Dataset Information

Prevalence and correlates of depression in individuals with and without type 1 diabetes.

ABSTRACT:

Objective

Depression is associated with poor glycemic control and complications in people with type 1 diabetes. We assessed the prevalence of depression and antidepressant medication use among adults with and without type 1 diabetes and the association between depression and diabetes complications.

Research design and methods

In 2006-2008, the Coronary Artery Calcification in Type 1 Diabetes Study applied the Beck Depression Inventory II (BDI-II) to 458 participants with type 1 diabetes (47% male, aged 44 +/- 9 years, type 1 diabetes duration 29 +/- 9 years) and 546 participants without diabetes (nondiabetic group) (51% male, aged 47 +/- 9 years). Use of antidepressant medication was self-reported. Depression was defined as a BDI-II score >14 and/or use of antidepressant medication. Occurrence of diabetes complications (retinopathy, blindness, neuropathy, diabetes-related amputation, and kidney or pancreas transplantation) was self-reported.

Results

Mean BDI-II score, adjusted for age and sex, was significantly higher in participants with type 1 diabetes than in nondiabetic participants (least-squares mean +/- SE: 7.4 +/- 0.3 vs. 5.0 +/- 0.3; P < 0.0001). Type 1 diabetic participants reported using more antidepressant medications (20.7 vs. 12.1%, P = 0.0003). More type 1 diabetic than nondiabetic participants were classified as depressed by BDI-II cut score (17.5 vs. 5.7%, P < 0.0001) or by either BDI-II cut score or antidepressant use (32.1 vs. 16.0%, P < 0.0001). Participants reporting diabetes complications (n = 209) had higher mean BDI-II scores than those without complications (10.7 +/- 9.3 vs. 6.4 +/- 6.3, P < 0.0001).

Conclusions

Compared with nondiabetic participants, adults with type 1 diabetes report more symptoms of depression and more antidepressant medication usage. Depression is highly prevalent in type 1 diabetes and requires further study on assessment and treatment.

SUBMITTER: Gendelman N

PROVIDER: S-EPMC2660458 | biostudies-literature | 2009 Apr

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Prevalence and correlates of depression in individuals with and without type 1 diabetes.

Gendelman Nicole N Snell-Bergeon Janet K JK McFann Kim K Kinney Gregory G Paul Wadwa R R Bishop Franziska F Rewers Marian M Maahs David M DM

Diabetes care 20090126 4

<h4>Objective</h4>Depression is associated with poor glycemic control and complications in people with type 1 diabetes. We assessed the prevalence of depression and antidepressant medication use among adults with and without type 1 diabetes and the association between depression and diabetes complications.<h4>Research design and methods</h4>In 2006-2008, the Coronary Artery Calcification in Type 1 Diabetes Study applied the Beck Depression Inventory II (BDI-II) to 458 participants with type 1 di ...[more]

PMID: 19171719

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Project description:BACKGROUND:Evidence suggests that mobile health app use is beneficial for the prevention and management of type 2 diabetes (T2D) and its associated complications; however, population-based research on specific determinants of health app use in people with and without T2D is scarce. OBJECTIVE:This cross-sectional study aimed to provide population-based evidence on rates and determinants of health app use among adults with and without T2D, thereby covering a prevention perspective and a diabetes management perspective, respectively. METHODS:The study population included 2327 adults without a known diabetes diagnosis and 1149 adults with known T2D from a nationwide telephone survey in Germany conducted in 2017. Rates of smartphone ownership and health app use were estimated based on weighted sample proportions. Among smartphone owners, determinants of health app use were identified for both groups separately in multivariable logistic regression models. Sociodemographic factors, diabetes-related factors or indicators, psychological and health-related factors, and physician-provided information were selected as potential determinants. RESULTS:Among participants without known diabetes, 74.72% (1690/2327) were smartphone owners. Of those, 49.27% (717/1690) used health apps, most often to improve regular physical activity. Among participants with T2D, 42.26% (481/1149) were smartphone owners. Of those, 41.1% (171/481) used health apps, most commonly to target a healthy diet. Among people without known diabetes, determinants significantly (all P values <.05) associated with an increased likelihood of health app use compared with their reference group were as follows: younger and middle age of 18 to 44 or 45 to 64 years (odds ratios [ORs] 3.89; P<.001 and 1.76; P=.004, respectively), overweight or obesity (ORs 1.58; P<.001 and 2.07; P<.001, respectively), hypertension diagnosis (OR 1.31; P=.045), former or current smoking (ORs 1.51; P=.002 and 1.58; P<.001, respectively), perceiving health as very good (OR 2.21; P<.001), other chronic diseases (OR 1.48; P=.002), and having received health advice from a physician (OR 1.48; P<.001). A slight or high perceived diabetes risk (ORs 0.78; P=.04 and 0.23; P<.001, respectively) was significantly associated with a decreased likelihood of health app use. Among people with T2D, younger and middle age (18-64 years; OR 1.84; P=.007), female gender (OR 1.61; P=.02), and using a glucose sensor in addition or instead of a glucose meter (OR 2.74; P=.04) were significantly positively associated with health app use. CONCLUSIONS:In terms of T2D prevention, age, diabetes-related risk factors, psychological and health-related factors, and medical health advice may inform app development for specific target groups. In addition, health professionals may encourage health app use when giving advice on health behaviors. Concerning T2D management, only a few determinants seem relevant for explaining health app use among people with T2D, indicating a need for more future research on which people with T2D use health apps and why.

