A single-cell atlas of human and mouse white adipose tissue.

White adipose tissue, once regarded as morphologically and functionally bland, is now recognized to be dynamic, plastic and heterogenous, and is involved in a wide array of biological processes including energy homeostasis, glucose and lipid handling, blood pressure control and host defence1. High-fat feeding and other metabolic stressors cause marked changes in adipose morphology, physiology and cellular composition1, and alterations in adiposity are associated with insulin resistance, dyslipidemia and type 2 diabetes2. Here we provide detailed cellular atlases of human and mouse subcutaneous and visceral white fat at single-cell resolution across a range of body weight. We identify subpopulations of adipocytes, adipose stem and progenitor cells, vascular and immune cells and demonstrate commonalities and differences across species and dietary conditions. We link specific cell types to increased risk of metabolic disease and provide an initial blueprint for a comprehensive set of interactions between individual cell types in the adipose niche in leanness and obesity. These data comprise an extensive resource for the exploration of genes, traits and cell types in the function of white adipose tissue across species, depots and nutritional conditions.

Relationship of Fat Mass Ratio, a Biomarker for Lipodystrophy, With Cardiometabolic Traits.

Familial partial lipodystrophy (FPLD) is a heterogenous group of syndromes associated with a high prevalence of cardiometabolic diseases. Prior work has proposed DEXA-derived fat mass ratio (FMR), defined as trunk fat percentage divided by leg fat percentage, as a biomarker of FPLD, but this metric has not previously been characterized in large cohort studies. We set out to 1) understand the cardiometabolic burden of individuals with high FMR in up to 40,796 participants in the UK Biobank and 9,408 participants in the Fenland study, 2) characterize the common variant genetic underpinnings of FMR, and 3) build and test a polygenic predictor for FMR. Participants with high FMR were at higher risk for type 2 diabetes (odds ratio [OR] 2.30, P = 3.5 × 10-41) and metabolic dysfunction-associated liver disease or steatohepatitis (OR 2.55, P = 4.9 × 10-7) in UK Biobank and had higher fasting insulin (difference 19.8 pmol/L, P = 5.7 × 10-36) and fasting triglycerides (difference 36.1 mg/dL, P = 2.5 × 10-28) in the Fenland study. Across FMR and its component traits, 61 conditionally independent variant-trait pairs were discovered, including 13 newly identified pairs. A polygenic score for FMR was associated with an increased risk of cardiometabolic diseases. This work establishes the cardiometabolic significance of high FMR, a biomarker for FPLD, in two large cohort studies and may prove useful in increasing diagnosis rates of patients with metabolically unhealthy fat distribution to enable treatment or a preventive therapy.

The impact of genetic risk on the prevalence of advanced fibrosis and cirrhosis in prospectively assessed patients with type 2 diabetes.

BACKGROUND: Genetic factors contribute to the risk and severity of metabolic dysfunction-associated steatotic liver disease (MASLD). However, the utility of genetic testing in risk stratification remains poorly characterised. AIMS: To examine the influence of genetic risk on advanced fibrosis and cirrhosis in patients with type 2 diabetes mellitus (T2DM) and the utility of a polygenic risk score (PRS) in screening guidelines. METHODS: We prospectively enrolled adults aged ≥50 years with T2DM recruited from clinics. PRS was the sum of risk alleles in PNPLA3, TM6SF2 and SERPINA1 minus the protective variant in HSD17B13. We performed magnetic resonance elastography and vibration-controlled transient elastography to assess for advanced fibrosis and cirrhosis. RESULTS: Of 382 included patients, the mean age and BMI were 64.8 (±8.4) years and 31.7 (±6.2) kg/m2 respectively. The prevalence of advanced fibrosis and cirrhosis were 12.3% and 5.2% respectively; higher PRS was associated with increased risk of cirrhosis (2.7% vs. 7.5%, p = 0.037). High PRS was associated with increased risk of advanced fibrosis among those with fibrosis-4 index (FIB-4) index <1.3 (9.6% vs. 2.3%, p = 0.036) but was not significantly different in other FIB-4 categories. Incorporating PRS determination into the American Gastroenterological Association and American Association for the Study of Liver Diseases screening guidelines prevented approximately 20% of all participants with advanced fibrosis from being inappropriately classified as low risk. CONCLUSIONS: Utilising a well-phenotyped, prospective cohort of adults with T2DM, we found that adding an assessment of genetic risk to recommendations to screen at-risk populations may improve risk prediction.

Genomic analysis of lean individuals with NAFLD identifies monogenic disorders in a prospective cohort study.

