Abstract
Background
The relationship between the aggregate index of systemic inflammation (AISI) and the mortality risk of pan-cancer patients in the US population remains unclear. This study aimed to investigate the relationship between baseline AISI and all-cause mortality and specific types of mortality in adult cancer survivors in the United States.
Methods
We used the data from the National Health and Nutrition Examination Survey (NHANES) from 2003 to 2018. A multivariate Cox regression analysis model was constructed to determine the relationship between baseline AISI and outcomes. Outcome events include all-cause, cardiovascular disease (CVD), and cancer-related mortality. Nonlinear correlations were analyzed via restricted cubic spline (RCS) analysis. Subgroup analysis and interaction tests were also conducted.
Results
A total of 3,773 adult cancer survivors were recruited in this study. Among them, 1,772 (42.99%) were male, with an average age of 62.83±14.32 years. The AISI was respectively divided into the quartiles (Q1–Q4) as follows: ≤179.23, 179.24–279.03, 279.04–442.59, and >442.59. During a median follow-up period of 87 months, 1,137 (30.14%) all-cause deaths occurred. Among these deaths, 314 were attributed to CVD and 343 to cancer. For every additional standard deviation increase in AISI, the risks of all-cause mortality, CVD mortality, and cancer-related mortality increased by 16% [hazard ratio (HR) =1.16, 95% confidence interval (CI): 1.12–1.21], 21% (HR =1.21, 95% CI: 1.14–1.29), and 9% (HR =1.09, 95% CI: 1.01–1.18), respectively. The RCS analysis results showed that the AISI index had a significant linear relationship with all-cause and CVD mortality. However, AISI showed a significant nonlinear relationship with cancer-related mortality (P for nonlinearity =0.01). Similar findings were also revealed in the subgroup analysis.
Conclusions
Elevated AISI is positively correlated with all-cause mortality in cancer survivors, and the AISI may thus serve as a valuable indicator of poor prognosis among cancer survivors.
Keywords: Cancer survivors, mortality, inflammatory biomarker, National Health and Nutrition Examination Survey (NHANES)
Highlight box.
Key findings
• For every one-standard-deviation increase in aggregate index of systemic inflammation (AISI), the risks of all-cause mortality, cardiovascular disease mortality, and cancer-related mortality increase by 16%, 21%, and 9% respectively.
• The study provides evidence of a dose-response relationship between AISI and mortality, with distinct linear and nonlinear patterns for different causes of death.
What is known and what is new?
• Chronic inflammation affects cancer outcomes, and common inflammatory biomarkers are used for cancer prognosis. AISI has prognostic value in certain cancers and chronic diseases.
• The study highlights the utility of AISI as a cost-effective, readily available prognostic tool that integrates multiple blood-based inflammatory markers.
What is the implication, and what should change now?
• Clinicians should consider incorporating AISI into routine blood work for cancer survivors to monitor systemic inflammation and assess long-term prognosis.
• Future research should focus on longitudinal tracking of AISI to evaluate its dynamic changes and validate its predictive value in a diversity of cancer populations.
Introduction
Cancer remains a significant global public health challenge (1). In 2022 alone, there were approximately 20 million new cases and 10 million cancer-related deaths worldwide (2). Although in recent years, the development of medical technology has led to significant progress in the prevention and treatment of cancer, the cancer incidence rate continues to increase (3). Consequently, the population of cancer survivors is expanding substantially, with estimates projecting it will reach 21.6 million in the United States by 2030 and exceed 26 million by 2040 (4). Nevertheless, a significant number of these survivors face long-term and late-effects from both the disease and its treatments (5), which often result in a reduction in life expectancy (6). Therefore, investigating practical and efficient strategies to improve the long-term well-being of this growing population is of critical importance for advancing global public health and strengthening cancer prevention efforts.
