Abstract
Background
Inflammation and malnutrition are both closely associated with adverse outcomes after acute ischemic stroke (AIS), yet their combined effect and potential interaction remain insufficiently understood. This study aimed to evaluate the prognostic value of combined nutritional and inflammatory risk stratification based on nutritional risk screening-2002 (NRS-2002) and neutrophil-to-lymphocyte ratio (NLR), and to explore whether inflammatory burden mediates the relationship between nutritional status and functional outcomes.
Methods
We retrospectively analyzed 1,167 consecutive patients with AIS admitted within 48 h of symptom onset. Nutritional status was assessed using NRS-2002, and inflammatory status was evaluated using admission NLR. Patients were categorized into four groups: low-Risk, inflammatory-risk, nutritional-risk, and dual-risk. The primary outcome was poor functional outcome at 3 months (modified Rankin Scale ≥3). Multivariable logistic regression models were used to examine associations. Predictive performance was evaluated using AUC, NRI, and IDI. Mediation analysis was conducted using NLR area under the curve (NLR-AUC) to assess the indirect effect of inflammation.
Results
Compared with the low-risk group, the inflammatory-risk, nutritional-risk, and dual-risk groups were all independently associated with increased risk of poor functional outcomes (all P < 0.05). The dual-risk group showed the highest risk (fully adjusted OR = 3.59, P < 0.001). Nutritional–inflammatory risk stratification demonstrated superior predictive performance compared with traditional nutritional indices (all P < 0.05). Although it did not significantly improve AUC when added to the totaled health risks in vascular events (THRIVE) score, it significantly enhanced continuous NRI and IDI (both P < 0.05). Mediation analysis revealed that inflammatory burden partially mediated the association between malnutrition and poor outcomes in the basic model (mediation proportion: 8.73%), but this effect was attenuated after full adjustment.
Conclusions
Combined assessment of nutritional and inflammatory status provides meaningful risk stratification for functional outcomes in AIS. Malnutrition and systemic inflammation exert a synergistic effect on post-stroke prognosis, while inflammation may partially mediate this relationship but is not independent of key clinical factors.
Keywords: acute ischemic stroke, inflammation, malnutrition, prognosis, risk stratification
Introduction
Acute ischemic stroke (AIS) is a leading cause of death and disability worldwide (1). In 2021, it accounted for 69.9 million prevalent cases, 7.8 million incident cases, 3.6 million deaths, and 70.4 million disability-adjusted life years globally (1). Although age-standardized rates have declined, the absolute burden continues to rise due to population aging. Moreover, the proportion of IS among all stroke subtypes has increased from 68.32% in 1990 to 73.33% in 2021, making it the predominant contributor to the global stroke burden (2).
Accumulating evidence highlights the critical role of inflammation in determining the prognosis of ischemic stroke. Elevated high-sensitivity C-reactive protein levels within 24–72 h of stroke onset have been associated with an increased risk of poor long-term functional outcomes (3). Moreover, composite inflammatory indices, including the systemic inflammatory response index, systemic immune-inflammatory index, and inflammatory prognostic index, can predict unfavorable outcomes at 3 months in patients undergoing intravenous thrombolysis (4). A higher inflammatory burden index is also associated with an increased risk of 90-day unfavorable outcomes in patients treated with endovascular therapy (5). Novel inflammatory markers are similarly linked to stroke prognosis, with serum cytokine analysis showing Interleukin-5 as an independent protective factor and Interleukin-6 as an independent risk predictor in AIS patients (6).
Nutritional status is another factor closely linked to stroke prognosis. Malnutrition, as defined by the internationally recognized global leadership initiative on malnutrition criteria, is associated with poorer functional recovery, less favorable discharge outcomes, and an increased risk of mortality in stroke patients (7). Biochemistry-based nutritional risk assessment tools, including the geriatric nutritional risk index, controlling nutritional status score, and prognostic nutritional index, have demonstrated that better nutritional status reliably predicts improved functional recovery, a lower incidence of post-stroke complications, and reduced mortality in older adults with ischemic stroke (8, 9).
Nutritional and inflammatory composite indices are widely used across various medical fields. The systemic immune-inflammatory index and prognostic nutritional index have been employed to evaluate prognosis in patients with severe community-acquired pneumonia (10). Similarly, the combined use of immune and inflammatory markers has been applied to assess prognosis in patients with respiratory and gastrointestinal malignancies (11, 12). Incorporating nutritional and inflammatory indices into clinical indicators has also been shown to improve the diagnostic accuracy for severe pulmonary tuberculosis (13). However, research on the application of combined nutritional and inflammatory indices in stroke remains limited. The nutritional risk screening-2002 (NRS-2002) score is a well-established and widely used nutritional screening tool, recommended by both the European society for parenteral and enteral nutrition and the Chinese guidelines for acute ischemic stroke for assessing patients' nutritional status (14). The neutrophil-to-lymphocyte ratio (NLR) serves as the basis for many composite inflammatory indices and has been shown to be closely associated with prognosis in ischemic stroke (15, 16). This study aims to use NRS-2002 and NLR for risk stratification, to investigate their relationship with stroke outcomes, and to evaluate their predictive performance. In addition, we examine whether inflammatory status acts as a mediator between nutritional status and prognosis in patients with ischemic stroke.
Materials and methods
Patients
In this study, we retrospectively analyzed consecutive ischemic stroke patients admitted to the Department of Neurology at Tianxiang East Hospital and the Affiliated Dongyang Hospital of Wenzhou Medical University between January 2024 and June 2025. The study was approved by the Ethics Committees of Tianxiang East Hospital and the Affiliated Dongyang Hospital of Wenzhou Medical University. Written informed consent was obtained from all participants or their legal guardians upon admission. The study was conducted in accordance with the Declaration of Helsinki. The inclusion criteria were as follows: (1) a confirmed diagnosis of ischemic stroke based on the 2023 Chinese Guidelines for the diagnosis and treatment of acute ischemic stroke; (2) age >18 years; and (3) admission within 48 h of symptom onset. The exclusion criteria were as follows: (1) presence of acute infection at admission; (2) malignancy, autoimmune diseases, or severe hepatic or renal dysfunction; (3) a pre-stroke modified Rankin Scale (mRS) score ≥ 3; and (4) incomplete clinical data or loss to follow-up. As this was a retrospective study, no formal sample size calculation was performed. All consecutive eligible patients during the study period were included.
