{"id":1931,"date":"2025-12-25T12:01:23","date_gmt":"2025-12-25T03:01:23","guid":{"rendered":"https:\/\/lmi.jp\/articles\/?p=1931"},"modified":"2026-06-30T10:56:36","modified_gmt":"2026-06-30T01:56:36","slug":"associations-between-inflammatory-markers-and-all-cause-mortality-in-the-general-population-the-nagahama-study","status":"publish","type":"post","link":"https:\/\/lmi.jp\/articles\/2025\/12\/25\/associations-between-inflammatory-markers-and-all-cause-mortality-in-the-general-population-the-nagahama-study\/","title":{"rendered":"Associations between inflammatory markers and all-cause mortality in the general population: the Nagahama study."},"content":{"rendered":"\n

Aya Shoji-Asahina<\/strong><\/a>*1<\/sup>, Kazuya Setoh<\/a><\/strong>*2<\/sup>, Takahisa Kawaguchi<\/strong><\/a>*3<\/sup>, Takeo Nakayama<\/a>*1, 4<\/sup>, Fumihiko Matsuda<\/strong><\/a>*3<\/sup>, \u2020Yasuharu Tabara<\/a><\/strong>*1, 3<\/sup>  and the Nagahama Study Group<\/p>\n\n\n\n

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\u2003<\/span> Cite<\/span><\/i><\/i><\/span><\/summary>
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Shoji-Asahina A, Setoh K, Kawaguchi T, Nakayama T, Matsuda F, Tabara Y, the Nagahama Study Group. Associations between inflammatory markers and all-cause mortality in the general population: the Nagahama study. Lab Med Int 2025; 4(4): 105-113. doi: 10.51041\/lmi.4.4_105<\/p>\n<\/div><\/details>\n<\/div>\n\n\n\n

Original
Lab Med Int 2025; 4(4): 105-113<\/p>\n\n\n\n

\u2020\u0007Correspondence: Graduate School of Public Health, Shizuoka Graduate University of Public Health Kita-ando 4-27-2, Aoi-ku, Shizuoka 420-0881, Japan
Tel: +81-54-295-5400; Fax: +81-54-248-3520;
E-mail: tabara”@”s-sph.ac.jp
Received July 9, 2025; accepted July 28, 2025
*1 Graduate School of Public Health, Shizuoka Graduate University of Public Health, Shizuoka 420-8527, Japan
*2 Department of Epidemiology for Community Health and Medicine, Kyoto Prefectural University of Medicine, Kyoto 602-8566, Japan
*3 Center for Genomic Medicine, Kyoto University Graduate School of Medicine, Kyoto 606-8507, Japan
*4 Department of Health Informatics, Kyoto University School of Public Health, Kyoto 606-8501, Japan<\/strong><\/span><\/p>\n\n\n\n

Download PDF<\/span><\/a><\/div>\n\n\n\n

ABSTRACT<\/strong><\/h2>\n\n\n\n

Background:<\/strong> Inflammatory markers, especially C-reactive protein (CRP), have been reported to be associated with all-cause mortality. In addition, \u03b11-antitrypsin and white blood cell (WBC)-based inflammatory markers were suggested to represent mortality risk. We aimed to investigate whether simultaneous assessment of these markers was more useful for evaluating mortality risk than compared to individual assessment.
Methods:<\/strong> This longitudinal study included 5,970 Japanese community residents (mean age 62.9 years). Circulating levels of inflammatory markers were measured from baseline blood samples. All-cause mortality was ascertained by referring to the residential records.
Results:<\/strong> During a mean follow-up duration of 13.5 years, 550 deaths occurred. Kaplan\u2013Meier curves for mortality showed significant differences across the quintiles of each marker (log-rank test: P < 0.05). Results of the Cox proportional hazard model adjusted for potential covariates indicated that \u03b11-antitrypsin (fifth quintile: hazard ratio 1.73, P < 0.001) and CRP (fourth quintile: hazard ratio 1.49, P = 0.001; fifth quintile: hazard ratio 1.33, P = 0.068) were significantly associated with mortality. Among the WBC-based markers, platelet-to-lymphocyte ratio (hazard ratio 1.38, P = 0.002), systemic immune\u2013inflammation index (hazard ratio 1.27, P = 0.014), and lymphocyte-to-monocyte ratio (hazard ratio 1.39, P = 0.035) showed significant associations. When these markers were included in the same model, \u03b11-antitrypsin, but not CRP, showed pronounced association, whereas the WBC-based markers showed significant but weak associations.
Conclusions:<\/strong> \u03b11-antitrypsin was identified as a good marker for long-term mortality risk assessment in the general population. Combining these markers might help identify high risk populations.<\/p>\n\n\n\n

\u3014Lab Med Int 2025; 4(4): 105-113\u3015<\/p>\n\n\n\n

Key Words<\/strong><\/h3>\n\n\n\n

white blood cell count, \u03b11-antitrypsin, C-reactive protein, all-cause mortality, general population<\/p>\n\n\n\n

