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Article(id=1241025206974739203, tenantId=1146029695717560320, journalId=1227665162245664772, issueId=1241025201983508979, articleNumber=null, orderNo=null, doi=10.20043/j.cnki.MPM.202410253, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1728748800000, receivedDateStr=2024-10-13, revisedDate=null, revisedDateStr=null, acceptedDate=null, acceptedDateStr=null, onlineDate=1773813066476, onlineDateStr=2026-03-18, pubDate=1744214400000, pubDateStr=2025-04-10, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1773813066476, onlineIssueDateStr=2026-03-18, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1773813066476, creator=13701087609, updateTime=1773813066476, updator=13701087609, issue=Issue{id=1241025201983508979, tenantId=1146029695717560320, journalId=1227665162245664772, year='2025', volume='52', issue='7', pageStart='1153', pageEnd='1344', issueExtLink='null', onlineDate='null', pubDate='null', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1773813065285, creator=13701087609, updateTime=1773815493878, updator=13701087609, preIssue=null, nextIssue=null, ext={EN=IssueExt(id=1241035388320543403, tenantId=1146029695717560320, journalId=1227665162245664772, issueId=1241025201983508979, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1241035388320543404, tenantId=1146029695717560320, journalId=1227665162245664772, issueId=1241025201983508979, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null}, startPage=1216, endPage=1220, ext={EN=ArticleExt(id=1241025207306089244, articleId=1241025206974739203, tenantId=1146029695717560320, journalId=1227665162245664772, language=EN, title=Study on the association between gaseous air pollutants and inflammatory indices in patients with schizophrenia, columnId=1228016570660745413, journalTitle=Modern Preventive Medicine, columnName=Environmental and Occupational Health, runingTitle=null, highlight=null, articleAbstract=
Objective

To explore the relationship between gaseous air pollutants and inflammatory indices in patients with schizophrenia (SCZ).

Methods

A total of 866 hospitalized SCZ patients were included from October 2020 to December 2023.The daily average exposure values of gaseous air pollutants (SO2, NO2, O3, CO) for the seven days prior to hospitalization were estimated using Kriging interpolation. Based on the biochemical test results on the day of admission, four inflammatory indices were calculated for the SCZ patients: Platelet to Lymphocyte Ratio (PLR), Neutrophil to Lymphocyte Ratio (NLR), Systemic Immune Inflammation Index (SII), and Systemic Inflammation Response Index (SIRI). Spearman correlation and multiple linear regression models were employed to analyze the association between individual gaseous air pollutants and inflammatory indices;Bayesian Kernel Machine Regression (BKMR) was used to assess the joint effects of mixed gaseous air pollutants on inflammatory indices.

Results

The daily average exposure values of SO2, NO2, and CO in the seven days prior to hospitalization showed a positive correlation with PLR（rSO-PLR=0.08;rNO-PLR= 0.069; rCO-PLR=0.078）、NLR（rSO-NLR=0.075; rNO-NLR= 0.077; rCO-NLR=0.069） and SII（rSO-SII=0.081; rNO-SII=0.072; rCO-SII=0.075）, with statistical significance (all P<0.05). The multiple linear regression results indicated a positive correlation between the daily average NO2 exposure and NLR levels, with β=0.006(95%CI: 0.001-0.011). The BKMR analysis revealed that when the daily averages of the four gaseous air pollutants exceeded their 50th percentile (P50) in the seven days prior to hospitalization, the mixture of SO2, NO2, CO, and O3 had a positive joint effect on the NLR and SII of SCZ patients.

Conclusion

Short-term exposure to a mixture of gaseous air pollutants may influence the inflammatory response in SCZ patients.

