The Prognostic Significance of Body Mass Index and Metabolic Parameter Variabilities in Predialysis CKD: A Nationwide Observational Cohort Study

Abstract

BackgroundThe association between variabilities in body mass index (BMI) or metabolic parameters and prognosis of patients with CKD has rarely been studied.MethodsIn this retrospective observational study on the basis of South Korea’s national health screening database, we identified individuals who received ≥3 health screenings, including those with persistent predialysis CKD (eGFR <60 ml/min per 1.73 m2 or dipstick albuminuria ≥1). The study exposure was variability in BMI or metabolic parameters until baseline assessment, calculated as the variation independent of the mean and stratified into quartiles (with Q4 the highest quartile and Q1 the lowest). We used Cox regression adjusted for various clinical characteristics to analyze risks of all-cause mortality and incident myocardial infarction, stroke, and KRT.ResultsThe study included 84,636 patients with predialysis CKD. Comparing Q4 versus Q1, higher BMI variability was significantly associated with higher risks of all-cause mortality (hazard ratio [HR], 1.66; 95% confidence interval [95% CI], 1.53 to 1.81), P [for trend] <0.001), KRT (HR, 1.20; 95% CI, 1.09 to 1.33; P<0.001), myocardial infarction (HR, 1.19; 95% CI, 1.05 to 1.36, P=0.003), and stroke (HR, 1.19; 95% CI, 1.07 to 1.33, P=0.01). The results were similar in the subgroups divided according to positive or negative trends in BMI during the exposure assessment period. Variabilities in certain metabolic syndrome components (e.g., fasting blood glucose) also were significantly associated with prognosis of patients with predialysis CKD. Those with a higher number of metabolic syndrome components with high variability had a worse prognosis.ConclusionsHigher variabilities in BMI and certain metabolic syndrome components are significantly associated with a worse prognosis in patients with predialysis CKD.

Association between visit-to-visit blood pressure variability and risks of dementia in CKD patients: a nationwide observational cohort study

“…In the current study, following our previous study [ 12 ], we first identified non-dialysis-dependent CKD patients based on estimated glomerular filtration rate (eGFR) and dipstick urine albumin results measured in consecutive health screenings. After determining the visit-to-visit BP variability of the study population, the association between BP variability and the risk of dementia, identified from the claims database, was investigated.…”

“…We first screened individuals who had received baseline health screenings from 2013 to 2014. As three or more health screenings were necessary to determine visit-to-visit BP variability, those who had two or more health screenings before the baseline visits were included [ 12 ]. After excluding those with missing information for any collected variable, the target CKD population included those with persistent eGFR <60 mL/min/1.73 m 2 or dipstick albuminuria ≥1+ during the exposure assessment period.…”

“…BP measurements were performed by each health screening center via automated or manual reading. We calculated variability independent of the mean (VIM), which was the main exposure in previous studies regarding variabilities in metabolic parameters [ 12 , 14 ]. VIM has the advantage of being independent of mean levels, which is distinct from the standard deviation or coefficient of variation.…”

“…Baseline laboratory data were collected from the health screening results, including baseline eGFR, presence of dipstick albuminuria (≥1+), fasting glucose, high-density lipoprotein and low-density lipoprotein. The variability in eGFR during the exposure assessment period was also collected as a covariate, as kidney function variability may determine BP variability itself [ 12 , 17 ]. The variability of eGFR was also determined as VIM by including three or more eGFR values for each individual.…”

“…The variability of eGFR was also determined as VIM by including three or more eGFR values for each individual. In addition, the number of examinations was identified as a variable that could reflect the health-seeking behavior of a subject and reflect measurement errors that may occur when health screening examinees attend different health exam centers [ 12 ].…”