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Associations between naples prognostic score and stroke and mortality among older adults

European Journal of Medical Research volume 30, Article number: 327 (2025) Cite this article

Abstract

Background

Inflammation and malnutrition in the body are closely related to the incidence of stroke. Older adults often suffer from malnutrition and inflammation. Naples Prognostic Score (NPS), a novel inflammation-malnutrition score, can effectively assess the inflammation and nutritional status of the body. The aim of this study is to explore the connection between NPS and stroke among older adults, as well as the association between NPS and mortality in older adults.

Methods

Participants eligible for the study were collected from National Health and Nutrition Examination Survey(NHANES) data from 1999 to 2018. Logistic regression models were employed to investigate the link between NPS and stroke. Additionally, restricted cubic spline was utilized to explore the correlations. Subgroup analysis was adopted in order to ensure the credibility of the results. Kaplan–Meier Survival curve and cox regression models and were utilized to evaluate the link between NPS and mortality among older adults.

Results

16,940 older adults qualified for analysis. The participants with stroke had higher levels of NPS. In the logistic regression model, NPS was positively related to stroke (high NPS vs. low NPS, OR = 1.70 (95% CI 1.24–2.35), P < 0.001). Restricted cubic spline revealed a positive non-linear relationship (P for overall < 0.001, P for non-linear < 0.001). Subgroup analysis showed that the association between NPS and the incidence of stroke is more significant in the non-diabetes population (P < 0.001). The Kaplan–Meier curve shows that patients with high NPS have a significantly higher risk of all-cause mortality and cardiovascular mortality (P < 0.001). In the Cox regression model, a positive correlation was observed between NPS and mortality among older adults (all-cause mortality: HR = 1.36 (95% CI 1.30–1.42); cardiovascular mortality:HR = 1.59 (95% CI 1.45–1.75)).

Conclusions

A positive link was observed between NPS and stroke and mortality. Routine NPS screening may enhance risk stratification in geriatric clinics.

Introduction

With the gradual aging of the population, the incidence of cerebrovascular diseases is increasing, which brings huge economic and medical burden to the world [1]. The aggravation of cerebrovascular dysfunction can lead to focal or overall brain tissue damage, thereby inducing stroke. Despite continuous updates in medication and interventional treatment techniques, stroke remains one of the leading causes of death worldwide [2]. In the future, the number of deaths from stroke will be more concentrated in developing and low-income countries. The incidence rate of stroke is slowly rising, and the elderly are more seriously affected. The recurrence rate, disability rate and mortality rate remain at a high level [3]. The patients with stroke often have severe neurological deficits and consciousness disorders. Early prediction of stroke and identification of high-risk patients are key measures for stroke treatment [4, 5].

Previous research has found that chronic inflammation is involved in the occurrence and development of cerebrovascular dysfunction. Initially, inflammation promotes endothelial cell damage and releases adhesion molecules, recruiting monocytes to deposit under the endometrium and form macrophages [6]. On the one hand, macrophages engulf lipids to form foam cells, which aggravate vascular diseases. On the other hand, polarization of M1 macrophages can enhance the expression of inflammatory factors, further exacerbating vascular inflammation. At the same time, neutrophil infiltration, platelet activation, and the participation of various immune cells further aggravate the progress of atherosclerosis [7]. With the continuous destruction of vascular structure, plaque rupture and thrombus formation may occur, causing acute cerebrovascular disease [8]. Inflammation can recruit a large number of peripheral white blood cells into the brain parenchyma, promote the release of inflammatory factors, and cause damage to the blood–brain barrier [9].

When the body experiences an inflammatory response, neutrophil count, monocyte count, and platelet count often increase, while lymphocyte count often decreases. Therefore, scholars propose to combine these inflammatory indicators to further reflect the level of inflammation in the body. These inflammatory indices include neutrophil-to-lymphocyte ratio (NLR), lymphocyte-to-monocyte ratio (LMR), etc. [10, 11]. Research has also confirmed that these inflammatory indicators can effectively predict the occurrence of stroke [12]. In addition, older adults often have multiple diseases and their bodies are at a high level of inflammation. Therefore, inflammation related biomarkers have also been proposed to predict the risk of death in elderly patients[13].

