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Article(id=1241522860473250796, tenantId=1146029695717560320, journalId=1227665162245664772, issueId=1241522846384583426, articleNumber=null, orderNo=null, doi=10.20043/j.cnki.MPM.202307244, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1689091200000, receivedDateStr=2023-07-12, revisedDate=null, revisedDateStr=null, acceptedDate=null, acceptedDateStr=null, onlineDate=1773931716316, onlineDateStr=2026-03-19, pubDate=1707494400000, pubDateStr=2024-02-10, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1773931716316, onlineIssueDateStr=2026-03-19, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1773931716316, creator=13701087609, updateTime=1773931716316, updator=13701087609, issue=Issue{id=1241522846384583426, tenantId=1146029695717560320, journalId=1227665162245664772, year='2024', volume='51', issue='3', pageStart='385', pageEnd='576', issueExtLink='null', onlineDate='null', pubDate='null', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1773931712956, creator=13701087609, updateTime=1773931842201, updator=13701087609, preIssue=null, nextIssue=null, ext={EN=IssueExt(id=1241523388544504301, tenantId=1146029695717560320, journalId=1227665162245664772, issueId=1241522846384583426, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1241523388544504302, tenantId=1146029695717560320, journalId=1227665162245664772, issueId=1241522846384583426, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null}, startPage=395, endPage=400, ext={EN=ArticleExt(id=1241522860955594764, articleId=1241522860473250796, tenantId=1146029695717560320, journalId=1227665162245664772, language=EN, title=A longitudinal study on the relationship between alcohol consumption and hyperuricemia: the mediating effect of blood lipid level, columnId=1240413921954295836, journalTitle=Modern Preventive Medicine, columnName=Epidemiology and Statistical Methods, runingTitle=null, highlight=null, articleAbstract=
Objective

To evaluate the relationship between alcohol consumption and hyperuricemia in natural population in southwest China, and to further explore the mediating effect of blood lipid level on the relationship between alcohol consumption and hyperuricemia.

Methods

Based on the baseline data (2018 to 2019) and the first-round follow-up data (2020 to 2021) of the natural population cohort study in southwest China, a multivariate Logistic regression model was used to analyze the association between alcohol consumption and hyperuricemia. The potential mediating effect of serum lipids was evaluated by causal mediation analysis.

Results

A total of 1 615 people were enrolled in this study, including 162 new cases of hyperuricemia. Each 10g/d increase in pure alcohol intake was positively correlated with the risk of hyperuricemia (OR=1.22, 95%CI: 1.08-1.37). High density lipoprotein (HDL-C) and triglyceride (TG) partially mediated the relationship between alcohol consumption and hyperuricemia, with mediating effects of 16.58% (95%CI: 5.64%-54.01%) and 6.98% (95% CI:1.12%-24.84%), respectively.

Conclusion

The increase of alcohol consumption can increase the risk of hyperuricemia. HDL-C and TG are important mediating factors between alcohol consumption and hyperuricemia. Our study shows that the occurrence and development of hyperuricemia can be prevented by reducing alcohol consumption and controlling blood lipids.

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目的

本研究旨在评估我国西南地区自然人群中饮酒量与高尿酸血症的关系，并进一步探讨血脂水平对饮酒量与高尿酸血症关联的中介作用。

方法

基于中国西南区域自然人群队列研究的基线数据（2018—2019年）和第一轮随访数据（2020—2021年），使用多因素logistic回归模型分析饮酒量和高尿酸血症的关联。采用因果中介分析评价血脂水平的潜在中介作用。

结果

共有1 615人纳入研究，其中有162人新发高尿酸血症。纯酒精摄入量每增加10 g/d与高尿酸血症发生风险呈正相关（OR=1.22，95%CI：1.08～1.37）。高密度脂蛋白（HDL-C）和甘油三酯（TG）部分介导了饮酒量与高尿酸血症之间的关系，中介效应分别占16.58%（95%CI：5.64%～54.01%）和6.98%（95%CI：1.12%～24.84%）。

结论

饮酒量的增加可导致高尿酸血症的发生风险升高，HDL-C和TG是饮酒量与高尿酸血症关联的重要中介因素。本研究表明，可以通过减少饮酒及有针对性地控制血脂，来预防高尿酸血症的发生发展。

