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Article(id=1241322668599857760, tenantId=1146029695717560320, journalId=1227665162245664772, issueId=1241322661654098823, articleNumber=null, orderNo=null, doi=10.20043/j.cnki.MPM.202309044, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1693756800000, receivedDateStr=2023-09-04, revisedDate=null, revisedDateStr=null, acceptedDate=null, acceptedDateStr=null, onlineDate=1773883986854, onlineDateStr=2026-03-19, pubDate=1706112000000, pubDateStr=2024-01-25, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1773883986854, onlineIssueDateStr=2026-03-19, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1773883986854, creator=13701087609, updateTime=1773883986854, updator=13701087609, issue=Issue{id=1241322661654098823, tenantId=1146029695717560320, journalId=1227665162245664772, year='2024', volume='51', issue='2', pageStart='193', pageEnd='384', issueExtLink='null', onlineDate='null', pubDate='null', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1773883985198, creator=13701087609, updateTime=1773890256678, updator=13701087609, preIssue=null, nextIssue=null, ext={EN=IssueExt(id=1241348966214849502, tenantId=1146029695717560320, journalId=1227665162245664772, issueId=1241322661654098823, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1241348966214849503, tenantId=1146029695717560320, journalId=1227665162245664772, issueId=1241322661654098823, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null}, startPage=200, endPage=204, ext={EN=ArticleExt(id=1241322668994122360, articleId=1241322668599857760, tenantId=1146029695717560320, journalId=1227665162245664772, language=EN, title=Mendelian randomization study of risk factors associated with hyperuricemia, columnId=1228016567443718970, journalTitle=Modern Preventive Medicine, columnName=Epidemiology and Statistical Methods Advances, runingTitle=null, highlight=null, articleAbstract=
Objective

To further investigate the risk factors of Hyperuricemia(HUA) using Mendelian randomization study and its causal relationship, and to lay the theoretical foundation for the prevention and control of hyperuricemia.

Methods

In the first, we regarded the HUA-related factors as the exposure and the uric acid as outcomes, and two-sample Mendelian randomization analysis was made based on the data from genome-wide association study concerning European populations. Then, the variable possibly bearing a cause and effect relationship to the outcomes in the Mendelian study was further analyzed by multivariate Mendelian randomization analysis to determine the independent influence of the variables on the outcomes.

Results

Two-sample Mendelian randomization study results showed that the increase in the body mass index, the triglycerides level and the pork consumption and the decrease in the high-density lipoprotein cholesterol could lead to the increase in the uric acid. Multivariate Mendelian randomization study results showed that the causality between the three factors including BMI(b=0.246, P=8.89×10-9), HDL(b=-0.074, P=0.046) and TG(b=0.145, P=7.32×10-5) and UA still held, while causality between pork consumption and UA showed no statistical significance(b=-0.074, P=0.096).

Conclusion

Body mass index, high density lipoprotein cholesterol, triglyceride have a causal relationship with uric acid.

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目的

采用孟德尔随机化研究探究高尿酸血症（HUA）的危险因素与其因果关系，为人群高尿酸血症的预防和控制奠定理论基础。

方法

以与HUA相关的因素为暴露，以尿酸为结局，使用基于欧洲人群的全基因组关联研究数据进行两样本孟德尔随机化分析，将可能与结局具有因果关系的变量进一步进行多变量孟德尔随机化分析，得到该暴露因素对结局的独立影响。

结果

两样本孟德尔随机化分析结果显示身体质量指数，甘油三酯，猪肉摄入量的升高以及高密度脂蛋白胆固醇的降低会导致尿酸的升高。多变量孟德尔随机化结果显示在控制其他三个变量的情况下，身体质量指数（b=0.246，P=8.89×10-9）、高密度脂蛋白胆固醇（b=-0.074，P=0.046）、甘油三酯（b=0.145，P=7.32×10-5）与尿酸的因果关系仍然成立，而猪肉的摄入量和尿酸的因果关系无统计学意义（b=-0.074，P=0.096）。

