A saturated map of common genetic variants associated with human height.

Common single-nucleotide polymorphisms (SNPs) are predicted to collectively explain 40-50% of phenotypic variation in human height, but identifying the specific variants and associated regions requires huge sample sizes1. Here, using data from a genome-wide association study of 5.4 million individuals of diverse ancestries, we show that 12,111 independent SNPs that are significantly associated with height account for nearly all of the common SNP-based heritability. These SNPs are clustered within 7,209 non-overlapping genomic segments with a mean size of around 90 kb, covering about 21% of the genome. The density of independent associations varies across the genome and the regions of increased density are enriched for biologically relevant genes. In out-of-sample estimation and prediction, the 12,111 SNPs (or all SNPs in the HapMap 3 panel2) account for 40% (45%) of phenotypic variance in populations of European ancestry but only around 10-20% (14-24%) in populations of other ancestries. Effect sizes, associated regions and gene prioritization are similar across ancestries, indicating that reduced prediction accuracy is likely to be explained by linkage disequilibrium and differences in allele frequency within associated regions. Finally, we show that the relevant biological pathways are detectable with smaller sample sizes than are needed to implicate causal genes and variants. Overall, this study provides a comprehensive map of specific genomic regions that contain the vast majority of common height-associated variants. Although this map is saturated for populations of European ancestry, further research is needed to achieve equivalent saturation in other ancestries.

A multi-population phenome-wide association study of genetically-predicted height in the Million Veteran Program.

BACKGROUND: Height has been associated with many clinical traits but whether such associations are causal versus secondary to confounding remains unclear in many cases. To systematically examine this question, we performed a Mendelian Randomization-Phenome-wide association study (MR-PheWAS) using clinical and genetic data from a national healthcare system biobank. METHODS AND FINDINGS: Analyses were performed using data from the US Veterans Affairs (VA) Million Veteran Program in non-Hispanic White (EA, n = 222,300) and non-Hispanic Black (AA, n = 58,151) adults in the US. We estimated height genetic risk based on 3290 height-associated variants from a recent European-ancestry genome-wide meta-analysis. We compared associations of measured and genetically-predicted height with phenome-wide traits derived from the VA electronic health record, adjusting for age, sex, and genetic principal components. We found 345 clinical traits associated with measured height in EA and an additional 17 in AA. Of these, 127 were associated with genetically-predicted height at phenome-wide significance in EA and 2 in AA. These associations were largely independent from body mass index. We confirmed several previously described MR associations between height and cardiovascular disease traits such as hypertension, hyperlipidemia, coronary heart disease (CHD), and atrial fibrillation, and further uncovered MR associations with venous circulatory disorders and peripheral neuropathy in the presence and absence of diabetes. As a number of traits associated with genetically-predicted height frequently co-occur with CHD, we evaluated effect modification by CHD status of genetically-predicted height associations with risk factors for and complications of CHD. We found modification of effects of MR associations by CHD status for atrial fibrillation/flutter but not for hypertension, hyperlipidemia, or venous circulatory disorders. CONCLUSIONS: We conclude that height may be an unrecognized but biologically plausible risk factor for several common conditions in adults. However, more studies are needed to reliably exclude horizontal pleiotropy as a driving force behind at least some of the MR associations observed in this study.

Polygenic Risk Scores for Cardiovascular Disease: A Scientific Statement From the American Heart Association.

