Abstract
Purpose
The Naples Prognostic Score (NPS) is a widely recognized scoring system used to assess inflammation and nutrition status, yet its role in prostate cancer (PCa) remains unclear. This study aims to investigate the association between the modified NPS (MNPS) and PCa prevalence and mortality outcomes among participants with self-reported PCa.
Methods
Data from the National Health and Nutrition Examination Survey 1999–2018 were collected. The NPS was calculated using MaxStat analysis, while M1NPS and M2NPS were constructed with X-Tile analysis. Weighted logistic regression was used to estimate the odds ratio (OR) for PCa prevalence. Kaplan-Meier survival analysis, log-rank tests, and Cox proportional hazard ratios (HR) were employed to analyze the mortality risk.
Results
Among 22,027 individuals, 717 had PCa. After considering the interactions among different factors and adjusting for confounding variables, the prevalence of PCa was notably higher in participants with the highest MNPS groups (group 3) compared with those with the lowest groups (group 1) (OR (95% CI) = 2.96 (1.57–5.57) for M1NPS; 15.50 (1.78–134.71) for M2NPS). With a median follow-up of 9.92 years, 326 (45.47%) of all-cause deaths occurred among participants with self-reported PCa. Significant differences in all-cause mortality were observed between the MNPS groups (log-rank P < 0.001 for M1NPS and M2NPS). Compared to participants with self-reported PCa in group 1, the adjusted HRs for all-cause mortality in group 3 were 1.12 (95% CI: 0.49–2.57) for M1NPS and 2.02 (95% CI: 1.19–3.44) for M2NPS. Furthermore, a significant association was found between M2NPS and cardiovascular mortality (HR (95% CI) = 4.77 (1.95–11.65).
Conclusion
The modified and optimized NPS, which integrates inflammation and nutritional status, is associated with PCa prevalence and with all-cause and cardiovascular mortality among individuals with self-reported PCa.
Supplementary Information
The online version contains supplementary material available at https://doi.org/10.1186/s41043-026-01402-x.
Keywords: Naples prognostic score, Prostate cancer, NHANES, Inflammation, Mortality
Introduction
Globally, prostate cancer (PCa) ranks among the most common malignant tumors in men, accounting for approximately 30% of newly diagnosed cancer cases annually in the United States [1]. As the global trend of population aging intensifies, the incidence of PCa continues to rise [2]. The number of new PCa cases is projected to increase from 1.4 million annually in 2020 to 2.9 million by 2040 [3]. The rapidly increasing number of PCa patients poses significant challenges to public health systems. This highlights the necessity of identifying characteristic and modifiable indicators for PCa occurrence and prognosis.
Systemic inflammation and nutritional status contribute to tumor progression [4, 5]. Inflammation and malnutrition can trigger immune responses and metabolic alterations, impacting survival outcomes in cancer patients [6, 7]. Growing research suggests that inflammation is a key factor in PCa development and contributes to the progression of the disease to therapeutically-resistant PCa [8–10]. Certain inflammation-related indicators, such as the neutrophil-to-lymphocyte ratio (NLR) and lymphocyte-to-monocyte ratio (LMR), have been demonstrated to be independent risk factors for PCa and to impact the prognosis of PCa patients [11–13]. Furthermore, nutritional assessment tools such as the nutritional risk index (NRI) and prognostic nutritional index (PNI) have been shown to be associated with survival among PCa patients in multiple studies [14, 15]. Consequently, inflammation and nutritional status, as controllable risk factors, should be prioritized in the prevention and management of PCa.
The Naples prognostic score (NPS) is a composite index established on the basis of serum albumin (ALB), total cholesterol (TC), NLR, and LMR [16]. It is capable of simultaneously reflecting both systemic inflammation and nutritional status, and was initially used to assess the prognosis of patients with colorectal cancer [17]. Recent studies have indicated a correlation between a higher NPS and the onset and poor prognosis of gastric, liver, and breast cancer [18–21]. Additionally, NPS has been found to be associated with the clinical outcomes of small cell lung cancer patients [22, 23]. However, the relationship between NPS and the prevalence and survival of PCa remains underexplored. Therefore, this study used data from the National Health and Nutrition Examination Survey (NHANES) between 1999 and 2018 to evaluate the association between NPS and PCa prevalence in the male participants, as well as its correlation with mortality among those with PCa. Since NPS thresholds may vary across cancer types and treatment contexts, we developed the modified NPS (MNPS) to assess its clinical significance.
This study aims to investigate the associations of NPS and MNPS with PCa prevalence and mortality among individuals with self-reported PCa.