结论

身体质量指数、高密度脂蛋白胆固醇、甘油三酯与尿酸具有因果关系。

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赵灵燕，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=tufnaO6yXE4OYY96IRtHrQ==, magXml=onbWnX+bl6G1whwLLoFI5Q==, pdfUrl=null, pdf=zypFmurfEP2q1NHgTqYJpQ==, pdfFileSize=1046675, pdfExtLink=null, richHtmlUrl=null, mobilePdfUrl=null, reviewReport=null, pdfFirstPage=null, abstractGraph=Y9e9PoNvli+MVtvMVI43pA==, abstractGraphContent=null, abstractVideo=null, citation=null, cebUrl=null, magXmlContent=MfVnA52dTfJ7QF50Ijwztg==, mapNumber=null, authorCompany=null, fund=null, authors=

李娜（1998—），女，硕士在读，研究方向：慢性非传染性疾病流行病学研究

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李娜（1998—），女，硕士在读，研究方向：慢性非传染性疾病流行病学研究

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李娜（1998—），女，硕士在读，研究方向：慢性非传染性疾病流行病学研究

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注：图A、B、C、D分别表示BMI、HDL、TG和猪肉摄入量。

, figureFileSmall=415FdwMiJZiZYsnYKQMfaA==, figureFileBig=OGTOaVWaBoBJmp9d/sr4og==, tableContent=null), ArticleFig(id=1241322677789577348, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241322668599857760, language=EN, label=Table 1, caption=

Characteristics of interest for inclusion in Mendelian randomization studies

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变量特征变量类型数据来源样本量人群
尿酸结局慢性肾脏疾病遗传学联盟288 649European
身体质量指数暴露人体测量特征遗传调查联盟694 649European
高密度脂蛋白胆固醇暴露英国生物银行403 943European
低密度脂蛋白胆固醇暴露英国生物银行440 546European
甘油三酯暴露英国生物银行441 016European
空腹血糖暴露葡萄糖和胰岛素相关性状的meta分析联盟153 525European
饮酒暴露酒精和尼古丁使用测序联盟537 349European
职业为体力劳动暴露英国生物银行263 615European
饮食暴露英国生物银行161 625European
), ArticleFig(id=1241322677923795093, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241322668599857760, language=CN, label=表1, caption=

纳入孟德尔随机化研究的相关特征

, figureFileSmall=null, figureFileBig=null, tableContent=
变量特征变量类型数据来源样本量人群
尿酸结局慢性肾脏疾病遗传学联盟288 649European
身体质量指数暴露人体测量特征遗传调查联盟694 649European
高密度脂蛋白胆固醇暴露英国生物银行403 943European
低密度脂蛋白胆固醇暴露英国生物银行440 546European
甘油三酯暴露英国生物银行441 016European
空腹血糖暴露葡萄糖和胰岛素相关性状的meta分析联盟153 525European
饮酒暴露酒精和尼古丁使用测序联盟537 349European
职业为体力劳动暴露英国生物银行263 615European
饮食暴露英国生物银行161 625European
), ArticleFig(id=1241322678049624225, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241322668599857760, language=EN, label=Table 2, caption=

Multivariate MR of different exposures with UA

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暴露nSNPBeta(95% CI)SEP值
BMI2260.246（0.162，0.330）0.0438.89×10-9
HDL204-0.074（-0.147，-0.001）0.0370.046
TG1600.145（0.072，0.218）0.0377.32×10-5
猪肉摄入量2-0.074（-0.160，0.012）0.0440.096
), ArticleFig(id=1241322678141898920, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241322668599857760, language=CN, label=表2, caption=

不同暴露与UA的多变量MR

, figureFileSmall=null, figureFileBig=null, tableContent=
暴露nSNPBeta(95% CI)SEP值
BMI2260.246（0.162，0.330）0.0438.89×10-9
HDL204-0.074（-0.147，-0.001）0.0370.046
TG1600.145（0.072，0.218）0.0377.32×10-5
猪肉摄入量2-0.074（-0.160，0.012）0.0440.096
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高尿酸血症相关危险因素的孟德尔随机化研究
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李娜 1 , 董嘉慧 1 , 高玉敏 1, 2 , 庞慧 1, 2 , 赵燕平 3 , 郑海芳 1 , 赵灵燕 1, 2
现代预防医学 | 流行病与统计方法 2024,51(2): 200-204
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现代预防医学 | 流行病与统计方法 2024, 51(2): 200-204
高尿酸血症相关危险因素的孟德尔随机化研究
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李娜1, 董嘉慧1, 高玉敏1, 2, 庞慧1, 2, 赵燕平3, 郑海芳1, 赵灵燕1, 2
作者信息
  • 1.内蒙古医科大学 公共卫生学院，内蒙古 呼和浩特 010110
  • 2.内蒙古医科大学 慢性病分子流行病学重点实验室
  • 3.内蒙古医科大学附属医院 神经内科

