The neutrophil-percentage-to-albumin ratio(NPAR) shows a nonlinear correlation with all-cause mortality in patients with anemia

The neutrophil-percentage-to-albumin ratio(NPAR) shows a nonlinear correlation with all-cause mortality in patients with anemia | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article The neutrophil-percentage-to-albumin ratio(NPAR) shows a nonlinear correlation with all-cause mortality in patients with anemia Anqi Peng, Jizhe Li, Jun Huang, Yuqiong Yang, Yizhi Jiang, Dongping Huang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8161330/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 06 Apr, 2026 Read the published version in Clinical and Experimental Medicine → Version 1 posted 7 You are reading this latest preprint version Abstract Background: The neutrophil percentage-to-albumin ratio (NPAR) is an emerging indicator of inflammation that has been associated with the prognosis of various diseases, including hypertension, diabetes, and cardiovascular disease. NPAR is calculated by multiplying the neutrophil percentage by 100 and dividing it by the albumin value. This study aimed to evaluate the predictive value of NPAR for all-cause mortality in anemic patients. We employed Kaplan–Meier analysis, multiple regression models, restricted cubic splines (RCS), and threshold effect analysis to explore these associations. Methods: This study included 3,258 anemic individuals. The relationship between NPAR and all-cause mortality was evaluated using Kaplan–Meier analysis and multiple regression models. RCS curves were employed to assess potential nonlinear relationships, while threshold effect analysis was used to identify breakpoints in the association between NPAR and mortality risk. Subgroup analyses were conducted to examine variations in this relationship across different population strata. Results: The study population included 3,258 anemic patients, with a mean follow-up period of 94.3 months. During follow-up, 904 participants (27.75%) died. Kaplan–Meier analysis revealed that individuals in the highest NPAR quartile (Q4) exhibited significantly higher cumulative mortality compared with those in the lowest quartile (Q1) (p < 0.001). Multiple regression analysis showed that, in fully adjusted models, each unit increase in NPAR among individuals in the highest quartile was associated with a 65.4% increased risk of all-cause mortality. RCS and threshold effect analyses revealed a nonlinear relationship between NPAR and mortality, with a breakpoint at NPAR = 15.116. Below this threshold, no statistical association was observed, while above the threshold, NPAR was positively associated with increased mortality risk. Subgroup analyses demonstrated that the association between NPAR and mortality was particularly significant in individuals aged < 60 years and those with a body mass index (BMI) ≥ 30 kg/m². Conclusions: Our findings indicate that elevated NPAR is independently associated with increased all-cause mortality in anemic individuals. The nonlinear relationship and identified breakpoint suggest that NPAR may serve as a valuable prognostic biomarker for mortality risk in this population. Further research should focus on the clinical utility of NPAR in guiding therapeutic interventions for anemia. anemia NPAR inflammation all-cause mortality nonlinear correlation Figures Figure 1 Figure 2 Figure 3 Figure 4 1 Introduction Anemia is characterized by a lower-than-normal hemoglobin concentration in the blood, impairing oxygen transport and resulting in various clinical symptoms and pathophysiological changes( 1 ). Anemia in children can affect cognitive and motor development, while in adults, it is associated with increased risks of infection, heart failure, and mortality( 2 – 5 ). According to the World Health Organization (WHO), anemia is defined as hemoglobin levels < 12.0 g/dL for women and < 13.0 g/dL for men( 6 ). Common symptoms in adults include fatigue, reduced activity levels, and shortness of breath( 7 , 8 ). In severe cases, anemia can precipitate heart failure or death( 9 – 14 ). These findings underscore the need for improved strategies to prevent anemia, alleviate symptoms, and enhance patient outcomes. Neutrophils, as first responders during inflammation, release mediators like LTB4, TNF-α, and IL-1β, which exacerbate vascular permeability and oxidative stress( 15 , 16 ). Albumin, the most abundant plasma protein, provides antioxidant and immunomodulatory functions( 17 , 18 ). Inflammatory responses inhibit albumin synthesis and distribution, compounding oxidative stress( 19 – 21 ). Elevated NPAR, reflecting increased neutrophil counts and/or decreased albumin levels, thus serves as a marker of heightened inflammatory states ( 19 , 22 ). Inflammation is intricately linked to anemia, as it disrupts iron homeostasis, inhibits red blood cell production, and shortens red cell lifespan( 23 , 24 ). It is suggested that the percentage of neutrophils and the albumin ratio may serve as novel laboratory indicators for the identification of inflammation( 25 , 26 ). Although traditional inflammatory markers such as C-reactive protein (CRP) and erythrocyte sedimentation rate (ESR) are widely utilized within clinical practice, the percentage of neutrophils and albumin values are more readily obtainable and less expensive to test( 27 ). Elevated NPAR has been linked to increased mortality in conditions such as chronic obstructive pulmonary disease (COPD), diabetes, and heart failure( 28 – 30 ). However, its prognostic value in anemia-related mortality remains unexplored. This study seeks to address this gap by investigating NPAR as a predictor of all-cause mortality in anemic individuals. 2 Materials and Methods 2.1 Study population This study included 101,316 individuals between 1999 and 2018. Among them, 46,235 individuals under the age of 20 years were excluded from the study, as were 6,299 individuals with missing data on neutrophil percentage and albumin values, and 44,004 individuals with missing hemoglobin data but who were not anemic. Subsequently, a total of 1520 individuals with missing data on covariates were excluded, including 640 individuals with no history of alcohol consumption, 1 individual with no history of hypertension, 44 individuals with a history of smoking, 372 individuals with a history of diabetes mellitus, 5 pregnant women, 7 individuals with missing education levels, 30 individuals with missing marital status, 127 individuals with missing body mass index, 333 individuals with missing household income and poverty rates, and 1 individual with missing data on death. A total of 3258 anemic patients were included in the final analysis (Fig. 1 ). This study analyzed NHANES data spanning 1999–2018, comprising 101,316 individuals. After excluding participants aged < 20 years, those with missing neutrophil or albumin values, and non-anemic individuals, 3,258 anemic patients were included in the final analysis. Details of the selection process are shown in Fig. 1 . Figure 1 : A comprehensive outline of the participant selection process. 2.2 Exposure and Outcomes In this study, NPAR is the exposure variable. The NPAR formula is calculated by multiplying the neutrophil percentage by 100 and dividing it by the albumin value. The albumin value is obtained from the standard biochemical profile of laboratory data, and the neutrophil percentage is derived from the complete blood count and five-part differential white blood cell count. The study endpoint is defined as all-cause mortality, or mortality from any cause. For each participant, the follow-up duration was calculated from enrollment until either death or December 31, 2019, whichever came later. This date is the latest available in the NDI database ( https://www.cdc.gov/nchs/datalinkage/mortality-public.htm ). Mortality data were extracted from the 1999–2018 NHANES mortality files ( https://www.cdc.gov/nchs/nhanes/index.htm ). In this study, NPAR served as the exposure variable. The NPAR formula is calculated as the neutrophil percentage multiplied by 100 and then divided by the albumin value. Albumin values were obtained from the standard biochemical profile in laboratory data, while neutrophil percentages were derived from complete blood counts and five-part differential white blood cell counts. The study endpoint was defined as all-cause mortality, or mortality from any cause. Follow-up time was calculated for each participant from enrollment until death or December 31, 2019 (the latest data available in the NDI database). Mortality data were extracted from the 1999–2018 NHANES mortality files. 2.3 Covariates Variables studied included age, gender (including both men and women), and race (including Mexican American, Other Hispanic, Non-Hispanic White, Non-Hispanic Black, and Other). Marital status is categorized as married or cohabiting with a partner, and single (never married, divorced, separated, or widowed). Educational level is divided into three categories: less than high school, high school, and more than high school. Household income and poverty rate (PIR) were classified into three groups: ( 1 ) < 1.5; ( 2 ) 1.5–3.5; and ( 3 ) ≥ 3.5. Body mass index (BMI) was generally classified into the following three groups: ( 1 ) BMI < 25 kg/m²; ( 2 ) BMI 25–30 kg/m²; and ( 3 ) BMI ≥ 30 kg/m²( 31 ). The five categories of drinking status are as follows: ( 1 ) ex-drinkers, who used to drink but do not drink now; ( 2 ) never drinkers, who have not drunk any alcohol in their lifetime; ( 3 ) light drinkers, who consume one to two glasses of alcohol per day; ( 4 ) moderate drinkers, who consume no more than four glasses of alcohol per day for men and no more than three glasses of alcohol per day for women; and ( 5 ) heavy drinkers, who consume more than four glasses of alcohol per day for men and more than three glasses of alcohol per day for women. The smoking status of individuals is categorized into three distinct groupings: ( 1 ) never-smokers, defined as those who have smoked less than 100 cigarettes in their lifetime; ( 2 ) ex-smokers, characterized by having smoked more than 100 cigarettes in their lifetime, but are no longer active smokers; and ( 3 ) current smokers, comprising individuals who have smoked more than 100 cigarettes in their lifetime, and who currently smoke either regularly or daily( 32 ). Hypertension is diagnosed if any of the following conditions are met: Firstly, a physician diagnoses hypertension. Secondly, antihypertensive medication. Thirdly, the average systolic blood pressure must be ≥ 140 mmHg, and/or the average diastolic blood pressure must be ≥ 90 mmHg. The diagnosis of diabetes is made if any of the following conditions are met: ( 1 ) a doctor's diagnosis of diabetes; ( 2 ) a glycated hemoglobin (HbA1c) level ≥ 6.5%; ( 3 ) a fasting blood glucose level ≥ 126 mg/dl; ( 4 ) current use of diabetes medication or insulin. The present study included covariates related to anemia, including laboratory parameters for white blood cells (WBC), red blood cells (RBC), platelet count (PLT), mean corpuscular hemoglobin concentration (MCHC), mean corpuscular hemoglobin (MCH), and mean corpuscular volume (MCV). These data are available from the CDC National Health Survey ( https://www.cdc.gov/nchs/nhanes ). 