Metabolic phenotypes of obese, overweight, and normal weight individuals and risk of chronic kidney disease: a systematic review and meta-analysis.

OBJECTIVE: Chronic kidney disease (CKD) risk is inconsistent in the normal-weight, overweight, and obese individuals due to the heterogeneity of metabolic status. This meta-analysis aimed to examine the combined effects of body mass index (BMI) and metabolic status on CKD risk. MATERIALS AND METHODS: The MEDLINE, EMBASE, and Web of Knowledge databases were systematically searched up to March 2019 to identify all eligible studies investigating the CKD risk (defined as GFR < 60 mL/min per 1.73 m2 and/or microalbuminuria or proteinuria) associated with the body size phenotypes which are known as metabolically unhealthy normal-weight (MUNW), metabolically healthy overweight (MHOW), metabolically unhealthy overweight, metabolically healthy obese (MHO) and metabolically unhealthy obese (MUHO). The classification of subjects in included studies as metabolically unhealthy was based on the presence of three components of metabolic syndrome. BMI categorization was based on the criteria of included studies. The risk estimates and 95% confidence intervals (CIs) were extracted and pooled using random effects analysis. RESULTS: A total of 9 prospective cohort studies with 128773 participants and 4797 incident cases were included in the meta-analysis. Compared with healthy normal-weight individuals as reference, MUNW and MHO subjects showed an increased risk for CKD events with a pooled RR of 1.58 (95% CI = 1.28-1.96) in MUNW and 1.55 (95% CI = 1.34-1.79) in MHO persons. Also, MHOW was at increased risk for CKD (RR = 1.34, 95% CI = 1.20-1.51). MUHO individuals were at the highest risk for the development of CKD (RR = 2.13, 95% CI = 1.66-2.72). CONCLUSIONS: Individuals with metabolic abnormality, although at normal-weight, have an increased risk for CKD. Healthy overweight and obese individuals had higher risk; refuting the notion that metabolically healthy overweight and obese phenotypes are benign conditions.

Metabolic phenotypes of obese, overweight, and normal weight individuals and risk of chronic kidney disease: a systematic review and meta-analysis

ABSTRACT Objective Chronic kidney disease (CKD) risk is inconsistent in the normal-weight, overweight, and obese individuals due to the heterogeneity of metabolic status. This meta-analysis aimed to examine the combined effects of body mass index (BMI) and metabolic status on CKD risk. Materials and methods The MEDLINE, EMBASE, and Web of Knowledge databases were systematically searched up to March 2019 to identify all eligible studies investigating the CKD risk (defined as GFR < 60 mL/min per 1.73 m2 and/or microalbuminuria or proteinuria) associated with the body size phenotypes which are known as metabolically unhealthy normal-weight (MUNW), metabolically healthy overweight (MHOW), metabolically unhealthy overweight, metabolically healthy obese (MHO) and metabolically unhealthy obese (MUHO). The classification of subjects in included studies as metabolically unhealthy was based on the presence of three components of metabolic syndrome. BMI categorization was based on the criteria of included studies. The risk estimates and 95% confidence intervals (CIs) were extracted and pooled using random effects analysis. Results A total of 9 prospective cohort studies with 128773 participants and 4797 incident cases were included in the meta-analysis. Compared with healthy normal-weight individuals as reference, MUNW and MHO subjects showed an increased risk for CKD events with a pooled RR of 1.58 (95% CI = 1.28-1.96) in MUNW and 1.55 (95% CI = 1.34-1.79) in MHO persons. Also, MHOW was at increased risk for CKD (RR = 1.34, 95% CI = 1.20-1.51). MUHO individuals were at the highest risk for the development of CKD (RR = 2.13, 95% CI = 1.66-2.72). Conclusions Individuals with metabolic abnormality, although at normal-weight, have an increased risk for CKD. Healthy overweight and obese individuals had higher risk; refuting the notion that metabolically healthy overweight and obese phenotypes are benign conditions.

BayMiR: inferring evidence for endogenous miRNA-induced gene repression from mRNA expression profiles.

BACKGROUND: Popular miRNA target prediction techniques use sequence features to determine the functional miRNA target sites. These techniques commonly ignore the cellular conditions in which miRNAs interact with their targets in vivo. Gene expression data are rich resources that can complement sequence features to take into account the context dependency of miRNAs. RESULTS: We introduce BayMiR, a new computational method, that predicts the functionality of potential miRNA target sites using the activity level of the miRNAs inferred from genome-wide mRNA expression profiles. We also found that mRNA expression variation can be used as another predictor of functional miRNA targets. We benchmarked BayMiR, the expression variation, Cometa, and the TargetScan "context scores" on two tasks: predicting independently validated miRNA targets and predicting the decrease in mRNA abundance in miRNA overexpression assays. BayMiR performed better than all other methods in both benchmarks and, surprisingly, the variation index performed better than Cometa and some individual determinants of the TargetScan context scores. Furthermore, BayMiR predicted miRNA target sets are more consistently annotated with GO and KEGG terms than similar sized random subsets of genes with conserved miRNA seed regions. BayMiR gives higher scores to target sites residing near the poly(A) tail which strongly favors mRNA degradation using poly(A) shortening. Our work also suggests that modeling multiplicative interactions among miRNAs is important to predict endogenous mRNA targets. CONCLUSIONS: We develop a new computational method for predicting the target mRNAs of miRNAs. BayMiR applies a large number of mRNA expression profiles and successfully identifies the mRNA targets and miRNA activities without using miRNA expression data. The BayMiR package is publicly available and can be readily applied to any mRNA expression data sets.