Skip to main content
Download PDF

The Naples prognostic score serves as a predictor and prognostic indicator for cancer survivors in the community

BMC Cancer volume 24, Article number: 696 (2024) Cite this article

Abstract

Objective

Inflammation, malnutrition, and cancer are intricately interconnected. Despite this, only a few studies have delved into the relationship between inflammatory malnutrition and the risk of death among cancer survivors. This study aimed to specifically investigate the association between the categorically defined Naples prognostic score (NPS) and the prognosis of cancer survivors.

Methods

Data from 42,582 participants in the National Health and Nutrition Examination Survey (NHANES, 1999–2018) were subjected to analysis. Naples prognostic scores (NPS) were computed based on serum albumin (ALB), total cholesterol (TC), neutrophil to lymphocyte ratio (NLR), and lymphocyte to monocyte ratio (LMR), and participants were stratified into three groups accordingly. Cancer status was ascertained through a self-administered questionnaire, while mortality data were sourced from the National Death Index up to December 31, 2019. Multiple logistic regression was employed to estimate the odds ratio (OR) with a 95% confidence interval (CI) between NPS and cancer prevalence within the U.S. community population. Kaplan-Meier survival analysis and the Log-rank test were utilized to compare survival disparities among the three groups. Additionally, Cox proportional regression was utilized to estimate the hazard ratio (HR) with a 95% CI.

Results

The incidence of cancers was 9.86%. Among the participants, 8140 individuals (19.1%) were classified into Group 0 (NPS 0), 29,433 participants (69.1%) into Group 1 (NPS 1 or 2), and 5009 participants (11.8%) into Group 2 (NPS 3 or 4). After adjusting for confounding factors, the cancer prevalence for the highest NPS score yielded an odds ratio (OR) of 1.64 (95% CI: 1.36, 1.97) (P(for trend) < 0.05). In comparison to cancer survivors in Group 0, those with the highest NPS had adjusted hazard ratios (HRs) of 2.57 (95% CI: 1.73, 3.84) for all-cause mortality, 3.44 (95% CI: 1.64, 7.21) for cardiovascular mortality, 1.60 (95% CI: 1.01, 2.56) for cancer mortality, and 3.15 (95% CI: 1.74, 5.69) for other causes of mortality (All P(for trend) < 0.05). These associations remained consistent when stratified by age, sex, race, and body mass index.

Conclusions

This study indicates that the Naples prognostic score (NPS), serving as a novel prognostic metric integrating inflammation and nutritional status, is closely linked to cancer prognosis within the general population.

Peer Review reports

Introduction

Cancer stands as one of the foremost causes of death worldwide, imposing substantial economic burdens on public health systems [1, 2]. Despite significant progress in cancer treatments in recent decades and notable enhancements in overall survival rates, the 5-year survival rate for cancer patients remains relatively modest, hovering around approximately 68.1% [3]. The long-term survival of cancer patients hinges on a variety of factors, encompassing the extent of tissue damage and inflammation, alongside psychological well-being [4]. Inflammation and malnutrition induced by cancer can instigate alterations in immune response and metabolism, thereby influencing cancer survival outcomes. Meanwhile, cancer survivors incur an additional annual healthcare expenditure of $3000–4500 on average compared to non-cancer patients, resulting in a greater burden on healthcare economies [5,6,7]. Therefore, identifying characteristic predictive indicators for cancer survivors and modifiable factors that can improve the long-term prognosis of cancer patients is crucial.

Recently, mounting evidence indicates that several nutrition and inflammation-related elements could function as robust predictive markers for individuals diagnosed with cancer. Notably, the neutrophil-to-lymphocyte ratio (NLR) and lymphocyte-to-monocyte ratio (LMR) have emerged as pivotal factors influencing the progression and prognosis of cancer patients [8, 9]. Indices related to nutrition, including the Prognostic Nutritional Index (PNI), Nutritional Risk Index (NRI), and Controlling Nutritional Status (CONUT), represent independent risk factors impacting overall survival (OS) in cancer patients. Nevertheless, establishing fixed optimal predictive cutoff values for continuous variables across studies presents a challenge, thereby complicating their application for general population assessment [10, 11]. Hence, there exists a requirement for a straightforward, universally defined indicator with consistent classification criteria across studies to facilitate population analysis.

The Naples Prognostic Score (NPS) is a novel scoring system originally utilized in evaluating the prognosis of colorectal cancer [12]. It is distinguished by its straightforward definition and consistent classification criteria across various studies [13,14,15,16]. The NPS comprises serum albumin (ALB), total cholesterol (TC), neutrophil-to-lymphocyte ratio (NLR), and lymphocyte-to-monocyte ratio (LMR), thereby offering a simultaneous reflection of the inflammatory and nutritional status of the body. Notably, NPS has been recognized as an independent prognostic factor in diverse hospitalized patients with organic diseases [17,18,19,20,21]. However, whether NPS can independently predict the association between cancer incidence and survival rates in community populations remains to be evaluated. Therefore, this study utilized data from the National Health and Nutrition Examination Survey (NHANES) spanning from 1999 to 2018 to explore the relationship between NPS and cancer incidence in the general population. Additionally, the study examined the correlation between NPS and mortality rates among cancer survivors. The goal is to furnish a straightforward predictive indicator for identifying cancer patients in the general population and to provide prognostic guidance for cancer survivors.

Methods

Study population and data collection

The study utilized data from the National Health and Nutrition Examination Survey (NHANES, 1999–2018), a nationally representative survey conducted by the National Center for Health Statistics [22]. NHANES aims to evaluate the nutritional and health status of the non-institutionalized population in the United States. All data are available for download from the official website (https://www.cdc.gov/nchs/nhanes) [22].

