Abstract
Background
Reliable prognostic markers are essential for optimizing risk stratification and clinical management in patients with cardiovascular disease (CVD). The advanced lung cancer inflammation index (ALI), an integrated measure of systemic inflammatory and nutritional status, has shown promise as a predictor of cardiovascular outcomes. This study aimed to examine the association of ALI with the risk of all-cause and cardiovascular mortality in patients with CVD.
Methods
In this prospective cohort study, we utilized data from the National Health and Nutrition Examination Survey (NHANES) between 1999 and 2018, linked to mortality records from the National Death Index (NDI). A total of 4,247 adult CVD patients were included. The association of ALI with mortality risk was evaluated using Kaplan–Meier survival analysis with log-rank tests and Cox proportional hazards regression models. To examine the nonlinear association, restricted cubic spline (RCS) analysis was performed. Additionally, Time-dependent receiver operating characteristic (ROC) curve analysis was conducted to evaluate the predictive performance of ALI for survival outcomes.
Results
During a median follow-up period of 84 months, 1,795 deaths were recorded, including 741 were cardiovascular-related. Kaplan–Meier survival analysis demonstrated significantly improved survival for participants in the highest ALI tertile compared to those in lower tertiles, for both all-cause and cardiovascular mortality. Weighted Cox proportional hazards models revealed that patients with high ALIs had significantly lower risks of all-cause and cardiovascular mortality than did those with low ALIs. RCS analysis further supported a nonlinear, inverse dose-response relationship between ALI and all-cause and cardiovascular mortality. The ROC demonstrated moderate discriminative performance for short- and long-term mortality risk, with AUCs of 0.727, 0.728 at 1 year and 0.638, 0.639 at 10 years, for all-cause and cardiovascular mortality, respectively.
Conclusions
ALI is independently associated with mortality in patients with CVD, and low ALI levels are significantly correlated with an elevated risk of mortality. Therapeutic interventions focused on mitigating inflammation and optimizing nutritional status may have important clinical implications for reducing mortality risk in this patient population, given the observed association between ALI and mortality outcomes.
Supplementary Information
The online version contains supplementary material available at 10.1186/s12872-026-05631-0.
Keywords: Nutritional indicator, Inflammatory status, Albumin, Diabetes mellitus, Neutrophil-to-lymphocyte ratio
Introduction
Cardiovascular disease (CVD) remains a leading cause of morbidity, mortality, and healthcare expenditure worldwide [1–3]. The global CVD-related deaths increased from 13.1 million in 1990 to 19.2 million in 2023, which underscores the escalating public health burden this disease represents [4]. Consequently, the identification of reliable prognostic markers is essential to optimize optimizing risk stratification and inform clinical management for CVD patients.
Substantial evidence underscores the pivotal roles of inflammation and nutritional status play pivotal roles in determining cardiovascular outcomes. Persistent low-grade inflammatory contributes to endothelial function, increase vascular permeability, as well as atherosclerotic plaques and the development of myocardial injury, thereby elevating the risk of cardiovascular events [5]. Elevated levels of inflammatory biomarkers, including the neutrophil-to-lymphocyte ratio (NLR), and systemic immune inflammatory index are associated with unfavorable long-term prognosis in patients with CVD [6, 7]. Moreover, malnutrition is prevalent in this population and significantly impacts survival [8]. Body mass index (BMI) and serum albumin (Alb) are the most common nutritional indicators, which are closely related to CVD prognosis [9, 10].
The advanced lung cancer inflammation index (ALI) represents a novel and comprehensive biomarker that integrates nutritional and inflammatory indicators. By integrating BMI, Alb, and NLR, ALI enables a holistic evaluation of systemic health and demonstrates superior prognostic utility compared with that of individual markers. ALI was initially developed as a prognostic indicator in lung cancer and has since demonstrated predictive value for adverse outcomes in various malignancies and inflammation-related conditions, such as metabolic dysfunction-associated steatohepatopathy and chronic kidney disease [11–13]. Emerging research also indicates its potential applicability in cardiovascular settings. ALI studies have primarily focused on specific CVD subtypes, such as heart failure, coronary heart disease, and myocardial infarction; its prognostic value for overall CVD as an integrated entity remains underexplored [14–18]. Its prognostic utility for the broader spectrum of CVD, considered as an integrated entity, remains insufficiently investigated. Thus, this study aimed to evaluate the association between ALI and all-cause and cardiovascular mortality in a general population of patients with CVD.
Materials and methods
Study design and population
This study was designed as a prospective cohort analysis. We analyzed publicly available data from the National Health and Nutrition Examination Survey (NHANES), an ongoing, stratified, multistage probability survey of the non-institutionalized civilian population in the United States, conducted by the National Center for Health Statistics (NCHS). Baseline participant data were linked to mortality information from the National Death Index (NDI), with follow-up extending through December 31, 2019. The analysis included participants from the 1999–2018 survey cycles, with a total initial sample size of 101,316 individuals. Exclusions were applied sequentially for pregnant individuals (n = 1,722), participants aged < 20 years (n = 46,054), those without a CVD history (n = 47,211), and those with missing data on ALI, survival status, or key covariates. These exclusions resulted in a final analytical cohort of 4,247 participants (Fig. 1). The survey protocol received approval from the NCHS Ethics Review Board, and all participants provided written informed consent.
Fig. 1.
Flowchart of participant selection from NHANES 1999–2018. Abbreviation: NHANES, National Health and Nutrition Examination Survey
Definition of CVD
The presence of CVD was determined based on self-reported physician diagnoses obtained from a standardized medical condition questionnaire. Participants were asked about a history of clinical diagnosis with angina pectoris, congestive heart failure, coronary heart disease, myocardial infarction, or stroke. Those who reported any of these conditions were classified as having CVD.
Calculation of ALI
ALI was computed using the following formula:
 |
where neutrophil and lymphocyte counts were expressed in units of 10⁹/L.
Ascertainment of clinical outcomes
The primary study outcomes were all-cause and CVD mortality. Mortality status and cause of death were determined by linking NHANES participant records to the NDI, with follow-up continuing through December 31, 2019.
All-cause mortality included death from any cause.
CVD mortality was specified as death attributable to cardiovascular or cerebrovascular diseases, according to the International Classification of Diseases, Tenth Revision (ICD-10). Deaths coded under ICD-10 I00–I09, I11, I13, I20–I51 (heart diseases) or I60–I69 (cerebrovascular diseases) were categorized as CVD mortality.
By definition, all CVD mortality events constituted a subset of the all-cause mortality events within the cohort.