Data collection
Information on patients' demographics, vascular risk factors, comorbidities, treatment modalities, and laboratory results was obtained from electronic medical records. Patients treated with endovascular therapy, either alone or following intravenous thrombolysis (bridging therapy), were classified in the endovascular treatment group. Stroke etiology was determined for all patients using the TOAST classification based on clinical data (17). Hospital-acquired infection was defined as any infection developing 48 h or more after admission, including respiratory infections, pneumonia, and bloodstream infections. At 3 months ± 1 week after stroke onset, outcomes were evaluated via outpatient visits or telephone follow-up using the modified Rankin Scale (mRS) (18), with mRS ≥ 3 defined as a poor outcome.
Risk stratification based on nutritional and inflammatory status
The NRS-2002 was applied by trained nurses to evaluate the nutritional status of patients upon admission. The NRS-2002 score ranges from 0 to 7 and includes three components: (1) disease severity (indicating increased nutritional needs), ranging from 0 to 3 based on comorbidities and medical history; (2) nutritional impairment, based on BMI, body weight, and food intake, with a score range of 0 to 3; and (3) age, with one point assigned for patients aged 70 years or older (14). Patients were classified into a high nutritional risk group (NRS-2002 ≥3) and a low nutritional risk group (NRS-2002 < 3). NLR was calculated from the first complete blood count performed upon admission by dividing the absolute neutrophil count by the absolute lymphocyte count (NLR = neutrophil count ÷ lymphocyte count; 15). For patients with a follow-up NLR measured 5 ± 1 days after admission, and without infection at the time of retesting, the cumulative inflammatory burden was quantified using the area under the curve of NLR (NLR-AUC), calculated by the trapezoidal method as NLR-AUC= (admission NLR + retest NLR) × days/2, with “days” representing the time interval between the two measurements. Receiver operating characteristic curve analysis based on admission NLR identified 3.6 as the optimal threshold for predicting 3-month unfavorable outcomes (mRS ≥ 3). Patients were classified into a high inflammatory status group (admission NLR ≥ 3.6) and a low inflammatory status group (admission NLR < 3.6). Based on the combination of nutritional and inflammatory status, patients were further stratified into four nutritional–inflammatory risk groups: low-risk group (low nutritional risk and low inflammatory status), inflammatory-risk group (low nutritional risk but high inflammatory status), nutritional-risk group (high nutritional risk but low inflammatory status), and dual-risk group (high nutritional risk and high inflammatory status).
Statistical analysis
Continuous variables were presented as medians with interquartile ranges (IQRs), and categorical variables as counts and percentages. Comparisons of continuous variables among the four independent groups were performed using the Kruskal–Wallis H test, followed by pairwise comparisons with Bonferroni correction. Differences in categorical variables were assessed using Fisher's exact test or the Chi-square (X2) test, as appropriate. Four logistic regression models incorporating different covariates were constructed to examine the association between nutritional–inflammatory risk stratification and poor functional outcomes at 3 months. The predictive performance of nutritional–inflammatory risk stratification for poor outcomes in ischemic stroke was evaluated using the area under the receiver operating characteristic curve (AUC), net reclassification improvement (NRI), and integrated discrimination improvement (IDI). Subgroup analyses were performed to explore potential effect modifiers in the association between nutritional–inflammatory risk stratification and 3-month poor functional outcomes. NLR-AUC was used as the mediator in mediation analysis to evaluate whether inflammatory burden mediates the relationship between nutritional status and 3-month poor functional outcomes. Statistical significance was defined as a two-sided P < 0.05. All analyses were performed using SPSS version 26.0 and R version 4.1.1.
Results
Baseline characteristics by nutritional–inflammatory risk stratification
A total of 1,167 patients with acute ischemic stroke were retrospectively included and stratified into four groups according to nutritional and inflammatory status: low-risk (n = 605), inflammatory-risk(n = 203), nutritional-risk (n = 211), and dual-risk (n = 148), as detailed in Table 1. Age increased progressively across the four groups, with median values of 62.00, 67.00, 77.00, and 82.00 years, respectively, while the proportion of males was lowest in the nutritional-risk group. Patients in the inflammatory-risk and dual-risk groups were more likely to undergo reperfusion therapy (P < 0.001). Compared with the other groups, the dual-risk group had a lower proportion of small-artery occlusion and a higher proportion of cardioembolic stroke (P < 0.001). Moreover, the dual-risk group exhibited a higher incidence of hospital-acquired infections, greater stroke severity, and a significantly higher rate of poor functional outcomes (P < 0.001).
Table 1.
Baseline characteristics of patients with acute ischemic stroke stratified by nutritional and inflammatory status.
