Abstract
Introduction
Heart failure (HF), a growing public health concern, is primarily driven by metabolic disorders. While the systemic inflammatory response index (SIRI) has demonstrated prognostic value in cardiometabolic diseases, its role in predicting HF remains unclear. Given the link between obesity and inflammation, integrating SIRI with obesity-related measures may enhance the stratification of HF risk. This study aims to examine the association between SIRI, integrated with obesity-related indices, and HF.
Methods
Data from NHANES 2017–2020 were used, including 6572 adults aged 20–80 years with complete data on key indices. HF was defined based on self-reported physician diagnosis. SIRI was calculated as (neutrophil × monocyte)/lymphocyte count. Receiver operating characteristic (ROC) analysis was performed to assess the predictive value of inflammatory and obesity indices on HF risk. Multivariable logistic regression models, restricted cubic spline (RCS) and Interaction tests were used to examine the association between the index of interest and HF.
Results
Of 6572 participants, 170 (2.6%) had HF. The SIRI × BMI × WHR index showed the highest predictive value (AUC: 0.68), improving in non-smokers (AUC: 0.73) and individuals with diabetes (AUC: 0.71). RCS analysis indicated a linear, dose-response relationship, with multivariable logistic regression analysis revealed the strongest association in the fourth quartile (AOR: 2.00, 95% CI: 1.07–3.75), and stronger effects in non-smokers (AOR: 7.25, 95% CI: 2.04–25.76) and those with diabetes (AOR: 5.63, 95% CI: 1.25–25.39).
Conclusion
The SIRI × BMI × WHR index demonstrated predictive ability and an association with HF, particularly among individuals with diabetes and non-smokers. Given its accessibility and cost-effectiveness, this index may serve as a valuable tool for HF screening.
Keywords: Systemic inflammation response index, obesity, heart failure, population health, effect modifier
Introduction
Heart failure (HF) remains a significant public health challenge, affecting more than 64 million individuals worldwide and contributing to substantial morbidity, mortality, and healthcare costs. 1 In the United States, HF prevalence continues to rise, driven in part by the increasing burden of metabolic disorders such as obesity. 2 Obesity is a well-established risk factor for HF, promoting adverse cardiac remodeling, systemic hypertension, and metabolic dysregulation. Additionally, emerging evidence suggests that chronic low-grade inflammation plays a key role in the pathophysiology of HF.3,4
The systemic inflammatory response index (SIRI), a novel inflammatory biomarker derived from neutrophil, monocyte, and lymphocyte counts, has been explored as a prognostic indicator in various cardiovascular and metabolic conditions. 4 However, its role in HF prediction remains under-investigated. Given that obesity is often accompanied by heightened systemic inflammation, 5 integrating SIRI with obesity measures may provide a more comprehensive, cost-effective, and easily accessible approach to HF risk stratification.
Despite obesity and inflammation being established contributors to HF, few studies have tested their joint predictive value in large, nationally representative cohorts. Prior work has focused on individual inflammatory markers, leaving the predictive potential of SIRI, especially in conjunction with obesity indices, underexplored. Hence, this study aims to examine whether integrating SIRI with obesity indices strengthens the association with HF and enhances its predictive performance. The findings may improve early risk stratification and inform targeted prevention strategies for HF.
Methods
Study design and subjects
Data for this study were derived from the National Health and Nutrition Examination Survey (NHANES) 2017–2020, a nationally representative, cross-sectional survey conducted by the Centers for Disease Control and Prevention (CDC). 6 Of the 15,560 individuals initially recruited, 6328 participants aged under 20 were excluded. Additional exclusions were made for participants with missing data on key variables: 1116 with missing neutrophil, lymphocyte, or monocyte counts; and 1544 with missing obesity indices (body mass index (BMI) and waist-to-hip ratio (WHR)) or HF status. After applying these criteria, a final of 6572 participants was included (Figure 1).
Figure 1.
The flow of the enrolled participants.
Data collection
The data collection process included in-home interviews, standardized physical examinations conducted at a mobile examination center (MEC), and laboratory analysis of blood and urine samples collected at the MEC. This comprehensive approach combined self-reported information with objective measurements to assess individuals’ health and nutritional status. HF was determined based on a self-reported questionnaire asking, “Has a doctor or other health professional ever diagnosed you with congestive heart failure?”
Statistical analysis
Data analysis was performed using IBM SPSS version 29 and R 4.3.3 (https://www.R-project.org). Categorical variables were summarized as frequencies and percentages, while continuous variables were reported as means with standard deviations (SD). The SIRI was calculated as (neutrophil × monocyte) / lymphocyte count. 6 The WHR was computed by dividing waist circumference (cm) by hip circumference (cm). 7 The SIRI × BMI × WHR index was calculated by multiplying the values of the SIRI, BMI, and WHR for each participant. This combined product was used as a composite indicator to capture the joint effect of systemic inflammation, general adiposity, and central obesity on the outcome. Independent t-tests and chi-square tests were used to compare characteristics between participants with and without HF.