In recent years, the role of inflammation in cancer occurrence and progression has been widely reported (7,8). The inflammatory response is involved in all stages of cancer progression. The ultimate result of a chronically inflammatory microenvironment is the enhancement of tumor promotion, acceleration of tumor progression, invasion of surrounding tissues, inducement of angiogenesis, and the emergence of metastasis (1,6,9). Similarly, in the tumor microenvironment, the numbers of inflammatory cells such as neutrophils, lymphocytes, monocytes, and platelets change significantly, and these changes can reflect the degree of cancer progression (10-13). Previous research has demonstrated that these indicators are helpful in predicting and evaluating tumor progression and prognosis (14,15). Compared with single indicators, combined indicators, such as the neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR), and lymphocyte-to-monocyte ratio (LMR) provide stronger predictive power (14,16).
In recent years, a new method for evaluating systemic inflammation using multiple hematological parameters, the aggregate index of systemic inflammation (AISI), has been widely used to examine the relationship between inflammation and various diseases. Studies have shown that AISI is significantly associated with hypertension (17), pulmonary fibrosis (18), rheumatoid arthritis (19), and chronic obstructive pulmonary disease (20). In addition, AISI has been used to predict the prognosis of patients with various solid tumors, including prostate cancer (21), esophageal cancer (22), and gastric cancer (23). AISI has become a convenient, effective, and efficient marker of chronic inflammation, providing significant predictive value for clinical applications. However, although AISI has been confirmed to be closely correlated to certain cancers, research on its prognostic value for pan-cancer survivors remains limited. Therefore, this study had the following objectives: (I) to characterize the independent relationship between baseline AISI and the risk of all-cause and cause-specific mortality in a national cohort of adult cancer survivors; (II) to explore the potential nonlinearity of these relationships; and (III) to assess the prognostic value of AISI for long-term survival outcomes. We present this article in accordance with the STROBE reporting checklist (available at https://tcr.amegroups.com/article/view/10.21037/tcr-2025-2045/rc).
Methods
Data source and study population
The data for this study were all obtained from the 2003–2018 National Health and Nutrition Examination Survey (NHANES). The purpose of this database is to assess the health and nutritional conditions of both adults and children living in the United States. The NHANES database uses a stratified, multistage probability sampling design. The protocol of NHANES has been formally approved by the National Center for Health Statistics (NCHS) Research Ethics Review Board. Each participant provided informed consent by signing an agreement. The study was conducted in accordance with the Declaration of Helsinki and its subsequent amendments.
In this study, the data from eight cycles of NHANES [2003–2018] were reviewed, comprising 80,312 participants. Survivorship status was confirmed through physician-verified malignancy history via the query, “Have you ever received a cancer diagnosis from a healthcare provider?” After participants aged <18 years (n=32,549), non-cancer survivors (n=43,503), those with missing AISI indicators (n=471), pregnant participants (n=1), and those with missing follow-up information (n=15) were excluded, a total of 3,773 adult cancer survivor participants were included in this study. The research process is shown in Figure 1.
Figure 1.
Flowchart of participant inclusion. AISI, aggregate index of systemic inflammation; NHANES, National Health and Nutrition Examination Survey.
Baseline characteristics of the study population
Table 1 presents the baseline characteristics of participants with different AISI quartiles in this study. Overall, 3,773 cancer survivors were included in this study. Among them, 1,772 (42.99%) were males, and the average age was 62.83±14.32 years. Compared with participants in the lowest quartile, those in the higher AISI quartiles were older and more likely to be male, non-Hispanic white, obese, smokers, and nondrinkers. They also had a higher prevalence of diabetes and hypertension. Moreover, participants in the higher quartiles exhibited higher levels of monocytes, neutrophils, and platelets, in addition to lower levels of total cholesterol (TC), estimated glomerular filtration rate (eGFR), healthy eating index (HEI), and lymphocytes. The baseline characteristics of the participants’ follow-up status are shown in Table S1.
