Literature DB >> 31532013

Depression prevalence in Type 2 diabetes is not related to diabetes-depression symptom overlap but is related to symptom dimensions within patient self-report measures: a meta-analysis.

K A Harding1, M E Pushpanathan2, S R Whitworth3, S Nanthakumar2, R S Bucks2, T C Skinner4.   

Abstract

AIM: Depression is common in Type 2 diabetes, yet rates vary. Overlap between symptoms of depression and diabetes may account for this variability in depression prevalence rates. We examined to what extent depression prevalence was a function of the proportion of depression-diabetes symptom overlap (items within symptom dimensions) and sample characteristics.
METHODS: Electronic and hand searching of published and unpublished works identified 147 eligible papers. Of 3656 screened, 147 studies (149 samples, N = 17-229 047, mean sample age 25.4-82.8 years, with 152 prevalence estimates), using 24 validated depression questionnaires were selected. Sample size, publication type, sample type, gender, age, BMI, HbA1c , depression questionnaire and prevalence rates were extracted.
RESULTS: Prevalence rates ranged from 1.8% to 88% (mean = 28.30%) and were higher in younger samples, samples with higher mean HbA1c and clinic samples. Diabetes-depression symptom overlap did not affect prevalence. A higher proportion of anhedonia, cognition, cognitive, negative affect and sleep disturbance symptoms, and a lower proportion of somatic symptoms were consistently associated with higher depression prevalence.
CONCLUSIONS: The lack of an overall effect of diabetes-depression symptom overlap might suggest that assessment of depression in Type 2 diabetes is generally not confounded by co-occuring symptoms. However, questionnaires with proportionally more or fewer items measuring other symptom categories were associated with higher estimates of depression prevalence. Depression measures that focus on the cardinal symptoms of depression (e.g. negative affect and cognition), limiting symptoms associated with increasing diabetes symptomatology (e.g. sleep disturbance, cognitive) may most accurately diagnose depression.
© 2019 Diabetes UK.

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Year:  2019        PMID: 31532013     DOI: 10.1111/dme.14139

Source DB:  PubMed          Journal:  Diabet Med        ISSN: 0742-3071            Impact factor:   4.359


  5 in total

1.  Characterization of Symptoms and Symptom Clusters for Type 2 Diabetes Using a Large Nationwide Electronic Health Record Database.

Authors:  Veronica Brady; Meagan Whisenant; Xueying Wang; Vi K Ly; Gen Zhu; David Aguilar; Hulin Wu
Journal:  Diabetes Spectr       Date:  2022-01-11

2.  Improvements in Depression Outcomes Following a Digital Cognitive Behavioral Therapy Intervention in a Polychronic Population: Retrospective Study.

Authors:  Aarathi Venkatesan; Benjamin Forster; Prasanna Rao; Melissa Miller; Michael Scahill
Journal:  JMIR Form Res       Date:  2022-07-05

Review 3.  The burden and risks of emerging complications of diabetes mellitus.

Authors:  Dunya Tomic; Jonathan E Shaw; Dianna J Magliano
Journal:  Nat Rev Endocrinol       Date:  2022-06-06       Impact factor: 47.564

4.  Adults with type 2 diabetes benefit from self-management support intervention regardless of depressive symptoms.

Authors:  Lindsay S Mayberry; Lyndsay A Nelson; Jeffrey S Gonzalez
Journal:  J Diabetes Complications       Date:  2021-08-18       Impact factor: 2.852

5.  The prevalence of and factors associated with antenatal depression among all pregnant women first attending antenatal care: a cross-sectional study in a comprehensive teaching hospital.

Authors:  Jiamei Guo; Anhai Zheng; Jinglan He; Ming Ai; Yao Gan; Qi Zhang; Lulu Chen; Sisi Liang; Xiaoyu Yu; Li Kuang
Journal:  BMC Pregnancy Childbirth       Date:  2021-10-26       Impact factor: 3.007

  5 in total

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