Long-Term Body Mass Index Variability, Weight Change Slope, and Risk of Cardiovascular Outcomes: 7-Year Prospective Study in Chinese Hypertensive Subjects

High Variability of Body Mass Index Is Independently Associated With Incident Heart Failure

BACKGROUND

Heart failure (HF) is a serious condition with increasing prevalence, high morbidity, and increased mortality. Obesity is an established risk factor for HF. Fluctuation in body mass index (BMI) has shown a higher risk of cardiovascular outcomes. We investigated the association between BMI variability and incident HF.

METHODS AND RESULTS

In the UK Biobank, we established a prospective cohort after excluding participants with prevalent HF or cancer at enrollment. A total of 99 368 White participants with ≥3 BMI measures during >2 years preceding enrollment were included, with a median follow-up of 12.5 years. The within-participant variability of BMI was evaluated using standardized SD and coefficient of variation. The association of BMI variability with incident HF was assessed using Fine and Gray's competing risk model, adjusting for confounding factors and participant-specific rate of BMI change. Higher BMI variability measured in both SD and coefficient of variation was significantly associated with higher risk in HF incidence (SD: hazard ratio [HR], 1.05 [95% CI, 1.03-1.08], P<0.0001; coefficient of variation: HR, 1.07 [95% CI, 1.04-1.10], P<0.0001).

CONCLUSIONS

Longitudinal health records capture BMI fluctuation, which independently predicts HF incidence.

High Variability of Body Mass Index Independently Associated with Incident Heart Failure

Background

Heart failure (HF) is a serious condition with increasing prevalence, high morbidity, and increased mortality. Obesity is an established risk factor for cardiovascular diseases, including HF. Fluctuation in body mass index (BMI) has shown a higher risk of cardiovascular outcomes. We investigated the association between BMI variability and incident HF.

Methods

In the UK Biobank, we established a prospective cohort after excluding participants with prevalent HF or cancer at enrollment. A total of 99,368 White (British, Irish, and any other white background) participants with ≥ 3 BMI measures during > 2 years preceding enrollment were included, with a median follow-up of 12.5 years. The within-participant variability of BMI was evaluated using standardized standard deviation (SD) and coefficient of variation (CV). The association of BMI variability with incident HF was assessed using Fine and Gray's competing risk model, and adjusted for age, sex, smoking history, alcohol consumption, diabetes, hypertension, history of heart attack, stroke, atrial fibrillation, lipids, estimated glomerular filtration rate and mean BMI per individual.

Results

In the fully adjusted model, higher BMI variability measured in both SD and CV were significantly associated with higher risk in HF incidence (SD: Hazard Ratio [HR] 1.05, 95% Confidence Interval [CI] 1.02 - 1.07, p = 0.0002; CV: HR 1.06, 95% CI 1.04 - 1.09, p < 0.0001).

Conclusions

Longitudinal health records capture BMI fluctuation, which independently predicts HF incidence. Integration of long-term BMI and other routinely measured health factors may improve risk prediction of HF and other cardiovascular outcomes.

Variability in Lipid Levels and Risk for Cardiovascular Disease: An Electronic Health Record-Based Population Cohort Study

Background Larger within-patient variability of lipid levels has been associated with increased risk of cardiovascular disease (CVD); however, measures of lipid variability require ≥3 measurements and are not currently used clinically. We investigated the feasibility of calculating lipid variability within a large electronic health record-based population cohort and assessed associations with incident CVD. Methods and Results We identified all individuals ≥40 years of age who resided in Olmsted County, MN, on January 1, 2006 (index date), without prior CVD, defined as myocardial infarction, coronary artery bypass graft surgery, percutaneous coronary intervention, or CVD death. Patients with ≥3 measurements of total cholesterol, low-density lipoprotein cholesterol, high-density lipoprotein cholesterol, or triglycerides during the 5 years before the index date were retained. Lipid variability was calculated using variability independent of the mean. Patients were followed through December 31, 2020 for incident CVD. We identified 19 652 individuals (mean age 61 years; 55% female), who were CVD-free and had variability independent of the mean calculated for at least 1 lipid type. After adjustment, those with highest total cholesterol variability had a 20% increased risk of CVD (Q5 versus Q1 hazard ratio, 1.20 [95% CI, 1.06-1.37]). Results were similar for low-density lipoprotein cholesterol and high-density lipoprotein cholesterol. Conclusions In a large electronic health record-based population cohort, high variability in total cholesterol, high-density lipoprotein cholesterol, and low-density lipoprotein cholesterol was associated with an increased risk of CVD, independent of traditional risk factors, suggesting it may be a possible risk marker and target for intervention. Lipid variability can be calculated in the electronic health record environment, but more research is needed to determine its clinical utility.