I. Introduction<\/strong><\/h2>\n\n\n\n

Systemic inflammation is exaggerated in patients with cardiovascular diseases1)2)<\/sup> <\/strong>and cancers2)<\/strong><\/sup>. Among several peripheral blood markers for systemic inflammation, high-sensitivity C-reactive protein (hsCRP) is a well-investigated marker for indicating increased risk of cardiovascular3)<\/strong><\/sup> and all-cause mortalities4)-7)<\/strong><\/sup>. hsCRP is an acute-phase reactant, the production of which is stimulated by interleukin-6 released from activated immune cells1)<\/strong><\/sup> . A previous report revealed that hsCRP was associated with all-cause mortality in a general Japanese population aged 50 years or over8)<\/strong><\/sup>. \u03b11-antitrypsin (AAT), another acute-phase inflammatory marker, has also been reported to be associated with cardiovascular disease events9)-13)<\/strong><\/sup> and all-cause mortality8)<\/strong><\/sup> in general populations. AAT is a major serine protease inhibitor with broad-spectrum anti-inflammatory, immunomodulatory, and anti-infective tissue-repair functions14)<\/strong><\/sup>. In addition, several markers based on white blood cell (WBC) counts, such as neutrophil-to-lymphocyte ratio (NLR), have been suggested to indicate systemic inflammation and reported to be associated with the prognosis of certain cancers15)-16)<\/strong><\/sup>, sepsis18)<\/strong><\/sup>, and stroke19)<\/strong><\/sup>. NLR has also been reported to be associated with the severity of coronary artery disease patients20)<\/strong><\/sup>. Other WBC-based markers, including platelet-to-lymphocyte ratio (PLR), systemic immune\u2013inflammation index (SII), and lymphocyte-to-monocyte ratio (LMR), have also been suggested to be associated with a worse prognosis in cancer patients21)-23)<\/strong><\/sup>. Even in a general population, NLR, PLR, SII, and LMR were suggested to be associated with all-cause mortality24)-28)<\/strong><\/sup>.
Therefore, we hypothesized that the simultaneous assessment of conventional and WBC-based inflammatory markers may be more useful for mortality risk evaluation than individual assessment. Furthermore, previous findings on WBC-based markers have been focused on Western populations24)-26)28)<\/strong><\/sup>, with limited results in Asian populations27)29)<\/strong><\/sup>. To our knowledge, no reports have been published on Japanese cohorts. We aimed to investigate this hypothesis by the analysis of large-scale longitudinal study of a general Japanese population. <\/p>\n\n\n\n

II. Methods<\/strong><\/h2>\n\n\n\n

Study population<\/em><\/strong>
We analyzed the data of the Nagahama Study8)30)<\/strong><\/sup>, an ongoing longitudinal study based on community residents of Nagahama City, Japan, located in central Japan with approximately 113,000 inhabitants in 2024. Participants of the Nagahama Study were recruited at a baseline survey performed between 2008 and 2010. Nagahama City residents aged 30\u201386 years who were living independently without physical impairment or dysfunction were eligible to participate. Of the baseline population (N = 9,764), 5,970 were ultimately included in this study after excluding participants who met the following exclusion criteria; younger than 50 years (N = 3,736), using hemodialysis therapy (N = 4), pacemaker implantation (N = 11), having clinical values widely deviated from their distributions [platelet count
\u2265 550 \u00d7 109<\/sup> \/L (N = 1), NLR \u2265 9 (N = 5), gamma-glutamyl transferase (\u03b3GT) \u2265 500 IU\/L (N = 10), ALT \u2265 200 IU\/L (N = 2)], and incomplete measurement of required clinical values (N = 25).
All procedures were approved by the Ethics Committee of Kyoto University Graduate School of Medicine and the Nagahama Municipal Review Board. Written informed consent was obtained from all participants.<\/p>\n\n\n\n

All-cause mortality<\/em><\/strong>
All-cause mortality was identified by reviewing residential registry records managed by the Nagahama City Office. Participants who had relocated out of Nagahama City were censored. Follow-up period was calculated from participation in the baseline survey to the date of relocation, death or to current end of the follow-up period (March 31, 2024).<\/p>\n\n\n\n

Inflammatory markers<\/em><\/strong>
Serum levels of AAT and hsCRP were measured using a blood sample drawn at baseline in a commercial laboratory (SRL Inc., Tokyo, Japan) using the N-antiserum to Human Alpha-1-Antitrypsin Kit or N-Latex CRP II Kit (Siemens Healthcare Diagnostics, Munich, Germany). Other blood markers were measured using the same sera in another commercial laboratory (Medic Inc., Shiga, Japan). Blood cell counts and blood cell fractions were measured using an automated hematology analyzer (Sysmex XE\u20132100, Sysmex Corporation, Kobe, Japan) using the blood specimens drawn at baseline. NLR, PLR, SII, and LMR were calculated using the following formulas:<\/p>\n\n\n\n

NLR = neutrophil count\/lymphocyte count
PLR = platelet count\/lymphocyte count
SII = (neutrophil count \u00d7 platelet count)\/lymphocyte count
LMR = lymphocyte count\/monocyte count<\/p>\n\n\n\n

Basic clinical parameters<\/em><\/strong>
Other clinical parameters used in this study were obtained at baseline. Data on histories of cancers and cardiovascular diseases, medication use, and smoking and drinking habits were obtained using a structured questionnaire. Heavy drinking was defined as consuming \u2265 2 Go (men) or \u2265 1 Go (women) of alcohol per sitting. Go is a Japanese traditional liquor unit that corresponds to 22 g of ethanol. Blood pressure was measured twice after a few minutes of rest in a sitting position using a cuff-oscillometric device (HEM-9000AI; Omron Healthcare, Kyoto, Japan). The mean of two readings was used as the representative value.<\/p>\n\n\n\n