| Variables | Low-risk group (N = 605) | Inflammatory-risk group (N = 203) | Nutritional-risk group (N = 211) | Dual-risk group (N=148) | P |
|---|---|---|---|---|---|
| Demographic data | |||||
| Age (years), median (IQR) | 62.00 (54.00,70.00) | 67.00 (56.00,74.00) a | 77.00 (73.00,83.00) ab | 82.00 (76.00,87.00) ab | < 0.001 |
| Sex, male, n (%) | 400 (66.1) 24.65 (22.27,26.70) | 143 (70.4) | 111 (52.6) ab | 86 (58.1) | < 0.001 |
| BMI (kg/m2), Median (IQR) | 24.17 (21.97,26.57) | 22.86 (20.81,25.10) ab | 22.88 (20.76,25.39) ab | < 0.001 | |
| Vascular risk factors, n (%) | |||||
| Hypertension | 459 (75.9) | 159 (78.3) | 169 (80.1) | 113 (76.4) | 0.612 |
| Diabetes mellitus | 212 (35.0) | 76 (37.4) | 61 (28.9) | 39 (26.4) | 0.059 |
| Ischemic heart disease | 68 (11.2) | 30 (14.8) | 37 (17.5) | 36 (24.3) a | < 0.001 |
| History of stroke/TIA | 86 (14.2) | 31 (15.3) | 31 (14.7) | 27 (18.2) | 0.673 |
| Current smoke | 210 (34.7) | 56 (27.6) | 25 (11.8) ab | 15 (10.1) ab | < 0.001 |
| Excess alcohol consumption | 119 (19.7) | 41 (20.2) | 32 (15.2) | 24 (16.2) | 0.391 |
| Laboratory data, median (IQR) | |||||
| Hb (g/L) | 141.00 (129.00,151.00) | 141.00 (130.00,152.00) | 130.00 (115.00,138.00) ab | 137.00 (123.00,148.00) abc | < 0.001 |
| TC (mmol/L) | 4.26 (3.58,5.03) | 4.29 (3.62,4.93) | 3.93 (3.33,4.71) ab | 3.87 (3.34,4.50) ab | < 0.001 |
| TG (mmol/L) | 1.36 (0.97,1.91) | 1.07 (0.81,1.54) a | 0.98 (0.75,1.35) a | 0.85 (0.61,1.21) abc | < 0.001 |
| LDL (mmol/L) | 2.35 (1.77,3.01) | 2.32 (1.69,2.98) | 2.02 (1.48,2.65) ab | 1.90 (1.36,2.37) ab | < 0.001 |
| HDL (mmol/L) | 1.01 (0.86,1.22) | 1.00 (0.86,1.14) | 1.05 (0.89,1.30) b | 1.01 (0.87,1.23) | 0.034 |
| Hemoglobin A1c (%) | 6.00 (5.70,6.95) | 6.00 (5.70,6.90) | 5.90 (5.70,6.40) | 5.80 (5.60,6.07) ab | < 0.001 |
| Reperfusion therapy, n (%) | < 0.001 | ||||
| No reperfusion therapy | 535 (88.4%) | 149 (73.4%) a | 183 (86.7%) b | 109 (73.6%) ac | |
| Intravenous thrombolysis | 58 (9.6%) | 21 (10.3%) a | 24 (11.4%) b | 22 (14.9%) ac | |
| Endovascular treatment | 12 (2.0%) | 33 (16.3%) a | 4 (1.9%) b | 17 (11.5%) ac | |
| TOAST classification, n (%) | < 0.001 | ||||
| Small-vessel occlusion | 338 (55.9) | 71 (35.0) a | 100 (47.4) ab | 40 (27.0) abc | |
| Large-artery atherosclerosis | 137 (22.6) | 76 (37.4) a | 43 (20.4) ab | 44 (29.7) abc | |
| Cardioembolism | 32 (5.3) | 21 (10.3) a | 25 (11.8) ab | 33 (22.3) abc | |
| Other/undetermined | 98 (16.2) | 35 (17.2) a | 43 (20.4) ab | 31 (20.9) abc | |
| Clinical characteristics and outcomes | |||||
| Hospital-acquired infection, n (%) | 28 (4.6) | 34 (16.7) a | 29 (13.7) a | 55 (37.2) abc | < 0.001 |
| Admission NIHSS score, median (IQR) | 1.00 (1.00,3.00) | 2.00 (1.00,6.00) a | 2.00 (1.00,3.00) a | 4.00 (2.00,12.00) abc | < 0.001 |
| Admission NIHSS score >5, n (%) | 34 (5.6%) | 63 (31.0%) a | 24 (11.4%) ab | 66 (44.6%) ac | < 0.001 |
| mRS≥3, n (%) | 60 (9.9) | 48 (23.6) a | 40 (19.0) a | 66 (44.6) abc | < 0.001 |
BMI, body mass index; TIA, transient ischemic attack; Hb, hemoglobin; TC, total cholesterol; TG, triglyceride; LDL, low-density lipoprotein cholesterol; HDL, high-density lipoprotein cholesterol; NIHSS, national institutes of health stroke scale; TOAST, trial of Org 10172 in acute stroke treatment; mRS, modified rankin scale; IQR, interquartile range.
a indicates significant difference compared with low-risk group; b indicates significant difference compared with inflammatory-risk group; c indicates significant difference compared with nutritional-risk group (Bonferroni-adjusted p-values for pairwise comparisons).
Association between nutritional-inflammatory risk stratification and functional outcome
Table 2 presents the associations between nutritional–inflammatory risk stratification and 3-month poor functional outcomes in patients with acute ischemic stroke, as assessed using four logistic regression models with the low-risk group as the reference. The inflammatory-risk, nutritional-risk, and dual-risk groups were all significantly associated with an increased risk of poor outcomes across all models (all P < 0.05). With progressive adjustment for potential confounders, the odds ratios were attenuated but remained statistically significant. Notably, the dual-risk group consistently showed the highest risk across all models (crude OR = 7.31; fully adjusted OR = 3.59; both P < 0.001).
Table 2.