Methods
Study design and population
The NHANES is a nationally representative study conducted by the National Center for Health Statistics (NCHS), which is part of the Centers for Disease Control and Prevention (CDC) (https://www.cdc.gov/nchs/nhanes/). It employs a complex, multistage, probability sampling design, with data collected through structured interviews, physical examinations, and laboratory assessments. Standardized and rigorously regulated procedures are implemented in the NHANES to ensure the reliability of the data and to protect the privacy of the participants. Written informed consent was obtained from all the participants in the NHANES, and the survey protocol was approved by the NCHS Research Ethics Review Board.
Typically, the NHANES releases a new dataset at two-year intervals. The information utilized in this study was derived from NHANES database spanning from 1999 to 2018, with a total of 101,316 individuals. First, participants under the age of 18, female, and lacking cancer assessment data were excluded (N = 74,866). Subsequently, participants lacking NPS-related data (ALB, TC, NLR, and LMR) (N = 2663), those with malignancies other than PCa (N = 1592), and those lacking PIR, BMI, smoking, or alcohol consumption data (N = 127) were excluded from the study. Finally, participants lacking weighted information or follow-up information were also excluded (N = 41) (Supplemental Fig. 1).
Assessment of NPS and MNPS
The NPS was calculated based on four clinical parameters (ALB, TC, NLR, and LMR) via the method of Galizia et al. with MaxStat analysis [16]. Specifically, participants with serum ALB < 40 g/L, TC ≤ 180 mg/dL, NLR ≥ 2.96 or LMR ≤ 4.44 were scored as 1, whereas those with the opposite condition were scored as 0. The NPS is calculated by adding together the scores for these four metrics. The participants were then divided into three groups according to their NPS scores: group 1 (NPS = 0), group 2 (NPS = 1–2), and group 3 (NPS = 3–4). Notably, given the particularity of PCa, we also developed a modified Naples prognostic score (MNPS) to assess its association with prognosis in participants with self-reported PCa (Fig. 1). X-Tile (version 3.6.1; Yale University, New Haven, USA) was employed to determine the optimal thresholds for the four indicators (ALB, TC, NLR, and LMR) based on the maximum chi-square and the lowest P-value from the log-rank tests, thereby establishing the MNPS [24]. Since TC was not significantly associated with prognosis in participants with self-reported PCa at the optimal threshold, we separately constructed M1NPS including TC and M2NPS excluding TC to compare their differences in assessing survival rates (Supplemental Table 1). The workflow for constructing the modified scores is illustrated in Supplemental Fig. 2.
Fig. 1.
Optimal cut-points of ALB, TC, NLR, and LMR determined by the X-Tile analysis. The plot displays the chi-square log-rank values generated when the cohort was divided into low and high populations based on optimal cut-points (black circle in the left panels). Red cut-points indicate negative correlations with survival, while green denotes positive correlations. The middle panels present the histograms of the optimal 2-population cut-point for the cohort, and the corresponding Kaplan-Meier plots are presented in the right panels. For overall survival, the optimal thresholds of ALB (A), TC (B), NLR (C), and LMR (D) were 40.0 g/L, 180.0 mg/dL, 3.10, and 1.90, respectively. Abbreviations: ALB, albumin; TC, total cholesterol; NLR, neutrophil-to-lymphocyte ratio; LMR, lymphocyte-to-monocyte ratio
Assessment of PCa
The PCa status was determined based on self-reported physician diagnoses obtained through the medical condition questionnaire in the NHANES. Responses to the questions “Have you ever been told that you had cancer or malignancy?” (MCQ220) and “What kind of cancer was it?” (MCQ230) were used to identify participants with PCa and help exclude participants with other types of cancer.
Assessment of mortality
NHANES data were linked to death certificate records from the National Death Index (NDI), which captured cause-of-death information from the date of survey participation until death or study follow-up completion (December 31, 2019). The specific cause of death was described in accordance with the International Classification of Diseases, Tenth Revision (ICD-10) [25]. In this study, we examined all-cause, cardiovascular, and cancer mortality among participants with self-reported PCa.
Covariates
Drawing on a comprehensive literature review, we incorporated multiple key covariates to control for potential confounding effects. These covariates encompass demographic parameters, including age (< 65 years or ≥ 65 years), race (Mexican American, non-Hispanic black, non-Hispanic white, and other races), poverty income ratio (PIR) (≤ 1.3, 1.3–3.5, and > 3.5), education level (below high school, high school, and above high school), and marital status (never married, divorced or separated or widowed, married or living with a partner); lifestyle behaviors, including smoking status (never smoker, former smoker, and current smoker), drinking status (non-drinker, low-to-moderate drinker, and heavy drinker); anthropometric measures, namely BMI (< 25.0, 25.0–30.0, and ≥ 30.0 kg/m²); clinical conditions, including diabetes (yes, no), hypertension (yes, no), anemia (yes, no); and biochemical indicators, including hemoglobin, total bilirubin, serum uric acid, triglycerides, alanine aminotransferase (ALT), and aspartate aminotransferase (AST). For more details, see the Supplemental Methods.