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赵星，E-mail:
, copyrightStatement=本刊刊出的所有文章不代表中华预防医学会和本刊编委会的观点，除非特别声明。, copyrightOwner=中华预防医学会和四川大学华西公共卫生学院, extLink=null, articleAbsUrl=null, sourceXml=7r6svHmn6N/xQQaPmBiE1Q==, magXml=s8+AQEADwblQzsb2hr9jmA==, pdfUrl=null, pdf=YCFHxAqWCMoemDWZbu5tOw==, pdfFileSize=684061, pdfExtLink=null, richHtmlUrl=null, mobilePdfUrl=null, reviewReport=null, pdfFirstPage=null, abstractGraph=ANN2/muuY2xA1XEr6DRuZg==, abstractGraphContent=null, abstractVideo=null, citation=null, cebUrl=null, magXmlContent=uQAo5enTIdE5tUa0N3+sZw==, mapNumber=null, authorCompany=null, fund=null, authors=

陈柯君（1999—），女，硕士在读，研究方向：卫生统计学

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陈柯君（1999—），女，硕士在读，研究方向：卫生统计学

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陈柯君（1999—），女，硕士在读，研究方向：卫生统计学

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PeerJ, 2023, 11: e14554., articleTitle=The biomarkers discovery of hyperuricemia and gout: proteomics and metabolomics, refAbstract=null), Reference(id=1241680456140256026, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241522860473250796, doi=null, pmid=null, pmcid=null, year=2023, volume=37, issue=13/14, pageStart=null, pageEnd=null, url=null, language=null, rfNumber=[24], rfOrder=28, authorNames=Yadegar A, Mohammadi F, Rabizadeh S, journalName=Journal of Clinical Laboratory Analysis, refType=null, unstructuredReference=Yadegar A, Mohammadi F, Rabizadeh S, et al. Correlation between different levels and patterns of dyslipidemia and glomerular filtration rate in patients with type 2 diabetes: A cross-sectional survey of a regional cohort[J]. 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注：箭头上数值表示自然直接/间接效应OR（95%CI）；圆圈中数值表示中介占比%（95%CI）；该模型调整了年龄、性别、婚姻状况、教育程度、BMI、家庭年收入、地中海膳食评分、体力活动、吸烟状况、二手烟状况和高脂血症治疗状况；*表示P<0.05。

, figureFileSmall=XIKOX1eDQfUeSAoQBjayVQ==, figureFileBig=noQQQJIf0NnlGcNqrGdPgQ==, tableContent=null), ArticleFig(id=1241680450108846530, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241522860473250796, language=EN, label=Table 1, caption=

Basic characteristics of participants (n=1 615) ［n（%），（）］

, figureFileSmall=null, figureFileBig=null, tableContent=
变量总人群（n=1 615）非HUA（n=1 453）HUA（n=162）t/χ2值P值
协变量
年龄（岁）1.6050.205
<651 366 (84.6)1 235 (85.0)131 (80.9)
≥65249 (15.4)218 (15.0)31 (19.1)
性别4.8900.027
男性652 (40.4)573 (39.4)79 (48.8)
女性963 (59.6)880 (60.6)83 (51.2)
婚姻状况2.2020.138
未同居135 (8.4)116 (8.0)19 (11.7)
同居1 480 (91.6)1 337 (92.0)143 (88.3)
教育程度5.6860.338
文盲135 (8.4)123 (8.5)12 (7.4)
小学264 (16.3)237 (16.3)27 (16.7)
初中549 (34.0)497 (34.2)52 (32.1)
高中348 (21.5)312 (21.5)36 (22.2)
大专215 (13.3)197 (13.6)18 (11.1)
大学及以上104 (6.4)87 (6.0)17 (10.5)
家庭年收入（元）0.9670.915
0～12 000106 (6.6)98 (6.7)8 (4.9)
>12 000～20 000173 (10.7)156 (10.7)17 (10.5)
>20 000～60 000601 (37.2)541 (37.2)60 (37.0)
>60 000～100 000354 (21.9)318 (21.9)36 (22.2)
>100 000381 (23.6)340 (23.4)41 (25.3)
BMI（kg/m2）7.6310.006
0～<281 459 (90.3)1 323 (91.1)136 (84.0)
≥28156 (9.7)130 (8.9)26 (16.0)
地中海膳食评分（分）26.19 ± 4.1726.22 ± 4.1425.90 ± 4.420.9230.356
体力活动（METs/d）21.01 ± 14.7220.99 ± 14.5521.20 ± 16.22-0.1760.860
吸烟状况0.0300.863
从不吸烟1 142 (70.7)1 026 (70.6)116 (71.6)
吸烟/戒烟473 (29.3)427 (29.4)46 (28.4)
二手烟状况0.4760.490
未接触771 (47.7)689 (47.4)82 (50.6)
接触844 (52.3)764 (52.6)80 (49.4)
高脂血症治疗状况1.2750.259
是51 (3.2)43 (3.0)8 (4.9)
否1 564 (96.8)1 410 (97.0)154 (95.1)
暴露
纯酒精摄入量（g/d）4.53 ± 12.504.19 ± 11.947.55 ± 16.42-3.2520.001
潜在中介
TC（mmol/L）4.83 ± 0.974.82 ± 0.964.86 ± 1.04-0.4700.639
LDL-C（mmol/L）2.99 ± 0.722.98 ± 0.723.04 ± 0.79-0.8380.402
HDL-C（mmol/L）1.33 ± 0.291.34 ± 0.291.23 ± 0.274.503<0.001
TG（mmol/L）1.55 ± 1.351.51 ± 1.261.92 ± 1.95-3.667<0.001
), ArticleFig(id=1241680450347921870, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241522860473250796, language=CN, label=表1, caption=