Nutritional-inflammatory risk stratification and 3-month poor functional outcome in acute ischemic stroke.
| NNRS | Model1 | Model2 | Model3 | Model4 | ||||
|---|---|---|---|---|---|---|---|---|
| OR (95% CI) | P | OR (95% CI) | P | OR (95% CI) | P | OR (95% CI) | P | |
| Low-risk | 1.00 (Reference) | 1.00 (Reference) | 1.00 (Reference) | 1.00 (Reference) | ||||
| Inflammatory-risk | 2.81 (1.85–4.28) | < 001 | 3.00 (1.96–4.60) | < 001 | 1.80 (1.08–3.01) | 0.024 | 1.78 (1.06–2.99) | 0.030 |
| Nutritional-risk | 2.12 (1.37–3.28) | < 001 | 2.31 (1.40–3.80) | 0.001 | 2.02 (1.15–3.54) | 0.014 | 1.98 (1.12–3.51) | 0.018 |
| Dual-risk | 7.31 (4.81–11.12) | < 001 | 8.46 (5.05–14.17) | < 001 | 3.81 (2.05–7.07) | < 001 | 3.59 (1.91–6.75) | < 001 |
Model 1: Crude model. Model 2: Adjusted for age, gender, smoking, hypertension, diabetes mellitus, coronary artery disease, history of stroke/TIA. Model 3: Adjusted for Model 2 covariates plus reperfusion therapy, TOAST classification, admission NIHSS, hospital-acquired infection. Model 4: Adjusted for Model 3 covariates plus total cholesterol, triglycerides, low-density lipoprotein cholesterol, high-density lipoprotein cholesterol, hemoglobin A1c. NNRS, nutritional-inflammatory risk stratification; TIA, transient ischemic attack; TOAST, trial of Org 10172 in acute stroke treatment; NIHSS, national institutes of health stroke scale; OR, odds ratio; CI, confidence interval. Bold values indicate statistical significance (p < 0.05).
Incremental prognostic value of nutritional-inflammatory risk stratification
As shown in Figure 1, nutritional-inflammatory risk stratification exhibited a higher AUC compared with traditional nutritional indices, including NRS-2002, PNI, and CONUT (all P < 0.05). For inflammatory markers, nutritional–inflammatory risk stratification showed a significantly greater AUC than SII (P < 0.05). Although its AUC was also higher than that of NLR and SIRI, these differences were not statistically significant (both P > 0.05). As presented in Table 3, the addition of nutritional–inflammatory risk stratification to the THRIVE score did not significantly improve AUC or categorical NRI (P > 0.05), but it significantly enhanced continuous NRI and IDI (both P < 0.05).
Figure 1.
Comparison of area under the receiver operating characteristic curves (AUCs) for nutritional-inflammatory risk stratification (NNRS) and other inflammatory and nutritional markers in predicting poor outcome after ischemic stroke. The AUC of NNRS was significantly higher than those of SII, NRS-2002, PNI, and CONUT (DeLong test, P < 0.05), and higher than NLR and SIRI, but the differences were not statistically significant (DeLong test, P > 0.05). NRS-2002, Nutritional risk screening 2002; NLR, neutrophil-to-lymphocyte ratio (neutrophils ÷ lymphocytes); SIRI, systemic inflammatory response index (neutrophils × monocytes ÷ lymphocytes); SII, systemic immune-inflammation index (platelets × neutrophils ÷ lymphocytes); PNI, prognostic nutritional index [10 × albumin (g/Dl) + 0.005 × lymphocytes (per mm3)]; CONUT, controlling nutritional status score (based on serum albumin, lymphocyte count, and total cholesterol; each component scored 0–6 according to standard thresholds, summed to give total score).
Table 3.
Incremental prognostic value of nutritional–inflammatory risk stratification in acute ischemic stroke.
| Predictive performance | THRIVE score | THRIVE score + NNRS | P |
|---|---|---|---|
| AUC (95% CI) | 0.828 (0.796, 0.860) | 0.830 (0.898, 0.862) | 0.832 |
| NRIcat (95% CI) | Reference | −0.030 (−0.076, 0.016) | 0.200 |
| NRIcont (95% CI) | Reference | 0.217 (0.070, 0.365) | 0.004 |
| IDI (95% CI) | Reference | 0.016 (0.006, 0.026) | 0.001 |
AUC, area under the curve; CI, confidence interval; THRIVE, totaled health risks in vascular events; NRIcat, categorical net reclassification improvement; NRIcont, continuous net reclassification improvement; IDI, integrated discrimination improvement; NNRS, nutritional-inflammatory risk stratification.
THRIVE score: A validated prognostic scale for acute ischemic stroke based on age, baseline NIHSS, and the presence of hypertension, diabetes, and atrial fibrillation; higher scores indicate greater risk of poor outcomes.
Subgroup analysis of nutritional–inflammatory risk stratification and poor functional outcomes
Subgroup analyses were conducted to examine the association between nutritional–inflammatory risk stratification and 3-month poor functional outcomes in patients with acute ischemic stroke across different clinical subgroups in Table 4. The association between nutritional–inflammatory risk stratification and poor prognosis was particularly pronounced in elderly patients and those with comorbidities such as hypertension and diabetes. A stronger relationship was also observed in patients with moderate-to-severe stroke and in those who did not undergo reperfusion therapy. Notably, the dual-risk group demonstrated a markedly increased risk of poor functional outcome in patients with large-artery atherosclerosis as well as in those with other or undetermined stroke etiologies. No significant interaction was observed (all P for interaction > 0.05).
Table 4.
Subgroup analysis of nutritional–inflammatory risk stratification and 3-month poor outcomes (low-risk reference).
