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Exp Clin Endocrinol Diabetes 2023; 131(10): 554-561
DOI: 10.1055/a-2148-9789
Article

Association Between Diabetes and Personality Traits Among the Elderly in China: A Latent Class Analysis

Peisheng Xiong‡
1   Zhanggong District Center for Disease Control and Prevention, Ganzhou, Jiangxi, Peoples R China
,
Wanbao Ye‡
2   Cancer Hospital of Shantou University Medical College, Shantou, Guangdong, Peoples R China
,
Meijuan Xiong
3   Shenzhen Cancer Hospital, Shenzhen, Guangdong, Peoples R China
,
Kangkang Chen
4   Shaoxing Center for Disease Control and Prevention, Shaoxing, Zhejiang, Peoples R China
,
Kai Xu
1   Zhanggong District Center for Disease Control and Prevention, Ganzhou, Jiangxi, Peoples R China
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Abstract

Background The present study aimed to identify individuals with different personalities using latent class analysis and further distinguish those with a high risk of diabetes among different clusters.

Methods Data were utilized from a large-scale, cross-sectional epidemiological survey conducted in 2018 across 23 provinces in China, employing a multi-stage, stratified sampling technique. Latent class cluster analysis was performed to identify distinct personality clusters based on a series of variables concerning life attitudes. Logistic regression was used to calculate adjusted odds ratios (AORs) after controlling for potential confounding variables, including age, gender, body mass index, smoking status, alcohol consumption, hypertension, and physical activity levels, to determine the association between these groups and diabetes.

Results Four distinct personality clusters were identified, namely the energy-poor (2.0%), self-domination (61.3%), optimistic (21.3%), and irritable (15.4%) groups. The prevalence of diabetes in these groups was 14.6%, 9.7%, 9.3%, and 11.6%, respectively. After adjusting for potential confounders, the “energy-poor group” exhibited more odds of having diabetes as compared to the “optimistic group” (AOR 1.683, 95%CI: 1.052–2.693; P=0.030).

Conclusion This study identified an energy-poor group of individuals with a high risk of diabetes. Targeted interventions should consider the emotional and personality characteristics of the elderly.

Key words

latent class analysis - personality - diabetes - chinese longitudinal healthy longevity survey

Additional material



Publication History

Received: 08 June 2023
Received: 17 July 2023

Accepted: 03 August 2023

Accepted Manuscript online:
04 August 2023

Article published online:
14 September 2023

© 2023. Thieme. All rights reserved.

Georg Thieme Verlag KG
Rüdigerstraße 14, 70469 Stuttgart, Germany

 