In addition to the inflammatory state of the body, malnutrition assessment is also of great significance in stroke and prognosis. Malnutrition refers to the lack of sufficient nutrients in the body to maintain normal physiological functions. Malnutrition can increase the risk of various diseases, including cancer, coronary heart disease, stroke, etc. [14, 15]. In addition, the lack of nutrients in the body also triggers inflammation. This will lead to increased permeability of brain endothelial cells, production of intracellular reactive oxygen species(ROS), and damage to the blood–brain barrier [16]. Stroke patients often have neurological deficits, abnormal limb movements, eating disorders, weakened immunity, emotional changes, and intestinal dysfunction caused by stress, which further exacerbates the degree of malnutrition after stroke[17]. In addition, the elderly population often suffers from malnutrition. Studies have also found that malnutrition is associated with an increased risk of long-term mortality in older adults [18]. In recent years, some tools for evaluating the nutritional status of the body have been gradually developed, including controlling nutritional status (CONUT) score, prognostic nutritional index (PNI) and geriatric nutritional risk index (GNRI) [19]. Although these nutritional indicators include total cholesterol, albumin, etc., they do not take into account the potential impact of inflammation. Therefore, these nutritional indicators may have certain limitations in practical applications.

The inflammation and malnutrition are currently receiving increasing attention. The Naples Prognostic Score (NPS) consists of albumin (ALB), total cholesterol (TC), NLR, and LMR, which can more effectively evaluate the body's inflammation, nutrition, and immune status. NPS adopts specific calculation methods in different diseases and populations. The NPS was defined based on the levels of serum albumin, TC, NLR, and LMR by the method of Galizia et al. (the cutoff values of NLR and LMR were defined by MaxStat analysis)[20]. NPS is calculated using four specific criteria and these cutoffs are universally validated. Initially, NPS was used to assess the prognosis of colorectal cancer patients. Recently, NPS has since been extended to various malignancies and applied to asthma and nonalcoholic fatty liver disease [21,22,23]. Furthermore, the research on NPS in the NHANES database has also been validated. Therefore, we would like to further evaluate the value of NPS in older adults. Recent studies have found that NPS is associated with the incidence and prognosis of various diseases such as cancer and severe aortic stenosis [24,25,26]. However, the relationship between NPS and stroke and mortality among older adults is not yet clear. The aim of this study is to explore the connection between NPS and stroke among older adults, as well as the association between NPS and mortality in older adults.

Materials and methods

Sample selection

Data for this study were collected from the National Health and Nutrition Examination Survey (NHANES) database (www.cdc.gov/nchs/nhanes.com). We included 101,316 participants from 1999 to 2018. We excluded participants under the age of 60 (n = 82,229) and those missing stroke information (n = 56). In addition, we also excluded individuals missing NPS information (n = 2068) and follow-up data (n = 23). Finally, 16,940 participants were included in this study. The selection process for participants is shown in Fig. 1.