Cardiovascular disease is the leading contributor to years lost due to disability or premature death among adults. Current efforts focus on risk prediction and risk factor mitigation' which have been recognized for the past half-century. However, despite advances, risk prediction remains imprecise with persistently high rates of incident cardiovascular disease. Genetic characterization has been proposed as an approach to enable earlier and potentially tailored prevention. Rare mendelian pathogenic variants predisposing to cardiometabolic conditions have long been known to contribute to disease risk in some families. However, twin and familial aggregation studies imply that diverse cardiovascular conditions are heritable in the general population. Significant technological and methodological advances since the Human Genome Project are facilitating population-based comprehensive genetic profiling at decreasing costs. Genome-wide association studies from such endeavors continue to elucidate causal mechanisms for cardiovascular diseases. Systematic cataloging for cardiovascular risk alleles also enabled the development of polygenic risk scores. Genetic profiling is becoming widespread in large-scale research, including in health care-associated biobanks, randomized controlled trials, and direct-to-consumer profiling in tens of millions of people. Thus, individuals and their physicians are increasingly presented with polygenic risk scores for cardiovascular conditions in clinical encounters. In this scientific statement, we review the contemporary science, clinical considerations, and future challenges for polygenic risk scores for cardiovascular diseases. We selected 5 cardiometabolic diseases (coronary artery disease, hypercholesterolemia, type 2 diabetes, atrial fibrillation, and venous thromboembolic disease) and response to drug therapy and offer provisional guidance to health care professionals, researchers, policymakers, and patients.

Mendelian randomization study of diabetes and dementia in the Million Veteran Program.

INTRODUCTION: Diabetes and dementia are diseases of high health-care burden worldwide. Individuals with diabetes have 1.4 to 2.2 times higher risk of dementia. Our objective was to evaluate evidence of causality between these two common diseases. METHODS: We conducted a one-sample Mendelian randomization (MR) analysis in the US Department of Veterans Affairs Million Veteran program. The study included 334,672 participants ≥65 years of age with type 2 diabetes and dementia case-control status and genotype data. RESULTS: For each standard deviation increase in genetically predicted diabetes, we found increased odds of three dementia diagnoses in non-Hispanic White participants (all-cause: odds ratio [OR] = 1.07 [1.05-1.08], P = 3.40E-18; vascular: OR = 1.11 [1.07-1.15], P = 3.63E-09, Alzheimer's disease [AD]: OR = 1.06 [1.02-1.09], P = 6.84E-04) and non-Hispanic Black participants (all-cause: OR = 1.06 [1.02-1.10], P = 3.66E-03, vascular: OR = 1.11 [1.04-1.19], P = 2.20E-03, AD: OR = 1.12 [1.02-1.23], P = 1.60E-02) but not in Hispanic participants (all P > 0.05). DISCUSSION: We found evidence of causality between diabetes and dementia using a one-sample MR study, with access to individual level data, overcoming limitations of prior studies using two-sample MR techniques.

Heterogeneous treatment effects of intensive glycemic control on major adverse cardiovascular events in the ACCORD and VADT trials: a machine-learning analysis.

BACKGROUND: Evidence to guide type 2 diabetes treatment individualization is limited. We evaluated heterogeneous treatment effects (HTE) of intensive glycemic control in type 2 diabetes patients on major adverse cardiovascular events (MACE) in the Action to Control Cardiovascular Risk in Diabetes Study (ACCORD) and the Veterans Affairs Diabetes Trial (VADT). METHODS: Causal forests machine learning analysis was performed using pooled individual data from two randomized trials (n = 12,042) to identify HTE of intensive versus standard glycemic control on MACE in patients with type 2 diabetes. We used variable prioritization from causal forests to build a summary decision tree and examined the risk difference of MACE between treatment arms in the resulting subgroups. RESULTS: A summary decision tree used five variables (hemoglobin glycation index, estimated glomerular filtration rate, fasting glucose, age, and body mass index) to define eight subgroups in which risk differences of MACE ranged from - 5.1% (95% CI - 8.7, - 1.5) to 3.1% (95% CI 0.2, 6.0) (negative values represent lower MACE associated with intensive glycemic control). Intensive glycemic control was associated with lower MACE in pooled study data in subgroups with low (- 4.2% [95% CI - 8.1, - 1.0]), intermediate (- 5.1% [95% CI - 8.7, - 1.5]), and high (- 4.3% [95% CI - 7.7, - 1.0]) MACE rates with consistent directions of effect in ACCORD and VADT alone. CONCLUSIONS: This data-driven analysis provides evidence supporting the diabetes treatment guideline recommendation of intensive glucose lowering in diabetes patients with low cardiovascular risk and additionally suggests potential benefits of intensive glycemic control in some individuals at higher cardiovascular risk.