Covariates
The covariates in this study included demographic information (age, sex, race, education, marital status, and poverty-to-income ratio [PIR]), lifestyle factors (smoking status and alcohol intake), clinical indicators (blood pressure, glycosylated hemoglobin type A1c [HbA1c], total cholesterol [TC], high-density lipoprotein cholesterol [HDL-C], and serum creatinine [Scr], C-reactive protein [CRP]), medication use (lipid-lowering drugs), and comorbidities (hypertension, diabetes, and cancer). Hypertension was diagnosed based on meeting any of the following criteria: systolic blood pressure ≥ 130 mmHg, diastolic blood pressure ≥ 80 mmHg, a self-reported physician diagnosis, or the current use of antihypertensive agents. Diabetes mellitus (DM) was identified by fasting glucose levels ≥ 125 mg/dL, HbA1c ≥ 6.5%, a prior medically confirmed diagnosis, or the use of glucose-lowering medication. A history of cancer was ascertained through self-report of any malignant tumor diagnosis.
Statistical analysis
To address right-skewness in the distribution, ALIs were natural log-transformed (lnALI). To ensure a balanced distribution of participants and adequate sample size within each stratum for stable risk estimation the primary analysis categorized participants into tertiles based on lnALI. Baseline characteristics were compared among these tertile groups using a one-way analysis of variance for normally distributed continuous variables, the Kruskal–Wallis test for non-normally distributed continuous variables, and Pearson’s chi-square test for categorical variables.
Survival probability differences across lnALI tertiles were evaluated using Kaplan–Meier survival analysis. Multivariate Cox proportional hazards models were used to assess associations, with three sequential models constructed: Model 1 was unadjusted; Model 2 was adjusted for age, sex, and race; Model 3 further incorporated socioeconomic factors (education level, PIR, and marital status), lifestyle (smoking and alcohol intake), clinical measures (systolic blood pressure, diastolic blood pressure, HbA1c, TC, HDL-C, and Scr), medication use (lipid-lowering agents), and comorbidities (hypertension, DM, and cancer). Multicollinearity was assessed using variance inflation factors (VIFs); all VIFs were below 2.0 (complete results provided in Supplementary Table S1). To evaluate the potential impact of unmeasured confounding, we calculated E-values [19], indicating the minimum strength of association an unmeasured confounder would require with both exposure and outcome to explain away the observed associations. The potential nonlinear relationships of lnALI with all-cause and cardiovascular mortality were examined using restricted cubic splines (RCS). The 4 knots were placed at the 5th, 35th, 65th, and 95th percentiles of the lnALI distribution. Subgroup analyses were performed to assess potential interactions by sex, age, race, smoking status, alcohol consumption, hypertension, DM, antihyperlipidemic use and cancer. Time-dependent ROC curves were generated to compare the prognostic performance of lnALI, modified body mass index (mBMI = BMI (kg/m²) × serum Alb (g/dL)), NLR, and CRP for survival prediction at multiple time points.
To test the robustness of our findings, we performed the following sensitivity analyses for both Kaplan–Meier and Cox models: (1) using an optimal dichotomous cut-point for lnALI derived from time-dependent ROC analysis (maximizing the log-rank statistic for 5-year mortality); (2) repeating analyses using lnALI quartiles; (3) repeating analyses based on participants with or without a cancer history.
All statistical analyses were conducted using the R software (version 4.4.2), and a two-sided P-value < 0.05 was considered statistically significant.
Results
Baseline characteristics
Table 1 summarizes the baseline characteristics of the 4,247 participants stratified by lnALI tertiles. The overall cohort had a median age of 66 years, and 55.67% of the participants were men. Age, sex, race, PIR, diastolic blood pressure, HbA1c, TC, HDL-C, Scr, eGFR, CRP, congestive heart failure, coronary heart disease and cancer exhibited significant differences across the lnALI tertiles (P < 0.05). During a median follow-up of 84 months (7 years), 1,795 all-cause deaths occurred, 741 of which were CVD-related.
Table 1.
Weighted baseline characteristics of participants
| Variable | Total (n = 4,247) | T1 (1.04–3.79) (n = 1,461) |
T2 (3.79–4.24) (n = 1,426) |
T3 (4.24–7.94) (n = 1,360) |
P |
|---|---|---|---|---|---|
| Age, M (IQR), years | 66.00 (56.00, 75.00) | 71.00 (62.00, 80.00) | 66.00 (55.00, 75.00) | 62.00 (53.00, 70.00) | < 0.001 |
| Male, n (%) | 2480 (55.67) | 939 (60.09) | 826 (54.11) | 715 (52.86) | 0.006 |
| Race, n (%) | < 0.001 | ||||
| Mexican American | 468 (3.92) | 130 (3.22) | 186 (4.53) | 152 (4.00) | |
| Other Hispanic | 246 (3.11) | 67 (2.44) | 98 (3.73) | 81 (3.14) | |
| Non-Hispanic White | 2470 (77.30) | 1012 (83.85) | 846 (78.58) | 612 (69.43) | |
| Non-Hispanic Black | 845 (10.23) | 175 (5.71) | 226 (7.96) | 444 (17.09) | |
| Other race | 218 (5.43) | 77 (4.78) | 70 (5.20) | 71 (6.33) | |
| Education, n (%) | 0.273 | ||||
| High school or below | 2522 (52.59) | 865 (54.00) | 862 (53.33) | 795 (50.41) | |
| Greater than high school | 1725 (47.41) | 596 (46.00) | 564 (46.67) | 565 (49.59) | |
