Leg and arm adiposity is inversely associated with diastolic hypertension in young and middle-aged United States adults

Background We sought to determine the association between appendicular adiposity and hypertension, with the purpose of better understanding the role of body fat distribution on blood pressure (BP). Methods We included 7411 adults aged 20 to 59 who were not taking antihypertensives and without cardiovascular disease from the 2011 to 2018 National Health and Nutrition Examination Surveys. Leg & arm adiposity, determined via dual-energy X-ray absorptiometry scans, was defined as percent of total body fat present in legs/arms (leg/total%, arm/total%). Measures were categorized into sex-specific tertiles. We estimated change in BP and odds ratios (ORs) of hypertension (BP ≥ 130/80) and hypertension subtypes using multivariable, survey design-adjusted linear & logistic regression, respectively. Results Of the participants, 49% were female, the average (standard deviation) age was 37.4 (0.3) years, and 24% had hypertension. Those in the highest tertile (T3) of leg/total% had 30% decreased adjusted ORs (aOR) of hypertension compared to the lowest tertile (T1; aOR, 0.70; 95% confidence interval [95% CI], 0.55–0.89). This association was not significant for arm/total% (0.89, 0.68–1.17). T3 of leg/total% was associated with 49% lower, 41% lower, and unchanged relative odds of isolated diastolic hypertension (IDH), systolic-diastolic hypertension (SDH), and isolated systolic hypertension (ISH) compared to T1 (IDH: 0.51, 0.37–0.70; SDH: 0.59, 0.43–0.80; ISH: 1.06, 0.70–1.59). For every 10% increase in leg/total%, diastolic BP decreased by an adjusted mean 3.5 mmHg (95% CI, − 4.8 to − 2.2) in males and 1.8 mmHg (95% CI, − 2.8 to − 0.8) in females (P < 0.001 for both). Conclusions A greater proportional distribution of fat around the legs is inversely, independently associated with hypertension, and more specifically, diastolic hypertension (IDH and SDH).

Estimating body fat distribution - a driver of cardiometabolic health - from silhouette images

Background: Inter-individual variation in fat distribution is increasingly recognized as clinically important but is not routinely assessed in clinical practice because quantification requires medical imaging. Objectives: We hypothesized that a deep learning model trained on an individual's body shape outline - or silhouette - would enable accurate estimation of specific fat depots, including visceral (VAT), abdominal subcutaneous (ASAT), and gluteofemoral (GFAT) adipose tissue volumes, and VAT/ASAT ratio. We additionally set out to study whether silhouette-estimated VAT/ASAT ratio may stratify risk of cardiometabolic diseases independent of body mass index (BMI) and waist circumference. Methods: Two-dimensional coronal and sagittal silhouettes were constructed from whole-body magnetic resonance images in 40,032 participants of the UK Biobank and used to train a convolutional neural network to predict VAT, ASAT, and GFAT volumes, and VAT/ASAT ratio. Logistic and Cox regressions were used to determine the independent association of silhouette-predicted VAT/ASAT ratio with type 2 diabetes and coronary artery disease. Results: Mean age of the study participants was 65 years and 51% were female. A deep learning model trained on silhouettes enabled 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 studied VAT/ASAT ratio, a nearly BMI- and waist circumference-independent marker of unhealthy fat distribution. While the comparator model poorly predicted VAT/ASAT ratio (R2: 0.17-0.26), a silhouette-based model enabled significant improvement (R2: 0.50-0.55). Silhouette-predicted VAT/ASAT ratio was associated with increased prevalence of type 2 diabetes and coronary artery disease. Conclusions: Body silhouette images can estimate important measures of fat distribution, laying the scientific foundation for population-based assessment.

A Rare Mutation in LMNB2 Associated with Lipodystrophy Drives Premature Cell Senescence

Many proteins are causative for inherited partial lipodystrophies, including lamins, the essential constituents of the nuclear envelope scaffold called the lamina. By performing high throughput sequencing on a panel of genes involved in lipodystrophies, we identified a heterozygous mutation in LMNB2 gene (c.700C > T p.(Arg234Trp)) in a female patient presenting early onset type II diabetes, hypertriglyceridemia, and android fat distribution. This mutation is rare in the general population (frequency 0.013% in GnomAD) and was predicted pathogenic by a set of pathogenicity prediction software. Patient-derived fibroblasts showed nuclear shape abnormalities and premature senescence features, which are two typical cellular phenotypes associated with laminopathies. Moreover, we observed an atypical aggregation of lamin B2 in nucleoplasm, which co-distributes with emerin and lamin A/C, along with an abnormal distribution of lamin A/C at the nuclear envelope. Finally, reducing lamin B2 expression level by siRNA targeted toward LMNB2 transcripts resulted in decreased nuclear anomalies and senescence-associated beta-galactosidase, suggesting a role of the mutated protein in the occurrence of the observed cellular phenotype. Altogether, these results suggest that mutations in lamin B2 could produce premature senescence and partial lipodystrophy features as observed with certain mutants of lamin A/C.