A calibration approach to transportability and data-fusion with observational data.

Two important considerations in clinical research studies are proper evaluations of internal and external validity. While randomized clinical trials can overcome several threats to internal validity, they may be prone to poor external validity. Conversely, large prospective observational studies sampled from a broadly generalizable population may be externally valid, yet susceptible to threats to internal validity, particularly confounding. Thus, methods that address confounding and enhance transportability of study results across populations are essential for internally and externally valid causal inference, respectively. These issues persist for another problem closely related to transportability known as data-fusion. We develop a calibration method to generate balancing weights that address confounding and sampling bias, thereby enabling valid estimation of the target population average treatment effect. We compare the calibration approach to two additional doubly robust methods that estimate the effect of an intervention on an outcome within a second, possibly unrelated target population. The proposed methodologies can be extended to resolve data-fusion problems that seek to evaluate the effects of an intervention using data from two related studies sampled from different populations. A simulation study is conducted to demonstrate the advantages and similarities of the different techniques. We also test the performance of the calibration approach in a motivating real data example comparing whether the effect of biguanides vs sulfonylureas-the two most common oral diabetes medication classes for initial treatment-on all-cause mortality described in a historical cohort applies to a contemporary cohort of US Veterans with diabetes.

Potential unrealized mortality benefit of glucagon-like peptide-1 receptor agonists and sodium-glucose co-transport-2 inhibitors: A report from the Veterans Health Administration Clinical Assessment, Reporting and Tracking program.

AIM: To assess the unrealized potential of glucagon-like peptide-1 receptor agonist (GLP-1RA) or sodium-glucose co-transport-2 inhibitor (SGLT2i) use to reduce mortality in veterans with type 2 diabetes (T2D), coronary artery disease (CAD), and other characteristics congruent with clinical trial cohorts that established the efficacy of these agents. METHODS: Veterans with T2D and CAD on angiography in 2014 who were untreated with either a GLP-1RA or a SGLT2i were assessed for key eligibility criteria of the LEADER (GLP-1RA) and EMPA-REG OUTCOME (SGLT2i) trials. Trial hazard ratios and 95% confidence intervals for all-cause death were applied to deaths observed in veterans through 2018 to estimate the potential benefit of GLP-1RA or SGLT2i use. RESULTS: Median observation was 4.3 years. Of 15 987 veterans with T2D and CAD, 1186 (7.4%) were excluded for GLP-1RA or SGLT2i treatment, and 1386 lacked glycated haemoglobin measurement. Of the remaining 13 415 patients, 4103 (30.1%) and 5313 (39.6%) fulfilled the key criteria for the LEADER and EMPA-REG OUTCOME trials, respectively. Death occurred in 1009 (24.6%) of LEADER-eligible patients and 1335 (25.1%) of EMPA-REG OUTCOME-eligible patients. Under treatment with liraglutide in LEADER-eligible veterans, a 3.5% (0.7%-6.2%) potential absolute mortality reduction, corresponding to 144 (28-253) fewer deaths (0.88 [0.17-1.56] per 100 person-years), might have been expected. Similarly, under treatment with empagliflozin in EMPA-REG OUTCOME-eligible veterans, a 7.9% (4.5%-10.8%) potential absolute mortality reduction, corresponding to 418 (230-573) fewer deaths (1.98 [1.14-2.72] per 100 person-years), might have been expected. CONCLUSIONS: This analysis indicates unrealized opportunities to reduce mortality in selected veterans with T2D and CAD via increased GLP-1RA and SGLT2i use.

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

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.

Transporting experimental results with entropy balancing.

