The genetic underpinnings of body fat distribution

Protective Effects of Melatonin against Obesity-Induced by Leptin Resistance

Consumption of an exceedingly high-fat diet with irregular eating and sleeping habits is typical in the current sedentary lifestyle, leading to chronic diseases like obesity and diabetes mellitus. Leptin is a primary appetite-regulating hormone that binds to its receptors in the hypothalamic cell membrane and regulates downstream appetite-regulating neurons NPY/AgRp and POMC in the hypothalamus. Based on the fat content of the adipose tissue, leptin is secreted, and excess accumulation of fat in adipose tissue stimulates the abnormal secretion of leptin. The secreted leptin circulating in the bloodstream uses its transporters to cross the blood-brain barrier (BBB) and reach the CSF. There is a saturation limit for leptin bound to its transporters to cross the BBB, and increased leptin secretion in adipose tissue has a defect in its transport across the BBB. Leptin resistance is due to excess leptin, a saturation of its transporters, and deficiency in either the receptor level or signalling in the hypothalamus. Leptin resistance leads to obesity due to excess food intake and less energy expenditure. Normal leptin secretion follows a rhythm, and alteration in the lifestyle leads to hormonal imbalances and increases ROS generation leading to oxidative stress. The sleep disturbance causes obesity with increased lipid accumulation in adipose tissue. Melatonin is the master regulator of the sleep-wake cycle secreted by the pineal gland during the night. It is a potent antioxidant with anti-inflammatory properties. Melatonin is secreted in a pattern called the circadian rhythm in humans as well. Research indicates that melatonin plays a vital role in hormonal regulation and energy metabolism, including leptin signalling and secretion. Studying the role of melatonin in leptin regulation will help us combat the pathologies of obesity caused by leptin resistance.

Machine Learning based histology phenotyping to investigate the epidemiologic and genetic basis of adipocyte morphology and cardiometabolic traits

Genetic studies have recently highlighted the importance of fat distribution, as well as overall adiposity, in the pathogenesis of obesity-associated diseases. Using a large study (n = 1,288) from 4 independent cohorts, we aimed to investigate the relationship between mean adipocyte area and obesity-related traits, and identify genetic factors associated with adipocyte cell size. To perform the first large-scale study of automatic adipocyte phenotyping using both histological and genetic data, we developed a deep learning-based method, the Adipocyte U-Net, to rapidly derive mean adipocyte area estimates from histology images. We validate our method using three state-of-the-art approaches; CellProfiler, Adiposoft and floating adipocytes fractions, all run blindly on two external cohorts. We observe high concordance between our method and the state-of-the-art approaches (Adipocyte U-net vs. CellProfiler: R2visceral = 0.94, P < 2.2 × 10-16, R2subcutaneous = 0.91, P < 2.2 × 10-16), and faster run times (10,000 images: 6mins vs 3.5hrs). We applied the Adipocyte U-Net to 4 cohorts with histology, genetic, and phenotypic data (total N = 820). After meta-analysis, we found that mean adipocyte area positively correlated with body mass index (BMI) (Psubq = 8.13 × 10-69, βsubq = 0.45; Pvisc = 2.5 × 10-55, βvisc = 0.49; average R2 across cohorts = 0.49) and that adipocytes in subcutaneous depots are larger than their visceral counterparts (Pmeta = 9.8 × 10-7). Lastly, we performed the largest GWAS and subsequent meta-analysis of mean adipocyte area and intra-individual adipocyte variation (N = 820). Despite having twice the number of samples than any similar study, we found no genome-wide significant associations, suggesting that larger sample sizes and a homogenous collection of adipose tissue are likely needed to identify robust genetic associations.