To ensure data reliability and completeness, the NHANES questionnaire collection process adheres to standardized and rigorously controlled procedures. The Computer-Assisted Personal Interviewing (CAPI) system, equipped with built-in consistency checks, plays a crucial role in minimizing data entry errors. Furthermore, the CAPI system incorporates an online help screen, offering valuable guidance to interviewers in precisely defining key terms in the survey questionnaire. This stringent quality assurance and control framework highlight our dedication to upholding high standards of data quality throughout NHANES, thereby bolstering the credibility and robustness of our study findings.

In this study, we analyzed NHANES data spanning from 1999 to 2018. Initially, participants under the age of 18 and those lacking data on cancer history assessment were excluded from the analysis. Subsequently, participants lacking assessment data for NPS (ALB, TC, NLR, and LMR), pregnant individuals, those with extreme energy intake (> 4200 or < 800 kcal/day for males; >3500 or < 500 kcal/day for females) [23], and those lacking weight information or follow-up information were also excluded.

NPS assessment

(1)The NPS was defined based on ALB, TC, NLR, and LMR [12]. The optimal cut-off points for these indicators are determined using the MaxStat R package [24, 25], which identifies the values that maximize the log-rank statistic. This classification method has been employed since its initial reporting [26,27,28]. As described in previous literature, participants with serum albumin ≥ 40 g/L, TC > 180 mg/dL, NLR < 2.96, or LMR > 4.44 were assigned a score of 0, while those with serum albumin < 40 g/L, TC ≤ 180 mg/dL, NLR ≥ 2.96, or LMR ≤ 4.44 were assigned a score of 1. The NPS is calculated as the sum of scores for each of the four factors [12]. Patients were then categorized into three groups based on their NPS scores: group 0 (score of 0), group 1 (score of 1 or 2), and group 2 (score of 3 or 4).

Cancer assessment

The NHANES study collected information on cancer history through a self-administered questionnaire [22]. Participants meeting the following two criteria were included: (1) Individuals who answered “Yes” to the question “Have you ever been told you had cancer or any type of malignant tumor?“; (2) Participants for whom a record of response to the question “What kind of cancer?” was available. Participants answering “No” to either question were used as the control group.

Mortality assessment

The survival status of participants up to December 31, 2019, was determined by linking the study data with the National Death Index (NDI) [29]. This file provides the most recent linkage between selected National Center for Health Statistics(NCHS) surveys and the NDI. The International Classification of Diseases, Tenth Revision (ICD-10), was employed to delineate specific causes of death [30]. We scrutinized both all-cause mortality and specific causes of death, encompassing cardiovascular diseases (ICD-10: I00-I09, I11, I13, I20-I51) and malignant neoplasms (ICD-10: C00-C97). The baseline time for calculating survival time was defined as the time of NHANES data collection.

Covariates

Baseline data on study participants were gathered through questionnaires and measurement data [22]. Self-reported variables such as age (in years), gender (male or female), education level (less than high school, high school, or higher than high school), race/ethnicity (non-Hispanic white, non-Hispanic black, Mexican American, or other race). Body mass index was measured by NHANES (BMI; <25.0, 25.0–29.9, or > 29.9 kg/m2). Poverty status was assessed using the poverty income ratio, calculated as the family poverty income ratio(PIR) divided by the poverty threshold determined based on family size according to guidelines from the U.S. Department of Health and Human Services. It was categorized as ≤ 1.0, 1.1-3.0, and > 3.0 [31, 32]. Individuals who smoked fewer than 100 cigarettes in their lifetime were classified as never smokers. Former smokers were defined as those who had smoked more than 100 cigarettes in their lifetime but had subsequently quit. Current smokers were defined as individuals who were currently smoking [33]. Alcohol consumption status was classified as follows: non-drinkers (individuals who reported no alcohol consumption in the past 12 months), light to moderate drinkers (men consuming < 3 drinks per day, women consuming < 2 drinks per day, or individuals engaging in binge drinking < 5 times in the past 30 days), or heavy drinkers (men consuming ≥ 4 drinks per day, women consuming ≥ 3 drinks per day, or individuals engaging in binge drinking ≥ 5 times in the past 30 days) [34, 35]. To ensure data accuracy, energy intake (kcal/day) was obtained through first 24-hour dietary recall interview [36].

Statistical analysis

The NCHS analysis guidelines stipulate that all statistical analyses utilizing continuous NHANES data must incorporate the complex survey design to generate estimates representative of the civilian non-institutionalized population of the United States. Hence, the analysis includes sample weights as well as geographic clustering indicators (primary sampling units and strata) [37] .

Continuous variables are reported as weighted mean (standard error, SE), while categorical variables are presented as counts (weighted frequencies). Logistic regression analysis is utilized to compute adjusted odds ratios (OR) and 95% confidence intervals (CI) to examine the association between NPS and cancer incidence. Kaplan-Meier survival analysis and log-rank tests are employed to determine cumulative survival rates and compare them based on NPS score categorized into three groups (group 0: 0; group 1: 1–2; group 2: 3–4). Cox proportional hazards models are utilized to calculate adjusted hazard ratios (HR) and 95% CI for all-cause and cause-specific mortality rates among cancer survivors based on NPS.

Stratified analyses are performed based on age (< 45, ≥ 45 years) [38,39,40], gender (male, female), race (white, other), and BMI (< 30, ≥ 30) [41]. The significance of interactions is tested using p-values of the product terms between NPS and stratification variables. Trend tests for categorical variables are conducted based on NPS scores.