Table 1. Baseline characteristics of the study participants according to quartiles of AISI index.
| Characteristic | Total (n=3,773) | AISI quartiles | P | |||
|---|---|---|---|---|---|---|
| Q1 (n=943) | Q2 (n=944) | Q3 (n=943) | Q4 (n=943) | |||
| Sex | 0.04 | |||||
| Male | 1,772 (42.99) | 390 (39.25) | 423 (40.67) | 459 (44.76) | 500 (47.52) | |
| Female | 2,001 (57.01) | 553 (60.75) | 521 (59.33) | 484 (55.24) | 443 (52.48) | |
| Age (years) | 62.83±14.32 | 60.22±14.15 | 61.68±14.35 | 63.69±14.49 | 65.88±13.64 | <0.001 |
| Race | <0.001 | |||||
| Non-Hispanic White | 2,625 (86.32) | 561 (83.01) | 652 (86.60) | 690 (88.24) | 722 (87.40) | |
| Non-Hispanic Black | 510 (5.33) | 205 (8.69) | 118 (4.46) | 106 (4.60) | 81 (3.63) | |
| Mexican American | 249 (2.51) | 71 (3.04) | 66 (2.48) | 58 (2.15) | 54 (2.39) | |
| Other | 389 (5.84) | 106 (5.26) | 108 (6.46) | 89 (5.01) | 86 (6.59) | |
| IPR | 0.04 | |||||
| ≤1.30 | 915 (15.66) | 252 (17.54) | 231 (15.75) | 206 (14.78) | 226 (14.56) | |
| 1.31–3.50 | 1,532 (35.99) | 350 (31.60) | 349 (33.13) | 419 (38.81) | 414 (40.71) | |
| >3.50 | 1,326 (48.35) | 341 (50.86) | 364 (51.12) | 318 (46.41) | 303 (44.73) | |
| Education level | 0.50 | |||||
| Less than high | 831 (13.92) | 203 (12.78) | 206 (13.27) | 203 (14.35) | 219 (15.37) | |
| High school grad or equivalent | 866 (21.38) | 211 (22.29) | 198 (19.44) | 236 (23.01) | 221 (20.93) | |
| College or above | 2,076 (64.69) | 529 (64.92) | 540 (67.29) | 504 (62.64) | 503 (63.70) | |
| BMI (kg/m2) | 0.006 | |||||
| <25.0 | 1,052 (28.91) | 277 (34.04) | 249 (24.11) | 258 (28.52) | 268 (29.41) | |
| 25.0–29.9 | 1,303 (33.86) | 321 (34.72) | 337 (38.86) | 309 (30.61) | 336 (30.80) | |
| ≥30 | 1,418 (37.23) | 345 (31.25) | 358 (37.03) | 376 (40.87) | 339 (39.79) | |
| Smoking status | 0.049 | |||||
| Never | 1,709 (45.75) | 468 (49.69) | 453 (47.39) | 408 (42.88) | 380 (42.91) | |
| Former | 1,474 (37.99) | 354 (36.18) | 338 (36.66) | 389 (42.29) | 393 (36.88) | |
| Current | 590 (16.26) | 121 (14.13) | 153 (15.95) | 146 (14.83) | 170 (20.21) | |
| Drink status | 0.04 | |||||
| Never | 2,821 (70.29) | 694 (67.02) | 686 (68.81) | 717 (68.45) | 724 (77.14) | |
| Moderate | 575 (16.87) | 161 (19.26) | 148 (16.82) | 137 (18.64) | 129 (12.69) | |
| Heavy | 377 (12.83) | 88 (13.71) | 110 (14.37) | 89 (12.91) | 90 (10.17) | |
| Diabetes | 982 (20.87) | 237 (18.51) | 219 (18.08) | 263 (23.23) | 263 (23.92) | 0.03 |
| Hypertension | 2,393 (57.66) | 536 (47.15) | 587 (58.15) | 612 (60.95) | 658 (64.40) | <0.001 |
| TC (mmol/L) | 5.10±1.12 | 5.14±1.09 | 5.18±1.12 | 5.09±1.14 | 4.97±1.12 | 0.04 |
| HDL-C (mmol/L) | 1.42±0.45 | 1.46±0.45 | 1.42±0.43 | 1.41±0.45 | 1.39±0.45 | 0.05 |
| eGFR (mL/min/1.73 m2) | 81.83±20.95 | 84.78±19.04 | 82.46±21.26 | 81.00±20.60 | 79.01±22.39 | <0.001 |
| HEI | 50.94±12.50 | 51.72±12.86 | 51.24±12.82 | 50.67±12.12 | 50.09±12.11 | 0.20 |
Data are presented as number (%) or mean ± standard deviation. Q1: ≤179.23; Q2: 179.24–279.03; Q3: 279.04–442.59; Q4: >442.59. AISI, aggregate index of systemic inflammation; BMI, body mass index; eGFR, estimated glomerular filtration rate; HDL-C, high-density lipoprotein cholesterol; HEI, healthy eating index; IPR, family income-to-poverty ratio; TC, total cholesterol.
