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

Background & aimsLean 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.MethodsThis 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.ResultsSix 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.ConclusionsIn 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 implicationsAlthough 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

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 (trunk fat %) divided by leg fat percentage (leg fat %) – 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 (OR = 2.30, p = 3.5 x 10-41) and MASLD/MASH (OR = 2.55, p = 4.9 x 10-7) in UK Biobank, and had higher fasting insulin (difference = +19.8 pmol/L, p = 5.7 x 10-36) and fasting triglycerides (difference = +36.1 mg/dL, p = 2.5 x 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 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.</p

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

Different location of adipose tissue may have different consequences to cardiometabolic risk. Here the authors report that deep learning enabled accurate prediction of specific adipose tissue volumes, and that after adjustment for BMI, visceral adiposity was associated with increased risk of cardiometabolic disease, while gluteofemoral adiposity was associated with reduced risk

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

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

Funder: Massachusetts General Hospital (MGH); doi: https://doi.org/10.13039/100005294Funder: Broad Institute; doi: https://doi.org/10.13039/100013114Funder: Harvard Catalyst (Harvard Clinical and Translational Science Center); doi: https://doi.org/10.13039/100007299Identification 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

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

none38noneMargo P. Emont, Christopher Jacobs, Adam L. Essene, Deepti Pant, Danielle Tenen, Georgia Colleluori, Angelica Di Vincenzo, Anja M. Jørgensen, Hesam Dashti, Adam Stefek, Elizabeth McGonagle, Sophie Strobel, Samantha Laber, Saaket Agrawal,Gregory P. Westcott, Amrita Kar, Molly L. Veregge, Anton Gulko, Harini Srinivasan, Zachary Kramer, Eleanna De Filippis, Erin Merkel, Jennifer Ducie, Christopher G. Boyd, William Gourash, Anita Courcoulas, Samuel J. Lin, Bernard T. Lee, Donald Morris, Adam Tobias, Amit V. Khera, Melina Claussnitzer, Tune H. Pers, Antonio Giordano, Orr Ashenberg, Aviv Regev, Linus T. Tsai & Evan D. RosenEmont, Margo P.; Jacobs, Christopher; Essene, Adam L.; Pant, Deepti; Tenen, Danielle; Colleluori, Georgia; DI VINCENZO, Angelica; Jørgensen, Anja M.; Dashti, Hesam; Stefek, Adam; Mcgonagle, Elizabeth; Strobel, Sophie; Laber, Samantha; Agrawal, Saaket; Westcott, Gregory P.; Kar, Amrita; Veregge, Molly L.; Gulko, Anton; Srinivasan, Harini; Kramer, Zachary; De Filippis, Eleanna; Merkel, Erin; Ducie, Jennifer; Boyd, Christopher G.; Gourash, William; Courcoulas, Anita; Lin, Samuel J.; Lee, Bernard T.; Morris, Donald; Tobias, Adam; Khera, Amit V.; Claussnitzer, Melina; Pers, Tune H.; Giordano, Antonio; Ashenberg, Orr; Regev, Aviv; Tsai &amp;, Linus T.; Rosen, Evan D