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基于营养状况指标构建简易模型对北京地区老年体检人群认知功能损害的预测价值

李海静 朱峰 刘慧娟 关锐

李海静, 朱峰, 刘慧娟, 关锐. 基于营养状况指标构建简易模型对北京地区老年体检人群认知功能损害的预测价值[J]. 中华全科医学, 2024, 22(1): 1-4. doi: 10.16766/j.cnki.issn.1674-4152.003317
引用本文: 李海静, 朱峰, 刘慧娟, 关锐. 基于营养状况指标构建简易模型对北京地区老年体检人群认知功能损害的预测价值[J]. 中华全科医学, 2024, 22(1): 1-4. doi: 10.16766/j.cnki.issn.1674-4152.003317
LI Haijing, ZHU Feng, LIU Huijuan, GUAN Rui. Predictive value of a simple model based on nutritional status indicators for cognitive impairment in elderly people undergoing physical examination in Beijing area[J]. Chinese Journal of General Practice, 2024, 22(1): 1-4. doi: 10.16766/j.cnki.issn.1674-4152.003317
Citation: LI Haijing, ZHU Feng, LIU Huijuan, GUAN Rui. Predictive value of a simple model based on nutritional status indicators for cognitive impairment in elderly people undergoing physical examination in Beijing area[J]. Chinese Journal of General Practice, 2024, 22(1): 1-4. doi: 10.16766/j.cnki.issn.1674-4152.003317

基于营养状况指标构建简易模型对北京地区老年体检人群认知功能损害的预测价值

doi: 10.16766/j.cnki.issn.1674-4152.003317
基金项目: 

北京首都卫生发展科研专项项目 2022-4-9085

详细信息
    通讯作者:

    关锐,E-mail: rain_315@yeah.net

  • 中图分类号: R151.42  R592

Predictive value of a simple model based on nutritional status indicators for cognitive impairment in elderly people undergoing physical examination in Beijing area

  • 摘要:   目的  营养状况与认知功能的关系是研究热点,本研究建立基于营养状况指标的简易模型并分析对北京地区老年体检人群认知功能损害(CI)的预测价值。  方法  选取2022年2月—2023年4月首都医科大学附属北京康复医院劳模健康管理中心的670名老年体检者,采用蒙特利尔认知评估量表(MoCA)分为认知障碍组(MoCA<26分,CI组)和无认知障碍组(n-CI组),比较2组一般资料、微型营养评估量表(MNA)和血清白蛋白(ALB)指标,分析营养状况指标与受检者CI发生的关系。  结果  670名受检者CI发生率为9.10%(61/670),MoCA评分为(22.78±3.15)分。CI组受教育程度、MNA评分[(18.47±3.57) 分vs. (23.92±3.95) 分]、血清ALB[(30.25±3.86) g/L vs. (38.70±4.26) g/L]均低于n-CI组(P<0.05)。随着MNA评分、血清ALB水平逐渐下降,CI发生率明显升高(P<0.05)。MoCA评分与MNA评分、血清ALB均呈正相关关系(P<0.05)。较低的MNA评分、血清ALB均为老年体检人群CI发生的独立危险因素(P<0.05)。基于MNA评分、血清ALB构建简易模型筛查CI发生的AUC为0.891,均高于单独预测(P<0.05)。  结论  营养状况与老年CI密切相关,基于MNA评分、血清ALB构建简易模型对北京地区老年体检人群CI发生的筛查预测价值显著。

     

  • 图  1  营养状况指标筛查北京地区老年体检人群CI发生的ROC曲线

    Figure  1.  ROC curve of CI occurrence in elderly people screened by nutritional status indicators in Beijing area

    表  1  CI组与n-CI组老年体检人群相关资料比较

    Table  1.   Data comparison between CI group and n-CI group

    组别 例数 男性/女性(例) 年龄(x±s,岁) 城市/农村(例) 吸烟史[例(%)] 饮酒史[例(%)] 在婚/其他[例(%)] ≥2种/≤1种基础疾病(例) 小学及以下/初中/高中及以上(例) MNA量表评分(x±s,分) 血清ALB(x±s, g/L)
    CI组 61 39/22 68.12±6.17 29/32 20(32.79) 24(39.34) 40(65.57) 15/46 12/22/27 18.47±3.57 30.25±3.86
    n-CI组 609 370/239 66.93±6.03 360/249 159(26.11) 190(31.20) 435(71.43) 99/510 87/210/312 23.92±3.95 38.70±4.26
    统计量 0.236a 1.466b 2.249a 1.263a 1.692a 0.921a 2.727a 6.838a 10.359b 14.618b
    P 0.627 0.143 0.134 0.261 0.193 0.337 0.099 0.033 <0.001 <0.001
    注:a为χ2值,bt值。
    下载: 导出CSV

    表  2  不同营养状况体检人群的CI发生率

    Table  2.   Comparison of CI incidence among people with different nutritional status

    MNA评分(分) 例数 CI[例(%)] 血清ALB(g/L) 例数 CI[例(%)]
    ≥24 387 36(9.30) ≥35 400 41(10.25)
    17~23 159 30(18.87) 28-34 162 37(22.84)
    <17 124 61(49.19) ≤27 108 56(51.85)
    合计 670 127(18.96) 合计 670 134(20.00)
    下载: 导出CSV

    表  3  老年体检人群MoCA评分与MNA评分、血清ALB的相关性

    Table  3.   Correlation between MoCA score, MNA score and serum ALB in elderly people undergoing physical examination

    组别 例数 MNA评分 血清ALB
    r P r P
    CI组 61 0.742 <0.001 0.754 <0.001
    n-CI组 609 0.713 <0.001 0.708 <0.001
    总受检者 670 0.726 <0.001 0.720 <0.001
    下载: 导出CSV

    表  4  北京地区670例老年体检人群CI发生的多因素logistic分析

    Table  4.   Multivariate logistic analysis of CI occurrence in 670 elderly people in Beijing

    变量 B SE Waldχ2 P OR 95% CI
    受教育程度 -0.337 0.150 5.048 <0.001 0.714 0.483~0.936
    MNA评分 0.608 0.238 6.526 <0.001 1.837 1.257~3.771
    血清ALB 0.704 0.271 6.748 <0.001 2.021 1.383~5.027
    下载: 导出CSV

    表  5  营养状况指标筛查北京地区老年体检人群CI发生的ROC曲线分析

    Table  5.   ROC curve analysis of CI occurrence in the elderly population screened by nutritional status indicators in Beijing area

    变量 截断值 AUC(95% CI) SE P
    MNA评分 19.35分 0.812(0.714~0.910) 0.054 <0.001
    血清ALB 33.01 g/L 0.770(0.650~0.888) 0.060 <0.001
    简易模型 0.891(0.806~0.977) 0.044 <0.001
    下载: 导出CSV
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  • 收稿日期:  2023-11-01
  • 网络出版日期:  2024-03-09

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