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Integrating proteomic, sociodemographic and clinical data to predict future depression diagnosis in subthreshold symptomatic individuals.

ABSTRACT: Individuals with subthreshold depression have an increased risk of developing major depressive disorder (MDD). The aim of this study was to develop a prediction model to predict the probability of MDD onset in subthreshold individuals, based on their proteomic, sociodemographic and clinical data. To this end, we analysed 198 features (146 peptides representing 77 serum proteins (measured using MRM-MS), 22 sociodemographic factors and 30 clinical features) in 86 first-episode MDD patients (training set patient group), 37 subthreshold individuals who developed MDD within two or four years (extrapolation test set patient group), and 86 subthreshold individuals who did not develop MDD within four years (shared reference group). To ensure the development of a robust and reproducible model, we applied feature extraction and model averaging across a set of 100 models obtained from repeated application of group LASSO regression with ten-fold cross-validation on the training set. This resulted in a 12-feature prediction model consisting of six serum proteins (AACT, APOE, APOH, FETUA, HBA and PHLD), three sociodemographic factors (body mass index, childhood trauma and education level) and three depressive symptoms (sadness, fatigue and leaden paralysis). Importantly, the model demonstrated a fair performance in predicting future MDD diagnosis of subthreshold individuals in the extrapolation test set (AUC?=?0.75), which involved going beyond the scope of the model. These findings suggest that it may be possible to detect disease indications in subthreshold individuals up to four years prior to diagnosis, which has important clinical implications regarding the identification and treatment of high-risk individuals.

SUBMITTER: Han SYS

PROVIDER: S-EPMC6838310 | biostudies-literature | 2019 Nov

REPOSITORIES: biostudies-literature

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Integrating proteomic, sociodemographic and clinical data to predict future depression diagnosis in subthreshold symptomatic individuals.

Han Sung Yeon Sarah SYS Cooper Jason D JD Ozcan Sureyya S Rustogi Nitin N Penninx Brenda W J H BWJH Bahn Sabine S

Translational psychiatry 20191107 1

Individuals with subthreshold depression have an increased risk of developing major depressive disorder (MDD). The aim of this study was to develop a prediction model to predict the probability of MDD onset in subthreshold individuals, based on their proteomic, sociodemographic and clinical data. To this end, we analysed 198 features (146 peptides representing 77 serum proteins (measured using MRM-MS), 22 sociodemographic factors and 30 clinical features) in 86 first-episode MDD patients (traini ...[more]

PMID: 31699963

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Project description:ObjectivesTo summarise the current evidence regarding interventions for accurate and timely cancer diagnosis among symptomatic individuals.DesignA scoping review following the Joanna Briggs Institute's methodological framework for the conduct of scoping reviews and reported in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews checklist.Data sourcesMEDLINE (Ovid), CINAHL (EBSCOhost) and PsycINFO (Ovid) bibliographic databases, and websites of relevant organisations. Published and unpublished literature (grey literature) of any study type in the English language were searched for from January 2017 to January 2021.Eligibility and criteriaStudy participants were individuals of any age presenting at clinics with symptoms indicative of cancer. Interventions included practice guidelines, care pathways or other initiatives focused on achieving predefined benchmarks or targets for wait times, streamlined or rapid cancer diagnostic services, multidisciplinary teams and patient navigation strategies. Outcomes included accuracy and timeliness of cancer diagnosis.Data extraction and synthesisWe summarised findings graphically and descriptively.ResultsFrom 21 298 retrieved citations, 88 unique published articles and 16 unique unpublished documents (on 18 study reports), met the eligibility for inclusion. About half of the published literature and 83% of the unpublished literature were from the UK. Most of the studies were on interventions in patients with lung cancer. Rapid referral pathways and technology for supporting and streamlining the cancer diagnosis process were the most studied interventions. Interventions were mostly complex and organisation-specific. Common themes among the studies that concluded intervention was effective were multidisciplinary collaboration and the use of a nurse navigator.ConclusionsMultidisciplinary cooperation and involvement of a nurse navigator may be unique features to consider when designing, delivering and evaluating interventions focused on improving accurate and timely cancer diagnosis among symptomatic individuals. Future research should examine the effectiveness of the interventions identified through this review.

Dataset Information

Integrating proteomic, sociodemographic and clinical data to predict future depression diagnosis in subthreshold symptomatic individuals.

Publications

Integrating proteomic, sociodemographic and clinical data to predict future depression diagnosis in subthreshold symptomatic individuals.

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