| Marital status, n (%) | 0.459 | ||||
| Married or living with a partner | 2437 (62.54) | 811 (60.81) | 846 (63.37) | 780 (63.44) | |
| Widowed/divorced/separated/ never married | 1810 (37.46) | 650 (39.19) | 580 (36.63) | 580 (36.56) | |
| PIR, n (%) | 0.027 | ||||
| <1 | 913 (15.78) | 273 (15.21) | 300 (14.54) | 340 (17.63) | |
| ≥ 1, ≤3 | 2123 (45.36) | 778 (47.81) | 721 (47.30) | 624 (40.93) | |
| >3 | 1211 (38.85) | 410 (36.98) | 405 (38.17) | 396 (41.44) | |
| Smoking status, n(%) | 0.282 | ||||
| Never | 1608 (36.71) | 508 (34.15) | 557 (39.20) | 543 (36.72) | |
| Former | 1775 (41.05) | 666 (43.11) | 564 (38.53) | 545 (41.58) | |
| Current | 864 (22.24) | 287 (22.74) | 305 (22.26) | 272 (21.70) | |
| Alcohol intake, n (%) | 0.680 | ||||
| Never | 581 (11.70) | 197 (12.07) | 206 (12.62) | 178 (10.39) | |
| Former | 839 (19.04) | 284 (19.40) | 271 (18.71) | 284 (19.03) | |
| Current | 2827 (69.25) | 980 (68.52) | 949 (68.67) | 898 (70.58) | |
| SBP, M (IQR), mm Hg | 127.00 (115.00, 142.00) | 129.00 (114.00, 143.00) | 126.00 (116.00, 140.00) | 127.00 (115.00, 141.00) | 0.489 |
| DBP, M (IQR), mm Hg | 68.00 (60.00, 77.00) | 66.00 (58.00, 74.00) | 68.00 (60.00, 77.00) | 71.00 (62.00, 79.00) | < 0.001 |
| HbA1c, M (IQR), % | 5.70 (5.40, 6.30) | 5.70 (5.40, 6.10) | 5.70 (5.40, 6.30) | 5.80 (5.40, 6.30) | 0.009 |
| TC, M (IQR), mg/dL | 181.00 (153.00, 212.00) | 173.00 (146.00, 206.00) | 183.00 (155.00, 213.00) | 185.00 (158.00, 217.00) | < 0.001 |
| HDL-C, M (IQR), mg/dL | 47.00 (39.00, 58.00) | 48.00 (40.00, 60.00) | 46.00 (38.00, 58.00) | 46.00 (39.00, 56.00) | 0.003 |
| Scr, M (IQR), mg/dL | 0.96 (0.80, 1.14) | 1.00 (0.84, 1.25) | 0.93 (0.80, 1.11) | 0.91 (0.78, 1.10) | < 0.001 |
| eGFR, M (IQR), ml/min/1.73m2 | 91.04 (83.20, 99.28) | 87.28 (79.41, 94.71) | 91.15 (83.71, 100.24) | 94.26 (87.30, 101.45) | < 0.001 |
| Albumin, M (IQR), g/dL | 4.20 (4.00, 4.40) | 4.10 (3.90, 4.30) | 4.20 (4.00, 4.40) | 4.20 (4.00, 4.40) | < 0.001 |
| BMI, M (IQR), kg/m2 | 29.39 (25.62, 33.90) | 26.51 (23.39, 30.74) | 29.60 (26.07, 34.10) | 31.90 (28.05, 36.30) | < 0.001 |
| Neutrophil, M (IQR), ×109/L | 4.30 (3.40, 5.40) | 5.10 (4.10, 6.30) | 4.40 (3.50, 5.40) | 3.50 (2.80, 4.30) | < 0.001 |
| Lymphocyte, M (IQR), ×109/L | 1.90 (1.50, 2.40) | 1.40 (1.10, 1.80) | 1.90 (1.60, 2.30) | 2.40 (2.00, 2.90) | < 0.001 |
| CRP, M (IQR), (mg/L) | 2.77 (1.20, 6.13) | 2.81 (1.20, 6.90) | 2.52 (1.10, 5.70) | 2.90 (1.30, 5.90) | 0.044 |
| Antihyperlipidemic use, n (%) | 2377 (58.31) | 844 (60.89) | 800 (56.79) | 733 (57.30) | 0.223 |
| Hypertension, n (%) | 3553 (80.49) | 1204 (80.27) | 1172 (79.00) | 1177 (82.24) | 0.269 |
| DM, n (%) | 1615 (33.92) | 503 (31.25) | 550 (35.81) | 562 (34.64) | 0.146 |
| Cancer, n (%) | 884 (21.50) | 374 (25.85) | 280 (19.56) | 230 (19.13) | 0.002 |
| Angina pectoris | 1149 (29.10) | 368 (27.19) | 395 (29.19) | 386 (30.81) | 0.351 |
| Congestive heart failure | 1223 (26.92) | 470 (30.52) | 399 (26.87) | 354 (23.37) | 0.001 |
| Coronary heart disease | 1653 (41.44) | 617 (46.37) | 556 (40.37) | 480 (37.61) | 0.002 |
| Myocardial infarction | 1682 (39.58) | 603 (41.21) | 585 (40.00) | 494 (37.51) | 0.256 |
| Stroke | 1400 (30.93) | 477 (29.77) | 478 (33.44) | 445 (29.50) | 0.124 |
| All-cause mortality, n (%) | 1795 (35.95) | 804 (47.07) | 582 (34.71) | 409 (26.10) | < 0.001 |
| Cardiovascular mortality, n (%) | 741 (14.43) | 331 (19.05) | 256 (14.29) | 154 (9.95) | < 0.001 |
| Follow-up time, M (IQR), months | 84.00 (44.00, 137.00) | 73.00 (37.00, 123.00) | 88.00 (47.00, 145.00) | 95.00 (51.00, 147.00) | < 0.001 |
Abbreviations: T Tertiles, M Median, PIR Poverty income ratio, SBP Systolic blood pressure, DBP Diastolic blood pressure, HbA1c Glycosylated hemoglobin A1c, TC Total cholesterol, HDL-C High-density lipoprotein cholesterol, Scr Serum creatinine, eGFR Estimated glomerular filtration rate, CRP C-reactive protein, BMI Body mass index, DM Diabetes mellitus
Association of LnALI with all-cause and cardiovascular mortality
Kaplan–Meier analysis revealed that high lnALI levels were associated with significantly reduced risks of all-cause and cardiovascular mortality in patients with CVD (Fig. 2). The Cox proportional hazards regression analyses for the association between lnALI and mortality outcomes are presented in Table 2. Considering all-cause mortality, a significant inverse association was observed in the fully adjusted model (Model 3). Compared with those of the lowest tertile (T1), the hazard ratios (HRs) and 95% confidence intervals (CIs) of the higher tertiles T2 and T3 were 0.82 (0.72–0.93) and 0.66 (0.56–0.77), respectively, with a significant trend across tertiles (P for trend < 0.001). When analyzed as a continuous variable, each one-unit increase in lnALI was associated with a 34% lower risk of all-cause mortality (HR: 0.66, 95% CI: 0.58–0.75). Similarly, elevated lnALI levels were significantly associated with reduced risk of cardiovascular mortality. After full adjustment, the HRs (95% CIs) for cardiovascular mortality across the higher tertiles (T2 and T3) were 0.82 (0.69–0.98) and 0.63 (0.47–0.83), respectively, relative to that of T1 (P for trend < 0.001). Per one-unit increment in lnALI, the risk of cardiovascular mortality decreased by 39% (HR: 0.61, 95% CI: 0.50–0.76). The E-values for these associations ranged from 1.26 to 1.99 (Table S2).
