Abstract
Background
The neutrophil-to-lymphocyte ratio (NLR) has emerged as a novel inflammatory marker related to disease prognosis, this study aimed to evaluate the association between NLR and mortality in metabolic syndrome (MetS) patients.
Methods
This study used data from 13,156 participants with MetS, derived from the National Health and Nutrition Examination Survey from 1999 to 2020. The NLR was calculated, and its associations with cardiovascular disease (CVD) mortality and all-cause mortality were assessed by multivariate Cox regression, restricted cubic spline and Kaplan-Meier curves. The study performed subgroup analyses to validate the robustness of the findings in different populations. The predictive ability of NLR was evaluated using time-dependent receiver operating characteristic curve. The indirect impact of eGFR was explored by mediation analysis.
Results
As NLR values increased, there was an obvious rise in the risk of mortality in MetS. The fully adjusted continuous model revealed a 16.0%, 14.4% elevated risk of CVD mortality (HR = 1.160; 95% CI: 1. 090-1.234, p < 0.0001) and all-cause mortality (HR = 1.144; 95% CI: 1. 086-1.206, p < 0.0001), respectively, with each one-unit increment in NLR. Comparing the highest to the lowest quartile of NLR, the top quartile exhibited a significantly increased risk of CVD mortality (HR = 2. 447; 95% CI: 1. 561-3. 836, p < 0.0001), and all-cause mortality (HR = 1. 53; 95% CI: 1. 188-1. 972, p = 0.001) among individuals with MetS. Subgroup analyses substantiated the stability of these associations in most populations. The curve under area for the 3, 5, and 10 years were 0.650, 0.716, and 0.645 for CVD mortality, and 0.746, 0.688, and 0.635 for all-cause mortality. Significantly, the eGFR acted as an intermediary in the relationship of NLR with CVD mortality and all-cause mortality, accounting for 9.85% and 9.86% of the effect, respectively.
Conclusion
The NLR served as a significant indicator for assessing the risk of mortality in the MetS population. Consequently, we recommended the regular assessment of NLR in MetS populations as a potentially advantageous method for evaluating their risk of mortality.
Supplementary Information
The online version contains supplementary material available at 10.1186/s12872-024-04284-1.
Keywords: Neutrophil-to-lymphocyte ratio, NLR, Metabolic syndrome, MetS, Mortality, NHANES
Introduction
Metabolic Syndrome (MetS) is a disorder characterized by a range of metabolic abnormalities, which has become a significant factor affecting global health due to its increasing incidence [1–3]. Previous studies have reported that over 30% of the population in the United States, particularly the elderly, are affected by MetS [4, 5]. Moreover, recent analyses of the epidemiological trends in the U.S. show that the prevalence of MetS has been on the rise over the past few decades, increasing from 27.6 to 32.3% [6]. A growing body of evidence indicates that the symptoms intricately associated with MetS, such as insulin resistance, hypertension, and obesity, not only elevate the risk of developing various chronic diseases but also increase the likelihood of premature death in later life [7–9]. Despite these compelling insights, the identification of prognostic biomarkers and the formulation of personalized guidance for individuals with MetS remain a complex and challenging task [10].
The complete blood count-derived neutrophil-to-lymphocyte ratio (NLR) is a vital hematological indicator, signifying systemic inflammation [11]. It served as a significant biomarker for a series of diseases, such as metabolic disorders, chronic inflammatory response, mood disorders, and cancer [12–15]. More evidences have consistently demonstrated a positive relationship between an elevated NLR and a range of cardiovascular disease (CVD), such as hypertension, atherosclerosis, and heart failure [15–17]. A comprehensive study utilizing data from the National Health and Nutrition Examination Survey (NHANES) from 1999 to 2014, which included 32,454 participants, underscored the link between a heightened NLR and overall mortality, as well as mortality specifically due to heart disease, stroke, and kidney disease [18]. Despite these findings, the connection between NLR and the risk of death in individuals with MetS has not been extensively clarified.
Consequently, the objective of this research was to explore the relationship between NLR and the risk of death from CVD mortality and all-cause mortality among the MetS population. This investigation was conducted through a population-based study, providing insights into the health conditions of adults in the United States.
Materials and methods
Study population
This observational research drew on data collected over 11 iterations of the NHANES conducted across the United States from 1999 to 2020. Annually, NHANES selected approximately 5,000 individuals from 15 distinct geographical regions for inclusion in its study. For comprehensive insights into the survey’s methodology, one should refer to the NHANES Plan and Operations manual. The survey was approved by the National Center for Health Statistics Research Ethics Review Board, adhering to established protocols. Since this investigation is a secondary analysis of anonymized NHANES data, no additional ethical approval is warranted. All methods were performed in accordance with the relevant guidelines and regulations.