Background & Aims: Lean patients with non-alcoholic fatty liver disease (NAFLD) represent 10-20% of the affected population and may have heterogeneous drivers of disease. We have recently proposed the evaluation of patients with lean NAFLD without visceral adiposity for rare monogenic drivers of disease. Here, we aimed to validate this framework in a well-characterised cohort of patients with biopsy-proven NAFLD by performing whole exome sequencing. Methods: This prospective study included 124 patients with biopsy-proven NAFLD and paired liver biopsies who underwent standardised research visits including advanced magnetic resonance imaging (MRI) assessment of liver fat and stiffness. Results: Six patients with lean NAFLD were identified and underwent whole exome sequencing. Two lean patients (33%) were identified to have monogenic disorders. The lean patients with monogenic disorders had similar age, and anthropometric and MRI characteristics to lean patients without a monogenic disorder. Patient 1 harbours a rare homozygous pathogenic mutation in ALDOB (aldolase B) and was diagnosed with hereditary fructose intolerance. Patient 2 harbours a rare heterozygous mutation in apolipoprotein B (APOB). The pathogenicity of this APOB variant (p.Val1856CysfsTer2) was further validated in the UK Biobank and associated with lower circulating APOB levels (beta = -0.51 g/L, 95% CI -0.65 to -0.36 g/L, p = 1.4 × 10-11) and higher liver fat on MRI (beta = +10.4%, 95% CI 4.3-16.5%, p = 8.8 × 10-4). Hence, patient 2 was diagnosed with heterozygous familial hypobetalipoproteinaemia. Conclusions: In this cohort of well-characterised patients with lean NAFLD without visceral adiposity, 33% (2/6) had rare monogenic drivers of disease, highlighting the importance of genomic analysis in this NAFLD subtype. Impact and Implications: Although most people with non-alcoholic fatty liver disease (NAFLD) are overweight or obese, a subset are lean and may have unique genetic mutations that cause their fatty liver disease. We show that 33% of study participants with NAFLD who were lean harboured unique mutations that cause their fatty liver, and that these mutations had effects beyond the liver. This study demonstrates the value of genetic assessment of NAFLD in lean individuals to identify distinct subtypes of disease.

BMI-adjusted adipose tissue volumes exhibit depot-specific and divergent associations with cardiometabolic diseases.

For any given body mass index (BMI), individuals vary substantially in fat distribution, and this variation may have important implications for cardiometabolic risk. Here, we study disease associations with BMI-independent variation in visceral (VAT), abdominal subcutaneous (ASAT), and gluteofemoral (GFAT) fat depots in 40,032 individuals of the UK Biobank with body MRI. We apply deep learning models based on two-dimensional body MRI projections to enable near-perfect estimation of fat depot volumes (R2 in heldout dataset = 0.978-0.991 for VAT, ASAT, and GFAT). Next, we derive BMI-adjusted metrics for each fat depot (e.g. VAT adjusted for BMI, VATadjBMI) to quantify local adiposity burden. VATadjBMI is associated with increased risk of type 2 diabetes and coronary artery disease, ASATadjBMI is largely neutral, and GFATadjBMI is associated with reduced risk. These results - describing three metabolically distinct fat depots at scale - clarify the cardiometabolic impact of BMI-independent differences in body fat distribution.

A multi-ancestry polygenic risk score improves risk prediction for coronary artery disease.

Identification of individuals at highest risk of coronary artery disease (CAD)-ideally before onset-remains an important public health need. Prior studies have developed genome-wide polygenic scores to enable risk stratification, reflecting the substantial inherited component to CAD risk. Here we develop a new and significantly improved polygenic score for CAD, termed GPSMult, that incorporates genome-wide association data across five ancestries for CAD (>269,000 cases and >1,178,000 controls) and ten CAD risk factors. GPSMult strongly associated with prevalent CAD (odds ratio per standard deviation 2.14, 95% confidence interval 2.10-2.19, P < 0.001) in UK Biobank participants of European ancestry, identifying 20.0% of the population with 3-fold increased risk and conversely 13.9% with 3-fold decreased risk as compared with those in the middle quintile. GPSMult was also associated with incident CAD events (hazard ratio per standard deviation 1.73, 95% confidence interval 1.70-1.76, P < 0.001), identifying 3% of healthy individuals with risk of future CAD events equivalent to those with existing disease and significantly improving risk discrimination and reclassification. Across multiethnic, external validation datasets inclusive of 33,096, 124,467, 16,433 and 16,874 participants of African, European, Hispanic and South Asian ancestry, respectively, GPSMult demonstrated increased strength of associations across all ancestries and outperformed all available previously published CAD polygenic scores. These data contribute a new GPSMult for CAD to the field and provide a generalizable framework for how large-scale integration of genetic association data for CAD and related traits from diverse populations can meaningfully improve polygenic risk prediction.

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.

Silhouette images enable estimation of body fat distribution and associated cardiometabolic risk.