    • 李娜（1998—），女，硕士在读，研究方向：慢性非传染性疾病流行病学研究

通讯作者:

赵灵燕，E-mail：
Mendelian randomization study of risk factors associated with hyperuricemia
Na LI1, Jia-hui DONG1, Yu-min GAO1, 2, Hui PANG1, 2, Yan-ping ZHAO3, Hai-fang ZHENG1, Ling-yan ZHAO1, 2
Affiliations
  • Inner Mongolia Medical University, School of Public Health, Hohhot, Inner Mongolia 010110, China
出版时间: 2024-01-25 doi: 10.20043/j.cnki.MPM.202309044
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摘要
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目的

采用孟德尔随机化研究探究高尿酸血症（HUA）的危险因素与其因果关系，为人群高尿酸血症的预防和控制奠定理论基础。

方法

以与HUA相关的因素为暴露，以尿酸为结局，使用基于欧洲人群的全基因组关联研究数据进行两样本孟德尔随机化分析，将可能与结局具有因果关系的变量进一步进行多变量孟德尔随机化分析，得到该暴露因素对结局的独立影响。

结果

两样本孟德尔随机化分析结果显示身体质量指数，甘油三酯，猪肉摄入量的升高以及高密度脂蛋白胆固醇的降低会导致尿酸的升高。多变量孟德尔随机化结果显示在控制其他三个变量的情况下，身体质量指数（b=0.246，P=8.89×10-9）、高密度脂蛋白胆固醇（b=-0.074，P=0.046）、甘油三酯（b=0.145，P=7.32×10-5）与尿酸的因果关系仍然成立，而猪肉的摄入量和尿酸的因果关系无统计学意义（b=-0.074，P=0.096）。

结论

身体质量指数、高密度脂蛋白胆固醇、甘油三酯与尿酸具有因果关系。

高尿酸血症  /  孟德尔随机化  /  危险因素
Objective

To further investigate the risk factors of Hyperuricemia(HUA) using Mendelian randomization study and its causal relationship, and to lay the theoretical foundation for the prevention and control of hyperuricemia.

Methods

In the first, we regarded the HUA-related factors as the exposure and the uric acid as outcomes, and two-sample Mendelian randomization analysis was made based on the data from genome-wide association study concerning European populations. Then, the variable possibly bearing a cause and effect relationship to the outcomes in the Mendelian study was further analyzed by multivariate Mendelian randomization analysis to determine the independent influence of the variables on the outcomes.

Results

Two-sample Mendelian randomization study results showed that the increase in the body mass index, the triglycerides level and the pork consumption and the decrease in the high-density lipoprotein cholesterol could lead to the increase in the uric acid. Multivariate Mendelian randomization study results showed that the causality between the three factors including BMI(b=0.246, P=8.89×10-9), HDL(b=-0.074, P=0.046) and TG(b=0.145, P=7.32×10-5) and UA still held, while causality between pork consumption and UA showed no statistical significance(b=-0.074, P=0.096).

Conclusion

Body mass index, high density lipoprotein cholesterol, triglyceride have a causal relationship with uric acid.