The research pool was sourced from participants in the NHANES, covering the period from 1999 to 2020. Initially, we had 116,876 potential subjects. We first excluded those younger than 18 and those without a diagnosis of MetS, which left us with 101,725 participants. Next, 1,876 participants were removed due to missing mortality data. Finally, 119 participants were omitted because they lacked the necessary physical measurements to determine the NLR. In the end, our study encompassed 13,156 participants, as shown in Fig. 1.
Fig. 1.
Flowchart of participants’ selection
Diagnosis of MetS
The criteria for diagnosing MetS were established according to the guidelines of the National Cholesterol Education Program (NCEP) and its Adult Treatment Panel III. An individual is considered to have MetS if they exhibit three or more of the following conditions: a fasting blood glucose (FBG) level exceeding 100 mg/dL or are undergoing medication for diabetes; a low level of high-density lipoprotein cholesterol (HDL-C), defined as less than 50 mg/dL for women and less than 40 mg/dL for men, or are receiving treatment for reduced HDL-C levels; elevated triglycerides (TG) with a plasma concentration above 150 mg/dL or are on medication for high TG levels; an increased waist circumference, which is more than 88 cm for women and more than 102 cm for men; high blood pressure, with readings above 130/85 mmHg or are on treatment for elevated blood pressure [19, 20].
Measurement of NLR
The levels of neutrophil and lymphocyte were achieved by performing a comprehensive blood test on blood samples, utilizing an automated hematology analyzer from Beckman Coulter3 within a medical examination center. These levels were presented in units of ×10 cells per microliter. The NLR was derived by dividing the total count of neutrophils by that of lymphocytes.
Assessment of mortality
To assess the mortality rates among our study participants over time, we used the NHANES-linked mortality database, which was updated to December 31, 2019. The National Center for Health Statistics has carefully merged this database with the National Death Index using a detailed probabilistic matching process. We used the International Statistical Classification of Diseases, 10th Revision (ICD-10) to pinpoint deaths from specific causes. Deaths due to heart conditions, such as rheumatic heart disease, high blood pressure-related heart disease, heart attacks, and other heart issues, including heart failure, were categorized under ICD-10 codes ranging from I00 to I09, I11, I13, and I20 to I51. This methodical approach ensures an accurate and thorough examination of heart-related deaths in our study group [21]. This systematic classification allows for a precise and comprehensive analysis of cardiovascular-related mortality within the study population.
Potential confounders
In our analysis, we took into account demographic factors that could affect the results, such as gender, age, race (White, Black, Mexican, or other), education level (below high school, high school or equivalent, and above high school), and marital status (married, never married, or other). We also looked at lifestyle habits like smoking (non-smoker or smoker), drinking alcohol (non-drinker or drinker), and the family’s income compared to the poverty line (IPR). Physical health measures like systolic blood pressure (SBP) and diastolic blood pressure (DBP) were part of the study. We reviewed medical histories, focusing on diseases like diabetes, CVD, and chronic kidney disease (CKD). We checked various blood indicators, including TG, total cholesterol (TC), HDL, low-density lipoprotein (LDL) cholesterol, and liver function tests with glutamic pyruvic transaminase (ALT) and glutamic oxaloacetic transaminase (AST), as well as FBG and hemoglobin A1c (HbA1c). levels. To evaluate kidney health, we used the CKD-EPI 2021 formula to calculate the estimated Glomerular Filtration Rate (eGFR).
Statistical analysis
The statistical analysis was conducted using R software, specifically version 4.1.1. The criterion for statistical significance was set at a P value of less than 0.05, considering both directions of the test (two-tailed). To ensure the generalizability of the study findings and to prevent any bias due to overrepresentation, sampling weights were incorporated into the analysis. Data that was continuous in nature was reported as either the mean with 95% confidence intervals (CI). Discrete data was summarized by the number of occurrences and their respective percentages.
For a more nuanced analysis, multivariate Cox regression models were employed to assess the influence of the NLR on mortality rates within the MetS cohort. The selection of covariates for this study was informed by existing literature on MetS survival studies [20]. Specifically, crude model represented the unadjusted analysis. Model 1 incorporated basic adjustments for age, gender, and ethnicity. The fully adjusted model (Model 2) considered a comprehensive set of variables including age, gender, ethnicity, marital status, education level, IPR, smoking habits, alcohol consumption, BMI, presence of diabetes, CVD, CKD, SBP, DBP, eGFR and serum levels of TG, TC, HDL-C, LDL-C, ALT, AST, FBG, and HbA1c. Subgroup analyses were conducted and are reported, stratified using a comprehensive Model 2 that included all adjustments. To ensure the robustness of the association, sensitivity analysis was conducted, we included physical activity and C-reactive protein in the analysis based on model 2. The potential nonlinear associations were assessed utilizing restricted cubic spline (RCS) curves. The diagnostic and predictive capabilities of the NLR for mortality were compared using the area under the receiver operating characteristic (ROC) curve (AUC). This method was a standard approach in evaluating the performance of a diagnostic test or a predictive model, with the AUC providing a single value that represents the overall accuracy of the test or model. A higher AUC value indicates a better predictive or diagnostic ability. Kaplan-Meier (KM) curves were utilized to illustrate censored data and to compare survival patterns among different quartiles of the NLR and groups stratified by the NLR cutoff value within the MetS population. A mediating role of eGFR in the relation between NLR and mortality was investigated through a mediation analysis.