Fig. 2.
Kaplan–Meier survival curves for all-cause (A) and cardiovascular mortality (B) across lnALI tertiles. Abbreviation: lnALI, log-transformed advanced lung cancer inflammation index
Table 2.
Multivariate-adjusted Cox regression models of LnALI for all-cause and cardiovascular mortality
| Model 1 | Model 2 | Model 3 | ||||
|---|---|---|---|---|---|---|
| HR (95% CI) | P | HR (95% CI) | P | HR (95% CI) | P | |
| All-cause mortality | ||||||
| LnALI | 0.48 (0.42–0.55) | < 0.001 | 0.65 (0.57–0.74) | < 0.001 | 0.66 (0.58–0.75) | < 0.001 |
| LnALI (tertiles) | ||||||
| T1 | 1.00 (Reference) | 1.00 (Reference) | 1.00 (Reference) | |||
| T2 | 0.62 (0.53–0.72) | < 0.001 | 0.79 (0.69–0.90) | < 0.001 | 0.82 (0.72–0.93) | 0.002 |
| T3 | 0.44 (0.38–0.52) | < 0.001 | 0.66 (0.56–0.78) | < 0.001 | 0.66 (0.56–0.77) | < 0.001 |
| P for trend | < 0.001 | < 0.001 | < 0.001 | |||
| Cardiovascular mortality | ||||||
| LnALI | 0.46 (0.37–0.56) | < 0.001 | 0.63 (0.51–0.78) | < 0.001 | 0.61 (0.50–0.76) | < 0.001 |
| LnALI (tertiles) | ||||||
| T1 | 1.00 (Reference) | 1.00 (Reference) | 1.00 (Reference) | |||
| T2 | 0.63 (0.52–0.77) | < 0.001 | 0.82 (0.69–0.97) | 0.020 | 0.82 (0.69–0.98) | 0.030 |
| T3 | 0.42 (0.32–0.56) | < 0.001 | 0.66 (0.49–0.88) | 0.004 | 0.63 (0.47–0.83) | 0.001 |
| P for trend | < 0.001 | 0.003 | < 0.001 | |||
Abbreviations: ALI Advanced Lung Cancer Inflammation Index, HR Hazard Ratio, PIR Poverty income ratio, SBP Systolic blood pressure, DBP Diastolic blood pressure, HbA1c Glycosylated hemoglobin A1c, TC Total cholesterol, HDL-C High-density lipoprotein cholesterol, Scr Serum creatinine, DM Diabetes mellitus
Model 1: No covariates were adjusted for
Model 2: Adjusted for age, sex, and race
Model 3: Adjusted for age, sex, race, education, marital status, PIR, smoking status, alcohol intake, SBP, DBP, HbA1c, TC, HDL-C, Scr, antihyperlipidemic use, hypertension, DM, and cancer
After full adjustment for covariates, RCS regression was used to examine the potential nonlinear association between lnALI and mortality. RCS analysis with 4 knots revealed significant nonlinear associations between lnALI and both all-cause mortality (P for nonlinearity < 0.001) and cardiovascular mortality (P for nonlinearity < 0.001) (Fig. 3).
Fig. 3.
RCS analysis of the association between lnALI and all-cause (A) and cardiovascular mortality (B). Abbreviations: RCS, restricted cubic spline; lnALI, log-transformed advanced lung cancer inflammation index
Subgroup analysis
The consistency of the association between lnALI and mortality outcomes was assessed through stratified and interaction analyses (Table 3). We analyzed the interaction effects of age, sex, race, smoking status, alcohol intake, hypertension, DM, antihyperlipidemic use, and cancer. A significant interaction was observed for diabetes status (P for interaction = 0.046) on all-cause mortality. The inverse association between lnALI and the risk of all-cause mortality was significant in both subgroups. However, this association was substantially stronger in patients with diabetes (HR: 0.52, 95% CI: 0.44–0.62, P < 0.001) than in those without diabetes (HR: 0.78, 95% CI: 0.68–0.89, P = 0.004).
Table 3.
Subgroup analysis of the association between LnALI index and all-cause and cardiovascular mortality
| All-cause mortality | Cardiovascular mortality | |||||
|---|---|---|---|---|---|---|
| HR (95%CI) | P | P interaction | HR (95%CI) | P | P interaction | |
| Age | 0.993 | 0.200 | ||||
| <65 years | 0.68 (0.54–0.85) | 0.013 | 0.46 (0.32–0.67) | 0.003 | ||
| ≥ 65 years | 0.69 (0.61–0.77) | < 0.001 | 0.71 (0.60–0.85) | 0.003 | ||
| Gender | 0.430 | 0.143 | ||||
| Male | 0.65 (0.57–0.75) | < 0.001 | 0.55 (0.44–0.68) | < 0.001 | ||
| Female | 0.70 (0.60–0.82) | < 0.001 | 0.73 (0.57–0.94) | 0.054 | ||
| Race | 0.929 | 0.991 | ||||
| Mexican American | 0.61 (0.42–0.90) | 0.014 | 0.36 (0.20–0.65) | 0.001 | ||
| Other Hispanic | 0.50 (0.26–0.94) | 0.180 | 0.20 (0.07–0.56) | 0.002 | ||
| Non-Hispanic White | 0.69 (0.60–0.79) | < 0.001 | 0.67 (0.54–0.83) | 0.002 | ||
| Non-Hispanic Black | 0.72 (0.58–0.89) | 0.004 | 0.63 (0.46–0.87) | 0.009 | ||
| Other Race | 0.25 (0.12–0.54) | 0.003 | 0.19 (0.04–0.80) | 0.031 | ||
| Smoking status | 0.933 | 0.620 | ||||
| Never | 0.69 (0.58–0.82) | 0.001 | 0.65 (0.50–0.83) | 0.015 | ||
| Former | 0.65 (0.56–0.76) | < 0.001 | 0.70 (0.55–0.90) | 0.036 | ||
| Current | 0.68 (0.53–0.87) | 0.016 | 0.42 (0.28–0.63) | < 0.001 | ||
| Alcohol intake | 0.439 | 0.437 | ||||
| Never | 0.79 (0.62–1.01) | 0.214 | 0.81 (0.55–1.21) | 0.555 | ||
| Former | 0.68 (0.55–0.84) | 0.014 | 0.59 (0.43–0.81) | 0.010 | ||
| Current | 0.65 (0.57–0.75) | < 0.001 | 0.58 (0.47–0.72) | < 0.001 | ||
| Hypertension | 0.135 | 0.624 | ||||
| No | 0.93 (0.67–1.30) | 0.723 | 0.72 (0.42–1.25) | 0.298 | ||
| Yes | 0.65 (0.59–0.73) | < 0.001 | 0.63 (0.53–0.74) | < 0.001 | ||
| DM | 0.046 | 0.643 | ||||
| No | 0.78 (0.68–0.89) | 0.004 | 0.63 (0.52–0.78) | < 0.001 | ||
| Yes | 0.52 (0.44–0.62) | < 0.001 | 0.62 (0.48–0.80) | 0.012 | ||
| Antihyperlipidemic use | 0.685 | 0.314 | ||||
| No | 0.68 (0.59–0.79) | < 0.001 | 0.57 (0.45–0.73) | < 0.001 | ||
| Yes | 0.67 (0.58–0.77) | < 0.001 | 0.66 (0.53–0.82) | 0.006 | ||
| Cancer | 0.989 | 0.966 | ||||
| No | 0.66(0.54–0.82) | 0.003 | 0.69(0.49–0.97) | 0.070 | ||
| Yes | 0.68(0.60–0.77) | < 0.001 | 0.62(0.52–0.75) | < 0.001 | ||
The model was adjusted for age, gender, race, education, marital status, PIR, smoking status, alcohol intake, SBP, DBP, HbA1c, TC, HDL-C, Scr, antihyperlipidemic use, hypertension, DM and cancer
