| Background:Chronic kidney disease (CKD), caused by hypertension, diabetes, kinds of inflammation or metabolic disorders and characterized by chronic advancing loss of renal function, substantially elevates the risk of cardiovascular diseases, end-stage renal disease (ESRD) and other complications, and is now recognized as a worldwide public health problem. A better understanding of the modifiable risk factors of renal function decline (RFD) in apparently healthy population, leading to early detection and prevention, might alleviate the future burden of CKD and associated complications.Metabolic syndrome (Mets), characterized by a cluster of abdominal obesity, elevated blood pressure, dyslipidemia (elevated triglycerides and low high-density lipoprotein cholesterol), and elevated fasting glucose, is associated with an increased risk for diabetes, cardiovascular disease, as well as cardiovascular death and all-cause mortality. A central feature of Mets is insulin resistance (IR), although the pathogenesis remains unclear. Due to the economic development and consequential changes in lifestyle and diet, obesity and Mets have also become increasingly common in China.However, prospective studies in Chinese population investigating the association between Mets, IR, obesity and renal dysfunction were limited and inconsistent, particularly in healthy populations. Therefore, in the current study, we aimed to evaluate the effect of Mets, IR and body mass index (BMI) on the risk of renal dysfunction in a rural Chinese cohort with normal renal function (estimated glomerular filtration rate (eGFR) value of≥60mL/min/1.73m2), and to identify possible effect modifiers.Objectives:We first (Chapter1) aimed to examine the effect of Mets and IR on renal function decline in a Chinese cohort with normal renal function and then (Chapter2) examine the relationship of body mass index with the decline rate of renal function in this same cohort.Methods:Study participants were from an epidemiological study of Mets conducted during2003-2005in rural communities in Anqing, Anhui province of China. Detailed protocol of the study was described previously. In2011,6,301of the study subjects aged≥40years at baseline were invited for a follow-up visit, and2,901(46%) of them responded. The non-responders did not differ substantially with respect to baseline characteristics compared with the responders. This study was approved by the Institutional Review Boards from the Nanfang Hospital in Guangzhou and the Institute of Biomedicine in the Anhui Medical University. Written informed consent was obtained from each study participant.Baseline data including questionnaires on sociodemographic status, occupation, diet, lifestyle, health behavior, and medical history, blood pressure and anthropometric measurements, as well as blood tests was collected by trained research staff according to protocols described previously. Physical activity level was self-reported as mild, moderate, and heavy.Venous blood was drawn from the forearm of each participant in the fasting status. Serum and plasma were separated from blood cells in the field within30minutes and kept frozen at-20℃. Serum creatinine concentrations were determined using an enzymatic method (sarcosine oxidase-PAP). Fasting plasma glucose (FPG), total cholesterol (TC), triglyceride (TG), and high-density lipoprotein-cholesterol (HDL-C) were measured on the Hitachi7020Automatic Analyzer. Plasma insulin was measured by using an enhanced chemiluminescence method on an Elecsys2010system (Roche, Basel, Switzerland).GFR was estimated using the following equation according to the Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI)[16]: eGFR=141×min(Scr/κ,1)α×max(Scr/κ,1)-1.209×0.993Age×1.018[if female] where Scr is serum creatinine [mg/dL],κis0.7for females and0.9for males, α is-0.329for females and-0.411for males, min indicates the minimum of Scr/κor1, and max indicates the maximum of Scr/κor1.Chapter1:The Mets was defined according to the recent harmonized criterion [18] set by the NHLBI/AHA (National Heart, Lung and Blood Institute/American Heart Association) and IDF (International Diabetes Federation) as the presence of three or more of the following conditions:(1) abdominal obesity (WC≥90cm in men or≥80cm in women);(2) elevated TG (≥1.7mmol/L);(3) low HDL-C (<1.0mmol/L in men or<1.3mmol/L in women);(4) elevated blood pressure (SBP≥130mmHg or DBP≥85mmHg, or self-reported diagnosis of hypertension); and (5) elevated FPG (≥5.6mmol/L or self-reported diagnosis of diabetes).The insulin resistance index of homeostasis model assessment (HOMA-IR) was calculated from the fasting concentrations of insulin and glucose using the formula: fasting serum insulin (μU/mL)×fasting plasma glucose (mmol/L)/22.5. Within the cohort, we measured HOMA-IR in2,359non-diabetic subjects.The primary outcome of interest was RFD, which was defined by the Kidney Disease:Improving Global Outcome (KDIGO)2012as follows:a drop in GFR category (≥90[G1],60-89[G2],45-59[G3a],30-44[G3b],15-29[G4],<15[G5] ml/min/1.73m2) accompanied by a25%or greater drop in eGFR from baseline or a sustained decline in eGFR of more than5mL/min/1.73m2/year. The rate of eGFR decline was calculated as (eGFR at baseline-eGFR at revisit)/follow-up year. The secondary outcomes of interest were rapid eGFR decline, defined as a decline of eGFR of greater than3mL/min/1.73m2/year, and new incidence of CKD, defined as an eGFR at revisit below60mL/min/1.73m2.The effects of Mets status, individual Mets component, as well as the number of Mets components on the risks of RFD, rapid eGFR decline, and incident CKD were estimated using logistic regression models with adjustment