We show how entropy balancing can be used for transporting experimental treatment effects from a trial population onto a target population. This method is doubly robust in the sense that if either the outcome model or the probability of trial participation is correctly specified, then the estimate of the target population average treatment effect is consistent. Furthermore, we only require the sample moments of the effect modifiers drawn from the target population to consistently estimate the target population average treatment effect. We compared the finite-sample performance of entropy balancing with several alternative methods for transporting treatment effects between populations. Entropy balancing techniques are efficient and robust to violations of model misspecification. We also examine the results of our proposed method in an applied analysis of the Action to Control Cardiovascular Risk in Diabetes Blood Pressure trial transported to a sample of US adults with diabetes taken from the National Health and Nutrition Examination Survey cohort.

Association of Glycemic Control Trajectory with Short-Term Mortality in Diabetes Patients with High Cardiovascular Risk: a Joint Latent Class Modeling Study.

BACKGROUND: The relationship between risk factor or biomarker trajectories and contemporaneous short-term clinical outcomes is poorly understood. In diabetes patients, it is unknown whether hemoglobin A1c (HbA1c) trajectories are associated with clinical outcomes and can inform care in scenarios in which a single HbA1c is uninformative, for example, after a diagnosis of coronary artery disease (CAD). OBJECTIVE: To compare associations of HbA1c trajectories and single HbA1c values with short-term mortality in diabetes patients evaluated for CAD DESIGN: Retrospective observational cohort study PARTICIPANTS: Diabetes patients (n = 7780) with and without angiographically defined CAD MAIN MEASURES: We used joint latent class mixed models to simultaneously fit HbA1c trajectories and estimate association with 2-year mortality after cardiac catheterization, adjusting for clinical and demographic covariates. KEY RESULTS: Three HBA1c trajectory classes were identified: individuals with stable glycemia (class A; n = 6934 [89%]; mean baseline HbA1c 6.9%), with declining HbA1c (class B; n = 364 [4.7%]; mean baseline HbA1c 11.6%), and with increasing HbA1c (class C; n = 482 [6.2%]; mean baseline HbA1c 8.5%). HbA1c trajectory class was associated with adjusted 2-year mortality (3.0% [95% CI 2.8, 3.2] for class A, 3.1% [2.1, 4.2] for class B, and 4.2% [3.4, 4.9] for class C; global P = 0.047, P = 0.03 comparing classes A and C, P > 0.05 for other pairwise comparisons). Baseline HbA1c was not associated with 2-year mortality (P = 0.85; hazard ratios 1.01 [0.96, 1.06] and 1.02 [0.95, 1.10] for HbA1c 7-9% and ≥ 9%, respectively, relative to HbA1c < 7%). The association between HbA1c trajectories and mortality did not differ between those with and without CAD (interaction P = 0.1). CONCLUSIONS: In clinical settings where single HbA1c measurements provide limited information, HbA1c trajectories may help stratify risk of complications in diabetes patients. Joint latent class modeling provides a generalizable approach to examining relationships between biomarker trajectories and clinical outcomes in the era of near-universal adoption of electronic health records.

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.

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.

Risk of Developing Diabetes Among Refugees and Immigrants: A Longitudinal Analysis.

To determine the difference in risk of developing diabetes for refugees, immigrants, and American-born participants living in the same communities, and to explore potential mediators of that difference. Retrospective longitudinal cohort from January 1, 2003 and December 31, 2013. Refugees aged ≥18 years were matched in a 1:3 ratio by age, gender, and date of care initiation to (1) Spanish-speaking non-refugee immigrants, and (2) English-speaking controls receiving care in the same community health center. We used proportional hazards regression to estimate the risk of incident diabetes. We tested whether differences in education or baseline obesity mediated diabetes risk using counterfactual mediation analysis. We included 3174 participants. Among refugee participants, the most common countries of origin were Somalia (17.8 %), Iraq (16.7 %) and Bhutan (8.8 %). Diabetes incidence rate was 1.94, 1.91, and 1.22 cases per 100 person-years follow-up for refugees, immigrants, and controls, respectively. In adjusted models, both refugee (HR 2.08 95 % CI 1.32-3.30) and immigrant (HR 1.51 95 % CI 1.01-2.24) statuses were associated with increased diabetes risk compared with controls. Risk between refugees and immigrants did not differ (adjusted HR for refugees 1.37 95 % CI 0.91-2.06). In mediation analyses, educational attainment mediated 36 % (p = 0.007) of the difference in diabetes risk between refugees/immigrants and controls. Baseline obesity did not mediate difference in diabetes risk (proportion mediated 1 %, p = 0.84). Refugees and immigrants had significantly increased risk for diabetes, partially mediated by education. Education-based lifestyle interventions may be a promising strategy to prevent diabetes for these vulnerable patients.