Definitions of exposure and outcome variables
The main exposure variable in this study was the AISI, which was calculated with the following formula: AISI = (neutrophils × monocytes × platelets)/lymphocytes. Blood cell counts were determined with the DxH 800 analyzer (Beckman Coulter, Brea, CA, USA) and reported as ×103 cells/µL. Participants were divided into four groups according to the quartiles of the AISI index, with the group in the first quartile serving as the reference.
The primary outcome of this study was all-cause mortality among adult cancer survivors. The secondary outcomes were cardiovascular disease (CVD) mortality and cancer-related mortality among adult cancer survivors.
Death-related data were obtained from the NHANES data files up to December 31, 2019. This file was linked to the National Death Index (NDI) of the NCHS via a probabilistic matching algorithm. The International Classification of Diseases, 10th Revision (ICD-10) was used to assess the causes of death. CVD mortality was defined by codes I00–I09, I11, I13, I20–I51, and I60–I69, and cancer-related mortality was defined by codes C00–C97.
Assessment of covariates
Covariates included sex (male and female), age, race (non-Hispanic White, non-Hispanic Black, Mexican American, or other), education level (≤ high school, college, or > college), family income-to-poverty ratio (IPR; <1.3, 1.31–3.50, or >3.50), body mass index (BMI), smoking status (never, former, or current), drinking status (never, moderate, or heavy), diabetes (yes or no), hypertension (yes or no), eGFR, TC, high-density lipoprotein cholesterol (HDL-C), HEI. BMI was calculated as weight (kg) divided by the square of height (m). Diabetes was defined as a self-reported diagnosis, hemoglobin A1c ≥6.5%, fasting blood glucose ≥126 mg/dL, 2-hour post-meal blood glucose ≥200 mg/dL, or use of insulin or oral hypoglycemic agents (24). Hypertension was defined as self-reported diagnosis, mean systolic blood pressure (SBP) ≥140 mmHg, mean diastolic blood pressure (DBP) ≥90 mmHg, or use of antihypertensive medications (25). The eGFR was calculated via the Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI) equation (26).
Statistical analysis
In accordance with the analysis guidelines of the NHANES database, all analyses in this study incorporated sample weights, clustering, and stratification to estimate the representativeness of adult cancer survivors in the entire US adult population. The study participants were divided into four groups based on the quartiles (Q1–Q4) of the AISI index. Continuous variables are presented as the mean and standard deviation (SD), while categorical variables are presented as frequencies and percentages. Multiple imputation methods were used to address missing covariates in the survey data. Baseline characteristics across AISI quartiles were compared using analysis of variance (ANOVA) or the Kruskal-Wallis test for continuous variables, as appropriate, and the Chi-squared test for categorical variables. The Kaplan-Meier curve was used to estimate survival over time, and the log-rank test was used to evaluate the differences between various survival curves. Multivariable Cox proportional hazards models were used to assess the associations between AISI and all-cause mortality, CVD mortality, and cancer-related mortality. The results are presented as hazard ratios (HRs) and 95% confidence intervals (CIs). Model 1 was unadjusted; Model 2 was adjusted for age, sex, race, education level, and IPR; and Model 3 was adjusted for age, sex, race, education level, IPR, BMI, smoking status, drink status, hypertension, diabetes, eGFR, TC, HDL-C, and HEI. Restricted cubic spline (RCS) curves were used to visualize the dose-response relationship between AISI and mortality.
Furthermore, the data were stratified by sex (male and female), age (≤60 and >60 years), race (non-Hispanic white and others), IPR (≤3.5 and >3.5), education level (less than high and college or above), BMI (≤30 and >30 kg/m2), smoking status (never and former or current), drink status (never and moderate or heavy), diabetes (no and yes), and hypertension (no and yes). The likelihood ratio test was used to examine the influence of potential covariates on the effects.