Weight fluctuation, mortality, and cardiovascular disease in adults in 18 years of follow-up: Tehran Lipid and Glucose Study

PURPOSE

Controversies exist in the effect of body weight loss and fluctuation on cardiovascular disease (CVD) and mortality. This study aims to assess the effect of weight variability on CVD and all-cause and cardiovascular mortality in the Tehran Lipid and Glucose Study (TLGS) cohort.

METHOD

Participants aged  ≥ 40 year at the baseline period with at least 3 BMI measurements were included in this study. After excluding individuals with cancer, CVD, end-stage renal disease, systemic use of glucocorticoids, pregnancy, and missing covariates at the baseline, a total of 3461 participants were enrolled and followed for 18 years. BMI variability was defined using root mean squared error (RMSE) and average successive variability (ASV). In the RMSE method, BMI variability was calculated using the best-fitting model for BMI trend of each subject. Multivariate Cox proportional hazard models were applied to assess BMI variability's effect on CVD and mortality.

RESULTS

Among the 3461 participants in this study, the group with the highest weight variability had an increased risk of death for all-cause (HR 1.65; 95% CI 1.21-2.25), non-cardiovascular (HR 1.77; 95% CI 1.24-2.53), and non-cancer (HR 1.77; 95% CI 1.25-2.50) mortality. However, BMI variability showed to be protective against CVD (HR 0.76; 95% CI 0.6-0.97). These findings were significant in males, non-smokers, participants with age  ≤ 60 year, BMI < 30, negative BMI slope, and both diabetic and non-diabetic subjects.

CONCLUSION

High BMI variability is associated with increased risk of all-cause, non-CVD, and non-cancer mortality, although protective for the CVD event. Appropriate strategies for body weight maintenance after weight loss could be adopted to avoid weight variability, particularly in non-obese subjects.

Weight Change over 4 Years and Risk of Cardiovascular Diseases in China: The China Health and Retirement Longitudinal Study

INTRODUCTION

Previous studies had reported the impact of weight changes in middle age on the incidence of cardiovascular disease (CVD), but the results were inconsistent. In present study, we aimed to investigate the impact of a 4-year weight change on the risk of CVD in middle-aged and elderly Chinese individuals.

METHODS

Using nationally representative data from the China Health and Retirement Longitudinal Study, 7,530 participants (age: 58.2 ± 8.9 years) were included. Weight change was calculated by subtracting weight at baseline from that at 4-year follow-up. Weight change over 4 years was divided into 5 categories (loss ≥5 kg; loss 2-5 kg; stable (change ≤2 kg); gain 2-5 kg; and gain ≥5 kg).

RESULTS

During the follow-up period, a total of 758 respondents experienced CVD (including 319 stroke and 477 cardiac events). The multivariable ORs of CVD for gain ≥5 kg compared to stable weight (change ≤2 kg) was 1.50 (95% CI, 1.14-1.97) versus 1.41(1.09-1.83) for losing ≥5 kg. Multivariable-adjusted logistic regression model with restricted cubic splines showed a U-shaped association between weight change and the risk of CVD (p for nonlinearity <0.001). The significant associations did not change in subgroup and sensitivity analysis. Weight change was also associated with higher risk of stroke and cardiac events.

CONCLUSION

Weight changes (weight gain or loss more than 5 kg) during middle age were associated with an increased risk of CVD in middle-aged and elderly Chinese individuals.