A synergistic impact of body mass index and gamma gap on heart failure and mortality rate among older patients with coronary artery disease: a prospective study with 10-year follow-up

The study addresses whether and how BMI and gamma gap jointly influence heart failure and all-cause mortality among older patients with coronary artery disease (CAD). While high BMI is traditionally considered a cardiovascular risk factor, reports of an obesity paradox suggest lower mortality among patients with obesity in some CAD contexts. Gamma gap (total protein minus albumin), reflecting inflammation/immune activity, has been linked to all-cause mortality, but its effect across BMI strata in CAD is unclear. This prospective study with 10-year follow-up evaluates the individual and combined associations of BMI and gamma gap with heart failure and mortality in older CAD patients, aiming to clarify potential synergistic effects and inform risk stratification.

Prior work has shown mixed associations between BMI and outcomes in cardiovascular disease, including reports of an obesity paradox with lower mortality among higher-BMI patients in certain CAD and heart failure cohorts. Elevated gamma gap has been associated with increased all-cause mortality in general and very elderly populations and is considered a marker of systemic inflammation and immune dysregulation. However, data on gamma gap in CAD, particularly its interaction with BMI, have been lacking. This study builds on these findings by jointly examining BMI and gamma gap in an elderly CAD cohort.

Design and setting: Prospective cohort study conducted in the Department of Geriatric Cardiology, Chinese People's Liberation Army (PLA) General Hospital, with approximately 10 years of follow-up. Participants: 987 consecutive patients aged ≥60 years with a clinical diagnosis of CAD based on history, angina symptoms, cardiac biomarkers, and diagnostic testing (ECG, echocardiography, nuclear imaging, CT, and/or coronary angiography) per ACC/AHA/ESC guidelines. Exclusion criteria included severe aortic stenosis, anticipated heart transplantation, or ventricular assist device. Ethics: Approved by the Ethics Committee of Chinese PLA General Hospital; informed consent obtained; conducted in accordance with the Helsinki Declaration. Baseline data: Demographics (age, sex), physical measurements (height, weight, heart rate, systolic and diastolic blood pressure), and laboratory measures (hemoglobin, albumin, total cholesterol, HDL-C, LDL-C, fasting plasma glucose, creatinine, C-reactive protein, NT-proBNP, total protein). BMI was calculated as kg/m². Gamma gap was defined as total serum protein minus albumin (g/L). Exposure categorization: BMI categorized as high (≥25 kg/m²) vs low (<25 kg/m²). Gamma gap categorized as high (≥30 g/L) vs low (<30 g/L). Patients were grouped into four categories: high BMI/low gamma gap, low BMI/low gamma gap, high BMI/high gamma gap, and low BMI/high gamma gap. Data management: Trained physicians entered data into a database; separate physicians performed logical checks for accuracy. Follow-up and endpoints: Follow-up obtained via hospital records and telephone interviews; mortality verified by legal documentation of date and place of death. The primary endpoint was all-cause mortality. Heart failure occurrence was also analyzed as an outcome. The average follow-up duration was 1836 days (median 1871 days; IQR 384–3225 days), with no loss to follow-up reported. Statistical analysis: Continuous variables with skewed distributions summarized as median (IQR); categorical variables as counts (percentages). Group comparisons used Kruskal–Wallis (continuous) and chi-square tests (categorical). Kaplan–Meier curves compared survival across BMI/gamma gap groups (log-rank test). Multivariate logistic regression assessed associations with heart failure. Multivariate Cox proportional hazards regression assessed associations with mortality. Models were built unadjusted (Model 1), adjusted for age and sex (Model 2), and fully adjusted (Model 3: age, sex, heart rate, diastolic blood pressure, hemoglobin, albumin, HDL-C, creatinine, fasting plasma glucose, C-reactive protein, and NT-proBNP). A two-sided P<0.05 was considered statistically significant; analyses conducted with IBM SPSS 22.0.