Fig. 1
figure 1

The flow chart of participant selection

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Covariates

Based on previous research on stroke, we collected variables from NHANES, including age, gender, race, educational levels, blood lipids, ALB, uric acid (UA), Glycosylated hemoglobin (HbA1c) smoking and drinking. The pro-oxidative and pro-inflammatory properties of UA cause damage to small arteries, leading to increased permeability of the vascular wall and disruption of the blood–brain barrier, which promotes the occurrence of stroke. Long term hyperuricemia leads to sustained damage to vascular function, causing serious adverse events and subsequently increasing mortality rates [27]. Previous studies based on NHANES have found that uric acid is associated with an increased risk of stroke and death [28, 29].Furthermore, chronic hyperglycemia accelerates the progression of cerebral atherosclerosis by inducing endothelial injury and smooth muscle cell dysfunction, and increases the risk of long-term adverse cardiovascular events in patients. many studies have found that HbA1c is closely related to stroke and poor prognosis for patients [30]. Therefore, we included UA and HbA1c as independent variables. In addition, certain important variables such as dietary information and physical activity levels could also influence both inflammation and nutritional status. Low physical activity increases the risk of obesity and insulin resistance, leading to endothelial dysfunction and dyslipidemia, which can increase the risk of stroke and death [31]. Furthermore, previous studies have shown that a decrease in antioxidant levels and an increase in free radical oxidation products in the body both affect the occurrence and development of stroke. The main pathogenic mechanism is the body's oxidative stress response, which generates a certain degree of free radicals and increases the risk of stroke and long-term death [32, 33]. In addition, low levels of vitamin D are associated with endothelial dysfunction, abnormal lipid metabolism, increased risk of vascular oxidative stress and inflammation, which can induce stroke and adverse cardiovascular events [34]. In summary, many studies have found that low physical activity, low intake of antioxidant diets and reduced intake of vitamin D are closely related to the occurrence and prognosis of stroke. Therefore, we also included physical activity and vitamin D intake as variables. We calculated the composite dietary antioxidant index (CDAI). CDAI can effectively measure the body's dietary antioxidant capacity. It comprises a composite score of six dietary antioxidants: vitamins A, C, and E, as well as selenium, zinc, and carotenoids [35]. Besides, hypertension and diabetes (DM) are recognized risk factors for stroke and cardiovascular death. Therefore, we collected some comorbidities, including hypertension and DM.

Calculation of inflammatory and nutritional indicators

We calculate inflammatory markers based on clinically relevant indicators. The calculation formula for NLR and LMR are as follows: NLR: neutrophil count/lymphocyte count [36]. LMR: lymphocyte count/monocyte count [36]. The CONUT score acts as a measure for assessing nutritional and immune status by taking into account the composition of lymphocytes, levels of total cholesterol, and albumin. The CONUT score is the sum of the scores corresponding to three indicators. The total scores range from 0 to 12 [37]. The specific scoring criteria are shown in Table S1. The calculation formula for PNI is as follows: serum albumin(g/L) + 5 × total lymphocyte count(109/L) [38]. The calculation formula for GNRI is as follows: 1.489 × serum albumin(g/L) + 41.7 × (ideal weight/current weight). The ideal weight for men is calculated as 22 × height (m)2. The ideal weight for women is calculated as 21 × height (m)2. If the current weight exceeds the ideal weight, the weight-to-ideal weight ratio is set to 1[39]. NPS is the sum of the scores corresponding to four indicators, including NLR, LMR, TC and ALB. For each parameter, thresholds were assigned as follows: serum albumin ≥ 40 g/L, TC ≥ 4.65 mmol/L, NLR < 2.96, or LMR ≥ 4.44 scored 0; levels outside these thresholds scored 1. The specific scoring criteria are shown in Table S2 [40].

Mortality

The mortality statistics were connected up to December 31, 2019 (https://www.cdc.gov/nchs/data-linkage/mortality.htm). Outcomes were divided into categories of all-cause mortality and cardiovascular mortality. Death causes were categorized according to ICD-10 codes. Cardiovascular mortality refers to deaths caused by heart disease (I00-I09, I11, I13, and I20-I51) and stroke (I60-I69) [41].