Abbreviations: ALI Advanced Lung Cancer Inflammation Index, HR Hazard Ratio, PIR Poverty income ratio, SBP Systolic blood pressure, DBP Diastolic blood pressure, HbA1c Glycosylated hemoglobin A1c, TC Total cholesterol, HDL-C High-density lipoprotein cholesterol, Scr Serum creatinine, DM Diabetes mellitus
Predictive ability of ALI for all-cause and cardiovascular mortality in patients with CVD
For all-cause mortality, the time-dependent ROC analysis demonstrated varying predictive performance across different time points (Fig. 4). At 1-year follow-up, lnALI exhibited the highest discriminative ability with an AUC of 0.727, outperforming both mBMI (AUC = 0.676), NLR (AUC = 0.68) and CRP (AUC = 0.642). This trend persisted across longer follow-up periods, with lnALI maintaining the highest AUC values at 3 years (0.678), 5 years (0.658), and 10 years (0.638). Notably, lnALI demonstrated superior predictive performance compared with that of mBMI, NLR, and CRP. For CVD-specific mortality, Similar patterns were observed for CVD mortality prediction. The result showed in Fig. 5.
Fig. 4.
Time-dependent ROC curves of the ALI for predicting all-cause mortality. Abbreviation: ROC, receiver operating characteristic; AUC, area under the curve; ALI, advanced lung cancer inflammation index; CRP, C-reactive protein; NLR, neutrophil-to-lymphocyte ratio
Fig. 5.
Time-dependent ROC curves of the ALI for predicting cardiovascular mortality. Abbreviation: ROC, receiver operating characteristic; AUC, area under the curve; ALI, advanced lung cancer inflammation index; CRP, C-reactive protein; NLR, neutrophil-to-lymphocyte ratio
Sensitivity analyses
Kaplan–Meier analysis revealed that high lnALI levels were associated with significantly reduced risks of all-cause and cardiovascular mortality in patients with CVD according to determining an optimal dichotomous cut-point via time-dependent ROC analysis (the cutpoint of lnALI was 3.611 for all-cause mortality and 3.387 for cardiovascular mortality), using quartiles of lnALI and in the non-cancer population. (Figure S1-3).
The Cox proportional hazards regression analyses for the association between lnALI (based on quartiles) and mortality outcomes are presented in Table S3. Considering all-cause mortality, a significant inverse association was observed in the fully adjusted model (Model 3). Compared with those of the Q1, the HRs and 95% CIs of the Q2, Q3 and Q4 were 0.75(0.65–0.85), 0.69(0.59–0.80) and 0.61(0.51–0.73), respectively, with a significant trend across quartiles (P for trend < 0.001). Similar patterns were observed for CVD mortality prediction.
The Cox proportional hazards regression analyses for the association between lnALI based on ROC cut point and mortality outcomes are presented in Table S4. Considering all-cause mortality, a significant inverse association was observed in the fully adjusted model (Model 3). Compared with low lnALI, the HRs and 95% CIs of the high lnALI was 0.68(0.60–0.76). Similar patterns were observed for CVD mortality prediction.
Discussion
In this study, we employed multiple analytical approaches to comprehensively examine the association between ALI and all-cause and cardiovascular mortality in individuals with CVD. Drawing on data from 4,247 patients with CVD in NHANES, our findings reveal a significant inverse relationship between ALI and the risk of all-cause and cardiovascular mortality. Stratified analyses supported the general consistency of these associations and revealed a significant interaction with the diabetes status for all-cause mortality. Furthermore, ALI showed better predictive performance for 1-, 3-, 5-, and 10-year mortality than did mBMI, NLR, and CRP when the indicators were evaluated individually.
Inflammation plays a pivotal role in the initiation and progression of CVD, connecting immune dysregulation, oxidative stress, and metabolic imbalance. Inflammatory activation promotes endothelial dysfunction and lipid oxidation, thereby accelerating atherosclerosis and plaque instability [20]. Persistent inflammation disrupts protein synthesis and increases catabolism, leading to malnutrition and perpetuating systemic inflammation—a vicious cycle that worsens prognosis [21]. Alb, the most abundant plasma protein traditionally viewed as a nutritional marker, represents an independent predictor of cardiovascular outcomes [22]. The prognostic significance of hypoalbuminemia stems from its reflection of both malnutrition and systemic inflammation, considering the antioxidant, anti-inflammatory, and antithrombotic properties of Alb [23]. BMI, another indicator of nutritional status, exhibits a complex relationship with CVD outcomes. Although obesity is a well-established risk factor for disease onset, low BMI remains consistently associated with elevated mortality—a pattern termed the “obesity paradox” [24]. This paradox may reflect disease-related wasting in low-BMI patients, including sarcopenia or cardiac cachexia, both of which are strong predictors of death [25]. Furthermore, in end-stage heart failure, anorexia and metabolic disturbances often drive progressive weight loss. Additionally, BMI fails to account for body composition or fat distribution, thereby limiting its precision in reflecting metabolic health [26]. An elevated NLR, which captures the dual pathology of systemic inflammation and immune suppression, predicts poor cardiovascular outcomes [27, 28]. ALI, a composite index incorporating Alb levels, BMI, and NLR, enhances prognostic assessment by providing a concurrent evaluation of inflammation, nutritional status, and metabolic health.