2.4 Modeling The NHANES data have undergone a complex stratified, multi-stage sampling design; if it is not weighted, this may lead to biased results. Consequently, the implementation of weighting methodologies is imperative to enhance the generalisability of the study sample. The sampling weight is divided into two parts, and the calculation method is as follows: Specifically, the 4-year weight WTMEC4YR x 2/10 is utilized for the period 1999–2002, while the 2-year weight WTMEC2YR/10 is employed for the subsequent period from 2003–2018( 33 ). Ultimately, the weights described above are amalgamated through the implementation of a meticulous calculation approach for NHANES composite survey cycle weights ( https://wwwn.cdc.gov/nchs/nhanes/tutorials/Weighting.aspx ), thereby yielding the aggregate weight. Continuous variables that follow a normal distribution are represented by the mean and standard deviation; continuous variables with non-normal distributions are represented by the median (Q1, Q3). Categorical variables are represented by percentages. The participants' baseline characteristics are represented by quartiles from NPAR. The Kaplan-Meier method was employed to ascertain the association between variations in NPAR levels and all-cause mortality in anemic patients. A multiple regression model was employed to evaluate the relationship between NPAR levels and all-cause mortality in anemic patients. To eliminate the potential impact of confounding factors, three models have been developed: Model 1 is not adjusted for covariates, and Model 2 is adjusted for the variables of gender, age, and race. The third model is adjusted for all potential confounding factors, including gender, age, race, education level, marital status, PIR, smoking history, drinking history, and medical history of hypertension, diabetes, white blood cells, red blood cells, platelets, MCHC, MCH, and MCV. The present study employed restricted cubic spline analysis to explore the nonlinear relationship between NPAR and all-cause mortality in anemic patients. Furthermore, we proceeded to utilize threshold effect analysis to ascertain the breakpoint of the nonlinear relationship between NPAR and all-cause mortality in anemic individuals based on Model 3. Stratified analysis and interaction tests were performed to explore whether there was a statistically significant association between NPAR and all-cause mortality in anemic individuals in different subgroups. 2.5 Statistical analysis All statistical analyses were performed using the following software: Empower Stats 4.0, Free Statistics 1.9.2, and R Studio 4.4.2. A p-value of less than 0.05 was considered statistically significant. 3 Results 3.1 Baseline characteristics of participants Among the 3,258 participants, 37.02% were male and 62.98% were female. The mean age was 56.199 ± 18.315 years, and the median NPAR was 14.526. The demographic composition of the study sample is shown in Table 1 . Participants were grouped by NPAR quartiles: Q1 (below 12.543), Q2 (12.543–14.525), Q3 (14.526–16.424), and Q4 (above 16.424). Compared to individuals in the lowest NPAR quartile (Q1), those in the highest quartile (Q4) were more likely to exhibit the following characteristics: ( 1 ) male; ( 2 ) older age; ( 3 ) non-Hispanic white; ( 4 ) less than a high school education; ( 5 ) married or cohabiting with a partner; ( 6 ) moderate income; ( 7 ) higher body mass index; ( 8 ) history of diabetes; ( 9 ) history of hypertension; ( 10 ) prior smoking; ( 11 ) prior alcohol consumption; ( 12 ) higher white blood cell count; ( 13 ) higher platelet count; and ( 14 ) lower red blood cell count. No statistically significant differences in mean red blood cell hemoglobin concentration were observed between different NPAR groups. Table 1 Baseline characteristics of study subjects expressed as NPAR quartiles. Q1(16.424) P- value N = 814 N = 815 N = 814 N = 815 Age(years) 54(39,69) 52(40,70) 58(42,75) 64(46,76.5) < 0.001 WBC(1000 cells/uL) 5.65(4.6,6.9) 6.3(5.3,7.7) 6.7(5.6,8.1) 7.6(6.3,9.2) < 0.001 RBC(million cells/uL) 4.09(3.75,4.44) 4.09(3.82,4.39) 4.095(3.8,4.44) 3.99(3.69,4.35) < 0.001 MCV(fl) 86(78.15,91.4) 85.6(77.9,91) 84.7(77.6,91.25) 86(80.05,92.1) < 0.001 MCH (pg) 28.4(25.3,30.8) 28.5(25.4,30.8) 28.2(25.125,30.8) 28.7(26.05,31) 0.016 MCHC (g/dL) 32.9(32.1,33.8) 33.2(32.4,34) 33.1(32.2,33.9) 33.2(32.3,34) 0.475 PLT(1000 cells/uL) 246.5(198,304) 255(210,312) 253(210,315) 260(208,333) < 0.001 Gender (%) < 0.001 Male Female 31.267 23.968 31.517 36.015 68.733 76.032 68.483 63.985 Race/ethnicity (%) < 0.001 Mexican American Other Hispanic Non-Hispanic White Non-Hispanic Black Other Race 8.333 7.76 8.017 6.87 5.014 7.475 6.09 4.417 36.044 50.818 52.987 58.219 42.701 27.359 26.547 23.984 7.908 6.589 6.359 6.51 Education (%) 0.045 Less than high school High school More than high school 22.199 20.105 20.641 26.36 22.909 24.59 23.743 23.921 54.892 55.305 55.615 49.719 Marital status (%) < 0.001 married or cohabiting with a partner Single 52.485 58.112 63.816 54.937 47.515 41.888 36.184 45.063 BMI (%) < 0.001 < 25 25–30 ≥ 30 39.857 31.928 32.092 26.002 29.426 29.727 25.716 24.123 30.717 38.345 42.192 49.876 PIR (%) < 0.001 <1.5 1.5–3.5 ≥3.5 36.17 32.584 29.292 35.716 34.404 32.153 35.115 38.752 29.427 35.262 35.593 25.532 Smoking (%) < 0.001 Never Former Now 62.579 63.632 65.844 55.568 23.503 23.968 24.218 30.931 13.918 12.399 9.939 13.5 Drinking (%) < 0.001 Never Former Mild Moderate Heavy 20.354 19.495 15.771 15.903 17.804 16.818 17.755 29.278 34.149 31.507 36.931 32.459 12.122 18.344 16.351 11.057 15.571 13.836 13.193 11.302 Hypertension history(%) < 0.001 Yes No 45.701 45.012 48.257 64.358 54.299 54.988 51.743 35.642 Diabetes(%) < 0.001 Yes Prediabetes No 16.352 20.478 26.601 33.523 4.945 3.766 5.209 8.739 78.704 75.756 68.19 57.737 Table 1 : Q1-Q4 are grouped according to NPAR quartiles. Continuous variables are expressed as mean ± standard deviation, and p-values were calculated using a weighted linear regression model. Percentages indicate categorical variables, and p-values were calculated using a weighted chi-square test. WBC: white blood cells; RBC: red blood cells; PLT: platelet count; MCHC: mean corpuscular hemoglobin concentration; MCH: mean corpuscular hemoglobin; MCV: mean corpuscular volume; PIR: household income-to-poverty ratio; BMI: body mass index. 3.2 Association of NPAR with all-cause mortality in individuals with anemia Because the endpoint of this study was death, a Kaplan-Meier analysis was performed to examine the effect of NPAR on all-cause mortality in anemic individuals over time. After a mean follow-up period of 94.337 months (range: 0–248 months), 904 out of 3,258 participants (27.75%) died. (See Fig. 2 .) In the Kaplan-Meier analysis, which was stratified by NPAR quartiles and adjusted for all covariates, individuals in the highest NPAR quartile (Q4) exhibited the highest all-cause mortality during the follow-up period, compared to individuals in the other quartiles. Meanwhile, individuals in the lowest NPAR quartile (Q1) exhibited the lowest all-cause mortality, with significant differences in all-cause mortality rates observed among the four groups (p < 0.001). Figure 2 : Adjusted variables such as gender, race, age, education level, marital status, household income to poverty ratio, body mass index, hypertension, smoking, drinking, diabetes, WBC, RBC, PLT, MCH, MCHC, and MCV. In Model 1 (the unadjusted model), the highest quartile (Q4) of NPAR was associated with a 2.460-fold increased risk of all-cause mortality (HR: 1.597; 95% CI: 2.004–3.020; p < 0.001) when the lowest quartile (Q1) of NPAR was used as the reference value. (See Table 2 .) Model 2 incorporated demographic variables, including gender, age, and ethnicity. Compared to Q1, each additional unit of NPAR in Q4 individuals was associated with a 1.597-fold increased risk of all-cause mortality (HR: 1.597; 95% CI: 1.297–1.967; p < 0.001). Model 3 included all covariates: gender, race, age, education level, marital status, poverty ratio, body mass index, hypertension, smoking status, alcohol consumption, diabetes status, white blood cell count, red blood cell count, platelet count, mean hemoglobin concentration, mean corpuscular hemoglobin, and mean corpuscular volume. The results showed that, compared to Q1, each additional unit of NPAR in Q4 individuals was associated with a 1.654-fold increase in the risk of all-cause mortality (HR: 1.654; 95% CI: 1.321–2.071; p < 0.001). Table 2 Multiple regression model of NPAR and all-cause mortality in individuals with anemia. Exposure Crude model Model 1 Model 2 HR (95% CI) P value HR (95% CI) P value HR (95% CI) P value All-cause mortality NPAR 1.136(1.107, 1.166) < 0.001 1.069(1.040, 1.098) < 0.001 1.073(1.046, 1.100) < 0.001 NPAR category Q1 Q2 Q3 Q4 Reference Reference Reference 1.102(0.864,1.405)0.434 0.977(0.762,1.253)0.856 1.654(1.321,2.071) < 0.001 1.057(0.832, 1.345)0.649 1.033(0.815, 1.309)0.787 1.255(0.981, 1.604)0.07 0.913( 0.729, 1.144)0.429 2.460( 2.004, 3.020) < 0.001 1.597( 1.297, 1.967) < 0.001 Trend test < 0.001 < 0.001 < 0.001 Table 2 : Model 1 was not adjusted for covariates. Model 2 was adjusted for gender, age, and race. Model 3 was adjusted for gender, race, age, education level, marital status, PIR, BMI, hypertension, smoking, drinking, diabetes, white blood cells, red blood cells, platelets, MCHC, MCH, and MCV. Compared to Model 1 (the unadjusted model), the all-cause mortality risk for the highest quartile (Q4) of NPAR decreased from 2.460 to 1.597 after adjusting for variables in Model 2. This suggests that one of these variables is a risk factor for NPAR and that adjusting for it reduces both NPAR and mortality risk. However, after adjusting for all covariates in Model 3, the all-cause mortality risk in the highest quartile increased from 1.597 to 1.654. This result suggests that certain variables act as suppressors of NPAR. Adjusting for these variables increases NPAR and elevates all-cause mortality risk. All three models yielded p-values < 0.001. Although the all-cause mortality risk in the highest quartile fluctuated across these models, the finding that higher NPAR correlates with increased all-cause mortality risk remained consistent. Our results confirm that NPAR is a highly reliable biological marker for predicting all-cause mortality in individuals with anemia. Using restricted cubic splines within a fully adjusted model, we determined the association between NPAR and all-cause mortality, revealing a nonlinear relationship in anemic individuals (see Fig. 3 ). Specifically, we found that NPAR was positively associated with all-cause mortality in anemic individuals to the right of the breakpoint. Subsequently, we conducted a threshold effect analysis on the fully adjusted model (Table 3 ). The results revealed a nonlinear relationship between NPAR and all-cause mortality, with a breakpoint at 15.116. To the left of the breakpoint, NPAR showed no statistically significant association with all-cause mortality in individuals with anemia. However, to the right of the breakpoint, NPAR was positively correlated with all-cause mortality in anemic individuals (HR: 1.129; 95% CI: 1.095–1.165; p < 0.001). The log-likelihood ratio test yielded p < 0.001. Figure 3 : The solid red line shows the cumulative risk of death from any cause for patients with anemia, and the two dashed blue lines indicate the 95% confidence interval. Table 3 NPAR has a non-linear relationship with the all-cause mortality rate of anemic individuals. Adjusted HR (95% CI), p -value Fitting by the standard linear model Linear Effect 1.065 (1.043, 1.087) < 0.001 Fitting by a two-piecewise linear model Breakpoint(K) 15.116 NPAR 15.116 1.129 (1.095, 1.165) < 0.001 The effect difference between 2 and 1 1.149 (1.084, 1.217) < 0.001 Log likelihood ratio < 0.001 Table 3 : The adjustment variables are the same as in Model 3. 