Results
Baseline characteristics
During the study period, a total of 13,156 participants diagnosed with metabolic syndrome were included in the research. The median age of the MetS population was 52.11 years. Additionally, the proportion of females was slightly higher than that of males, accounting for 54.86% (n = 7,217). Nearly half of the MetS population were non-Hispanic whites, representing 46.15% (n = 6,071). Over a median follow-up period of 100 months, there were 2,618 (19.9%) recorded deaths, of which 725 (5.51%) were attributed to cardiovascular causes. Table 1 presented the baseline demographics categorized into quartiles based on NLR. The median NLR value was 2.24. Except educational level, alcohol consumption, IPR, ALT, AST, TG, HDL-C, other variations were observed across the quartiles in terms of age, gender, ethnicity, marital status, smoking status, SBP, DBP, eGFR, FBG, HbA1c, TC, LDL-C, diabetes, and CKD, all of which were statistically significant (p-value < 0.05). When comparing the lowest NLR quartile to the higher ones, there was a progressive increase in the likelihood of CVD mortality (Q1: 114 deaths, 2.60%; Q2: 165 deaths, 3.77%; Q3: 162 deaths, 3.88%; Q4: 284 deaths, 6.80%; p-value < 0.0001), and all-cause mortality (Q1: 482 deaths, 11.47%; Q2: 599 deaths, 13.76%; Q3: 583 deaths, 13.62%; Q4: 954 deaths, 23.00%; p-value < 0.0001). This indicates a clear rising trend of mortality risks with increasing NLR scores in MetS.
Table 1.
Basic characteristics of participants by NLR
| Variable | Total (n = 13,156) | Q1 (n = 3,358) | Q2 (n = 3,442) | Q3 (n = 3,069) | Q4 (n = 3,287) | P value |
|---|---|---|---|---|---|---|
| NLR | 2.24(2.21,2.27) | 1.20(1.19,1.21) | 1.77(1.76,1.77) | 2.30(2.29,2.31) | 3.56(3.51,3.61) | < 0.0001 |
| Age, years old | 52.11(51.70,52.52) | 50.99(50.33,51.65) | 51.44(50.82,52.07) | 50.80(50.02,51.58) | 54.96(54.27,55.64) | < 0.0001 |
| Sex, % | 0.01 | |||||
| Female | 7217(54.86) | 1959(55.31) | 1947(53.57) | 1663(53.59) | 1648(49.46) | |
| Male | 5939(45.14) | 1399(44.69) | 1495(46.43) | 1406(46.41) | 1639(50.54) | |
| Race/ethnicity, % | < 0.0001 | |||||
| Non-Hispanic White | 6071(46.15) | 1117(59.86) | 1492(69.48) | 1557(73.79) | 1905(78.63) | |
| Non-Hispanic Black | 2202(16.74) | 964(16.35) | 533(7.97) | 364(5.93) | 341(4.71) | |
| Mexican American | 2736(20.8) | 661(9.35) | 809(10.37) | 679(9.14) | 587(7.18) | |
| Others | 2147(16.32) | 616(14.45) | 608(12.18) | 469(11.15) | 454(9.48) | |
| Educational level, % | 0.07 | |||||
| Above high school | 5631(42.84) | 1428(52.00) | 1452(50.79) | 1344(51.91) | 1407(50.92) | |
| High school or equivalent | 3299(25.1) | 803(25.31) | 861(27.92) | 755(27.95) | 880(29.50) | |
| Under high school | 4214(32.06) | 1125(22.69) | 1128(21.29) | 968(20.15) | 993(19.58) | |
| Marital status, % | 0.01 | |||||
| Married | 7195(55.65) | 1831(60.04) | 1922(61.56) | 1711(59.36) | 1731(57.26) | |
| Never married | 1444(11.17) | 400(11.90) | 317(9.23) | 360(12.29) | 367(11.23) | |
| Other | 4290(33.18) | 1070(28.06) | 1139(29.21) | 944(28.35) | 1137(31.51) | |
| Smoking status, % | < 0.001 | |||||
| No | 6564(50.54) | 1789(52.91) | 1785(49.14) | 1532(50.74) | 1458(45.62) | |