The statistical analysis was performed with R software version 4.2.1. A P value of less than 0.05 was considered statistically significant.
Results
Relationships of AISI with mortality
During the median follow-up period of 87 months, 1,137 (30.14%) cases of all-cause death occurred, among which 314 and 343 cases were attributed to CVD and cancer death, respectively. Additionally, the Kaplan-Meier survival plots, as shown in Figure 2, indicated significant differences in all-cause death, cardiovascular death, and cancer-related death among participants in the AISI quartile groups.
Figure 2.
Kaplan-Meier survival analysis of all-cause mortality (A), CVD mortality (B) and cancer-related mortality (C) in quartiles of the AISI among cancer survivors. AISI, aggregate index of systemic inflammation; CVD, cardiovascular disease.
Table 2 illustrates the relationships between AISI and all-cause death, CVD death, and cancer-related death. In all models, AISI as a continuous variable was significantly associated with all-cause death, CVD death, and cancer-related death. For every one-standard-deviation increase in AISI, the risks of all-cause death, CVD death, and cancer-related mortality increased by 16% [hazard ratio (HR) =1.16, 95% confidence interval (CI): 1.12–1.21], 21% (HR =1.21, 95% CI: 1.14–1.29), and 9% (HR =1.09, 95% CI: 1.01–1.18), respectively. As a categorical variable, the multivariable-adjusted associations between AISI quartiles and mortality are shown in Table 2. Compared to the lowest quartile (Q1), the HR for all-cause mortality in Q4 was 1.26 (95% CI: 1.02–1.56); for CVD mortality, it was 1.16 (95% CI: 0.74–1.83); and for cancer-related mortality, it was 1.09 (95% CI: 0.76–1.56).
Table 2. Association of AISI with all-cause and cause-specific death in cancer survivors.
| AISI | Model 1 | Model 2 | Model 3 | |||||
|---|---|---|---|---|---|---|---|---|
| HR (95% CI) | P value | HR (95% CI) | P value | HR (95% CI) | P value | |||
| Outcome: all-cause mortality | ||||||||
| AISI (per 1 SD) | 1.18 (1.13–1.24) | <0.001 | 1.19 (1.14–1.25) | <0.001 | 1.16 (1.12–1.21) | <0.001 | ||
| Q1 | Reference | – | Reference | – | Reference | - | ||
| Q2 | 0.98 (0.76–1.27) | 0.88 | 0.92 (0.72–1.18) | 0.50 | 0.89 (0.69–1.15) | 0.36 | ||
| Q3 | 1.29 (1.02–1.64) | 0.03 | 1.02 (1.01–1.26) | 0.02 | 1.08 (1.01–1.17) | 0.04 | ||
| Q4 | 1.96 (1.57–2.46) | <0.001 | 1.37 (1.11–1.69) | 0.004 | 1.26 (1.02–1.56) | 0.03 | ||
| P for trend | <0.001 | <0.001 | 0.001 | |||||
| Outcome: cardiovascular mortality | ||||||||
| AISI (per 1 SD) | 1.21 (1.14–1.30) | <0.001 | 1.24 (1.16–1.32) | <0.001 | 1.21 (1.14–1.29) | <0.001 | ||
| Q1 | Reference | – | Reference | – | Reference | – | ||
| Q2 | 0.91 (0.55–1.51) | 0.71 | 0.84 (0.50–1.38) | 0.49 | 0.79 (0.47–1.34) | 0.39 | ||
| Q3 | 1.43 (0.86–2.39) | 0.17 | 1.02 (0.62–1.68) | 0.94 | 0.92 (0.56–1.53) | 0.76 | ||
| Q4 | 2.11 (1.32–3.37) | 0.002 | 1.26 (0.80–1.99) | 0.31 | 1.16 (0.74–1.83) | 0.51 | ||
| P for trend | 0.09 | 0.10 | 0.17 | |||||
| Outcome: cancer mortality | ||||||||
| AISI (per 1 SD) | 1.11 (1.02–1.20) | 0.01 | 1.12 (1.04-1.21) | 0.004 | 1.09 (1.01–1.18) | 0.04 | ||
| Q1 | Reference | – | Reference | – | Reference | – | ||
| Q2 | 0.78 (0.50–1.20) | 0.25 | 0.76 (0.50–1.15) | 0.19 | 0.74 (0.49–1.11) | 0.15 | ||
| Q3 | 1.12 (0.76–1.64) | 0.56 | 0.96 (0.66–1.39) | 0.81 | 0.90 (0.62–1.32) | 0.60 | ||