Hyperuricemia  /  Mendelian randomization  /  Risk factor
李娜, 董嘉慧, 高玉敏, 庞慧, 赵燕平, 郑海芳, 赵灵燕. 高尿酸血症相关危险因素的孟德尔随机化研究. 现代预防医学, 2024 , 51 (2) : 200 -204 . DOI: 10.20043/j.cnki.MPM.202309044
Na LI, Jia-hui DONG, Yu-min GAO, Hui PANG, Yan-ping ZHAO, Hai-fang ZHENG, Ling-yan ZHAO. Mendelian randomization study of risk factors associated with hyperuricemia[J]. Modern Preventive Medicine, 2024 , 51 (2) : 200 -204 . DOI: 10.20043/j.cnki.MPM.202309044
正文
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尿酸是人体嘌呤代谢的终产物，当机体出现嘌呤代谢紊乱，尿酸分泌过多或肾脏排泄功能障碍时，会使尿酸聚集在血液中，从而导致高尿酸血症（Hyperuricemia, HUA）[1]。HUA在全球成年人中非常普遍，美国患病率为20.1%[1]，韩国HUA的患病率为11.4%[2]。近年来，中国人的饮食模式发生了由传统的以蔬菜和碳水化合物为主到以肉类等富含嘌呤的食物为主的转变[3]，这可能是HUA在中国的患病率上升的原因。最新的一项有关中国大陆31个省份的横断面研究结果显示中国成人HUA总体标准化患病率为17.7%，且呈逐年升高的趋势[4-5]。
孟德尔随机化研究（Mendelian randomisation analysis, MR）是一种可使用观察性研究得到的非实验性数据进行暴露和结局间的因果推断的研究[6-7]。基于两样本孟德尔随机化分析，多项研究探究了尿酸与其他疾病之间的因果关系，发现血清尿酸与阿尔茨海默病的发病风险之间有因果关联[8]，尿酸是冠心病和充血性心力衰竭的危险因素，但与心房颤动、慢性肾脏病均不存在因果关联[9-10]。但尚未发现高尿酸的相关危险因素与其之间的因果关系研究。
因此本研究将以相关因素的单核苷酸多态性（Single Nucleotide Polymorphism, SNP）作为工具变量，深入探讨成年人群HUA的危险因素与其因果关系，为成年人HUA的防治提供重要的依据。
1　研究方法
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1.1　数据来源
通过前期对2018—2020年在内蒙古自治区进行的现场流行病学调查的横断面研究数据的整理与分析发现，与蒙古族人群HUA相关的变量有：身体质量指数（Body Mass Index, BMI）、血脂、血糖、饮酒、职业为体力劳动、猪肉、羊肉、禽肉、豆类及其制品、奶及奶制品的摄入量[11]。MR数据来自公开可用全基因组关联研究（Genome-Wide Association Studies, GWAS）的汇总级数据，包含GWAS的所有研究均已获得其各自原始机构审查委员会的现有伦理许可，包括参与者知情同意书和严格的质量控制，见表1。
1.2　暴露相关遗传变异的筛选与确定
本研究用于MR分析的工具变量是根据以下标准选择的：（1）选择暴露相关的GWAS数据中P<5×10-8的SNPs。（2）以r2=0.001，kb=10 000为参数删除掉存在连锁不平衡的SNP。（3）设置次等位基因频率（Minor Allele Frequency，MAF）>0.01用来排除回文SNPs的干扰。（4）计算每个暴露工具变量的F值，且保证F>10。
1.3　因果效应的计算
本研究使用R（4.2.1版本）进行孟德尔随机化分析，使用的软件包为“TwoSampleMR”（版本 0.5.6）。为了结合多个SNPs评估暴露与结局的因果关系，采用四种方法进行MR，包括逆方差加权法（inverse-variance weighted, IVW）（固定效应模型、随机效应模型）、Mr-Egger回归、加权中位数法（weighted median estimator，WME）和MR-PRESSO法。IVW是一种获得对多个SNP的每个Wald比率进行汇总分析的MR传统方法，加权中位数法，允许存在50%的无效SNPs。MR-Egger回归，则允许在SNP之间存在水平多效性。若三个方法结果相同，则证明该结果是可靠的[12]。MR-PRESSO将SNP结果估计数与SNP暴露估计数进行回归，以检验离群的SNPs，并输出修正后的MR估计数。最后为了得到每个因素对HUA的独立影响，使用MVMR进行了深入分析。
1.4　质量控制
使用MR-egger回归法计算多效性的大小；异质性检验使用Cochran Q检验；敏感性分析使用R包中的“leave-one-out”法，判断结果的稳定性。检验水准α=0.05。
2　结果