| Yes | 6424(49.46) | 1527(47.09) | 1614(50.86) | 1487(49.26) | 1796(54.38) | |
| Alcohol user, % | 0.13 | |||||
| No | 2044(17.19) | 562(15.24) | 562(13.13) | 460(13.21) | 460(12.76) | |
| Yes | 9849(82.81) | 2481(84.76) | 2540(86.87) | 2316(86.79) | 2512(87.24) | |
| IPR | 2.85(2.78,2.91) | 2.79(2.69,2.89) | 2.91(2.82,3.00) | 2.86(2.77,2.96) | 2.81(2.73,2.90) | 0.1 |
| BMI | 33.33(33.15,33.51) | 32.55(32.24,32.86) | 33.05(32.76,33.33) | 33.84(33.52,34.17) | 33.82(33.46,34.17) | < 0.0001 |
| SBP, mmHg | 128.85(128.39,129.32) | 128.67(127.74,129.61) | 127.95(127.26,128.64) | 128.67(127.69,129.65) | 130.07(129.23,130.92) | 0.001 |
| DBP, mmHg | 74.33(73.89,74.77) | 75.40(74.75,76.05) | 74.55(73.95,75.15) | 74.40(73.71,75.10) | 73.11(72.39,73.83) | < 0.0001 |
| ALT, U/L | 29.78(29.06,30.49) | 30.43(29.52,31.34) | 30.41(29.53,31.29) | 29.24(28.33,30.15) | 29.08(26.97,31.19) | 0.16 |
| AST, U/L | 26.35(25.93,26.78) | 26.90(26.35,27.45) | 26.61(25.95,27.27) | 25.79(25.19,26.39) | 26.14(25.01,27.28) | 0.05 |
| eGFR, mL/min/1.73m2 | 89.10(88.53,89.67) | 91.45(90.53,92.37) | 90.47(89.57,91.37) | 90.44(89.42,91.45) | 84.48(83.41,85.55) | < 0.0001 |
| Fast glucose, mmol/L | 6.77(6.70,6.85) | 6.55(6.42,6.69) | 6.69(6.56,6.81) | 6.77(6.64,6.91) | 7.03(6.87,7.18) | < 0.0001 |
| HbA1c, % | 6.05(6.02,6.08) | 6.04(5.98,6.09) | 6.00(5.95,6.06) | 6.04(5.97,6.10) | 6.12(6.06,6.18) | 0.03 |
| Triglyceride, mmol/L | 2.70(2.65,2.74) | 2.77(2.68,2.86) | 2.73(2.64,2.82) | 2.69(2.62,2.76) | 2.61(2.51,2.71) | 0.1 |
| Total cholesterol, mmol/L | 5.22(5.19,5.25) | 5.37(5.31,5.43) | 5.28(5.23,5.34) | 5.21(5.16,5.26) | 5.03(4.97,5.10) | < 0.0001 |
| HDL-C, mmol/L | 1.08(1.08,1.09) | 1.09(1.07,1.10) | 1.09(1.07,1.10) | 1.08(1.06,1.09) | 1.09(1.07,1.10) | 0.52 |
| LDL-C, mmol/L | 3.07(3.03,3.10) | 3.21(3.14,3.28) | 3.13(3.06,3.20) | 3.09(3.03,3.15) | 2.87(2.81,2.93) | < 0.0001 |
| DM, % | < 0.0001 | |||||
| No | 8089(61.49) | 2114(71.37) | 2222(71.33) | 1920(68.90) | 1833(61.60) | |
| Yes | 5067(38.51) | 1244(28.63) | 1220(28.67) | 1149(31.10) | 1454(38.40) | |
| CKD, % | < 0.0001 | |||||
| No | 1809(13.82) | 298(7.50) | 416(9.59) | 383(10.54) | 712(15.81) | |
| Yes | 11,281(86.18) | 3043(92.50) | 3008(90.41) | 2672(89.46) | 2558(84.19) | |
| CVD, % | < 0.0001 | |||||
| No | 10,661(82.44) | 2866(87.60) | 2868(86.55) | 2494(86.73) | 2433(79.88) | |
| Yes | 2271(17.56) | 443(12.40) | 510(13.45) | 510(13.27) | 808(20.12) | |
| CVD mortality, % | < 0.0001 | |||||
| No | 12,431(94.49) | 3244(97.40) | 3277(96.23) | 2907(96.12) | 3003(93.20) | |
| Yes | 725(5.51) | 114(2.60) | 165(3.77) | 162(3.88) | 284(6.80) | |
| All-cause mortality, % | < 0.0001 | |||||
| No | 10,538(80.1) | 2876(88.53) | 2843(86.24) | 2486(86.38) | 2333(77.00) | |
| Yes | 2618(19.9) | 482(11.47) | 599(13.76) | 583(13.62) | 954(23.00) |
* NLR, Neutrophil-to-lymphocyte ratio; IPR, Family income-to-poverty ratio; BMI, Body mass index; SBP, Systolic blood pressure; DBP: Diastolic blood pressure; ALT, Alanine aminotransferase; AST, Aspartate aminotransferase; eGFR, Glomerular filtration rate; FBG, Fasting blood glucose; HbA1c, Glycated hemoglobin; HDL-C, High-density lipoprotein-cholesterol; LDL-C, Low-density lipoprotein-cholesterol; CKD, Chronic kidney disease; CVD, Cardiovascular diseases