The study employed three multivariable logistic regression models to investigate the associations between BMI, WHR, and SIRI and HF, reporting adjusted odds ratios (AORs) with 95% confidence intervals (CIs). Additionally, smooth curve fitting was applied to assess the non-linear relationship between the final modified obesity and inflammatory index (SIRI × BMI × WHR) and HF. Receiver operating characteristic (ROC) analysis was conducted to determine the accuracy of model predictions.
An interaction analysis was conducted to evaluate whether baseline characteristics modified the association between the obesity-inflammatory index and HF, followed by subgroup analyses to explore these potential effect modifications further. All statistical analyses were two-sided, with a P-value of less than 0.05 considered statistically significant. Additionally, sensitivity analysis was performed to address potential unmeasured confounding by estimating E-values using the e-value package. 8
Results
Baseline characteristics of participants
This study included 6572 participants, of whom 170 (2.6%) had HF as demonstrated in Table 1. The mean age was 50.2 ± 17.3 years, and 52.1% were female. Non-Hispanic White individuals comprised 35.4% of the sample. Alcohol consumption and smoking were reported by 90.7% and 40.4% of participants, respectively. Common comorbidities included hypertension (35.8%), diabetes (13.7%), coronary artery disease (3.6%), and stroke (4.5%). The mean BMI was 28.9 ± 5.9 kg/m2; waist circumference, 98.6 ± 14.9 cm; total cholesterol 186.8 ± 40.5 mg/dL; LDL cholesterol 111.4 ± 35.9 mg/dL; HDL cholesterol 54.3 ± 16.1 mg/dL; triglyceride 108.1 ± 95.5; eGFR 94.7 ± 21.7 mL/min/1.73 m2; HS C-reactive protein 3.3 ± 6.2 mg/L; HbA1c, 5.8 ± 1.1%; SIRI 1.0 ± 0.5; and SIRI × BMI × WHR, 28.4 ± 16.0.
Table 1.
Demographic, behavioral characteristics and laboratory tests stratified by history of heart failure (N = 6572).
| Variables | Total | Non-heart failure | Heart failure | P-value |
|---|---|---|---|---|
| n (%) | n (%) | n (%) | ||
| Number of participants | 6572 | 6402 | 170 | |
| Age (years) (mean ± SD) | 50.2 ± 17.3 | 49.8 ± 17.2 | 66.2 ± 11.6 | <0.001 a |
| Age (years) | <0.001 b | |||
| 20–39 | 2068 (31.5%) | 2063 (99.7%) | 5 (0.3%) | |
| 40–59 | 2205 (33.5%) | 2171 (98.5%) | 34 (1.5%) | |
| 60–80 | 2299 (35.0%) | 2168 (94.3%) | 131 (5.7%) | |
| Gender | 0.002 b | |||
| Male | 3147(47.9%) | 3046 (96.8%) | 101 (3.2%) | |
| Female | 3425(52.1%) | 3356 (98.0%) | 69 (2.0%) | |
| Body mass index (kg/m2) (mean ± SD) | 28.9 ± 5.9 | 28.9 ± 5.9 | 30.6 ± 5.8 | <0.001 a |
| Waist circumference (cm) (mean ± SD) | 98.6 (14.9) | 98.4 (14.9) | 106.4 (14.2) | <0.001 a |
| Race | <0.001 b | |||
| Mexican American | 793 (12.7%) | 783 (98.7%) | 10 (1.3%) | |
| Other Hispanic | 705 (11.2%) | 692 (98.2%) | 13 (1.8%) | |
| Non-Hispanic White | 2216 (35.4%) | 2145 (96.8%) | 71 (3.2%) | |
| Non-Hispanic Black | 1694 (27.0%) | 1634 (96.5%) | 60 (3.5%) | |
| Other race | 860 (13.7%) | 853 (99.2%) | 7 (0.8%) | |
| Education level | <0.001 b | |||
| Less than 9th grade | 2760 (42.0%) | 2668 (96.7%) | 92 (3.3%) | |
| 9–11th grade (Includes 12th grade with no diploma) | 2108 (32.1%) | 2054 (97.4%) | 54 (2.6%) | |