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目的

旨在探索气态空气污染物与精神分裂症（schizophrenia，SCZ）患者炎症指数的关系。

方法

纳入2020年10月—2023年12月866名入院SCZ患者。使用克里金插值法估计个体SCZ患者入院前7 d气态空气污染物（SO2、NO2、O3、CO）日均暴露值；根据入院当天血生化检查结果计算SCZ患者四种炎症指数水平：血小板与淋巴细胞数比值（platelet to lymphocyte ratio， PLR）、中性粒细胞数与淋巴细胞数比值（neutrophil to lymphocyte ratio， NLR）、全身免疫炎症指数（the systemic immune inflammation index， SII）和全身炎症反应指数（the systemic inflammation response index， SIRI）。采用斯皮尔曼相关、多元线性回归模型，分析单一气态空气污染物与炎症指数之间的关联；采用贝叶斯核机回归模型分析混合气态空气污染物对炎症指数的联合影响。

结果

入院前7 d的SO2、NO2、CO日均暴露值与PLR（rSO-PLR=0.08;rNO-PLR= 0.069; rCO-PLR=0.078）、NLR（rSO-NLR=0.075; rNO-NLR=0.077; rCO-NLR=0.069）、SII（rSO-SII=0.081; rNO-SII=0.072; rCO-SII=0.075）有正相关关系，相关性具有统计学意义（均为P<0.05）。多元线性回归结果显示，入院前7 d的NO2日均暴露值与NLR水平呈正相关，β为0.006(95%CI：0.001～0.011)。BKMR分析结果显示，入院前7 d四种气态空气污染物日均值均高于其第50分位数(P50)时，由SO2、NO2、CO和O3组成的混合气态污染物对于SCZ患者的NLR、SII具有正向联合效应影响。

结论

短期的气态空气污染物混合暴露可能影响SCZ患者的炎症反应。

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苏莉， E-mail:
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陈栢桦（2001—），男，硕士在读，研究方向：环境流行病学

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陈栢桦（2001—），男，硕士在读，研究方向：环境流行病学

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Clinical Interventions in Aging, 2021, 16: 1997-2007., articleTitle=The predictive role of systemic inflammation response index (SIRI) in the prognosis of stroke patients, refAbstract=null)], funds=[Fund(id=1241025216512586107, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241025206974739203, awardId=82360664; 82060615, language=CN, fundingSource=国家自然科学基金(82360664; 82060615), fundOrder=null, country=null)], companyList=[AuthorCompany(id=1241025211273900045, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241025206974739203, xref=1., ext=[AuthorCompanyExt(id=1241025211282288653, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241025206974739203, companyId=1241025211273900045, language=EN, country=null, province=null, city=null, postcode=null, companyName=null, departmentName=null, remark=School of Public Health, Guangxi Medical University, Nanning, Guangxi 530021, China), AuthorCompanyExt(id=1241025211290677262, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241025206974739203, companyId=1241025211273900045, language=CN, country=null, province=null, city=null, postcode=null, companyName=null, departmentName=null, remark=1.广西医科大学公共卫生学院，广西 南宁 530021)]), AuthorCompany(id=1241025211382951961, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241025206974739203, xref=2., ext=[AuthorCompanyExt(id=1241025211399729179, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241025206974739203, companyId=1241025211382951961, language=CN, country=null, province=null, city=null, postcode=null, companyName=null, departmentName=null, remark=2.广西壮族自治区中国-东盟公共卫生与健康大数据应用创新工程研究中心，广西 南宁 530021)])], figs=[ArticleFig(id=1241025215107494156, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241025206974739203, language=EN, label=Figure 1, caption=Analysis of the characteristics of the combined effect of mixed gaseous pollutants on inflammatory index NLR 7 d before admission., figureFileSmall=qdC61FBdKoDLCBSqTXKGKQ==, figureFileBig=r1wyxVpZlyuV3z3udLeUmA==, tableContent=null), ArticleFig(id=1241025215191380243, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241025206974739203, language=CN, label=图1, caption=入院前7d混合气态空气污染物对炎症指数NLR联合作用特征分析

注：a为SO2；b为NO2; c为O3；d为CO；图A为混合物对NLR的混合效应的估计值；图B为当其他污染物固定在P25， P50和P75时，单个污染物的单独效应；图C为h(污染物)-当其他污染物固定在P50时，单个污染物的暴露-反应关系；图D为污染物间的两两交互作用的估计值。

, figureFileSmall=qdC61FBdKoDLCBSqTXKGKQ==, figureFileBig=r1wyxVpZlyuV3z3udLeUmA==, tableContent=null), ArticleFig(id=1241025215401095459, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241025206974739203, language=EN, label=Figure 2, caption=Analysis of the characteristics of the combined effect of mixed gaseous pollutants on the inflammation index SII 7 days before admission, figureFileSmall=whKFJF4sD04abU3EFLBBXw==, figureFileBig=U59j7ZjclaLAtdJsp+WCqw==, tableContent=null), ArticleFig(id=1241025215501758761, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241025206974739203, language=CN, label=图2, caption=入院前7d混合气态空气污染物对炎症指数SII联合作用特征分析