| Subgroup | Inflammatory-risk group | Nutritional-risk group | Dual-risk group | P for interaction | |
|---|---|---|---|---|---|
| Gender | 0.822 | ||||
| Male (N = 740) | 1.959 (1.049, 3.658)* | 2.695 (1.227, 5.921)* | 4.987 (2.224, 11.182)*** | ||
| Female (N = 427) | 1.218 (0.458, 3.240) | 2.209 (0.935, 5.217) | 3.970 (1.551, 10.163)** | ||
| Age | 0.291 | ||||
| ≥65 (N = 718) | 1.928 (0.915, 4.065) | 2.284 (1.192, 4.374)* | 4.815 (2.429, 9.545)*** | ||
| < 65 (N = 449) | 1.161 (0.509, 2.649) | 15.388 (1.221, 193.920)* | 3.401 (0.128, 90.220) | ||
| Hypertension | 0.439 | ||||
| Yes (N = 900) | 1.789 (0.984, 3.254) | 2.384 (1.240, 4.582)** | 5.255 (2.628, 10.508)*** | ||
| No (N = 267) | 1.322 (0.397,4.401) | 3.052 (0.898, 10.372) | 4.570 (1.181, 17.683)* | ||
| Diabetes mellitus | 0.697 | ||||
| Yes (N = 388) | 2.062 (0.894, 4.755) | 3.690 (1.447, 9.411)** | 7.015 (2.469, 19.927)*** | ||
| No (N = 779) | 1.503 (0.761, 2.967) | 1.879 (0.901, 3.919) | 3.550 (1.662, 7.584)* | ||
| NIHSS score | 0.957 | ||||
| >5 (N = 187) | 4.846 (1.537, 15.277)** | 6.464 (1.497, 27.924)* | 18.255 (4.400, 75.742)*** | ||
| ≤ 5 (N = 980) | 1.518 (0.791, 2.910) | 2.026 (1.073, 3.827)* | 3.910 (1.900, 8.047)*** | ||
| TOAST classification | 0.101 | ||||
| SVO (N = 549) | 1.141 (0.482, 2.702) | 2.512 (1.097, 5.749)* | 2.821 (0.969, 8.208) | ||
| LAA (N = 300) | 3.603 (1.345, 9.654)* | 1.692 (0.504, 5.685) | 8.375 (2.530, 27.718)** | ||
| CE (N = 111) | 0.261 (0.016, 4.202) | 0.419 (0.039, 4.514) | 2.306 (0.257, 20.688) | ||
| OU (N=207) | 13.109 (1.221, 140.709)* | 35.090 (2.137, 576.073)* | 63.538 (3.677, 1097.795)** | ||
| Reperfusion therapy | 0.08 | ||||
| No (N = 976) | 1.914 (1.047, 3.500)* | 2.083 (1.098, 3.951)* | 4.443 (2.232, 8.844)*** | ||
| Intravenous thrombolysis (N = 125) | 0.820 (0.191, 3.510) | 2.898 (0.613, 13.699) | 1.508 (0.288, 7.890) | ||
NNRS, nutritional-inflammatory risk stratification; NIHSS, national institutes of health stroke scale; TOAST, trial of Org 10172 in acute stroke treatment; SVO, small-vessel occlusion; LAA, large-artery atherosclerosis; CE, Cardioembolism; OU, Other/undetermined; N, Number. Adjusted for covariates in Model 4. Statistical significance is denoted as: * p < 0.05, ** p < 0.01, *** p < 0.001.
Mediating role of inflammation in nutrition and functional outcomes
Mediation analyses were conducted to evaluate whether inflammatory status mediates the association between nutritional status and prognosis in patients with acute ischemic stroke in Figure 2. In Model 1, NLR-AUC demonstrated a significant partial mediating effect (indirect effect = 0.012, 95% CI: 0.001–0.026, P = 0.040), accounting for 8.73% of the total effect of NRS-2002 on 3-month mRS. In Model 2, the mediating effect of NLR-AUC was attenuated and no longer statistically significant (indirect effect = 0.009, 95% CI: −0.002 to 0.021, P = 0.112).
Figure 2.
Mediating effect of inflammation burden, measured by neutrophil-to-lymphocyte ratio area under the curve (NLR-AUC; mediator), on the association between malnutrition (nutritional risk screening 2002 ≥3; exposure) and poor 3-month functional outcome (modified Rankin scale ≥3; outcome) in patients with acute ischemic stroke. NLR-AUC partially mediated this association in Model 1 (accounting for 8.73% of the total effect), whereas the mediating effect was attenuated and became non-significant in Model 2. Model 1: adjusted for age, sex, cigarette smoking, hypertension, diabetes mellitus, coronary artery disease, and history of stroke or transient ischemic attack. Model 2: adjusted for all Model 1 covariates plus reperfusion therapy, Trial of Org 10172 in Acute Stroke Treatment classification, admission National Institutes of Health Stroke Scale score, hospital-acquired infection, total cholesterol, triglycerides, low-density lipoprotein cholesterol, high-density lipoprotein cholesterol, and glycated hemoglobin.
Discussion
After ischemic stroke, hypoxia and ischemia cause neuronal necrosis and the release of damage-associated molecular patterns, which activate microglia and trigger central neuroinflammation (19). This response leads to excessive production of pro-inflammatory cytokines (e.g., TNF-α, IL-1β, and IL-6), disruption of the blood–brain barrier, and infiltration of peripheral immune cells, especially neutrophils, into the brain, further worsening neuronal injury and promoting the progression of the ischemic penumbra to irreversible infarction (19, 20). Overactivation of central immune responses also disrupts peripheral immunity, resulting in immunosuppression that increases susceptibility to infections (20). At the same time, malnutrition is common in the acute phase of stroke, with reported prevalence ranging from 6.1% to 62%, and elderly patients being particularly vulnerable (21). Malnutrition reduces protein synthesis and glucose utilization in the ischemic penumbra, limiting neuronal repair (22), while also weakening immune defenses, increasing the risk of complications such as stroke-associated pneumonia and gastrointestinal bleeding (23), and contributing to muscle weakness that impairs rehabilitation and functional recovery (24). Beyond its impact on the acute phase, malnutrition may also adversely affect long-term recovery through persistent energy deficiency and the development of post-stroke sarcopenia (25). Inadequate nutritional intake promotes muscle protein breakdown and loss of muscle mass and strength, which can reduce physical performance, limit participation in rehabilitation programs, and ultimately impair functional recovery (26). Furthermore, chronic inflammation may exacerbate skeletal muscle wasting and cachexia through persistent catabolic signaling, creating a vicious cycle between malnutrition, inflammation, and sarcopenia that contributes to long-term disability after stroke (27). Consistent with these mechanisms, our study showed that patients with both high nutritional risk and high inflammatory status had the highest rate of poor functional outcomes at 3 months. To our knowledge, this is the first study to combine nutritional and immune–inflammatory indicators for risk stratification and prognosis prediction in ischemic stroke, highlighting the synergistic impact of malnutrition and systemic inflammation on post-stroke recovery.