Several studies have explored the prognostic significance of ALI in CVD recently. Among patients with non-ST-segment elevation myocardial infarction, Karaca demonstrated that ALI was independently associated with 1-year major adverse cardiovascular and cerebrovascular events (MACCEs) [29]. Similarly, Wang et al. reported a nonlinear relationship between ALI and 1-year risk of major adverse cardiovascular events in an acute coronary syndrome cohort, indicating that when the ALI value was below the threshold of 334.96, higher ALI levels were associated with a lower risk of all-cause mortality [30]. Moreover, Trimarchi et al. found that low ALI levels were linked to increased all-cause mortality among patients with ST-segment elevation myocardial infarction undergoing primary percutaneous coronary intervention [31]. Additionally, Chen proposed that ALI is a predictor of long-term outcomes in patients with heart failure, where high ALI levels were associated with a significant reduction in all-cause and cardiovascular mortality compared with the death events under low ALI levels [32].
Our study indicated an L-shaped association between ALI and mortality risk, aligning with previous findings in survivors of MACCEs, where high ALI levels were linked to reduced mortality [33]. Conversely, Peng et al. reported a U-shaped relationship between ALI and all-cause mortality in patients with CHD, suggesting that extremely low and high ALI levels may be detrimental [17]. The discrepancy may emerge from methodological and population differences. Logarithmic transformation in our analysis corrected for right-skewed ALI distribution [34], revealing a potentially more plausible L-shaped trend. Our study enrolled a general CVD population, which included a substantial proportion of individuals with heart failure—a subgroup where the “obesity paradox” is most pronounced [35]. In this context, moderate overnutrition may act as a critical metabolic reserve to counteract catabolic processes, thereby conferring a protective effect that plateaus at high ALI levels. In contrast, in the above-mentioned CHD cohort, overnutrition—predominantly mediated by elevated BMI—and its concurrent pathogenic burdens of insulin resistance, dyslipidemia, and chronic inflammation might counteract the potential protective effects observed at extreme ALI levels, contributing to an increased mortality risk [36, 37]. Future research employing standardized ALI measurement protocols and larger heterogeneous CVD cohorts is needed to validate the L-shaped association observed herein and further clarify the underlying mechanisms driving the divergent findings across different patient populations.
Our subgroup analysis indicated that the protective association between ALI and all-cause mortality was most pronounced in patients with DM. This enhanced protective effect might be explained by the broader spectrum of fatal etiologies in patients with DM, who are more inherently susceptible to life-threatening complications (such as severe infections and end-stage renal disease) than their non-DM counterparts with CVD [38–40]. The protective role of ALI against these non-cardiovascular causes of death may be more prominent in the DM subgroup. Adequate nutritional status bolsters immune function to counteract infectious insults [41]. while attenuating the systemic inflammatory burden alleviates the risk of progressive renal damage [42]. Furthermore, DM is frequently accompanied by insulin resistance, a pathological state closely linked to chronic inflammation and nutritional dysregulation. By potentially improving nutritional homeostasis and dampening inflammatory responses, ALI may indirectly ameliorate insulin resistance, thereby reducing the risk of insulin resistance-related sequelae (e.g., metabolic derangements and vascular endothelial dysfunction) that contribute to non-cardiovascular mortality [43]. Further research is needed to validate these hypotheses, particularly through assessing the effect of ALI on distinct mortality subtypes and exploring the underlying molecular pathways linking ALI to DM-related pathological processes.
From a clinical perspective, the inverse association between ALI and mortality highlights the potential therapeutic importance of addressing systemic inflammation and nutritional deficiency in patients with CVD. We posit that ALI’s advantage may stem from its integrative nature, simultaneously capturing the synergistic interplay of nutrition (via BMI and albumin) and systemic inflammation (via NLR), which might reflect the “nutrition-inflammation imbalance” more holistically than a single inflammatory marker like CRP. Interventions such as anti-inflammatory therapy, individualized nutritional supplementation, and resistance training aimed at preserving skeletal muscle mass could potentially improve ALI levels and improve cardiovascular outcomes [44–46]. Moreover, given its simplicity, affordability, and reproducibility, ALI may serve as a dynamic indicator for monitoring the effectiveness of multidisciplinary management strategies in cardiovascular practice.
Several limitations of this study should be acknowledged. First, ALI was measured once at baseline, precluding assessment of longitudinal changes in ALI and their potential influence on cardiovascular and all-cause mortality outcomes. Second, although various covariates were adjusted for, residual confounding cannot be ruled out. E-value analyses suggested that our findings for lnALI (E-values 1.97–1.99) and higher ALI categories (E-values 1.68–1.90) exhibit moderate robustness; however, associations with cardiovascular mortality, particularly for ALI category T2 (E-value = 1.26), may be more susceptible to unmeasured confounding. Factors such as environmental exposures, CVD management strategies, and disease duration may have contributed to the observed associations and warrant further investigation when data become available. Third, the analysis relied solely on NHANES data, which may introduce potential bias related to region, healthcare context, and patient selection. Additionally, the diagnostic criteria for hypertension and diabetes, while ensuring standardization, are based on fixed definitions within NHANES and may not fully align with current common clinical practice, potentially affecting the generalizability of prevalence estimates. Future studies incorporating multi-center or multinational cohorts are necessary to enhance the generalizability and external validity of these findings. Finally, prospective and interventional research should explore whether modifying ALI levels could effectively reduce CVD-related mortality.
Conclusion
The present study demonstrated that low ALI was independently associated with increased all-cause and cardiovascular mortality in patients with CVD. These findings position ALI as a promising indicator of nutrition-inflammation status and a prognostic correlate for mortality risk, adding to the evidence base that warrants future investigation into its potential clinical relevance. Further large-scale prospective studies are warranted to confirm these associations.
Supplementary Information
Acknowledgements
The authors are grateful to all the NHANES staff and participants for their contributions to the collection and sharing of data.
