Adaptive selection at G6PD and disparities in diabetes complications.

Diabetes complications occur at higher rates in individuals of African ancestry. Glucose-6-phosphate dehydrogenase deficiency (G6PDdef), common in some African populations, confers malaria resistance, and reduces hemoglobin A1c (HbA1c) levels by shortening erythrocyte lifespan. In a combined-ancestry genome-wide association study of diabetic retinopathy, we identified nine loci including a G6PDdef causal variant, rs1050828 -T (Val98Met), which was also associated with increased risk of other diabetes complications. The effect of rs1050828 -T on retinopathy was fully mediated by glucose levels. In the years preceding diabetes diagnosis and insulin prescription, glucose levels were significantly higher and HbA1c significantly lower in those with versus without G6PDdef. In the Action to Control Cardiovascular Risk in Diabetes (ACCORD) trial, participants with G6PDdef had significantly higher hazards of incident retinopathy and neuropathy. At the same HbA1c levels, G6PDdef participants in both ACCORD and the Million Veteran Program had significantly increased risk of retinopathy. We estimate that 12% and 9% of diabetic retinopathy and neuropathy cases, respectively, in participants of African ancestry are due to this exposure. Across continentally defined ancestral populations, the differences in frequency of rs1050828 -T and other G6PDdef alleles contribute to disparities in diabetes complications. Diabetes management guided by glucose or potentially genotype-adjusted HbA1c levels could lead to more timely diagnoses and appropriate intensification of therapy, decreasing the risk of diabetes complications in patients with G6PDdef alleles.

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.

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.

Glycemic and sleep effects of daytime compared with those of overnight infusions of home parenteral nutrition in adults with short bowel syndrome: A quasi-experimental pilot trial.

BACKGROUND: Patients with short bowel syndrome (SBS) dependent on home parenteral nutrition (HPN) commonly cycle infusions overnight, likely contributing to circadian misalignment and sleep disruption. METHODS: The objective of this quasi-experimental, single-arm, controlled, pilot trial was to examine the feasibility, safety, and efficacy of daytime infusions of HPN in adults with SBS without diabetes. Enrolled patients were fitted with a continuous glucose monitor and wrist actigraph and were instructed to cycle their infusions overnight for 1 wk, followed by daytime for another week. The 24-h average blood glucose, the time spent >140 mg/dL or <70 mg/dL, and sleep fragmentation were derived for each week and compared using Wilcoxon signed-rank test. Patient-reported quality-of-life outcomes were also compared between the weeks. RESULTS: Twenty patients (mean age, 51.7 y; 75% female; mean body mass index, 21.5 kg/m2) completed the trial. Overnight infusions started at 21:00 and daytime infusions at 09:00. No serious adverse events were noted. There were no differences in 24-h glycemia (daytime-median: 93.00 mg/dL; 95% CI: 87.7-99.9 mg/dL, compared with overnight-median: 91.1 mg/dL; 95% CI: 89.6-99.0 mg/dL; P = 0.922). During the day hours (09:00-21:00), the mean glucose concentrations were 13.5 (5.7-22.0) mg/dL higher, and the time spent <70 mg/dL was 15.0 (-170.0, 22.5) min lower with daytime than with overnight HPN. Conversely, during the night hours (21:00-09:00), the glucose concentrations were 16.6 (-23.1, -2.2) mg/dL lower with daytime than with overnight HPN. There were no differences in actigraphy-derived measures of sleep and activity rhythms; however, sleep timing was later, and light at night exposure was lower with daytime than with overnight HPN. Patients reported less sleep disruptions due to urination and fewer episodes of uncontrollable diarrhea or ostomy output with daytime HPN. CONCLUSIONS: Daytime HPN was feasible and safe in adults with SBS and, compared with overnight HPN, improved subjective sleep without increasing 24-h glucose concentrations. This trial was registered at clinicaltrials.gov as NCT04743960 (https://classic. CLINICALTRIALS: gov/ct2/show/NCT04743960).

Precision subclassification of type 2 diabetes: a systematic review.