- Cohort: 987 older CAD patients; median age 86 years (IQR 82–90). High BMI: 41.9% (414/987). High gamma gap: 31.8% (314/987). - Crude outcomes by group: Low BMI/high gamma gap had the highest heart failure (46.2%) and mortality (84.4%). High BMI/low gamma gap had the lowest heart failure (18.9%) and mortality (62.9%). - Correlations and crude comparisons: High BMI negatively correlated with heart failure (r = -0.13, P<0.001) and mortality (r = -0.07, P<0.05). High gamma gap positively correlated with heart failure (r = 0.16, P<0.001) and mortality (r = 0.17, P<0.001). Heart failure was higher in low vs high BMI (34.9% vs 22.9%, P<0.001) and in high vs low gamma gap (40.4% vs 25.0%, P<0.001). Mortality was higher in low vs high BMI (75.2% vs 69.1%, P<0.05) and in high vs low gamma gap (83.8% vs 67.5%, P<0.001). - Multivariable associations (individual exposures): • Heart failure (logistic): High vs low BMI HR 0.56 (95% CI 0.42–0.74, P<0.001); High vs low gamma gap HR 2.04 (95% CI 1.54–2.72, P<0.001). • Mortality (Cox): High vs low BMI HR 0.80 (95% CI 0.69–0.93, P<0.05); High vs low gamma gap HR 1.81 (95% CI 1.55–2.10, P<0.001). - Multivariable associations (four-group comparison; reference: high BMI/low gamma gap): • Heart failure (Model 3 fully adjusted): Low BMI/low gamma gap HR 1.62 (95% CI 1.08–2.42, P=0.019); High BMI/high gamma gap HR 1.58 (95% CI 0.94–2.65, P=0.082); Low BMI/high gamma gap HR 2.82 (95% CI 1.79–4.48, P<0.001). • Mortality (Model 3 fully adjusted): Low BMI/low gamma gap HR 1.19 (95% CI 0.98–1.43, P=0.075); High BMI/high gamma gap HR 1.51 (95% CI 1.19–1.93, P=0.001); Low BMI/high gamma gap HR 1.65 (95% CI 1.32–2.07, P<0.001). - Survival analysis: Kaplan–Meier survival significantly differed among groups (log-rank P<0.001), with the worst survival in the low BMI/high gamma gap group and the best in the high BMI/low gamma gap group.

The study demonstrates that in older CAD patients, lower BMI and elevated gamma gap are independently associated with higher risks of heart failure and all-cause mortality, and that the combination of low BMI and high gamma gap confers the greatest risk. These findings address the research question by showing a synergistic effect: patients with high BMI and low gamma gap had the most favorable outcomes, whereas those with low BMI and high gamma gap had the worst. This aligns with observations of the obesity paradox in cardiovascular disease, suggesting that higher BMI may be protective in specific contexts, potentially via better nutritional reserves and metabolic profiles. Elevated gamma gap likely reflects systemic inflammation and immune dysregulation and/or lower albumin, factors implicated in atherosclerosis progression and adverse cardiac remodeling. The synergy may reflect a state of chronic malnutrition and inflammation—in which low BMI denotes poorer nutritional status and high gamma gap indicates heightened inflammatory/immune activity—culminating in higher susceptibility to heart failure and mortality. These results underscore the importance of integrating inflammatory/nutritional markers with anthropometrics for risk stratification in elderly CAD populations and suggest potential benefits of interventions targeting nutrition and inflammation.

In a prospective cohort of older CAD patients with approximately 10-year follow-up, low BMI and high gamma gap were each associated with increased heart failure and mortality, and their combination exerted a synergistic adverse effect. Patients with low BMI/high gamma gap had the highest risk, whereas those with high BMI/low gamma gap had the lowest. Future research should elucidate mechanisms linking nutritional status and inflammatory/immune activation to outcomes and evaluate whether nutritional and immunological interventions can improve prognosis in this high-risk group.