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- Hough, Christina M., et al.
(författare)
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Higher serum DHEA concentrations before and after SSRI treatment are associated with remission of major depression
- 2017
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Ingår i: Psychoneuroendocrinology. - : Elsevier BV. - 0306-4530. ; 77, s. 122-130
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Tidskriftsartikel (refereegranskat)abstract
- Background Dehydroepiandrosterone (DHEA) and its sulfated ester DHEA-sulfate (DHEA-S), (together DHEA[S]), are the most abundant adrenal steroids in humans and are found in blood and the brain, where they function as neurosteroids with direct receptor affinities. Preclinical studies suggest that DHEA(S) has antidepressant/neuroprotective properties, and exogenously administered DHEA has shown antidepressant efficacy in humans. Nonetheless, the role of endogenous DHEA(S) levels in major depressive disorder (MDD) and antidepressant outcomes remains unclear. Methods Morning fasting serum DHEA(S) concentrations were determined in 36 healthy, unmedicated MDD adults with Hamilton Depression (HDRS) ratings ≥17, and 75 healthy controls. MDD participants then completed eight weeks of open-label SSRI treatment before DHEA(S) levels were re-sampled; those with post-treatment HDRS ratings ≤7 were classified as “Remitters.” Pre- and post-treatment DHEA(S) levels of Remitters and Non-remitters were compared, controlling for age, sex, and BMI. Results Pre-treatment HDRS ratings did not differ between Remitters and Non-remitters (p = 0.179). Baseline DHEA levels of Remitters were significantly higher than both Non-remitters (p = 0.008) and controls (p = 0.004); baseline DHEA-S levels of Remitters were also higher than Non-remitters (p = 0.040) but did not significantly differ from controls (p = 0.096). Non-remitters did not significantly differ from controls. Post-treatment DHEA(S) levels remained higher in Remitters compared to Non-remitters (DHEA: p = 0.013; DHEA-S: p = 0.040). Conclusions These data suggest that higher circulating DHEA(S) levels (while unmedicated and after eight weeks of SSRI treatment) predict SSRI-associated remission in MDD. This raises the possibility that endogenous DHEA(S) abundance is a physiological adjunct to SSRI efficacy, as suggested by prior preclinical and clinical studies.
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| 2. |
- Lugner, Moa, et al.
(författare)
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Identifying top ten predictors of type 2 diabetes through machine learning analysis of UK Biobank data
- 2024
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Ingår i: Scientific Reports. - 2045-2322. ; 14:1
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Tidskriftsartikel (refereegranskat)abstract
- The study aimed to identify the most predictive factors for the development of type 2 diabetes. Using an XGboost classification model, we projected type 2 diabetes incidence over a 10-year horizon. We deliberately minimized the selection of baseline factors to fully exploit the rich dataset from the UK Biobank. The predictive value of features was assessed using shap values, with model performance evaluated via Receiver Operating Characteristic Area Under the Curve, sensitivity, and specificity. Data from the UK Biobank, encompassing a vast population with comprehensive demographic and health data, was employed. The study enrolled 450,000 participants aged 40–69, excluding those with pre-existing diabetes. Among 448,277 participants, 12,148 developed type 2 diabetes within a decade. HbA1c emerged as the foremost predictor, followed by BMI, waist circumference, blood glucose, family history of diabetes, gamma-glutamyl transferase, waist-hip ratio, HDL cholesterol, age, and urate. Our XGboost model achieved a Receiver Operating Characteristic Area Under the Curve of 0.9 for 10-year type 2 diabetes prediction, with a reduced 10-feature model achieving 0.88. Easily measurable biological factors surpassed traditional risk factors like diet, physical activity, and socioeconomic status in predicting type 2 diabetes. Furthermore, high prediction accuracy could be maintained using just the top 10 biological factors, with additional ones offering marginal improvements. These findings underscore the significance of biological markers in type 2 diabetes prediction.
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