Statistical analysis

R software is used for statistical analysis. Baseline characteristics were stratified by stroke status. Continuous variables were represented by mean ± SEM (standard error of the mean), while categorical variables were represented as proportions (95% CI), with P values < 0.05 considered statistically significant. All covariates were missing at < 10%, and the mice package was used to perform random forest multiple interpolation for missing covariates. In addition, baseline data is divided into three groups based on NPS values: low NPS group, medium NPS group and high NPS group. We further compare the differences in data between different NPS groups. Age, gender, and related variables were incorporated into the multivariate logistic regression model. Two models were developed: Model I, and Model II. Model I was adjusted for age, gender and race. Model II was adjusted for age, gender, race, education levels, BMI, smoking, drinking, TC, HDL-C, HbA1c, uric acid, hypertension and DM. Restrictive cubic spline is utilized to model the correlation between continuous variables and dependent variables. In regression analysis, it allows for capturing non-linear correlations and avoiding overfitting while maintaining smoothness. Nonlinear is closer to the essence of objective things and is one of the significant methods for understanding complex problems. We used the rms package in R software to create a restricted cubic spline model. In addition, we selected the minimum number of nodes for the Akaike information criterion(AIC) to ensure the stability of the model. Restricted cubic spline was used to investigate the relationship between NPS and stroke. Previous studies have indicated that results from weighted analysis and unweighted analysis may occasionally differ. Therefore, we conducted unweighted logistic regression to further verify our results [42]. In the sensitivity analysis, we also compared the analysis of baseline information before and after the inclusion of the exclusion of NPS. Inverse Probability Weighting (IPW) was applied to adjust for potential bias due to the exclusion of participants with missing NPS scores, and weighted multivariate logistic regression and Cox regression analyses to assess the robustness of the findings. Additionally, we conducted subgroup analysis to further clarify the relationship between NPS and the incidence of stroke in different subgroups. Kaplan–Meier Survival curve was utilized to explore the differences in mortality risk among different NPS groups. Cox regression was utilized to evaluate the link between NPS and mortality among older adults. ROC curve was used to analyze the predictive value of NPS, CONUT score, PNI, and GNRI for the incidence of stroke and mortality in older adults.

Results

The baseline characteristics of participants

This study included 16,940 patients. Table 1 showed the baseline data of patients. There were statistically significant differences between non-stroke group and stroke group in terms of age, race,]educational levels, HbA1c, TC, HDL, UA, NPS, smoking, drinking, hypertension and DM (P < 0.05). Specifically, patients in the stroke group had higher age(73.12 ± 0.24vs.69.94 ± 0.10), HbA1c(6.11 ± 0.04vs.5.90 ± 0.01), and UA(353.55 ± 3.33vs.334.94 ± 0.99). patients in the stroke group had a higher proportion of patients with hypertension(83.49%vs.66.86%) and DM(40.11%vs.25.53%) (P < 0.05). On the contrary, the stroke group had lower PNI(50.84 ± 0.38vs.51.80 ± 0.15), GNRI (117.69 ± 0.86vs.120.85 ± 0.25), HDL(1.36 ± 0.02vs.1.44 ± 0.01) and TC(4.86 ± 0.04vs.5.14 ± 0.01) (P < 0.05). It is worth noting that NPS in the stroke group was higher(1.91 ± 0.04vs.1.56 ± 0.01, P < 0.001). The CONUT score of stroke patients is also higher(1.32 ± 0.06vs.0.92 ± 0.02, P < 0.001). In addition, we divided the data into low NPS, medium NPS and high NPS group as showed in table S3. Compared with the low NPS group, patients in the high NPS group have higher age (72.44 ± 0.19vs.67.91 ± 0.19), BMI (29.66 ± 0.16vs.28.12 ± 0.19), HbA1c(6.09 ± 0.02vs.5.86 ± 0.02) and UA (349.74 ± 2.78vs.312.98 ± 2.39). The proportion of smoking (57.30%vs.44.55%), hypertension (74.54%vs.62.83%) and DM(39.12%vs. 20.09) in patients with high NPS was higher. Patients in the high NPS group had lower levels of TC (4.16 ± 0.02vs.5.87 ± 0.03) and HDL (1.31 ± 0.01vs.1.52 ± 0.02). In addition, compared with the low NPS group, patients with high NPS have higher rates of all-cause mortality(43.48% vs. 23.10%) and cardiovascular mortality(13.38%vs.6.22%).