Associations between NLR and CVD mortality in MetS
Table 2 showed a positive correlation between an elevated NLR and an increased risk of CVD mortality. This relationship existed in both the crude analysis (hazard ratio (HR) = 1.244, 95% CI 1.185–1.307, p-value < 0.0001) and the minimally adjusted model (HR = 1.210, 95% CI 1. 145–1. 278, p-value < 0.0001). After comprehensive adjustment, the link between NLR and CVD mortality was still evident (HR = 1.160, 95% CI 1.090–1.234, p-value < 0.0001), indicating that each increment in WWI was correlated with a 16.0% rise in the risk of CVD mortality. When NLR was stratified into quartiles, the fully adjusted models revealed that individuals in the top NLR quartile had a notably higher risk of CVD mortality by 144.7% compared to those in the lowest quartile (HR = 2.447, 95% CI 1. 561–3.836, p-value < 0.0001). RCS analysis revealed a non-linear association between NLR and CVD mortality (p for nonlinear = 0.02) (Fig. 2, Table S1).
Table 2.
Association between NLR and mortality in MetS population
| Character | Crude model | Model 1 | Model 2 | |||
|---|---|---|---|---|---|---|
| HR 95%CI | P value | HR 95%CI | P value | HR 95%CI | P value | |
| CVD mortality | ||||||
| NLR | 1.244(1.185,1.307) | < 0.0001 | 1.210(1.145,1.278) | < 0.0001 | 1.160(1.090,1.234) | < 0.0001 |
| NLR category | ||||||
| NLR-Q1 | ref | ref | ref | |||
| NLR-Q2 | 1.515(1.123,2.042) | 0.007 | 1.361(1.023,1.810) | 0.034 | 1.604(1.026,2.509) | 0.038 |
| NLR-Q3 | 1.567(1.104,2.223) | 0.012 | 1.430(1.011,2.024) | 0.043 | 1.479(0.947,2.309) | 0.085 |
| NLR-Q4 | 3.047(2.230,4.163) | < 0.0001 | 2.278(1.664,3.119) | < 0.0001 | 2.447(1.561,3.836) | < 0.0001 |
| p for trend | < 0.0001 | < 0.0001 | 0.003 | |||
| All-cause mortality | ||||||
| NLR | 1.223(1.172,1.276) | < 0.0001 | 1.180(1.121,1.243) | < 0.0001 | 1.144(1.086,1.206) | < 0.0001 |
| NLR category | ||||||
| NLR-Q1 | ref | ref | ref | |||
| NLR-Q2 | 1.250(1.070,1.460) | 0.005 | 1.139(0.987,1.314) | 0.076 | 1.108(0.826,1.485) | 0.493 |
| NLR-Q3 | 1.242(1.064,1.450) | 0.006 | 1.150(0.997,1.326) | 0.055 | 1.048(0.800,1.374) | 0.732 |
| NLR-Q4 | 2.323(1.998,2.702) | < 0.0001 | 1.800(1.558,2.080) | < 0.0001 | 1.531(1.188,1.972) | 0.001 |
| p for trend | < 0.0001 | < 0.0001 | < 0.001 | |||
* NLR, Neutrophil-to-lymphocyte ratio; CVD, Cardiovascular diseases; IPR, Family income-to-poverty ratio; BMI, Body mass index; SBP, Systolic blood pressure; DBP: Diastolic blood pressure; ALT, Alanine aminotransferase; AST, Aspartate aminotransferase; eGFR, Glomerular filtration rate; FBG, Fasting blood glucose; HbA1c, Glycated hemoglobin; TG, Triglyceride; TC, Total cholesterol,; HDL-C, High-density lipoprotein-cholesterol; LDL-C, Low-density lipoprotein-cholesterol; CKD, Chronic kidney disease. OR, Odds ratio; CI, Confidence interval; Ref, Reference
Crude model: univariate regression analyses. Model 1: adjusted for age, gender and race. Model 2: adjusted for age, gender and race, marriage, education, smoking status, alcohol user, IPR, BMI, SBP, DBP, eGFR, ALT, AST, FBG, HbA1c, TC, TG, HDL-C, LDL-C, diabetes, CVD, CKD
Fig. 2.
