Effects of ten year body weight variability on cardiovascular risk factors in Japanese middle-aged men and women

Abstract

OBJECTIVE:The purpose of the present study was to determine the effects of weight variability on cardiovascular risk factors (CRF) based on a large sample of community-resident Japanese males and females. METHOD: A total of 3564 men and 1955 women, all Japanese, aged 30±69 y in the baseline year (1987), were followed-up for up to 10 y (end-point in 1996). Height, body weight, systolic and diastolic blood pressure, fasting serum total cholesterol, triglyceride and fasting plasma glucose were measured as an annual health check-up. At least six times in 10 y, body mass index (BMI) mean was calculated as an index of the BMI level of each subject (BMI mean). Direction and magnitude of the change in a subject's BMI was determined by a regression slope of BMI values over time (BMI slope). BMI¯uctuation was de®ned as the root mean square error (BMI RMSE) of a regression line. The slopes of the ®ve CRF were calculated using each regression equation over time. RESULTS: The BMI slope strongly correlated to each CRF slope independent of baseline age, baseline CRF value, smoking habit, BMI mean and BMI RMSE. BMI RMSE did not correlate to any CRF slopes. CONCLUSION: This study indicates that weight gain and weight loss have a much greater effect on CRF change than does weight¯uctuation in Japanese middle-aged men and women.

Five-year alterations in BMI are associated with clustering of changes in cardiovascular risk factors in a gender-dependant way: the Stanislas study

“…22 When analyzing individual parameters or clusters taking the confounders into account, our results are in accordance with worldwide previous studies showing associations of blood pressure and lipid profile changes over time with changes in BMI for both sexes. 4,6,8,23,24 In addition, similarly to previous investigations, changes in GGT activity and uric acid concentrations 15 were significantly associated with BMI gain in both genders and no association was found with changes in fasting blood glucose levels. 4,7 Conversely, BMI changes were associated with changes in ALP and in inflammatory markers (hs-CRP, haptoglobin, orosomucoid and summary cluster inflammation) in women only.…”

Body fat distribution of overweight females with a history of weight cycling

“…[18][19][20][21][22] However, recent studies discuss the weak adverse effects of weight cycling on cardiovascular risk factors. 23,24 Several reviews remark that these studies have some limitations and that information about the causes of the weight change is missing. 18,25 It has been suggested that the adverse effects of weight cycling could be due to a redistribution of body fat to the upper body compartments.…”

Weight fluctuations and risk for metabolic syndrome in an adult cohort

“…Consistent with other studies, we found no interaction between WF and weight change on the risk for MS or each MS component. 10,[31][32][33] There are a number of previous studies that have investigated the relationships between WF and each individual component of the MS. 10,[31][32][33][34][35][36][37] In our study, we found that WF had an increased risk for BP, HDL-c and WC. ORs for the fasting blood glucose and TG components followed the same trend and we assume that their nonsignificant results were related to insufficient statistical power (the number of cases was smaller than those for other components).…”

“…35 However, other studies did not support the hypothesis that WF have deleterious effect on these CVD risk factors. 10,[31][32][33]37 These contradictions could be attributed to the different methods used for estimating WF. There is no single definition of weight cycling which makes comparisons between studies difficult.…”

“…First, it has a longitudinal design with a 7-year follow-up, which allows to estimate incident MS risk. Second, whereas some previous papers used the RMSE method 10,23,33 for WF estimation, we used a continuous reproducible WF estimation. This original statistical method developed to analyze longitudinal weight data could be used for other settings.…”