Statistical analysis
According to the NHANES Analytic Guidelines, given the complex survey design, sampling weights were applied to produce nationally representative estimates for the U.S. population. The associations between NPS (or MNPS) and PCa prevalence were examined using weighted logistic regression analyses. Kaplan-Meier (KM) survival curves and the log-rank test were used to evaluate the differences in the survival among different NPS groups (or MNPS groups). Weighted Cox proportional hazards regression analysis was employed to further analyze the relationships between NPS (or MNPS) and mortality in participants with self-reported PCa. Furthermore, for variables exhibiting interaction effects, the cross-product terms between these variables and NPS (or MNPS) were included in the multivariable regression analysis simultaneously [26, 27]. Interaction terms were included because certain variables, such as age, anemia, smoking, may influence inflammation- and nutrition-related biomarkers [28–30]. The inclusion of interaction terms accounts for the mutual effects between the variables and NPS (or MNPS), thus enabling a more accurate assessment of the associations between NPS (or MNPS) and outcomes [31]. Trend tests were conducted based on NPS and MNPS groups. In the final stage of the research, stratified analyses were performed [32].
All the statistical analyses in this study were performed using R (version 4.5.1; R Foundation, Vienna, Austria), with a two-tailed significance level of P-value < 0.05. A detailed description of the statistical analysis is provided in the Supplemental Methods.
Results
Characteristics of participants
A total of 22,027 participants were included in the analysis, comprising 717 participants with self-reported PCa. Supplemental Table 2 presents the characteristics of participants according to the three NPS groups from the 1999–2018 NHANES. The included participants had an average age of 45.28 (16.14) years, with 85.96% being under 65 years. Compared to NPS group 1, participants in group 3 were generally more likely to be older non-Hispanic whites with higher BMIs, former smokers, non-drinkers, and a higher prevalence of anemia, hypertension, and diabetes.
With a median follow-up of 9.92 years, there were 326 (45.47%) all-cause deaths of the 717 participants with self-reported PCa, including 72 deaths caused by heart disease and 101 deaths caused by cancer (Table 1). Compared to participants with self-reported PCa who remained alive, those who died from all causes were more likely to be older, have lower education and income levels, be non-drinkers, and have a higher prevalence of anemia and hypertension.
Table 1.
Characteristics of participants with self-reported prostate cancer
| Characteristics | Total | All-cause mortality | P-value | |
|---|---|---|---|---|
| Alive | Deceased | |||
| Participants, N | 717 | 391 | 326 | |
| Age, years | 71.96 (7.91) | 69.70 (7.81) | 75.37 (6.77) | < 0.001 |
| Age (%) | ||||
| < 65 | 123 (18.56) | 96 (26.34) | 27 ( 6.88) | < 0.001 |
| ≥ 65 | 594 (81.44) | 295 (73.66) | 299 (93.12) | |
| Hgb, g/dL | 14.43 (1.53) | 14.65 (1.47) | 14.11 (1.57) | < 0.001 |
| Anemia (%) | ||||
| No | 568 (83.59) | 329 (88.61) | 239 (76.04) | < 0.001 |
| Yes | 149 (16.41) | 62 (11.39) | 87 (23.96) | |
| Bilirubin, mg/dL | 0.74 (0.33) | 0.73 (0.35) | 0.76 (0.31) | 0.401 |
| Uric acid, mg/dL | 5.95 (1.37) | 5.94 (1.36) | 5.96 (1.39) | 0.841 |
| Triglyceride, mg/dL | 150.57 (92.84) | 151.81 (93.46) | 148.70 (92.03) | 0.757 |