Table 1 Clinical characteristics of population
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Logistic regression analysis

As a continous variable, a positive relationship was showed between NPS and the incidence of stroke with an OR of 1.22 (95% CI 1.11–1.35) in adjusted logistic regression analysis(Table 2). Similarly, as a categorical variable, high NPS is associated with an increased incidence of stroke(OR = 1.70 (95% CI 1.24–2.35), P = 0.001) in model II. This suggests that high NPS may double stroke risk in older adults.

Table 2 Logistic regression analysis on the correlation between NPS and stroke
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Restricted cubic spline

It was utilized to explore the curve relationship between NPS and the incidence of stroke. Our results revealed a non-linear positive relationship between NPS and the incidence of stroke (P for overall < 0.001, P for non-linear < 0.001)(Fig. 2). The results indicated that there was a non-linear positive correlation between NPS and the incidence of stroke. As NPS increased, the odds of stroke exhibited a gradual rise up to a certain threshold. Beyond the inflection point (NPS = 2), the risk of stroke has sharply increased(Fig. 2). We need to pay attention to people with NPS greater than 2, which may be helpful for stroke screening in the elderly population(NPS < 2: OR 95% CI 1.03 (0.81–1.31), NPS ≥ 2, OR 95% CI1.48(1.31–1.67)). Interestingly, this positive correlation has been noted in different age groups (Figs. 3A). This positive correlation has also been noted in both male and female participants (Figs. 3B).

Fig. 2
figure 2

The relationship between NPS and the incidence of stroke. NPS, naples prognostic score

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Fig. 3
figure 3

The relationship between NPS and the incidence of stroke in different subgroups. A Age; B Sex; NPS naples prognostic score

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Sensitivity analysis

Additionally, sensitivity analysis was used to investigate the correlation between NPS and stroke after excluding participants with serious diseases, including severe renal insufficiency, heart failure, and cancer. The results indicated that high NPS is still associated with an increased risk of stroke(OR = 1.72 (95% CI 1.22–2.41))(Table 3). This suggests that high NPS may double stroke risk in older adults. Previous studies have found that results from weighted analysis and unweighted analysis may differ. Therefore, unweighted method was utilized to confirm the reliability of our results. As a continuous variable, a positive correlation was illustrated between NPS and the incidence of stroke, with an OR of 1.21 (95% CI 1.13–1.30) in Model II. Compared to the low NPS group, participants in the high NPS group exhibits a higher incidence of stroke (OR = 1.66 (95% CI 1.29–2.15)) in Model II (Table S4). In sensitivity analysis, We also compared the baseline characteristics of 2,091 participants excluded due to missing NPS variables with 16,940 included participants (Table S5). Inverse Probability Weighting (IPW) was then applied in multivariate logistic and cox regression analysis to validate the stability of these relationships(Table S6-S7).

Table 3 Sensitivity analysis to investigate the correlation between NPS and stroke after excluding participants with serious diseases
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Subgroups analysis

Subgroup analysis was utilized to investigate the association between NPS and the incidence of stroke in different subgroups based on age, sex, BMI, race, education levels, smoking, different eGFR levels, rheumatoid arthritis, hypertension and DM. Interestingly, Statistically significant interactions were found in different HbA1c levels(P = 0.03) and DM(P = 0.03) subgroups(Table 4).