for baseline covariates including age, sex, eGFR, smoking status, drinking status, total cholesterol, and physical activity level.Chapter2:Body mass index (BMI) was calculated as weight/height squared (kg/m2). BMI was categorized into underweight (<19kg/m2), normal weight (≥19kg/m2and<23kg/m2), overweight (≥23kg/m2and<27kg/m2), and obesity (>27kg/m2), based on the results in the Hong Kong population. Our primary outcome was annual rate of eGFR decline calculated as (eGFR at baseline-eGFR at revisit)/follow-up year. The secondary outcomes included rapid eGFR decline, and RFD.The effects of BMI (as a continuous variable and a categorical variable, respectively) on the annual rate of eGFR decline and the risks of rapid eGFR decline and RFD were estimated using linear or logistic regression models accordingly, with adjustment for baseline covariates including age, sex, eGFR, hypertension, diabetes, dyslipidemia (hypercholesterolemia, high TG and low HDL-C), cigarette smoking, alcohol drinking, and physical activity level. Regression analysis with a generalized additive model was used to examine the nonlinear association between BMI and annual rate of eGFR decline, as well as rapid eGFR decline and RFD.Two-tailed P<0.05was considered statistically significant in all analyses. R software, version2.15.1(http://www.R-project.org/) was used to perform all statistical analyses.Results:Of the2,901subjects revisited,66had one or more missing baseline values in age, cigarette smoking status, alcohol drinking status, physical activity level, or fasting glucose, and139had an eGFR value of<60mL/min/1.73m2. We excluded above subjects in our analyses, resulting in a final sample size of2,696(1,423men and1,273women). Of the2,696study subjects, mean (sd) and median annual rates of eGFR decline during the7-year follow-up (Inter-quartile Range:7.0-7.1) was1.7(1.9) and1.8mL/min/1.73m2/year, respectively,19.8%had rapid eGFR decline,9.0%had RFD and1.7%had incident CKD.Chapter1:Mets status was associated with an increased risk of RFD, rapid eGFR decline and CKD, with odds ratios [ORs](95%CI) of1.77(1.25to2.52),1.54(1.17to2.04), and1.59(0.75to3.35), respectively. There was also a graded increase in the risk of RFD and rapid eGFR decline with the number of Mets components, while the most significant stepwise jump in risk was observed between two and three Mets components. Compared with subjects with≤1Mets component (OR=1.00), those with≥4components had adjusted ORs (95%CI) of1.91(1.05to3.46) and1.90(1.18to3.05), respectively, for RFD and rapid eGFR decline (Table2). Furthermore, there were significant interactions of Mets status with age and cholesterol on the risk of RFD and rapid eGFR decline. The ORs of Mets status for RFD and rapid eGFR decline in the older group (≥55years) were2.14(1.06to4.33) and1.95(1.06to3.57) times, respectively, of that in the younger group (<55years). The corresponding ORs of Mets in hypercholesterolemia group (≥5.2mmol/L) were2.26(1.07to4.78) and2.04(1.11to3.77) times, respectively, of that in subjects without hypercholesterolemia. Among the five individual components of Mets, elevated blood pressure and abdominal obesity were the major determinants for RFD and rapid eGFR decline, with ORs ranging from1.26(0.96to1.67) to1.51(1.02to2.24). Exclusion of the subjects with hypertension at baseline may not substantially change the association between Mets and the risk of RFD (n=1,987;1.65;0.99to2.75). However, the relationship of Mets with the risk of rapid eGFR decline was attenuated and became insignificant (1.38;0.91to2.10). Furthermore, exclusion of the subjects with diabetes at baseline did not substantially change the association between Mets and RFD (n=2,605;1.86;1.30to2.67) or rapid eGFR decline (1.57;1.17to2.09). Log-transformed HOMA-IR itself was not associated with the risk of RFD, while its significant association with the risk of rapid eGFR decline also disappeared after adjustment for Mets. However, Mets was still significantly associated with the increased risk for RFD (1.72;1.15to2.57) or rapid eGFR decline (1.42;1.04to1.94) even after further adjustment for HOMA-IR.Chapter2:When analyzed as a continuous exposure,1kg/m2increase in BMI was associated with a more rapid annual rate of eGFR decline (β,0.05;95%CI:0.03to0.08mL/min/1.73m2/year), and increased risks of rapid eGFR decline and RFD with ORs of1.05(95%CI:1.01to1.09) and1.05(95%CI:0.99to1.09), respectively. Compared with subjects with normal weight, those with overweight had an increase of0.33(95%CI:0.18to0.47) mL/min/1.73m2/year in annual rate of eGFR decline as well as significantly increased risks of rapid eGFR decline (OR:1.52;95%CI:1.20to1.93) and RFD (OR:1.3995%CI:1.01to1.91), but not underweight or obese subjects. In regression analysis with a generalized additive model, S-shaped curves between BMI and annual rate of eGFR decline, as well as rapid eGFR loss and RFD were observed. In the19-27kg/m2range, BMI was positively linearly associated with annual eGFR decline (β2,0.09;95%CI:0.05to0.12), whereas in the<19(β1,-0.21;95%CI:-0.45to0.02; p2versus β1:P<0.01) or>27(p3,-0.24;95%CI:-0.48to0.00; β3versus β2:P<0.01) range, a negative but statistically insignificant trend of association was detected.Conclusions:1) Metabolic syndrome, but not insulin resistance was associated with an incre-ased risk of RFD among Chinese adults with normal renal function. And there was a detrimental interaction of Mets with older age and hypercholesterolemia on the risk of RFD.2) Our results demonstrated an S-shaped association between BMI and reducti-on rate of eGFR among Chinese adults with normal renal function. |
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