BACKGROUND: Heterogeneity in type 2 diabetes presentation and progression suggests that precision medicine interventions could improve clinical outcomes. We undertook a systematic review to determine whether strategies to subclassify type 2 diabetes were associated with high quality evidence, reproducible results and improved outcomes for patients. METHODS: We searched PubMed and Embase for publications that used 'simple subclassification' approaches using simple categorisation of clinical characteristics, or 'complex subclassification' approaches which used machine learning or 'omics approaches in people with established type 2 diabetes. We excluded other diabetes subtypes and those predicting incident type 2 diabetes. We assessed quality, reproducibility and clinical relevance of extracted full-text articles and qualitatively synthesised a summary of subclassification approaches. RESULTS: Here we show data from 51 studies that demonstrate many simple stratification approaches, but none have been replicated and many are not associated with meaningful clinical outcomes. Complex stratification was reviewed in 62 studies and produced reproducible subtypes of type 2 diabetes that are associated with outcomes. Both approaches require a higher grade of evidence but support the premise that type 2 diabetes can be subclassified into clinically meaningful subtypes. CONCLUSION: Critical next steps toward clinical implementation are to test whether subtypes exist in more diverse ancestries and whether tailoring interventions to subtypes will improve outcomes.

In people with type 2 diabetes there may be differences in the way people present, including for example, their symptoms, body weight or how much insulin they make. We looked at recent publications describing research in this area to see whether it is possible to separate people with type 2 diabetes into different subgroups and, if so, whether these groupings were useful for patients. We found that it is possible to group people with type 2 diabetes into different subgroups and being in one subgroup can be more strongly linked to the likelihood of developing complications over others. This might mean that in the future we can treat people in different subgroups differently in ways that improves their treatment and their health but it requires further study.

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.

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.

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.

Prognostic value of insulin resistance and hyperglycemia biomarkers for long-term risks of cardiometabolic outcomes.

We found that individuals in the top tertile of HOMA-IR and with HbA1c-defined prediabetes have elevated risk of cardiometabolic outcomes.

Discovering cellular programs of intrinsic and extrinsic drivers of metabolic traits using LipocyteProfiler.

A primary obstacle in translating genetic associations with disease into therapeutic strategies is elucidating the cellular programs affected by genetic risk variants and effector genes. Here, we introduce LipocyteProfiler, a cardiometabolic-disease-oriented high-content image-based profiling tool that enables evaluation of thousands of morphological and cellular profiles that can be systematically linked to genes and genetic variants relevant to cardiometabolic disease. We show that LipocyteProfiler allows surveillance of diverse cellular programs by generating rich context- and process-specific cellular profiles across hepatocyte and adipocyte cell-state transitions. We use LipocyteProfiler to identify known and novel cellular mechanisms altered by polygenic risk of metabolic disease, including insulin resistance, fat distribution, and the polygenic contribution to lipodystrophy. LipocyteProfiler paves the way for large-scale forward and reverse deep phenotypic profiling in lipocytes and provides a framework for the unbiased identification of causal relationships between genetic variants and cellular programs relevant to human disease.

Genome-wide association analysis identifies ancestry-specific genetic variation associated with acute response to metformin and glipizide in SUGAR-MGH.