Table 4 Subgroup analysis for the relationship between NPS and stroke
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The relationship between NPS and mortality

Kaplan–Meier Survival curve and cox regression analysis was utilized to investigate the association between NPS and mortality among older adults. The Kaplan–Meier curve shows that patients with high NPS have a significantly higher risk of all-cause mortality and cardiovascular mortality (P < 0.001)(Fig. 4A, B). In the Cox regression model, as a continous variable, a positive correlation was observed between NPS and mortality among older adults in model II(all-cause mortality: HR = 1.36 (95% CI 1.30–1.42); cardiovascular mortality: HR = 1.59 (95% CI 1.45–1.75)). Similarly, as a categorical variable, high NPS is associated with an increased risk of mortality in model II(all-cause mortality: HR = 2.22 (95% CI 1.90–2.61); cardiovascular mortality: HR = 2.65 (95% CI 1.90–3.70)) (Table 5). We also found that NPS is associated with an increased risk of death from heart disease(HR = 1.40(95% C:1.33–1.47)) and stroke(HR = 1.26(95% CI 1.09–1.45)) (Table S8).

Fig. 4
figure 4

Kaplan–Meier survival analysis curves for mortality in different NPS groups. A all-cause mortality; B cardiovascular mortality. NPS naples prognostic score

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Table 5 Weighted cox regression analysis on the relationship between NPS and mortality
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ROC curve analysis

ROC curve was used to analyze the predictive value of NPS, CONUT score, PNI, and GNRI for the incidence of stroke and mortality in older adults. The results indicate that NPS has a better ability to predict the incidence of stroke compared with other indicators (NPS vs. CONUT score vs. PNI vs. GNRI: 0.610 vs. 0.598 vs. 0.568 vs.0.550)(Fig. 5A). Similarly, NPS also has better predictive value for the mortality risk of older adults(all-cause mortality: NPS vs. CONUT score vs. PNI vs. GNRI: 0.625 vs. 0.603 vs. 0.613 vs.0.601; cardiovascular mortality: NPS vs. CONUT score vs. PNI vs. GNRI: 0.625 vs. 0.612 vs. 0.623 vs.0.573)(Fig. 5B, C).

Fig. 5
figure 5

ROC curve analysis of the predictive value of NPS, CONUT score, PNI, and GNRI for the incidence of stroke and mortality in older adults. A the incidence of stroke; B all-cause mortaliy; C cardiovascular mortality; ROC receiver operating characteristic; CONUT controlling nutritional status, PNI prognostic nutritional index, GNRI geriatric nutritional risk index

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Discussion

Inflammation and malnutrition were closely associated with stroke. Our research found that the NPS, which combines inflammation and nutritional status, is positively correlated with the incidence of stroke in older adults. In addition, NPS is positively correlated with the risk of all-cause and cardiovascular mortality in older adults.

Inflammation plays an important role in cerebral atherosclerosis. After endothelial cell injury, adhesion molecules are released, attracting a large number of inflammatory cells (including neutrophils, lymphocytes, monocytes, etc.) to infiltrate the plaque site [42]. Monocytes are deposited under the intima to form macrophages, which swallow lipids to form foam cells. Macrophages can release a large number of inflammatory factors, thereby promoting the proliferation of smooth muscle cells and exacerbating cerebral atherosclerosis[43]. The activation of NLRP3 inflammasome can promote the release of inflammatory mediators, thereby inducing plaque instability and rupture, and further progressing to stroke. Brain cells are extensively damaged due to ischemia and hypoxia, and damaged neurons produce excessive reactive oxygen species, disrupting the blood–brain barrier. Peripheral immune cell infiltration induces an inflammatory cascade reaction. At the same time, microglia transform into pro-inflammatory cells, and the infiltration of inflammatory factors leads to increased permeability of cerebral vascular endothelium, further inducing brain tissue damage[44].