Statistical analysis<\/strong>
Values were expressed as means \u00b1 standard deviations, medians and interquartile ranges, or frequencies. The Student\u2019s t-test, analysis of variances, Mann\u2013Whitney U test and Kruskal\u2013Wallis test were used to assess group differences in numerical variables, whereas chi-squared tests were used to assess frequency differences. Spearman\u2019s rank correlation coefficient was used to examine correlations among numerical variables. Mortality rate was calculated per 10,000 person-years. Survival curves across quintiles of inflammatory markers were depicted using the Kaplan\u2013Meier method, and group differences in the survival curves were assessed using the log-rank test.
We first used Cox proportional hazards models to identify factors independently associated with all-cause mortality. Each model included quintiles of one WBC-based marker and was adjusted for standard covariates, including age, sex, body mass index, current smoking, heavy drinking, history of cancer, history of cardiovascular disease, mean blood pressure, hemoglobin A1c, high-density lipoprotein cholesterol, low-density lipoprotein cholesterol, creatinine, albumin, alanine aminotransferase, and gamma-glutamyl transferase. Based on the results of these initial analyses, we then conducted Cox proportional hazards models that included the most abnormal quintiles of the statistically significant WBC-based markers, in order to evaluate their associations after further adjustment for serum inflammatory markers. The inflammatory markers included in this analysis were the fifth quintile (Q5) of AAT and a high hsCRP group, defined as either the fourth (Q4) or fifth (Q5) quintile. Finally, to provide clinically interpretable results, we conducted additional Cox proportional hazards analyses combining the high levels of these statistically significant inflammatory and WBC-based markers without adjusting for other covariates, in order to calculate crude hazard ratios. To account for multicollinearity, each WBC-based marker was included in the model separately. The results are presented as hazard ratios (HRs) with 95% confidence intervals (CIs).
Statistical analyses were primarily conducted using JMP software, version 17.0.0 (SAS Institute, Cary, NC, USA). In addition, restricted cubic spline analyses using the Cox proportional hazards model were performed in R software, version 4.5.1, with the survival and rms packages. P-values <0.05 were considered significant.<\/p>\n\n\n\n

Table 1<\/strong> Baseline clinical characteristics of the study participants (N = 5,970)<\/p>\n\n\n\n

\"\"<\/figure>\n\n\n\n

Values are mean \u00b1 standard deviation, median and interquartile range, or frequency. Statistical significance
was assessed by the analysis of variance or the Chi-squared test. Cardiovascular diseases include symptomatic
stroke, angina pectoris, and myocardial infarction. NLR, neutrophil-to-lymphocyte ratio; PLR, platelet-to-lymphocyte
ratio; SII, systemic immune\u2013inflammation index; LMR, lymphocyte-to-monocyte ratio.<\/p>\n\n\n\n

III. Results<\/strong><\/h2>\n\n\n\n

The mean age of the study participants was 62.9 \u00b1 6.4 years, and 35.4% were men. During a mean follow-up duration of 13.5 years, 550 deaths were observed. Supplementary Figure 1 illustrates cubic splines showing the association between baseline WBC-based markers or serum inflammatory markers and the HRs for all-cause mortality. The baseline clinical characteristics of the study participants are summarized in Table 1 <\/strong>separately for cases of death and survival. The death group was older, included more men and smokers, and had a higher prevalence of cancer and cardiovascular disease at baseline. hsCRP, AAT, NLR and SII were significantly higher, and LMR was significantly lower in the death group. Clinical characteristics of study populations in studies that investigated the prognostic significance of WBC-based markers are summarized in Supplementary Table 1<\/strong>. Our study population had a relatively small body size, a low frequency of smokers and patients with diabetes, and a low mortality rate. In addition, levels of WBC-based markers in this population were better than those in other populations.
Table 2<\/strong> shows mortality rates by the quintiles of each inflammatory marker. Differences in the survival curves among the quintiles (Figure 1<\/strong>) were statistically significant for all markers. Because the clinical characteristics differed significantly among the quintiles (Supplementary Tables 2-7<\/strong>), a covariate-adjusted Cox proportional hazards model analysis was performed to identify markers independently associated with all-cause mortality (Table 3<\/strong>). AAT showed marked association with mortality even when individuals with circulating AAT levels less than 100 mg\/dL (n = 130), possible cases of congenital AAT deficiency, were excluded from the analysis (Supplementary Table 8<\/strong>). Other markers, namely PLR, SII, LMR, and hsCRP, but not NLR, were also associated with mortality, although the significance was relatively weak. When the conventional inflammatory markers and one of the significant WBC-based inflammatory markers were included in the same model, PLR and SII were independently associated with all-cause mortality along with AAT (PLR [Q5] HR = 1.38, 95% CI: 1.13\u20131.69, P = 0.002, SII [Q5] HR = 1.27, 95% CI: 1.05\u20131.54. P = 0.014) (Table 4<\/strong>). hsCRP did not show significant association in any model. Figure 2<\/strong> shows crude HR for all-cause mortality by the combination of AAT and PLR or SII, indicating a stepwise association between the combined markers and all-cause mortality. For the combination of PLR and AAT, the highest HR was observed in the group with both high PLR and high AAT (HR = 2.74, 95% CI: 2.05\u20133.67, P < 0.001), followed by the group with high AAT only (HR = 2.20, 95% CI: 1.80\u20132.69, P < 0.001), and the group with high PLR only (HR = 1.17, 95% CI: 0.91\u20131.50, P = 0.232), using the group with neither high PLR nor high AAT as the reference. Similarly, for the combination of SII and AAT, the highest HR was observed in the group with both high SII and high AAT (HR = 2.89, 95% CI: 2.23\u20133.76, P < 0.001), followed by the group with high AAT only (HR = 2.23, 95% CI: 1.81\u20132.76, P < 0.001), and the group with high SII only (HR = 1.46, 95% CI: 1.14\u20131.87, P = 0.003), using the group with neither high SII nor high AAT as the reference.
Supplementary Figure 2<\/strong> shows changes in the adjusted HR of each marker with increasing follow-up duration. The HR of AAT was consistently significant, whereas those of other markers decreased gradually, indicating that the prognostic significance of each marker varied with follow-up duration.<\/p>\n\n\n\n