AIMS/HYPOTHESIS: Characterisation of genetic variation that influences the response to glucose-lowering medications is instrumental to precision medicine for treatment of type 2 diabetes. The Study to Understand the Genetics of the Acute Response to Metformin and Glipizide in Humans (SUGAR-MGH) examined the acute response to metformin and glipizide in order to identify new pharmacogenetic associations for the response to common glucose-lowering medications in individuals at risk of type 2 diabetes. METHODS: One thousand participants at risk for type 2 diabetes from diverse ancestries underwent sequential glipizide and metformin challenges. A genome-wide association study was performed using the Illumina Multi-Ethnic Genotyping Array. Imputation was performed with the TOPMed reference panel. Multiple linear regression using an additive model tested for association between genetic variants and primary endpoints of drug response. In a more focused analysis, we evaluated the influence of 804 unique type 2 diabetes- and glycaemic trait-associated variants on SUGAR-MGH outcomes and performed colocalisation analyses to identify shared genetic signals. RESULTS: Five genome-wide significant variants were associated with metformin or glipizide response. The strongest association was between an African ancestry-specific variant (minor allele frequency [MAFAfr]=0.0283) at rs149403252 and lower fasting glucose at Visit 2 following metformin (p=1.9×10-9); carriers were found to have a 0.94 mmol/l larger decrease in fasting glucose. rs111770298, another African ancestry-specific variant (MAFAfr=0.0536), was associated with a reduced response to metformin (p=2.4×10-8), where carriers had a 0.29 mmol/l increase in fasting glucose compared with non-carriers, who experienced a 0.15 mmol/l decrease. This finding was validated in the Diabetes Prevention Program, where rs111770298 was associated with a worse glycaemic response to metformin: heterozygous carriers had an increase in HbA1c of 0.08% and non-carriers had an HbA1c increase of 0.01% after 1 year of treatment (p=3.3×10-3). We also identified associations between type 2 diabetes-associated variants and glycaemic response, including the type 2 diabetes-protective C allele of rs703972 near ZMIZ1 and increased levels of active glucagon-like peptide 1 (GLP-1) (p=1.6×10-5), supporting the role of alterations in incretin levels in type 2 diabetes pathophysiology. CONCLUSIONS/INTERPRETATION: We present a well-phenotyped, densely genotyped, multi-ancestry resource to study gene-drug interactions, uncover novel variation associated with response to common glucose-lowering medications and provide insight into mechanisms of action of type 2 diabetes-related variation. DATA AVAILABILITY: The complete summary statistics from this study are available at the Common Metabolic Diseases Knowledge Portal ( https://hugeamp.org ) and the GWAS Catalog ( www.ebi.ac.uk/gwas/ , accession IDs: GCST90269867 to GCST90269899).

Systematic review of precision subclassification of type 2 diabetes.

Heterogeneity in type 2 diabetes presentation, progression and treatment has the potential for precision medicine interventions that can enhance care and outcomes for affected individuals. We undertook a systematic review to ascertain whether strategies to subclassify type 2 diabetes are associated with improved clinical outcomes, show reproducibility and have high quality evidence. We reviewed publications that deployed 'simple subclassification' using clinical features, biomarkers, imaging or other routinely available parameters or 'complex subclassification' approaches that used machine learning and/or genomic data. We found that simple stratification approaches, for example, stratification based on age, body mass index or lipid profiles, had been widely used, but no strategy had been replicated and many lacked association with meaningful outcomes. Complex stratification using clustering of simple clinical data with and without genetic data did show reproducible subtypes of diabetes that had been associated with outcomes such as cardiovascular disease and/or mortality. Both approaches require a higher grade of evidence but support the premise that type 2 diabetes can be subclassified into meaningful groups. More studies are needed to test these subclassifications in more diverse ancestries and prove that they are amenable to interventions.

Genetic Effect on Body Mass Index and Cardiovascular Disease Across Generations.

BACKGROUND: Whether genetics contribute to the rising prevalence of obesity or its cardiovascular consequences in today's obesogenic environment remains unclear. We sought to determine whether the effects of a higher aggregate genetic burden of obesity risk on body mass index (BMI) or cardiovascular disease (CVD) differed by birth year. METHODS: We split the FHS (Framingham Heart Study) into 4 equally sized birth cohorts (birth year before 1932, 1932 to 1946, 1947 to 1959, and after 1960). We modeled a genetic predisposition to obesity using an additive genetic risk score (GRS) of 941 BMI-associated variants and tested for GRS-birth year interaction on log-BMI (outcome) when participants were around 50 years old (N=7693). We repeated the analysis using a GRS of 109 BMI-associated variants that increased CVD risk factors (type 2 diabetes, blood pressure, total cholesterol, and high-density lipoprotein) in addition to BMI. We then evaluated whether the effects of the BMI GRSs on CVD risk differed by birth cohort when participants were around 60 years old (N=5493). RESULTS: Compared with participants born before 1932 (mean age, 50.8 yrs [2.4]), those born after 1960 (mean age, 43.3 years [4.5]) had higher BMI (median, 25.4 [23.3-28.0] kg/m2 versus 26.9 [interquartile range, 23.7-30.6] kg/m2). The effect of the 941-variant BMI GRS on BMI and CVD risk was stronger in people who were born in later years (GRS-birth year interaction: P=0.0007 and P=0.04 respectively). CONCLUSIONS: The significant GRS-birth year interactions indicate that common genetic variants have larger effects on middle-age BMI and CVD risk in people born more recently. These findings suggest that the increasingly obesogenic environment may amplify the impact of genetics on the risk of obesity and possibly its cardiovascular consequences.