In our study, comparison between the inflammatory-risk group and the nutritional-risk group revealed distinct patterns of inflammatory and nutritional risk among different stroke populations. Patients in the nutritional-risk group were older, had a higher proportion of females, and lower lipid levels, reflecting not only decline in nutritional reserves and muscle mass but also potential differences in metabolic and hormonal status that predispose them to malnutrition (28). By comparison, the inflammatory-risk group had a higher proportion of large-artery atherosclerotic strokes and a lower proportion of small-artery occlusion strokes, with higher baseline NIHSS scores, as large-artery atherosclerotic infarctions generally involve a larger infarct volume than small-artery occlusions (29), eliciting a more pronounced systemic immune–inflammatory response and resulting in more severe neurological deficits (30, 31). Notably, the dual-risk group had a higher proportion of cardioembolic strokes, which are typically sudden in onset and lack effective collateral circulation (32), leading to larger infarct volumes and more pronounced systemic inflammatory responses. Moreover, these patients often present with impaired consciousness or dysphagia in the acute phase, limiting energy and nutrient intake and thereby increasing the risk of malnutrition (33). The incidence of hospital-acquired infections was higher in all three risk groups compared with the low-risk group, indirectly indicating that impaired immune–inflammatory status and poor nutritional status may increase the risk of poor outcomes by promoting in-hospital complications.
Logistic regression analysis demonstrated that combined immune–inflammatory and nutritional risk stratification was more strongly associated with poor post-stroke outcomes than either inflammatory or nutritional indicators alone, and this association remained robust after adjustment for multiple potential confounders. Subgroup analyses further showed that this association was broadly consistent across patient subgroups, with no significant interactions observed. Notably, a trend toward stronger associations was evident in high-risk populations, including elderly patients, those with hypertension or diabetes, and patients with moderate-to-severe stroke. These populations are typically characterized by diminished physiological and nutritional reserves alongside heightened immune–inflammatory responses (28, 31), potentially predisposing them to worse outcomes. Furthermore, the association appeared more pronounced in patients without reperfusion therapy and in certain stroke subtypes, such as large-artery atherosclerosis, possibly reflecting both prolonged ischemic exposure and sustained systemic inflammatory responses in the absence of timely reperfusion (31), as well as the larger infarct burden and more extensive inflammatory activation typically observed in these conditions (30), which together may amplify the detrimental effects of nutritional and inflammatory disturbances.
Nevertheless, nutritional-inflammatory risk stratification did not improve the predictive AUC compared with NLR, and its overall discriminative ability remained modest. This may be attributable to the heterogeneity of the study population, as patients across a wide age range and receiving different treatment modalities were included, potentially diluting its predictive performance. The THRIVE score, a validated prognostic tool incorporating age, baseline NIHSS score, and key vascular comorbidities (34), demonstrated strong baseline predictive ability. The addition of nutritional-inflammatory risk stratification to the THRIVE score did not improve AUC or categorical NRI, likely because age and NIHSS already capture substantial prognostic information and may partially overlap with nutritional-inflammatory risk stratification. However, nutritional-inflammatory risk stratification significantly improved continuous NRI and IDI, suggesting that it provides incremental predictive value and enhances individual-level risk stratification beyond that achieved by the THRIVE score alone.
Previous studies have suggested that malnutrition may exacerbate post-stroke immune–inflammatory responses through impaired immune regulation, negative energy balance, and barrier dysfunction, leading to a pro-inflammatory and immunosuppressed state (35, 36). Based on this rationale, we conducted a mediation analysis to examine whether inflammation contributes to the association between malnutrition and stroke outcomes. Considering that post-stroke immune–inflammatory responses evolve dynamically and typically plateau within the first week (19), we used inflammatory burden (NLR-AUC) rather than admission NLR to capture the cumulative response. NLR-AUC partially mediated the association between malnutrition and poor functional outcomes in Model 1, accounting for 8.73% of the total effect, indicating that systemic inflammation is one pathway linking malnutrition to adverse prognosis. After further adjustment for clinical severity, stroke characteristics, and in-hospital factors in Model 2, the mediation effect was attenuated and no longer significant, suggesting that the observed mediation may be partly explained by factors closely related to both inflammation and outcomes, particularly stroke severity. These findings indicate that while inflammation may partially link malnutrition to poor outcomes, its effect is not independent of key clinical determinants, highlighting the complex interplay among nutritional status, systemic inflammation, and stroke severity in post-stroke recovery.
This study has several limitations. First, as a single-center retrospective study, it may be subject to selection bias and limit the generalizability of our findings. Second, patients with malignancy, autoimmune diseases, or severe hepatic or renal dysfunction were excluded to minimize their impact on inflammatory markers, but this may have overlooked their potential effects on nutritional status. Finally, although inflammatory burden was included in the mediation analysis, dynamic changes in nutritional status during hospitalization were not captured.
Conclusion
In conclusion, our study demonstrates that combined assessment of nutritional and inflammatory status using NRS-2002 and NLR provides meaningful risk stratification for functional outcomes in patients with acute ischemic stroke. Patients with both high nutritional risk and high inflammatory burden exhibited the poorest prognosis, supporting a synergistic effect of malnutrition and systemic inflammation on post-stroke recovery. While inflammation may partially mediate this association, its effect is not independent of key clinical factors.
Acknowledgments
We would like to express our gratitude to all the participants and investigators for their valuable contributions in collecting the data for this project.
Funding Statement
The author(s) declared that financial support was not received for this work and/or its publication.
Footnotes
Edited by: Dongliang Zhang, Beijing Jishuitan Hospital, China
Reviewed by: Jacek Budzyński, Nicolaus Copernicus University in Toruń, Poland
Fatmanur Hümeyra Zengin, Kastamonu University, Türkiye
Data availability statement
The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.
Ethics statement
The studies involving humans were approved by Ethics Committees of Tianxiang East Hospital and the Affiliated Dongyang Hospital of Wenzhou Medical University. The studies were conducted in accordance with the local legislation and institutional requirements. The participants provided their written informed consent to participate in this study.