注：a为SO2；b为NO2; c为O3；d为CO；图A为混合物对SII的混合效应的估计值；图B为当其他污染物固定在P25， P50和P75时，单个污染物的单独效应；图C为h(污染物)-当其他污染物固定在P50时，单个污染物的暴露-反应关系；图D为污染物间的两两交互作用的估计值。

, figureFileSmall=whKFJF4sD04abU3EFLBBXw==, figureFileBig=U59j7ZjclaLAtdJsp+WCqw==, tableContent=null), ArticleFig(id=1241025215602422065, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241025206974739203, language=EN, label=Table 1, caption=

Basic characteristics of the research subjects（n=866）

, figureFileSmall=null, figureFileBig=null, tableContent=
特征n (%) / （）
性别
女性666(76.91)
男性200(23.09)
年龄（岁）39.53±13.90
炎症指数
PLR174.32±99.08
NLR3.58±2.48
SII1 008.42±798.73
SIRI1.92±3.65
), ArticleFig(id=1241025215736639801, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241025206974739203, language=CN, label=表1, caption=

研究对象的基本特征（n=866）

, figureFileSmall=null, figureFileBig=null, tableContent=
特征n (%) / （）
性别
女性666(76.91)
男性200(23.09)
年龄（岁）39.53±13.90
炎症指数
PLR174.32±99.08
NLR3.58±2.48
SII1 008.42±798.73
SIRI1.92±3.65
), ArticleFig(id=1241025215921189185, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241025206974739203, language=EN, label=Table 2, caption=

Individual gas pollutant exposure values of the study subjects at 7 days before admission

, figureFileSmall=null, figureFileBig=null, tableContent=
暴露时点气态空气污染物范围
入院前7 dSO2(μg/m3)4.35～21.799.43±2.79
NO2(μg/m3)7.17～48.2917.76±6.95
O3(μg/m3)7.17～122.7943.69±25.8
CO(mg/m3)0.45～1.340.75±0.14
), ArticleFig(id=1241025216021852493, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241025206974739203, language=CN, label=表2, caption=

入院前7d四种气态空气污染物暴露值

, figureFileSmall=null, figureFileBig=null, tableContent=
暴露时点气态空气污染物范围
入院前7 dSO2(μg/m3)4.35～21.799.43±2.79
NO2(μg/m3)7.17～48.2917.76±6.95
O3(μg/m3)7.17～122.7943.69±25.8
CO(mg/m3)0.45～1.340.75±0.14
), ArticleFig(id=1241025216101544277, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241025206974739203, language=EN, label=Table 3, caption=

Correlation between four gaseous pollutants and inflammation index during the first 7 days of admission

, figureFileSmall=null, figureFileBig=null, tableContent=
变量PLRNLR SII SIRI SO2NO2O3CO
PLR1
NLR0.663**1
SII0.776**0.890**1
SIRI0.384**0.812**0.754**1
SO20.088**0.075*0.081*0.051
NO20.089**0.077*0.072*0.0310.631**1
O30.0450.0390.0560.0470.451**0.274**1
CO0.078*0.069*0.075*0.030.375**0.700**0.164**1
), ArticleFig(id=1241025216177041757, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241025206974739203, language=CN, label=表3, caption=

入院前7 d四种气态空气污染物与炎症指数相关性

, figureFileSmall=null, figureFileBig=null, tableContent=
变量PLRNLR SII SIRI SO2NO2O3CO
PLR1
NLR0.663**1
SII0.776**0.890**1
SIRI0.384**0.812**0.754**1
SO20.088**0.075*0.081*0.051
NO20.089**0.077*0.072*0.0310.631**1
O30.0450.0390.0560.0470.451**0.274**1
CO0.078*0.069*0.075*0.030.375**0.700**0.164**1
), ArticleFig(id=1241025216290287976, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241025206974739203, language=EN, label=Table 4, caption=

Relationship between pollutant exposure and inflammation index during the 7 d before admission