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 combined polygenic score of 21,293 rare and 22 common variants improves diabetes diagnosis based on hemoglobin A1C levels.

Polygenic scores (PGSs) combine the effects of common genetic variants1,2 to predict risk or treatment strategies for complex diseases3-7. Adding rare variation to PGSs has largely unknown benefits and is methodically challenging. Here, we developed a method for constructing rare variant PGSs and applied it to calculate genetically modified hemoglobin A1C thresholds for type 2 diabetes (T2D) diagnosis7-10. The resultant rare variant PGS is highly polygenic (21,293 variants across 154 genes), depends on ultra-rare variants (72.7% observed in fewer than three people) and identifies significantly more undiagnosed T2D cases than expected by chance (odds ratio = 2.71; P = 1.51 × 10-6). A PGS combining common and rare variants is expected to identify 4.9 million misdiagnosed T2D cases in the United States-nearly 1.5-fold more than the common variant PGS alone. These results provide a method for constructing complex trait PGSs from rare variants and suggest that rare variants will augment common variants in precision medicine approaches for common disease.

Mendelian Randomization Shows a Causal Effect of Low Vitamin D on Non-infectious Uveitis and Scleritis Risk.

PURPOSE: To investigate a causal relationship between Vitamin D levels and non-infectious uveitis and scleritis using Mendelian randomization (MR) techniques. DESIGN: Two-sample Mendelian randomization case-control study. METHODS: The study setting was a biobank of an academic, integrated health care system. The patient population comprised 375 case patients with a non-infectious uveitis and/or scleritis diagnosis and no diagnosis of infectious, trauma-related, or drug-induced uveitis/scleritis. In addition, there were 4167 controls with no uveitis or scleritis diagnosis. Causal effect estimates of low 25-hydroxy Vitamin D (25OHD) on uveitis/scleritis risk were calculated. RESULTS: We found an association of genetically decreased 25OHD with uveitis/scleritis risk (odds ratio [OR] = 2.16, 95% CI = 1.01-4.64, P = .049, per SD decrease in log25OHD). In a first sensitivity MR analysis excluding the genetic variants that are unlikely to have a role in biologically active 25OHD, effect estimates were consistent with those from the primary analysis (OR = 2.38, 95% CI =1.06-5.36, P = 0.035, per SD of log25OHD). Furthermore, in a second sensitivity analysis using only the 6 variants within the CYP2R1 locus (which encodes 25OHD hydroxylase, the liver enzyme responsible for converting Vitamin D to 25OHD), genetically decreased 25OHD was strongly associated with increased uveitis/scleritis risk (OR = 6.42, 95% CI = 3.19-12.89, P = 1.7 × 10-7, per SD of log25OHD). CONCLUSIONS: Our findings suggest a causal relationship between low Vitamin D levels and higher risk of non-infectious uveitis and scleritis. Vitamin D supplementation may be a low-cost, low-risk intervention to mitigate non-infectious uveitis and scleritis risk, and should be explored in a prospective trial.

Proteomic analysis of cardiometabolic biomarkers and predictive modeling of severe outcomes in patients hospitalized with COVID-19.

