Abstract
Abstract
Purpose
To estimate the health-related quality of life (HRQOL) according to glycemic status, and its relationship with sociodemographic and clinical factors in a population at risk of developing type 2 diabetes (T2D).
Methods
Cross-sectional study, using cluster sampling. Data were collected from 1135 participants over 30 years of age, at risk of developing T2D from the PREDICOL project. Participants' glycemic status was defined using an oral glucose tolerance test (OGTT). Participants were divided into normoglycemic subjects (NGT), prediabetes and diabetics do not know they have diabetes (UT2D). HRQOL was assessed using the EQ-5D-3L questionnaire of the EuroQol group. Logistic regression and Tobit models were used to examine factors associated with EQ-5D scores for each glycemic group.
Results
The mean age of participants was 55.6 ± 12.1 years, 76.4% were female, and one in four participants had prediabetes or unknown diabetes. Participants reported problems most frequently on the dimensions of Pain/Discomfort and Anxiety/Depression in the different glycemic groups. The mean EQ-5D score in NGT was 0.80 (95% CI 0.79–0.81), in prediabetes, 0.81 (95% CI 0.79–0.83), and in participants with UT2D of 0.79 (95% CI 0.76–0.82), respectively. Female sex, older age, city of residence, lower education, receiving treatment for hypertension, and marital status were significantly associated with lower levels of HRQOL in the Tobit regression analysis.
Conclusions
HRQOL of NGT, prediabetes, and UT2D participants was statistically similar. However, factors such as gender, age. and place of residence were found to be significant predictors of HRQOL for each glycemic group.
Publisher
Springer Science and Business Media LLC
Subject
Public Health, Environmental and Occupational Health
Reference49 articles.
1. Cho, N., Kirigia, J., Ogurstova, K., & Reja, A. (2021). IDF diabetes Atlas (10th ed., pp. 1–150). Brussels, Belgium. International Diabetes Federation. Accessed 20 June 2022 https://www.diabetesatlas.org
2. Saeedi, P., Petersohn, I., Salpea, P., Malanda, B., Karuranga, S., Unwin, N., et al. (2019). Global and regional diabetes prevalence estimates for 2019 and projections for 2030 and 2045: Results from the International Diabetes Federation Diabetes Atlas, 9th edition. Diabetes Research and Clinical Practice; 157(107843):107843. https://doi.org/10.1016/j.diabres.2019
3. Lindström, J., & Tuomilehto, J. (2003). The diabetes risk score: A practical tool to predict type 2 diabetes risk. Diabetes Care, 26(3), 725–731. https://doi.org/10.2337/diacare.26.3.725
4. Saaristo, T., Peltonen, M., Lindström, J., et al. (2005). Cross-sectional evaluation of the Finnish Diabetes Risk Score: A tool to identify undetected type 2 diabetes, abnormal glucose tolerance and metabolic syndrome. Diabetes and Vascular Disease Research, 2, 67–72. https://doi.org/10.3132/2Fdvdr.2005.011
5. Gabriel, R., Acosta, T., Florez, K., Anillo, L., Navarro, E., Boukichou, N., et al. (2021). Validation of the Finnish type 2 diabetes risk score (FINDRISC) with the OGTT in health care practices in Europe. Diabetes Research and Clinical Practice, 178(108976), 108976. https://doi.org/10.1016/j.diabres.2021.108976
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