinations (95% CI, 4.9-7.1) in smokers, 3.2 per 100 person-examinations (95% CI, 2.1-4.5) in recent quitters, 3.1 per 100 person-examinations (95% CI, 2.6-3.7) in long-term quitters, and 2.4 per 100 person-examinations (95% CI, 2.0-3.0) in nonsmokers. After adjustment for CVD risk factors, compared with smokers, recent quitters had a hazard ratio (HR) for CVD of 0.47 (95% CI, 0.23-0.94) and long-term quitters had an HR of 0.46 (95% CI, 0.34-0.63); these associations had only a minimal change after further adjustment for weight change. Among participants with diabetes, there were similar point estimates that did not reach statistical significance. CONCLUSIONS AND RELEVANCE: In this community-based cohort, smoking cessation was associated with a lower risk of CVD events among participants without diabetes, and weight gain that occurred following smoking cessation did not modify this association. This supports a net cardiovascular benefit of smoking cessation, despite subsequent weight gain.

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.