Association of Lean Body Mass and Fat Mass With 1-Year Mortality Among Patients With Heart Failure

Ge, Yilan; Liu, Shiyuan; Zhang, Lihua; Gao, Yan; Wang, Bin; Wang, Xiuling; Li, Jing; Zheng, Xin

doi:10.3389/fcvm.2022.824628

Cited by 4 publications

(5 citation statements)

References 42 publications

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“…Although VO 2 max did not predict CC and physical activity was not measured in this sample, the same postulation may explain the higher incidence of obesity among our cachectic patients who had lower baseline LVEF than the non-cachectic patients. Similar to depression (Carels, 2004; Qiu et al, 2021; Rumsfeld et al, 2003; Sullivan et al, 2004; Vaccarino et al, 2001), decreased LVEF is associated with lower functional capacity (Davos et al, 2003) and exercise levels due to dyspnea and marked fatiguability (Anker, Swan, et al, 1997) Thus, obesity in HF may not necessarily indicate the absence of cachexia and muscle wasting (Ge et al, 2022; Selthofer-Relatić et al, 2019) and might be explained by decreased physical activity due to the decreased LVEF and advanced HF.…”

Section: Discussionmentioning

confidence: 99%

Depression Predicts Cardiac Cachexia in Heart Failure Patients

Moughrabi

Habib

Evangelista

2023

Biological Research For Nursing

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Background Cardiac cachexia (CC) is associated with increased morbidity and mortality in persons with heart failure (HF). Compared to the biological underpinning of CC, little is known about the psychological factors. Thus, the overarching objective of this study was to determine whether depression predicts the onset of cachexia at 6 months in patients with chronic HF. Methods 114 participants with a mean age of 56.7 ± 13.0 years, LVEF of 33.13 ± 12.30% and NYHA class III (48.0%) were assessed for depression using the PHQ-9. Body weight was measured at baseline and at 6 months. Patients who had ≥6% non-edematous unintentional weight loss were classified as cachectic. Univariate and logistic multivariate regression were used to examine the relationship between CC and depression, controlling for clinical and demographic variables. Results Cachectic patients (11.4%) had significantly higher baseline BMI levels (31.35 ± 5.70 vs. 28.31 ± 4.73; p = .038), lower LVEF (mean = 24.50 ± 9.48 vs. 34.22 ± 12.18, p = .009), and depression scores (mean = 7.17 ± 6.44 vs. 4.27 ± 3.98, p = .049) when compared to their non-cachectic counterparts. In multivariate regression analysis, depression scores ( β = 1.193, p = .035) and LVEF ( β = .835, p = .031) predicted cachexia after controlling for age, gender, body mass index, VO2 max, and New York Heart Association class and accounted for 49% of the variance in Cardiac cachexia. When depression was dichotomized, depression and LVEF predicted 52.6% of the variance in CC. Conclusion Depression predicts CC in patients with HF. Additional studies are needed to expand the knowledge of the role of the psychological determinants of this devastating syndrome.

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Section: Discussionmentioning

confidence: 99%

Depression Predicts Cardiac Cachexia in Heart Failure Patients

Moughrabi

Habib

Evangelista

2023

Biological Research For Nursing

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“…Although BMI is well correlated with body fatness [ 18 ], it does not distinguish fat from fat-free mass and can overestimate adiposity in muscular subjects. BMI has been well validated with body fat percentage as assessed by dual X-ray absorptiometry and can be clinically useful [ 19 ], but lean body mass or lean body mass index might be a better indicator to predict future mortality [ 19 , 20 ]. However, data regarding lean body mass were not collected in this study.…”

Section: Discussionmentioning

confidence: 99%

Body mass index at baseline directly predicts new-onset diabetes and to a lesser extent incident cardio-cerebrovascular events, but has a J-shaped relationship to all-cause mortality