| ALT, U/L | 23.70 (13.65) | 25.15 (15.63) | 21.52 (9.56) | 0.006 |
| AST, U/L | 25.39 (10.79) | 25.49 (11.63) | 25.23 (9.40) | 0.819 |
| Race (%) | ||||
| Mexican American | 37 (1.81) | 20 ( 1.88) | 17 ( 1.70) | 0.330 |
| Non-Hispanic Black | 409 (77.67) | 190 (75.34) | 219 (81.16) | |
| Non-Hispanic White | 204 (12.97) | 129 (14.36) | 75 (10.87) | |
| Other | 67 (7.56) | 52 ( 8.41) | 15 ( 6.27) | |
| Education (%) | ||||
| Below high school | 177 (16.98) | 66 (10.35) | 111 (26.94) | < 0.001 |
| High School | 163 (20.54) | 86 (18.13) | 77 (24.17) | |
| Above high School | 377 (62.48) | 239 (71.51) | 138 (48.89) | |
| Marital status (%) | ||||
| Never married | 22 (1.93) | 17 ( 2.42) | 5 ( 1.20) | 0.195 |
| Divorced/Separated/Widowed | 520 (76.55) | 281 (78.25) | 239 (73.99) | |
| Married/Living with partner | 175 (21.52) | 93 (19.33) | 82 (24.81) | |
| PIR (%) | ||||
| < 1.3 | 140 (12.50) | 67 ( 9.55) | 73 (16.93) | < 0.001 |
| 1.3–3.5 | 326 (39.70) | 160 (32.83) | 166 (50.03) | |
| ≥ 3.5 | 251 (47.81) | 164 (57.63) | 87 (33.04) | |
| BMI, kg/m2, (%) | ||||
| < 25 | 197 (24.13) | 94 (21.18) | 103 (28.57) | 0.169 |
| 25–30 | 286 (41.30) | 163 (43.53) | 123 (37.95) | |
| ≥ 30 | 234 (34.57) | 134 (35.29) | 100 (33.49) | |
| Smoke (%) | ||||
| Never smoker | 286 (38.99) | 170 (42.95) | 116 (33.04) | 0.119 |
| Former smoker | 344 (50.62) | 174 (47.98) | 170 (54.58) | |
| Current smoker | 87 (10.39) | 47 ( 9.07) | 40 (12.38) | |
| Alcohol (%) | ||||
| Nondrinker | 233 (27.62) | 106 (22.36) | 127 (35.52) | 0.018 |
| Low-to-moderate drinker | 289 (40.95) | 175 (43.80) | 114 (36.65) | |
| Heavy drinker | 195 (31.44) | 110 (33.84) | 85 (27.83) | |
| Hypertension (%) | ||||
| No | 161 (26.58) | 93 (29.79) | 68 (21.74) | 0.036 |
| Yes | 556 (73.42) | 298 (70.21) | 258 (78.26) | |
| Diabetes (%) | ||||
| No | 513 (71.81) | 275 (71.18) | 238 (72.77) | 0.741 |
| Yes | 204 (28.19) | 116 (28.82) | 88 (27.23) | |
Hgb, hemoglobin; ALT, alanine aminotransferase; AST, aspartate aminotransferase; PIR, poverty income ratio; BMI, body mass index
Associations between NPS and MNPS and PCa prevalence
Using group 1 as the reference category, logistic regression analysis (crude model) revealed a positive correlation between NPS and MNPS and the prevalence of PCa (NPS: OR (95% CI) = 5.91 (3.51–9.93); M1NPS: OR (95% CI) = 5.49 (3.92–7.67); M2NPS: OR (95% CI) = 8.27 (5.26–13.00); Supplemental Fig. 3). Interestingly, significant multiplicative interaction effects were found between NPS, M1NPS, and M2NPS and 4 (age, anemia, smoking, and hypertension), 2 (anemia and smoking), and 8 variables (age, anemia, education, marital status, PIR, BMI, smoking, and hypertension), respectively (P-interaction < 0.05). Further analysis of the cross-product terms revealed that, although most of these factors were positively correlated with PCa prevalence, their interactions with NPS and MNPS weakened this association (Supplemental Figs. 4–6). Taking the interaction effects into account, the fully adjusted Model 3 showed that NPS and MNPS were still associated with the prevalence of PCa (NPS: OR (95% CI) = 33.07 (8.00–136.76); M1NPS: OR (95% CI) = 2.96 (1.57–5.57); M2NPS: OR (95% CI) = 15.50 (1.78–134.71); Table 2). It should be noted that the wide confidence interval indicates limited precision, likely due to small subgroup sizes or sparse events. Moreover, trend tests across the three fully adjusted models all revealed statistically significant associations (P-trend < 0.01).
Table 2.
