Depressive symptoms in hospitalized geriatric patients with and without cognitive impairment: a cross-sectional network analysis approach

Background Late-life depression is common and often co-occurs with cognitive impairment, complicating its assessment and clinical management. Network analysis allows for a nuanced understanding of how individual depressive symptoms interact. This study examines differences in the network structure of depressive symptoms in geriatric patients with and without cognitive impairment. Methods We included monocentric cross-sectional data of 3,990 hospitalized geriatric inpatients whose depressive symptoms were rated using the 15-item Geriatric Depression Scale (GDS). Patients were stratified into an unimpaired and impaired cognition group depending on the Mini-Mental State Examination (MMSE) (cut-off < 24 points). Network analyses were estimated separately for both groups using regularized logistic regression models. A network comparison test was conducted for group comparison. Results Our study showed that worthlessness was the most central depressive symptom. However, the network structures differed notably between the two groups, with less impact of feeling empty when cognitive impairment is present, as well as a stronger association between feeling unsatisfied and unhappy, and a weaker connection between feeling empty and bored. Conclusion These differences highlight the need for clinicians and public health professionals to adapt their screening and intervention strategies to take into account the subtle presentation of depressive symptoms in older adults according to cognitive status.

Authors

Institutions

Publication Details

Journal
Frontiers in Medicine
Published
2026-03-16
DOI
https://doi.org/10.3389/fmed.2026.1689283
Primary Topic
Mental Health Research Topics
Type
article
Field-Weighted Citation Impact
0.00

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Depressive symptoms in hospitalized geriatric patients with and without cognitive impairment: a cross-sectional network analysis approach

Tino Prell, Konstantin G. Heimrich, Aline Schönenberg
Frontiers in Medicine
Mental Health Research Topics
article

Depressive symptoms in hospitalized geriatric patients with and without cognitive impairment: a cross-sectional network analysis approach

Tino Prell, Konstantin G. Heimrich, Aline Schönenberg
article en

Abstract

Background Late-life depression is common and often co-occurs with cognitive impairment, complicating its assessment and clinical management. Network analysis allows for a nuanced understanding of how individual depressive symptoms interact. This study examines differences in the network structure of depressive symptoms in geriatric patients with and without cognitive impairment. Methods We included monocentric cross-sectional data of 3,990 hospitalized geriatric inpatients whose depressive symptoms were rated using the 15-item Geriatric Depression Scale (GDS). Patients were stratified into an unimpaired and impaired cognition group depending on the Mini-Mental State Examination (MMSE) (cut-off < 24 points). Network analyses were estimated separately for both groups using regularized logistic regression models. A network comparison test was conducted for group comparison. Results Our study showed that worthlessness was the most central depressive symptom. However, the network structures differed notably between the two groups, with less impact of feeling empty when cognitive impairment is present, as well as a stronger association between feeling unsatisfied and unhappy, and a weaker connection between feeling empty and bored. Conclusion These differences highlight the need for clinicians and public health professionals to adapt their screening and intervention strategies to take into account the subtle presentation of depressive symptoms in older adults according to cognitive status.

Frontiers in MedicineVol. 13
Jena University Hospital (DE), University Hospital in Halle (DE)
Deutsche Forschungsgemeinschaft, Bundesministerium für Bildung und Forschung
Openalex Percentile: Top 24%
Mental Health Research Topics
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.