Predicting the course of depression is necessary for personalized treatment. Impaired glucose metabolism (IGM) was introduced as a promising depression biomarker, but no consensus was made. This study aimed to predict IGM at the time of depression diagnosis and examine the relationship between long-term prognosis and predicted results.

Clinical data were extracted from four electronic health records in South Korea. The study population included patients with depression, and the outcome was IGM within 1 year. One database was used to develop the model using three algorithms. External validation was performed using the best algorithm across the three databases. The area under the curve (AUC) was calculated to determine the model’s performance. Kaplan–Meier and Cox survival analyses of the risk of hospitalization for depression as the long-term outcome were performed. A meta-analysis of the long-term outcome was performed across the four databases.

A prediction model was developed using the data of 3,668 people, with an AUC of 0.781 with least absolute shrinkage and selection operator (LASSO) logistic regression. In the external validation, the AUCs were 0.643, 0.610, and 0.515. Through the predicted results, survival analysis and meta-analysis were performed; the hazard ratios of risk of hospitalization for depression in patients predicted to have IGM was 1.20 (95% confidence interval [CI] 1.02–1.41, p = 0.027) at a 3-year follow-up.

We developed prediction models for IGM occurrence within a year. The predicted results were related to the long-term prognosis of depression, presenting as a promising IGM biomarker related to the prognosis of depression.

To propose a new anthropometric index that can be employed to better predict percent body fat (PBF) among young adults and to compare with current anthropometric indices.

Cross-sectional.

All measurements were taken in a controlled laboratory setting in Seoul (South Korea), between 1 December 2015 and 30 June 2016.

Eighty-seven young adults (18–35 years) who underwent dual-energy x-ray absorptiometry (DXA) were used for analysis. Multiple regression analyses were conducted to develop a body fat index (BFI) using simple demographic and anthropometric information. Correlations of DXA measured PBF (DXA_PBF) with previously developed anthropometric indices and the BFI were analysed. Receiver operating characteristic curve analyses were conducted to compare the ability of anthropometric indices to identify obese individuals.

BFI showed a strong correlation with DXA_PBF (r = 0·84), which was higher than the correlations of DXA_PBF with the traditional (waist circumference, r = 0·49; waist to height ratio, r = 0·68; BMI, r = 0·36) and alternate anthropometric indices (a body shape index, r = 0·47; body roundness index, r = 0·68; body adiposity index, r = 0·70). Moreover, the BFI showed higher accuracy at identifying obese individuals (area under the curve (AUC) = 0·91), compared with the other anthropometric indices (AUC = 0·71–0·86).

The BFI can accurately predict DXA_PBF in young adults, using simple demographic and anthropometric information that are commonly available in research and clinical settings. However, larger representative studies are required to build on our findings.