Plain English summary
Diabetes is one of the main noncommunicable diseases with an increasing prevalence in the world, which has turned it into a serious public health problem. In Colombia, in 2019, diabetes affected 8.4% of the adult Colombian population and more than one million adult Colombians in this age group have hidden or undetected diabetes. Diabetes is not only characterized by increased premature mortality, loss of productivity, economic impact, but it also entails a deterioration in the quality of life of people with diabetes and their respective families. However, little is known about health-related quality of life (HRQOL) in population with different glycemic states. This study investigated the quality of life in patients with diabetes risk, glucose intolerance, impaired fasting glucose, T2D, and its association with some sociodemographic, lifestyle, and background variables, and established the difference of these in two territories of Colombia. The results of this study indicated that no statistically significant associations were found in HRQOL in the NGT, prediabetes, and UT2D groups. However, some sociodemographic and clinical factors were significant predictors of HRQOL in some glycemic groups. Therefore, more attention should be paid to these determinants of HRQL to design and implement strategies to improve these variables, aiming to reduce the risk of deterioration in the quality of life of prediabetic or diabetic patients.
Type 2 diabetes (T2D) has become one of the fastest growing health emergencies of the twenty-first century. An estimated 537 (10.5%) million adults worldwide have diabetes, and 860 (16.8%) million have prediabetes, i.e., impaired glucose tolerance (IGT) and/or impaired fasting glycemia (IFG). It is expected that, by 2045, the prevalence of diabetes will be 12.2%, while that of prediabetes will reach 17.5% [1
]. In Colombia, the prevalence of diabetes and prediabetes is 8.3% and 20.7%, respectively, and it is estimated that at least 1 in 3 diabetics do not know they have diabetes (UT2D) [1
]. For these reasons, it is essential to identify people at risk of diabetes, prediabetes or unknown diabetes on a time [3
Multiple studies show that T2D negatively affects HRQOL, due to the chronicity condition, metabolic control, treatments, and complications of the disease [8
]. However, most of these studies are in population with a previous diagnosis of diabetes, so very little is known about what HRQOL is like in subjects with unknown type 2 diabetes (UT2D). Likewise, there is no clear consensus about HRQOL in prediabetic states, due to the limited and controversial literature on HRQOL in population with prediabetes [19
]. In addition, most studies assessing HRQOL in populations at risk of diabetes only include participants with glycemic impairment, so there are few studies that include normoglycemic subjects (NGT) who are at risk of developing T2D according to The Finnish Diabetes Risk Score (FINDRISC ≥ 12) [3
Measurement of HRQOL refers to the subjective assessment of a broad range of dimensions of current functional health that affect overall well-being [25
]. There is now a growing interest in including patient-reported outcome measures (PROs) because they capture aspects of treatment effect that may not be captured in the main clinical outcomes [26
]. Hence their great importance in the field of chronic diseases or epidemiological studies as an indicator of clinical effectiveness and health economic evaluation [26
HRQOL is assessed using disease-specific measures or generic instruments such as the Short Form 36 (SF-36) [28
], the health utility index (HUI) [29
], and the EQ-5D [30
], which stands out, initially assessing health status in severity levels by dimensions and then on a visual analog scale (VAS) [31
]. In recent scientific literature, the EQ-5D is the most used instrument to assess HRQOL in the T2D population; however, it has been little used in the population at risk for diabetes or prediabetes [33
]. In a previous study with the same data set, the determinants of HRQOL were established according to the risk of diabetes [24
]. However, that study did not consider establishing the HRQOL for each glycemic status, which is very important if one wishes to calculate quality-adjusted life years (QALYs) in those studies that seek to evaluate the effects of an intervention or the evaluation of a new health technology.
To our knowledge, there is no information Available at studies that establish HRQOL according to glycemic status in population at risk for T2D using the EQ-5D-3L in Colombia or Latin America. Therefore, the objective of our study was to estimate the HRQOL according to glycemic status, and its relationship with sociodemographic and clinical factors in a population at risk of developing T2D.