Abbreviations
- CVD
Cardiovascular disease
- ALI
Advanced lung cancer inflammation index
- NLR
Neutrophil-to-lymphocyte ratio
- BMI
Body mass index
- Alb
Albumin
- NHANES
National Health and Nutrition Examination Survey
- NCHS
National Center for Health Statistics
- PIR
Poverty-to-income ratio
- HbA1c
Hemoglobin type A1c
- TC
Total cholesterol
- HDL-C
High-density lipoprotein cholesterol
- Scr
Serum creatinine
- CRP
C-reactive protein
- DM
Diabetes mellitus
- RCS
Restricted cubic splines
- ROC
Receiver operating characteristic
- CIs
Confidence intervals
- HR
Hazard ratio
- AUC
Area under the curve
- MACCE
Major adverse cardiovascular and cerebrovascular events
Authors’ contributions
YPY conducted the data analysis and wrote the primary manuscript. XPY, XW, NQL and AMD reviewed and revised the manuscript. All the authors have approved the manuscript for publication.
Funding
The study was supported by National Key Research and Development Program of China (2022YFC3602405).
Data availability
The data for this study were obtained from the publicly accessible NHANES database (www.cdc.gov/nchs/nhanes/).
Declarations
Ethics approval and consent to participate
The NHANES survey protocol was approved by the National Center for Health Statistics Ethics Review Board, and all participants provided written informed consent. This study was conducted in accordance with the ethical standards of the Declaration of Helsinki.
Consent for publication
Not applicable.
Competing interests
The authors declare no competing interests.
Footnotes
Publisher’s Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
References
- 1.Global incidence. prevalence, years lived with disability (YLDs), disability-adjusted life-years (DALYs), and healthy life expectancy (HALE) for 371 diseases and injuries in 204 countries and territories and 811 subnational locations, 1990–2021: a systematic analysis for the global burden of disease study 2021. Lancet (London England). 2024;403(10440):2133–61. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 2.Vaduganathan M, Mensah GA, Turco JV, et al. The global burden of cardiovascular diseases and risk: A compass for future Health. J Am Coll Cardiol. 2022;80(25):2361–71. [DOI] [PubMed] [Google Scholar]
- 3.Woodruff RC, Tong X, Loustalot FV, et al. Cardiovascular disease mortality Trends, 2010–2022: an update with final Data. Am J Prev Med. 2025;68(2):391–5. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 4.Global Burden of Cardiovascular Diseases and Risks 2023 Collaborators. Global, Regional, and National Burden of Cardiovascular Diseases and Risk Factors in 204 Countries and Territories, 1990-2023. J Am College Cardiol. 2025;86(22):2167–2243. [DOI] [PubMed]
- 5.Ndumele CE, Neeland IJ, Tuttle KR, et al. A synopsis of the evidence for the science and clinical management of Cardiovascular-Kidney-Metabolic (CKM) syndrome: A scientific statement from the American heart Association. Circulation. 2023;148(20):1636–64. [DOI] [PubMed] [Google Scholar]
- 6.Xiao S, Wang Z, Zuo R, et al. Association of systemic immune inflammation index with All-Cause, cardiovascular Disease, and Cancer-Related mortality in patients with cardiovascular disease: A Cross-Sectional Study. J Inflamm Res. 2023;16:941–61. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 7.Zhu B, Liu Y, Liu W, et al. Association of neutrophil-to-lymphocyte ratio with all-cause and cardiovascular mortality in CVD patients with diabetes or pre-diabetes. Sci Rep. 2024;14(1):24324. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 8.Raposeiras Roubín S, Abu Assi E, Cespón Fernandez M, et al. Prevalence and prognostic significance of malnutrition in patients with acute coronary Syndrome. J Am Coll Cardiol. 2020;76(7):828–40. [DOI] [PubMed] [Google Scholar]
- 9.Chen Y, Copeland WK, Vedanthan R et al. Association between body mass index and cardiovascular disease mortality in East Asians and South asians: pooled analysis of prospective data from the Asia cohort Consortium. BMJ (Clinical research ed). 2013;347:f5446. [DOI] [PMC free article] [PubMed]
- 10.Phillips A, Shaper AG, Whincup PH. Association between serum albumin and mortality from cardiovascular disease, cancer, and other causes. Lancet (London England). 1989;2(8677):1434–6. [DOI] [PubMed] [Google Scholar]
- 11.Jafri SH, Shi R, Mills G. Advance lung cancer inflammation index (ALI) at diagnosis is a prognostic marker in patients with metastatic non-small cell lung cancer (NSCLC): a retrospective review. BMC Cancer. 2013;13:158. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 12.Li H, Chen G, Bao S, et al. Association between the advanced lung cancer inflammation index and all-cause mortality in the US MASLD/MetALD patient population: a cohort study. Int J Surg (London England). 2025;111(7):4412–21. [DOI] [PubMed] [Google Scholar]
- 13.Zhang X, Hu X, Qian L, et al. The association between nutritional-inflammatory status and chronic kidney disease prognosis: a population-based study. Ren Fail. 2025;47(1):2471016. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 14.Kurkiewicz K, Gąsior M, Szyguła-Jurkiewicz BE. Markers of malnutrition, inflammation, and tissue remodeling are associated with 1-year outcomes in patients with advanced heart failure. Pol Archives Intern Med. 2023;133(6):16411. [DOI] [PubMed]
- 15.Amin AM, Ghaly R, Elbenawi H et al. Impact of advanced lung cancer inflammation index on all-cause mortality among patients with heart failure: a systematic review and meta-analysis with reconstructed time-to-event data. Cardio-oncology (London, England). 2025;11(1):9. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 16.Sun X, Zhang X, Tang R, et al. Advanced lung cancer inflammation index is associated with mortality in critically ill patients with heart failure. ESC Heart Fail. 2025;12(1):508–16. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 17.Peng J, Xiang J, Hu J, et al. Nutritional-inflammatory balance assessed by advanced lung cancer inflammation index and its association with all-cause mortality in coronary heart disease: a retrospective cohort study. J Health Popul Nutr. 2025;44(1):317. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 18.Zhao HW, Wang CF. The relationship between advanced lung cancer inflammation index and adverse clinical outcomes in patients with myocardial infarction with No-Obstructive coronary Arteries. J Inflamm Res. 2025;18:9907–17. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 19.VanderWeele TJ, Ding P. Sensitivity analysis in observational research: introducing the E-Value. Ann Intern Med. 2017;167(4):268–74. [DOI] [PubMed] [Google Scholar]
