Identification of individuals with non-alcoholic fatty liver disease by the diagnostic criteria for the metabolic syndrome

AIM: To clarify the efficiency of the criterion of metabolic syndrome to detecting non-alcoholic fatty liver disease (NAFLD)

Correlation between renal function and common risk factors for chronic kidney disease in a healthy middle-aged population: a prospective observational 2-year study.

Age, proteinuria, metabolic syndrome, and hyperuricemia are the reported risk factors for chronic kidney disease (CKD) and cardiovascular disease (CVD). However, the best predictor of changes in renal function in the early stages of renal disease in a healthy middle-aged population is still unknown. Our study evaluated the correlation between changes in renal function and common risk factors to determine such a predictor.In total, 2,853 healthy persons aged ≤50 years participated in the study. They had no proteinuria and were not on medications for hypertension, diabetes mellitus, hyperlipidemia, or hyperuricemia. Over 2 years, participants underwent annual health screening. The relationship between changes in estimated glomerular filtration rate (eGFR) and changes in risk factors for CKD was evaluated using univariate and multivariate linear regression analyses.Over 2 years, eGFR showed a significant decrease. Univariate regression analysis revealed that changes in fasting plasma glucose (FPG), total cholesterol, LDL-cholesterol, serum uric acid levels, and hemoglobin showed a significant negative correlation with changes in eGFR. Multiple regression analysis confirmed that changes in FPG, serum uric acid levels, in particular, and hemoglobin had a significant negative correlation with changes in eGFR.The changes in eGFR and other variables over 2 years were small and could be within expected biologic variation. A longer observational study is needed to elucidate whether FPG, serum uric acid and hemoglobin represent the earliest markers of eGFR decline

Multiple regression models for factors related to ΔeGFR.

<p>Model 1: all variables, Model 2: all variables except total cholesterol, Model 3: significant variables in univariate analysis, Model 4: significant variables in univariate analysis except total cholesterol.</p><p>Abbreviations and symbols: eGFR, estimated glomerular filtration rate; Δ, change in the variable over 2 years; β, standardized regression coefficient; BMI, body mass index; BP, blood pressure; FPG, fasting plasma glucose; HbA_1c, hemoglobin A_1c; HDL, high density lipoprotein; LDL, low density lipoprotein; Hb, hemoglobin.</p><p>Multiple regression models for factors related to ΔeGFR.</p

Prevalence of hyperuricemia (male, n = 1,515; female, n = 1,257).

<p>Hyperuricemia: serum uric acid ≥7 mg/dL in males and ≥6 mg/dL in females.</p><p>Prevalence of hyperuricemia (male, n = 1,515; female, n = 1,257).</p

Disposition of the study population.

<p>Disposition of the study population.</p