IV. Discussion<\/strong><\/h2>\n\n\n\n

In this longitudinal study of a large general population, we showed that conventional inflammatory markers (such as AAT) and WBC-based markers (such as PLR and SII) were independently associated with all-cause mortality. The HRs increased linearly with the combination of these markers.
PLR and SII were significantly associated with all-cause mortality. However, their HRs in the combination analysis with AAT were substantially smaller than that of AAT, which did not support PLR and SII use in population health practice, despite their ease of measurement. The lack of or weak associations was possibly due to our study population being relatively healthy, with low BMI, less frequent smoking and diabetes, and low mortality rates, compared with previous studies that reported the prognostic significance of WBC-based markers. In addition, NLR, PLR, and SII levels were lower in our study population. Although it is difficult to directly compare the results of our study with those of previous studies owing to large population differences including ethnicity, WBC-based markers may be useful in populations with more pronounced systemic inflammations.
In our previous analysis of the Nagahama study population, we found that AAT and hsCRP were independently associated with all-cause mortality8)<\/strong><\/sup>. However, in the current analysis of the same population with approximately 4 years of extended follow-up period, the HRs of hsCRP did not reach statistical significance when hsCRP and AAT were included in the same model. A reason for the discrepancy may lie in the gradual decreases in the HR of hsCRP in proportion to follow-up duration. The HRs of PLR, SII, and LMR also showed gradual decline, and a similar trend was observed in the analysis of NLR in the Rotterdam study24)<\/strong><\/sup> and the National Health and Nutrition Examination Survey25)<\/strong><\/sup>, indicating that hsCRP and WBC-based markers may be useful in predicting relatively short-term prognosis. In contrast, AAT was associated with all-cause mortality in a study with a follow-up of more than 10 years probably due to AAT indicating long-term persistent low-level inflammation31)<\/strong><\/sup>.
Several studies on the prognostic significance of WBC-based markers did not include CRP in the model, in order to examine the usefulness of WBC-based markers as a proxy for CRP15)22)23)25)27)-29)<\/strong><\/sup>. In our study, WBC-based markers did not show clear associations with mortality even when hsCRP and AAT were not included in the model, indicating a limited usability of WBC-based markers. In settings where it is easily measurable, such as Japan, CRP is preferred for the assessment of potential inflammation in a general population. Our results strongly suggest that AAT should also be emphasized as an inflammatory marker in addition to CRP.
A strength of this study was the large sample size and availability of various clinical measures, which allowed for a comparison of the prognostic significance of WBC-based and conventional inflammatory markers. However, several study limitations should be considered with caution in interpreting the results. First, we did not consider cause of death owing to the limited number of deaths. Given the results of previous studies, WBC-based markers may be closely associated with cancer and cardiovascular mortalities. Second, WBC differential counts were measured using an automated hematology analyzer, whereas the visual method is considered as an objective standard. However, any discrepancies in the counts due to the differences in measurement methods were small. The use of automated hematology analyzers may not significantly affect the present results.
In conclusion, AAT was identified as a good marker for long-term mortality risk assessment in the general population. Some of WBC-based inflammatory markers were associated with mortality, although the prognostic significance was limited when WBC-based inflammatory markers were considered individually. However, combining these markers with AAT might be useful for identifying populations at higher risk.<\/p>\n\n\n\n

Author Contributions<\/strong><\/h3>\n\n\n\n

AS:<\/strong> Conceptualization, Methodology, Formal analysis, Writing – Original Draft. KS:<\/strong> Supervision, Investigation, Data Curation. TK:<\/strong> Supervision, Investigation, Data Curation. TN:<\/strong> Supervision, Project administration, Funding acquisition. FM:<\/strong> Supervision, Project administration, Funding acquisition. YT:<\/strong> Conceptualization, Methodology, Investigation, Resources, Data Curation, Supervision, Project administration, Funding acquisition, Writing \u2013 review & editing.<\/p>\n\n\n\n

Acknowledgements<\/strong><\/h3>\n\n\n\n

We would like to thank all members of the Nagahama study group for their assistance in conducting the Nagahama study. We are very grateful to the Nagahama City Office and the non-profit organization, Zeroji Club, for their assistance in the Nagahama study. We would like to thank Editage (www.editage.jp) for English language editing.<\/p>\n\n\n\n

Conflict of Interest<\/strong><\/h3>\n\n\n\n

The authors have no conflicts of interest directly relevant to the content of this article.<\/p>\n\n\n\n

Financial Support<\/strong><\/h3>\n\n\n\n

The work was supported by a university grant, the Center of Innovation Program, the Global University Project from the Ministry of Education, Culture, Sports, Science and Technology of Japan (25293141, 26670313, 25253059, 26293198, 17H04182, 17H04126, 18K18450, 17H04123, 21H04850); the Practical Research Project for Rare\/Intractable Diseases (ek0109070, ek0109283, ek0109196, ek0109348), the Program for an Integrated Database of Clinical and Genomic Information (kk0205008), the Research and Development Grants for Dementia (dk0207006, dk0207027), the Practical Research Project for Lifestyle-related Diseases including Cardiovascular Diseases and Diabetes Mellitus (ek0210066, ek0210096, ek0210116), the Research Program for Health Behavior Modification by Utilizing IoT (le0110005, le0110013), the Research and Development Grants for Longevity Science (dk0110040) from the Japan Agency for Medical Research and Development (AMED), Takeda Medical Research Foundation, Mitsubishi Foundation, Daiwa Securities Health Foundation, and Sumitomo Foundation.<\/p>\n\n\n\n