| High school graduate/GED or equivalent | 1696 (25.9%) | 1672 (98.6%) | 24 (1.4%) | |
| Alcohol intake | 5684 (90.7%) | 5533 (97.3%) | 151 (2.7%) | 0.3 b |
| Smoked | 2657 (40.4%) | 2556 (96.2%) | 101 (3.8%) | <0.001 b |
| Hypertension | 2351 (35.8%) | 2216 (94.3%) | 135 (5.7%) | <0.001 b |
| Diabetes | 875 (13.7%) | 805 (92.0%) | 70 (8.0%) | <0.001 b |
| Coronary artery disease | 239 (3.6%) | 177 (74.1%) | 62 (25.9%) | <0.001 b |
| Stroke | 298 (4.5%) | 255 (85.6%) | 43 (14.4%) | <0.001 b |
| Poverty-to-income ratio | <0.001 b | |||
| <1 | 1062 (18.6%) | 1035 (97.5%) | 27 (2.5%) | |
| 1 to 3 | 3012 (52.7%) | 2911 (96.7%) | 101 (3.3%) | |
| >3 | 1639 (28.7%) | 1616 (98.6%) | 23 (1.4%) | |
| Neutrophils count (1000 cells/μL) (mean ± SD) | 3.85 ± 1.31 | 3.84 ± 1.31 | 4.05 ± 1.28 | 0.043 a |
| Monocyte count (1000 cells/μL) (mean ± SD) | 0.54 ± 0.31 | 0.54 ± 0.16 | 0.59 ± 0.16 | <0.001 a |
| Lymphocyte count (1000 cells/μL) (mean ± SD) | 2.14 ± 0.63 | 2.15 ± 0.63 | 1.94 ± 0.61 | <0.001 a |
| Glycohemoglobin (%) (mean ± SD) | 5.8 ± 1.1 | 5.8 ± 1.1 | 6.4 ± 1.3 | <0.001 a |
| Total cholesterol (mg/dL) (mean ± SD) | 186.8 ± 40.5 | 187.2 ± 40.4 | 172.7 ± 44.0 | <0.001 a |
| LDL cholesterol (mg/dL) (mean ± SD) | 111.4 ± 35.9 | 111.8 ± 35.8 | 95.6 ± 37.2 | <0.001 a |
| HDL cholesterol (mg/dL) (mean ± SD) | 54.3 ± 16.1 | 54.4 ± 16.1 | 51.8 ± 16.2 | 0.051 a |
| Triglyceride (mg/dL) (mean ± SD) | 108.1 ± 95.5 | 107.9 ± 96.1 | 113.9 ± 70.6 | 0.446 a |
| eGFR (mL/min/1.73 m2) (mean ± SD) | 94.7 ± 21.7 | 95.3 ± 21.2 | 69.1 ± 25.0 | <0.001 a |
| HS C-reactive protein (mg/L) (Mean ± SD) | 3.3 ± 6.2 | 3.3 ± 6.0 | 5.3 ± 10.9 | <0.018 a |
| SIRI (mean ± SD) | 1.0 ± 0.5 | 1.0 ± 0.5 | 1.3 ± 0.5 | <0.001 a |
| SIRI × BMI × WHR (mean ± SD) | 28.4 ± 16.0 | 28.2 ± 15.9 | 38.9 ± 17.8 | <0.001 a |
eGFR: estimated glomerular filtration rate; SIRI: systemic inflammatory response index; BMI: body mass index; WHR: waist-to-hip ratio.
Independent t-test.
Chi-square.
When stratified by HF status, the HF group was significantly older (66.2 ± 11.6 years) compared to the non-HF group (49.8 ± 17.2 years, P < 0.001). HF patients had a higher mean BMI (30.6 ± 5.8 kg/m2 vs 28.9 ± 5.9 kg/m2, P < 0.001) and a larger waist circumference (106.4 ± 14.2 cm vs 98.4 ± 14.9 cm, P < 0.001). The SIRI was significantly higher in the HF group compared with controls (1.30 ± 0.54 vs 1.03 ± 0.51, P < 0.001). Similarly, hs-CRP levels were elevated in patients with HF (5.34 ± 10.86 mg/L vs 3.28 ± 5.97 mg/L, P = 0.018). Moreover, the combined SIRI × BMI × WHR index was significantly higher in HF patients than in controls (38.9 ± 17.8 vs 28.2 ± 15.9, P < 0.001).
Relationship between SIRI and its obesity-related derivatives with HF
Table 2 presents multivariable logistic regression analyses assessing the association of the SIRI, BMI, and WHR with HF risk across three models. Model 1 was unadjusted; Model 2 was adjusted for age, sex, education, and race; Model 3 was fully adjusted for sociodemographic factors, lifestyle behaviors, and comorbidities.
Table 2.
Univariable and multivariable logistic regression analysis of systemic inflammatory response index and its obesity-related derivatives and heart failure.