| Q4 | 1.51 (1.05–2.17) | 0.03 | 1.19 (0.84–1.69) | 0.33 | 1.09 (0.76–1.56) | 0.64 | ||
| P for trend | 0.16 | 0.12 | 0.28 | |||||
Model 1 was unadjusted. Model 2 was adjusted for sex, age, race, education level, and IPR. Model 3 was adjusted for age, sex, race, education level, IPR, BMI, smoking status, drink status, hypertension, diabetes, eGFR, TC, HDL-C, and HEI. Q1: ≤179.23; Q2: 179.24–279.03; Q3: 279.04–442.59; Q4: >442.59. AISI, aggregate index of systemic inflammation; BMI, body mass index; CI, confidence interval; eGFR, estimated glomerular filtration rate; HDL-C, high-density lipoprotein cholesterol; HEI, healthy eating index; HR, hazard ratio; IPR, family income-to-poverty ratio; SD, standard deviation; TC, total cholesterol.
The trend test showed that the risk of all-cause death increased with increasing quartiles of AISI (P for trend <0.001). However, no statistically significant trends were observed for CVD death or cancer-related death across the AISI quartiles.
Dose-response relationship between AISI and mortality
RCS curves were further applied to assess the potential nonlinear relationships between AISI and all-cause death, CVD death, and cancer-related death. As shown in Figure 3, the AISI exhibited approximately linear relationships with all-cause death (P overall <0.001 and P nonlinear =0.17) and CVD death (P overall <0.001 and P nonlinear =0.81), while a significant nonlinear relationship was observed between AISI and cancer-related death (P overall <0.001 and P nonlinear =0.01).
Figure 3.
Restricted cubic spline analysis of all-cause mortality (A), CVD mortality (B) and cancer-related mortality (C) in quartiles of the AISI among cancer survivors. AISI, aggregate index of systemic inflammation; CI, confidence interval; CVD, cardiovascular disease; HR, hazard ratio.
Subgroup analysis
For all-cause death (Figure 4), a strong and consistent positive association was observed across the vast majority of subgroups. A notable exception was the age ≤60 years subgroup, where this association was attenuated and lost statistical significance. In contrast, the associations between AISI and both CVD death (Figure 5) and cancer-related death (Figure 6) were markedly weaker and less consistent across subgroups. For these cause-specific outcomes, no statistically significant association was found in most subgroups.
Figure 4.
Stratified analyses of the associations between the AISI and all-cause mortality. Model was adjusted for age, sex, race, education level, IPR, BMI, smoking status, drink status, hypertension, diabetes, eGFR, TC, HDL-C, and HEI. AISI, aggregate index of systemic inflammation; BMI, body mass index; CI, confidence interval; eGFR, estimated glomerular filtration rate; HDL-C, high-density lipoprotein cholesterol; HEI, healthy eating index; HR, hazard ratio; IPR, family income-to-poverty ratio; TC, total cholesterol.
Figure 5.
Stratified analyses of the associations between AISI and CVD mortality. Model was adjusted for age, sex, race, education level, IPR, BMI, smoking status, drink status, hypertension, diabetes, eGFR, TC, HDL-C, and HEI. AISI, aggregate index of systemic inflammation; BMI, body mass index; CI, confidence interval; CVD, cardiovascular disease; eGFR, estimated glomerular filtration rate; HDL-C, high-density lipoprotein cholesterol; HEI, healthy eating index; HR, hazard ratio; IPR, family income-to-poverty ratio; TC, total cholesterol.
Figure 6.
