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中华肥胖与代谢病电子杂志 ›› 2021, Vol. 07 ›› Issue (01) : 24 -29. doi: 10.3877/cma.j.issn.2095-9605.2021.01.005

所属专题: 文献

论著

基于血糖检验指标及聚类分析构建门诊精神分裂症患者分类判别预测模型
黄丽红1, 萧鲲2,(), 张翠玲1, 江妙玲1, 余敏1   
  1. 1. 510370 广州,广州医科大学附属脑科医院门急诊科
    2. 510370 广州,广州医科大学附属脑科医院内科
  • 收稿日期:2020-08-28 出版日期:2021-02-28
  • 通信作者: 萧鲲
  • 基金资助:
    广东省医学科学技术研究基金项目(B2020042); 广州市卫生健康科技一般项目(20201A010031)

The classification discriminant prediction model of schizophrenia patients in outpatient department was established based on blood glucose test indexes and cluster analysis

Lihong Huang1, Kun Xiao2,(), Cuiling Zhang1, Miaoling Jiang1, Min Yu1   

  1. 1. Department of Outdoor Emergency, Affiliated Brain Hospital of Guangzhou Medical University, Guangzhou 510370, China
    2. Internal medicine, Affiliated Brain Hospital of Guangzhou Medical University, Guangzhou 510370, China
  • Received:2020-08-28 Published:2021-02-28
  • Corresponding author: Kun Xiao
引用本文:

黄丽红, 萧鲲, 张翠玲, 江妙玲, 余敏. 基于血糖检验指标及聚类分析构建门诊精神分裂症患者分类判别预测模型[J/OL]. 中华肥胖与代谢病电子杂志, 2021, 07(01): 24-29.

Lihong Huang, Kun Xiao, Cuiling Zhang, Miaoling Jiang, Min Yu. The classification discriminant prediction model of schizophrenia patients in outpatient department was established based on blood glucose test indexes and cluster analysis[J/OL]. Chinese Journal of Obesity and Metabolic Diseases(Electronic Edition), 2021, 07(01): 24-29.

目的

基于随机空腹血糖(FPG)、糖化血红蛋白(HbA1c)及糖化血清蛋白(GSP)及聚类分析构建门诊精神分裂症(schizophrenia,SP)患者的分类判别预测模型,为SP患者共患糖代谢异常的早期诊治、监测管理和成本控制等策略的优化提供参考。

方法

回顾2013年12月01日至2020年05月31于广州医科大学附属脑科医院门诊就诊SP患者的性别、年龄、门诊诊断、FPG、HbA1c及GSP等检验结果等资料,进行聚类及判别分析,并将分类后的数据进行对比分析。

结果

(1)共纳入2047例患者的资料,门诊SP中年患者的各类血糖指标异常的比例较高,HbA1c(5.898±1.354),GSP(1.877±1.354),FPG(7.055±430);(2)短期血糖指标监测中,男性升高的比例更高,差异具有统计学意义(P<0.05);(3)可将其聚类分为3类,构建判别分类预测模型,第一类的模型为Y1=-24.477+4.496HbA1c+6.781 GSP+1.641FPG,第二类的模型为Y2=-139.639+6.404HbA1c+8.733GSP+8.592FPG,第三类的模型为Y3=-49.354+5.502HbA1c+6.747GSP+3.831FPG;(4)对基于聚类的三组数据进行组间对比,发现这三类在年龄及HbA1c、GSP、FPG均存在显著差异(P<0.05)。

结论

门诊SP患者血糖管理普遍较差,尤其是在男性的短期血糖指标管理上;基于血糖指标及聚类法构建的分类判别预测模型效果较好,可利用该结果探讨对门诊SP中年患者实施个体化监测,为此类糖代谢异常患者提供早期诊治。

Objective

Based on random fasting plasma glucose (FPG), glycosylated hemoglobin A1c (HbA1c) and glycosylated serum protein (GSP) and cluster analysis to construct a classification discriminant prediction model for middle-aged outpatients with schizophrenia (SP), which could provide reference for the early diagnosis and treatment, monitoring management and cost control strategies of SP patients with comorbidity of glucose metabolism.

Methods

from December 1, 2013 to May 31, 2020, the data of gender, age, outpatient diagnosis, FPG, HbA1c and GSP test results of SP patients in the outpatient department of Brain Hospital Affiliated to Guangzhou Medical University were reviewed, and the clustering and discriminant analysis were conducted, and the classified data were compared and analyzed.