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2.1　两样本孟德尔随机化分析
以与HUA相关的变量BMI、血脂、血糖、饮酒、职业为体力劳动、猪肉、羊肉、禽肉、豆类及其制品、奶及奶制品的摄入量为暴露，以UA为结果，分别进行两样本孟德尔随机化研究，结果如图1所示。
对于BMI共纳入了437个SNPs作为工具变量，IVW法结果显示BMI与UA可能存在因果关系（b=0.298, P=6.61×10-139），且三种方法的结果一致，见图2。多效性检验显示截距项为0.000 2（P=0.91），不存在水平多效性。异质性检验结果为存在异质性（P<0.05），但随机效应IVW结果（b=0.298，P=4.05×10-23）仍然显著，使用leave-one-out法进行敏感性分析，结果仍然显著，因果关联方向与先前一致。
IVW固定效应模型结果显示高密度脂蛋白胆固醇（High Density Lipoprotein Cholesterol, HDL）可能与UA存在因果关系（b=-0.153，P=7.26×10-51），与IVW随机效应模型结果相同（b=-0.015，P=1.01×10-10）。异质性检验结果显示存在异质性（P<0.05），且多效性检验结果提示截距项为-0.005（P<0.05），进一步使用MR-PRESSO法剔除异常SNP后，结果仍然显著（P<0.05），leave-one-out法进行敏感性分析后提示仍存在因果关系。
IVW固定效应模型结果显示，甘油三酯（Triglyceride, TG）与UA之间可能存在因果关系（b=0.192，P=4.27×10-84），不同模型结果显示TG对UA的影响如图2所示，且均与IVW结果无差异。Cochran’s Q检验结果为存在异质性（P<0.05），但随机效应IVW结果（b=0.192，P=5.14×10-16）仍然显著。Mr-Egger法显示截距项为0.0037（P<0.05），存在水平多效性，进一步使用MR-PRESSO法剔除异常SNP后，结果仍然显著（P<0.05），且参考Mr-Egger回归法结果（b=0.100，P=0.004）是显著的，使用leave-one-out法进行敏感性分析，提示存在因果关系。
在饮酒与UA的两样本MR分析中，用同样的方法筛选了68个SNPs，不同模型下，MR结果不支持饮酒和UA存在因果关系：随机效应模型IVW法（b=-0.134，P=0.334）；MR-Egger回归法（b=-0.722，P=0.066）；加权中位数法（b=0.141，P=0.079）。
对于羊肉、禽肉、豆及豆制品和奶类及其制品摄入量，分别筛选出了4、5、2、2个与暴露相关的SNPs，结果（均为固定效应IVW模型）不支持羊肉（b=0.031，P=0.364）、禽肉（b=0.030，P=0.371）、豆及豆制品（b=-0.055 6，P=0.442）和奶类及其制品（b=0.169，P=0.270）摄入量与UA有因果关系。
在猪肉摄入量与UA的两样本MR中，筛选出7个SNPs作为工具变量，IVW随机效应模型结果显示（b=0.102，P=0.000 1），猪肉摄入量与UA可能存在因果关系，见图2。且多效性检验结果显示截距项为-0.008（P=0.559），即不存在水平多效性，虽然异质性检验结果提示存在异质性（P=0.001），但是随机效应IVW结果（b=0.102，P=0.025）仍是显著的，leave-one-out法的敏感性分析结果仍然显著。
2.2　多变量孟德尔随机化分析
经过两样本MR分析，在多个与HUA相关的因素中筛选出了BMI、HDL、TG、猪肉摄入量这四个与HUA可能存在因果关系的变量，以这四个变量为暴露，UA为结果进行多变量MR分析：在控制其他三个变量的情况下，BMI（b=0.246，P=8.89×10-9）、HDL（b=-0.074，P=0.046）、TG（b=0.145，P=7.32×10-5）与UA的因果关系仍然成立，而猪肉摄入量和UA的因果关系无统计学意义（b=-0.074，P=0.096），见表2。
3　讨论
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饮食与高尿酸血症的关系已引起了学者们的广泛关注。有研究表明，受试者的生活方式对高尿酸血症的发生有显著影响，如饮食习惯，以及一些人口学特征[13]。一项动物实验结果提示，高蛋白饮食提高了鸡的血清尿酸水平并长期维持在高水平[14]。在本研究中，两样本孟德尔随机化研究的结果提示猪肉摄入量与尿酸可能存在因果关系，这表明此因素与高尿酸血症患病的相关性最强。这可能是因为猪肉和羊肉是富含嘌呤的动物源性食物，且猪肉中嘌呤总含量较高[15]，长时间过量摄入会使机体尿酸摄入大于排泄，最终导致尿酸升高。而在多变量孟德尔随机化分析中，控制BMI，HDL，TG后，猪肉摄入量与尿酸之间因果关联消失，由此推断猪肉摄入量与尿酸之间的因果关联可能是由于BMI等指标介导的，二者之间不存在直接的因果关系。