- 20.Henein MY, Vancheri S, Longo G et al. The role of inflammation in cardiovascular Disease. Int J Mol Sci. 2022;23(21):12906. [DOI] [PMC free article] [PubMed]
- 21.Pecoits-Filho R, Lindholm B, Stenvinkel P. The malnutrition, inflammation, and atherosclerosis (MIA) syndrome -- the heart of the matter. Nephrology, dialysis, transplantation: official publication of the European Dialysis and transplant association. Eur Ren Association. 2002;17(Suppl 11):28–31. [DOI] [PubMed] [Google Scholar]
- 22.Djoussé L, Rothman KJ, Cupples LA, et al. Serum albumin and risk of myocardial infarction and all-cause mortality in the Framingham offspring Study. Circulation. 2002;106(23):2919–24. [DOI] [PubMed] [Google Scholar]
- 23.Manolis AA, Manolis TA, Melita H, et al. Low serum albumin: A neglected predictor in patients with cardiovascular disease. Eur J Intern Med. 2022;102:24–39. [DOI] [PubMed] [Google Scholar]
- 24.Andreotti F, Rio T, Lavorgna A. Body fat and cardiovascular risk: Understanding the obesity paradox. Eur Heart J. 2009;30(7):752–4. [DOI] [PubMed] [Google Scholar]
- 25.Fujimoto Y, Maeda D, Kagiyama N, et al. Prevalence and prognostic impact of the coexistence of cachexia and sarcopenia in older patients with heart failure. Int J Cardiol. 2023;381:45–51. [DOI] [PubMed] [Google Scholar]
- 26.Xia JY, Lloyd-Jones DM, Khan SS. Association of body mass index with mortality in cardiovascular disease: new insights into the obesity paradox from multiple perspectives. Trends Cardiovasc Med. 2019;29(4):220–5. [DOI] [PubMed] [Google Scholar]
- 27.Song S, Chen L, Yu R, et al. Neutrophil-to-lymphocyte ratio as a predictor of all-cause and cardiovascular mortality in coronary heart disease and hypertensive patients: a retrospective cohort study. Front Endocrinol. 2024;15:1442165. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 28.Zhang X, Wei R, Wang X, et al. The neutrophil-to-lymphocyte ratio is associated with all-cause and cardiovascular mortality among individuals with hypertension. Cardiovasc Diabetol. 2024;23(1):117. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 29.Karaca M, Kalyoncuoğlu M, Zengin A et al. The prognostic value of the advanced lung cancer inflammation index for major cardiovascular and cerebrovascular events in patients with Non-ST elevation myocardial infarction undergoing percutaneous coronary Intervention. J Clin Med. 2025;14(5):1403. [DOI] [PMC free article] [PubMed]
- 30.Wang X, Wei C, Fan W, et al. Advanced lung cancer inflammation index for predicting prognostic risk for patients with acute coronary syndrome undergoing percutaneous coronary Intervention. J Inflamm Res. 2023;16:3631–41. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 31.Trimarchi G, Pizzino F, Lilli A et al. Advanced lung cancer inflammation index as predictor of All-Cause mortality in ST-Elevation myocardial infarction patients undergoing primary percutaneous coronary Intervention. J Clin Med. 2024;13(20). [DOI] [PMC free article] [PubMed]
- 32.Chen W, Zhang G, Lei Q, et al. Prognostic value of advanced lung cancer inflammation index in heart failure patients: A comprehensive analysis. ESC Heart Fail. 2025;12(3):2298–309. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 33.Hou C, Huang X, Wang J, et al. Inflammation and nutritional status in relation to mortality risk from cardio-cerebrovascular events: evidence from NHANES. Front Nutr. 2024;11:1504946. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 34.Su X, Rao H, Zhao C, et al. Association between advanced lung cancer inflammation index and mortality in US adults with chronic obstructive pulmonary Disease. Int J Chron Obstruct Pulmon Dis. 2025;20:2481–92. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 35.Alebna PL, Mehta A, Yehya A, et al. Update on obesity, the obesity paradox, and obesity management in heart failure. Prog Cardiovasc Dis. 2024;82:34–42. [DOI] [PubMed] [Google Scholar]
- 36.Mann V, Sundaresan A, Shishodia S. Overnutrition and lipotoxicity: impaired efferocytosis and chronic inflammation as precursors to multifaceted disease Pathogenesis. Biology. 2024;13(4):241. [DOI] [PMC free article] [PubMed]
- 37.Sakamoto K, Butera MA, Zhou C, et al. Overnutrition causes insulin resistance and metabolic disorder through increased sympathetic nervous system activity. Cell Metabol. 2025;37(1):121–e137126. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 38.Carey IM, Critchley JA, Chaudhry UAR, et al. Contribution of infection to mortality in people with type 2 diabetes: a population-based cohort study using electronic records. Lancet Reg Health Europe. 2025;48:101147. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 39.Støvring H, Wild S. How we count matters - infections are a major cause of death among patients with type 2 diabetes. Lancet Reg Health Europe. 2025;48:101177. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 40.Cheng HT, Xu X, Lim PS, et al. Worldwide epidemiology of Diabetes-Related End-Stage renal Disease, 2000–2015. Diabetes Care. 2021;44(1):89–97. [DOI] [PubMed] [Google Scholar]
- 41.Lu C, Xu Y, Li X, et al. Nutritional status affects immune function and exacerbates the severity of pulmonary tuberculosis. Front Immunol. 2024;15:1407813. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 42.Gutiérrez OM, Shlipak MG, Katz R, et al. Associations of plasma biomarkers of Inflammation, Fibrosis, and kidney tubular injury with progression of diabetic kidney disease: A cohort Study. Am J Kidney Diseases: Official J Natl Kidney Foundation. 2022;79(6):849–e857841. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 43.Donath MY, Drucker DJ. Obesity, diabetes, and inflammation: pathophysiology and clinical implications. Immunity. 2025;58(10):2373–82. [DOI] [PubMed] [Google Scholar]
- 44.Samuel M, Tardif JC. Inflammation reduction with Colchicine in atherosclerotic cardiovascular disease. Eur Heart J. 2025;46(26):2552–2563. [DOI] [PubMed]
- 45.Zhou S, Cheng F, He J, et al. Effects of high-quality protein supplementation on cardiovascular risk factors in individuals with metabolic diseases: A systematic review and meta-analysis of randomized controlled trials. Clinical nutrition (Edinburgh. Scotland). 2024;43(8):1740–50. [DOI] [PubMed] [Google Scholar]
- 46.Liu W, Zhang H, Wang X, et al. Combined effect of skeletal muscle mass loss and elevated insulin resistance on heart failure risk in older adults: a community-based longitudinal cohort study. Cardiovasc Diabetol. 2025;24(1):157. [DOI] [PMC free article] [PubMed] [Google Scholar]
Associated Data
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Supplementary Materials
Data Availability Statement
The data for this study were obtained from the publicly accessible NHANES database (www.cdc.gov/nchs/nhanes/).