Author contributions
BJ: Project administration, Conceptualization, Methodology, Investigation, Writing – review & editing, Writing – original draft, Formal analysis, Data curation. RM: Resources, Methodology, Investigation, Conceptualization, Writing – original draft, Supervision, Project administration, Writing – review & editing. PZ: Investigation, Visualization, Software, Validation, Writing – original draft. RZ: Data curation, Conceptualization, Investigation, Writing – review & editing, Software, Methodology. SL: Investigation, Software, Writing – original draft. ZJ: Writing – original draft.
Conflict of interest
The author(s) declared that this work was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
Generative AI statement
The author(s) declared that Generative AI was not used in the creation of this manuscript.
Any alternative text (alt text) provided alongside figures in this article has been generated by Frontiers with the support of artificial intelligence and reasonable efforts have been made to ensure accuracy, including review by the authors wherever possible. If you identify any issues, please contact us.
Publisher's note
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.
References
- 1.Feigin LV, Brainin M, Norrving B, Martins SO, Pandian J, Lindsay P, et al. World stroke organization: global stroke fact sheet 2025. Int J Stroke (2025) 20:132–44. doi: 10.1177/17474930241308142 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 2.GBD 2021 stroke risk factor collaborators. Global, regional, and national burden of stroke and its risk factors, 1990-2021: a systematic analysis for the global burden of disease study 2021. Lancet Neurol. (2024) 23:973–1003. doi: 10.1016/S1474-4422(24)00369-7 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 3.Wang Y, Li J, Pan Y, Wang M, Meng X, Wang Y. Association between high-sensitivity C-reactive protein and prognosis in different periods after Ischemic stroke or transient Ischemic attack. J Am Heart Assoc. (2022) 11:e025464. doi: 10.1161/JAHA.122.025464 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 4.Ma F, Li L, Xu L, Wu J, Zhang A, Liao J, et al. The relationship between systemic inflammation index, systemic immune-inflammatory index, and inflammatory prognostic index and 90-day outcomes in acute ischemic stroke patients treated with intravenous thrombolysis. J Neuroinflammation. (2023) 20:220. doi: 10.1186/s12974-023-02890-y [DOI] [PMC free article] [PubMed] [Google Scholar]
- 5.Du M, Xu L, Zhang X, Huang X, Cao H, Qiu F, et al. Association between inflammatory burden index and unfavorable prognosis after endovascular thrombectomy in acute ischemic stroke. J inflamm Res. (2023) 16:3009–17. doi: 10.2147/JIR.S419087 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 6.Li X, Lin S, Chen X, Huang W, Li Q, Zhang H, et al. The prognostic value of serum Cytokines in patients with acute ischemic stroke. Aging Dis. (2019) 10:544. doi: 10.14336/AD.2018.0820 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 7.Li J, Li X, Yang J, Wang Y, Gong W. Performance of GLIM with different nutrition risk screening tools in predicting 90-day mortality in critically ill adults with acute stroke: a comparison study. JPEN J Parenter Enteral Nutr. (2025) 49:507–14. doi: 10.1002/jpen.2730 [DOI] [PubMed] [Google Scholar]
- 8.Jiang T-T, Zhu X-Y, Yin Y-W, Liu H-J, Zhang G-Y. The prognostic significance of malnutrition in older adult patients with acute ischemic stroke. FrontNutr. (2025) 12:1529754. doi: 10.3389/fnut.2025.1529754 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 9.Yuan K, Zhu S, Wang H, Chen J, Zhang X, Xu P, et al. Association between malnutrition and long-term mortality in older adults with ischemic stroke. Clini Nutr. (2021) 40:2535–42. doi: 10.1016/j.clnu.2021.04.018 [DOI] [PubMed] [Google Scholar]
- 10.Chen X, Hao L, Zhou Y, Zhang H, Wang H, Yu W. Combined systemic immune-inflammation index-prognostic nutritional index score in evaluating the prognosis of patients with severe community-acquired pneumonia. J Inflamm Res. (2025) 18:7105. doi: 10.2147/JIR.S521440 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 11.Fan R, Chen Y, Xu G, Pan W, Lv Y, Zhang Z. Combined systemic immune-inflammatory index and prognostic nutritional index predict outcomes in advanced non-small cell lung cancer patients receiving platinum-doublet chemotherapy. Front Oncol. (2023) 13:1485849. doi: 10.3389/fonc.2023.996312 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 12.Zhang Y, Tang M, Gu Q-H, Zhou L-N, Chen M-B. The prognostic value of combined systemic immune-inflammatory index (SII) and prognostic nutritional index (PNI) in solid tumor. Cancer Manag Res. (2025) 17:1351. doi: 10.2147/CMAR.S523419 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 13.Lu C, Xu Y, Li X, Wang M, Xie B, Huang Y, et al. Nutritional status affects immune function and exacerbates the severity of pulmonary tuberculosis. Front Immunol. (2024) 15:1407813. doi: 10.3389/fimmu.2024.1407813 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 14.Kondrup J, Allison SP, Elia M, Vellas B, Plauth M. ESPEN guidelines for nutrition screening 2002. Clinl Nutr. (2003) 22:415–21. doi: 10.1016/S0261-5614(03)00098-0 [DOI] [PubMed] [Google Scholar]