, figureFileSmall=null, figureFileBig=null, tableContent=
炎症指数空气污染物单因素模型多因素模型
β（95%CI）P值β（95%CI）P值
PLRSO20.01（0～0.021）0.0620.006(-0.004～0.017)0.243
NO20.004（0～0.008）0.0690.002(-0.002～0.007)0.253
O30（-0.001～0.002）0.4420(-0.001～0.001)0.693
CO0.233（0.016～0.449）0.0350.145(-0.069～0.36)0.184
NLRSO20.014（0.001～0.027）0.0380.013（-0.001～0.026）0.063
NO20.006（0.001～0.012）0.0180.006（0.001～0.011）0.030
O30.001（-0.001～0.002）0.2510.001（-0.001～0.002）0.419
CO0.266（-0.006～0.539）0.0560.235（-0.04～0.51）0.094
SIISO20.016（0.001～0.031）0.0410.013(-0.003～0.028)0.105
NO20.006（0～0.012）0.0480.005(-0.001～0.011)0.129
O30.001（0～0.003）0.1570.001(-0.001～0.002)0.473
CO0.339（0.023～0.654）0.0350.264(-0.053～0.58)0.103
SIRISO20.216（-0.672～1.103）0.6330.222(-0.673～1.117)0.627
NO20.13（-0.537～0.798）0.7020.119(-0.557～0.795)0.730
O3-0.214（-0.553～0.125）0.216-0.227(-0.575～0.121)0.201
CO0.008（-1.368～1.384）0.9910.013(-1.378～1.404)0.985
), ArticleFig(id=1241025216386756974, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241025206974739203, language=CN, label=表4, caption=

入院前7d空气污染物暴露与炎症指数关系

, figureFileSmall=null, figureFileBig=null, tableContent=
炎症指数空气污染物单因素模型多因素模型
β（95%CI）P值β（95%CI）P值
PLRSO20.01（0～0.021）0.0620.006(-0.004～0.017)0.243
NO20.004（0～0.008）0.0690.002(-0.002～0.007)0.253
O30（-0.001～0.002）0.4420(-0.001～0.001)0.693
CO0.233（0.016～0.449）0.0350.145(-0.069～0.36)0.184
NLRSO20.014（0.001～0.027）0.0380.013（-0.001～0.026）0.063
NO20.006（0.001～0.012）0.0180.006（0.001～0.011）0.030
O30.001（-0.001～0.002）0.2510.001（-0.001～0.002）0.419
CO0.266（-0.006～0.539）0.0560.235（-0.04～0.51）0.094
SIISO20.016（0.001～0.031）0.0410.013(-0.003～0.028)0.105
NO20.006（0～0.012）0.0480.005(-0.001～0.011)0.129
O30.001（0～0.003）0.1570.001(-0.001～0.002)0.473
CO0.339（0.023～0.654）0.0350.264(-0.053～0.58)0.103
SIRISO20.216（-0.672～1.103）0.6330.222(-0.673～1.117)0.627
NO20.13（-0.537～0.798）0.7020.119(-0.557～0.795)0.730
O3-0.214（-0.553～0.125）0.216-0.227(-0.575～0.121)0.201
CO0.008（-1.368～1.384）0.9910.013(-1.378～1.404)0.985
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气态空气污染物与精神分裂症患者炎症指数的关联研究
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陈栢桦 1 , 黄小兰 1 , 蓝雪宏 1 , 黎冬梅 1 , 苏莉 1, 2
现代预防医学 | 环境与职业卫生 2025,52(7): 1216-1220
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现代预防医学 | 环境与职业卫生 2025, 52(7): 1216-1220
气态空气污染物与精神分裂症患者炎症指数的关联研究
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陈栢桦1, 黄小兰1, 蓝雪宏1, 黎冬梅1, 苏莉1, 2
作者信息
  • 1.广西医科大学公共卫生学院，广西 南宁 530021
  • 2.广西壮族自治区中国-东盟公共卫生与健康大数据应用创新工程研究中心，广西 南宁 530021

    • 陈栢桦（2001—），男，硕士在读，研究方向：环境流行病学

通讯作者:

苏莉， E-mail:
Study on the association between gaseous air pollutants and inflammatory indices in patients with schizophrenia
Bai-hua CHEN1, Xiao-lan HUANG1, Xue-hong LAN1, Dong-mei LI1, Li SU1, 2
Affiliations
  • School of Public Health, Guangxi Medical University, Nanning, Guangxi 530021, China
出版时间: 2025-04-10 doi: 10.20043/j.cnki.MPM.202410253
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摘要
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目的

旨在探索气态空气污染物与精神分裂症（schizophrenia，SCZ）患者炎症指数的关系。

方法

纳入2020年10月—2023年12月866名入院SCZ患者。使用克里金插值法估计个体SCZ患者入院前7 d气态空气污染物（SO2、NO2、O3、CO）日均暴露值；根据入院当天血生化检查结果计算SCZ患者四种炎症指数水平：血小板与淋巴细胞数比值（platelet to lymphocyte ratio， PLR）、中性粒细胞数与淋巴细胞数比值（neutrophil to lymphocyte ratio， NLR）、全身免疫炎症指数（the systemic immune inflammation index， SII）和全身炎症反应指数（the systemic inflammation response index， SIRI）。采用斯皮尔曼相关、多元线性回归模型，分析单一气态空气污染物与炎症指数之间的关联；采用贝叶斯核机回归模型分析混合气态空气污染物对炎症指数的联合影响。

结果

入院前7 d的SO2、NO2、CO日均暴露值与PLR（rSO-PLR=0.08;rNO-PLR= 0.069; rCO-PLR=0.078）、NLR（rSO-NLR=0.075; rNO-NLR=0.077; rCO-NLR=0.069）、SII（rSO-SII=0.081; rNO-SII=0.072; rCO-SII=0.075）有正相关关系，相关性具有统计学意义（均为P<0.05）。多元线性回归结果显示，入院前7 d的NO2日均暴露值与NLR水平呈正相关，β为0.006(95%CI：0.001～0.011)。BKMR分析结果显示，入院前7 d四种气态空气污染物日均值均高于其第50分位数(P50)时，由SO2、NO2、CO和O3组成的混合气态污染物对于SCZ患者的NLR、SII具有正向联合效应影响。

结论

短期的气态空气污染物混合暴露可能影响SCZ患者的炎症反应。

气态空气污染物  /  炎症指数  /  精神分裂症
Objective

To explore the relationship between gaseous air pollutants and inflammatory indices in patients with schizophrenia (SCZ).

Methods

A total of 866 hospitalized SCZ patients were included from October 2020 to December 2023.The daily average exposure values of gaseous air pollutants (SO2, NO2, O3, CO) for the seven days prior to hospitalization were estimated using Kriging interpolation. Based on the biochemical test results on the day of admission, four inflammatory indices were calculated for the SCZ patients: Platelet to Lymphocyte Ratio (PLR), Neutrophil to Lymphocyte Ratio (NLR), Systemic Immune Inflammation Index (SII), and Systemic Inflammation Response Index (SIRI). Spearman correlation and multiple linear regression models were employed to analyze the association between individual gaseous air pollutants and inflammatory indices;Bayesian Kernel Machine Regression (BKMR) was used to assess the joint effects of mixed gaseous air pollutants on inflammatory indices.

Results

The daily average exposure values of SO2, NO2, and CO in the seven days prior to hospitalization showed a positive correlation with PLR（rSO-PLR=0.08;rNO-PLR= 0.069; rCO-PLR=0.078）、NLR（rSO-NLR=0.075; rNO-NLR= 0.077; rCO-NLR=0.069） and SII（rSO-SII=0.081; rNO-SII=0.072; rCO-SII=0.075）, with statistical significance (all P<0.05). The multiple linear regression results indicated a positive correlation between the daily average NO2 exposure and NLR levels, with β=0.006(95%CI: 0.001-0.011). The BKMR analysis revealed that when the daily averages of the four gaseous air pollutants exceeded their 50th percentile (P50) in the seven days prior to hospitalization, the mixture of SO2, NO2, CO, and O3 had a positive joint effect on the NLR and SII of SCZ patients.

Conclusion

Short-term exposure to a mixture of gaseous air pollutants may influence the inflammatory response in SCZ patients.