Weighted logistic regression analysis of the associations between NPS and MNPS and prostate cancer
| NPS | Model | Group | OR (95% CI) | P-value | P-trend |
|---|---|---|---|---|---|
| Model 1 | Group 1 | Ref. | 0.015 | ||
| Group 2 | 1.51 (0.73,3.14) | 0.265 | |||
| Group 3 | 4.59 (1.66,12.69) | 0.004 | |||
| Model 2 | Group 1 | Ref. | 0.014 | ||
| Group 2 | 1.55 (0.73,3.27) | 0.253 | |||
| Group 3 | 4.75 (1.70,13.28) | 0.004 | |||
| Model 3 | Group 1 | Ref. | < 0.001 | ||
| Group 2 | 8.01 (2.23,28.84) | 0.002 | |||
| Group 3 | 33.07 (8.00,136.76) | < 0.001 | |||
| M1NPS | |||||
| Model 1 | Group 1 | Ref. | 0.238 | ||
| Group 2 | 0.98 (0.75,1.28) | 0.876 | |||
| Group 3 | 1.44 (1.01,2.05) | 0.045 | |||
| Model 2 | Group 1 | Ref. | 0.135 | ||
| Group 2 | 1.02 (0.77,1.34) | 0.901 | |||
| Group 3 | 1.65 (1.10,2.46) | 0.016 | |||
| Model 3 | Group 1 | Ref. | 0.002 | ||
| Group 2 | 1.41 (0.99,2.01) | 0.063 | |||
| Group 3 | 2.96 (1.57,5.57) | 0.001 | |||
| M2NPS | |||||
| Model 1 | Group 1 | Ref. | < 0.001 | ||
| Group 2 | 5.35 (1.73,16.57) | 0.004 | |||
| Group 3 | 7.07 (0.86,58.06) | 0.071 | |||
| Model 2 | Group 1 | Ref. | 0.013 | ||
| Group 2 | 2.97 (0.92,9.53) | 0.070 | |||
| Group 3 | 6.76 (0.86,53.53) | 0.072 | |||
| Model 3 | Group 1 | Ref. | 0.002 | ||
| Group 2 | 3.43 (1.01,11.71) | 0.052 | |||
| Group 3 | 15.50 (1.78,134.71) | 0.014 |
Model 1: adjusted for age (< 65 or ≥ 65), race (Mexican American, Non-Hispanic Black, Non-Hispanic White, or other), and marital status (never married, divorced/separated/widowed, or married/living with partner); Model 2: further adjusted for family PIR (< 1.3, 1.3–3.5, or ≥ 3.5), anemia (No or Yes), triglyceride, ALT, and AST; Model 3: further adjusted for smoking status (never smoker, former smoker, or current smoker), drinking status (nondrinker, low-to-moderate drinker, or heavy drinker), hypertension (No or Yes), and diabetes (No or Yes). For variables exhibiting multiplicative interaction effects with NPS (or MNPS), their cross-product terms were incorporated into the models
Prognosis based on NPS and MNPS
Firstly, KM survival curves were plotted to examine the relationships between NPS and MNPS groups and all-cause and cardiovascular mortality among participants with self-reported PCa (Fig. 3). Visually, although individuals characterized by NPS group 3 had a poorer prognosis on all-cause mortality than those in NPS group 1, the difference was not statistically significant (log-rank P = 0.066). In contrast, the MNPS was significantly associated with all-cause mortality in participants with self-reported PCa (log-rank P < 0.001 for M1NPS; P < 0.001 for M2NPS). Furthermore, significant differences in cardiovascular mortality were observed across different MNPS groups (log-rank P < 0.001 for M1NPS; P = 0.003 for M2NPS). In terms of cancer mortality, the NPS and MNPS groups did not show marked differences (log-rank P > 0.05; Supplemental Fig. 7). Group sample sizes and censored counts for each NPS/MNPS category are provided in Fig. 2 and the Supplemental Fig. 7.
Fig. 3.
Weighted Cox regression analysis of variables associated with all-cause mortality in participants with self-reported prostate cancer. P1-interaction, statistical significance of interactions between variables and M1NPS; P2-interaction, statistical significance of interactions between variables and M2NPS. CIs are derived from model‑based standard errors, while P‑values use design‑based robust variance estimation. For discordant cases, the P‑value determines significance. Abbreviations: ALT, alanine aminotransferase; AST, aspartate aminotransferase; PIR, poverty income ratio; BMI, body mass index; NPS, Naples prognostic score; MNPS, modified Naples prognostic score; CI, confidence interval; HR, hazard ratio
Fig. 2.