The statistical analysis will be carried out using R (version 4.3.3). Two-sided p-values less than 0.05 will be deemed statistically significant.

Results

Baseline characteristics of study participants

Between 1999 and 2018, a total of 101,316 participants attended NHANES. Initially, participants under 18 years of age and those lacking cancer assessment data were excluded (n = 46,295). Subsequently, participants lacking NPS assessment data (n = 5,210), those who were pregnant during the survey period (n = 1,370), and those with excessive energy intake (n = 2,908) were also excluded. Participants without weight information, according to NHANES weighting guidelines [37], were further excluded (n = 2,951). Finally, 1 participant deemed unsuitable for follow-up was excluded from survival analysis. Therefore, a total of 42,582 participants were available for logistic regression, and 4,099 cancer survivors were included in survival analysis(Fig. 1).

Fig. 1
figure 1

Participant flow diagram

Full size image

Table 1 presents the baseline characteristics of three NPS score groups in NHANES from 1999 to 2018. The study population had a mean age of 47.74 (0.20) years, with males accounting for 47.45% and predominantly non-Hispanic whites (69.38%). Overall, the cancer incidence rate was 9.86%. Compared to Group 0, participants in Group 2 were more likely to be older non-Hispanic whites with lower levels of education and income, higher body mass index, and a higher prevalence of hypertension, with a lower prevalence of hyperlipidemia and diabetes. Additionally, the cancer incidence rate was significantly higher in Group 2 participants.

In a median follow-up period of 7.2 years, a total of 1,452 all-cause deaths were recorded, including 320 deaths attributed to heart disease and 430 deaths attributed to cancer(Table 2). Compared to adult cancer survivors, individuals who succumbed to various causes were more likely to be older Hispanic white males with higher NPS, lower levels of education and income, and lower energy intake. Additionally, participants who died from cancer exhibited a higher proportion of hypertension and diabetes.

Association between NPS and cancer incidence

NPS was divided into three groups, with group 0 serving as the reference category, and its association with cancer incidence was evaluated (Table 3). The crude model’s odds ratios (ORs) with 95% confidence intervals (CIs) revealed a positive correlation between NPS and cancer incidence (1.36 (1.18, 1.56) for group 1; 2.20 (1.88, 2.57) for group 2). In the fully adjusted multivariable regression model, relative to group 0, the fully adjusted ORs (95% CIs) for groups 1 and 2 were 1.20 (1.03, 1.40) and 1.64 (1.36, 1.97), respectively. Across all models, the trend test indicated statistically significant associations (All P(for trend) < 0.05).

Association between NPS and cancer survivor mortality

Firstly, Kaplan-Meier curves suggested significant differences in prognosis among cancer survivors across NPS groups (Fig. 2). The findings revealed that cancer survivors in Group 2 exhibited the highest risk of all-cause and other-cause mortality compared to the other two groups (log-rank test P < 0.0001). In the multivariable-adjusted models, hazard ratios (HRs) with 95% confidence intervals (CIs) indicated that, relative to Group 0, cancer survivors in Group 2 demonstrated elevated risks of mortality. The HRs (95% CIs) were 2.57 (1.73, 3.84) for all-cause mortality; 3.44 (1.64, 7.21) for cardiovascular mortality; 1.60 (1.01, 2.56) for cancer-specific mortality; and 3.15 (1.74, 5.69) for other-cause mortality (Table 4). In all models, the trend test revealed statistically significant associations (All P(for trend) < 0.05).

Fig. 2
figure 2

Kaplan‒Meier survival curve of mortality; (A) for all-cause mortality, (B) for cardiac mortality, (C) for cancer mortality, and (D) for other mortality

Full size image

Subgroup analysis

In the subgroup analysis (Table 5), when stratified by age (< 45, ≥ 45), gender (male, female), race (white, others), and BMI (< 30, ≥ 30), no significant differences in the impact of NPS on cancer survivor survival rates were observed among participants aged under 45 years. It is noteworthy that in the subgroup analysis, most results remained consistent with the main analysis trend. Regarding the mortality rates of cancer survivors, no significant interaction was found between each subgroup condition and NPS score (all P(interaction) > 0.05).

Table 1 Characteristics of adult participants in NHANES 1999–2018
Full size table
Table 2 Characteristics of adult with cancer in NHANES 1999–2018
Full size table
Table 3 ORs (95% CIs) of the prevalence of cancer according to the NPS in the NHANES 1999–2018 (n = 42,582)
Full size table
Table 4 HRs (95% CIs) for all-cause and cause-specific mortality among cancer patients according to NPS
Full size table
Table 5 Subgroup analyses of the association of the frailty score with all-cause mortality among patients with cancer
Full size table

Sensitivity analysis

In the sensitivity analysis, firstly, we divided NPS into 5 groups and re-conducted the aforementioned analyses. The results revealed that the positive correlation between NPS in 5 groups and cancer incidence rate, as well as the risk of mortality among cancer survivors, persisted (Table S1, Table S2). Secondly, we excluded participants who died within 2 years prior to follow-up and conducted survival analysis again. We confirmed the robustness of this relationship (Figure S1, Table S3).

Discussion

This study utilized data from the NHANES database, and by appropriately weighting the data, our analysis outcomes accurately reflect the general population’s situation in the United States [37]. To our knowledge, this is the inaugural investigation exploring the association between NPS and cancer incidence rates. Our findings reveal that cancer survivors exhibit higher NPS scores compared to non-cancer participants. Following adjustments for numerous pertinent factors, we observed a significant positive correlation between NPS and the overall and specific cause mortality risk among cancer survivors. Given the simplicity of NPS calculation and the advantage of consistent standards across various studies [13,14,15,16], our research offers promising insights for the prospective application of NPS in tumor diagnosis and prognosis assessment.

