Abdominal Obesity Genetic Variants Predict Waist Circumference Regain After Weight Loss.

Although many individuals are able to achieve weight loss, maintaining this loss over time is challenging. We aimed to study whether genetic predisposition to general or abdominal obesity predicts weight regain after weight loss. We examined the associations between genetic risk scores for higher BMI and higher waist-to-hip ratio adjusted for BMI (WHRadjBMI) with changes in weight and waist circumference up to 3 years after a 1-year weight loss program in participants (n = 822 women, n = 593 men) from the Look AHEAD (Action for Health in Diabetes) study who had lost ≥3% of their initial weight. Genetic predisposition to higher BMI or WHRadjBMI was not associated with weight regain after weight loss. However, the WHRadjBMI genetic score did predict an increase in waist circumference independent of weight change. To conclude, a genetic predisposition to higher WHRadjBMI predicts an increase in abdominal obesity after weight loss, whereas genetic predisposition to higher BMI is not predictive of weight regain. These results suggest that genetic effects on abdominal obesity may be more pronounced than those on general obesity during weight regain. ARTICLE HIGHLIGHTS: Nearly all individuals who intentionally lose weight experience weight regain. Individuals with a higher genetic risk for abdominal adiposity experience increased regain in waist circumference after weight loss. Genetic predisposition to higher BMI does not predict weight regain after weight loss.

The Genetic Basis of Hypertriglyceridemia.

PURPOSE OF REVIEW: Hypertriglyceridemia is a common dyslipidemia associated with an increased risk of cardiovascular disease and pancreatitis. Severe hypertriglyceridemia may sometimes be a monogenic condition. However, in the vast majority of patients, hypertriglyceridemia is due to the cumulative effect of multiple genetic risk variants along with lifestyle factors, medications, and disease conditions that elevate triglyceride levels. In this review, we will summarize recent progress in the understanding of the genetic basis of hypertriglyceridemia. RECENT FINDINGS: More than 300 genetic loci have been identified for association with triglyceride levels in large genome-wide association studies. Studies combining the loci into polygenic scores have demonstrated that some hypertriglyceridemia phenotypes previously attributed to monogenic inheritance have a polygenic basis. The new genetic discoveries have opened avenues for the development of more effective triglyceride-lowering treatments and raised interest towards genetic screening and tailored treatments against hypertriglyceridemia. The discovery of multiple genetic loci associated with elevated triglyceride levels has led to improved understanding of the genetic basis of hypertriglyceridemia and opened new translational opportunities.

Semantic text mining in early drug discovery for type 2 diabetes.

BACKGROUND: Surveying the scientific literature is an important part of early drug discovery; and with the ever-increasing amount of biomedical publications it is imperative to focus on the most interesting articles. Here we present a project that highlights new understanding (e.g. recently discovered modes of action) and identifies potential drug targets, via a novel, data-driven text mining approach to score type 2 diabetes (T2D) relevance. We focused on monitoring trends and jumps in T2D relevance to help us be timely informed of important breakthroughs. METHODS: We extracted over 7 million n-grams from PubMed abstracts and then clustered around 240,000 linked to T2D into almost 50,000 T2D relevant 'semantic concepts'. To score papers, we weighted the concepts based on co-mentioning with core T2D proteins. A protein's T2D relevance was determined by combining the scores of the papers mentioning it in the five preceding years. Each week all proteins were ranked according to their T2D relevance. Furthermore, the historical distribution of changes in rank from one week to the next was used to calculate the significance of a change in rank by T2D relevance for each protein. RESULTS: We show that T2D relevant papers, even those not mentioning T2D explicitly, were prioritised by relevant semantic concepts. Well known T2D proteins were therefore enriched among the top scoring proteins. Our 'high jumpers' identified important past developments in the apprehension of how certain key proteins relate to T2D, indicating that our method will make us aware of future breakthroughs. In summary, this project facilitated keeping up with current T2D research by repeatedly providing short lists of potential novel targets into our early drug discovery pipeline.