Our data indicate that gender, age, place of residence, educational level, marital status, and hypertension treatment were associated with HRQOL when compared by glycemic group. Likewise, we found that the greatest report of problems in each glycemic group was presented in the dimensions of Pain/Discomfort and Anxiety/Depression. The most frequent alterations in the dimensions of quality of life and the lowest HRQOL scores in the different glycemic groups were associated to a greater extent with female participants, residents of the larger city (Bogotá) and with older participants.
To our knowledge, this is one of the few studies that establishes the HRQOL of participant at risk for diabetes by means of the FINDRISC instrument, which includes participants with NGT, prediabetes, and UT2D, using the EQ-5D. Most studies of HRQOL in population at risk for diabetes do not include participants with NGT or have assessed HRQOL with other questionnaires such as the SF-36 [19
], SF-6D [23
], and the 15D HRQoL [22
]. Likewise, studies assessing HRQOL in patients with UT2D are scarce [45
]. Recent scientific literature revealed that the EQ-5D has been widely used to measure HRQOL in diabetic patients in different countries [8
], which is probably due to the ease of use of this instrument in the use of health economic evaluations, hence our interest in establishing the HRQOL of our participants with this questionnaire.
Our findings show that the dimensions of quality of life in participants with NGT, prediabetes, and UT2D with the highest reported problems are Pain/Discomfort and Anxiety/Depression, being like the results found in population at risk for diabetes [24
] and consistent with studies that measured HRQOL in patients with diabetes [8
], and, partially, with those involving patients with prediabetes or NGT [11
]. Previous studies in patients with type 2 diabetes have reported that the dimensions with the highest reported problems are Pain/Discomfort and Anxiety/Depression [8
], however, Sakamaki et al., in a 2006 study of diabetic patients in Japan reported greater problems in the dimension’s Pain/Discomfort and Mobility [13
Regarding people with prediabetes, there is no clear consensus, either due to the use of different methods of measuring HRQOL or to the scarce and controversial literature in this type of population. For example, while some studies in patients with prediabetes showed that the problems most likely reported were the dimensions of physical functioning and bodily pain [11
], the study by Seppälä et al. reported that prediabetes was not associated with low HRQOL [20
]. However, a study in Greece reported that mobility and psychological distress are the dimensions with the greatest impairment in patients with prediabetes (IGT) [22
]. In addition, Adriaanse MC et al. observed that depressive symptoms were higher in women with prediabetes compared to participants with normal glucose metabolism [46
], however, a meta-analysis in 2011 concluded that patients with normoglycemia, prediabetes, and undetected type two diabetes have similar risk of depression [47
In the present study, the mean EQ-5D score in participants with NGT, prediabetes, and UT2D was 0.80, 0.81, and 0.79 respectively, whereas the VAS scores in NGT, prediabetes, and UT2D were 75.0, 74.1, and 69.5, different from those reported in a recent study in Denmark where the mean EQ-5D scores in NGT, prediabetes, and UT2D were 0.90, 0.86, and 0.85 [45
]. In the study of Abedini et al. with diabetic patients in Iran, the mean EQ-5D and VAS scores were 0.89 and 65.22, respectively [8
]. Studies with diabetic patients in Indonesia and India showed similar results to ours, the mean EQ-5D scores were 0.77 and 0.803 respectively [16
]. Similar studies with diabetic patients using the EQ-5D in Iran, Korea, Japan, and the Netherlands reported mean EQ-5D and VAS scores of 0.70 and 56.8, 0.87 and 71.94, 0.86 and 74.3, 0.74 and 68.0, respectively [9
]. Other studies reported that the mean EQ-5D score in diabetic patients was 0.85 [48
] and 0.75 [14
On the other hand, Makrilakis et al. in 2018 in a study in Greece, using the 15D HRQOL questionnaire reported scores of 0.91, 0.90, and 0.86 for patients with NGT, prediabetes, and T2D each [22
]. Because various factors influence the HRQOL of individuals, the HRQOL scores of this study with those found in diabetic, prediabetic, or NGT population should be compared and interpreted with caution. These differences could be related to differences in the main characteristics of the subjects or to different methods of measuring HRQOL (the set of EQ-5D values used by each country is different). On the other hand, some of these studies worked with patients who were undergoing treatment for diabetes or already had complications due to the disease, whereas the participants in our study were administered the Quality-of-Life questionnaire before establishing their glycemic status, i.e. they were unaware of their diagnosis of prediabetes or occult diabetes. In our study, overall, the HRQOL of NGT, prediabetes, and UT2D were significantly equal, however, when examining certain characteristics within each glycemic group, significant differences were found in the EQ-5D score.