The relationship between cancer and inflammation has been a subject of study since the 19th century. This consideration stems primarily from observational studies, which have noted that tumors frequently develop in areas of chronic inflammation. Moreover, biopsy samples of tumors commonly reveal the presence of inflammatory cells [42, 43]. Timely evaluation of the inflammatory status in cancer patients is crucial for comprehending disease progression and selecting suitable treatment strategies [43]. A substantial prospective cohort study using data from the UK Biobank database showcased a positive correlation between NLR and the risk of seven malignancies, while LMR exhibited a negative correlation. Notably, this correlation was particularly pronounced among patients diagnosed with malignancies within one year of recruitment [44]. Statistically, potential bodily infections and inflammatory responses are linked to approximately 15–20% of all cancer-related deaths globally [43]. Additionally, nutritional status plays a pivotal role in cancer progression, impacting the body’s oxidative stress levels and modifying tissue metabolism [45, 46]. Nutritional markers such as TC and ALB are closely intertwined with the advancement of malignant tumors [47, 48]. The interplay among cancer, inflammation, and nutrition underscores the significance of a comprehensive assessment of inflammation and nutrition in guiding tumor treatment [49]. For instance, studies have indicated that the integrated assessment of inflammation and nutrition is closely associated with predicting the efficacy of immunotherapy in malignant tumors [50,51,52].

NPS serves as a comprehensive reflection of overall inflammation, malnutrition, and survival across diverse conditions, demonstrating superior predictive performance compared to PNI, NRI, and CONUT scores [15, 53]. Moreover, as a categorical scoring system, NPS maintains consistency across studies, facilitating simpler comprehension for clinicians and patients alike, unlike other scoring systems [14, 15]. Beyond oncological diseases, NPS exhibits close associations with non-oncological conditions as well. Research has revealed links between NPS and hospitalization rates, as well as follow-up outcomes in patients with acute cardiovascular events [17, 19, 54, 55]. However, it’s noteworthy that prior studies have primarily focused on hospitalized patients, and the inherent vulnerability of this particular group may limit the broader generalization of this indicator. In our investigation, we observed that higher NPS levels correlate with increased cancer incidence and are closely linked with elevated overall and specific cause mortality rates (cardiovascular and cancer) among cancer survivors. Given the significant prevalence of comorbidity among cancer survivors [56], chronic cardiovascular and endocrine conditions can influence an individual’s inflammation and nutritional status [57, 58], potentially affecting the predictive capability of NPS for cancer disease. Addressing these concerns, our Model 3, after full adjustment, incorporated chronic disease states such as hypertension, hyperlipidemia, and diabetes. Encouragingly, the results remained robust despite these additional adjustments. This observation was further validated through various sensitivity analysis approaches. Although interaction analysis did not unveil statistically significant differences, it’s essential to acknowledge that the impact of NPS on cancer survivors was primarily concentrated among participants aged 45 and above. This can be attributed to three main factors. Firstly, cancer occurrence often exhibits time dependency, resulting in a potentially lower proportion of younger cancer survivors [59]. Secondly, aging individuals are more prone to inflammation and nutritional imbalances compared to their younger counterparts [60, 61]. Finally, individuals over the age of 45 are more likely to have cancer, cardiovascular disease, and serious endocrine diseases [38,39,40].

At the same time, it is worth noting that the cancer mortality rate was generally differentiated among the NPS groups compared to the superior differentiation of other deaths. However, the phenomenon may be widespread [62,63,64,65,66,67]. The possible explanation is that 969 cases of skin cancer, 619 cases of prostate cancer and 604 cases of breast cancer in our study, which account for 2192 of the total cancer patients. Non-melanoma skin cancers have a very low mortality rate [68], and their poor survival rates are often associated with non-cancer causes [69, 70]. In addition, breast and prostate cancer patients were also less likely to die from cancer, but more likely to die from non-cancer causes, such as heart disease, infections and suicide [71, 72].

Compared to previous studies, our research offers several notable advantages. Firstly, we leveraged a relatively large sample size with national representativeness and employed weighted strategies recommended by the NCHS, enabling us to elucidate the true relationship between NPS and both cancer incidence and mortality rates among cancer survivors [37]. Secondly, while previous studies were confined to hospital populations, limiting the applicability of the indicators, we expanded our investigation to encompass the entire community population in the United States, thereby enhancing the relevance and generalizability of our findings. Thirdly, the NPS index integrates overall inflammatory status and nutritional status, representing a significant advancement over single inflammatory or nutritional indicators in evaluating tumor progression. Lastly, we corroborated the association between NPS and cancer progression using various sensitivity analysis methods, ensuring robust analytical outcomes. The utilization of these methods underscores the substantial contribution of our study, offering valuable insights into cancer progression across diverse populations and healthcare settings.

This study also presents several limitations. Firstly, cancer-related data relied on self-reported information from participants, which could introduce recall bias. Nonetheless, NHANES implements standardized and stringent control procedures to ensure the reliability and completeness of the included data. Secondly, despite efforts to control for various potential confounders such as age, gender, and chronic disease status, there may still be unmeasured confounding variables that could impact the analysis. Thirdly, due to limited study data, the heterogeneity of cancer-related characteristics may not have been adequately assessed. To mitigate this potential bias, we adjusted for cancer categories in the analysis. Fourthly, given the observational nature of the study on cancer incidence correlation, causal relationships cannot be inferred. However, our study findings align with those of another prospective study from the UK Biobank [44]. Lastly, the study population primarily comprises participants from the US community, lacking data from economically underdeveloped countries, highlighting the need for further global research to validate the findings.