Gaseous air pollutants  /  Inflammatory indices  /  Schizophrenia
陈栢桦, 黄小兰, 蓝雪宏, 黎冬梅, 苏莉. 气态空气污染物与精神分裂症患者炎症指数的关联研究. 现代预防医学, 2025 , 52 (7) : 1216 -1220 . DOI: 10.20043/j.cnki.MPM.202410253
Bai-hua CHEN, Xiao-lan HUANG, Xue-hong LAN, Dong-mei LI, Li SU. Study on the association between gaseous air pollutants and inflammatory indices in patients with schizophrenia[J]. Modern Preventive Medicine, 2025 , 52 (7) : 1216 -1220 . DOI: 10.20043/j.cnki.MPM.202410253
正文
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精神分裂症（schizophrenia， SCZ）是一种严重的精神疾病，其影响着全球约2 400万人[1]，该病严重影响病人的躯体健康和社会功能。但目前，SCZ的病因尚不明确。一般认为SCZ的发生、发展是遗传因素与环境因素共同作用的结果[2]。近年来，对于SCZ遗传学的研究较多，genome-wide association studies（GWAS）发现多个与SCZ有关的危险基因[3-4]。相对于遗传学研究，关于环境，尤其是自然环境对于SCZ影响的研究较少。研究表明短期的环境空气污染会增加精神分裂症的门诊就诊和复发入院[5]。据报道，炎症过程和神经免疫相互作用参与了其发病机制，许多研究表明炎症标志物和细胞因子与SCZ具有潜在因果关系[2]。基因层面上，研究发现白细胞介素（ILs）在SCZ患者的背外侧前额叶中表达增加[6]；同时，研究发现在首发和复发的SCZ患者的外周血中发现促炎因子升高现象[7]，临床研究中也有针对炎症辅助治疗精神分裂症的研究[8]。炎症指数是一种新型的炎症指标，其优势为：（1）可以通过常规的炎症指标计算获得；（2）比单一炎症指标更全面地反映机体炎症反应和免疫水平。炎症指数与SCZ关联的报道指出炎症指数在SCZ的首发、复发预测和预后评价中存在巨大潜在价值[9-10]。有报道PM2.5和O3的长期暴露可能与肺结节的患病率显著相关，一些炎症指数可能在其中发挥了中介效应[11]。但暂未发现空气污染与SCZ患者炎症指数相关的研究。因此，本文从环境因素出发，探讨空气污染与SCZ炎症反应关系，探索影响SCZ发病、复发的新路径，为科学防治SCZ提供新依据。
1　对象与方法
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1.1　研究对象
本文病例来自2020年10月—2023年12月在广西壮族自治区脑科医院收治的SCZ患者，经纳入排除标准后，共纳入866名SCZ患者。纳入标准：（1）符合ICD-10诊断标准的SCZ患者；（2）入组前1个月无输血史；（3）采血前3个月之内未进行抗精神病治疗的SCZ患者。排除标准：（1）各种脑器质性疾病或躯体疾病所致精神障碍；（2）罹患严重的躯体疾病或神经系统疾病者；（3）酒精滥用或其他物质滥用者；（4）妊娠或哺乳期妇女。（5）无居住地址的病例。本次研究经过广西医科大学医学伦理委员会审核和批准（2023年KY0023号）。
1.2　研究方法
1.2.1　炎症指数计算
根据SCZ患者入院时的生化检查结果中白细胞、中性粒细胞数、淋巴细胞数和血小板数，计算以下四项炎症指数指标：
（1）血小板与淋巴细胞数比值(platelet to lymphocyte ratio，PLR)=血小板数/淋巴细胞数，PLR可放大血小板和淋巴细胞对疾病变化的预测作用，是全身炎症反应的标志。
（2）中性粒细胞数与淋巴细胞数比值(neutrophil to lymphocyte ratio，NLR)=中性粒细胞数/淋巴细胞数，NLR和身体的免疫状况以及炎症表达密切相关，中性粒细胞属于固有免疫细胞，反应非特异性免疫情况。淋巴细胞是机体适应性免疫的核心细胞成分，两者结合可以更加全面地反映机体的炎症状态。