参与者的基本特征（n=1 615） ［n（%），（）］

, figureFileSmall=null, figureFileBig=null, tableContent=
变量总人群（n=1 615）非HUA（n=1 453）HUA（n=162）t/χ2值P值
协变量
年龄（岁）1.6050.205
<651 366 (84.6)1 235 (85.0)131 (80.9)
≥65249 (15.4)218 (15.0)31 (19.1)
性别4.8900.027
男性652 (40.4)573 (39.4)79 (48.8)
女性963 (59.6)880 (60.6)83 (51.2)
婚姻状况2.2020.138
未同居135 (8.4)116 (8.0)19 (11.7)
同居1 480 (91.6)1 337 (92.0)143 (88.3)
教育程度5.6860.338
文盲135 (8.4)123 (8.5)12 (7.4)
小学264 (16.3)237 (16.3)27 (16.7)
初中549 (34.0)497 (34.2)52 (32.1)
高中348 (21.5)312 (21.5)36 (22.2)
大专215 (13.3)197 (13.6)18 (11.1)
大学及以上104 (6.4)87 (6.0)17 (10.5)
家庭年收入（元）0.9670.915
0～12 000106 (6.6)98 (6.7)8 (4.9)
>12 000～20 000173 (10.7)156 (10.7)17 (10.5)
>20 000～60 000601 (37.2)541 (37.2)60 (37.0)
>60 000～100 000354 (21.9)318 (21.9)36 (22.2)
>100 000381 (23.6)340 (23.4)41 (25.3)
BMI（kg/m2）7.6310.006
0～<281 459 (90.3)1 323 (91.1)136 (84.0)
≥28156 (9.7)130 (8.9)26 (16.0)
地中海膳食评分（分）26.19 ± 4.1726.22 ± 4.1425.90 ± 4.420.9230.356
体力活动（METs/d）21.01 ± 14.7220.99 ± 14.5521.20 ± 16.22-0.1760.860
吸烟状况0.0300.863
从不吸烟1 142 (70.7)1 026 (70.6)116 (71.6)
吸烟/戒烟473 (29.3)427 (29.4)46 (28.4)
二手烟状况0.4760.490
未接触771 (47.7)689 (47.4)82 (50.6)
接触844 (52.3)764 (52.6)80 (49.4)
高脂血症治疗状况1.2750.259
是51 (3.2)43 (3.0)8 (4.9)
否1 564 (96.8)1 410 (97.0)154 (95.1)
暴露
纯酒精摄入量（g/d）4.53 ± 12.504.19 ± 11.947.55 ± 16.42-3.2520.001
潜在中介
TC（mmol/L）4.83 ± 0.974.82 ± 0.964.86 ± 1.04-0.4700.639
LDL-C（mmol/L）2.99 ± 0.722.98 ± 0.723.04 ± 0.79-0.8380.402
HDL-C（mmol/L）1.33 ± 0.291.34 ± 0.291.23 ± 0.274.503<0.001
TG（mmol/L）1.55 ± 1.351.51 ± 1.261.92 ± 1.95-3.667<0.001
), ArticleFig(id=1241680450444390872, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241522860473250796, language=EN, label=Table 2, caption=