2022

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Objective The prevalence of diabetes mellitus (DM), cardio-cerebrovascular diseases (CCVDs) has increased during recent decades. We aimed to investigate the relationship between body mass index (BMI) and each of several outcomes (DM, CCVDs, or mortality) based on the Korean National Health Insurance Service-Health Screening cohort. Methods BMI was categorized as appropriate for Asian populations, into underweight (< 18.5 kg/m2), normal (18.5–< 23 kg/m2), overweight (23–< 25 kg/m2), grade 1 obesity (25–< 30 kg/m2), grade 2 obesity (30–< 35 kg/m2), and grade 3 obesity (≥35 kg/m2). In addition, BMI was further stratified into one unit. Multivariate Cox proportional hazards regression analyses were conducted to examine the association between BMI category and the primary outcomes (DM, CCVDs, or mortality). Results A total of 311,416 individuals were included. The median follow-up was 12.5 years. Compared to normal BMI, underweight, overweight, and grade 1–3 obese individuals had a higher risk of the primary outcomes (hazard ratio [95% confidence intervals] 1.293 [1.224–1.365], 1.101 [1.073–1.129], 1.320 [1.288–1.353], 1.789 [1.689–1.897], and 2.376 [2.019–2.857], respectively, in men and 1.084 [1.010–1.163], 1.150 [1.116–1.185], 1.385 [1.346–1.425], 1.865 [1.725–2.019], and 2.472 [2.025–3.028], respectively, in women). Setting the reference BMI to 20–< 21 kg/m2 and categorizing into one unit increment, BMI was associated with the primary outcomes in a J-shaped manner in both sexes. The risk of DM increased with higher BMI in both sexes, while all-cause mortality decreased in men with a BMI 21–< 31 kg/m2 and women with BMI 22–< 30 kg/m2. Conclusions BMI was associated with all-cause mortality in a J-shaped manner in both sexes, while it was associated with risk of DM in a dose-response relationship. The relationship between BMI and the primary outcomes was J-shaped.

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“…Of course, this study has some limitations: First, body composition indicators, predicted FM and LBM, were calculated by anthropometric prediction equations rather than the gold standard method DXA measurements; furthermore, although Lee et al's anthropometric prediction equations take into account the effect of race and have been used to calculate body composition in several published studies in Asian populations (42)(43)(44), the high predictive power of the prediction equations has not been directly confirmed in Asian populations at this time and needs to be validated in future studies. Second, the diagnosis of NAFLD was based on abdominal ultrasound images rather than the liver biopsy (19), however, it is unethical to perform an invasive test on the general population attending a health check-up.…”

Section: Study Strengths and Limitationsmentioning

confidence: 99%

The role of predicted lean body mass and fat mass in non-alcoholic fatty liver disease in both sexes: Results from a secondary analysis of the NAGALA study

Kuang

Yang²,

Xie³

et al. 2023

Front. Nutr.

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ObjectiveHigh body mass index (BMI) is an important risk factor for non-alcoholic fatty liver disease (NAFLD). However, the association of body composition such as fat mass (FM) and lean body mass (LBM) with NAFLD has not been adequately studied. The purpose of this study was to clarify the contribution of body composition FM and LBM to NAFLD.MethodsWe analyzed data from 7,411 men and 6,840 women in the NAGALA cohort study. LBM and FM were estimated for all subjects using validated anthropometric prediction equations previously developed from the National Health and Nutrition Examination Survey (NHANES). Using multiple logistic regression and restricted cubic spline (RCS) to analyze the association and the dose-response curve of predicted LBM and FM with NAFLD in both sexes.ResultsThe prevalence of NAFLD in man and woman subjects was 27.37 and 6.99%, respectively. Predicted FM was positively and linearly associated with NAFLD in both sexes, with each 1 kg increase in predicted FM associated with a 27 and 40% increased risk of NAFLD in men and women, respectively. In contrast, predicted LBM was negatively associated with NAFLD in both sexes, with each 1 kg increase in predicted LBM reducing the risk of NAFLD by 4 and 19% in men and women, respectively. In addition, according to the RCS curve, the risk of NAFLD did not change in men when the predicted LBM was between 47 and 52 kg, and there seemed to be a saturation effect; further, the threshold value of the saturation effect was calculated to be about 52.08 kg by two-piecewise logistic regression, and the protective effect on NAFLD would be significantly enhanced when the man predicted LBM was greater than 52.08 kg.ConclusionThe current findings suggested that body composition LBM and FM had opposite associations with NAFLD in both sexes, with higher LBM associated with a lower risk of NAFLD and higher FM increasing the risk of NAFLD, especially in women.

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Association of Lean Body Mass and Fat Mass With 1-Year Mortality Among Patients With Heart Failure

Cited by 4 publications

References 42 publications

Depression Predicts Cardiac Cachexia in Heart Failure Patients

Depression Predicts Cardiac Cachexia in Heart Failure Patients

Body mass index at baseline directly predicts new-onset diabetes and to a lesser extent incident cardio-cerebrovascular events, but has a J-shaped relationship to all-cause mortality

The role of predicted lean body mass and fat mass in non-alcoholic fatty liver disease in both sexes: Results from a secondary analysis of the NAGALA study

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