Table 2<\/strong> Summary statistics of mortality rate by inflammatory marker quintile<\/p>\n\n\n\n

\"\"<\/figure>\n\n\n\n

The mortality rate is shown per 10,000 person-years.
NLR, neutrophil-to-lymphocyte ratio; PLR, platelet-to-lymphocyte ratio; SII, systemic immune\u2013inflammation
index; LMR, lymphocyte-to-monocyte ratio; hsCRP, high-sensitivity C-reactive protein; AAT, \u03b11-antitrypsin.<\/p>\n\n\n\n

\"\"<\/figure>\n\n\n\n

Figure 1 <\/strong>Kaplan-Meier curve for all-cause mortality. Statistical significance was assessed by log-rank test.
NLR: neutrophil-to-lymphocyte ratio, PLR: platelet-to-lymphocyte ratio, SII: systemic immune-inflammation index,
LMR: lymphocyte-to-monocyte ratio; hsCRP, high-sensitivity C-reactive protein; AAT, \u03b1 1-antitry<\/p>\n\n\n\n

Table 3<\/strong> Cox proportional hazards model analysis for all-cause
mortality across quintiles of inflammatory markers (N = 5,970)<\/p>\n\n\n\n

\"\"<\/figure>\n\n\n\n

Adjusted standard factors were age, sex, body mass index, current smoking, heavy drinking, history of cancer, history of cardiovascular disease, mean blood pressure, hemoglobin A1c, high-density lipoprotein cholesterol, low-density lipoprotein cholesterol, creatinine, albumin, alanine aminotransferase, and gamma-glutamyl transferase.
HR; hazard ratio; CI, confidence interval; NLR, neutrophil-to-lymphocyte ratio; PLR, platelet-to-lymphocyte ratio; SII, systemic immune\u2013inflammation index, LMR, lymphocyte-to-monocyte ratio; hsCRP, high-sensitivity C-reactive protein; AAT, \u03b11-antitrypsin.<\/p>\n\n\n\n

Table 4<\/strong> Cox proportional hazards model analysis of all-cause mortality (N = 5,970)<\/p>\n\n\n\n

\"\"<\/figure>\n\n\n\n

Adjusted factors were age, sex, body mass index, current smoking, heavy drinking, history of cancer, history of cardiovascular disease, mean blood pressure, hemoglobin A1c, high-density lipoprotein cholesterol, low-density lipoprotein cholesterol, creatinine, albumin, alanine aminotransferase, gamma-glutamyl transferase and AAT.
HR; hazard ratio; CI, confidence interval; NLR, neutrophil-to-lymphocyte ratio; SII, systemic immune\u2013inflammation index; hsCRP, high-sensitivity C-reactive protein; AAT, \u03b1 1-antitrypsin.<\/p>\n\n\n\n

\"\"<\/figure>\n\n\n\n

Figure 2<\/strong> Crude hazard ratio for all-cause mortality.
High \u03b1 1-antitrypsin (AAT): \u2265 146 mg\/dL; high platelet-to-lymphocyte ratio (PLR): \u2265 144; high systemic immuneinflammation index (SII): \u2265 495.<\/p>\n\n\n\n