Association between alcohol intake and serum lipids at baseline

, figureFileSmall=null, figureFileBig=null, tableContent=
潜在中介模型1a模型2b模型3c
β（95%CI）P值β（95%CI）P值β（95%CI）P值
TC0.03 (-0.01～0.07)0.1140.02 (-0.02～0.06)0.2610.02 (-0.02～0.07)0.242
LDL-C0.01 (-0.02～0.03)0.645-0.01 (-0.04～0.02)0.507-0.01 (-0.04～0.02)0.597
HDL-C0.00 (-0.01～0.01)0.7200.03 (0.01～0.04)<0.0010.02 (0.01～0.04)<0.001
TG0.15 (0.10～0.20)<0.0010.11 (0.05～0.16)<0.0010.11 (0.05～0.17)<0.001
), ArticleFig(id=1241680450557637087, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241522860473250796, language=CN, label=表2, caption=

饮酒量与血脂水平的关联

, figureFileSmall=null, figureFileBig=null, tableContent=
潜在中介模型1a模型2b模型3c
β（95%CI）P值β（95%CI）P值β（95%CI）P值
TC0.03 (-0.01～0.07)0.1140.02 (-0.02～0.06)0.2610.02 (-0.02～0.07)0.242
LDL-C0.01 (-0.02～0.03)0.645-0.01 (-0.04～0.02)0.507-0.01 (-0.04～0.02)0.597
HDL-C0.00 (-0.01～0.01)0.7200.03 (0.01～0.04)<0.0010.02 (0.01～0.04)<0.001
TG0.15 (0.10～0.20)<0.0010.11 (0.05～0.16)<0.0010.11 (0.05～0.17)<0.001
), ArticleFig(id=1241680450649911785, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241522860473250796, language=EN, label=Table 3, caption=

Association of alcohol intake, serum lipids at baseline and hyperuricemia at follow-up

, figureFileSmall=null, figureFileBig=null, tableContent=
变量模型1a模型2b模型3c
OR值（95%CI）P值OR值（95%CI）P值OR值（95%CI）P值
暴露
纯酒精摄入量1.18 (1.06～1.30)0.0021.16 (1.03～1.30)0.0111.22 (1.08～1.37)0.001
潜在中介
TC1.04 (0.88～1.23)0.6381.04 (0.88～1.23)0.6451.04 (0.88～1.23)0.662
LDL-C1.10 (0.88～1.37)0.4021.09 (0.87～1.36)0.4661.07 (0.85～1.34)0.574
HDL-C0.25 (0.13～0.46)<0.0010.26 (0.14～0.50)<0.0010.29 (0.15～0.56)<0.001
TG1.16 (1.06～1.27)0.0011.16 (1.06～1.27)0.0011.16 (1.06～1.27)0.002
), ArticleFig(id=1241680450742186483, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241522860473250796, language=CN, label=表3, caption=

饮酒量、血脂与随访时HUA的关联

, figureFileSmall=null, figureFileBig=null, tableContent=
变量模型1a模型2b模型3c
OR值（95%CI）P值OR值（95%CI）P值OR值（95%CI）P值
暴露
纯酒精摄入量1.18 (1.06～1.30)0.0021.16 (1.03～1.30)0.0111.22 (1.08～1.37)0.001
潜在中介
TC1.04 (0.88～1.23)0.6381.04 (0.88～1.23)0.6451.04 (0.88～1.23)0.662
LDL-C1.10 (0.88～1.37)0.4021.09 (0.87～1.36)0.4661.07 (0.85～1.34)0.574
HDL-C0.25 (0.13～0.46)<0.0010.26 (0.14～0.50)<0.0010.29 (0.15～0.56)<0.001
TG1.16 (1.06～1.27)0.0011.16 (1.06～1.27)0.0011.16 (1.06～1.27)0.002
), ArticleFig(id=1241680450842849790, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241522860473250796, language=EN, label=Table 4, caption=

The mediating effect of serum lipids at baseline on the association between alcohol intake and the risk of hyperuricemia at follow-up (with the interaction between alcohol intake and serum lipids)

, figureFileSmall=null, figureFileBig=null, tableContent=
潜在中介中介效应c中介占比c
OR值（95%CI）%（95%CI）
TC1.000 0 (0.999 7～1.000 6)0.17 (-2.51%～5.72%)
LDL-C1.000 0 (0.999 5～1.000 5)-0.08 (-7.91%～2.18%)
HDL-C0.997 2 (0.995 0～0.998 7) *-15.85 (-75.50%～-5.26%) *
TG1.001 3 (1.000 2～1.003 4) *6.89 (0.79%～30.67%) *
), ArticleFig(id=1241680450951901706, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241522860473250796, language=CN, label=表4, caption=