Conclusion

The study reveals a noteworthy positive correlation between elevated NPS scores and cancer incidence. Moreover, higher NPS scores among cancer survivors are linked to heightened risks of both all-cause mortality and cause-specific mortality. These findings carry considerable clinical implications, indicating the potential utility of NPS in predicting clinical outcomes among cancer survivors and informing subsequent treatment strategies.

Data availability

The data used for all analyses are sourced from the public database NHANES, available at: [https://www.cdc.gov/nchs/nhanes/index.htm].

Abbreviations

NPS:

Naples Prognostic Score

NHANES:

National Health and Nutrition Examination Survey

ALB:

Albumin

TC:

Total Cholesterol

NLR:

Neutrophil to Lymphocyte ratio

LMR:

Lymphocyte to Monocyte Ratio

OR:

Odds Ratio

HR:

Hazard Ratio

95%CI:

95%Confidence Interval

PNI:

Nutritional Index

NRI:

Nutritional Risk Index

CONUT:

Controlling Nutritional Status

OS:

Overall Survival

CAPI:

Computer-Assisted Personal Interviewing

NDI:

National Death Index

ICD-10:

The International Classification of Diseases, Tenth Revision

NCHS:

National Center for Health Statistics

SE:

Standard Error

References

  1. GBD 2021 Causes of Death Collaborators. Global burden of 288 causes of death and life expectancy decomposition in 204 countries and territories and 811 subnational locations, 1990–2021: a systematic analysis for the global burden of Disease Study 2021[J]. Lancet (London, England), 2024: S0140-6736(24)00367–2.

  2. Global Health Observatory. http://www.who.int/data/gho[EB/OL]. /2024-04-11. https://www.who.int/data/gho.

  3. Arvanitis M, Lowenstein CJ. Dyslipidemia[J]. Ann Intern Med. 2023;176(6):ITC81–96.

    Article  PubMed  Google Scholar 

  4. Jensen RE, Potosky AL, Moinpour CM, et al. United States Population-based estimates of patient-reported outcomes Measurement Information System Symptom and Functional Status reference values for individuals with Cancer[J]. J Clin Oncology: Official J Am Soc Clin Oncol. 2017;35(17):1913–20.

    Article  Google Scholar 

  5. Ekwueme DU, Zhao J, Rim SH et al. Annual out-of-Pocket expenditures and Financial Hardship among Cancer survivors aged 18–64 years - United States, 2011–2016[J]. MMWR. Morbidity and mortality weekly report, 2019, 68(22): 494–9.

  6. Guy GP, Ekwueme DU, Yabroff KR, et al. Economic burden of cancer survivorship among adults in the United States[J]. J Clin Oncology: Official J Am Soc Clin Oncol. 2013;31(30):3749–57.

    Article  Google Scholar 

  7. Ekwueme DU, Yabroff KR, Guy GP, et al. Medical costs and productivity losses of cancer survivors–United States, 2008–2011[J]. MMWR Morb Mortal Wkly Rep. 2014;63(23):505–10.

    PubMed  PubMed Central  Google Scholar 

  8. Zhou J, Wei S, Guo X, et al. Correlation between preoperative peripheral blood NLR, PLR, LMR and prognosis of patients with head and neck squamous cell carcinoma[J]. BMC Cancer. 2023;23(1):1247.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  9. Cupp MA, Cariolou M, Tzoulaki I, et al. Neutrophil to lymphocyte ratio and cancer prognosis: an umbrella review of systematic reviews and meta-analyses of observational studies[J]. BMC Med. 2020;18(1):360.

    Article  PubMed  PubMed Central  Google Scholar 

  10. Okadome K, Baba Y, Yagi T, et al. Prognostic Nutritional Index, Tumor-infiltrating lymphocytes, and prognosis in patients with esophageal Cancer[J]. Ann Surg. 2020;271(4):693–700.

    Article  PubMed  Google Scholar 

  11. Ramos R, Nadal E, Peiró I, et al. Preoperative nutritional status assessment predicts postoperative outcomes in patients with surgically resected non-small cell lung cancer[J]. Eur J Surg Oncology: J Eur Soc Surg Oncol Br Association Surg Oncol. 2018;44(9):1419–24.

    Article  Google Scholar 

  12. Galizia G, Lieto E, Auricchio A, et al. Naples Prognostic score, based on Nutritional and Inflammatory Status, is an independent predictor of long-term outcome in patients undergoing surgery for colorectal Cancer[J]. Dis Colon Rectum. 2017;60(12):1273–84.

    Article  PubMed  Google Scholar 

  13. Chen S, Liu S, Xu S, et al. Naples Prognostic score is an independent prognostic factor in patients with small cell Lung Cancer and Nomogram Predictive Model Established[J]. J Inflamm Res. 2022;15:3719–31.

    Article  PubMed  PubMed Central  Google Scholar 

  14. Xiu Y, Jiang C, Huang Q, et al. Naples score: a novel prognostic biomarker for breast cancer patients undergoing neoadjuvant chemotherapy[J]. J Cancer Res Clin Oncol. 2023;149(17):16097–110.