| Variables | Model 1 OR (95% CI) a | Model 2 OR (95% CI) b | Model 3 OR (95% CI) c |
|---|---|---|---|
| SIRI | 2.40 (1.84–3.12) | 1.67 (1.25–2.23) | 1.93 (1.37–2.70) |
| SIRI (Quartiles) | |||
| Quartile 1 (<0.62) | Ref | ||
| Quartile 2 (0.62–0.91) | 1.60 (0.88–2.92) | 1.38 (0.75–2.53) | 1.38 (0.66–2.87) |
| Quartile 3 (0.91–1.28) | 2.24 (1.27–3.95) | 1.63 (0.91–2.91) | 1.41 (0.69–2.87) |
| Quartile 4 (>1.28) | 3.79 (2.24–6.43) | 2.18 (1.26–3.78) | 2.31 (1.20–4.46) |
| SIRI × BMI | 2.77 (2.12–3.62) | 1.03 (1.01–1.03) | 1.03 (1.01–1.04) |
| SIRI × BMI (Quartiles) | |||
| Quartile 1 (<16.63) | Ref | ||
| Quartile 2 (16.63–25.51) | 1.64 (0.87–3.09) | 1.45 (0.76–2.77) | 1.17 (0.53–2.55) |
| Quartile 3 (25.51–38.01) | 2.43 (1.34–4.40) | 1.84 (1.00–3.39) | 1.64 (0.80–3.38) |
| Quartile 4 (>38.01) | 4.73 (2.72–8.21) | 3.00 (1.68–5.33) | 2.41 (1.22–4.76) |
| SIRI × WHR | 2.77 (2.12–3.62) | 1.72 (1.28–2.32) | 1.90 (1.34–2.69) |
| SIRI × WHR (Quartiles) | |||
| Quartile 1 (<0.56) | Ref | ||
| Quartile 2 (0.56–0.82) | 1.31 (0.69–2.48) | 1.06 (0.55–2.05) | 1.16 (0.53–2.53) |
| Quartile 3 (0.82–1.19) | 2.40 (1.35–4.27) | 1.68 (0.93–3.04) | 1.49 (0.72–3.09) |
| Quartile 4 (>1.19) | 4.15 (2.42–7.10) | 2.19 (1.24–3.84) | 2.12 (1.07–4.18) |
| BMI × WHR | 1.06 (1.04–1.09) | 1.06 (1.04–1.09) | 1.02 (0.99–1.05) |
| BMI × WHR (Quartiles) | |||
| Quartile 1 (<21.95) | Ref | ||
| Quartile 2 (21.95–26.24) | 1.70 (0.96–3.01) | 1.20 (0.66–2.18) | 0.91 (0.46–1.81) |
| Quartile 3 (26.24–30.92) | 1.98 (1.14–3.44) | 1.28 (0.71–2.30) | 1.00 (0.52–1.95) |
| Quartile 4 (>30.92) | 3.72 (2.25–6.16) | 2.74 (1.60–4.70) | 1.45 (0.77–2.74) |
| SIRI × BMI × WHR | 1.04 (1.03–1.04) | 1.03 (1.02–1.03) | 1.02 (1.01–1.04) |
| SIRI × BMI × WHR (Quartiles) | |||
| Quartile 1 (<16.08) | Ref | ||
| Quartile 2 (16.08–25.07) | 1.23 (0.66–2.30) | 1.03 (0.54–1.96) | 0.68 (0.31–1.49) |
| Quartile 3 (25.07–38.01) | 2.72 (1.58–4.70) | 1.93 (1.10–3.39) | 1.51 (0.78–2.90) |
| Quartile 4 (>38.01) | 4.75 (2.84–7.95) | 2.69 (1.56–4.63) | 2.00 (1.07–3.75) |
SIRI: systemic inflammatory response index; BMI: body mass index; WHR: waist-to-hip ratio.
Not adjusted for other variables.
Adjusted for age, gender, education level, race.
Adjusted for age, gender, education level, race, poverty-to-income ratio, smoking, alcohol intake, hypertension, diabetes, angina, stroke.
A one-unit increase in SIRI was associated with an increased risk of HF (AOR: 1.93, 95% CI: 1.37–2.70). A dose-response relationship was observed across SIRI quartiles. The combination of SIRI with BMI further strengthened this association; individuals in the highest quartile of SIRI × BMI (>38.01) had an AOR of 2.41 (95% CI: 1.22–4.76) in the fully adjusted model. SIRI × WHR was also significantly associated with HF risk, whereas the combined BMI × WHR alone was not significant in Model 3, whether treated as a continuous or categorical variable.
The SIRI × BMI × WHR index remained significantly associated with HF in the fourth quartiles across all models. Each unit increase in SIRI × BMI × WHR was associated to HF with an AOR of 1.02 (95% CI: 1.01–1.04). Furthermore, dose-response relationships were observed between SIRI × BMI × WHR and HF, with the highest quartile showing a significantly elevated risk of HF (AOR: 2.00, 95% CI: 1.07–3.75).
A restricted cubic spline (RCS) plot was performed to explore any nonlinear relationship between the SIRI × BMI × WHR index and HF. After adjusting for confounders—including age, gender, education level, race, poverty-to-income ratio, smoking, alcohol intake, hypertension, diabetes, angina and stroke, the plot demonstrated a significant linear relationship between the SIRI × BMI × WHR index and the risk of HF (P < 0.001), while the non-linear component was not statistically significant (P = 0.209) (Figure 2).
Figure 2.
Restricted cubic spline (RCS) plot between SIRI × BMI × WHR index and heart failure. The red line represents the inflection point at 25.73, while the black line indicates the reference (OR = 1).