3.3 Subgroup Analysis Subgroup analysis identified significant associations between NPAR and mortality in participants aged < 60 years and those with BMI ≥ 30 kg/m² (Fig. 4 ). Subgroups were stratified by age, sex, PIR, and BMI, smoking, alcohol consumption, hypertension, and diabetes. They were analyzed using fully adjusted models. Age was categorized as < 60 or ≥ 60 years. Interactions showed that after adjustment for all covariates, the association between NPAR and all-cause mortality in anemic patients was statistically significant (P < 0.05) in the subgroups of age < 60 years, ≥ 60 years, and body mass index < 25 kg/m 2 , 25–30 kg/m 2 , and ≥ 30 kg/m 2 . Of particular note, there was a significant positive association between anemia and all-cause mortality in those aged < 60 years and with a body mass index ≥ 30 kg/m 2 . In contrast, no statistically significant interactions were found between subgroups stratified by sex, PIR, smoking status and alcohol consumption, history of hypertension, and history of diabetes. Figure 4 Subgroup analysis of NPAR and all-cause mortality in individuals with anemia 3.4 Discussion Our study investigated the association between the novel inflammatory marker NPAR and all-cause mortality in anemic individuals. The Results indicated that, after adjusting for confounding factors, higher NPAR levels were positively correlated with all-cause mortality in anemic individuals. We observed differing associations between NPAR and all-cause mortality in anemic individuals on either side of the cutoff point (NPAR-15.116). When NPAR 15.116. We found that NPAR was significantly positively associated with all-cause mortality in anemic individuals, particularly in those younger than 60 years old and with a body mass index (BMI) ≥ 30 kg/m 2 . Therefore, we should promote NPAR level testing, which can assist clinicians in developing targeted treatment plans for anemic patients and improve their prognosis. According to existing research, anemia is associated with certain inflammatory markers. Elevated levels of the neutrophil-to-lymphocyte ratio (NLR), systemic inflammatory index (SII), and platelet-to-lymphocyte ratio (PLR) increase the likelihood of developing anemia ( 34 – 36 ). According to existing research, anemia is associated with certain inflammatory markers. Elevated levels of the neutrophil-to-lymphocyte ratio (NLR), systemic inflammatory index (SII), and platelet-to-lymphocyte ratio (PLR) increase the likelihood of developing anemia( 37 ). Reduced albumin levels weaken the body's antioxidant capacity, intensifying inflammatory responses and tissue damage( 38 ). The mechanisms by which inflammation causes anemia primarily involve the following points: when the body experiences inflammation, it enters a state of stress, which triggers the activation of immune cells and the release of inflammatory cytokines such as IL-6、IL-1、and TNF-α ( 39 ). IL-6 activates the STAT3 signalling pathway in the liver, thereby upregulating ferritin expression( 40 ). Ferritin has been shown to bind to ferroportin (FP1), inducing its degradation and consequently resulting in iron being stored in macrophages. This process impedes the absorption of iron in the duodenum, thereby reducing the amount of iron available as a raw material for the production of red blood cells and leading to anemia( 41 , 42 ). In addition, IL-1 and TNF have been demonstrated to inhibit the production of EPO by renal epithelial cells, thereby reducing red blood cell production and leading to the development of anemia( 43 ). It has been established that the occurrence of inflammation is accompanied by the deposition of autoantibodies and complement on red blood cells. This, in turn, results in enhanced phagocytosis of red blood cells by macrophages and increased red blood cell destruction. The consequence of these processes is the onset of anemia( 39 ). In comparison with conventional indicators of inflammation, the percentage of neutrophils and albumin values is readily discernible and economical and possesses the capacity to predict the prognosis of associated diseases( 44 ). The findings of this study demonstrate that the regulation of inflammation can enhance the prognoses of patients suffering from anemia( 45 , 46 ). The indicators representing inflammation, such as SII and PLR, are nonlinearly related to anemia, which is consistent with the results of this study. Furthermore, NPAR is nonlinearly related to the all-cause mortality of individuals suffering from anemia( 35 , 36 ). The findings of the present study demonstrate that a higher NPAR value is associated with a poorer prognosis in individuals suffering from anemia. This association can be used to predict the prognosis of such individuals. Our study demonstrated the predictive value of NPAR for anemia prognosis. Using Kaplan-Meier analysis, multivariable Cox proportional hazards models, restricted cubic splines, threshold effect analysis, stratified analysis, and interaction tests, we demonstrated a significant positive correlation between elevated NPAR levels and all-cause mortality. However, this study has limitations. Memory bias in the questionnaire may have influenced the results. Additionally, since the NHANES data only represent the U.S. population, the results may be biased when extrapolating predictions to other countries. Since neutrophil and albumin values are single-point measurements that fail to capture dynamic changes in NPAR levels, multiple measurements are required to enhance accuracy. Therefore, we should promote NPAR testing to help clinicians develop effective treatment plans and improve outcomes for patients with anemia. Declarations Author Contributions Conceptualization: Peng Anqi, Li Jizhe(both authors contributed equally to this study); Methodology: Peng Anqi, Li Jizhe; Software: Peng Anqi, Li Jizhe; Validation: Peng Anqi, Li Jizhe; Formal analysis: Peng Anqi, Li Jizhe; Visualization: Peng Anqi, Li Jizhe; Investigation: Huang Jun, Yang Yuqiong; Resources: Huang Jun, Yang Yuqiong; Data curation: Huang Jun, Yang Yuqiong; Writing – original draft: Peng Anqi, Li Jizhe, Huang Jun, Yang Yuqiong; Project administration: Jiang Yizhi, Huang Dongping; Supervision: Jiang Yizhi, Huang Dongping; Funding acquisition: Jiang Yizhi, Huang Dongping; Writing – review & editing: Jiang Yizhi, Huang Dongping. Availability of Data and Materials. The datasets supporting the conclusions of this article are available in the NHANES (https://www.cdc.gov / nchs /nhanes). Acknowledgements. We express our gratitude to the NHANES database and researchers for providing high-quality NHANES data. Funding. This research was funded by the Natural Science Foundation of Anhui Province (2023AH040255) and the National Natural Science Foundation of China (82200146). Author information. Authors and Affiliations. Department of Hematology, the First Affiliated Hospital of Wannan Medical College, Wuhu, 241001, China; The Second Affiliated Hospital of Anhui Medical University, Hefei 230601, China. Anqi Peng, Jizhe Li, Jun Huang, Yuqiong Yang, Yizhi Jiang , Dongping Huang. Anqi Peng: [email protected] Jizhe Li: [email protected] Jun Huang: [email protected] Yuqiong Yang: [email protected] Yizhi Jiang: [email protected] Dongping Huang: [email protected] Corresponding authors Correspondence to Dongping Huang. Ethics declarations The authors confirm that all experiments were performed in accordance with the Declaration of Helsinki. Ethical approval statement This study is an analysis derived from publicly available NHANES data, which was acquired with the approval of the Ethics Review Board of the National Center for Health Statistics (https://www.cdc.gov/nchs/nhanes/irba98.htm). Given that the NHANES public use data does not contain individually identifiable information, ethical review and approval were waived for this study. Consent for publication Not an application. Competing interests The author(s) declare(s) that they have no competing interests. References Chaparro CM, Suchdev PS. Anemia Epidemiology, Pathophysiology, and Etiology in Low- and Middle-Income Countries. Ann N Y Acad Sci. 2019;1450(1):15–31. https://doi.org/10.1111/nyas.14092 . Balarajan Y, Ramakrishnan U, Ozaltin E, Shankar AH, Subramanian SV. Anaemia in Low-Income and Middle-Income Countries. Lancet. 2011;378(9809):2123–35. 10.1016/s0140-6736(10)62304-5 . Epub 20110801. 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1","display":"","copyAsset":false,"role":"figure","size":41738,"visible":true,"origin":"","legend":"\u003cp\u003eA comprehensive outline of the participant selection process.\u003c/p\u003e","description":"","filename":"Onlinefloatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-8161330/v1/e58ea5dd7f0f9bd3fac1562a.png"},{"id":97118938,"identity":"0e734155-e6e8-405d-b98a-c90113db8fe1","added_by":"auto","created_at":"2025-12-01 07:41:47","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":51207,"visible":true,"origin":"","legend":"\u003cp\u003eAdjusted variables such as gender, race, age, education level, marital status, household income to poverty ratio, body mass index, hypertension, smoking, drinking, diabetes, WBC, RBC, PLT, MCH, MCHC, and MCV.\u003c/p\u003e","description":"","filename":"Onlinefloatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-8161330/v1/e15604bee71c077f63365c09.png"},{"id":97142409,"identity":"5ab1184a-c70a-4ba3-9514-2a3a25902805","added_by":"auto","created_at":"2025-12-01 10:07:36","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":30989,"visible":true,"origin":"","legend":"\u003cp\u003eNonlinear relationship between NPAR and all-cause mortality in anemic individuals using a restricted cubic spline.\u003c/p\u003e","description":"","filename":"Onlinefloatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-8161330/v1/c0358f0d884e358434ee1d75.png"},{"id":97118943,"identity":"ed00dfaa-fe8b-4183-a26f-a23a2b5767b1","added_by":"auto","created_at":"2025-12-01 07:41:47","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":483977,"visible":true,"origin":"","legend":"\u003cp\u003eSubgroup analysis of NPAR and all-cause mortality in individuals with anemia\u003c/p\u003e","description":"","filename":"Onlinefloatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-8161330/v1/3f37797ee98145548f166daf.png"},{"id":106809210,"identity":"4abfb282-754d-4f02-99ce-a6ab5f637c7b","added_by":"auto","created_at":"2026-04-13 16:08:21","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1400336,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8161330/v1/653bb778-7207-43be-b2fd-b3fb0bdffd75.