    Article  PubMed  Google Scholar 

  15. Sugimoto A, Fukuoka T, Shibutani M, et al. Prognostic significance of the Naples prognostic score in colorectal cancer patients undergoing curative resection: a propensity score matching analysis[J]. BMC Gastroenterol. 2023;23(1):88.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  16. Peng S-M, Ren J-J, Yu N, et al. The prognostic value of the Naples prognostic score for patients with non-small-cell lung cancer[J]. Sci Rep. 2022;12(1):5782.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  17. Erdogan A, Genc O, Ozkan E, et al. Impact of Naples Prognostic score at admission on In-Hospital and Follow-Up outcomes among patients with ST-Segment Elevation Myocardial Infarction[J]. Angiology. 2023;74(10):970–80.

    Article  CAS  PubMed  Google Scholar 

  18. Karakoyun S, Cagdas M, Celik AI et al. Predictive Value of the Naples Prognostic Score for Acute Kidney Injury in ST-Elevation Myocardial Infarction Patients Undergoing Primary Percutaneous Coronary Intervention[J]. Angiology, 2023: 33197231161922.

  19. Liu R, Chen C, Zhao Y, et al. The Osaka prognostic score and Naples prognostic score: novel biomarkers for predicting short-term outcomes after spontaneous intracerebral hemorrhage[J]. BMC Neurol. 2023;23(1):272.

    Article  PubMed  PubMed Central  Google Scholar 

  20. Liu J, Wang Z, Liu G, et al. Assessment of Naples prognostic score in predicting survival for small cell lung cancer patients treated with chemoradiotherapy[J]. Ann Med. 2023;55(2):2242254.

    Article  PubMed  PubMed Central  Google Scholar 

  21. Guo D, Liu J, Li Y, et al. Evaluation of predictive values of Naples Prognostic score in patients with Unresectable Stage III Non-small Cell Lung Cancer[J]. J Inflamm Res. 2021;14:6129–41.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. NHANES - NCHS Research Ethics Review Board Approval[EB/OL]. 2022-08-25/2024-04-12. https://www.cdc.gov/nchs/nhanes/irba98.htm.

  23. T Y, J Y et al. Y H,. Associations of Dietary Fats with All-Cause Mortality and Cardiovascular Disease Mortality among Patients with Cardiometabolic Disease[J]. Nutrients, Nutrients, 2022, 14(17).

  24. Oehme I, Deubzer HE, Wegener D, et al. Histone deacetylase 8 in neuroblastoma tumorigenesis[J]. Clin Cancer Research: Official J Am Association Cancer Res. 2009;15(1):91–9.

    Article  CAS  Google Scholar 

  25. George DJ, Halabi S, Shepard TF, et al. The prognostic significance of plasma interleukin-6 levels in patients with metastatic hormone-refractory prostate cancer: results from cancer and leukemia group B 9480[J]. Clin Cancer Research: Official J Am Association Cancer Res. 2005;11(5):1815–20.

    Article  CAS  Google Scholar 

  26. Nakagawa N, Yamada S, Sonohara F, et al. Clinical implications of Naples Prognostic score in patients with resected pancreatic Cancer[J]. Ann Surg Oncol. 2020;27(3):887–95.

    Article  PubMed  Google Scholar 

  27. Kano K, Yamada T, Yamamoto K, et al. The impact of Pretherapeutic Naples Prognostic Score on Survival in patients with locally advanced esophageal Cancer[J]. Ann Surg Oncol. 2021;28(8):4530–9.

    Article  PubMed  Google Scholar 

  28. Li Q, Cong R, Wang Y, et al. Naples prognostic score is an independent prognostic factor in patients with operable endometrial cancer: results from a retrospective cohort study[J]. Gynecol Oncol. 2021;160(1):91–8.

    Article  CAS  PubMed  Google Scholar 

  29. NCHS Data Linkage. - Mortality Data - Public-Use Files[EB/OL]. 2022-07-14/2024-04-12. https://www.cdc.gov/nchs/data-linkage/mortality-public.htm.

  30. Outland B, Newman MM, William MJ. Health Policy basics: implementation of the International classification of Disease, 10th Revision[J]. Ann Intern Med. 2015;163(7):554–6.

    Article  PubMed  Google Scholar 

  31. Zhu X, Cheang I, Tang Y, et al. Associations of serum carotenoids with risk of all-cause and Cardiovascular Mortality in Hypertensive Adults[J]. J Am Heart Association. 2023;12(4):e027568.

    Article  Google Scholar 

  32. 2024 Poverty Guidelines Computations[EB/OL]. ASPE. /2024-04-12. https://aspe.hhs.gov/topics/poverty-economic-mobility/poverty-guidelines/prior-hhs-poverty-guidelines-federal-register-references/2024-poverty-guidelines-computations.

  33. Qiu Z, Chen X, Geng T, et al. Associations of serum carotenoids with risk of Cardiovascular Mortality among individuals with type 2 diabetes: results from NHANES[J]. Diabetes Care. 2022;45(6):1453–61.

    Article  CAS  PubMed  Google Scholar 

  34. Rattan P, Penrice DD, Ahn JC, et al. Inverse Association of Telomere length with Liver Disease and Mortality in the US Population[J]. Hepatol Commun. 2022;6(2):399–410.

    Article  CAS  PubMed  Google Scholar 

  35. Hicks CW, Wang D, Matsushita K, et al. Peripheral neuropathy and all-cause and Cardiovascular Mortality in U.S. adults: a prospective cohort Study[J]. Ann Intern Med. 2021;174(2):167–74.

    Article  PubMed  Google Scholar 

  36. Liu S, Lu C, He L, et al. Vitamin E intake and prevalence rates of thyroid dysfunction and autoimmune thyroiditis: a cross-sectional analysis of NHANES data[J]. Thyroid: Official Journal of the American Thyroid Association; 2024.