Kaplan-Meier survival analysis according to NPS and MNPS group. A-C: survival analysis of all-cause mortality among different NPS (A), M1NPS (B), and M2NPS (C) groups, respectively; D-F: survival analysis of cardiac mortality among different NPS (D), M1NPS (E), and M2NPS (F) groups, respectively. Abbreviations: NPS, Naples prognostic score; MNPS, modified Naples prognostic score
Associations of NPS and MNPS with mortality
Similarly, in the Cox regression analysis, relative to group 1, NPS was not significantly associated with all-cause mortality in participants with self-reported PCa in the crude model (HR (95% CI) = 1.86 (1.03–3.37), P = 0.091), whereas M1NPS and M2NPS demonstrated significant correlations (M1NPS: HR (95% CI) = 2.50 (1.71–3.65); M2NPS: HR (95% CI) = 2.43 (1.46–4.04); Fig. 3). Additionally, we found that the cross-product terms between M1NPS and anemia weakened this association, while the cross-product terms with education level strengthened it (Supplemental Fig. 8). Given the weak association between NPS and prognosis in participants with self-reported PCa, we further analyzed the association of M1NPS and M2NPS in adjusted models. The results indicated that the fully adjusted model (Model 3) did not show an independent association between M1NPS and all-cause mortality in participants with self-reported PCa (HR (95% CI) = 1.12 (0.49–2.57); Table 3). In comparison, M2NPS remained associated with all-cause mortality in the fully adjusted model (HR (95% CI) = 2.02 (1.19–3.44); P-trend < 0.001). Besides, a significant association was also observed between M2NPS and cardiovascular mortality (HR (95% CI) = 4.77 (1.95–11.65); Supplemental Fig. 9).
Table 3.
Weighted Cox regression analysis of the association between MNPS and all-cause mortality among participants with self-reported prostate cancer
| M1NPS | Model | Group | HR (95% CI) | P-value | P-trend |
|---|---|---|---|---|---|
| Model 1 | Group 1 | Ref. | < 0.001 | ||
| Group 2 | 1.44 (1.10,1.89) | 0.012 | |||
| Group 3 | 1.89 (1.29,2.77) | < 0.001 | |||
| Model 2 | Group 1 | Ref. | < 0.001 | ||
| Group 2 | 1.41 (1.04,1.92) | 0.028 | |||
| Group 3 | 2.13 (1.37,3.32) | < 0.001 | |||
| Model 3 | Group 1 | Ref. | 0.808 | ||
| Group 2 | 1.04 (0.60,1.80) | 0.903 | |||
| Group 3 | 1.12 (0.49,2.57) | 0.711 | |||
| M2NPS | |||||
| Model 1 | Group 1 | Ref. | < 0.001 | ||
| Group 2 | 1.77 (1.39,2.26) | < 0.001 | |||
| Group 3 | 2.32 (1.39,3.89) | < 0.001 | |||
| Model 2 | Group 1 | Ref. | < 0.001 | ||
| Group 2 | 1.60 (1.25,2.06) | 0.002 | |||
| Group 3 | 2.09 (1.24,3.52) | 0.005 | |||
| Model 3 | Group 1 | Ref. | < 0.001 | ||
| Group 2 | 1.54 (1.19,1.98) | 0.004 | |||
| Group 3 | 2.02 (1.19,3.44) | 0.007 |
Model 1: adjusted for age (< 65 or ≥ 65), PIR (< 1.3, 1.3–3.5, or ≥ 3.5), and BMI (≤ 25, 25–30, or ≥ 30); Model 2: further adjusted for anemia (No or Yes), bilirubin, and ALT; Model 3: further adjusted for education level (below high school, high school, above high school), smoking status (never smoker, former smoker, or current smoker), drinking status (nondrinker, low-to-moderate drinker, or heavy drinker), and hypertension (No or Yes). For variables exhibiting multiplicative interaction effects with M1NPS, their cross-product terms were incorporated into the models
Subgroup analysis and sensitivity analysis
Multiple stratified analyses based on age, anemia, PIR, education level, smoking status, drinking status, and hypertension status were conducted. Generally, most of the results were consistent with the trend of the main analysis (Supplemental Fig. 10). Notably, no significant association was found between M2NPS and all-cause mortality in participants with self-reported PCa aged under 65 years, with anemia, and a PIR < 1.3. In the sensitivity analysis, M2NPS were divided into 4 groups, and the aforementioned analysis was then repeated. The results indicated that, in the fully adjusted model, the M2NPS in 4 groups remained significantly positively correlated with PCa prevalence and all-cause mortality (Supplemental Tables 3–4). Furthermore, after excluding participants with self-reported PCa who died within the first year of follow-up, the significant positive correlation between M2NPS and all-cause mortality was still observed (Supplemental Table 5).
Discussion
This study conducted weighted analyses using data from the NHANES database spanning from 1999 to 2018, with the findings representing the actual demographics of the U.S. population. To the best of our knowledge, this is the first investigation examining the association between MNPS and PCa prevalence and mortality. Our findings indicate that participants with self-reported PCa have elevated MNPS compared to individuals without cancer. M2NPS was found to be significantly associated with the PCa prevalence and all-cause mortality. In secondary exploratory analyses, M2NPS was also associated with cardiovascular mortality. These findings provide insights into the associations of M2NPS with PCa prevalence and mortality, which are hypothesis-generating.