BACKGROUND: The high heterogeneity in the symptoms and severity of COVID-19 makes it challenging to identify high-risk patients early in the disease. Cardiometabolic comorbidities have shown strong associations with COVID-19 severity in epidemiologic studies. Cardiometabolic protein biomarkers, therefore, may provide predictive insight regarding which patients are most susceptible to severe illness from COVID-19. METHODS: In plasma samples collected from 343 patients hospitalized with COVID-19 during the first wave of the pandemic, we measured 92 circulating protein biomarkers previously implicated in cardiometabolic disease. We performed proteomic analysis and developed predictive models for severe outcomes. We then used these models to predict the outcomes of out-of-sample patients hospitalized with COVID-19 later in the surge (N = 194). RESULTS: We identified a set of seven protein biomarkers predictive of admission to the intensive care unit and/or death (ICU/death) within 28 days of presentation to care. Two of the biomarkers, ADAMTS13 and VEGFD, were associated with a lower risk of ICU/death. The remaining biomarkers, ACE2, IL-1RA, IL6, KIM1, and CTSL1, were associated with higher risk. When used to predict the outcomes of the future, out-of-sample patients, the predictive models built with these protein biomarkers outperformed all models built from standard clinical data, including known COVID-19 risk factors. CONCLUSIONS: These findings suggest that proteomic profiling can inform the early clinical impression of a patient's likelihood of developing severe COVID-19 outcomes and, ultimately, accelerate the recognition and treatment of high-risk patients.

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.

Proteomic analysis of cardiometabolic biomarkers and predictive modeling of severe outcomes in patients hospitalized with COVID-19.

Background: The high heterogeneity in the symptoms and severity of COVID-19 makes it challenging to identify high-risk patients early in the disease. Cardiometabolic comorbidities have shown strong associations with COVID-19 severity in epidemiologic studies. Cardiometabolic protein biomarkers, therefore, may provide predictive insight regarding which patients are most susceptible to severe illness from COVID-19. Methods: In plasma samples collected from 343 patients hospitalized with COVID-19 during the first wave of the pandemic, we measured 92 circulating protein biomarkers previously implicated in cardiometabolic disease. We performed proteomic analysis and developed predictive models for severe outcomes. We then used these models to predict the outcomes of out-of-sample patients hospitalized with COVID-19 later in the surge (N=194). Results: We identified a set of seven biomarkers predictive of admission to the intensive care unit and/or death (ICU/death) within 28 days of presentation to care. Two of the biomarkers, ADAMTS13 and VEGFD, were associated with a lower risk of ICU/death. The remaining biomarkers, ACE2, IL-1RA, IL6, KIM1, and CTSL1, were associated with higher risk. When used to predict the outcomes of the future, out-of-sample patients, the predictive models built with these biomarkers outperformed all models built from standard clinical data, including known COVID-19 risk factors. Conclusions: These findings suggest that proteomic profiling can inform the early clinical impression of a patient’s likelihood of developing severe COVID-19 outcomes and, ultimately, accelerate the recognition and treatment of high-risk patients.

Association of GLP1R Polymorphisms With the Incretin Response.

CONTEXT: Polymorphisms in the gene encoding the glucagon-like peptide-1 receptor (GLP1R) are associated with type 2 diabetes but their effects on incretin levels remain unclear. OBJECTIVE: We evaluated the physiologic and hormonal effects of GLP1R genotypes before and after interventions that influence glucose physiology. DESIGN: Pharmacogenetic study conducted at 3 academic centers in Boston, Massachusetts. PARTICIPANTS: A total of 868 antidiabetic drug-naïve participants with type 2 diabetes or at risk for developing diabetes. INTERVENTIONS: We analyzed 5 variants within GLP1R (rs761387, rs10305423, rs10305441, rs742762, and rs10305492) and recorded biochemical data during a 5-mg glipizide challenge and a 75-g oral glucose tolerance test (OGTT) following 4 doses of metformin 500 mg over 2 days. MAIN OUTCOMES: We used an additive mixed-effects model to evaluate the association of these variants with glucose, insulin, and incretin levels over multiple timepoints during the OGTT. RESULTS: During the OGTT, the G-risk allele at rs761387 was associated with higher total GLP-1 (2.61 pmol/L; 95% CI, 1.0.72-4.50), active GLP-1 (2.61 pmol/L; 95% CI, 0.04-5.18), and a trend toward higher glucose (3.63; 95% CI, -0.16 to 7.42 mg/dL) per allele but was not associated with insulin. During the glipizide challenge, the G allele was associated with higher insulin levels per allele (2.01 IU/mL; 95% CI, 0.26-3.76). The other variants were not associated with any of the outcomes tested. CONCLUSIONS: GLP1R variation is associated with differences in GLP-1 levels following an OGTT load despite no differences in insulin levels, highlighting altered incretin signaling as a potential mechanism by which GLP1R variation affects T2D risk.