References<\/strong><\/h2>\n\n\n\n
    \n
  1. Hansson GK. Inflammation, atherosclerosis, and coronary artery disease. N Engl J Med. 2005; 352(16):1685-95. doi: 10.1056\/NEJMra043430.NEJM<\/a><\/span>\n<\/li>\n\n\n\n
  2. Furman D, Campisi J, Verdin E, et al. Chronic inflammation in the etiology of disease across the life span. Nat Med. 2019; 25(12): 1822-32. doi: 10.1038\/s41591-019-0675-0.PubMed<\/a><\/span>\n<\/li>\n\n\n\n
  3. Kaptoge S, Di Angelantonio E, Lowe G, et al. C-reactive protein concentration and risk of coronary heart disease, stroke, and mortality: an individual participant meta-analysis. Lancet. 2010; 375(9709): 132-40. doi: 10.1016\/S0140-6736(09)61717-7.PubMed<\/a><\/span>\n<\/li>\n\n\n\n
  4. Zacho J, Tybjaerg-Hansen A, Nordestgaard BG. C-reactive protein and all-cause mortality–the Copenhagen City Heart Study. Eur Heart J. 2010; 31(13): 1624-32. doi: 10.1093\/eurheartj\/ehq103.PubMed<\/a><\/span>\n<\/li>\n\n\n\n
  5. Li Y, Zhong X, Cheng G, et al. Hs-CRP and all-cause, cardiovascular, and cancer mortality risk: A meta-analysis. Atherosclerosis. 2017; 259: 75-82. doi: 10.1016\/j.atherosclerosis.PubMed<\/a><\/span>\n<\/li>\n\n\n\n
  6. Maluf CB, Barreto SM, Giatti L, et al. Association between C reactive protein and all-cause mortality in the ELSA-Brasil cohort. J Epidemiol Community Health. 2020; 74(5): 421-7. doi: 10.1136\/jech-2019-213289.PubMed<\/a><\/span>\n<\/li>\n\n\n\n
  7. Kawamoto R, Kikuchi A, Niomiya D, et al. High-sensitivity C-reactive protein is a predictor of all-cause mortality in a rural Japanese population. J Clin Lab Anal. 2024; 38(4): e25015. doi: 10.1002\/jcla.25015.PubMed<\/a><\/span>\n<\/li>\n\n\n\n
  8. Tabara Y, Setoh K, Kawaguchi T, et al. Association between serum \u03b11-antitrypsin levels and all-cause mortality in the general population: the Nagahama study. Sci Rep. 2021; 11(1): 17241. doi: 10.1038\/s41598-021-96833-3.PubMed<\/a><\/span>\n<\/li>\n\n\n\n
  9. Engstr\u00f6m G, Lind P, Hedblad B, et al. Effects of cholesterol and inflammation-sensitive plasma proteins on incidence of myocardial infarction and stroke in men. Circulation. 2002; 105(22): 2632-7. doi: 10.1161\/01.cir.0000017327.69909.ff.PubMed<\/a><\/span>\n<\/li>\n\n\n\n
  10. Engstr\u00f6m G, Lind P, Hedblad B, et al. Lung function and cardiovascular risk: relationship with inflammation-sensitive plasma proteins. Circulation. 2002; 106(20): 2555-60. doi: 10.1161\/01.cir.0000037220.00065.0d.PubMed<\/a><\/span>\n<\/li>\n\n\n\n
  11. Engstr\u00f6m G, Lind P, Hedblad B, et al. Long-term effects of inflammation-sensitive plasma proteins and systolic blood pressure on incidence of stroke. Stroke. 2002; 33(12): 2744-9. doi: 10.1161\/01.str.0000034787.02925.1f.PubMed<\/a><\/span>\n<\/li>\n\n\n\n
  12. Engstr\u00f6m G, Stavenow L, Hedblad B, et al. Inflammation-sensitive plasma proteins, diabetes, and mortality and incidence of myocardial infarction and stroke: a population-based study. Diabetes. 2003; 52(2): 442-7. doi: 10.2337\/diabetes.52.2.442.PubMed<\/a><\/span>\n<\/li>\n\n\n\n
  13. Engstr\u00f6m G, Hedblad B, Stavenow L, et al. Fatality of future coronary events is related to inflammation-sensitive plasma proteins: a population-based prospective cohort study. Circulation. 2004; 110(1): 27-31. doi: 10.1161\/01.CIR.0000133277.88655.00.PubMed<\/a><\/span>\n<\/li>\n\n\n\n
  14. de Serres F, Blanco I. Role of alpha-1 antitrypsin in human health and disease. J Intern Med. 2014; 276(4): 311-35. doi: 10.1111\/joim.12239.PubMed<\/a><\/span>\n<\/li>\n\n\n\n
  15. Yamanaka T, Matsumoto S, Teramukai S, et al. The baseline ratio of neutrophils to lymphocytes is associated with patient prognosis in advanced gastric cancer. Oncology. 2007; 73(3-4): 215-20. doi: 10.1159\/000127412.PubMed<\/a><\/span>\n<\/li>\n\n\n\n
  16. Templeton AJ, McNamara MG, \u0160eruga B, et al. Prognostic role of neutrophil-to-lymphocyte ratio in solid tumors: a systematic review and meta-analysis. J Natl Cancer Inst. 2014; 106(6): dju124. doi: 10.1093\/jnci\/dju124.PubMed<\/a><\/span>\n<\/li>\n\n\n\n
  17. Dolan RD, Lim J, McSorley ST, et al. The role of the systemic inflammatory response in predicting outcomes in patients with operable cancer: Systematic review and meta-analysis. Sci Rep. 2017; 7(1): 16717. doi: 10.1038\/s41598-017-16955-5.PubMed<\/a><\/span>\n<\/li>\n\n\n\n
  18. Huang Z, Fu Z, Huang W, et al. Prognostic value of neutrophil-to-lymphocyte ratio in sepsis: A meta-analysis. Am J Emerg Med. 2020; 38(3): 641-7. doi: 10.1016\/j.ajem.2019.10.023.PubMed<\/a><\/span>\n<\/li>\n\n\n\n
  19. Song SY, Zhao XX, Rajah G, et al. Clinical significance of baseline neutrophil-to-lymphocyte ratio in patients with ischemic stroke or hemorrhagic stroke: An updated meta-analysis. Front Neurol. 2019; 10: 1032. doi: 10.3389\/fneur.2019.01032.PubMed<\/a><\/span>\n<\/li>\n\n\n\n
  20. Verdoia M, Schaffer A, Barbieri L, et al. Impact of diabetes on neutrophil-to-lymphocyte ratio and its relationship to coronary artery disease. Diabetes Metab. 2015; 41(4): 304-11. doi: 10.1016\/j.diabet.2015.01.001.PubMed<\/a><\/span>\n<\/li>\n\n\n\n
  21. Templeton AJ, Ace O, McNamara MG, et al. Prognostic role of platelet to lymphocyte ratio in solid tumors: a systematic review and meta-analysis. Cancer Epidemiol Biomarkers Prev. 2014; 23(7): 1204-12. doi: 10.1158\/1055-9965.EPI-14-0146.PubMed<\/a><\/span>\n<\/li>\n\n\n\n
  22. Hu B, Yang XR, Xu Y, et al. Systemic immune-inflammation index predicts prognosis of patients after curative resection for hepatocellular carcinoma. Clin Cancer Res. 2014; 20(23): 6212-22. doi: 10.1158\/1078-0432.CCR-14-0442.PubMed<\/a><\/span>\n<\/li>\n\n\n\n
  23. Porrata LF, Ristow K, Habermann TM, et al. Peripheral blood lymphocyte\/monocyte ratio at diagnosis and survival in nodular lymphocyte-predominant Hodgkin lymphoma. Br J Haematol. 2012; 157(3): 321-30. doi: 10.1111\/j.1365-2141.2012.09067.xPubMed<\/a><\/span>\n<\/li>\n\n\n\n
  24. Fest J, Ruiter TR, Groot Koerkamp B, et al. The neutrophil-to-lymphocyte ratio is associated with mortality in the general population: The Rotterdam Study. Eur J Epidemiol. 2019; 34(5): 463-70. doi: 10.1007\/s10654-018-0472-y.PubMed<\/a><\/span>\n<\/li>\n\n\n\n
  25. Song M, Graubard BI, Rabkin CS, et al. Neutrophil-to-lymphocyte ratio and mortality in the United States general population. Sci Rep. 2021; 11(1): 464. doi: 10.1038\/s41598-020-79431-7.PubMed<\/a><\/span>\n<\/li>\n\n\n\n
  26. Mathur K, Kurbanova N, Qayyum R. Platelet-lymphocyte ratio (PLR) and all-cause mortality in general population: insights from national health and nutrition education survey. Platelets. 2019; 30(8): 1036-41. doi: 10.1080\/09537104.2019.1571188.PubMed<\/a><\/span>\n<\/li>\n\n\n\n
  27. Li H, Wu X, Bai Y, et al. Physical activity attenuates the associations of systemic immune-inflammation index with total and cause-specific mortality among middle-aged and older populations. Sci Rep. 2021; 11(1): 12532. doi: 10.1038\/s41598-021-91324-x.PubMed<\/a><\/span>\n<\/li>\n\n\n\n
  28. Hua Y, Sun JY, Lou YX, et al. Monocyte-to-lymphocyte ratio predicts mortality and cardiovascular mortality in the general population. Int J Cardiol. 2023; 379: 118-26. doi: 10.1016\/j.ijcard.2023.03.016.PubMed<\/a><\/span>\n<\/li>\n\n\n\n
  29. Chan SHT, Yu T, Zhang Z, et al. Total and differential white blood cell count and cause-specific mortality in 436 750 Taiwanese adults. Nutr Metab Cardiovasc Dis. 2022; 32(4): 937-47. doi: 10.1016\/j.numecd.2021.11.004.PubMed<\/a><\/span>\n<\/li>\n\n\n\n
  30. Tabara Y, Yamada H, Setoh K, et al. The association between the Moyamoya disease susceptible gene RNF213 variant and incident cardiovascular disease in a general population: the Nagahama study. J Hypertens. 2021; 39(12): 2521-26. doi: 10.1097\/HJH.0000000000002964.PubMed<\/a><\/span>\n<\/li>\n\n\n\n
  31. Janciauskiene S, DeLuca DS, Barrecheguren M, et al. Serum levels of alpha1-antitrypsin and their relationship with COPD in the general spanish population. Arch Bronconeumol. 2020; 56(2): 76-83. doi: 10.1016\/j.arbres.2019.03.001.PubMed<\/a><\/span>\n<\/li>\n<\/ol>\n\n\n\n