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"The neutrophil-percentage-to-albumin ratio(NPAR) shows a nonlinear correlation with all-cause mortality in patients with anemia","fulltext":[{"header":"1 Introduction","content":"\u003cp\u003eAnemia is characterized by a lower-than-normal hemoglobin concentration in the blood, impairing oxygen transport and resulting in various clinical symptoms and pathophysiological changes(\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e). Anemia in children can affect cognitive and motor development, while in adults, it is associated with increased risks of infection, heart failure, and mortality(\u003cspan additionalcitationids=\"CR3 CR4\" citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e). According to the World Health Organization (WHO), anemia is defined as hemoglobin levels\u0026thinsp;\u0026lt;\u0026thinsp;12.0 g/dL for women and \u0026lt;\u0026thinsp;13.0 g/dL for men(\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e). Common symptoms in adults include fatigue, reduced activity levels, and shortness of breath(\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e). In severe cases, anemia can precipitate heart failure or death(\u003cspan additionalcitationids=\"CR10 CR11 CR12 CR13\" citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e). These findings underscore the need for improved strategies to prevent anemia, alleviate symptoms, and enhance patient outcomes.\u003c/p\u003e\u003cp\u003eNeutrophils, as first responders during inflammation, release mediators like LTB4, TNF-α, and IL-1β, which exacerbate vascular permeability and oxidative stress(\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e). Albumin, the most abundant plasma protein, provides antioxidant and immunomodulatory functions(\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e). Inflammatory responses inhibit albumin synthesis and distribution, compounding oxidative stress(\u003cspan additionalcitationids=\"CR20\" citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e). Elevated NPAR, reflecting increased neutrophil counts and/or decreased albumin levels, thus serves as a marker of heightened inflammatory states (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e). Inflammation is intricately linked to anemia, as it disrupts iron homeostasis, inhibits red blood cell production, and shortens red cell lifespan(\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eIt is suggested that the percentage of neutrophils and the albumin ratio may serve as novel laboratory indicators for the identification of inflammation(\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e). Although traditional inflammatory markers such as C-reactive protein (CRP) and erythrocyte sedimentation rate (ESR) are widely utilized within clinical practice, the percentage of neutrophils and albumin values are more readily obtainable and less expensive to test(\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e). Elevated NPAR has been linked to increased mortality in conditions such as chronic obstructive pulmonary disease (COPD), diabetes, and heart failure(\u003cspan additionalcitationids=\"CR29\" citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e). However, its prognostic value in anemia-related mortality remains unexplored. This study seeks to address this gap by investigating NPAR as a predictor of all-cause mortality in anemic individuals.\u003c/p\u003e"},{"header":"2 Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003e2.1 Study population\u003c/h2\u003e\u003cp\u003eThis study included 101,316 individuals between 1999 and 2018. Among them, 46,235 individuals under the age of 20 years were excluded from the study, as were 6,299 individuals with missing data on neutrophil percentage and albumin values, and 44,004 individuals with missing hemoglobin data but who were not anemic. Subsequently, a total of 1520 individuals with missing data on covariates were excluded, including 640 individuals with no history of alcohol consumption, 1 individual with no history of hypertension, 44 individuals with a history of smoking, 372 individuals with a history of diabetes mellitus, 5 pregnant women, 7 individuals with missing education levels, 30 individuals with missing marital status, 127 individuals with missing body mass index, 333 individuals with missing household income and poverty rates, and 1 individual with missing data on death. A total of 3258 anemic patients were included in the final analysis (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). This study analyzed NHANES data spanning 1999\u0026ndash;2018, comprising 101,316 individuals. After excluding participants aged\u0026thinsp;\u0026lt;\u0026thinsp;20 years, those with missing neutrophil or albumin values, and non-anemic individuals, 3,258 anemic patients were included in the final analysis. Details of the selection process are shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e\u003cp\u003eFigure \u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e: A comprehensive outline of the participant selection process.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\u003ch2\u003e2.2 Exposure and Outcomes\u003c/h2\u003e\u003cp\u003eIn this study, NPAR is the exposure variable. The NPAR formula is calculated by multiplying the neutrophil percentage by 100 and dividing it by the albumin value. The albumin value is obtained from the standard biochemical profile of laboratory data, and the neutrophil percentage is derived from the complete blood count and five-part differential white blood cell count. The study endpoint is defined as all-cause mortality, or mortality from any cause. For each participant, the follow-up duration was calculated from enrollment until either death or December 31, 2019, whichever came later. This date is the latest available in the NDI database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.cdc.gov/nchs/datalinkage/mortality-public.htm\u003c/span\u003e\u003cspan address=\"https://www.cdc.gov/nchs/datalinkage/mortality-public.htm\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). Mortality data were extracted from the 1999\u0026ndash;2018 NHANES mortality files (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.cdc.gov/nchs/nhanes/index.htm\u003c/span\u003e\u003cspan address=\"https://www.cdc.gov/nchs/nhanes/index.htm\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). In this study, NPAR served as the exposure variable. The NPAR formula is calculated as the neutrophil percentage multiplied by 100 and then divided by the albumin value. Albumin values were obtained from the standard biochemical profile in laboratory data, while neutrophil percentages were derived from complete blood counts and five-part differential white blood cell counts. The study endpoint was defined as all-cause mortality, or mortality from any cause. Follow-up time was calculated for each participant from enrollment until death or December 31, 2019 (the latest data available in the NDI database). Mortality data were extracted from the 1999\u0026ndash;2018 NHANES mortality files.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\u003ch2\u003e2.3 Covariates\u003c/h2\u003e\u003cp\u003eVariables studied included age, gender (including both men and women), and race (including Mexican American, Other Hispanic, Non-Hispanic White, Non-Hispanic Black, and Other). Marital status is categorized as married or cohabiting with a partner, and single (never married, divorced, separated, or widowed). Educational level is divided into three categories: less than high school, high school, and more than high school. Household income and poverty rate (PIR) were classified into three groups: (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e)\u0026thinsp;\u0026lt;\u0026thinsp;1.5; (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e) 1.5\u0026ndash;3.5; and (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e)\u0026thinsp;\u0026ge;\u0026thinsp;3.5. Body mass index (BMI) was generally classified into the following three groups: (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e) BMI\u0026thinsp;\u0026lt;\u0026thinsp;25 kg/m\u0026sup2;; (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e) BMI 25\u0026ndash;30 kg/m\u0026sup2;; and (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e) BMI\u0026thinsp;\u0026ge;\u0026thinsp;30 kg/m\u0026sup2;(\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e). The five categories of drinking status are as follows: (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e) ex-drinkers, who used to drink but do not drink now; (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e) never drinkers, who have not drunk any alcohol in their lifetime; (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e) light drinkers, who consume one to two glasses of alcohol per day; (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e) moderate drinkers, who consume no more than four glasses of alcohol per day for men and no more than three glasses of alcohol per day for women; and (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e) heavy drinkers, who consume more than four glasses of alcohol per day for men and more than three glasses of alcohol per day for women. The smoking status of individuals is categorized into three distinct groupings: (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e) never-smokers, defined as those who have smoked less than 100 cigarettes in their lifetime; (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e) ex-smokers, characterized by having smoked more than 100 cigarettes in their lifetime, but are no longer active smokers; and (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e) current smokers, comprising individuals who have smoked more than 100 cigarettes in their lifetime, and who currently smoke either regularly or daily(\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e). Hypertension is diagnosed if any of the following conditions are met: Firstly, a physician diagnoses hypertension. Secondly, antihypertensive medication. Thirdly, the average systolic blood pressure must be \u0026ge;\u0026thinsp;140 mmHg, and/or the average diastolic blood pressure must be \u0026ge;\u0026thinsp;90 mmHg. The diagnosis of diabetes is made if any of the following conditions are met: (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e) a doctor's diagnosis of diabetes; (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e) a glycated hemoglobin (HbA1c) level\u0026thinsp;\u0026ge;\u0026thinsp;6.5%; (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e) a fasting blood glucose level\u0026thinsp;\u0026ge;\u0026thinsp;126 mg/dl; (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e) current use of diabetes medication or insulin. The present study included covariates related to anemia, including laboratory parameters for white blood cells (WBC), red blood cells (RBC), platelet count (PLT), mean corpuscular hemoglobin concentration (MCHC), mean corpuscular hemoglobin (MCH), and mean corpuscular volume (MCV). These data are available from the CDC National Health Survey (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.cdc.gov/nchs/nhanes\u003c/span\u003e\u003cspan address=\"https://www.cdc.gov/nchs/nhanes\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\u003ch2\u003e2.4 Modeling\u003c/h2\u003e\u003cp\u003eThe NHANES data have undergone a complex stratified, multi-stage sampling design; if it is not weighted, this may lead to biased results. Consequently, the implementation of weighting methodologies is imperative to enhance the generalisability of the study sample. The sampling weight is divided into two parts, and the calculation method is as follows: Specifically, the 4-year weight WTMEC4YR x 2/10 is utilized for the period 1999\u0026ndash;2002, while the 2-year weight WTMEC2YR/10 is employed for the subsequent period from 2003\u0026ndash;2018(\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e). Ultimately, the weights described above are amalgamated through the implementation of a meticulous calculation approach for NHANES composite survey cycle weights (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://wwwn.cdc.gov/nchs/nhanes/tutorials/Weighting.aspx\u003c/span\u003e\u003cspan address=\"https://wwwn.cdc.gov/nchs/nhanes/tutorials/Weighting.aspx\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), thereby yielding the aggregate weight.