Increasing evidence indicates that chronic inflammation plays a role in regulating cellular activities during the initiation and progression of PCa [33–35]. Specifically, immune cells recruited to prostate inflammatory lesions may promote the release of pro-inflammatory cytokines and chemokines, which can enhance oxidative stress and genetic instability in epithelial cells [33]. Chronic inflammation induces DNA damage by generating reactive oxygen species and reactive nitrogen species, which lead to epigenetic and genomic alterations [34]. Furthermore, the secretion of cytokines and generation of free radicals by myeloid-derived immune cells in peripheral blood further fuels inflammation, inducing “inflammatory storms” and driving prostate carcinogenesis [35]. Therefore, timely assessment of the inflammatory status is crucial for understanding disease progression and formulating treatment strategies. Notably, the NLR and LMR, as markers of inflammation and immunity, have been proven to be associated with PCa prevalence and prognosis [12, 13, 36]. Furthermore, nutritional status significantly influences the progression of PCa, affecting both tissue metabolic levels and immune status [14, 15]. Nutritional markers, such as ALB, exhibit significant alterations in PCa patients, with diagnostic performance comparable to that of prostate-specific antigen (PSA) [37].
Beyond roles in tumor biology, these systemic inflammation and nutritional indicators are also implicated in the pathogenesis of cardiovascular disease. Elevated NLR and decreased LMR have been linked to endothelial dysfunction and atherosclerosis, while hypoalbuminemia reflects a chronic pro-inflammatory state that adversely affects cardiovascular health [38, 39]. These shared pathways highlight the broad clinical relevance of composite scores that integrate inflammatory and nutritional status. However, this cardiovascular mortality analysis was limited by a relatively small number of events and the absence of a competing‑risk framework. Therefore, these findings should not be interpreted as definitive evidence of cardiovascular prognostic discrimination.
The effects of inflammation and nutrition on tumors highlight the importance of a comprehensive assessment of inflammatory and nutritional status in the management of PCa. As a comprehensive index, NPS reflects overall inflammatory and nutritional status, with extensive applications in both oncological and non-oncological fields [38–41]. Evidence indicates that the classic NPS is associated with the occurrence and progression of gastrointestinal tumors such as colorectal cancer and gastric cancer, and its prognostic performance significantly outperforms indicators such as PNI and controlling nutritional status (CONUT) [40, 41]. However, considering that PCa is a dynamic and highly heterogeneous disease, with patients exhibiting complex inflammatory, immune, and nutritional statuses, the direct use of conventional NPS may not be applicable to PCa [35]. Therefore, in this study, the MNPS scoring system, composed of inflammatory, immune, and nutritional status-related indicators, was constructed using X-tile analysis. Notably, previous studies have reported no significant association between TC levels and either PCa prevalence or overall survival [42]. The relationship between serum cholesterol and PCa biology is complex, influenced by tumor metabolism, hormonal regulation, and systemic inflammatory pathways [43, 44]. These factors may reduce the reliability of TC as a prognostic biomarker in this context. Therefore, we constructed M2NPS without TC to simplify the indicator.
As anticipated, our study showed that the conventional NPS is not associated with the prognosis of PCa. In contrast, the streamlined M2NPS shows significant associations with PCa prevalence, all-cause mortality, and cardiovascular mortality. These findings indicate that M2NPS, as a composite marker of systemic inflammation and nutritional status, reflects biological pathways that are relevant to PCa progression. The components of M2NPS (ALB, NLR, LMR) are modifiable factors linked to inflammation and nutrition [16]. Addressing nutritional status or reducing systemic inflammation might influence M2NPS levels. Such possibilities could be examined in controlled studies. Among the two modified scores, M2NPS demonstrated more consistent associations with both prevalence and mortality. Although M2NPS is not intended for screening or treatment decisions at this stage, it represents a hypothesis‑generating marker for exploring the role of inflammation and nutrition in PCa.
This study revealed a significant interaction between NPS (or M2NPS) and age on the PCa prevalence. A possible explanation lies in age-related chronic inflammation, characterized by an increased neutrophil count and decreased lymphocyte and albumin levels [45, 46]. This characteristic results in a greater overall NPS in the elderly population, which may attenuate its association with PCa prevalence in this group. However, in survival analyses, this interaction was no longer significant, possibly because participants with self-reported PCa were predominantly in the older age groups. Additionally, the interaction between NPS (or M2NPS) and anemia, as well as smoking, also exhibits similar manifestations. Recent evidence suggests that anemia and smoking may induce a systemic pro-inflammatory state, characterized by increased NLR and decreased LMR and ALB levels, leading to elevated NPS and thereby affecting its efficacy as a prognostic marker [47–51].