Inflammation participates in the occurrence and development of cerebral atherosclerosis. Inflammation induces endothelial damage, recruits proinflammatory immune cells to promote cytokine release and accelerates the formation of foam cells, thus leading to early atherosclerosis [45]. The worsening inflammatory response further triggers plaque rupture, bleeding, and disruption of the cerebral vascular barrier, leading to severe cerebrovascular events [46]. Recent studies have suggested that combining peripheral blood cell counts can better reflect the overall level of inflammation in the body. A study explored the relationship between different systemic inflammation indexes and the risk of stroke in older adults. The results showed that NLR and LMR exhibited good discriminatory ability between stroke and healthy control groups. NLR and LMR can be used as indicators to evaluate the risk of stroke[47]. Similarly, higher NLR is associated with an increased risk of stroke, and a longitudinal increase of more than 5% is associated with an increased risk of stroke in the elderly population [48]. Another study found that the systemic inflammatory immune index is closely related to all-cause mortality in elderly patients with frailty. This study emphasized the potential benefits of maintaining a certain low level of SI I[49]. Therefore, inflammation has important value in evaluating the risk of stroke and death in elderly patients.

Additionally, the lack of sufficient nutrition in the body can lead to weakened immunity and trigger inflammatory reactions. This can lead to damage and increased permeability of cerebral vascular endothelial cells, infiltration of pro-inflammatory immune cells, and disruption of the blood–brain barrier [50, 51]. Furthermore, inflammation and malnutrition may also interact with each other. Inflammation can further exacerbate the degree of malnutrition. Under the dual effects of inflammation and malnutrition, it ultimately leads to serious adverse cardiovascular events. Malnutrition is closely related to the risk of stroke in older adults. A study from UK Biobank study found that malnutrition is associated with an increased risk of abnormal brain structural parameters. Furthermore, patients with malnutrition have a higher risk of stroke [52]. Prognostic nutritional index (PNI) is an indicator for evaluating the nutritional status of the body. A large cross-sectional study found that the incidence rate of stroke in patients with high PNI was lower than that in patients with low PNI. Low PNI is a risk factor for the occurrence of stroke [15]. Elderly patients often have multiple comorbidities. Research has found that malnutrition is closely related to poor prognosis in elderly patients. And incorporating malnutrition indicators into survival prediction models significantly improves the predictive ability of mortality in elderly patients [53]. Therefore, it is necessary to closely monitor malnutrition in elderly patients.Previous studies have shown that the CHA2DS2-VASc score plays an important role in assessing stroke risk. Specifically, CHA2DS2-VASc score is widely used in patients with atrial fibrillation to assess stroke risk according to clinical factors such as congestive heart failure, hypertension, age, diabetes, stroke or transient ischemic attack history, vascular disease and gender. However, this tool mainly focuses on specific populations and does not consider the nutritional and inflammatory factors of patients, which may limit its application [54]. NPS is a recently proposed indicator that combines inflammation and nutritional status. Research has found that NPS is associated with an increased incidence of heart failure and a higher risk of mortality in patients with heart failure[26]. In addition, NPS is associated with an increased risk of long-term mortality in patients with heart failure (HR:2.194(95% CI 1.176–4.091), P = 0.014) [55]. In a large-scale cross-sectional study, NPS was closely associated with lung diseases. And NPS is associated with an increased risk of all-cause mortality in patients with respiratory diseases [56]. A propensity matching analysis found that NPS is more effective in predicting the prognosis of colorectal cancer patients compared to PNI and CONUT scores [57]. Another study also found similar conclusions that NPS has higher value in predicting the prognosis of rectal cancer patients compared to other inflammatory and nutritional indicators [58]. In addition, NPS has certain superiority in predicting the risk of death in patients with ST-segment elevation myocardial infarction compared with the systemic immune inflammation index(SII) [59]. Similarly, Our study found that high NPS is associated with an increased incidence of stroke(OR = 1.70(95% CI 1.24–2.35), P = 0.001). An OR of 1.60 suggests that high NPS may double stroke risk in elderly populations. And ROC curve analysis shows that compared with PNI, GNRI, and CONUT scores, NPS has the largest area under the curve in predicting stroke occurrence and death in older adults.

