Gut microbiome has been identified in the past decade as an important factor involved in obesity, but the magnitude of its contribution to obesity and its related comorbidities is still uncertain. Among the vast quantity of factors attributed to obesity, environmental, dietary, lifestyle, genetic, and others, the microbiome has aroused curiosity, and the scientific community has published many original articles. Most of the studies related to microbiome and obesity have been reported based on the associations between microbiota and obesity, and the in-depth study of the mechanisms related has been studied mainly in rodents and exceptionally in humans. Due to the quantity and diverse information published, the need of reviews is mandatory to recapitulate the relevant achievements. In this systematic review, we provide an overview of the current evidence on the association between intestinal microbiota and obesity. Additionally, we analyze the effects of an extreme weight loss intervention such as bariatric surgery on gut microbiota. The review is divided into 2 sections: first, the association of obesity and related metabolic disorders with different gut microbiome profiles, including metagenomics studies, and second, changes on gut microbiome after an extreme weight loss intervention such as bariatric surgery.

Metabolic syndrome is defined as a cluster of glucose intolerance, hypertension, dyslipidemia and central obesity with insulin resistance as the source of pathogenesis. Although several different combinations of criteria have been used to define metabolic syndrome, a recently published consensus recommends the use of ethnic‐specific criteria, including waist circumference as an indicator of central obesity, triglyceride and high‐density lipoprotein (HDL) cholesterol as indicators of dyslipidemia, and blood pressure greater than 130/85 mmHg. The definition of dysglycemia, and whether central obesity and insulin resistance are essential components remain controversial. Regardless of the definition, the prevalence of metabolic syndrome is increasing in Western and Asian countries, particularly in developing areas undergoing rapid socioenvironmental changes. Numerous clinical trials have shown that metabolic syndrome is an important risk factor for cardiovascular disease (CVD), type 2 diabetes mellitus and all‐cause mortality. Therefore, metabolic syndrome might be useful as a practical tool to predict these two major metabolic disorders. Comprehensive management of risk factors is very important to the improvement of personal and public health. However, recent studies have focused on the role metabolic syndrome plays as a risk factor for CVD; its importance in the prediction of incident diabetes is frequently overlooked. In the present review, we summarize the known evidence supporting metabolic syndrome as a predictor for type 2 diabetes mellitus and CVD. Additionally, we suggest how metabolic syndrome might be useful in clinical practice, especially for the prediction of diabetes.

To determine whether the TyG index, a product of the levels of triglycerides and fasting plasma glucose (FPG) might be a valuable marker for predicting future diabetes. A total of 5,354 nondiabetic subjects who had completed their follow-up visit for evaluating diabetes status were selected from a large cohort of middle-aged Koreans in the Chungju Metabolic Disease Cohort study. The risk of diabetes was assessed according to the baseline TyG index, calculated as ln[fasting triglycerides (mg/dL) × FPG (mg/dL)/2]. The median follow-up period was 4.6 years. During the follow-up period, 420 subjects (7.8%) developed diabetes. The baseline values of the TyG index were significantly higher in these subjects compared with nondiabetic subjects (8.9±0.6 vs. 8.6±0.6; P<0.0001) and the incidence of diabetes increased in proportion to TyG index quartiles. After adjusting for age, gender, body mass index, waist circumference, systolic blood pressure, high-density lipoprotein (HDL)-cholesterol level, a family history of diabetes, smoking, alcohol drinking, education level and serum insulin level, the risk of diabetes onset was more than fourfold higher in the highest vs. the lowest quartile of the TyG index (relative risk, 4.095; 95% CI, 2.701–6.207). The predictive power of the TyG index was better than the triglyceride/HDL-cholesterol ratio or the homeostasis model assessment of insulin resistance. The TyG index, a simple measure reflecting insulin resistance, might be useful in identifying individuals at high risk of developing diabetes.