肥胖已被公认为各种临床疾病和不良健康结果的危险因素，高尿酸血症就是其中之一。一项基于NHANES 2017—2018年数据的研究表明BMI与尿酸之间存在显著正相关，超重和肥胖个体的尿酸水平高于BMI正常者[16]。儿童期的超重肥胖可能会增加高尿酸血症的风险[17]。一项2022年基于中国西南地区壮族人口的队列研究的结果显示，高BMI和中心性肥胖是高尿酸血症的危险因素[18]，而且这一结果在多变量孟德尔随机化研究中得到了印证，即BMI与尿酸有独立的因果关系。有研究表明肥胖在饮食与高尿酸血症因果之间的关系中起着关键的中介作用[19]，这也解释了本研究中单变量孟德尔随机化研究提示猪肉与尿酸可能存在因果关系，而在多变量孟德尔随机化研究中这一因果关系并不显著，可能就是由于BMI的中介效应导致的。
一项中国西北地区的研究显示血脂异常会增加高尿酸血症的风险[20]，在3 884名受试者中展开的一项回顾性研究结果提示高甘油三酯血症人群发生高尿酸血症的风险是正常甘油三酯的2.353倍[21]。Liu等人在中国进行的为期4年的队列研究表明，TG/HDL-C比率有可能作为预防高尿酸血症的独立风险指标[22]。这与本研究是一致的：甘油三酯升高以及高密度脂蛋白的降低与尿酸升高存在独立的因果关系，表明高水平甘油三酯以及低水平高密度脂蛋白胆固醇可能会升高高尿酸血症的风险。
在流行病学调查结果中显示，饮酒和高尿酸血症的发生呈正相关[11]，一项探究中国成年人长期饮酒行为改变模式及其与高尿酸血症的关系的研究结果显示，继续饮酒与男性参与者高尿酸血症的高风险呈正相关[23]。而本研究饮酒和尿酸的两样本孟德尔随机化的结果则提示，饮酒和尿酸之间可能不存在因果关系，这与Syed等人的孟德尔随机化研究结果一致，每周饮酒与痛风风险和血清尿酸水平均无因果关系，且饮酒量增加与痛风的发展无关[24]。孟德尔随机化分析的一个主要限制是用于饮酒的数据集没有区分饮酒的类型。不同的酒精类型可能会带来不同的高尿酸血症的发生风险，啤酒尤其如此，与其他酒类相比，其分解会导致血清尿酸水平升高。
本研究存在一定的局限性，在进行基于大型GWAS数据的孟德尔随机化研究时，使用了欧洲人群的基因数据，不同人种间基因数据存在一定差异，但仍具备一定的参考价值。所有风险因素合并，对高尿酸血症的判别效率依然较为有限，尚需寻找更强的发病因素，使模型具有实质性的临床应用价值。
综上所述，身体质量指数、高密度脂蛋白胆固醇、甘油三酯与尿酸具有因果关系。限制高嘌呤、高脂食物的摄入，控制体重，同时加强关于高尿酸血症的健康教育是高尿酸血症防治的科学措施。
基金
收起
  • 国家自然科学基金资助项目(81660564)
  • 内蒙古自然科学基金资助项目(2023MS08059)
  • 内蒙古自治区医疗卫生科技计划项目(202201182)
  • 内蒙古医科大学“致远”人才计划(ZY0201013)
  • 2020年度内蒙古自治区“草原英才”工程青年创新创业人才培养计划
文献
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2024年第51卷第2期
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doi: 10.20043/j.cnki.MPM.202309044
  • 接收时间：2023-09-04
  • 首发时间：2026-03-19
  • 出版时间：2024-01-25
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  • 收稿日期：2023-09-04
基金
国家自然科学基金资助项目(81660564)
内蒙古自然科学基金资助项目(2023MS08059)
内蒙古自治区医疗卫生科技计划项目(202201182)
内蒙古医科大学“致远”人才计划(ZY0201013)
2020年度内蒙古自治区“草原英才”工程青年创新创业人才培养计划
作者信息
    1.内蒙古医科大学 公共卫生学院，内蒙古 呼和浩特 010110
    2.内蒙古医科大学 慢性病分子流行病学重点实验室
    3.内蒙古医科大学附属医院 神经内科

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Family		 属数
Number of
genus		 种数
Number of
species		 占总种数比例
Percentage of
total species (%)		 属
Genus		 种数
Number of
species		 占总种数比例
Percentage of total
species (%)
鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
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
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
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