The association of NLR with mortality in MetS population. (A) CVD mortality; (B) all-cause mortality. Hazard ratios were adjusted for model 2
The KM curves revealed that individuals in the MetS cohort with NLR values in the lower quartile had significantly better cardiovascular disease-specific survival rates compared to those in the upper quartiles. Similarly, KM curve analysis revealed a reduction in survival rates for the group with elevated NLR in contrast to the group with lower NLR, with a statistically significant p-value of less than 0.0001, as depicted in Fig. 3. The threshold value of NLR that optimally predicted survival was identified as 2.41, which divided the participants into two distinct groups: a higher NLR group (NLR > 2.41, n = 4,273) and a lower NLR group (NLR = < 2.41, n = 8,883), as illustrated in Figure S1. Furthermore, the Cox proportional hazards regression analysis indicated a significant uptrend in CVD mortality risk for the group with higher NLR: Model 1 (HR 1.953, 95% CI 1.593–2.392, p-value < 0.0001), Model 2 (HR 1.588, 95% CI 1.299–1.942, p-value < 0.0001), Model 3 (HR 1.535, 95% CI 1.044–2.258, p-value = 0.03), as detailed in Table S2.
Fig. 3.
KM curve analysis of NLR in MetS population. (A) cardiovascular-specific survival with different quartiles of NLR; (B) cardiovascular-specific survival grouped by cutoff value of NLR; (C) overall survival with different quartiles of NLR; (D) overall survival grouped by cutoff value of NLR
A stratified analysis was performed to explore the correlation between NLR and CVD mortality, taking into account various factor such as age, sex, race, smoking status, alcohol consumption, diabetes and history of CVD and CKD. The study found that a positive correlation between the NLR and CVD mortality was consistently present across most subgroups, with the exception of non-drinkers, non-Hispanic black, Mexican American, and other ethnicities (Fig. 4). To confirm the reliability of the findings, a sensitivity analysis was performed, the correlation between NLR and CVD mortality in MetS was found to be statistically robust (Table S3-S4).
Fig. 4.
Stratified analyses of NLR in MetS population. (A) associations between NLR and CVD mortality; (B) associations between NLR and all-cause mortality. Adjusted for model 2, using Group Q1 as a reference, the risk of mortality for Group Q4
Associations between NLR and all-cause mortality in MetS
Table 2 showed a positive correlation between an elevated NLR and an increased risk of all-cause mortality. This relationship existed in both the crude analysis (HR = 1.223, 95% CI 1. 172–1. 276, p-value < 0.0001) and the minimally adjusted model (HR = 1.180, 95% CI 1. 121–1. 243, p-value < 0.0001). After comprehensive adjustment, the link between NLR and CVD mortality was still evident (HR = 1.144, 95% CI 1. 086–1. 206, p-value < 0.0001), indicating that each increment in WWI was correlated with a 14.4% rise in the risk of all-cause mortality. When NLR was stratified into quartiles, the fully adjusted models revealed that individuals in the top NLR quartile had a notably higher risk of CVD mortality by 53.1% compared to those in the lowest quartile (HR = 1.531, 95% CI 1. 188–1.972, p-value = 0.001). RCS analysis revealed a positive linear association between NLR and all-cause mortality (p for nonlinear > 0.05) (Fig. 2).
The KM curves revealed that individuals in the MetS cohort with NLR values in the lower quartile had significantly better overall survival rates compared to those in the upper quartiles. Similarly, KM curve analysis revealed a reduction in survival rates for the group with elevated NLR in contrast to the group with lower NLR, with a statistically significant p-value of less than 0.0001, as depicted in Fig. 3. Furthermore, the Cox proportional hazards regression analysis indicated a significant uptrend in all-cause mortality risk for the group with higher NLR: Model 1 (HR 1.802, 95% CI 1.615–2.010, p-value < 0.0001), Model 2 (HR 1.506, 95% CI 1.355–1.675, p-value < 0.0001), Model 3 (HR 1.366, 95% CI 1.144–1.632, p-value = < 0.001), as detailed in Table S2.