（3）全身免疫炎症指数(the systemic immune inflammation index，SII)=血小板数×中性粒细胞数/淋巴细胞数，SII结合了外周血小板、中性粒细胞和淋巴细胞的综合参数，与单一的炎症指标相比更全面地反映了机体炎症状态。
（4）全身炎症反应指数(the systemic inflammation response index，SIRI)=单核细胞数×中性粒细胞数/淋巴细胞数。
1.2.2　气态空气污染物的个体暴露值计算
本文从中国环境监测总站（http://www.cnemc.cn/）收集全国监测站点的四种气态空气污染物（SO2、NO2、CO、O3）逐小时浓度。通过高德地图标准化提取地址，将SCZ患者的常住住址转换成具体的经纬度坐标（WGS-84坐标系）；使用克里斯金插值法将研究对象经纬度数据与暴露气态空气污染物数据进行衔接，匹配并估算入院前7 d的气态空气污染物日均暴露值。
1.2.3　统计学分析
关于研究人群的特征，连续型变量通过（）表示。采用斯皮尔曼相关分析方法，研究气态空气污染物与SCZ患者的四种炎症指数之间的关联；采用多元线性回归方法，以气态污染物为自变量，炎症指数为因变量研究气态空气污染物与SCZ患者的炎症指数的关系；采用贝叶斯核机模型研究气态污染物对SCZ患者炎症指数的联合效应。以上的分析使用软件为R 4.41，双侧检验，检验水准为α=0.05。
2　结果
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2.1　基本情况
本次研究对象以女性为主，多为青壮年人群，平均年龄为（39.53±13.9）岁。866名SCZ患者的炎症指数的水平，见表1。入院前7d四种气态空气污染物暴露值，见表2。
2.2　相关性分析
斯皮尔曼相关性分析结果显示，入院前7 d的SO2、NO2、CO的日均暴露值与PLR（;）、NLR（;）、SII（）有显著正相关关系，相关性具有统计学意义（均为P<0.05），见表3。SO2、NO2、CO、O3日均暴露值与SIRI值的相关性均无统计学意义（P>0.05）。
2.3　多元线性回归模型结果
采用多元线性回归模型分析SCZ患者入院前7 d气态空气污染物暴露值与炎症指数关系。未调整其他因素时，入院前7 d的CO与PLR和SII的β分别为0.233（95%CI：0.016～0.449）、0.339（95%CI：0.023～0.654）；NO2与NLR、SII的β分别为0.006（95%CI：0.001～0.012）和0.006（95%CI：0～0.012）；SO2与SII的β为0.016（95%CI：0.001～0.031）。纳入了协变量性别、年龄调整多元线性回归模型，结果显示，NO2与NLR的β为0.006(95%CI：0.001～0.011)。见表4。
2.4　混合气态污染物的联合作用
通过BKMR模型进行联合作用分析，结果显示，当入院前7 d四种气态空气污染物日均暴露值均高于其各自P50时，混合气态空气污染物对炎症指数NLR存在联合作用，呈正相关，见图1A。当入院前7 d四种气态空气污染物日均暴露值均高于其各自P60时，混合气态空气污染物对炎症指数SII存在联合作用、呈正相关。见图2A。混合气态空气污染物对于PLR和SIRI的联合作用和单独作用不存在显著性。
3　讨论
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本次研究中，通过2020年10月—2023年12月866名入院SCZ患者的个体气态空气污染物估计暴露值与其炎症指数构建了多元线性回归模型和BKMR模型。结果提示气态空气污染物对SCZ患者的炎症指数存在影响，暴露于气态空气污染可能会引起炎症指数的升高。SO2、NO2、O3与PLR、NLR、SII呈正相关；混合气态污染物增加可能升高NLR和SII水平。
线性回归模型结果的回归系数可以直观地解释单个气态空气污染物每变化一个单位时对炎症指数的影响程度，但面对混合污染物，往往采用多元线性回归模型。混合空气污染物之间可能存在高度的共线性，以及非线性、非相加和交互作用等复杂关系[12]，这时再使用多元线性回归模型可能导致预测结果与实际情况不符。BKMR模型被用于环境流行病学的“混合污染物”研究，已报道阐述该模型在混合空气污染物的适用性[13]。其通过先验分布结合样本数据得到后验分布来进行结果推断和预测，能够克服混合气态空气污染物间的多重共线性问题，因此更加可靠地评估混合气态污染物对炎症指数的影响。因此，本次研究采用多元线性回归模型探讨单一气态空气污染物对于炎症指数的影响，采用BKMR模型探索混合气态污染物对于炎症指数的联合效应。本次研究通过BKMR模型结果发现混合气态污染物在NLR和SII中存在正向联合作用。