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.

Secreted transcription factor controls Mycobacterium tuberculosis virulence.

Bacterial pathogens trigger specialized virulence factor secretion systems on encountering host cells. The ESX-1 protein secretion system of Mycobacterium tuberculosis-the causative agent of the human disease tuberculosis-delivers bacterial proteins into host cells during infection and is critical for virulence, but how it is regulated is unknown. Here we show that EspR (also known as Rv3849) is a key regulator of ESX-1 that is required for secretion and virulence in mice. EspR activates transcription of an operon that includes three ESX-1 components, Rv3616c-Rv3614c, whose expression in turn promotes secretion of ESX-1 substrates. EspR directly binds to and activates the Rv3616c-Rv3614c promoter and, unexpectedly, is itself secreted from the bacterial cell by the ESX-1 system that it regulates. Efflux of the DNA-binding regulator results in reduced Rv3616c-Rv3614c transcription, and thus reduced ESX-1 secretion. Our results reveal a direct negative feedback loop that regulates the activity of a secretion system essential for virulence. As the virulence factors secreted by the ESX-1 system are highly antigenic, fine control of secretion may be critical to successful infection.

Increased Genetic Risk for ß-Cell Failure Is Associated With ß-Cell Function Decline in People With Prediabetes.

Partitioned polygenic scores (pPS) have been developed to capture pathophysiologic processes underlying type 2 diabetes (T2D). We investigated the association of T2D pPS with diabetes-related traits and T2D incidence in the Diabetes Prevention Program. We generated five T2D pPS (ß-cell, proinsulin, liver/lipid, obesity, lipodystrophy) in 2,647 participants randomized to intensive lifestyle, metformin, or placebo arms. Associations were tested with general linear models and Cox regression with adjustment for age, sex, and principal components. Sensitivity analyses included adjustment for BMI. Higher ß-cell pPS was associated with lower insulinogenic index and corrected insulin response at 1-year follow-up with adjustment for baseline measures (effect per pPS SD -0.04, P = 9.6 × 10-7, and -8.45 µU/mg, P = 5.6 × 10-6, respectively) and with increased diabetes incidence with adjustment for BMI at nominal significance (hazard ratio 1.10 per SD, P = 0.035). The liver/lipid pPS was associated with reduced 1-year baseline-adjusted triglyceride levels (effect per SD -4.37, P = 0.001). There was no significant interaction between T2D pPS and randomized groups. The remaining pPS were associated with baseline measures only. We conclude that despite interventions for diabetes prevention, participants with a high genetic burden of the ß-cell cluster pPS had worsening in measures of ß-cell function.

Type 1 Diabetes Genetic Risk in 109,954 Veterans With Adult-Onset Diabetes: The Million Veteran Program (MVP).