    This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License (CC BY-NC-ND 4.0)<\/p>\n\n\n\n

    Supplementary materials<\/strong><\/h2>\n\n\n\n

    Associations between inflammatory markers and all-cause mortality in the general population: the Nagahama study<\/p>\n\n\n\n

    Corresponding author
    Yasuharu Tabara<\/p>\n\n\n\n

    Graduate School of Public Health, Shizuoka Graduate University of Public Health
    Kita-ando 4-27-2, Aoi-ku, Shizuoka 420-0881, Japan
    Tel: +81-54-295-5400, Fax: +81-54-248-3520
    E-mail: tabara”@”s-sph.ac.jp<\/p>\n\n\n\n

    Supplementary Table 1<\/strong>. Comparisons of participant characteristics in longitudinal studies on the prognostic significance of WBC-based inflammatory markers<\/p>\n\n\n\n

    \"\"<\/figure>\n\n\n\n

    The mortality rate is shown per 10,000 person-years. a Including past smoking. NHANES, National Health and Nutrition Examination Survey; BMI, body mass index; WBC, white blood cell; NLR, neutrophil-to-lymphocyte ratio; PLR, platelet-to-lymphocyte ratio; SII, systemic immune\u2013inflammation index; LMR, lymphocyte-to-monocyte ratio.<\/p>\n\n\n\n

    Supplementary Table 2<\/strong>.  Baseline clinical characteristics of participants by NLR quintile (N = 5,970)<\/p>\n\n\n\n

    \"\"<\/figure>\n\n\n\n

    Values are represented as the mean \u00b1 standard deviation, median [interquartile range], or frequency. Significance was assessed by analysis of variance or Chi-squared test. Cardiovascular disease (CVD) includes symptomatic stroke, angina pectoris, and myocardial infarction.
    NLR, neutrophil-to-lymphocyte ratio; BMI, body mass index; ALT, alanine aminotransferase; \u03b3-GT, gamma-glutamyl transferase; hsCRP, high-sensitivity C-reactive protein.<\/p>\n\n\n\n

