Association between depressive symptoms and diagnosis of diabetes and its complications: A network analysis in electronic health records

Abstract

ObjectivesDiabetes and its complications are commonly associated with depressive symptoms, and few studies have investigated the diagnosis effect of depressive symptoms in patients with diabetes. The present study used a network-based approach to explore the association between depressive symptoms, which are annotated from electronic health record (EHR) notes by a deep learning model, and the diagnosis of type 2 diabetes mellitus (T2DM) and its complications.MethodsIn this study, we used anonymous admission notes of 52,139 inpatients diagnosed with T2DM at the first affiliated hospital of Nanjing Medical University from 2008 to 2016 as input for a symptom annotation model named T5-depression based on transformer architecture which helps to annotate depressive symptoms from present illness. We measured the performance of the model by using the F1 score and the area under the receiver operating characteristic curve (AUROC). We constructed networks of depressive symptoms to examine the connectivity of these networks in patients diagnosed with T2DM, including those with certain complications.ResultsThe T5-depression model achieved the best performance with an F1-score of 91.71 and an AUROC of 96.25 compared with the benchmark models. The connectivity of depressive symptoms in patients diagnosed with T2DM (p = 0.025) and hypertension (p = 0.013) showed a statistically significant increase 2 years after the diagnosis, which is consistent with the number of patients diagnosed with depression.ConclusionThe T5-depression model proposed in this study can effectively annotate depressive symptoms in EHR notes. The connectivity of annotated depressive symptoms is associated with the diagnosis of T2DM and hypertension. The changes in the network of depressive symptoms generated by the T5-depression model could be used as an indicator for screening depression.

A network analysis of depressive symptoms in adults with and without diabetes: findings from the Irish longitudinal study on ageing

“…Those with diabetes at Wave 4 were predominately (87.74%) people with relatively new (within the last 5-7 years) diagnoses of diabetes. This is in line with recent research by Wan et al (2022), which found a statistically significant increase in depressive symptom connectivity in the 2 years following diagnosis. In contrast, Airaksinen et al (2020) found the connectivity of depressive symptoms remained unchanged before and after diagnosis (Airaksinen et al, 2020).…”

“…They found that although the mean level of depressive symptoms increased after diagnoses, these changes were not reflected in the depressive network structure. Recent research, also examining depressive symptoms in individuals before and after diagnosis of type 2 diabetes or hypertension, found a significant increase in depressive symptom connectivity (Wan et al, 2022). However, in both studies depressive symptoms were scored using binary values (1 for yes, and 0 for no), and therefore the network models may suffer from a lack of specificity of the strength of depressive symptom interactions.…”