Metabolic Unhealthiness Research Articles

Obesity and Metabolic Unhealthiness Have Different Effects on Colorectal Neoplasms.

Obesity and insulin resistance are risk factors for colorectal neoplasms (CRN), but data regarding metabolic status, obesity, and CRN are lacking. To investigate the relationship between metabolic status, obesity, and CRN in Koreans who underwent colonoscopy. Retrospective, cross-sectional. Subjects were divided based on metabolic and obesity criteria, as follows: metabolically healthy nonobese (MHNO), metabolically healthy obese (MHO), metabolically unhealthy nonobese (MUNO), and metabolically unhealthy obese (MUO). Multiple regression was used to identify CRN and advanced CRN risk factors, with the MHNO group as reference. A total of 10,235 subjects was included, as follows: 5096 MHNO, 1538 MHO, 1746 MUNO, and 1855 MUO. Of these, 3297 had CRN (32.2%), and 434 (4.2%) had advanced CRN. Number of subjects with CRN in each group were: MHNO 25.8%, MHO 33.9%, MUNO 38.9%, and MUO 42.0% (P for trend < 0.001). Risk of CRN was increased in the MHO [odds ratio (OR) 1.239, 95% confidence interval (CI) 1.082 to 1.418, P = 0.002], the MUNO (OR 1.233, 95% CI 1.086 to 1.400, P = 0.001), and the MUO groups (OR 1.510, 95% CI 1.338 to 1.706, P < 0.001), whereas risk of advanced CRN was increased in the MUNO (OR 1.587, 95% CI 1.222 to 2.062, P = 0.001) and the MUO groups (OR 1.456, 95% CI 1.116 to 1.900, P = 0.006). Obesity increased CRN risk with metabolically unhealthy status adding risk. For advanced CRN, metabolically unhealthy status increased the risk but obesity did not.

Atrial fibrillation risk in metabolically healthy obesity: A nationwide population-based study

BackgroundMetabolically healthy obese (MHO) individuals are reported to have a marginal increase in cardiovascular risk; however, their atrial fibrillation (AF) risk is unclear. We aimed to assess AF risk in MHO individuals and identify whether AF development is associated with obesity or influenced by metabolic comorbidities. MethodsA retrospective cohort of 389,321 individuals (age, 45.6±14.5years; male, 52.1%) was extracted from the Korean National Health Insurance sample database between 2004 and 2006 and followed-up for new-onset AF until 2013. Subjects with diabetes mellitus, hypertension, and/or dyslipidemia were classified as “metabolically unhealthy.” The cohort was stratified into four groups according to obesity and metabolic healthiness: metabolically healthy non-obese (MHNO), metabolically unhealthy non-obese (MUNO), MHO, and metabolically unhealthy obese (MUO). ResultsAF was newly diagnosed in 5106 (1.3%) individuals during a mean follow-up of 7.5±1.5years. The AF incidence rates for the MHNO, MUNO, MHO, and MUO groups were 0.76, 2.66, 1.10, and 2.88 per 1000 person-years, respectively. Compared with the MHNO group, the MHO group had increased AF risk (adjusted hazard ratio, 1.30; 95% CI, 1.14–1.48) on multivariate analysis. One fourth of the MHO cohort became metabolically unhealthy, contributing to increased AF risk. Obesity was an independent risk factor for AF, and increased AF risk by 20%. Metabolic unhealthiness increased AF risk by around 40%, and of its components, hypertension contributed the most. ConclusionsMHO individuals are at increased risk for AF development, and obesity was independently associated with elevated AF risk.

Adipokine Profiles and Metabolic Health.

group compared with their metabolically healthy counterparts. However, the difference between MHO subjects and their metabolically unhealthy counterparts was not significant. The authors also showed that the levels of these adipokines were significantly correlated with several parameters that are usually used to define metabolic health [10]. Because insulin resistance and excess adiposity are considered core pathophysiologies of metabolic unhealthiness, it could be easily assumed that adipokines might have a role in the pathogenic mechanism or could be influenced by metabolic health status. Although prospective studies to define the cause-and-effect relationship are lacking, previous studies have also investigated the crosssectional relationship between various adipokines and metabolic health. Similar to the results of Lee et al. [10], individuals with the MUHNO phenotype were known to have higher circulating levels of leptin, TNF-α, and interleukin 6 (IL-6) and lower levels of adiponectin compared with the MHNW group [11-13]. Several reports also demonstrated lower levels of TNF-α, IL-6, plasminogen activator inhibitor-1, progranulin, retinol-binding protein-4, and chemerin and higher levels of adiponectin in individuals with the MHO phenotype compared with metabolically unhealthy obese subjects [13-15]. Because there is no consensus on how to define metabolic health status, the clinical characteristics, metabolic profiles and outcomes can be largely affected by the diagnostic criteria used [8,16]. Although some conflicting results for adipokine levels also exist, current data support a close relationship to

Association between metabolically unhealthy overweight/obesity and chronic kidney disease: The role of inflammation

AimOur study explored the association between subtypes of increased fat mass (with or without associated metabolic alterations) and the presence of chronic kidney disease (CKD). MethodsIn this cross-sectional survey in China, body mass index (BMI) was used to assess fat mass. Metabolically healthy was defined as no insulin resistance or any metabolic syndrome components except abdominal obesity. We also used two previous definitions of metabolically healthy. Multiple logistic regression models were used. Normal weight with metabolic health was designated the reference group. Three other subgroups included normal weight with metabolic unhealthiness, overweight/obesity with metabolic health and overweight/obesity with metabolic unhealthiness. ResultsOf the 2324 subjects, 11.77% overweight/obese subjects were metabolically healthy. Compared with normal-weight subjects who were metabolically healthy, overweight/obese subjects who were metabolically healthy did not have an increased risk of CKD (OR: 0.79, 95% CI: 0.29–2.14; P=0.64), whereas overweight/obese subjects who were metabolically unhealthy had a significantly higher risk of CKD (OR: 2.47, 95% CI: 1.5–3.95; P<0.001). Normal-weight subjects who were metabolically unhealthy also had a higher risk of CKD, but the P value was of borderline significance. On further adjusting for C-reactive protein (CRP) levels, ORs were much attenuated, but did not alter the associations observed. Using two other definitions of metabolically healthy resulted in similar results. ConclusionMetabolically unhealthy overweight/obesity, but not metabolically healthy overweight/obesity, is associated with an increased risk of CKD. Inflammation might mediate at least part of the association between metabolic changes and CKD prevalence.