\u003c/p\u003e\u003cp\u003eContinuous variables that follow a normal distribution are represented by the mean and standard deviation; continuous variables with non-normal distributions are represented by the median (Q1, Q3). Categorical variables are represented by percentages. The participants' baseline characteristics are represented by quartiles from NPAR. The Kaplan-Meier method was employed to ascertain the association between variations in NPAR levels and all-cause mortality in anemic patients. A multiple regression model was employed to evaluate the relationship between NPAR levels and all-cause mortality in anemic patients. To eliminate the potential impact of confounding factors, three models have been developed: Model 1 is not adjusted for covariates, and Model 2 is adjusted for the variables of gender, age, and race. The third model is adjusted for all potential confounding factors, including gender, age, race, education level, marital status, PIR, smoking history, drinking history, and medical history of hypertension, diabetes, white blood cells, red blood cells, platelets, MCHC, MCH, and MCV. The present study employed restricted cubic spline analysis to explore the nonlinear relationship between NPAR and all-cause mortality in anemic patients. Furthermore, we proceeded to utilize threshold effect analysis to ascertain the breakpoint of the nonlinear relationship between NPAR and all-cause mortality in anemic individuals based on Model 3. Stratified analysis and interaction tests were performed to explore whether there was a statistically significant association between NPAR and all-cause mortality in anemic individuals in different subgroups.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\u003ch2\u003e2.5 Statistical analysis\u003c/h2\u003e\u003cp\u003eAll statistical analyses were performed using the following software: Empower Stats 4.0, Free Statistics 1.9.2, and R Studio 4.4.2. A p-value of less than 0.05 was considered statistically significant.\u003c/p\u003e\u003c/div\u003e"},{"header":"3 Results","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\u003ch2\u003e3.1 Baseline characteristics of participants\u003c/h2\u003e\u003cp\u003eAmong the 3,258 participants, 37.02% were male and 62.98% were female. The mean age was 56.199\u0026thinsp;\u0026plusmn;\u0026thinsp;18.315 years, and the median NPAR was 14.526. The demographic composition of the study sample is shown in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. Participants were grouped by NPAR quartiles: Q1 (below 12.543), Q2 (12.543\u0026ndash;14.525), Q3 (14.526\u0026ndash;16.424), and Q4 (above 16.424). Compared to individuals in the lowest NPAR quartile (Q1), those in the highest quartile (Q4) were more likely to exhibit the following characteristics: (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e) male; (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e) older age; (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e) non-Hispanic white; (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e) less than a high school education; (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e) married or cohabiting with a partner; (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e) moderate income; (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e) higher body mass index; (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e) history of diabetes; (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e) history of hypertension; (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e) prior smoking; (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e) prior alcohol consumption; (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e) higher white blood cell count; (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e) higher platelet count; and (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e) lower red blood cell count. No statistically significant differences in mean red blood cell hemoglobin concentration were observed between different NPAR groups.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eBaseline characteristics of study subjects expressed as NPAR quartiles.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"6\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eQ1(\u0026lt;12.543)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eQ2(12.543\u0026ndash;14.525)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eQ3(14.526\u0026ndash;16.424)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eQ4(\u0026gt;16.424)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cem\u003eP-\u003c/em\u003e value\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eN\u0026thinsp;=\u0026thinsp;814\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eN\u0026thinsp;=\u0026thinsp;815\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eN\u0026thinsp;=\u0026thinsp;814\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eN\u0026thinsp;=\u0026thinsp;815\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eAge(years)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e54(39,69)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e52(40,70)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e58(42,75)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e64(46,76.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eWBC(1000 cells/uL)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e5.65(4.6,6.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e6.3(5.3,7.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e6.7(5.6,8.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e7.6(6.3,9.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eRBC(million cells/uL)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4.09(3.75,4.44)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4.09(3.82,4.39)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e4.095(3.8,4.44)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e3.99(3.69,4.35)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eMCV(fl)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e86(78.15,91.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e85.6(77.9,91)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e84.7(77.6,91.25)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e86(80.05,92.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eMCH (pg)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e28.4(25.3,30.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e28.5(25.4,30.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e28.2(25.125,30.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e28.7(26.05,31)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.016\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eMCHC (g/dL)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e32.9(32.1,33.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e33.2(32.4,34)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e33.1(32.2,33.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e33.2(32.3,34)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.475\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003ePLT(1000 cells/uL)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e246.5(198,304)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e255(210,312)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e253(210,315)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e260(208,333)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eGender (%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eMale\u003c/p\u003e\u003cp\u003eFemale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e31.267\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e23.968\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e31.517\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e36.015\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e68.733\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e76.032\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e68.483\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e63.985\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eRace/ethnicity (%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e\u003cp\u003eMexican American\u003c/p\u003e\u003cp\u003eOther Hispanic\u003c/p\u003e\u003cp\u003eNon-Hispanic White\u003c/p\u003e\u003cp\u003eNon-Hispanic Black\u003c/p\u003e\u003cp\u003eOther Race\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e8.333\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e7.76\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e8.017\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e6.87\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\" morerows=\"4\" rowspan=\"5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e5.014\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e7.475\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e6.09\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e4.417\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e36.044\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e50.818\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e52.987\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e58.219\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e42.701\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e27.359\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e26.547\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e23.984\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e7.908\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e6.589\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e6.359\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e6.51\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eEducation (%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.045\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003eLess than high school\u003c/p\u003e\u003cp\u003eHigh school\u003c/p\u003e\u003cp\u003eMore than high school\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e22.199\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e20.105\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e20.641\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e26.36\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\" morerows=\"2\" rowspan=\"3\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e22.909\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e24.59\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e23.743\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e23.921\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e54.892\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e55.305\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e55.615\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e49.719\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eMarital