血脂对饮酒量与HUA关联的中介作用

, figureFileSmall=null, figureFileBig=null, tableContent=
潜在中介中介效应c中介占比c
OR值（95%CI）%（95%CI）
TC1.000 0 (0.999 7～1.000 6)0.17 (-2.51%～5.72%)
LDL-C1.000 0 (0.999 5～1.000 5)-0.08 (-7.91%～2.18%)
HDL-C0.997 2 (0.995 0～0.998 7) *-15.85 (-75.50%～-5.26%) *
TG1.001 3 (1.000 2～1.003 4) *6.89 (0.79%～30.67%) *
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饮酒量与高尿酸血症关联的纵向研究——血脂水平的中介效应
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陈柯君 1 , 古苓汐 1 , 高绪芳 2 , 黄守瑞 1 , 郭冰 1 , 赵星 1
现代预防医学 | 流行病与统计方法 2024,51(3): 395-400
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现代预防医学 | 流行病与统计方法 2024, 51(3): 395-400
饮酒量与高尿酸血症关联的纵向研究——血脂水平的中介效应
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陈柯君1, 古苓汐1, 高绪芳2, 黄守瑞1, 郭冰1, 赵星1
作者信息
  • 1.四川大学华西公共卫生学院/华西第四医院，四川 成都 610041
  • 2.成都市疾病预防控制中心，四川 成都 610041

    • 陈柯君（1999—），女，硕士在读，研究方向：卫生统计学

通讯作者:

赵星，E-mail:
A longitudinal study on the relationship between alcohol consumption and hyperuricemia: the mediating effect of blood lipid level
Ke-jun CHEN1, Ling-xi GU1, Xu-fang GAO2, Shou-rui HUANG1, Bing GUO1, Xing ZHAO1
Affiliations
  • West China School of Public Health, Sichuan University / West China Fourth Hospital, Sichuan University, Chengdu, Sichuan 610041, China
出版时间: 2024-02-10 doi: 10.20043/j.cnki.MPM.202307244
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摘要
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目的

本研究旨在评估我国西南地区自然人群中饮酒量与高尿酸血症的关系，并进一步探讨血脂水平对饮酒量与高尿酸血症关联的中介作用。

方法

基于中国西南区域自然人群队列研究的基线数据（2018—2019年）和第一轮随访数据（2020—2021年），使用多因素logistic回归模型分析饮酒量和高尿酸血症的关联。采用因果中介分析评价血脂水平的潜在中介作用。

结果

共有1 615人纳入研究，其中有162人新发高尿酸血症。纯酒精摄入量每增加10 g/d与高尿酸血症发生风险呈正相关（OR=1.22，95%CI：1.08～1.37）。高密度脂蛋白（HDL-C）和甘油三酯（TG）部分介导了饮酒量与高尿酸血症之间的关系，中介效应分别占16.58%（95%CI：5.64%～54.01%）和6.98%（95%CI：1.12%～24.84%）。

结论

饮酒量的增加可导致高尿酸血症的发生风险升高，HDL-C和TG是饮酒量与高尿酸血症关联的重要中介因素。本研究表明，可以通过减少饮酒及有针对性地控制血脂，来预防高尿酸血症的发生发展。

饮酒量  /  高尿酸血症  /  血脂  /  中介分析
Objective

To evaluate the relationship between alcohol consumption and hyperuricemia in natural population in southwest China, and to further explore the mediating effect of blood lipid level on the relationship between alcohol consumption and hyperuricemia.

Methods

Based on the baseline data (2018 to 2019) and the first-round follow-up data (2020 to 2021) of the natural population cohort study in southwest China, a multivariate Logistic regression model was used to analyze the association between alcohol consumption and hyperuricemia. The potential mediating effect of serum lipids was evaluated by causal mediation analysis.

Results

A total of 1 615 people were enrolled in this study, including 162 new cases of hyperuricemia. Each 10g/d increase in pure alcohol intake was positively correlated with the risk of hyperuricemia (OR=1.22, 95%CI: 1.08-1.37). High density lipoprotein (HDL-C) and triglyceride (TG) partially mediated the relationship between alcohol consumption and hyperuricemia, with mediating effects of 16.58% (95%CI: 5.64%-54.01%) and 6.98% (95% CI:1.12%-24.84%), respectively.