    Supplementary Table 3<\/strong>. Baseline clinical characteristics of participants by PLR quintile (N = 5,970).<\/p>\n\n\n\n

    \"\"<\/figure>\n\n\n\n

    Values are represented as the mean \u00b1 standard deviation, median [interquartile range], or frequency. Significance was assessed by analysis of variance or Chi-squared test. Cardiovascular disease (CVD) includes symptomatic stroke, angina pectoris, and myocardial infarction.
    PLR, platelet-to-lymphocyte ratio; BMI, body mass index; ALT, alanine aminotransferase; \u03b3-GT, gamma-glutamyl transferase; hsCRP, high-sensitivity C-reactive protein.<\/p>\n\n\n\n

    Supplementary Table 4<\/strong>.  Baseline clinical characteristics of participants by SII quintile (N = 5,970).<\/p>\n\n\n\n

    \"\"<\/figure>\n\n\n\n

    Values are represented as the mean \u00b1 standard deviation, median [interquartile range], or frequency. Significance was assessed by analysis of variance or Chi-squared test. Cardiovascular disease (CVD) includes symptomatic stroke, angina pectoris, and myocardial infarction.
    SII, systemic immune-inflammation index; BMI, body mass index; ALT, alanine aminotransferase; \u03b3-GT, gamma-glutamyl transferase; hsCRP, high-sensitivity C-reactive protein.<\/p>\n\n\n\n

    Supplementary Table 5<\/strong>.  Baseline clinical characteristics of participants by LMR quintile (N = 5,970).<\/p>\n\n\n\n

    \"\"<\/figure>\n\n\n\n

    Values are represented as the mean \u00b1 standard deviation, median [interquartile range], or frequency. Significance was assessed by analysis of variance or Chi-squared test. Cardiovascular disease (CVD) includes symptomatic stroke, angina pectoris, and myocardial infarction.
    LMR, lymphocyte-monocyte ratio; BMI, body mass index; ALT, alanine aminotransferase; \u03b3-GT, gamma-glutamyl transferase; hsCRP, high-sensitivity C-reactive protein.<\/p>\n\n\n\n

    Supplementary Table 6<\/strong>.  Baseline clinical characteristics of participants by hsCRP quintile (N = 5,970)<\/p>\n\n\n\n

    \"\"<\/figure>\n\n\n\n

    Values are represented as the mean \u00b1 standard deviation, median [interquartile range], or frequency. Significance was assessed by analysis of variance or Chi-squared test. Cardiovascular disease (CVD) includes symptomatic stroke, angina pectoris, and myocardial infarction.
    hsCRP, high-sensitivity C-reactive protein; BMI, body mass index; ALT, alanine aminotransferase; \u03b3-GT, gamma-glutamyl transferase.<\/p>\n\n\n\n

    Supplementary Table 7<\/strong>.  Baseline clinical characteristics of participants by AAT quintile (N = 5,970).<\/p>\n\n\n\n

    \"\"<\/figure>\n\n\n\n

    Values are represented as the mean \u00b1 standard deviation, median [interquartile range], or frequency. Significance was assessed by analysis of variance or Chi-squared test. Cardiovascular disease (CVD) includes symptomatic stroke, angina pectoris, and myocardial infarction.
    BMI, body mass index; ALT, alanine aminotransferase; \u03b3-GT, gamma-glutamyl transferase; hsCRP, high-sensitivity C-reactive protein.<\/p>\n\n\n\n

    Supplementary Table 8<\/strong>.  Cox proportional hazard analysis for all-cause mortality excluding the patients with AAT < 100 mg\/dL (N = 5,840).<\/p>\n\n\n\n

    \"\"<\/figure>\n\n\n\n

    Adjusted standard factors were age, sex, body mass index, current smoking, heavy drinking, history of cancer, history of cardiovascular disease, mean blood pressure, hemoglobin A1c, high-density lipoprotein cholesterol, low-density lipoprotein cholesterol, creatinine, albumin, alanine aminotransferase, and gamma-glutamyl transferase.
    CI, confidence interval; AAT, \u03b11-antitrypsin.<\/p>\n\n\n\n

    \"\"<\/figure>\n\n\n\n

    Supplementary Figure 1. <\/strong>Spline curves of the associations between white blood cell\u2013based markers or serum inflammatory markers and the hazard ratio for all-cause mortality in the crude Cox proportional hazards model.
    Solid lines represent hazard ratios, and shaded areas indicate 95% confidence intervals of baseline. NLR, neutrophil-to-lymphocyte ratio; PLR, platelet-to-lymphocyte ratio; SII, systemic immune\u2013inflammation index; LMR, lymphocyte-to-monocyte ratio, hsCRP, high-sensitivity C-reactive protein; AAT, \u03b11-antitrypsin.<\/p>\n\n\n\n

    \"\"<\/figure>\n\n\n\n

    Supplementary Figure 2. <\/strong>Hazard ratios for all-cause mortality by follow-up period.
    Number of deaths were as follows; 0-2 years: 27; 2-4 years: 41, 4-6 years: 58, 6-8 years: 59, 8-10 years: 95, 10-12 years: 119, and after 12 years: 151. Adjusted factors were age, sex, body mass index, current smoking. Asterisks indicates a statistical significance. NLR, neutrophil-to-lymphocyte ratio; PLR, platelet-to-lymphocyte ratio; SII, systemic immune\u2013inflammation index; LMR, lymphocyte-to-monocyte ratio, hsCRP, high-sensitivity C-reactive protein; AAT, \u03b11-antitrypsin<\/p>\n\n\n\n

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