    Google Scholar 

  37. NHANES Tutorials - Weighting Module[EB/OL]. /2024-04-13. https://wwwn.cdc.gov/nchs/nhanes/tutorials/Weighting.aspx.

  38. Holman DM, Grossman M, Henley SJ, et al. Opportunities for cancer prevention during midlife: highlights from a meeting of experts[J]. Am J Prev Med. 2014;46(3 Suppl 1):S73–80.

    Article  PubMed  PubMed Central  Google Scholar 

  39. Ory MG, Anderson LA, Friedman DB, et al. Cancer prevention among adults aged 45–64 years: setting the stage[J]. Am J Prev Med. 2014;46(3 Suppl 1):S1–6.

    Article  PubMed  PubMed Central  Google Scholar 

  40. Al-Qurayshi Z, Kandil E, Randolph GW. Cost-effectiveness of routine calcitonin screening and fine-needle aspiration biopsy in preoperative diagnosis of medullary thyroid Cancer in the United States[J]. Oral Oncol. 2020;110:104878.

    Article  PubMed  Google Scholar 

  41. Flegal KM, Kruszon-Moran D, Carroll MD, et al. Trends in obesity among adults in the United States, 2005 to 2014[J]. JAMA. 2016;315(21):2284–91.

    Article  CAS  PubMed  Google Scholar 

  42. Mantovani A, Allavena P, Sica A, et al. Cancer-related inflammation[J]. Nature. 2008;454(7203):436–44.

    Article  CAS  PubMed  Google Scholar 

  43. Balkwill F, Mantovani A. Inflammation and cancer: back to Virchow?[J]. Lancet (London England). 2001;357(9255):539–45.

    Article  CAS  PubMed  Google Scholar 

  44. Nøst TH, Alcala K, Urbarova I, et al. Systemic inflammation markers and cancer incidence in the UK Biobank[J]. Eur J Epidemiol. 2021;36(8):841–8.

    Article  PubMed  PubMed Central  Google Scholar 

  45. Redman LM, Smith SR, Burton JH, et al. Metabolic slowing and reduced oxidative damage with sustained caloric restriction support the rate of living and oxidative damage theories of Aging[J]. Cell Metabol. 2018;27(4):805–e8154.

    Article  CAS  Google Scholar 

  46. Luo H, Chiang H-H, Louw M, et al. Nutrient sensing and the oxidative stress Response[J]. Trends Endocrinol Metab. 2017;28(6):449–60.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  47. Bai G, Zhou Y, Rong Q, et al. Development of Nomogram models based on peripheral blood score and clinicopathological parameters to Predict Preoperative Advanced Stage and Prognosis for epithelial ovarian Cancer Patients[J]. J Inflamm Res. 2023;16:1227–41.

    Article  PubMed  PubMed Central  Google Scholar 

  48. Ruan G-T, Xie H-L, Gong Y-Z, et al. Prognostic importance of systemic inflammation and insulin resistance in patients with cancer: a prospective multicenter study[J]. BMC Cancer. 2022;22(1):700.

    Article  PubMed  PubMed Central  Google Scholar 

  49. Mecca M, Picerno S, Cortellino S. The Killer’s web: interconnection between inflammation, epigenetics and Nutrition in Cancer[J]. Int J Mol Sci. 2024;25(5):2750.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  50. Yamaguchi O, Kaira K, Imai H, et al. Clinical utility of inflammatory and nutritious index as therapeutic prediction of Nivolumab plus Ipilimumab in Advanced Non-small Cell Lung Cancer[J]. Oncology. 2024;102(3):271–82.

    Article  CAS  PubMed  Google Scholar 

  51. Sipe LM, Chaib M, Korba EB, et al. Response to immune checkpoint blockade improved in pre-clinical model of breast cancer after bariatric surgery[J]. eLife. 2022;11:e79143.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  52. Matsubara T, Takamori S, Haratake N, et al. The impact of immune-inflammation-nutritional parameters on the prognosis of non-small cell lung cancer patients treated with atezolizumab[J]. J Thorac Disease. 2020;12(4):1520–8.

    Article  Google Scholar 

  53. Sugimoto A, Fukuoka T, Nagahara H, et al. Predictive value of the Naples prognostic score on postoperative outcomes in patients with rectal cancer[J]. Langenbeck’s Archives Surg. 2023;408(1):113.

    Article  Google Scholar 

  54. Birdal O, Pay L, Aksakal E, et al. Naples Prognostic score and prediction of left ventricular ejection Fraction in STEMI Patients[J]. Angiology. 2024;75(1):36–43.

    Article  CAS  PubMed  Google Scholar 

  55. Zhu N, Lin S, Cao C. A novel prognostic prediction indicator in patients with acute pulmonary embolism: Naples prognostic score[J]. Thromb J. 2023;21(1):114.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  56. Esbenshade AJ, Lu L, Friedman DL et al. Accumulation of Chronic Disease among survivors of Childhood Cancer predicts early Mortality[J]. J Clin Oncology: Official J Am Soc Clin Oncol, 2023: JCO2202240.

  57. Mahemuti N, Jing X, Zhang N, et al. Association between systemic immunity-inflammation index and hyperlipidemia: a Population-based study from the NHANES (2015–2020)[J]. Nutrients. 2023;15(5):1177.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  58. Wu Y, Wei Q, Li H, et al. Association of remnant cholesterol with hypertension, type 2 diabetes, and their coexistence: the mediating role of inflammation-related indicators[J]. Lipids Health Dis. 2023;22(1):158.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  59. Qi J, Li M, Wang L, et al. National and subnational trends in cancer burden in China, 2005-20: an analysis of national mortality surveillance data[J]. Lancet Public Health. 2023;8(12):e943–55.