Conclusion

The increase of alcohol consumption can increase the risk of hyperuricemia. HDL-C and TG are important mediating factors between alcohol consumption and hyperuricemia. Our study shows that the occurrence and development of hyperuricemia can be prevented by reducing alcohol consumption and controlling blood lipids.

Alcohol consumption  /  Hyperuricemia  /  Blood lipids  /  Mediation analysis
陈柯君, 古苓汐, 高绪芳, 黄守瑞, 郭冰, 赵星. 饮酒量与高尿酸血症关联的纵向研究——血脂水平的中介效应. 现代预防医学, 2024 , 51 (3) : 395 -400 . DOI: 10.20043/j.cnki.MPM.202307244
Ke-jun CHEN, Ling-xi GU, Xu-fang GAO, Shou-rui HUANG, Bing GUO, Xing ZHAO. A longitudinal study on the relationship between alcohol consumption and hyperuricemia: the mediating effect of blood lipid level[J]. Modern Preventive Medicine, 2024 , 51 (3) : 395 -400 . DOI: 10.20043/j.cnki.MPM.202307244
正文
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高尿酸血症（hyperuricemia，HUA）是仅次于糖尿病的第二大常见代谢性疾病[1-2]，其特点是血清尿酸生成增加或者排泄减少。许多研究表明，HUA是多种不良健康状况的独立危险因素，如痛风、糖尿病和心血管疾病等[3]。近几十年来，HUA在全球越来越普遍，中国HUA的患病率已达到13.3%[4]，患病人数超过1.8亿，远高于其他国家。由此可见，HUA、尿酸水平升高及其并发症已成为重大的公共卫生问题。
《中国高尿酸血症与痛风诊疗指南(2019)》提出高嘌呤饮食、吸烟、饮酒等是HUA的危险因素[5]。孟德尔随机化研究等均发现饮酒会增加患HUA的风险[6-7]。酒精可能造成肝脏损伤，影响尿酸代谢酶和尿酸转运体的产生，从而使血清尿酸增加[6]，此外，研究提示酒精还可能通过改变循环中的血脂水平进而导致尿酸升高[8-10]。然而尚未有人群研究调查过血脂水平在饮酒量和高尿酸血症关联中的中介作用，酒精通过影响血脂水平进而导致HUA是一条尚未被探索的、需要明确的通路。因此本研究基于中国西南区域自然人群队列（China multi-ethnic cohort，CMEC）的两期数据，探讨饮酒量与HUA的关系，并通过因果中介分析来调查血脂水平在二者关联间的中介作用，本文的研究成果将为饮酒引起HUA的机制和预防提供新的见解。
1　对象与方法
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1.1　研究对象
本研究基于国家重点研发计划CMEC队列中的四川地区汉族人群开展[11]。CMEC通过多阶段、分层抽样方法，在中国西南地区的五个省份共招募了99 556名 30～79岁的参与者，收集了参与者的问卷、全面体检和临床实验室检测数据。为保证研究假设的因果时序性，本研究纳入了四川地区汉族人群中同时参加基线研究（2018—2019年）和第一轮随访研究（2020—2021年）的2 116名调查对象，排除了符合以下标准的调查对象：（1）自述患有痛风者；（2）基线时患HUA者；（3）相关数据缺失者。最终有1 615人纳入研究。研究由四川大学医学伦理审查委员会(K2016038, K2020022)审查和批准，每位调查对象都签署了知情同意书。
1.2　相关指标定义及标准
1.2.1　纯酒精摄入量
询问调查对象基线前一年的饮酒频率和饮酒量，并根据不同酒类的酒精浓度，计算得到每日纯酒精摄入量（g/d）。所有调查对象的数据均由训练有素的工作人员通过面对面访谈收集。
1.2.2　血脂水平
采集调查对象禁食8 h后静脉血样本，使用AU5800全自动化学分析仪(Beckman Coulter Commercial Enterprise, Shanghai, China)测定总胆固醇（TC）、低密度脂蛋白（LDL-C）、高密度脂蛋白（HDL-C）、甘油三酯（TG）水平。仪器在检测前经过校准，检测过程由经过培训的人员操作。