  • References

  • 1 Group CPMCJJoH. Severe hypertension in China: Results from the China PEACE Million Persons Project 2021; 39: 461-470
    PubMed
  • 2 Xie Y, Bowe B, Mokdad AH. et al. Analysis of the Global Burden of Disease study highlights the global, regional, and national trends of chronic kidney disease epidemiology from 1990 to 2016. Kidney Int 2018; 94: 567-581
    CrossrefPubMedSearch in Google Scholar
  • 3 Islam Z, Akter S, Inoue Y. et al. Prediabetes, diabetes, and the risk of all-cause and cause-specific mortality in a Japanese working population: Japan Epidemiology Collaboration on Occupational Health Study. Diabetes Care 2021; 44: 757-764
    CrossrefPubMedSearch in Google Scholar
  • 4 Cho NH, Shaw JE, Karuranga S. et al. IDF Diabetes Atlas: Global estimates of diabetes prevalence for 2017 and projections for 2045. Diabetes Res Clin Pract 2018; 138: 271-281
    CrossrefPubMedSearch in Google Scholar
  • 5 Alkaf B, Blakemore AI, Järvelin MR. et al. Secondary analyses of global datasets: Do obesity and physical activity explain variation in diabetes risk across populations?. Int J Obes 2021; 45: 944-956
    CrossrefPubMedSearch in Google Scholar
  • 6 Jayedi A, Soltani S, Abdolshahi A. et al. Healthy and unhealthy dietary patterns and the risk of chronic disease: An umbrella review of meta-analyses of prospective cohort studies. Br J Nutr 2020; 124: 1133-1144
    CrossrefPubMedSearch in Google Scholar
  • 7 Mustafina SV, Rymar OD, Shcherbakova LV. et al. The risk of type 2 diabetes mellitus in a Russian population cohort according to data from the HAPIEE Project. J Pers Med 2021; 11
    CrossrefPubMedSearch in Google Scholar
  • 8 Luo J, Manson JE, Weitlauf JC. et al. Personality traits and diabetes incidence among postmenopausal women. Menopause 2019; 26: 629-636
    CrossrefPubMedSearch in Google Scholar
  • 9 Judge TA, Higgins CA, Thoresen CJ. et al. The big five personality traits, general mental ability, and career success across the life span 2006; 52: 621-652
    PubMedSearch in Google Scholar
  • 10 Digman JM. Personality structure: Emergence of the five-factor model. Annu Rev Psychol 1990; 41: 417-440
    CrossrefPubMedSearch in Google Scholar
  • 11 Goodwin RD, Friedman HS. Health status and the five-factor personality traits in a nationally representative sample. J Health Psychol 2006; 11: 643-654
    CrossrefPubMedSearch in Google Scholar
  • 12 Jokela M, Elovainio M, Nyberg ST. et al. Personality and risk of diabetes in adults: Pooled analysis of 5 cohort studies. Health Psychol 2014; 33: 1618-1621
    CrossrefPubMedSearch in Google Scholar
  • 13 Rhodes RE, Smith NE. Personality correlates of physical activity: A review and meta-analysis. Br J Sports Med 2006; 40: 958-965
    CrossrefPubMedSearch in Google Scholar
  • 14 Phillips AC, Batty GD, Weiss A. et al. Neuroticism, cognitive ability, and the metabolic syndrome: The Vietnam Experience Study. J Psychosom Res 2010; 69: 193-201
    CrossrefPubMedSearch in Google Scholar
  • 15 Zeng Y, Zheng Z. Longitudinal survey and interdisciplinary research on healthy aging. China Popul Dev Stud 2020; 3: 293-296
    CrossrefPubMedSearch in Google Scholar
  • 16 Zheng Z. Twenty years’ follow-up on elder people’s health and quality of life. China Popul Dev Stud 2020; 3: 297-309
    CrossrefPubMedSearch in Google Scholar
  • 17 Kim SY. Determining the number of latent classes in single- and multi-phase growth mixture models. Struct Equ Model 2014; 21: 263-279
    CrossrefPubMedSearch in Google Scholar
  • 18 Lewis JB, DAJJoss Linzer. poLCA: An R package for polytomous variable latent class analysis. J Stat Softw 2011; 42: 1-29
    CrossrefPubMedSearch in Google Scholar
  • 19 Li Y, Teng D, Shi X. et al. Prevalence of diabetes recorded in mainland China using 2018 diagnostic criteria from the American Diabetes Association: National cross sectional study. BMJ 2020; 369: m997
    CrossrefPubMedSearch in Google Scholar
  • 20 Wasserman LI, Trifonova EA. Diabetes mellitus as a model of psychosomatic and somatopsychic interrelationships. Span J Psychol 2006; 9: 75-85
    CrossrefPubMedSearch in Google Scholar
  • 21 Kolb H, Martin S. Environmental/lifestyle factors in the pathogenesis and prevention of type 2 diabetes. BMC Med 2017; 15: 131
    CrossrefPubMedSearch in Google Scholar
  • 22 Cancan H, Jianran S, Datong D. et al. Study on the relationship between personality traits and diabetes. Medical Recapitulate. 2017
    PubMedSearch in Google Scholar