Subgroup analysis of demographic and behavioral factors
Interaction analyses were performed to evaluate whether demographic and behavioral factors—including age, sex, alcohol intake, and smoking—modified the association between SIRI × BMI × WHR and HF. The association was notably stronger among non-smokers and individuals with diabetes (Figure 3). When stratified by quartiles, individuals in the highest quartile of SIRI × BMI × WHR had significantly increased odds of HF compared to the lowest quartile in both the diabetic group (AOR: 5.63, 95% CI: 1.25–25.39) and the non-smoking group (AOR: 7.25, 95% CI: 2.04–25.76). In contrast, no significant associations were observed among non-diabetic individuals or current smokers (Figure 4).
Figure 3.
Forest plot demonstrating interaction analysis between SIRI × BMI × WHR and heart failure.
Figure 4.
Subgroup analysis of the association between SIRI × BMI × WHR and heart failure among individuals with diabetes and smokers.
aAdjusted for age, gender, education level, race, poverty-to-income ratio, smoking, alcohol intake, hypertension, diabetes, angina, stroke.
ROC analysis of the SIRI and its obesity-related derivatives to predict HF
The ability of obesity-related derivatives to predict HF is illustrated in Figure 5. The optimal cutoff points for predicting HF using SIRI, SIRI × BMI, SIRI × WHR, BMI × WHR, and SIRI × BMI × WHR were 1.11, 28.79, 1.07, 28.99, and 24.48, respectively, with corresponding AUC values of 0.643, 0.662, 0.663, 0.623, and 0.676.
Figure 5.
ROC analysis of systemic inflammatory response index and its obesity-related derivatives to predict heart failure.
Focusing on the modified inflammatory-obesity index (SIRI × BMI × WHR), the optimal cutoff points were 27.34 in smokers and 25.81 in individuals with diabetes (Figure 6(a)). The AUC was 0.62 (sensitivity = 0.70, specificity = 0.49) in smokers and 0.71 (sensitivity = 0.67, specificity = 0.64) in individuals with diabetes (Figure 6(b)).
Figure 6.
ROC analysis of SIRI × BMI × WHR for predicting heart failure. (a) Subgroup analysis by smoking status. (b) Subgroup analysis by diabetes status.
Discussion
In this study, we observed a dose-response and linear association between the combined SIRI × BMI × WHR index and HF. Although the overall predictive performance was modest, it was substantially stronger among non-smokers and individuals with diabetes. This study included 6572 participants, of whom 170 (2.6%) had heart HF—a prevalence higher than that reported in developed countries such as those in Europe (1.9%), China (1.3%), and Japan (1.0%).5,9,10 Even though the overall sample was predominantly female (52.1%), HF prevalence was higher among males, consistent with previous studies suggesting cardiovascular disease may be underrecognized in women due to the misconception of their lower risk. 11
Regarding inflammatory markers, hs-CRP, SIRI and its obesity-related derivatives were elevated in the HF samples. Since the 1990s, elevated plasma levels of pro-inflammatory cytokines in patients with HF have provided early evidence of an inflammatory component in HF pathogenesis. 12 The CANTOS trial further underscored the clinical relevance of inflammation, showing that IL-1β inhibition with canakinumab reduced HF-related hospitalizations and mortality in patients with prior myocardial infarction. 13
Innate immune receptors, particularly toll-like receptors and nod-like receptors, are key mediators that trigger inflammatory cascades, upregulating cytokines such as TNF-α and IL-6 and promoting recruitment of neutrophils and monocytes.14–16 These cells contribute to myocardial remodeling and dysfunction—monocytes via macrophage differentiation and cytokine secretion, 17 neutrophils through oxidative stress and inflammatory mediators, 18 and lymphocytes by regulating adaptive immunity. Notably, reduced lymphocyte counts have been independently associated with greater HF severity and increased mortality, irrespective of ejection fraction. 19 The SIRI, which integrates neutrophil, monocyte, and lymphocyte counts, thus captures this immune imbalance and may reflect the inflammatory burden contributing to HF progression.
Despite the widespread use of hs-CRP as an inflammatory marker, SIRI and its obesity-related derivatives may offer additional value. In this study, hs-CRP levels were relatively high, likely reflecting participant characteristics such as older age and higher BMI—both established risk factors for systemic inflammation and elevated hs-CRP. In HF, increased wall stress, neurohormonal activation, and congestion can cause endothelial and tissue injury, thereby promoting systemic inflammation and higher hs-CRP levels. 20 However, hs-CRP primarily reflects acute infection or injury and does not fully capture the chronic low-grade inflammation characteristic of HF. Accordingly, even after adjustment for hs-CRP, the association between SIRI × BMI × WHR and HF remained robust, whereas hs-CRP alone showed inferior predictive performance (Supplemental Table 2 and Supplemental Figure 1). In addition, generalizability may be limited, as hs-CRP is not routinely measured in standard health checkups and is often unavailable in low- and middle-income countries.21,22
This study demonstrated a significant association between SIRI and obesity indices with HF, with the combined SIRI × BMI × WHR index emerging as the strongest predictor. Unlike prior studies that assessed inflammatory markers alone, our RCS analysis revealed a clear linear relationship, underscoring the enhanced predictive value of the combined index. 23 Quartile stratification further confirmed a dose-response association, particularly in the highest quartile (>38.01). These findings contrast with previous reports showing non-linear associations between SIRI alone and HF 24 or cardiovascular disease. 25
Although SIRI alone was positively associated with HF, incorporating routinely measured obesity indices (BMI and WHR) better captures the multidimensional metabolic–inflammatory burden underlying HF pathophysiology. This combined index showed stronger associations and superior predictive performance, while avoiding overemphasis on individuals who are abnormal in only a single dimension. We observed higher SIRI levels in the HF group, which also had higher BMI and WHR. Notably, WHR—a marker of central obesity—is more consistently linked to adverse cardiac structure and function than BMI. 26 Consequently, individuals with a normal BMI but elevated WHR may still be at increased risk of HF. In addition, WHR helps account for cardiac cachexia, which affects approximately 5–15% of patients with HF and is characterized in part by low BMI (<20 kg/m2), a context in which BMI can be misleading. 27 Unlike BMI, WHR is less influenced by muscle mass, bone density, sex, or ethnicity, making it a more robust indicator of fat distribution.