1.2.3　HUA
由专业的医护人员检测尿酸水平，操作同血脂水平的检测。男性的血清尿酸>7.0 mg/dl(417 μmol/L)，女性血清尿酸>6.0 mg/dl (357 μmol/L)被判定为HUA[1]。
1.2.4　协变量
基于既往研究[9,12]，为控制混杂，我们将以下协变量纳入主要分析：年龄（<65岁、≥65岁），性别（男性、女性），婚姻状况（未同居、同居），教育程度（文盲、小学、初中、高中、大专、大学及以上），体质指数（body mass index，BMI；0～<28 kg/m2、≥28 kg/m2）、家庭年收入（0～12 000元、>12 000～20 000元、>20 000～60 000元、>60 000～100 000元、>100 000元）、地中海膳食评分、体力活动（metabolic equivalent of task, METs/d）、吸烟状况（从不吸烟、吸烟/戒烟）、二手烟状况(未接触、接触)、高脂血症治疗状况（是、否）。地中海膳食模式是一种与心脏代谢疾病相关的膳食模式[13]。考虑参与者的职业、交通、家务和休闲活动，使用代谢当量(METs/d)来估计参与者的体力活动[14]。
1.3　统计学分析
数据分析采用R 4.0.3软件。将研究对象按是否患HUA分组，分别进行统计描述，连续性变量用（均数±标准差）表示，分类变量用频数（构成比）表示。采用t检验或χ2检验比较两组之间的差异性。使用多因素线性回归和logistic回归模型分析饮酒量、血脂水平和HUA三者之间的关联。为了探索结果的稳健性，回归分析包括3个模型，模型1不调整任何协变量；模型2调整年龄、性别、婚姻状况、教育程度；模型3在模型2的基础上进一步调整BMI、家庭年收入、地中海膳食评分、体力活动、吸烟状况、二手烟状况和高脂血症治疗状况。后续分析所用协变量与模型3一致。
使用“mediation”包[15]，分析血脂水平对饮酒量和HUA关联的中介效应。并比较饮酒量和血脂有或没有交互作用时的中介效应，以检验结果的稳定性。使用自然间接效应表示饮酒量通过血脂对HUA的影响[15]，使用公式：（自然间接效应/总效应）×100%，计算得到中介效应占比。分析使用双侧检验，α=0.05。
2　结果
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2.1　基本特征描述
研究最终纳入1 615人，检出HUA新发病例162人，发病率为10.0%。研究对象中有15.4%的人≥65岁，患病人群中年龄≥65岁的占比为19.1%。不患病人群年龄≥65岁的占比为15.0%。与不患病人群相比，患病人群有着更高的纯酒精摄入量、TC、LDL-C和TG水平。见表1。
2.2　饮酒量和血脂水平的关联
表2显示饮酒量和基线时血脂水平的关联，纯酒精摄入量每增加10 g/d，HDL-C升高0.02 mmol/L（95%CI：0.01～0.04），TG升高0.11 mmol/L（95%CI：0.05～0.17）。
2.3　饮酒量、血脂和HUA的关联
表3显示了纯酒精摄入量、血脂水平与随访时新出现的HUA之间的关系。在三个模型中均观察到纯酒精摄入量、TG的增加可升高HUA的发病风险，而HDL-C的增加会降低HUA的发病风险。纯酒精摄入量每增加10 g/d所对应的OR（95%CI）为1.22（1.08～1.37）； HDL-C、TG水平每增加1 mmol/L的OR（95%CI）分别为0.29（0.15～0.56）和1.16（1.06～1.27）。
2.4　血脂在饮酒量和HUA关联中的中介作用
图1显示HDL-C和TG部分介导了饮酒量与HUA的关联。结果表明HDL-C降低和TG升高分别解释了16.58%和6.98%的效应。而TC和LDL-C的中介作用没有统计学意义。当纳入饮酒量和血脂的交互项时，也观察到了类似的中介占比结果，见表4。
3　讨论
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本研究基于CMEC项目的两期数据，首次探讨了饮酒量与HUA关联的中间途径，发现饮酒量与HDL-C、TG水平升高有关，饮酒量、TG水平的增加可使HUA发病风险升高，而HDL-C水平升高可降低HUA发病风险，并且HDL-C和TG部分介导了饮酒量与HUA之间的关系，其中HDL-C的中介作用占比更大。
HUA是痛风的前兆，可引起多种心血管代谢性疾病[3]。已有基因研究证明了人类饮酒与HUA之间的关联，比如与饮酒行为有关的基因rs671，同时也被发现与痛风相关[16]。一项孟德尔随机化研究为饮酒与HUA之间的因果关系也提供了证据[17]。其中潜在的原因可能是，酒精(特别是乙醇)的摄入可以加速腺嘌呤核苷三磷酸降解的速度，而腺嘌呤核苷三磷酸分解的增加会导致嘌呤和尿酸的产生增加[18]。此外，酒精代谢可增加血液中的乳酸水平，乳酸阻止尿酸通过肾小管排泄[19]，从而导致尿酸升高。