Results

(1) A total of 2047 patients were included in the study. The proportion of abnormal blood glucose indexes was higher in middle-aged patients with SP, HbA1c (5.898 ± 1.354), GSP (1.877 ± 1.354), FPG (7.055 ± 430); (2) In the short-term blood glucose monitoring, the proportion of male increased was higher, the difference was statistically significant (P<0.05); (3) The cluster can be divided into three categories, and the discriminant classification prediction model is constructed, the first kind is Y1=-24.477+4.496HbA1c+6.781GSP+1.641FPG, the second kind is Y2=-139.639+6.404HbA1c+8.733GSP+8.592FPG, the third one is Y3=-49.354+5.502HbA1c+6.747GSP+3.831FPG; (4) Comparing the three groups of data based on clustering, it was found that there were significant differences in age, HbA1c, GSP and Glu among the three groups (P<0.05).

Conclusions

The results show that the management of blood glucose in outpatients with SP is generally poor, especially in the management of short-term blood glucose indicators of men; the classification discrimination prediction model based on blood glucose index and clustering method has a good effect, which can be used to explore the implementation of individualized monitoring for middle-aged patients with SP in outpatient department, so as to provide early diagnosis and treatment for patients with abnormal glucose metabolism.

表1 不同性别的血糖指标的对比[n(%)]
表2 系统聚类系数变化表
表3 单因素方差(聚类数=3)
图1 聚类分析碎石图
表4 特征值
表5 Wilks的Lambda
表6 分类函数系数(Fisher的线性判别式函数)
表7 协方差矩阵预测分类结果
表8 基于聚类分组的三组间数据对比
[1]
Dixon L, Weiden P, Delahanty J. Prevalence and correlates of diabetes in national schizophrenic samples[J]. Schizophrenia Bulletin, 2001, 264: 903-912.
[2]
陈大春,李艳丽,王宁, 等. 首发SP患者糖代谢异常病例对照研究[J]. 中国神经精神疾病杂志, 2009, 35(8): 490-492.
[3]
WU Xiaoli. The glycometabolism abnormality among schizophrenia patients[J]. China Medical Abstracts, 2013(01): 62-62.
[4]
陈传刚. 内分泌-免疫网络失衡与精神分裂症的相关性研究[D]. 山东: 青岛大学, 2016.
[5]
中华医学会糖尿病学分会. 中国2型糖尿病防治指南(2017年版)[J]. 中国实用内科杂志, 2018, 38(04): 34-86.
[6]
中华医学会糖尿病学分会. 中国血糖监测临床应用指南(2015年版)[J]. 中华糖尿病杂志, 2015, 7(10): 603-613.
[7]
刘美玲. 精神分裂症外周血单个核细胞代谢组学研究[D]. 重庆医科大学, 2015: 1-76.
[8]
李娜. 住院2型糖尿病患者聚类分型及其与相关并发症的关系研究[D]. 山西医科大学, 2020.
[9]
王炜炜,宋晓燕. 糖化血红蛋白水平与老年2型糖尿病患者早期认知功能减退的关系[J]. 中华保健医学杂志, 2020, 22(3): 176-179.
[10]
刘志军,谭东云,饶荣. 糖化血红蛋白联合糖化血清蛋白检测在鉴别高血糖性质中的应用[J]. 中华实用诊断与治疗杂志, 2011, 25(1): 67-68.
[11]
尤加永,束飞. 长期住院慢性精神分裂症患者代谢综合征特点分析[J]. 齐齐哈尔医学院学报, 2020, 41(2): 169-172.
[12]
李浩军,景兰,孟根花. 4种抗精神病药物对精神分裂症患者脂糖代谢及血清催乳素水平的影响[J]. 中国医师杂志, 2020, 22(8): 1255-1258.
[13]
Mingrone C, Rocca P, Castagna F, et al. Insight in stable schizophrenia: Relations with psychopathology and cognition[J].Compr Psychiatry, 2013, 54(5): 484-492.
[14]
Lysaker PH, Buck KD, Salvatore G, et al. Lack of awareness of illness in schizophrenia: conceptualizations, correlates and treatment approaches[J]. Expert Rev Neurother, 2009, 9(7): 1035-1043.
[15]
黄丽宏,徐艳华,崔艳波, 等. 精神分裂症与糖尿病共病的居家护理[J]. 中国医药科学, 2014, 10(8): 106-108.
[16]
王晶,褚庆文,聂志文, 等. 低能量膳食对奥氮平所致体质量增加的男性精神分裂症患者糖脂代谢的影响[J]. 临床精神医学杂志, 2020, 30(3): 182-184.
[17]
罗兴能,周文洁,黄浩然, 等. 重庆市沙坪坝区精神分裂症合并糖尿病患者疾病特征分析[J]. 现代医药卫生, 2020, 36(2): 177-179.
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