A stratified analysis was performed to explore the correlation between NLR and all-cause mortality, taking into account various factor such as age, sex, race, smoking status, alcohol consumption, diabetes and history of CVD and CKD. The study found that a positive correlation between the NLR and CVD mortality was consistently present across most subgroups, with the exception of non-drinkers, non-smokers, non-Hispanic black, Mexican American, and other ethnicities (Fig. 4). The sensitivity analysis revealed that the relationship between the NLR and all-cause mortality among individuals with MetS was statistically robust, as demonstrated in Tables S3 and S4.
The predictive ability of NLR for mortality in MetS
The time-varying ROC curve analysis demonstrated that the AUC for NLR was 0.650, 0.716, and 0.645 for predicting CVD mortality at 3, 5, and 10 years, respectively. Similarly, for all-cause mortality, the AUC for NLR was 0.746, 0.688, and 0.635 over the same time frames (Fig. 5). These findings suggested that the predictive efficacy of NLR for mortality remains consistent over various durations. Furthermore, the study assessed the predictive value of lymphocyte and neutrophil counts individually for all-cause and CVD mortality in patients with MetS. The outcomes reveal that, regardless of the time span, the predictive power of individual lymphocyte and neutrophil counts was found to be less effective compared to that of the NLR (Figure S2).
Fig. 5.
Time-dependent ROC curves of the NLR for mortality in MetS population. (A) predicting CVD mortality; (B) predicting all-cause mortality; (C) AUC of cardiovascular mortality; (D) AUC of all-cause mortality
Mediation analysis of NLR for mortality in MetS
A mediation analysis was conducted to evaluate the mediating role of eGFR in the association between NLR and mortality from all causes as well as CVD. The eGFR was found to have a significant mediating effect on survival rates due to CVD (β = 0.0013, 95% CI 0.0009–0.0018, p-value < 0.0001) and from all causes (β = 0.0036, 95% CI 0.0028–0.0044, p-value < 0.0001). In conclusion, eGFR mediated 9.85% (95% CI 5.97% -16.00%) of the relationship between NLR and the risk of CVD mortality, and 9.86% (95% CI 7.61–12.00%) of the association with all-cause mortality, as illustrated in Fig. 6.
Fig. 6.
The mediating effect of eGFR on the relationship between NLR and survival. (A) cardiovascular death; (B) all-cause death. Adjusted for model 2
Discussion
This study was the first time to comprehensively analyze a positive correlation was identified between elevated NLR and CVD mortality and all-cause mortality in MetS populations. This association was consistent across crude, minimally, and comprehensively adjusted models. The highest NLR quartile demonstrated a notably increased risk of CVD mortality and all-cause mortality compared to the lowest quartile. KM curves confirmed better survival rates for those with lower NLR values, with a significant difference in survival rates between groups with elevated and lower NLR. Stratified analysis showed a consistent positive correlation between NLR and CVD mortality across most subgroups, with some exceptions. The study also evaluated the predictive ability of NLR for mortality, with the AUC indicating consistent predictive efficacy over 3, 5, and 10 years. The predictive power of individual lymphocyte and neutrophil counts was found to be less effective than that of the NLR. Finally, a mediation analysis revealed that eGFR significantly mediated the relationship between NLR and mortality, accounting for a substantial proportion of the risk associated with CVD and all-cause mortality. In summary, the study highlighted the importance of NLR as a prognostic marker for mortality in individuals with MetS, with eGFR playing a significant mediating role in this association.
Given its reliance on the counts of neutrophils and lymphocytes, the NLR offers the benefits of being cost-effective and commonly included in standard blood tests. An elevated neutrophil count and a reduced lymphocyte count are indicative of an active nonspecific inflammatory response and a potentially weakened immune system, respectively [22]. Acting as a composite marker that encompasses two opposing immune mechanisms, the NLR is recognized for its superior predictive capabilities compared to the assessment of neutrophils or lymphocytes in isolation [23]. Research has shown that immune system activation and chronic inflammation are integral to the progression of MetS [24–26]. However, there was limited research on the relationship between the NLR and MetS and had some controversy exists. A study conducted on an Asian Indian population has shown that the NLR was positively correlated with MetS and the number of its metabolic abnormalities, even after adjusting for age, gender, and body mass index [27]. Another study conducted among obese children did not find such a correlation [28]. Interestingly, it has been verified that an increased NLR in MetS may be indicative of renal impairment and adverse cardiac remodeling [29]. Furthermore, the relationship between the NLR and mortality has been widely confirmed. Firstly, in the general population from United States, NLR was a biomarker associated with overall and cause-specific mortality in the general population, the HR for overall mortality per quartile increase in NLR was 1.14 [18]. In another study conducted among Chinese centenarians, a positive correlation between the NLR and mortality was also confirmed (HR = 1.05) [30]. A comprehensive, forward-looking cohort study within a community-based aging population in Rotterdam had determined that NLR levels were distinctly and positively linked to a heightened risk of all-cause mortality (HR = 1.64), as well as CVD mortality (HR = 1.92) [31]. Secondly, the relationship between the NLR and mortality also existed within specific populations. A recent cross-sectional study involving 3,251 subjects has suggested that an elevated NLR could be an independent predictor of mortality in diabetic patients [32]. This correlation has also been identified in individuals with hypertension, a long-term cohort study of 3,067 participants revealed that higher NLR values are linked to a greater risk of both cardiovascular and all-cause mortality [33]. Collectively, these findings suggest that the NLR holds promise as a prognostic indicator for mortality stratification among MetS patients.