本研究显示血脂处于饮酒量与HUA关联的一条重要的因果路径上。饮酒量与HDL-C和TG水平呈正相关，据相关研究报道酒精可对脂肪组织起到一定的刺激作用，可通过增加载脂蛋白A1和A2的运输速率，使体内HDL-C升高[20]。此外，酒精可抑制脂蛋白酯酶活性，使肝脏合成LDL-C增多，而血中LDL-C的清除速度减慢，从而导致TG水平的提高[21]。本文表2模型1结果显示饮酒量与HDL-C无显著性关联，可能是由于模型2某些关键混杂因素对结果产生的影响，在调整这些混杂因素后，结果具有统计学意义，由此可见，酒精是一个不可否认的干扰脂质代谢的因素。本文发现基线时血脂代谢的改变与随访时HUA的高风险有关。美国的一项队列研究显示，基线TG、HDL-C及其变化与随访时的血清尿酸显著相关，这与本研究的发现一致[22]。来自临床和实验研究的大量证据支持脂质特征的改变可能造成肾脏功能障碍，进而导致肾脏排泄尿酸减少[23-24]。因此，脂质相关的肾滤过功能障碍可以解释饮酒量与HUA之间的潜在影响机制。在发现的中介因素中，HDL-C的中介占比更大，这表明与胆固醇逆向转运相关的HDL-C通路在HUA的发生中发挥了更为关键的作用[9]。总的来说，这一新发现为精确干预提供了有价值的信息，提示饮酒量较高人群，不仅可通过减少饮酒来防范HUA，还可以通过饮食控制和适量运动，降低体脂量，避免超重或肥胖，在生活中加强对血脂的监测[12]，接受针对性健康教育，以预防HUA及其严重并发症的发生。
饮酒量对HUA发病风险的总影响只有一部分可以用血脂的变化来解释。酒精影响血清尿酸的可能途径是多种多样的。未来的研究还需考虑纳入其他潜在的中介，以进一步探索酒精诱导HUA的机制，并制定新的公共卫生预防策略。本研究存在一定的局限性：一是虽然在分析中纳入了大部分协变量，但可能仍然存在未测量或未考虑的潜在混杂因素；二是研究中的饮酒量、吸烟状况、体力活动等数据是通过调查问卷获得的，可能存在一定的回忆偏倚。尽管如此，本研究也具有许多优势：第一，这是第一篇对饮酒量与HUA关联的中介研究，提升了大家对关联机制的全面了解；第二，研究对象来自中国四川地区的普通成年人，这可能对决策者设计人群水平的干预措施更有价值；第三，使用的前瞻性数据确保了暴露、中介和结局的时间序列，这比横断面数据提供了更为可靠的结果。
综上所述，饮酒量的增加可使HUA发生风险升高，HDL-C和TG部分介导了饮酒量和HUA之间的关系，尤其是HDL-C。这些发现可以为血脂的中介作用提供证据，并对制定通过干预血脂来减轻HUA及其继发病的公共政策具有重要意义。未来的研究还需要考虑其他途径来进一步阐明饮酒量和HUA之间的关联。
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  • 西南区域自然人群队列研究(2017YFC0907300)
  • 精准医学健康大型人群队列构建关键技术及其在四川民族地区应用(2020JDJQ0014)
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2024年第51卷第3期
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doi: 10.20043/j.cnki.MPM.202307244
  • 接收时间：2023-07-12
  • 首发时间：2026-03-19
  • 出版时间：2024-02-10
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  • 收稿日期：2023-07-12
基金
西南区域自然人群队列研究(2017YFC0907300)
精准医学健康大型人群队列构建关键技术及其在四川民族地区应用(2020JDJQ0014)
作者信息
    1.四川大学华西公共卫生学院/华西第四医院，四川 成都 610041
    2.成都市疾病预防控制中心，四川 成都 610041

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species		 占总种数比例
Percentage of
total species (%)		 属
Genus		 种数
Number of
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鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
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多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
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