status (%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003emarried or cohabiting with a partner\u003c/p\u003e\u003cp\u003eSingle\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e52.485\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e58.112\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e63.816\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e54.937\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e47.515\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e41.888\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e36.184\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e45.063\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eBMI (%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;25\u003c/p\u003e\u003cp\u003e25\u0026ndash;30\u003c/p\u003e\u003cp\u003e\u0026ge;\u0026thinsp;30\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e39.857\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e31.928\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e32.092\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e26.002\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\" morerows=\"2\" rowspan=\"3\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e29.426\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e29.727\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e25.716\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e24.123\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e30.717\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e38.345\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e42.192\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e49.876\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003ePIR (%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003e\u0026lt;1.5\u003c/p\u003e\u003cp\u003e1.5\u0026ndash;3.5\u003c/p\u003e\u003cp\u003e\u0026ge;3.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e36.17\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e32.584\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e29.292\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e35.716\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\" morerows=\"2\" rowspan=\"3\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e34.404\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e32.153\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e35.115\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e38.752\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e29.427\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e35.262\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e35.593\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e25.532\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eSmoking (%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003eNever\u003c/p\u003e\u003cp\u003eFormer\u003c/p\u003e\u003cp\u003eNow\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e62.579\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e63.632\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e65.844\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e55.568\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\" morerows=\"2\" rowspan=\"3\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e23.503\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e23.968\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e24.218\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e30.931\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e13.918\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e12.399\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e9.939\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e13.5\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eDrinking (%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e\u003cp\u003eNever\u003c/p\u003e\u003cp\u003eFormer\u003c/p\u003e\u003cp\u003eMild\u003c/p\u003e\u003cp\u003eModerate\u003c/p\u003e\u003cp\u003eHeavy\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e20.354\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e19.495\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e15.771\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e15.903\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\" morerows=\"4\" rowspan=\"5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e17.804\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e16.818\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e17.755\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e29.278\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e34.149\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e31.507\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e36.931\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e32.459\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e12.122\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e18.344\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e16.351\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e11.057\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e15.571\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e13.836\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e13.193\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e11.302\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eHypertension history(%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e45.701\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e45.012\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e48.257\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e64.358\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e54.299\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e54.988\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e51.743\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e35.642\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eDiabetes(%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003cp\u003ePrediabetes\u003c/p\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e16.352\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e20.478\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e26.601\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e33.523\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\" morerows=\"2\" rowspan=\"3\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4.945\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3.766\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e5.209\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e8.739\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e78.704\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e75.756\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e68.19\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e57.737\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e: Q1-Q4 are grouped according to NPAR quartiles. Continuous variables are expressed as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation, and p-values were calculated using a weighted linear regression model. Percentages indicate categorical variables, and p-values were calculated using a weighted chi-square test. WBC: white blood cells; RBC: red blood cells; PLT: platelet count; MCHC: mean corpuscular hemoglobin concentration; MCH: mean corpuscular hemoglobin; MCV: mean corpuscular volume; PIR: household income-to-poverty ratio; BMI: body mass index.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\u003ch2\u003e3.2 Association of NPAR with all-cause mortality in individuals with anemia\u003c/h2\u003e\u003cp\u003eBecause the endpoint of this study was death, a Kaplan-Meier analysis was performed to examine the effect of NPAR on all-cause mortality in anemic individuals over time. After a mean follow-up period of 94.337 months (range: 0\u0026ndash;248 months), 904 out of 3,258 participants (27.75%) died. (See Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.) In the Kaplan-Meier analysis, which was stratified by NPAR quartiles and adjusted for all covariates, individuals in the highest NPAR quartile (Q4) exhibited the highest all-cause mortality during the follow-up period, compared to individuals in the other quartiles. Meanwhile, individuals in the lowest NPAR quartile (Q1) exhibited the lowest all-cause mortality, with significant differences in all-cause mortality rates observed among the four groups (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eFigure \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e: Adjusted variables such as gender, race, age, education level, marital status, household income to poverty ratio, body mass index, hypertension, smoking, drinking, diabetes, WBC, RBC, PLT, MCH, MCHC, and MCV.\u003c/p\u003e\u003cp\u003eIn Model 1 (the unadjusted model), the highest quartile (Q4) of NPAR was associated with a 2.460-fold increased risk of all-cause mortality (HR: 1.597; 95% CI: 2.004\u0026ndash;3.020; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) when the lowest quartile (Q1) of NPAR was used as the reference value. (See Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.) Model 2 incorporated demographic variables, including gender, age, and ethnicity. Compared to Q1, each additional unit of NPAR in Q4 individuals was associated with a 1.597-fold increased risk of all-cause mortality (HR: 1.597; 95% CI: 1.297\u0026ndash;1.967; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Model 3 included all covariates: gender, race, age, education level, marital status, poverty ratio, body mass index, hypertension, smoking status, alcohol consumption, diabetes status, white blood cell count, red blood cell count, platelet count, mean hemoglobin concentration, mean corpuscular hemoglobin, and mean corpuscular volume. The results showed that, compared to Q1, each additional unit of NPAR in Q4 individuals was associated with a 1.654-fold increase in the risk of all-cause mortality (HR: 1.654; 95% CI: 1.321\u0026ndash;2.071; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eMultiple regression model of NPAR and all-cause mortality in individuals with anemia.