Diagnostic Criteria for Depression in Type 2 Diabetes: A Data-Driven Approach

被引:13
|
作者
Starkstein, Sergio E. [2 ]
Davis, Wendy A. [1 ]
Dragovic, Milan [2 ]
Cetrullo, Violetta [1 ]
Davis, Timothy M. E. [1 ]
Bruce, David G. [1 ]
机构
[1] Univ Western Australia, Sch Med & Pharmacol, Crawley, WA, Australia
[2] Univ Western Australia, Sch Psychiat & Clin Neurosci, Crawley, WA, Australia
来源
PLOS ONE | 2014年 / 9卷 / 11期
基金
英国医学研究理事会;
关键词
MAJOR DEPRESSION; ANXIETY; DISORDERS; METAANALYSIS; COMORBIDITY; DISTRESS; SAMPLE; HEALTH;
D O I
10.1371/journal.pone.0112049
中图分类号
O [数理科学和化学]; P [天文学、地球科学]; Q [生物科学]; N [自然科学总论];
学科分类号
07 ; 0710 ; 09 ;
摘要
Background: While depression is a frequent psychiatric comorbid condition in diabetes and has significant clinical impact, the syndromal profile of depression and anxiety symptoms has not been examined in detail. Aims: To determine the syndromal pattern of the depression and anxiety spectrum in a large series of patients with type 2 diabetes, as determined using a data-driven approach based on latent class analysis (LCA). Method: Type 2 diabetes participants from the observational community- based Fremantle Diabetes Study Phase II underwent assessment of lifetime depression using the Brief Lifetime Depression Scale, the Patient Health Questionnaire 9-item version (PHQ-9) for current depression symptoms, and the Generalized Anxiety Disorder Scale that was specifically developed and validated for this study. The main outcome measure was classes of patients with a specific syndromal profile of depression and anxiety symptoms based on LCA. Results: LCA identified four classes that were interpreted as "major anxious depression'', "minor anxious depression'', "subclinical anxiety'', and "no anxious depression''. All nine DSM-IV/5 diagnostic criteria for major depression identified a class with a high frequency of major depression. All symptoms of anxiety had similar high probabilities as symptoms of depression for the "major depression-anxiety'' class. There were significant differences between classes in terms of history of depression and anxiety, use of psychoactive medication, and diabetes-related variables. Conclusions: Patients with type 2 diabetes show specific profiles of depression and anxiety. Anxiety symptoms are an integral part of major depression in type 2 diabetes. The different classes identified here provide empirically validated phenotypes for future research.
引用
收藏
页数:9
相关论文
共 50 条
  • [11] A DATA-DRIVEN APPROACH TO AN ADAPTIVE-LEARNING DIAGNOSTIC ASSISTANT
    HANCOCK, JP
    TRAN, LP
    AIAA COMPUTERS IN AEROSPACE VII CONFERENCE, PTS 1 AND 2: A COLLECTION OF PAPERS, 1989, : 518 - 522
  • [12] Data-Driven Approach for Fault Detection and Diagnostic in Semiconductor Manufacturing
    Fan, Shu-Kai S.
    Hsu, Chia-Yu
    Tsai, Du-Ming
    He, Fei
    Cheng, Chun-Chung
    IEEE TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERING, 2020, 17 (04) : 1925 - 1936
  • [13] An Approach of Integrating Domain Knowledge into Data-Driven Diagnostic Model
    Su, Guanxu
    Wen, Jianghui
    Zhu, Zhaowei
    Liu, Zhuo
    Zhao, Wei
    Sun, Xingzhi
    Hu, Gang
    Xie, Guotong
    MEDINFO 2019: HEALTH AND WELLBEING E-NETWORKS FOR ALL, 2019, 264 : 1594 - 1595
  • [14] A BENCHMARKING ANALYSIS OF A DATA-DRIVEN GAS TURBINE DIAGNOSTIC APPROACH
    Loboda, Igor
    Luis Perez-Ruiz, Juan
    Yepifanov, Sergiy
    PROCEEDINGS OF THE ASME TURBO EXPO: TURBOMACHINERY TECHNICAL CONFERENCE AND EXPOSITION, 2018, VOL 6, 2018,
  • [15] Data-driven screening for type 2 diabetes: Machine Learning on Belgian Health Expenditure Data
    Katrien Mortelmans
    TBV – Tijdschrift voor Bedrijfs- en Verzekeringsgeneeskunde, 2017, 25 (8): : 376 - 377
  • [16] Data-driven criteria for quantum correlations
    Krawczyk, Mateusz
    Pawlowski, Jaroslaw
    Maska, Maciej M.
    Roszak, Katarzyna
    PHYSICAL REVIEW A, 2024, 109 (02)
  • [17] Data-driven type 2 diabetes patient clusters predict metabolic surgery outcomes
    Flannick, Jason
    LANCET DIABETES & ENDOCRINOLOGY, 2022, 10 (03): : 150 - 151
  • [18] A Data-Driven Diagnostic Framework for Wind Turbine Structures: A Holistic Approach
    Bogoevska, Simona
    Spiridonakos, Minas
    Chatzi, Eleni
    Dumova-Jovanoska, Elena
    Hoeffer, Rudiger
    SENSORS, 2017, 17 (04)
  • [19] Machine Learning Models for Data-Driven Prediction of Diabetes by Lifestyle Type
    Qin, Yifan
    Wu, Jinlong
    Xiao, Wen
    Wang, Kun
    Huang, Anbing
    Liu, Bowen
    Yu, Jingxuan
    Li, Chuhao
    Yu, Fengyu
    Ren, Zhanbing
    INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH, 2022, 19 (22)
  • [20] Characterization of data-driven clusters in diabetes-free adults and their utility for risk stratification of type 2 diabetes
    Mendez, Diego Yacaman
    Zhou, Minhao
    Lagerros, Ylva Trolle
    Velasco, Donaji V. Gomez
    Tynelius, Per
    Gudjonsdottir, Hrafnhildur
    de Leon, Antonio Ponce
    Eeg-Olofsson, Katarina
    Ostenson, Claes-Goran
    Brynedal, Boel
    Aguilar Salinas, Carlos A.
    Ebbevi, David
    Lager, Anton
    BMC MEDICINE, 2022, 20 (01)