However, it is interesting that the relationship between NPS and the incidence of stroke is weakened in different HbA1c levels(P = 0.03) and DM(P = 0.03) subgroups. We are considering several possible reasons. First, in older adults with DM or those with chronic poor blood glucose control, the body has chronic inflammation and malnutrition to a certain extent. It may reflect competing metabolic dysregulation or ceiling effects from chronic inflammation. Second, this may also be related to drug interactions. In addition, a positive correlation was observed between NPS and mortality among older adults (all-cause mortality:HR = 1.36(95% CI 1.30–1.42); cardiovascular mortality: HR = 1.59(95%CI1.45–1.75)). This suggests that high NPS may double mortality risk in elderly populations.

Older adults are often more susceptible to inflammation and malnutrition. This study may provide assistance for stroke prevention and disease management among older adults. Older adults with higher NPS scores in screening need to be given special attention. According to our research findings, we need to pay attention to people with NPS greater than 2, which may be helpful for stroke screening in older adults. Adding NPS score screening to clinical guidelines may identify high-risk patients and prevent stroke and poor prognosis. However, our research also has some limitations. First, we only evaluated the relationship between NPS and stroke and mortality in older adults. We did not extend it to the general population. Second, the current research relies on baseline NPS measurements. Detecting the dynamic changes in inflammation and nutritional status over time will provide a more comprehensive risk profile. Longitudinal monitoring of NPS could reveal how changes correlate with the progression of stroke risk and mortality. We did not monitor the dynamic changes of NPS, which may provide more prognostic information. Third, this article compares NPS with common nutritional indicators using ROC curves and finds that NPS has better predictive value for stroke and death. However, due to the lack of CHA2DS2-VASc score in the NHANES database, we are unable to directly compare NPS with CHA2DS2-VASc score. Moreover, NPS can be obtained by detecting blood routine and biochemical indicators, with relatively simple operation and low cost. The use of NPS in hospital settngs or primary care environments may have certain practicality and convenience. However, before incorporating NPS into the elderly risk stratification, we still need to conduct large-scale studies to determine the measurement frequency of NPS. Finally, although various confounders such as hypertension, diabetes mellitus, and lipid profiles were included, there might still be other unmeasured confounders that could affect the results, including dietary patterns, socioeconomic status, medication use and inflammatory diseases. Longitudinal studies are still needed to validate NPS's temporal predictive utility in the future.

Conclusion

A positive link was observed between NPS and stroke and mortality. Routine NPS screening may enhance risk stratification in geriatric clinics.

Ethics approval and consent for participation

The data is accessible to the public (found in the NHANES database), therefore there is no need for an ethical approval statement or informed consent for the study.

Consent for publication

Not applicable.

Competing interests

The authors declare no competing interests.

Availability of data and materials

The datasets produced in the present research can be accessed in the database(https://www.cdc.gov/nchs/nhanes/).

Abbreviations

NLR:

Neutrophil-to-lymphocyte ratio

LMR:

Lymphocyte-to-monocyte ratio

CONUT:

Controlling nutritional status

ROS:

Reactive oxygen species

PNI:

Prognostic nutritional index

GNRI:

Geriatric nutritional risk index

NPS:

Naples Prognostic Score

ALB:

Albumin

TC:

Total cholesterol

UA:

Uric acid

HbA1c:

Glycosylated hemoglobin

DM:

Diabetes mellitus

SEM:

Standard Error of Mean

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  1. Department of Neurology, The Second Hospital of Dalian Medical University, Dalian, 116021, Liaoning, People’s Republic of China

    Junting Song & Lin Yin

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The study was designed by L.Y. The manuscript was written by JT. S. Data collection was carried out by JT. S. The manuscript was reviewed and edited by L.Y. All the authors read and approved the final version of the manuscript.

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Song, J., Yin, L. Associations between naples prognostic score and stroke and mortality among older adults. Eur J Med Res 30, 327 (2025). https://doiorg.publicaciones.saludcastillayleon.es/10.1186/s40001-025-02613-4

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European Journal of Medical Research

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