Our study showed that mean EQ-5D scores in participants with NGT or prediabetes were lower in females as compared to males, being similar and consistent with studies in diabetic population using the same questionnaire [8
]. Along the same lines, Neumann et al. reported that male sex was associated with a higher quality of life score [23
]. In contrast, Makrilakis et al., in 2018, reported that male sex was significantly associated with lower HRQOL [22
]. The results of our study showed that older age was associated with lower HRQOL in NGT participants like that reported in another study [23
]. The HRQOL scores of our UT2D participants were similar across age groups. In contrast to our results, other studies with diabetic patients report that older age groups were associated with lower HRQOL [10
], however, O'Reilly et al., in their study, report that the EQ-5D score of diabetics increases with age [14
]. These differences could be due to the lack of knowledge about diabetes or the characteristics of our participants. Another significant finding in the present study is that participants with NGT, prediabetes, or UT2D who resided in the larger city had lower HRQOL scores compared to those who resided in the smaller city, being consistent with the results reported by Javanbakht et al. in diabetic patients [15
Our results showed that larger city, older age, female sex, education, being on medication for hypertension, and low physical activity are associated with higher odds of reporting problems in some dimension of the EQ-5D. Reporting problems on the MO dimension are more likely in participants residing in the largest city and having the oldest age, similar with the results found in patients with T2D [8
]. Participants with NGT or prediabetics who are female and reside in the largest city are more likely to report problems in the P/D and A/D dimensions, likewise, participants with UT2D who reside in the largest city are more likely to report problems in the A/D dimension, partially correlating with the results of Abedini MR et al. in a diabetic population [8
]. Participants with NGT living in larger cities are more likely to report problems in the SC dimension just like the results of Javanbakht M, but in patients with T2D [15
]. The results of the Tobit regression model showed that female sex, older age, living in the largest city, being on hypertension medication, marital status and lower education were significantly associated with lower EQ-5D scores. Javanbakht et al. demonstrated, using a similar model in diabetic patients, that female sex, living in the largest city, and lower education were significantly associated with lower EQ-5D scores [15
The results of this study are important because they provide utility values by glycemic status for this population in Colombia, facilitating the calculation of quality-adjusted life years (QALYs), essential for health economic evaluations. However, future studies should replicate this study to validate utility values in this type of population, to determine whether self-reported perceptions of HRQOL by glycemic status are consistent with the participants' level of utility. Naturally, our study has some limitations related to the fact that the examined population is not necessarily representative of the general population, since the participants in this study are from lower socioeconomic strata; therefore, the findings are not necessarily applicable to the general population. Another limitation of this study is not having a set of EQ-5D-3L values adjusted for Colombia, so it was necessary to use the set of Latin American values [39
], assuming some similarity in the Latin American context at the population level, language, and cultural habits/customs. Likewise, the ceiling effect of the EQ-5D-3L and the low sensitivity to detect small changes in HRQOL compared with the 5-level version (EQ-5D-5L) [49
] could be considered another limitation; however, we preferred to use the 3-level version because it is the version most recommended by health economic evaluation agencies for the calculation of QALYs [40
]. We also did not include potentially useful variables such as participants' current treatments or comorbidities. Finally, as this was a cross-sectional study, the associations observed are not necessarily causal.
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