Several potential mechanisms could explain the correlation observed between the NLR and mortality in patients with MetS. Firstly, NLR reflected the level of systemic inflammatory status, an elevated neutrophil count intensified ongoing inflammation, while a reduction in lymphocytes undermined the immune system’s defensive capabilities, this dynamic resulted in a weakened immune response and a diminished capacity to combat illnesses in affected individuals [34–36]. Secondly, factors contributing to MetS (including obesity, hyperlipidemia, and hypertension) all contributed to a pro-inflammatory state in the body, thereby increasing the release of inflammatory factors, including interleukin-6 and tumor necrosis factor [10], these factors not only directly cause oxidative stress damage to organs but also exacerbate endothelial damage by depleting NO, ultimately leading to the dysfunction of the cardiovascular system [37, 38]. Thirdly, consistent with previous studies, in the group with higher NLR, there was a noticeable increase in metabolic-related blood indicators, such as FBG, HbA1c, TC, LDL-C, it also exhibited lower kidney function and a higher prevalence of CVD. This suggested that NLR may mediate the adverse prognosis of MetS by affecting the aforementioned factors. Our study confirmed that NLR increased the mortality in patients with MetS through eGFR. The decline in eGFR served as a biomarker for more than just renal impairment; it also reflected a range of underlying pathological changes, included microvascular complications, and heightened oxidative stress, all of which are prevalent mechanisms in the progression of both cardiovascular and chronic kidney diseases, these factors cumulatively contribute to the risk of mortality [39]. Furthermore, diminished eGFR impacts the circulation’s capacity, which can result in arterial medial calcification and the enlargement of the heart muscle, consequently elevating the likelihood of cardiovascular death [40].
The strength of this study was its use of data from the NHANES, renowned for employing a stratified, multi-stage probability sampling approach. This rigorous sampling technique enhanced the study’s credibility and ensures that the findings were generalizable to the wider population. However, the study was not without its limitations. Firstly, the reliance on a single baseline measurement of NLR restricted our insight into the impact of changes in this index over time on the risk of mortality in MetS patients. It was important to consider the dynamic change of NLR and their potential influence on mortality. Secondly, while the study controlled for several known confounding factors, we recognized that there may be residual confounders that were not considered, including unmeasured environmental factors and diet. Finally, the findings of this study are derived from a cohort of MetS patients in the United States, it is imperative to conduct additional research to ascertain the generalizability of these conclusions to MetS individuals in different regions. These limitations should be taken into account when interpreting the study’s results and when designing future research.
Conclusion
In summary, our research analyzed 13,156 individuals with MetS from 11 cycles of NHANES and clarified a positive relationship of NLR with CVD and all-cause mortality risks. Consequently, we recommended the regular assessment of NLR in MetS populations as a potentially advantageous method for evaluating their risk of mortality.
Electronic supplementary material
Below is the link to the electronic supplementary material.
Acknowledgements
The authors thank the staff and the participants of the NHANES study for their valuable contributions.
Author contributions
ZT and MW conceived and designed the study and wrote the manuscript. MW analyzed the data. GM took the quality control of data and critically revised the manuscript. All authors read and approved the final manuscript.
Funding
The National Natural Science Foundation of China [82370346] and Young Scientists Fund of the National Natural Science Foundation of China [82000382] provided funding for this work.
Data availability
Publicly available datasets were analyzed in this study. This data can be found here: https://www.cdc.gov/nchs/nhanes/index.htm.
Declarations
Ethical approval
The studies involving human participants were reviewed and approved by the NCHS Research Ethics Review Board (ERB). All participants provided written informed consent.
Consent for publication
Not applicable.
Clinical trial number
Not applicable.
Competing interests
The authors declare no competing interests.
Footnotes
Publisher’s note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Contributor Information
Genshan Ma, Email: magenshan@hotmail.com.
Zaixiao Tao, Email: taozaixiao@163.com.
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Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Supplementary Materials
Data Availability Statement
Publicly available datasets were analyzed in this study. This data can be found here: https://www.cdc.gov/nchs/nhanes/index.htm.