Diabetes was found as an effect modifier in the association between the SIRI × BMI × WHR index and HF, likely due to the pro-inflammatory state characteristic of diabetes.18,28,29 Neutrophils contribute to endothelial dysfunction and oxidative stress via myeloperoxidase and NADPH oxidase, while circulating monocytes facilitate plaque formation through vascular infiltration. 18 Supporting this mechanism, Lin et al. (2023) found that elevated SIRI was independently associated with increased cardiovascular risk in individuals with diabetes, particularly those with a BMI >24 kg/m2, and demonstrated a dose-response relationship between log-transformed SIRI and CVD risk. 30 These findings and studies support the potential utility of inflammatory indices, such as SIRI, in identifying high-risk diabetic populations and guiding targeted prevention strategies.
The association between SIRI × BMI × WHR and HF was more pronounced among non-smokers, suggesting that the interplay between systemic inflammation, obesity-related factors, and metabolic dysfunction may have a greater impact on HF risk in the absence of smoking. Smoking is known to induce chronic inflammation and oxidative stress—well-established contributors to cardiovascular disease. 31 In smokers, this pre-existing inflammatory burden may overshadow the additional impact of obesity-related inflammatory pathways, thereby attenuating the observed effect of the SIRI × BMI × WHR index. In contrast, among non-smokers, where tobacco-induced inflammation is absent, the role of obesity-related systemic inflammation may be more apparent in driving HF risk. These findings underscore the importance of accounting for lifestyle factors such as smoking status when evaluating inflammatory-metabolic risk markers and suggest that non-smokers with elevated SIRI × BMI × WHR may represent a high-risk group warranting targeted prevention strategies.
To our knowledge, this is the first study to evaluate the joint predictive value of SIRI and obesity indices for HF in the U.S. population. We found that the SIRI × BMI × WHR index was significantly higher in the HF group than in those without HF, highlighting the potential utility of a combined inflammatory-obesity index for HF risk stratification, particularly in settings where conventional tools such as echocardiography may be limited by cost and accessibility. By offering a straightforward, data-driven approach, this study lays the groundwork for enhancing secondary prevention and comprehensive risk assessment. This index could serve as a cost-effective screening measure in routine clinical care, particularly for patients with obesity, metabolic syndrome, or diabetes, and may precede more advanced imaging. Importantly, smoking status should be considered as tobacco-induced inflammation may mask the index's predictive utility. Future research should validate this index in prospective cohorts, explore integration into electronic health records, and evaluate its role in guiding targeted anti-inflammatory strategies.
Strength and limitations
A key strength of this study is its introduction of a novel inflammatory-obesity index (SIRI × BMI × WHR) for predicting HF, demonstrating a clear linear and dose-response relationship with HF risk. Unlike prior studies that evaluated inflammatory or obesity markers in isolation, our integrated approach offers a more comprehensive and biologically plausible risk assessment. The association was particularly strong among individuals with diabetes and non-smokers. These findings support the index's potential utility in clinical risk stratification and targeted prevention strategies.
Several limitations should be acknowledged. First, the cross-sectional design limits causal inference between the SIRI × BMI × WHR index and HF. Additionally, SIRI and BMI are dynamic markers that may fluctuate over time, whereas HF is a chronic condition influenced by long-term inflammatory and metabolic processes. This temporal mismatch may attenuate the observed associations. Second, approximately 28.8% of participants (2660 out of 9232) were excluded due to missing data, which may have affected the generalizability of the findings and introduced selection bias. Therefore, results should be interpreted with caution. Third, key confounding variables—such as medication use and lifestyle factors (e.g. physical activity and diet)—were unavailable, which may have influenced both inflammation and HF risk. Nonetheless, the association between SIRI × BMI × WHR and HF remained robust, and sensitivity analysis yielded a high E-value (Supplemental Table 1), suggesting that unmeasured confounding would need to be substantial to fully explain the observed effect. Fourth, the HF status is based on self-report, which may lead to misclassification or underestimation of true HF prevalence. Nevertheless, previous epidemiologic studies have shown that self-reported HF can reasonably reflect underlying disease.12,24,32
Conclusion
This study highlights the complex interplay between SIRI, BMI, WHR, and HF, with diabetes and non-smoking status emerging as significant effect modifiers. Combining SIRI with BMI and WHR improves predictive ability for HF, particularly among individuals with diabetes and non-smokers. These findings underscore the potential of SIRI, when combined with obesity indices, to enhance risk stratification and support the development of personalized prevention strategies for HF.