In the subgroup analyses, no significant association was found between M2NPS and all-cause mortality among participants aged under 65 years, those with anemia, and those with a PIR below 1.3. These null findings may partly reflect limited statistical power, as the number of events in these subgroups was relatively small. Beyond power considerations, biological factors may also play a role. Younger patients may have less age-related chronic inflammation, which could attenuate the association between a composite inflammation–nutrition score and mortality [52, 53]. In participants with anemia, the condition itself is both a component and a consequence of systemic inflammation, and its presence may confound or dilute the independent prognostic signal of MNPS [47, 54]. For those with low PIR, unmeasured socioeconomic and healthcare access factors may influence mortality through pathways not fully captured by MNPS alone.
Compared to previous studies, our research has several notable strengths that enhance its credibility. First, we used nationally representative samples from the NHANES database to strengthen the generalizability of the findings. Second, we specifically accounted for the effects of interactions and adjusted for multiple potential confounding factors, including demographic characteristics, lifestyle factors, and comorbidities. Finally, the median follow-up period of 9.92 years enabled us to assess the prognostic value of NPS and MNPS more accurately in participants with self-reported PCa. However, despite these advantages, several limitations of our study warrant attention. First, data related to PCa rely on self-reported information from participants, which may introduce recall bias; objective confirmation via PSA or biopsy was not available for the majority of participants, and therefore the study cannot establish PCa incidence or PCa–specific prognostic discrimination. Second, although known potential confounders were controlled for, the possibility of residual confounding cannot be entirely ruled out, and causality cannot be inferred from these observational data. Third, the wide confidence intervals observed for some estimates, such as OR (95% CI) = 15.50 (1.78–134.71), suggest limited precision. Therefore, these imprecise estimates should not be used to support strong clinical recommendations. In addition, the complex survey weighting and robust variance estimation used in weighted Cox models may occasionally produce apparent discrepancies between confidence intervals and P-values. In such cases, we relied on the P-value as the primary criterion for determining statistical significance. Fourth, the NHANES dataset lacks detailed oncologic variables such as PSA levels, Gleason score, TNM stage, and treatment history; consequently, this study cannot determine whether MNPS provides incremental prognostic value beyond these established clinical factors. Future validation in independent cohorts with linked cancer registry data and comprehensive oncologic information, comparison with established prognostic tools, ideally incorporating risk discrimination metrics and calibration metrics as well as competing risk analyses, is warranted before clinical integration.
Conclusion
The modified and optimized NPS, as a novel metric that comprehensively considers inflammation and nutritional status, is associated with PCa prevalence and with all-cause and cardiovascular mortality among individuals with self-reported PCa. Currently, this score is not validated for screening, treatment selection, or clinical decision-making. High-quality prospective studies with detailed oncologic data are needed to validate these findings and to determine its potential clinical utility.
Supplementary Information
Acknowledgements
We would like to express our sincere gratitude to the NHANES administration for making the data available that enabled us to complete this paper.
Author contributions
HZ and FY designed the study and composed the manuscript. QW, TZ, and JC collected and interpreted the data. TF, SJ, and ZX performed the statistical analysis. ZL, YC, and CT were involved in the editing and collation of the images. ZZ revised the manuscript. YZ and WY censored and revised the article critically. All the authors contributed to the article and approved to the submitted version.
Funding
This work was financially supported by the National Key R&D Program of China (No. 2023YFC3605700 and 2023YFC3605703), the National High Level Hospital Clinical Research Funding (2022-PUMCH-A-063, 2022-PUMCH-B-009, and 2025-PUMCH-C-049) and the CAMS Innovation Fund for Medical Sciences (CIFMS) (grant number 2025-I2M-C&T-B-009).
Data availability
The data utilized in this study were obtained from the publicly accessible NHANES database (https://wwwn.cdc.gov/nchs/nhanes/default.aspx).
Declarations
Ethics approval and consent to participate
The NHANES protocols have been approved by the NCHS Ethics Review Board, and written informed consent was obtained from all participants prior to the study. Thus, no additional institutional review was required for our study, as it was based exclusively on publicly available NHANES data.
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.
Hong Zeng and Fan Yang contributed equally to this work.
Contributor Information
Yi Zhou, Email: zhouyixh@sina.com.
Weigang Yan, Email: pumchywg@sina.com.
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Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Supplementary Materials
Data Availability Statement
The data utilized in this study were obtained from the publicly accessible NHANES database (https://wwwn.cdc.gov/nchs/nhanes/default.aspx).