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老年人血脂水平与阿尔茨海默病发病风险系统评价

吕旋瑞 徐静 林晓南 周锦 李旭晗 宗晓郁 宋晶 张灵健 李欣鑫 宫建

吕旋瑞, 徐静, 林晓南, 周锦, 李旭晗, 宗晓郁, 宋晶, 张灵健, 李欣鑫, 宫建. 老年人血脂水平与阿尔茨海默病发病风险系统评价[J]. 中国公共卫生, 2022, 38(8): 1066-1069. doi: 10.11847/zgggws1138716
引用本文: 吕旋瑞, 徐静, 林晓南, 周锦, 李旭晗, 宗晓郁, 宋晶, 张灵健, 李欣鑫, 宫建. 老年人血脂水平与阿尔茨海默病发病风险系统评价[J]. 中国公共卫生, 2022, 38(8): 1066-1069. doi: 10.11847/zgggws1138716
LÜ Xuan-rui, XU Jing, LIN Xiao-nan, SONG Jing, . Lipid level and risk of Alzheimer′s disease: a systematic review and meta-analysis[J]. Chinese Journal of Public Health, 2022, 38(8): 1066-1069. doi: 10.11847/zgggws1138716
Citation: LÜ Xuan-rui, XU Jing, LIN Xiao-nan, SONG Jing, . Lipid level and risk of Alzheimer′s disease: a systematic review and meta-analysis[J]. Chinese Journal of Public Health, 2022, 38(8): 1066-1069. doi: 10.11847/zgggws1138716

老年人血脂水平与阿尔茨海默病发病风险系统评价

doi: 10.11847/zgggws1138716
详细信息
    作者简介:

    吕旋瑞(1998 – ),女,吉林四平人,硕士在读,研究方向:药物流行病学与临床药物评价

    通讯作者:

    宫建,E-mail:fanxing1230@163.com

  • 中图分类号: R 183

Lipid level and risk of Alzheimer′s disease: a systematic review and meta-analysis

  • 摘要:   目的  系统评价多种血脂水平在预测阿尔茨海默病(AD)发病风险过程中的作用。  方法  检索中国知网数据库、万方数据知识服务平台、维普数据库、Pubmed、Web of Science、Springer、Cocharne Library,并辅以手工检索和文献追溯法收集建库至2021年6月国内外公开发表的关于血脂水平与AD发病风险的相关文献;按照纽卡斯尔-渥太华质量量表评价纳入研究的质量;用RevMan5.3软件对纳入的文献进行meta分析。  结果  按照纳入和排除标准共筛选出15篇符合要求的研究文献,收集1435名健康老年人以及2162例患有AD老年人的多种血脂水平。经过meta分析结果显示,AD组老年人的总胆固醇、甘油三酯水平与健康对照组老年人差异无统计学意义;低密度脂蛋白胆固醇(LDL-C)水平显著高于对照组(MD = 3.59,95 % CI = 0.98~6.21);高密度脂蛋白胆固醇(HDL-C)水平显著低于对照组(MD = – 3.47,95 % CI = – 5.94~– 0.99)。  结论  老年人LDL-C水平偏高以及HDL-C水平偏低预示可能将有更高的AD患病风险。
  • 图  1  文献筛选流程

    表  1  纳入的15篇文献的基本信息

    纳入研究及年份国家研究对象样本量年龄
    (岁,$\bar x \pm s$)
    HDL-C
    (mg/dL,$\bar x \pm s$)
    LDL-C
    (mg/dL,$\bar x \pm s$)
    TC
    (mg/dL,$\bar x \pm s$)
    TG
    (mg/dL,$\bar x \pm s$)
    2017[8]中国AD组11871.81 ± 9.6648.43 ± 13.9106.29 ± 50.5
    对照组12070.45 ± 9.5252.52 ± 13.1105.40 ± 38.1
    2012[9]中国AD组10477.8 ± 6.7451.43 ± 1295.13 ± 25.5173.24 ± 34.4120.46 ± 54.9
    对照组10476.5 ± 6.1455.68 ± 1292.81 ± 31.7158.55 ± 40.2116.92 ± 62.9
    2014[10]中国AD组4480.00 ± 8.92171.31 ± 40.2118.69 ± 68.2
    对照组6279.63 ± 7.85164.35 ± 48.0124.89 ± 85.9
    2014[11]中国AD组4869.32 ± 5.5345.9 ± 10.3138.2 ± 34.7
    对照组3771.06 ± 5.8757.8 ± 14.6136.8 ± 16.4
    2015[12]中国AD组20176.79 ± 5.6551.04 ± 13.9100.54 ± 24.2197.99 ± 37.9163.86 ± 99.2
    对照组25775.88 ± 6.5051.43 ± 29.895.13 ± 43.7188.32 ± 26.7175.38 ± 124.0
    2016[13]中国AD组20780.67 ± 8.18850.27 ± 13.1107.5 ± 34.4184.07 ± 41.4
    对照组25681.66 ± 6.38253.36 ± 15.9104.41 ± 38.7176.72 ± 48
    2005[14]中国AD组3569.1 ± 8.256.84 ± 11.6158.55 ± 34.42246.71 ± 41.8
    对照组1669.6 ± 6.955.3 ± 10.1141.53 ± 24.0222.35 ± 34.4
    2000[15]日本AD组3677.3 ± 4.969 ± 21110 ± 26194 ± 33103 ± 52
    对照组1571.8 ± 6.164 ± 19114 ± 36207 ± 40142 ± 96
    2017[16]日本AD组4280.5 ± 5.7118.8 ± 39.4199.3 ± 45.1119.1 ± 68.4
    对照组1875.6 ± 5.5119.2 ± 35.7211.1 ± 39.9140.7 ± 66.3
    2009[17]日本AD组19780 ± 1458 ± 14123 ± 2882 ± 42.0
    对照组4775 ± 6.8656 ± 20.6121 ± 27.486 ± 27.4
    2005[18]日本AD组10679 ± 7106 ± 34181 ± 3792 ± 50.0
    对照组22776 ± 10100 ± 37184 ± 41104 ± 42.0
    2005[19]土耳其AD组12074.0 ± 7.6129.1 ± 44.2213.9 ± 44.6147.1 ± 86.5
    对照组80371.4 ± 5.9130.3 ± 60.9212.0 ± 46.5144.7 ± 76.0
    2017[20]塞尔维亚AD组6273.1 ± 5.850.27 ± 11.6132.86 ± 62.0
    对照组4068.4 ± 5.561.48 ± 11.6141.72 ± 97.4
    2018[21]巴西AD组4078 ± 8.495.16 ± 25.01157.01 ± 24.91
    对照组4076.5 ± 7.688.57 ± 28.15165.32 ± 34.03
    2014[22]印度AD组7540.5 ± 9.5104.4 ± 28.8170.7 ± 31.7129.0 ± 43.7
    对照组12042.4 ± 8.3100.6 ± 21.8167.6 ± 22.8122.6 ± 31.3
    下载: 导出CSV
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出版历程
  • 收稿日期:  2022-04-22
  • 网络出版日期:  2022-07-29
  • 刊出日期:  2022-08-31

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