Supplemental Material
Supplemental material, sj-docx-1-cvd-10.1177_20480040261419613 for Systemic inflammatory response index and its obesity-related derivatives as predictors of heart failure: A cross-sectional study from NHANES 2017–2020 by Chutawat Kookanok, Methavee Poochanasri and Sethapong Lertsakulbunlue in JRSM Cardiovascular Disease
Acknowledgement
The authors extend their sincere appreciation to everyone who contributed to the successful completion of this study and provided valuable support and insights throughout the research process.
Footnotes
ORCID iDs: Chutawat Kookanok https://orcid.org/0009-0009-1646-0486
Methavee Poochanasri https://orcid.org/0009-0001-2730-6646
Sethapong Lertsakulbunlue https://orcid.org/0000-0002-9349-2088
Human ethics and consent to participate: NHANES was approved by NCHS ERB (Protocols 2011-17, 2018-01).
Consent for publication: Not applicable. Because the present analysis used de-identified secondary data, it was deemed exempt from additional institutional review board approval according to federal regulations.
Author contributions: Chutawat Kookanok contributed to writing—review & editing, formal analysis, data curation, and conceptualization. Methavee Poochanasri was responsible for methodology, data curation, and conceptualization. Sethapong Lertsakulbunlue contributed to writing—original draft, visualization, methodology, formal analysis, supervision, and conceptualization.
Funding: The authors received no financial support for the research, authorship, and/or publication of this article.
The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.
Availability of data and materials: This study utilized publicly available data from the National Health and Nutrition Examination Survey (NHANES), conducted by the National Center for Health Statistics (NCHS). All NHANES datasets are accessible to the public and can be obtained from the NCHS website (https://www.cdc.gov/nchs/nhanes/index.htm). Researchers can freely access and use the data following the guidelines and protocols established by NCHS.
Supplemental material: Supplemental material for this article is available online.
References
- 1.Savarese G, Becher PM, Lund LH, et al. Global burden of heart failure: a comprehensive and updated review of epidemiology. Cardiovasc Res 2023; 118: 3272–3287. [DOI] [PubMed] [Google Scholar]
- 2.Conrad N, Judge A, Tran J, et al. Temporal trends and patterns in heart failure incidence: a population-based study of 4 million individuals. Lancet Lond Engl 2018; 391: 572–580. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 3.Ebong IA, Goff DC, Rodriguez CJ, et al. Mechanisms of heart failure in obesity. Obes Res Clin Pract 2014; 8: e540–e548. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 4.Zhao Y, Shao W, Zhu Q, et al. Association between systemic immune-inflammation index and metabolic syndrome and its components: results from the National Health and Nutrition Examination Survey 2011–2016. J Transl Med 2023; 21: 691. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 5.Kawai T, Autieri MV, Scalia R. Adipose tissue inflammation and metabolic dysfunction in obesity. Am J Physiol Cell Physiol 2021; 320: C375–C391. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 6.Qi Q, Zhuang L, Shen Y, et al. A novel systemic inflammation response index (SIRI) for predicting the survival of patients with pancreatic cancer after chemotherapy. Cancer 2016; 122: 2158–2167. [DOI] [PubMed] [Google Scholar]
- 7.Fauziana R, Jeyagurunathan A, Abdin E, et al. Body mass index, waist-hip ratio and risk of chronic medical condition in the elderly population: results from the Well-being of the Singapore Elderly (WiSE) study. BMC Geriatr 2016; 16: 125. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 8.Linden A, Mathur MB, VanderWeele TJ. Conducting sensitivity analysis for unmeasured confounding in observational studies using E-values: the evalue package. Stata J 2020; 20: 162–175. [Google Scholar]
- 9.Fu L, Jiang J-R, Lin W-D, et al. Prevalence and incidence of heart failure among community in China during a three-year follow-up. J Geriatr Cardiol JGC 2023; 20: 284–292. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 10.Rosano GMC, Seferovic P, Savarese G, et al. Impact analysis of heart failure across European countries: an ESC-HFA position paper. ESC Heart Fail 2022; 9: 2767–2778. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 11.Gao Z, Chen Z, Sun A, et al. Gender differences in cardiovascular disease. Med Nov Technol Devices 2019; 4: 100025. [Google Scholar]
- 12.Siontis GC, Bhatt DL, Patel CJ. Secular trends in prevalence of heart failure diagnosis over 20 years (from the US NHANES). Am J Cardiol 2022; 172: 161–164. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 13.Everett BM, Cornel JH, Lainscak M, et al. Anti-inflammatory therapy with canakinumab for the prevention of hospitalization for heart failure. Circulation 2019; 139: 1289–1299. [DOI] [PubMed] [Google Scholar]