A high visit-to-visit variability in cholesterol levels has been suggested to be an independent predictor of major adverse cardiovascular events in patients with coronary artery disease (CAD). Because whether this notion applies to general population is not known, we aimed to investigate the associations between total cholesterol (TC) variability and the risk of all-cause mortality, myocardial infarction (MI), and stroke. We identified 3 656 648 subjects without a history of MI and stroke who underwent ≥3 health examinations from 2002 to 2007 in the Korean National Health Insurance System cohort. Total cholesterol variability was measured using the coefficient of variation (CV), standard deviation (SD), and variability independent of the mean (VIM). There were 84 625 deaths (2.3%), 40 991 cases of MI (1.1%), and 42 861 cases of stroke (1.2%) during the median follow-up of 8.3 years. There was a linear association between higher TC variability and outcome measures. In the multivariable adjusted model, the hazard ratios and 95% confidence intervals comparing the highest vs. lowest quartiles of CV of TC were 1.26 (1.24–1.28) for all-cause mortality, 1.08 (1.05–1.11) for MI, and 1.11 (1.08–1.14) for stroke, which was independent of mean TC levels and the use of lipid-lowering agents. The results were consistent when modelling variability of TC using SD and VIM, and in various sensitivity analyses. High variability in lipid levels is associated with adverse health-related outcomes. These findings suggest that lipid variability is an important risk factor in the general population.

A high-calorie diet and sedentary behaviors are the most commonly cited explanations for the obesity epidemic and are the main targets for obesity prevention.¹ However, exposure to artificial light at night (ALAN) may contribute to the obesity pandemic.^2,3 Increasing trends in light pollution during the past few decades parallel the rapid increase of obesity in the United States.⁴ Evidence from animal studies supports the hypothesis that light exposure at night may have direct effects on melatonin signaling, sleep disruption, and circadian rhythms, which could result in weight gain and obesity.^4,5 ALAN has been shown to suppress expression of circadian clock genes and, in turn, alter feeding behaviors and lead to weight gain in rodents.^6-8

Background: Beyond intakes of total energy and individual nutrient, eating patterns may influence health, and thereby the risk of adverse outcomes. How different diet measures relate to frailty—a general measure of increased vulnerability to unfavorable health outcomes—and mortality risk, and how this might vary across the life course, is not known. We investigated the associations of five dietary indices (Nutrition Index (NI), the energy-density Dietary Inflammatory Index (E-DII™), Healthy Eating Index-2015 (HEI-2015), Mediterranean Diet Score (MDS), and Dietary Approaches to Stop Hypertension (DASH)) with frailty and mortality.Methods: We included 15,249 participants aged ≥ 20 years from the 2007–2012 cohorts of the National Health and Nutrition Examination Survey (NHANES).The NI combined 31 nutrition-related deficits. The E-DII is a literature-derived dietary index associated with inflammation. The HEI-2015 assesses adherence to the Dietary Guidelines of Americans. The MDS represents adherence to the traditional Mediterranean diet. DASH combines macronutrients and micronutrients to prevent hypertension. Frailty was evaluated using a 36-item frailty index. Mortality status was ascertained up to December 31, 2015.Results: Participants’ mean age was 47.2 ± 16.7 years and 51.7% were women. After adjusting for age, sex, race, educational level, marital and employment status, smoking, BMI, and study cohort, higher NI and E-DII scores and lower HEI-2015, MDS, and DASH scores were individually significantly associated with frailty. All dietary scores were significantly associated with 8-year mortality risk after adjusting for basic covariates and frailty: NI (hazard ratio per 0.1 point, 1.15, 95%CI 1.10–1.21), E-DII (per 1 point, 1.05, 1.01–1.08), HEI-2015 (per 10 points, 0.93, 0.89–0.97), MDS (per 1 point, 0.94, 0.90–0.97), and DASH (per 1 point, 0.96, 0.93–0.99). The associations of E-DII, HEI-2015, and MDS scores with 8-year mortality risk persisted after additionally adjusting for NI.Conclusions: NI, E-DII, HEI-2015, MDS, and DASH scores are associated with frailty and 8-year mortality risk in adults across all ages. Nevertheless, their mechanisms and sensitivity to predict health outcomes may differ. Nutrition scores have the potential to include measures of both consumption and laboratory and physical measures of exposure.