First person – Yixing Wu and Ying Bai

ABSTRACT First Person is a series of interviews with the first authors of a selection of papers published in Biology Open, helping early-career researchers promote themselves alongside their papers. Yixing Wu and Ying Bai are co-first authors on ‘ Palmitoylated small GTPase ARL15 is translocated within Golgi network during adipogenesis’, published in BiO. Yixing is a research fellow in the lab of Frances Wiseman at UCL Queen Square Institute of Neurology, London, UK, investigating Down's syndrome and Alzheimer's disease-related endo-lysosomal pathways and cathepsin deficits. Ying is a postdoc in the lab of Roger D. Cox at MRC Harwell Institute, Didcot, UK, investigating how fat cells are formed, and genes that are involved in regulating body fat distribution.

Association Between the Triglyceride–Glucose Index and the Incidence of Diabetes in People With Different Phenotypes of Obesity: A Retrospective Study

AimWe aimed to examine the relationship between the Triglyceride–glucose (TyG) index and the incidence of type 2 diabetes in people with different phenotypes of obesity.MethodsFrom May 1, 1994 to December 31, 2016, 15,464 participants were enrolled in the medical examination program at the Murakami Memorial Hospital to determine the relationship between the TyG index and the incidence of type 2 diabetes in people with different phenotypes of obesity after 5.38 years of follow-up.ResultsBesides triglycerides, HbA1c%, and FPG, the incidence of type 2 diabetes was found to be significantly associated with the TyG index (p &lt;0.001), age (p &lt;0.001), BMI (p = 0.033), current smoker (p &lt;0.001), and fatty liver (p &lt;0.001). In participants with visceral fat obesity and/or ectopic fat obesity and normal BMI, the TyG index was significantly associated with the incidence of type 2 diabetes after adjusting for confounding factors. In patients with BMI ≥25 mg/m2, although there was a trend of the relationship between the TyG index and the incidence of type 2 diabetes, the relationship was no longer positive.ConclusionIn participants with obesity involving visceral fat obesity and/or fatty liver and normal BMI which is not a measure of body fat distribution, there was a significant association between the TyG index and incidence of T2DM.

Body Fat Distribution and Associated Risk of Cardiovascular Disease in Adults With Cerebral Palsy

Objective: Fat distribution has increasingly been acknowledged as a more significant health parameter than general obesity, in terms of the risk of cardiovascular disease (CVD). We aimed to investigate the regional fat distribution pattern and general body fat characteristics of adults with cerebral palsy (CP), and we explored the risk of CVD in this population.Methods: People aged ≥20 years who were diagnosed with CP were recruited between February 2014 and November 2014. The subjects underwent a structured interview, laboratory studies, and physical examination. The amount and distribution of fat were determined directly by dual-energy X-ray absorptiometry. Laboratory analysis was performed to measure total cholesterol and triglyceride, high-density lipoprotein (HDL), low-density lipoprotein, and fasting plasma glucose levels. The Framingham risk score (FRS) was used to present the 10-year risk for having CVD, and predictors such as sex, age, total cholesterol, HDL, systolic blood pressure, treatment for hypertension, and smoking status were used to calculate the FRS.Results: Ninety-nine adults (58 men, mean age 41.77 ± 8.95 years) with CP were included. The participants consisted of all five levels of the Gross Motor Function Classification System. The mean body mass index (BMI) was 22.52 ± 4.58 kg/m2. According to BMI criteria, 54.9% were overweight and 27.3% were obese. The fat mass index criteria revealed 10.1% excess fat and 7.6% obesity. In univariable regression analysis, age, the timing of physical function deterioration, and android fat percentage were associated with the FRS (p &lt;0.001, p &lt;0.001, and p = 0.007, respectively). In multiple regression analysis, the FRS was associated with age and android fat percentage, based on the following formula: “FRS=−18.549 + 0.410 ∗ Age + 0.577 ∗ Android percent fat (%) (R2=0.528)′′ (p<0.001).Conclusions: Body fat distribution in the android area is significantly associated with future CVD risk in adults with CP.