- 15.Ma X, Zhou Y, Li Z, Mao G, Wei H, Zhao T. Comparison of the predictive performance of systemic immune-inflammation index and neutrophil-to-lymphocyte ratio for three-month poor functional outcome in ischemic stroke: a systematic review and meta-analysis. Ann Med. (2026) 58:2612820. doi: 10.1080/07853890.2026.2612820 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 16.Bi Y, Shen J, Chen S-C, Chen J-X, Xia Y-P. Prognostic value of neutrophil to lymphocyte ratio in acute ischemic stroke after reperfusion therapy. Sci Rep. (2021) 11:6177. doi: 10.1038/s41598-021-85373-5 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 17.Adams HP, Bendixen BH, Kappelle LJ, Biller J, Love BB, Gordon DL. Classification of subtype of acute ischemic stroke. Definitions for use in a multicenter clinical trial. TOAST. Trial of Org 10172 in acute stroke treatment. Stroke. (1993) 24:35–41. doi: 10.1161/01.STR.24.1.35 [DOI] [PubMed] [Google Scholar]
- 18.van Swieten JC, Koudstaal PJ, Visser MC, Schouten HJ, van Gijn J. Interobserver agreement for the assessment of handicap in stroke patients. Stroke. (1988) 19:604–7. doi: 10.1161/01.STR.19.5.604 [DOI] [PubMed] [Google Scholar]
- 19.DeLong JH, Ohashi SN, O'Connor KC, Sansing LH. Inflammatory responses after ischemic stroke. Semin Immunopathol. (2022) 44:625–48. doi: 10.1007/s00281-022-00943-7 [DOI] [PubMed] [Google Scholar]
- 20.Iadecola C, Buckwalter MS, Anrather J. Immune responses to stroke: mechanisms, modulation, and therapeutic potential. J Clin Invest. (2020) 130:2777–88. doi: 10.1172/JCI135530 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 21.Foley N Salter KL Robertson J Teasell RW Woodbury MG Which Which reported estimate of the prevalence of malnutrition after stroke Is Valid? Stroke. (2009) 40:e66-74. doi: 10.1161/STROKEAHA.108.518910 [DOI] [PubMed] [Google Scholar]
- 22.Xie J, Kittur FS Li PA, Hung C-Y. Rethinking the necessity of low glucose intervention for cerebral ischemia/reperfusion injury. Neural Regen Res. (2021) 17:1397–403. doi: 10.4103/1673-5374.330592 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 23.Martineau J, Bauer JD, Isenring E, Cohen S. Malnutrition determined by the patient-generated subjective global assessment is associated with poor outcomes in acute stroke patients. Clin Nutr. (2005) 24:1073–7. doi: 10.1016/j.clnu.2005.08.010 [DOI] [PubMed] [Google Scholar]
- 24.Chen H, Fu C, Fang W, Wang Z, Zhang D, Zhang H. Influence of nutritional status on rehabilitation efficacy of patients after stroke—a scoping review. Front Neurol. (2025) 16:1502772. doi: 10.3389/fneur.2025.1502772 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 25.Scherbakov N, von Haehling S, Anker SD, Dirnagl U, Doehner W. Stroke induced Sarcopenia: muscle wasting and disability after stroke. Int J Cardiol. (2013) 170:89–94. doi: 10.1016/j.ijcard.2013.10.031 [DOI] [PubMed] [Google Scholar]
- 26.Siotto M, Germanotta M, Guerrini A, Pascali S, Cipollini V, Cortellini L, et al. Relationship between nutritional status, food consumption and sarcopenia in post-stroke rehabilitation: preliminary data. Nutrients. (2022) 14:4825. doi: 10.3390/nu14224825 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 27.Scherbakov N, Pietrock C, Sandek A, Ebner N, Valentova M, Springer J, et al. Body weight changes and incidence of cachexia after stroke. J Cachexia Sarcopenia Muscle. (2019) 10:611–20. doi: 10.1002/jcsm.12400 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 28.Sabbouh T, Torbey MT. Malnutrition in stroke patients: risk factors, assessment, and management. Neurocrit Care. (2018) 29:374–84. doi: 10.1007/s12028-017-0436-1 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 29.Havenon AD, Zaidat OO, Amin-Hanjani S, Nguyen TN, Bangad A, Abbasi M, et al. Large vessel occlusion stroke due to intracranial atherosclerotic disease: identification, medical and interventional treatment, and Outcomes. Stroke. (2023) 54:1695–705. doi: 10.1161/STROKEAHA.122.040008 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 30.Rao Z, Zhang Y, Zhu C. Association of systemic immune-inflammation index with severity in acute ischemic stroke patients: a cross-sectional study. Front Neurol. (2025) 16:1553730. doi: 10.3389/fneur.2025.1553730 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 31.Audebert HJ, Rott MM, Eck T, Haberl RL. Systemic inflammatory response depends on initial stroke severity but is attenuated by successful thrombolysis. Stroke. (2004) 35:2128–33. doi: 10.1161/01.STR.0000137607.61697.77 [DOI] [PubMed] [Google Scholar]
- 32.Bang OY, Saver JL, Kim SJ, Kim G-M, Chung C-S, Ovbiagele B, et al. Collateral flow predicts response to endovascular therapy for acute ischemic stroke. Stroke. (2011) 42:693–9. doi: 10.1161/STROKEAHA.110.595256 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 33.Yoon J, Baek S, Jang Y, Lee CH, Lee ES, Byun H, et al. Malnutrition and associated factors in acute and subacute stroke patients with dysphagia. Nutrients. (2023) 15:3739. doi: 10.3390/nu15173739 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 34.Flint AC, Faigeles BS, Cullen SP, Kamel H, Rao VA, Gupta R, et al. VISTA Collaboration. THRIVE score predicts ischemic stroke outcomes and thrombolytic hemorrhage risk in VISTA. Stroke. (2013) 44:3365–9. doi: 10.1161/STROKEAHA.113.002794 [DOI] [PubMed] [Google Scholar]
- 35.Ciancarelli I, Morone G, Iosa M, Cerasa A, Calabrò RS, Iolascon G, et al. Influence of oxidative stress and inflammation on nutritional status and neural plasticity: new perspectives on post-stroke neurorehabilitative outcome. Nutrients. (2022) 15:108. doi: 10.3390/nu15010108 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 36.Uzun H, Fenercioglu AK, Senyigit A, Can G. Relationship between nutritional status and systemic immune–inflammation indices across BMI categories. Nutrients. (2025) 17:3799. doi: 10.3390/nu17233799 [DOI] [PMC free article] [PubMed] [Google Scholar]
Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Data Availability Statement
The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.