OBJECTIVE: To characterize high type 1 diabetes (T1D) genetic risk in a population where type 2 diabetes (T2D) predominates. RESEARCH DESIGN AND METHODS: Characteristics typically associated with T1D were assessed in 109,594 Million Veteran Program participants with adult-onset diabetes, 2011-2021, who had T1D genetic risk scores (GRS) defined as low (0 to <45%), medium (45 to <90%), high (90 to <95%), or highest (≥95%). RESULTS: T1D characteristics increased progressively with higher genetic risk (P < 0.001 for trend). A GRS ≥90% was more common with diabetes diagnoses before age 40 years, but 95% of those participants were diagnosed at age ≥40 years, and their characteristics resembled those of individuals with T2D in mean age (64.3 years) and BMI (32.3 kg/m2). Compared with the low-risk group, the highest-risk group was more likely to have diabetic ketoacidosis (low GRS 0.9% vs. highest GRS 3.7%), hypoglycemia prompting emergency visits (3.7% vs. 5.8%), outpatient plasma glucose <50 mg/dL (7.5% vs. 13.4%), a shorter median time to start insulin (3.5 vs. 1.4 years), use of a T1D diagnostic code (16.3% vs. 28.1%), low C-peptide levels if tested (1.8% vs. 32.4%), and glutamic acid decarboxylase antibodies (6.9% vs. 45.2%), all P < 0.001. CONCLUSIONS: Characteristics associated with T1D were increased with higher genetic risk, and especially with the top 10% of risk. However, the age and BMI of those participants resemble those of people with T2D, and a substantial proportion did not have diagnostic testing or use of T1D diagnostic codes. T1D genetic screening could be used to aid identification of adult-onset T1D in settings in which T2D predominates.

Primary care providers' preferences for the communication and management of actionable genomic findings from a research biobank.

Purpose: Little is known about non-genetics health care specialists' attitudes toward the return and utilization of actionable genomic results from a research biobank. We surveyed primary care providers (PCPs) to explore their perspectives on these results and their preferences for return. Methods: We administered a paper and web-based 27-question survey to PCPs residing locally and caring for adult patients. Recruitment was conducted in person and by email, focusing on PCPs likely to interact with results generated by our institution's biobank. Results: Of the ~482 PCPs contacted, 77 (16%) returned surveys. Although most respondents (90%) prefer that a genetics specialist be involved in communicating biobank-generated genomic results to patients, about 40% of respondents reported that a PCP shares the responsibility to discuss these results along with other specialists. A majority of respondents (74%) felt uncomfortable communicating these results to patients. However, respondents reported significantly greater comfort with this process when offered targeted educational resources (62% with vs 10% without resources; P < 10-5). Conclusion: PCPs recognize the need to engage with their patients' biobank-generated genomic results but feel uncomfortable in doing so. Relevant resources are needed to improve PCPs' confidence in the use of these types of results to affect patient care.

Association between Timing of Clinical Evaluation by a Physician or Advanced Practitioner and Risk of Rehospitalization in Older Adults Admitted to a Skilled Nursing Facility Following Hospitalization: A Cohort Study.

OBJECTIVES: How transitional care services are provided to patients receiving post-acute care in skilled nursing facilities (SNFs) is not well understood. We aimed to determine the association of timing of physician or advanced practice provider (APP) visit after SNF admission with rehospitalization risk in a national cohort of older adults. DESIGN: Retrospective cohort study. SETTING AND PARTICIPANTS: 2,482,616 Medicare fee-for-service beneficiaries aged ≥66 years who entered an SNF for post-acute care following hospitalization. METHODS: We measured the relative risk of being rehospitalized within 14 days of SNF admission as a function of time to the first PAP visit, using time to follow-up as a time-dependent covariate, adjusted for patient demographics and clinical characteristics. We also evaluated whether findings extended across groups with different SNF prognosis on admission. RESULTS: Patients seen sooner after admission to an SNF (0-1 days) were less likely to be rehospitalized compared to patients seen later (≥2 days). The relative difference was similar across different risk groups. CONCLUSIONS AND IMPLICATIONS: Timely evaluation by a physician or APP after SNF admission may protect against rehospitalization. Investment in the workforce such as training programs, practice innovations, and equitable reimbursement for SNF visits after hospital discharge may mitigate labor shortages that were exacerbated by the COVID pandemic.