Association between Controlling Nutritional Status and Allostatic Load with Heart Failure across Different Depressive States: A Cross-Sectional Study Using NHANES Data
Description
Both the controlling nutritional status (CONUT) and allostatic load (AL) have been established as significant correlations of heart failure (HF). Given that depressive status associated with metabolic dysregulation may modulate these associations, the study aims to investigate the relationships between CONUT, AL, and HF across different depressive states. This study analyzed data from 4632 participants in the National Health and Nutrition Examination Survey (NHANES) 2005–2018. The predictive performance of CONUT and AL, two clinical indices for HF stratified by depressive status, was assessed across multiple models. In Model 1/2, both CONUT and AL positively correlated with HF (model1: CONUT: OR [95% CI], 1.434 [1.258–1.634], p < 0.001, AL: 1.226 [1.140–1.318], p < 0.001; model2: CONUT: OR [95% CI], 1.286 [1.119–1.478], p < 0.001, AL: 1.141 [1.048–1.241], p=0.002). Depressive status moderated the association between CONUT and HF (p for interaction = 0.035). The result revealed that AL was significantly associated with HF in the depressive subgroup (AUC, 0.6048 [95% CI, 0.5162–0.6934]). Model 4 showed no significant difference between CONUT, AL, and HF across different depressive states (depression: NRI: –0.0982 [95% CI, –0.1267-0.0794], p=0.0585, IDI: –0.0025 [95% CI, –0.0105-0.0054], p=0.5308; non-depression: NRI: 0.0625 [95% CI, –0.1713-0.2601], p=0.5610, IDI: –0.0030 [95% CI, –0.0217-0.0158], p=0.7569). CONUT and AL demonstrated positive associations with HF, and the associations could be attenuated by depressive status. It’s necessary to integrate nutritional-metabolic, socio-environmental, and psychiatric factors to optimize the predictive accuracy of HF.
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Data from the National Health and Nutrition Examination Survey (NHANES) 2005–2018 were analyzed, encompassing demographic, physical examination, laboratory testing, and questionnaire-based information. Specifically, demographic variables included age, sex, race, income, and educational level; cardiovascular-related parameters comprised hypertension, diabetes, coronary heart disease, heart attack, anemia, and stroke; and CONUT/AL related components were also collected. To ensure adequate sample size, data from adults aged ≥20 years across the 14-year study period were included. For non-dichotomous variables, only missing data were excluded, whereas for dichotomous variables, cases with unclear diagnoses or missing responses were additionally excluded. Following the weighting procedure, samples that underwent no weighting adjustment were excluded from the analysis. Ultimately, 4632 participants were included in the final analysis. In the NHANES database, HF data were derived from personal interviews conducted during the questionnaire component, specifically based on response to question MCQ160b: " Ever told had congestive heart failure?" A positive response ("Yes") was coded as a confirmed HF diagnosis. CONUT score comprised three components: serum albumin, total lymphocyte count, and total cholesterol. AL score was derived from thresholds of total cholesterol (mg/dl), high-density lipoprotein (HDL) (mg/dl), low-density lipoprotein (LDL) (mg/dl), triglycerides (mg/dl), systolic blood pressure (mmHg), diastolic blood pressure (mmHg), waist circumference (cm), body mass index (BMI) (kg/m2), fasting blood glucose (mg/dl), insulin (uU/dl), and serum creatinine (mg/dl). Thresholds were defined as the first quartile (Q1) or third quartile (Q3) for each parameter: HDL levels below Q1 were scored as 1, while other parameters exceeding Q3 were scored as 1. The sum of individual scores was categorized as low AL (sum <3) or high AL (sum ≥3). Depressive status was assessed using the Patient Health Questionnaire-9 (PHQ-9), and its symptoms rated on a 4-point Likert scale ranging from 0 ("not at all") to 3 ("nearly every day"). A total score ≥10 indicated a diagnosis of depression.Potentially relevant covariates including age, sex, race , education level, poverty income ratio, alcohol use history, and smoking history, as well as medical history of hypertension, coronary heart disease, diabetes, anemia, heart attack, and stroke. This study utilized R Studio (version 4.5.0) and SPSS (version 27.0) for statistical analysis, incorporating the complex survey design and weighting procedures of NHANES data. Binary logistic regression was employed to analyze the associations between the two indices and HF. Subgroup analyses were conducted to explore potential effect modifiers. RCS was employed to assess potential linear or nonlinear relationships between the indices and heart failure. Model performance was evaluated through ROC curves, AUC, NRI, and IDI.
Institutions
- Capital Medical University Affiliated Anzhen Hospital
- Jianghan University