Stratified analyses of the associations between AISI and cancer-related mortality. Model was adjusted for age, sex, race, education level, IPR, BMI, smoking status, drink status, hypertension, diabetes, eGFR, TC, HDL-C, and HEI. AISI, aggregate index of systemic inflammation; BMI, body mass index; CI, confidence interval; eGFR, estimated glomerular filtration rate; HDL-C, high-density lipoprotein cholesterol; HEI, healthy eating index; HR, hazard ratio; IPR, family income-to-poverty ratio; TC, total cholesterol.
Discussion
In our study, we identified a positive correlation between AISI and mortality in adult cancer survivors. This association was mainly reflected in the all-cause mortality outcome, and this finding remained significant in the comprehensively adjusted model. The results of the subgroup analysis indicated that this positive correlation was similar across different population settings.
Previous studies have evaluated the association between AISI and mortality in different populations. Li et al. found that an elevated AISI at baseline was associated with an increased risk of cardiovascular and cerebrovascular diseases (HR =1.18, 95% CI: 1.11–1.26) and malignant tumor mortality (HR =1.20, 95% CI: 1.10–1.30) in the diabetic population (27). In our study, which specifically focused on cancer survivors, we also observed a strong association between AISI and all-cause mortality in participants with diabetes. However, we did not find statistically significant associations for CVD or cancer-related mortality in this subgroup. Our cohort consisted of cancer survivors, a population characterized by a markedly high background risk of all-cause mortality. In this context, the specific signals of CVD or cancer mortality might be attenuated or masked by other competing events, such as cancer recurrence, other complications, or the long-term effects of anti-cancer therapies. Similarly, another study reported a significant positive correlation between a high AISI and new-onset hypertension (17).
The prognostic value of AISI observed in our study is firmly rooted in its embodiment of systemic inflammation, a known driver of diverse pathological processes. The association we observed between AISI and mortality in cancer survivors is mechanistically supported by its established links to the progression of other chronic, inflammation-driven conditions such as idiopathic pulmonary fibrosis (18), rheumatoid arthritis (19), and contrast-induced acute kidney injury (28). This consistency across disparate diseases underscores that AISI captures a fundamental, trans-disease inflammatory burden which is also critically operative in the cancer survivorship state. More importantly, our findings significantly advance the field beyond the existing cancer-specific literature.
While previous investigations have convincingly demonstrated the prognostic utility of AISI in single-cancer types, including prostate (21), esophageal (22), and gastric cancers (23), its relevance across the heterogeneous population of survivors from all cancer types remained unestablished. Our work bridges this gap. We not only confirm AISI’s association with mortality in this broader context but also reveal that its relationship with cause-specific death (CVD and cancer) is more nuanced, being detectable as a continuous dose-response relationship rather than a simple threshold effect. This suggests that for these specific outcomes, the inflammatory risk is graded and may not have a clear clinical cut-off in this complex population. Furthermore, the extensive subgroup analyses we conducted move beyond mere description. The significant interactions we identified, for instance by income and hypertension status, are not limitations but valuable insights. They reveal that the mortality risk conveyed by an elevated AISI is not uniform but is significantly modified by key patient characteristics, highlighting populations that may be particularly vulnerable. This heterogeneity reinforces the broad applicability of our findings and argues for a personalized approach to risk assessment. In conclusion, our study generalizes AISI from a biomarker for specific cancers to a versatile indicator of all-cause mortality risk in the general cancer survivor community. Its ability to reflect a pervasive inflammatory state, combined with its nuanced associations and interaction effects, positions AISI as a practical tool for identifying high-risk survivors. Crucially, as the AISI is derived from routine, inexpensive complete blood count data, it can be seamlessly integrated into clinical practice at minimal additional cost, making it a promising tool for guiding risk-adapted survivorship care.