Project description:ObjectiveTo investigate the prevalence of microalbuminuria (MAU) among Chinese individuals without diabetes and the relationship between MAU and metabolic factors, individual socioeconomic status (SES), and regional economic development level.DesignCross-sectional study of prevalence of MAU.Setting152 urban street districts and 112 rural villages from northeast, north, east, south central, northwest and southwest China.Participants46 239 participants were recruited using a multistage stratified sampling design from 2007 to 2008. A total of 41 290 participants without diabetes determined by oral glucose tolerance test were included in the present study. Urine albumin/creatinine ratio results of 35 430 individuals were available.Primary and secondary outcome measuresPositive detection of MAU was determined using an ACR of 22.1-299.9 mg/g in men 30.9-299.9 mg/g in women.ResultsThe prevalence of MAU in men was 22.4% and 24.5% in women. In developed, intermediate-developed and under-developed areas, the prevalence of MAU in men was 20.7%, 21.9% and 32.5%, respectively; in women the prevalence was 19.6%, 26.0% and 29.5%, respectively. The prevalence of MAU increased as the number of metabolic disorders present increased, and as the number of lower SES components increased (farmer, below university education level and low income). Prevalence of MAU in developed and intermediate developed areas had adjusted risk ratios of 0.52 (95% CI 0.42 to 0.60) and 0.65 (95% CI 0.57 to 0.76), respectively. Multivariate logistic analyses demonstrated MAU was strongly associated with older age, high-blood pressure, higher blood glucose low education level, low occupational level and residence in under-developed region.ConclusionsSeveral factors had independent correlations to MAU in China: older age, metabolic abnormalities, lower SES level and living in economically under-developed areas, which encourage the development of strategies to lower the risk for MAU in these susceptible populations.

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Project description:There is increasing awareness of the high prevalence of insomnia symptoms in individuals with type 2 diabetes (T2D). Past studies have established the importance of measuring sleep parameters using measures of central tendency and variability. Additionally, subjective and objective methods involve different constructs due to the discrepancies between the two approaches. Therefore, this study is aimed at comparing the averages of sleep parameters in individuals with T2D with and without insomnia symptoms and comparing the variability of sleep parameters in these individuals. This study assessed the between-group differences in the averages and variability of sleep efficiency (SE) and total sleep time (TST) of 59 participants with T2D with and without insomnia symptoms. Actigraph measurements and sleep diaries were used to assess sleep parameter averages and variabilities calculated by the coefficient of variation across 7 nights. Mann-Whitney U tests were utilized to compare group differences in the outcomes. Validated instruments were used to assess the symptoms of depression, anxiety, and pain as covariates. Objective SE was found to be statistically lower on average (85.98 ± 4.29) and highly variable (5.88 ± 2.57) for patients with T2D and insomnia symptoms than in those with T2D only (90.23 ± 6.44 and 3.82 ± 2.05, respectively). The subjective average and variability of SE were also worse in patients with T2D and insomnia symptoms, with symptoms of depression, anxiety, and pain potentially playing a role in this difference. TST did not significantly differ between the groups on averages or in variability even after controlling for age and symptoms of depression, anxiety, and pain. Future studies are needed to investigate the underlying mechanisms of worse averages and variability of SE in individuals with T2D and insomnia symptoms. Additionally, prompting the associated risk factors of insomnia symptoms in individuals with T2D might be warranted.

Dataset Information

Prevalence and correlates of depression in individuals with and without type 1 diabetes.

Objective

Research design and methods

Results

Conclusions

Publications

Prevalence and correlates of depression in individuals with and without type 1 diabetes.

Similar Datasets

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