Abstract 4368073: Heart Failure Symptom Cluster Phenotypes and Their Influencing Factors Vary Across Population Groups: A Secondary Data Analysis from the <i>All of Us</i> Research Program

Introduction: Limited knowledge exists on physiological, psychological, and situational influencing factors of heart failure (HF) symptom clusters across different population groups. Hypotheses: HF symptom clusters differ in prevalence and composition across groups (Non-Hispanic White [NHW], Non-Hispanic Black [NHB], Hispanic/Latino [HL]); symptom cluster membership influencing factors vary by group. Methods: This secondary analysis used de-identified data from the All of Us Research Program. Participants ≥18 years with ≥3 of 8 HF symptoms (chest pain, edema, dizziness, dyspnea, nausea, fatigue, depression, anxiety) were included. Influencing factors were age, biological sex, body mass index (BMI), ability to perform physical activity, mood, education, health literacy, satisfaction with social relationships, marital and employment status, income, and health insurance. The study was guided by the Theory of Unpleasant Symptoms. Descriptive statistics summarized participant characteristics. Latent class analysis (LCA) identified distinct symptom-based subgroups. Model fit was assessed using Akaike and Bayesian Information Criteria. Symptom distributions were compared using chi-square tests. Standardized residuals (SR) explored group differences. Binary logistic regression identified IFs of class membership by group. The authors acknowledge the assistance of ChatGPT (OpenAI) in refining the language and remain solely responsible for the content. Results: The sample (N=1,540; mean age 56.6 years; 32.5% male) included 52% NHWs, 27.7% NHBs, and 16.1% HLs. Although the 5-class model showed slightly better fit indices, the 4-class solution was selected based on parsimony, interpretability, and theoretical coherence. Identified clusters were (Figure 1): Class 0- Edema-Psychological , Class 1- Nausea-Dominant , Class 2- Chest Pain-Psychological , and Class 3- Fatigue-Dominant . HLs were overrepresented in Class 2 (SR=2.08) and underrepresented in Class 3 (SR=–2.67); NHWs were overrepresented in Class 3 (SR=2.59). BMI was the only significant factor for Class 0 across all groups. For Class 1, age and BMI were significant among NHWs and NHBs. For Class 2, age and sex were significant among NHWs, and BMI among HLs. For Class 3, education was significant for NHWs and NHBs, and age for HLs. Conclusions: Findings reveal group-specific HF symptom clusters and influencing factors. Symptom management strategies should be tailored to underlying physiological and psychological factors.

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Journal
Circulation
Published
2025-11-03
DOI
https://doi.org/10.1161/circ.152.suppl_3.4368073
Primary Topic
Cardiovascular Health and Risk Factors
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article
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article

Abstract 4368073: Heart Failure Symptom Cluster Phenotypes and Their Influencing Factors Vary Across Population Groups: A Secondary Data Analysis from the All of Us Research Program

D. L. Newman, Yosun Yoon, Rita de Cássia Gengo e Silva Butcher, Judyta Kociolek
Circulation
Cardiovascular Health and Risk Factors
article

Abstract 4368073: Heart Failure Symptom Cluster Phenotypes and Their Influencing Factors Vary Across Population Groups: A Secondary Data Analysis from the All of Us Research Program

D. L. Newman, Yosun Yoon, Rita de Cássia Gengo e Silva Butcher, Judyta Kociolek
article en

Abstract

Introduction: Limited knowledge exists on physiological, psychological, and situational influencing factors of heart failure (HF) symptom clusters across different population groups. Hypotheses: HF symptom clusters differ in prevalence and composition across groups (Non-Hispanic White [NHW], Non-Hispanic Black [NHB], Hispanic/Latino [HL]); symptom cluster membership influencing factors vary by group. Methods: This secondary analysis used de-identified data from the All of Us Research Program. Participants ≥18 years with ≥3 of 8 HF symptoms (chest pain, edema, dizziness, dyspnea, nausea, fatigue, depression, anxiety) were included. Influencing factors were age, biological sex, body mass index (BMI), ability to perform physical activity, mood, education, health literacy, satisfaction with social relationships, marital and employment status, income, and health insurance. The study was guided by the Theory of Unpleasant Symptoms. Descriptive statistics summarized participant characteristics. Latent class analysis (LCA) identified distinct symptom-based subgroups. Model fit was assessed using Akaike and Bayesian Information Criteria. Symptom distributions were compared using chi-square tests. Standardized residuals (SR) explored group differences. Binary logistic regression identified IFs of class membership by group. The authors acknowledge the assistance of ChatGPT (OpenAI) in refining the language and remain solely responsible for the content. Results: The sample (N=1,540; mean age 56.6 years; 32.5% male) included 52% NHWs, 27.7% NHBs, and 16.1% HLs. Although the 5-class model showed slightly better fit indices, the 4-class solution was selected based on parsimony, interpretability, and theoretical coherence. Identified clusters were (Figure 1): Class 0- Edema-Psychological , Class 1- Nausea-Dominant , Class 2- Chest Pain-Psychological , and Class 3- Fatigue-Dominant . HLs were overrepresented in Class 2 (SR=2.08) and underrepresented in Class 3 (SR=–2.67); NHWs were overrepresented in Class 3 (SR=2.59). BMI was the only significant factor for Class 0 across all groups. For Class 1, age and BMI were significant among NHWs and NHBs. For Class 2, age and sex were significant among NHWs, and BMI among HLs. For Class 3, education was significant for NHWs and NHBs, and age for HLs. Conclusions: Findings reveal group-specific HF symptom clusters and influencing factors. Symptom management strategies should be tailored to underlying physiological and psychological factors.

CirculationVol. 152(Suppl_3)
Atlantic University (US), Atlantic Clinical Research Collaborative (United States) (US), Florida Atlantic University (US)
Openalex Percentile: Top 51%
Cardiovascular Health and Risk Factors
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