既往研究表明，炎症指数对于疾病发生发展及其预后评价具有重要意义[14-15]。有关炎症指数在SCZ中的研究报道发现，SII和NLR等炎症指数与PANSS量表阳性评分呈正相关[16]；同时该研究也指出，从临床实际考虑，外周血常规检查结果较于核磁共振较为经济方便。综上，炎症指数应用于SCZ的优势有以下三点：（1）炎症指数被多次报道在疾病的诊断、复发预测和预后评价的价值；（2）相较于单一炎症指标，炎症指标能更为全面评价机体炎症和免疫应答情况；（3）炎症指数可通过血常规检查获得，经济方便。
本次的研究提示短期的气态空气污染物暴露可能影响SCZ炎症指数。气态空气污染物引起炎症反应有多种途径，一方面，空气污染物进入上呼吸道，刺激呼吸道上皮细胞，降低上皮细胞防御和改变免疫，为细菌入侵创造有利环境[17]，从而引起气道炎症反应。另一方面，气态空气污染物，深入肺部，可能激活肺神经反射和局部炎症，从而改变自主神经系统，诱导全身炎症和氧化应激[18]。此外，刺激性的空气污染物进入呼吸道会被呼吸道黏膜上皮细胞识别，进而激活固有免疫细胞释放白细胞介素- 8（IL-8）等趋化因子[19]，吸引更多的免疫细胞聚集到呼吸道黏膜处，启动局部的免疫反应。局部的炎症和免疫信号会通过血液或神经传递至神经中枢，影响神经发育。综上，暴露于气态空气污染可能引起中枢神经系统炎症和免疫反应，进而介导了SCZ，通过炎症指数可以更为全面地评价机体的炎症和免疫应答情况。
一项meta分析结果显示短期暴露于O3、NO2和SO2会引起CPR的升高水平升高，与本次研究中关注的炎症指标结果相似。对于气态污染的混合效应分析结果显示，入院前7d的污染物存在联合效应，而单个污染物对于组合炎症指标影响不存在显著效应，其原因可能是不同的污染物可能通过不同的生物学途径影响健康。在混合暴露的情况下，这些不同的生物学途径可能相互作用，导致健康效应的增强。各污染物之间不存在显著的交互作用，这与多项研究混合气体污染物交互作用的研究结果相似[13，20]。本次研究发现，入院前7d的空气污染物暴露与炎症指数SIRI相关关系不显著，SIRI目前多用于评价临床重大疾病的预后效果[21]，短期的空气污染物暴露对于SIRI的影响可能需要长期观察。
本次研究优势在于，利用克里金插值法对患者个体暴露值进行了估算；另外，以往对于气体污染物与炎症指标的研究往往局限于CRP，本次研究选择了具有预测和预后评价的炎症指数研究气态污染物与SCZ炎症关系。本研究也存在一定的局限性：（1）本次研究未控制平均温度、湿度等气象变量的影响，因而在全面认识气态空气污染物对SCZ炎症指数的影响存在局限；（2）本次研究对象来自广西壮族自治区脑科医院，多数纳入的SCZ患者来自与广西壮族自治区，本次研究结果得出关于SCZ患者的炎症指数与气态空气污染物关系可能无法全面、准确反映其他地区SCZ患者的实际情况。
综上，本研究显示短期的气态空气污染物暴露可能会升高SCZ患者的炎症水平。这提示有必要对空气污染采取干预措施，以降低SCZ的疾病负担。
基金
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  • 国家自然科学基金(82360664; 82060615)
文献
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2025年第52卷第7期
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doi: 10.20043/j.cnki.MPM.202410253
  • 接收时间：2024-10-13
  • 首发时间：2026-03-18
  • 出版时间：2025-04-10
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  • 收稿日期：2024-10-13
基金
国家自然科学基金(82360664; 82060615)
作者信息
    1.广西医科大学公共卫生学院，广西 南宁 530021
    2.广西壮族自治区中国-东盟公共卫生与健康大数据应用创新工程研究中心，广西 南宁 530021

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