- 14.Alogna A, Koepp KE, Sabbah M, et al. Interleukin-6 in patients with heart failure and preserved ejection fraction. JACC Heart Fail 2023; 11: 1549–1561. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 15.Anker SD, von Haehling S. Inflammatory mediators in chronic heart failure: an overview. Heart Br Card Soc 2004; 90: 464–470. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 16.Ridker PM, Rane M. Interleukin-6 signaling and anti-interleukin-6 therapeutics in cardiovascular disease. Circ Res 2021; 128: 1728–1746. [DOI] [PubMed] [Google Scholar]
- 17.Wang X, Wang M, Shen Y. Higher systemic inflammation response index is associated with increased risk of heart failure in adults: an observational study. Medicine (Baltimore) 2024; 103: e38625. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 18.Swirski FK, Nahrendorf M. Leukocyte behavior in atherosclerosis, myocardial infarction, and heart failure. Science 2013; 339: 161–166. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 19.Majmundar M, Kansara T, Park H, et al. Absolute lymphocyte count as a predictor of mortality and readmission in heart failure hospitalization. Int J Cardiol Heart Vasc 2022; 39: 100981. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 20.Santas E, Villar S, Palau P, et al. High-sensitivity C-reactive protein and risk of clinical outcomes in patients with acute heart failure. Sci Rep 2024; 14: 21672. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 21.Yousuf O, Mohanty BD, Martin SS, et al. High-sensitivity C-reactive protein and cardiovascular disease. J Am Coll Cardiol 2013; 62: 397–408. [DOI] [PubMed] [Google Scholar]
- 22.Escadafal C, Incardona S, Fernandez-Carballo BL, et al. The good and the bad: using C reactive protein to distinguish bacterial from non-bacterial infection among febrile patients in low-resource settings. BMJ Glob Health 2020; 5: e002396. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 23.Wang X, Ni Q, Wang J, et al. Systemic inflammation response index is a promising prognostic marker in elderly patients with heart failure: a retrospective cohort study. Front Cardiovasc Med 2022; 9, Epub ahead of print 14 July 2022. doi: 10.3389/fcvm.2022.871031 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 24.Zheng H, Yin Z, Luo X, et al. Associations between systemic immunity-inflammation index and heart failure: Evidence from the NHANES 1999–2018. Int J Cardiol 2024; 395, Epub ahead of print 15 January 2024. doi: 10.1016/j.ijcard.2023.131400 [DOI] [PubMed] [Google Scholar]
- 25.Ma J, Li K. Systemic immune-inflammation index is associated with coronary heart disease: a cross-sectional study of NHANES 2009–2018. Front Cardiovasc Med 2023; 10, Epub ahead of print 7 July 2023. doi: 10.3389/fcvm.2023.1199433 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 26.Ammar KA, Redfield MM, Mahoney DW, et al. Central obesity: association with left ventricular dysfunction and mortality in the community. Am Heart J 2008; 156: 975–981. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 27.Nguyen HTT, Ha TTT, Tran HB, et al. Relationship between BMI and prognosis of chronic heart failure outpatients in Vietnam: a single-center study. Front Nutr 2023; 10: 1251601. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 28.Lecube A. [Impact of obesity and diabetes on health and cardiovascular disease]. Aten Primaria 2024; 56: 103045. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 29.Welsh C, Welsh P, Mark PB, et al. Association of total and differential leukocyte counts with cardiovascular disease and mortality in the UK biobank. Arterioscler Thromb Vasc Biol 2018; 38: 1415–1423. [DOI] [PubMed] [Google Scholar]
- 30.Lin G-M, Tsai K-Z, Lavie CJ. Waist-to-height ratio for the obesity paradox in heart failure: is it a matter of fitness? Eur Heart J 2023; 44: 3386–3387. [DOI] [PubMed] [Google Scholar]
- 31.Kamceva G, Arsova-Sarafinovska Z, Ruskovska T, et al. Cigarette smoking and oxidative stress in patients with coronary artery disease. Open Access Maced J Med Sci 2016; 4: 636. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 32.Zhong VW, Juhaeri J, Mayer-Davis EJ. Trends in hospital admission for diabetic ketoacidosis in adults with type 1 and type 2 diabetes in England, 1998–2013: a retrospective cohort study. Diabetes Care 2018; 41: 1870–1877. [DOI] [PubMed] [Google Scholar]
Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Supplementary Materials
Supplemental material, sj-docx-1-cvd-10.1177_20480040261419613 for Systemic inflammatory response index and its obesity-related derivatives as predictors of heart failure: A cross-sectional study from NHANES 2017–2020 by Chutawat Kookanok, Methavee Poochanasri and Sethapong Lertsakulbunlue in JRSM Cardiovascular Disease