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eExposure\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCrude model\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eModel 1\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eModel 2\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eHR (95% CI)\u003cem\u003eP\u003c/em\u003e value\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eHR (95% CI)\u003cem\u003eP\u003c/em\u003e value\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eHR (95% CI)\u003cem\u003eP\u003c/em\u003e value\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAll-cause mortality\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNPAR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.136(1.107, 1.166)\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.069(1.040, 1.098)\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.073(1.046, 1.100)\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNPAR category\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e\u003cp\u003eQ1\u003c/p\u003e\u003cp\u003eQ2\u003c/p\u003e\u003cp\u003eQ3\u003c/p\u003e\u003cp\u003eQ4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eReference\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eReference\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\" morerows=\"3\" rowspan=\"4\"\u003e\u003cp\u003eReference\u003c/p\u003e\u003cp\u003e1.102(0.864,1.405)0.434\u003c/p\u003e\u003cp\u003e0.977(0.762,1.253)0.856\u003c/p\u003e\u003cp\u003e1.654(1.321,2.071)\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.057(0.832, 1.345)0.649\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.033(0.815, 1.309)0.787\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.255(0.981, 1.604)0.07\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.913( 0.729, 1.144)0.429\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2.460( 2.004, 3.020)\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.597( 1.297, 1.967)\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTrend test\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e: Model 1 was not adjusted for covariates. Model 2 was adjusted for gender, age, and race. Model 3 was adjusted for gender, race, age, education level, marital status, PIR, BMI, hypertension, smoking, drinking, diabetes, white blood cells, red blood cells, platelets, MCHC, MCH, and MCV.\u003c/p\u003e\u003cp\u003eCompared to Model 1 (the unadjusted model), the all-cause mortality risk for the highest quartile (Q4) of NPAR decreased from 2.460 to 1.597 after adjusting for variables in Model 2. This suggests that one of these variables is a risk factor for NPAR and that adjusting for it reduces both NPAR and mortality risk. However, after adjusting for all covariates in Model 3, the all-cause mortality risk in the highest quartile increased from 1.597 to 1.654. This result suggests that certain variables act as suppressors of NPAR. Adjusting for these variables increases NPAR and elevates all-cause mortality risk. All three models yielded p-values\u0026thinsp;\u0026lt;\u0026thinsp;0.001. Although the all-cause mortality risk in the highest quartile fluctuated across these models, the finding that higher NPAR correlates with increased all-cause mortality risk remained consistent. Our results confirm that NPAR is a highly reliable biological marker for predicting all-cause mortality in individuals with anemia.\u003c/p\u003e\u003cp\u003eUsing restricted cubic splines within a fully adjusted model, we determined the association between NPAR and all-cause mortality, revealing a nonlinear relationship in anemic individuals (see Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Specifically, we found that NPAR was positively associated with all-cause mortality in anemic individuals to the right of the breakpoint. Subsequently, we conducted a threshold effect analysis on the fully adjusted model (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). The results revealed a nonlinear relationship between NPAR and all-cause mortality, with a breakpoint at 15.116. To the left of the breakpoint, NPAR showed no statistically significant association with all-cause mortality in individuals with anemia. However, to the right of the breakpoint, NPAR was positively correlated with all-cause mortality in anemic individuals (HR: 1.129; 95% CI: 1.095\u0026ndash;1.165; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). The log-likelihood ratio test yielded p\u0026thinsp;\u0026lt;\u0026thinsp;0.001.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eFigure \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e: The solid red line shows the cumulative risk of death from any cause for patients with anemia, and the two dashed blue lines indicate the 95% confidence interval.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eNPAR has a non-linear relationship with the all-cause mortality rate of anemic individuals.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"2\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAdjusted HR (95% CI), \u003cem\u003ep\u003c/em\u003e-value\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFitting by the standard linear model\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLinear Effect\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.065 (1.043, 1.087)\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFitting by a two-piecewise linear model\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBreakpoint(K)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e15.116\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNPAR\u0026thinsp;\u0026lt;\u0026thinsp;15.116\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.983 (0.946, 1.021) 0.373\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNPAR\u0026thinsp;\u0026gt;\u0026thinsp;15.116\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.129 (1.095, 1.165)\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eThe effect difference between 2 and 1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.149 (1.084, 1.217)\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLog likelihood ratio\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e: The adjustment variables are the same as in Model 3.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003e3.3 Subgroup Analysis\u003c/h2\u003e\u003cp\u003eSubgroup analysis identified significant associations between NPAR and mortality in participants aged\u0026thinsp;\u0026lt;\u0026thinsp;60 years and those with BMI\u0026thinsp;\u0026ge;\u0026thinsp;30 kg/m\u0026sup2; (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). Subgroups were stratified by age, sex, PIR, and BMI, smoking, alcohol consumption, hypertension, and diabetes. They were analyzed using fully adjusted models. Age was categorized as \u0026lt;\u0026thinsp;60 or \u0026ge;\u0026thinsp;60 years. Interactions showed that after adjustment for all covariates, the association between NPAR and all-cause mortality in anemic patients was statistically significant (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05) in the subgroups of age\u0026thinsp;\u0026lt;\u0026thinsp;60 years, \u0026ge; 60 years, and body mass index\u0026thinsp;\u0026lt;\u0026thinsp;25 kg/m\u003csup\u003e2\u003c/sup\u003e, 25\u0026ndash;30 kg/m\u003csup\u003e2\u003c/sup\u003e, and \u0026ge;\u0026thinsp;30 kg/m\u003csup\u003e2\u003c/sup\u003e. Of particular note, there was a significant positive association between anemia and all-cause mortality in those aged\u0026thinsp;\u0026lt;\u0026thinsp;60 years and with a body mass index\u0026thinsp;\u0026ge;\u0026thinsp;30 kg/m\u003csup\u003e2\u003c/sup\u003e. In contrast, no statistically significant interactions were found between subgroups stratified by sex, PIR, smoking status and alcohol consumption, history of hypertension, and history of diabetes.\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eFigure \u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e\u003c/strong\u003e\u003cp\u003eSubgroup analysis of NPAR and all-cause mortality in individuals with anemia\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003e3.4 Discussion\u003c/h2\u003e\u003cp\u003eOur study investigated the association between the novel inflammatory marker NPAR and all-cause mortality in anemic individuals. The Results indicated that, after adjusting for confounding factors, higher NPAR levels were positively correlated with all-cause mortality in anemic individuals. We observed differing associations between NPAR and all-cause mortality in anemic individuals on either side of the cutoff point (NPAR-15.116). When NPAR\u0026thinsp;\u0026lt;\u0026thinsp;15.116, a positive correlation with all-cause mortality was found, whereas no statistically significant association was observed when NPAR\u0026thinsp;\u0026gt;\u0026thinsp;15.116. We found that NPAR was significantly positively associated with all-cause mortality in anemic individuals, particularly in those younger than 60 years old and with a body mass index (BMI)\u0026thinsp;\u0026ge;\u0026thinsp;30 kg/m\u003csup\u003e2\u003c/sup\u003e. Therefore, we should promote NPAR level testing, which can assist clinicians in developing targeted treatment plans for anemic patients and improve their prognosis.\u003c/p\u003e\u003cp\u003eAccording to existing research, anemia is associated with certain inflammatory markers. Elevated levels of the neutrophil-to-lymphocyte ratio (NLR), systemic inflammatory index (SII), and platelet-to-lymphocyte ratio (PLR) increase the likelihood of developing anemia (\u003cspan additionalcitationids=\"CR35\" citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e). According to existing research, anemia is associated with certain inflammatory markers. Elevated levels of the neutrophil-to-lymphocyte ratio (NLR), systemic inflammatory index (SII), and platelet-to-lymphocyte ratio (PLR) increase the likelihood of developing anemia(\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e). Reduced albumin levels weaken the body's antioxidant capacity, intensifying inflammatory responses and tissue damage(\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e). The mechanisms by which inflammation causes anemia primarily involve the following points: when the body experiences inflammation, it enters a state of stress, which triggers the activation of immune cells and the release of inflammatory cytokines such as IL-6、IL-1、and TNF-α (\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e). IL-6 activates the STAT3 signalling pathway in the liver, thereby upregulating ferritin expression(\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e). Ferritin has been shown to bind to ferroportin (FP1), inducing its degradation and consequently resulting in iron being stored in macrophages. This process impedes the absorption of iron in the duodenum, thereby reducing the amount of iron available as a raw material for the production of red blood cells and leading to anemia(\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e). In addition, IL-1 and TNF have been demonstrated to inhibit the production of EPO by renal epithelial cells, thereby reducing red blood cell production and leading to the development of anemia(\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e). It has been established that the occurrence of inflammation is accompanied by the deposition of autoantibodies and complement on red blood cells. This, in turn, results in enhanced phagocytosis of red blood cells by macrophages and increased red blood cell destruction. The consequence of these processes is the onset of anemia(\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eIn comparison with conventional indicators of inflammation, the percentage of neutrophils and albumin values is readily discernible and economical and possesses the capacity to predict the prognosis of associated diseases(\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e). The findings of this study demonstrate that the regulation of inflammation can enhance the prognoses of patients suffering from anemia(\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e, \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e). The indicators representing inflammation, such as SII and PLR, are nonlinearly related to anemia, which is consistent with the results of this study. Furthermore, NPAR is nonlinearly related to the all-cause mortality of individuals suffering from anemia(\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e). The findings of the present study demonstrate that a higher NPAR value is associated with a poorer prognosis in individuals suffering from anemia. This association can be used to predict the prognosis of such individuals.