It is widely recognized that chronic inflammation has a strong connection with every phase of tumorigenesis and its development. This encompasses processes such as cell proliferation, tumor invasion, metastasis, and apoptosis (8,28). The AISI is calculated from neutrophils, monocytes, lymphocytes, and platelets. These cells can represent most blood cell types, thereby enabling the assessment of the systemic inflammatory status. The literature indicates that these indicators play important roles in inflammation and the tumor microenvironment (8,29). Neutrophils can promote the progression and metastasis of inflammation and cancer by producing inflammatory mediators such as interleukin-8 and matrix metalloproteinase 9 (30). Similarly, neutrophils inhibit antitumor immunity by releasing reactive oxygen species and promoting tumor invasion and metastasis (31). A previous study has shown that an increased monocyte count indicates a lower survival rate in cancer survivors (32). Monocytes play important roles in immune defense and inflammation. Within the tumor microenvironment, monocytes undergo differentiation to become tumor-associated macrophages (TAMs). These TAMs facilitate immune suppression and angiogenesis, enabling tumor cells to escape immune monitoring (33). Moreover, it has been reported that TAMs, which originate from circulating monocytes, are capable of penetrating the extracellular matrix. Once they penetrate, these macrophages can carry out multiple activities such as facilitating tumor cell proliferation, promoting metastasis, inducing angiogenesis, and causing immunosuppression (34-36). Lymphocytes play a significantly beneficial role in tumor-related immunology through their antitumor immune activities, including inhibiting tumor cell proliferation and inducing tumor cell apoptosis (37). Low lymphocyte counts are associated with immune suppression, and this change provides a favorable tumor microenvironment for tumor proliferation and migration (38,39). Platelets are known to have complex functions in both physiological and pathological conditions. Beyond their widely recognized roles in hemostasis and thrombosis, they participate in modulating immune responses, fueling chronic inflammation, and driving disease progression. For instance, in sterile inflammation, such as atherosclerosis, platelets engage with damage-associated molecular patterns. This interaction triggers the activation of signaling cascades, including the MAPK and NF-κB pathways. Subsequently, platelets secrete powerful inflammatory mediators, such as HMGB1 (40,41). These composite indicators may be superior because inflammation is more prominent in the tumor microenvironment of patients with cancer, which also suggests a poorer prognosis. Notably, research has shown that compared with other inflammatory indicators such as NLR and PLR, the AISI performs better in predicting the survival outcomes of patients with IPF (42). Similarly, compared with lymphocyte count and PLR, AISI has better accuracy in identifying chronic kidney disease and a low eGFR (43). These findings further highlight the value of the AISI in clinical applications. Despite several discrepancies, the majority of the relevant studies have emphasized the potential of these emerging indices. It is reasonable to believe that these indicators can be productively applied in clinic for more timely intervention in cancer survivors.
There are several limitations to our study that should be considered. First, we only used the baseline AISI, and we were unable to evaluate how the temporal changes of this biomarker affect the association with cause-specific mortality. Second, despite strict quality control in the NHANES questionnaire, self-reported measurements and potential result misclassification may still affect the study’s validity. Underreporting of malignancy status by patients could lead to an underestimation of the true disease prevalence. Third, although we attempted to consider potential confounding covariates, it was impossible to entirely rule out the existence of residual confounding factors. Fourth, reverse causality could not be completely avoided. Finally, as we only included cancer survivors from the United States, caution should be exercised when generalizing the results to other populations, and future studies are needed to confirm their applicability to patients in other countries.
Conclusions
In a national sample of cancer survivors in the United States, we found an association between the AISI and mortality. Against the backdrop of the increasing burden of cancer-related diseases and limited medical resources, this study has identified an alternative indicator for predicting the mortality of cancer survivors that is not only convenient and cost-effective but is also highly predictive.
Supplementary
The article’s supplementary files as
Acknowledgments
None.
Ethical Statement: The authors are accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved. The protocol of NHANES has been formally approved by the National Center for Health Statistics (NCHS) Research Ethics Review Board. Each participant provided informed consent by signing an agreement. The study was conducted in accordance with the Declaration of Helsinki and its subsequent amendments.
Reporting Checklist: The authors have completed the STROBE reporting checklist. Available at https://tcr.amegroups.com/article/view/10.21037/tcr-2025-2045/rc
Funding: None.
Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://tcr.amegroups.com/article/view/10.21037/tcr-2025-2045/coif). The author has no conflicts of interest to declare.
(English Language Editor: J. Gray)
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