Inter-individual variation in fat distribution is increasingly recognized as clinically important but is not routinely assessed in clinical practice, in part because medical imaging has not been practical to deploy at scale for this task. Here, we report a deep learning model trained on an individual's body shape outline-or "silhouette" -that enables accurate estimation of specific fat depots of interest, including visceral (VAT), abdominal subcutaneous (ASAT), and gluteofemoral (GFAT) adipose tissue volumes, and VAT/ASAT ratio. Two-dimensional coronal and sagittal silhouettes are constructed from whole-body magnetic resonance images in 40,032 participants of the UK Biobank and used as inputs for a convolutional neural network to predict each of these quantities. Mean age of the study participants is 65 years and 51% are female. A cross-validated deep learning model trained on silhouettes enables accurate estimation of VAT, ASAT, and GFAT volumes (R2: 0.88, 0.93, and 0.93, respectively), outperforming a comparator model combining anthropometric and bioimpedance measures (ΔR2 = 0.05-0.13). Next, we study VAT/ASAT ratio, a nearly body-mass index (BMI)-and waist circumference-independent marker of metabolically unhealthy fat distribution. While the comparator model poorly predicts VAT/ASAT ratio (R2: 0.17-0.26), a silhouette-based model enables significant improvement (R2: 0.50-0.55). Increased silhouette-predicted VAT/ASAT ratio is associated with increased risk of prevalent and incident type 2 diabetes and coronary artery disease independent of BMI and waist circumference. These results demonstrate that body silhouette images can estimate important measures of fat distribution, laying the scientific foundation for scalable population-based assessment.

Inherited basis of visceral, abdominal subcutaneous and gluteofemoral fat depots.

For any given level of overall adiposity, individuals vary considerably in fat distribution. The inherited basis of fat distribution in the general population is not fully understood. Here, we study up to 38,965 UK Biobank participants with MRI-derived visceral (VAT), abdominal subcutaneous (ASAT), and gluteofemoral (GFAT) adipose tissue volumes. Because these fat depot volumes are highly correlated with BMI, we additionally study six local adiposity traits: VAT adjusted for BMI and height (VATadj), ASATadj, GFATadj, VAT/ASAT, VAT/GFAT, and ASAT/GFAT. We identify 250 independent common variants (39 newly-identified) associated with at least one trait, with many associations more pronounced in female participants. Rare variant association studies extend prior evidence for PDE3B as an important modulator of fat distribution. Local adiposity traits (1) highlight depot-specific genetic architecture and (2) enable construction of depot-specific polygenic scores that have divergent associations with type 2 diabetes and coronary artery disease. These results - using MRI-derived, BMI-independent measures of local adiposity - confirm fat distribution as a highly heritable trait with important implications for cardiometabolic health outcomes.

Selection of 51 predictors from 13,782 candidate multimodal features using machine learning improves coronary artery disease prediction.

Current cardiovascular risk assessment tools use a small number of predictors. Here, we study how machine learning might: (1) enable principled selection from a large multimodal set of candidate variables and (2) improve prediction of incident coronary artery disease (CAD) events. An elastic net-based Cox model (ML4HEN-COX) trained and evaluated in 173,274 UK Biobank participants selected 51 predictors from 13,782 candidates. Beyond most traditional risk factors, ML4HEN-COX selected a polygenic score, waist circumference, socioeconomic deprivation, and several hematologic indices. A more than 30-fold gradient in 10-year risk estimates was noted across ML4HEN-COX quintiles, ranging from 0.25% to 7.8%. ML4HEN-COX improved discrimination of incident CAD (C-statistic = 0.796) compared with the Framingham risk score, pooled cohort equations, and QRISK3 (range 0.754-0.761). This approach to variable selection and model assessment is readily generalizable to a broad range of complex datasets and disease endpoints.

Fatal Lactobacillus endocarditis in a patient with transcatheter aortic valve replacement.

Lactobacillus species may translocate from the gastrointestinal tract into systemic circulation from ingested probiotics or commensal flora. Their pathogenic potential is still debated. Lactobacillus endocarditis is a rare entity with only around 120 cases reported in the literature. Here, we report the first case of fatal Lactobacillus endocarditis with involvement of a transcatheter aortic valve replacement with the following goals: to reaffirm the pathological significance of Lactobacillus spp, to demonstrate the potential limitations of the modified Duke criteria in diagnosing infective endocarditis of transcatheter aortic valve replacement, and to urge clinicians to aggressively search for and consider empiric treatment for endocarditis in patients with prosthetic valves who develop Lactobacillus bacteraemia.

Impact of CYP2C9-Interacting Drugs on Warfarin Pharmacogenomics.

Precise dosing of warfarin is important to achieve therapeutic benefit without adverse effects. Pharmacogenomics explains some interindividual variability in warfarin response, but less attention has been paid to drug-drug interactions in the context of genetic factors. We investigated retrospectively the combined effects of cytochrome P450 (CYP)2C9 and vitamin K epoxide reductase complex (VKORC)1 genotypes and concurrent exposure to CYP2C9-interacting drugs on long-term measures of warfarin anticoagulation. Study participants predicted to be sensitive responders to warfarin based on CYP2C9 and VKORC1 genotypes, had significantly greater international normalized ratio (INR) variability over time. Participants who were concurrently taking CYP2C9-interacting drugs were found to have greater INR variability and lesser time in therapeutic range. The associations of INR variability with genotype were driven by the subgroup not exposed to interacting drugs, whereas the effect of interacting drug exposure was driven by the subgroup categorized as normal responders. Our findings emphasize the importance of considering drug interactions in pharmacogenomic studies.