    Article  PubMed  Google Scholar 

  60. Mocchegiani E, Costarelli L, Giacconi R, et al. Vitamin E-gene interactions in aging and inflammatory age-related diseases: implications for treatment. A systematic review[J]. Ageing Res Rev. 2014;14:81–101.

    Article  CAS  PubMed  Google Scholar 

  61. Bastard P, Gervais A, Le Voyer T, et al. Autoantibodies neutralizing type I IFNs are present in ~ 4% of uninfected individuals over 70 years old and account for ~ 20% of COVID-19 deaths[J]. Sci Immunol. 2021;6(62):eabl4340.

    Article  PubMed  PubMed Central  Google Scholar 

  62. Cao C, Friedenreich CM, Yang L. Association of Daily Sitting Time and leisure-time physical activity with Survival among US Cancer Survivors[J]. JAMA Oncol. 2022;8(3):395–403.

    Article  PubMed  PubMed Central  Google Scholar 

  63. Yao J, Chen X, Meng F, et al. Combined influence of nutritional and inflammatory status and depressive symptoms on mortality among US cancer survivors: findings from the NHANES [J]. Brain Behav Immun. 2024;115:109–17.

    Article  CAS  PubMed  Google Scholar 

  64. Yang Y, Chen D, Zhong D, et al. Association of body mass index with survival in U.S. cancer survivors: a cross-sectional study of NHANES 1999–2018[J]. Front Oncol. 2023;13:1180442.

    Article  PubMed  PubMed Central  Google Scholar 

  65. Wang P, Zhao S, Hu X et al. Association of dietary total antioxidant capacity and its distribution across three meals with all-cause, cancer, and non-cancer mortality among cancer survivors: the US National Health and Nutrition Examination Survey, 1999–2018[J]. Frontiers in Nutrition, 2023, 10: 1141380.

  66. Cao C, Wang N, Liu R, et al. Leisure-time physical activity, daily sitting time, and mortality among US skin cancer survivors[J]. Supportive Care Cancer: Official J Multinational Association Supportive Care Cancer. 2023;31(12):718.

    Article  Google Scholar 

  67. Zhang D, Spiropoulos KA, Wijayabahu A, Nutrition et al. (Burbank, Los Angeles County, Calif.), 2023, 107: 111934.

  68. Barton V, Armeson K, Hampras S, et al. Nonmelanoma skin cancer and risk of all-cause and cancer-related mortality: a systematic review[J]. Arch Dermatol Res. 2017;309(4):243–51.

    Article  PubMed  PubMed Central  Google Scholar 

  69. Eisemann N, Jansen L, Castro FA, et al. Survival with nonmelanoma skin cancer in Germany[J]. Br J Dermatol. 2016;174(4):778–85.

    Article  CAS  PubMed  Google Scholar 

  70. Linos E, Parvataneni R, Stuart SE, et al. Treatment of nonfatal conditions at the end of life: nonmelanoma skin cancer[J]. JAMA Intern Med. 2013;173(11):1006–12.

    Article  PubMed  PubMed Central  Google Scholar 

  71. Elmehrath AO, Afifi AM, Al-Husseini MJ, et al. Causes of death among patients with metastatic prostate Cancer in the US from 2000 to 2016[J]. JAMA Netw open. 2021;4(8):e2119568.

    Article  PubMed  PubMed Central  Google Scholar 

  72. Zaorsky NG, Churilla TM, Egleston BL, et al. Causes of death among cancer patients[J]. Annals Oncology: Official J Eur Soc Med Oncol. 2017;28(2):400–7.

    Article  CAS  Google Scholar 

Download references

Acknowledgements

Thank you to the NHANES team for your outstanding contribution.

Funding

Not Applicable.

Author information

Authors and Affiliations

  1. Department of Otolaryngology Head and Neck Surgery, The Second Affiliated Hospital of Anhui Medical University, Hefei, China

    Chaoqun Liang, Chao Zhang, Jun Song, Lin Yan, Yun Xiao, Nan Cheng, Han Wu, Xiaohong Chen & Jianming Yang

Authors
  1. Chaoqun Liang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Chao Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Jun Song
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Lin Yan
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Yun Xiao
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Nan Cheng
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Han Wu
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Xiaohong Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Jianming Yang
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

CQL and CZ put forward ideas; CQL, CZ, and JS wrote the main manuscript text; CQL, LY, YX, prepared figures; CQL, LY, YX, NC and HW were used for data retrieval and statistical analysis. XHC and JMY provide funding and proofreading. All authors reviewed the manuscript.

Corresponding authors

Correspondence to Xiaohong Chen or Jianming Yang.

Ethics declarations

Ethics approval and consent to participate

The research protocol received approval from the Institutional Review Board of the National Center for Health Statistics (NCHS), and informed consent was obtained from all participants [22].

Consent for publication

Not Applicable.

Competing interests

The authors declare no competing interests.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Electronic supplementary material

Below is the link to the electronic supplementary material.

Supplementary Material 1

Supplementary Material 2

Supplementary Material 3

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Liang, C., Zhang, C., Song, J. et al. The Naples prognostic score serves as a predictor and prognostic indicator for cancer survivors in the community. BMC Cancer 24, 696 (2024). https://doi.org/10.1186/s12885-024-12448-7

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1186/s12885-024-12448-7

Keywords

BMC Cancer

ISSN: 1471-2407

Contact us