\u003c/p\u003e\u003cp\u003eOur study demonstrated the predictive value of NPAR for anemia prognosis. Using Kaplan-Meier analysis, multivariable Cox proportional hazards models, restricted cubic splines, threshold effect analysis, stratified analysis, and interaction tests, we demonstrated a significant positive correlation between elevated NPAR levels and all-cause mortality. However, this study has limitations. Memory bias in the questionnaire may have influenced the results. Additionally, since the NHANES data only represent the U.S. population, the results may be biased when extrapolating predictions to other countries. Since neutrophil and albumin values are single-point measurements that fail to capture dynamic changes in NPAR levels, multiple measurements are required to enhance accuracy. Therefore, we should promote NPAR testing to help clinicians develop effective treatment plans and improve outcomes for patients with anemia.\u003c/p\u003e\u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAuthor Contributions\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eConceptualization: Peng Anqi, Li Jizhe(both authors contributed equally to this study); Methodology: Peng Anqi, Li Jizhe; Software: Peng Anqi, Li Jizhe; Validation: Peng Anqi, Li Jizhe; Formal analysis: Peng Anqi, Li Jizhe; Visualization: Peng Anqi, Li Jizhe; Investigation: Huang Jun, Yang Yuqiong; Resources: Huang Jun, Yang Yuqiong; Data curation: Huang Jun, Yang Yuqiong; Writing – original draft: Peng Anqi, Li Jizhe, Huang Jun, Yang Yuqiong; Project administration: Jiang Yizhi, Huang Dongping; Supervision: Jiang Yizhi, Huang Dongping; Funding acquisition: Jiang Yizhi, Huang Dongping; Writing – review \u0026amp; editing: Jiang Yizhi, Huang Dongping.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of Data and Materials.\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets supporting the conclusions of this article are available in the NHANES (https://www.cdc.gov / nchs /nhanes).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements.\u0026nbsp;\u003c/strong\u003eWe express our gratitude to the NHANES database and researchers for providing high-quality NHANES data.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding.\u003c/strong\u003e This research was funded by the Natural Science Foundation of Anhui Province (2023AH040255) and the National Natural Science Foundation of China (82200146).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor information.\u0026nbsp;\u003c/strong\u003eAuthors and Affiliations.\u003c/p\u003e\n\u003cp\u003eDepartment of Hematology, the First Affiliated Hospital of Wannan Medical College, Wuhu, 241001, China; The Second Affiliated Hospital of Anhui Medical University, Hefei 230601, China.\u003c/p\u003e\n\u003cp\u003eAnqi Peng, Jizhe Li, Jun Huang, Yuqiong Yang, Yizhi Jiang\u003cstrong\u003e,\u0026nbsp;\u003c/strong\u003eDongping Huang.\u003c/p\u003e\n\u003cp\u003eAnqi Peng: [email protected]\u003c/p\u003e\n\u003cp\u003eJizhe Li: [email protected]\u003c/p\u003e\n\u003cp\u003eJun Huang: [email protected]\u003c/p\u003e\n\u003cp\u003eYuqiong Yang: [email protected]\u003c/p\u003e\n\u003cp\u003eYizhi Jiang: [email protected]\u003c/p\u003e\n\u003cp\u003eDongping Huang: [email protected]\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCorresponding authors\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCorrespondence to Dongping Huang.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics declarations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors confirm that all experiments were performed in accordance with the Declaration of Helsinki.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthical approval statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study is an analysis derived from publicly available NHANES data, which was acquired with the approval of the Ethics Review Board of the National Center for Health Statistics (https://www.cdc.gov/nchs/nhanes/irba98.htm). Given that the NHANES public use data does not contain individually identifiable information, ethical review and approval were waived for this study.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot an application.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe author(s) declare(s) that they have no competing interests.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eChaparro CM, Suchdev PS. Anemia Epidemiology, Pathophysiology, and Etiology in Low- and Middle-Income Countries. 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Epub 20221214. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3390/nu14245318\u003c/span\u003e\u003cspan address=\"10.3390/nu14245318\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLucendo AJ, Roncero \u0026Oacute;, Serrano-Duenas MT, Herv\u0026iacute;as D, Alc\u0026aacute;zar LM, Miriam Ruiz P, et al. Effects of Anti-Tnf-Alpha Therapy on Hemoglobin Levels and Anemia in Patients with Inflammatory Bowel Disease. Dig Liver Dis. 2020;52(4):400\u0026ndash;7. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.dld.2019.11.019\u003c/span\u003e\u003cspan address=\"10.1016/j.dld.2019.11.019\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. Epub 20191228.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"clinical-and-experimental-medicine","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"clem","sideBox":"Learn more about [Clinical and Experimental Medicine](https://www.springer.com/journal/10238)","snPcode":"10238","submissionUrl":"https://submission.nature.com/new-submission/10238/3","title":"Clinical and Experimental Medicine","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"anemia, NPAR, inflammation, all-cause mortality, nonlinear correlation","lastPublishedDoi":"10.21203/rs.3.rs-8161330/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8161330/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground:\u003c/h2\u003e\u003cp\u003eThe neutrophil percentage-to-albumin ratio (NPAR) is an emerging indicator of inflammation that has been associated with the prognosis of various diseases, including hypertension, diabetes, and cardiovascular disease. NPAR is calculated by multiplying the neutrophil percentage by 100 and dividing it by the albumin value. This study aimed to evaluate the predictive value of NPAR for all-cause mortality in anemic patients. We employed Kaplan\u0026ndash;Meier analysis, multiple regression models, restricted cubic splines (RCS), and threshold effect analysis to explore these associations.\u003c/p\u003e\u003ch2\u003eMethods:\u003c/h2\u003e\u003cp\u003eThis study included 3,258 anemic individuals. The relationship between NPAR and all-cause mortality was evaluated using Kaplan\u0026ndash;Meier analysis and multiple regression models. RCS curves were employed to assess potential nonlinear relationships, while threshold effect analysis was used to identify breakpoints in the association between NPAR and mortality risk. Subgroup analyses were conducted to examine variations in this relationship across different population strata.\u003c/p\u003e\u003ch2\u003eResults:\u003c/h2\u003e\u003cp\u003eThe study population included 3,258 anemic patients, with a mean follow-up period of 94.3 months. During follow-up, 904 participants (27.75%) died. Kaplan\u0026ndash;Meier analysis revealed that individuals in the highest NPAR quartile (Q4) exhibited significantly higher cumulative mortality compared with those in the lowest quartile (Q1) (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Multiple regression analysis showed that, in fully adjusted models, each unit increase in NPAR among individuals in the highest quartile was associated with a 65.4% increased risk of all-cause mortality. RCS and threshold effect analyses revealed a nonlinear relationship between NPAR and mortality, with a breakpoint at NPAR\u0026thinsp;=\u0026thinsp;15.116. Below this threshold, no statistical association was observed, while above the threshold, NPAR was positively associated with increased mortality risk. Subgroup analyses demonstrated that the association between NPAR and mortality was particularly significant in individuals aged\u0026thinsp;\u0026lt;\u0026thinsp;60 years and those with a body mass index (BMI)\u0026thinsp;\u0026ge;\u0026thinsp;30 kg/m\u0026sup2;.\u003c/p\u003e\u003ch2\u003eConclusions:\u003c/h2\u003e\u003cp\u003eOur findings indicate that elevated NPAR is independently associated with increased all-cause mortality in anemic individuals. The nonlinear relationship and identified breakpoint suggest that NPAR may serve as a valuable prognostic biomarker for mortality risk in this population. Further research should focus on the clinical utility of NPAR in guiding therapeutic interventions for anemia.\u003c/p\u003e","manuscriptTitle":"The neutrophil-percentage-to-albumin ratio(NPAR) shows a nonlinear correlation with all-cause mortality in patients with anemia","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-12-01 07:41:42","doi":"10.21203/rs.3.rs-8161330/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-01-12T17:30:50+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-01-05T15:09:42+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"263660848673732485986099169830478344162","date":"2025-12-24T02:52:31+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-11-22T08:47:17+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-11-21T13:43:50+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-11-21T13:41:36+00:00","index":"","fulltext":""},{"type":"submitted","content":"Clinical and Experimental Medicine","date":"2025-11-20T06:57:08+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"clinical-and-experimental-medicine","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"clem","sideBox":"Learn more about [Clinical and Experimental Medicine](https://www.springer.com/journal/10238)","snPcode":"10238","submissionUrl":"https://submission.nature.com/new-submission/10238/3","title":"Clinical and Experimental Medicine","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"32fa5160-2c3a-44fc-b91a-ec728445759f","owner":[],"postedDate":"December 1st, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2026-04-13T16:04:34+00:00","versionOfRecord":{"articleIdentity":"rs-8161330","link":"https://doi.org/10.1007/s10238-026-02119-1","journal":{"identity":"clinical-and-experimental-medicine","isVorOnly":false,"title":"Clinical and Experimental Medicine"},"publishedOn":"2026-04-06 15:59:22","publishedOnDateReadable":"April 6th, 2026"},"versionCreatedAt":"2025-12-01 07:41:42","video":"","vorDoi":"10.1007/s10238-026-02119-1","vorDoiUrl":"https://doi.org/10.1007/s10238-026-02119-1","workflowStages":[]},"version":"v1","identity":"rs-8161330","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8161330","identity":"rs-8161330","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}