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.

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.

Insulin resistance is one of the most important contributing factors to cardiovascular disease. This study aimed to investigate the association between coronary artery stenosis (CAS) and triglyceride glucose index (TyG index), a simple insulin resistance marker, in asymptomatic subjects with type 2 diabetes. We recruited asymptomatic adults with type 2 diabetes but without previous history of coronary heart disease (n = 888). Significant CAS was defined as maximum intraluminal stenosis ≥70 % by coronary CT angiography. TyG index was calculated as log [fasting triglycerides (mg/dl) x fasting glucose (mg/dl)/2]. Mean age was 63.8 ± 9.5 and 58.9 % of the subjects were men. We analyzed the participants according to the tertile of TyG index. The TyG index was correlated with HOMA-IR (r = 0.397, P < 0.001), and subjects with higher tertile of TyG index were younger but showed worse clinical and metabolic parameters. The prevalence of CAS was higher in subjects with higher tertile of TyG compared with those with lower tertile of TyG (14 % vs. 7.8 %, P = 0.022). On multiple regression analysis, the highest tertile of TyG index was an independent risk factor for CAS after adjustment for other confounders (odds ratio, 3.19 [95 % CI, 1.371–7.424]). Subgroup analysis showed that TyG index showed more significant association with CAS in patients with risk factors such as old age, longer duration of diabetes, poor glycemic control, no statin use, and male gender. Higher TyG index is associated with increased risk of CAS in asymptomatic subjects with type 2 diabetes, particularly when they have risk factors for cardiovascular disease. This study was retrospectively registered in ClinicalTrials. gov with the registration number of NCT02070926 in Feb 23, 2014.

Recently, medical research using big data has become very popular, and its value has become increasingly recognized. The Korean National Health Information Database (NHID) is representative of big data that combines information obtained from the National Health Insurance Service collected for claims and reimbursement of health care services and results obtained from general health examinations provided to all Korean adults. This database has several strengths and limitations. Given the large size, various laboratory data, and questionnaires obtained from medical check-ups, their longitudinal nature, and long-term accumulation of data since 2002, carefully designed studies may provide valuable information that is difficult to obtain from other forms of research. However, consideration of possible bias and careful interpretation when defining causal relationships is also important because the data were not collected for research purposes. After the NHID became publicly available, research and publications based on this database have increased explosively, especially in the field of diabetes and metabolism. This article reviews the history, structure, and characteristics of the Korean NHID. Recent trends in big data research using this database, commonly used operational diagnosis, and representative studies have been introduced. We expect further progress and expansion of big data research using the Korean NHID.

Insulin, a pleotrophic hormone, has diverse effects in the body. Recent work has highlighted the important role of insulin's action in the nervous system on glucose and energy homeostasis, memory, and mood. Here we review experimental and clinical work that has broadened the understanding of insulin's diverse functions in the central and peripheral nervous systems, including glucose and body weight homeostasis, memory and mood, with particular emphasis on intranasal insulin. Implications for the treatment of obesity, type 2 diabetes, dementia, and mood disorders are discussed in the context of brain insulin action. Intranasal insulin may have potential in the treatment of central nervous system-related metabolic disorders.