Background
Studies have demonstrated that excessive adipose tissue accumulation increases the risk of incident HF [1-3]. Established indices of adiposity include BMI, waist circumference (WC), and waist-hip ratio (WHR). In the last years, novel indices of adiposity have been introduced that are also easy to measure and calculate, but better reflect the body fat distribution and total fat mass than the traditional indices. Novel indices of adiposity include body shape index (BSI) [4], body roundness index (BRI) [5], weight-adjusted-weight index (WWI) [6] and relative fat mass (RFM) [7].
Aim of the study
This study investigated the association between multiple indices of adiposity (BMI, WC, WHR. BSI, BRI, WWI and RFM) and the risk of incident HF in a community-based cohort.
Methods
This study included 8295 individuals from the PREVEND observational cohort [8-10]. PREVEND enrolled adults from the general population of the city of Groningen, The Netherlands.
Mean age was 49.8±12.6 years, 49.8% were women, 41% were overweight and 16% were obese. Body weight, hight, WC and hip circumference were measured at baseline. These measures were used to calculate the different indices of adiposity.
Individuals were followed for the first occurrence of HF or death within 13.5 years of baseline. Associations of adiposity indices with incident HF were analyzed. Reported hazard ratios (HR) represent HR per standard deviation (SD) change in adiposity index and are adjusted for age, sex, smoking, cholesterol, SBP, glucose, and history of MI, stroke and AF.
Association between adiposity indices and incident HF
Association between adiposity indices and incident HFpEF and HFrEF
Improvement in model fit
This study assessed the association between multiple novel and established indices of adiposity and the risk of incident HF in community-dwelling individuals. Results showed that RFM was most strongly associated with incident HF risk.
The authors of the article wrote ‘RFM could potentially be used in routine clinical practice or public health surveillance programmes—even in resource poor settings. This is because RFM not only correlates strongly with HF risk, but can also be calculated using a relatively simple formula*, requiring only height and waist circumference—both of which could be determined using a measuring tape.’ They also noted that the value of including RFM in HF risk prediction models should be subject of future studies.
* [64 - (20 × Height/WC) + (12 × sex), with sex = 0 in men, and sex = 1 in women] [7]
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8. Brouwers, F. P. et al. Incidence and epidemiology of new onset heart failure with preserved vs. reduced ejection fraction in a community-based cohort: 11-year follow-up of PREVEND. Eur. Heart J. 34, 1424–1431 (2013).
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10. Suthahar, N. et al. High-sensitivity troponin-T and cardiovascular outcomes in the community: Differences between women and men. Mayo Clin. Proc. 95, 1158–1168 (2020).
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