  • 23 Croxson SC, Burden AC, Bodington M. et al. The prevalence of diabetes in elderly people. Diabet Med 1991; 8: 28-31
    CrossrefPubMedSearch in Google Scholar
  • 24 Lin X, Xu Y, Pan X. et al. Global, regional, and national burden and trend of diabetes in 195 countries and territories: An analysis from 1990 to 2025. Sci Rep 2020; 10: 14790
    CrossrefPubMedSearch in Google Scholar
  • 25 Creviston T, Quinn L. Exercise and physical activity in the treatment of type 2 diabetes. Nurs Clin North Am 2001; 36: 243-271 vi
    CrossrefPubMedSearch in Google Scholar
  • 26 Balducci S, Sacchetti M, Haxhi J. et al. Physical exercise as therapy for type 2 diabetes mellitus. Diabetes Metab Res Rev 2014; 30: 13-23
    CrossrefPubMedSearch in Google Scholar
  • 27 Sanz C, Gautier JF, Hanaire H. Physical exercise for the prevention and treatment of type 2 diabetes. Diabetes Metab 2010; 36: 346-351
    CrossrefPubMedSearch in Google Scholar
  • 28 Ni W, Yuan X, Zhang J. et al. Factors associated with treatment and control of hypertension among elderly adults in Shenzhen, China: A large-scale cross-sectional study. BMJ Open 2021; 11: e044892
    CrossrefPubMedSearch in Google Scholar
  • 29 Li D, Lv J, Liu F. et al. Hypertension burden and control in mainland China: Analysis of nationwide data 2003-2012. Int J Cardiol 2015; 184: 637-644
    CrossrefPubMedSearch in Google Scholar
  • 30 Kotsis V, Tsioufis K, Antza C. et al. Obesity and cardiovascular risk: A call for action from the European Society of Hypertension Working Group of Obesity, Diabetes and the High-risk Patient and European Association for the Study of Obesity: part B: obesity-induced cardiovascular disease, early prevention strategies and future research directions. J Hypertens 2018; 36: 1441-1455
    CrossrefPubMedSearch in Google Scholar
  • 31 Jia CF, Jiang YN, Yang ZQ. et al. Ascending aortic elasticity and related risk factors study on prehypertension patients. Am J Hypertens 2017; 30: 61-66
    CrossrefPubMedSearch in Google Scholar
  • 32 Scuteri A, Ferrucci L. Blood pressure, arterial function, structure, and aging: The role of hormonal replacement therapy in postmenopausal women. J Clin Hypertens 2003; 5: 219-225
    CrossrefPubMedSearch in Google Scholar
  • 33 Sowers JR, Epstein M, Frohlich ED. Diabetes, hypertension, and cardiovascular disease: An update. Hypertension 2001; 37: 1053-1059
    CrossrefPubMedSearch in Google Scholar
  • 34 Gross JL, de Azevedo MJ, Silveiro SP. et al. Diabetic nephropathy: Diagnosis, prevention, and treatment. Diabetes Care 2005; 28: 164-176
    CrossrefPubMedSearch in Google Scholar
  • 35 Conigrave KM, Hu BF, Camargo CA. et al. A prospective study of drinking patterns in relation to risk of type 2 diabetes among men. Diabetes 2001; 50: 2390-2395
    CrossrefPubMedSearch in Google Scholar
  • 36 Schrieks IC, Heil AL, Hendriks HF. et al. The effect of alcohol consumption on insulin sensitivity and glycemic status: A systematic review and meta-analysis of intervention studies. Diabetes Care 2015; 38: 723-732
    CrossrefPubMedSearch in Google Scholar
  • 37 Rosoff DB, Davey Smith G, Mehta N. et al. Evaluating the relationship between alcohol consumption, tobacco use, and cardiovascular disease: A multivariable Mendelian randomization study. PLoS Med 2020; 17: e1003410
    CrossrefPubMedSearch in Google Scholar
  • 38 Ruixing Y, Yiyang L, Meng L. et al. Interactions of the apolipoprotein C-III 3238C>G polymorphism and alcohol consumption on serum triglyceride levels. Lipids Health Dis 2010; 9: 86
    CrossrefPubMedSearch in Google Scholar
  • 39 Zhao X, Zhou R, Li H. et al. The effects of moderate alcohol consumption on circulating metabolites and gut microbiota in patients with coronary artery disease. Front Cardiovasc Med 2021; 8: 767692
    CrossrefPubMedSearch in Google Scholar
  • 40 Bloomgarden ZT. Obesity and diabetes. Diabetes Care 2000; 23: 1584-1590
    CrossrefPubMedSearch in Google Scholar
  • 41 Jackson JM, Defor TA, Crain AL. et al. Self-reported diabetes is a valid outcome in pragmatic clinical trials and observational studies. J Clin Epidemiol 2013; 66: 349-350
    CrossrefPubMedSearch in Google Scholar
  • 42 Malouff JM, NSJJoPP Schutte. Can psychological interventions increase optimism? A meta-analysis. J Posit Psychol 2016; 1-11
    PubMedSearch in Google Scholar
  • 43 Mohammadi N, Aghayousefi A, Nikrahan GR. et al. A randomized trial of an optimism training intervention in patients with heart disease. Gen Hosp Psychiatry 2018; 51: 46-53
    CrossrefPubMedSearch in Google Scholar