Dataset Information

T2D and Depression Risk Gene Proteasome Modulator 9 is Linked to Insomnia.

ABSTRACT: Insomnia increases type-2 diabetes (T2D) risk. The 12q24 locus is linked to T2D, depression, bipolar disorder and anxiety. At the 12q24 locus, the Proteasome-Modulator 9 (PSMD9) single nucleotide polymorphisms (SNPs) rs74421874 [intervening sequence (IVS) 3+nt460-G>A], rs3825172 (IVS3+nt437-C>T) and rs14259 (E197G-A>G) are linked to: T2D, depression, anxiety, maturity-onset-diabetes-of the young 3/MODY3, obesity, waist circumference, hypertension, hypercholesterolemia, T2D-macrovascular disease, T2D-microvascular disease, T2D-neuropathy, T2D-carpal-tunnel syndrome, T2D-nephropathy, T2D-retinopathy and non-diabetic retinopathy. PSMD9 SNP rs1043307/rs14259 (E197G-A>G) plays a role in anti-depressant therapy response, depression and schizophrenia. We aimed at determining PSMD9 rs74421874/rs3825172/rs14259 SNPs potential linkage to primary insomnia and sleep hours in T2D families. We recruited 200 Italian T2D families phenotyping them for primary insomnia and sleep hours per night. PSMD9-T2D-risk SNPs rs74421874/rs3825172 and rs1043307/rs14259 were tested for linkage with insomnia and sleep hours. Non-parametric-linkage analysis, linkage-disequilibrium-model analysis, single-SNP analysis, cluster-based-parametric analysis, quantitative-trait and variant-component analysis were performed using Merlin software. To validate data, 1000 replicates were executed for the significant non-parametric data. PSMD9 rs74421874 (IVS3+nt460-G>A), rs3825172 (IVS3+nt437-C>T) and rs1043307/rs14259 (E197G-A>G) SNPs are linked to insomnia in our Italian families.

SUBMITTER: Hao H

PROVIDER: S-EPMC4648424 | biostudies-literature | 2015

REPOSITORIES: biostudies-literature

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T2D and Depression Risk Gene Proteasome Modulator 9 is Linked to Insomnia.

Hao Han H Haas Michael J MJ Wu Rongling R Gragnoli Claudia C

Scientific reports 20150713

Insomnia increases type-2 diabetes (T2D) risk. The 12q24 locus is linked to T2D, depression, bipolar disorder and anxiety. At the 12q24 locus, the Proteasome-Modulator 9 (PSMD9) single nucleotide polymorphisms (SNPs) rs74421874 [intervening sequence (IVS) 3+nt460-G>A], rs3825172 (IVS3+nt437-C>T) and rs14259 (E197G-A>G) are linked to: T2D, depression, anxiety, maturity-onset-diabetes-of the young 3/MODY3, obesity, waist circumference, hypertension, hypercholesterolemia, T2D-macrovascular disease, ...[more]

PMID: 26166263

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Project description:OBJECTIVE:Insomnia and major depressive disorders (MDD) often co-occur, and such comorbidity has been associated with poorer outcomes for both conditions. However, individual differences in depressive symptom trajectories during and after treatment are poorly understood in comorbid insomnia and depression. This study explored the heterogeneity in long-term depression change trajectories, and examined their correlates, particularly insomnia-related characteristics. METHOD:Participants were 148 adults (age M ± SD = 46.6 ± 12.6, 73.0% female) with insomnia and MDD who received antidepressant pharmacotherapy, and were randomized to 7-session Cognitive Behavioral Therapy for Insomnia or control conditions over 16 weeks with 2-year follow-ups. Depression and insomnia severity were assessed at baseline, biweekly during treatment, and every 4 months thereafter. Sleep effort and beliefs about sleep were also assessed. RESULTS:Growth mixture modeling revealed three trajectories: (a) Partial-Responders (68.9%) had moderate symptom reduction during early treatment (p value < .001) and maintained mild depression during follow-ups. (b) Initial-Responders (17.6%) had marked symptom reduction during treatment (p values < .001) and low depression severity at posttreatment, but increased severity over follow-up (p value < .001). (c) Optimal-Responders (13.5%) achieved most gains during early treatment (p value < .001), continued to improve (p value < .01) and maintained minimal depression during follow-ups. The classes did not differ significantly on baseline measures or treatment received, but differed on insomnia-related measures after treatment began (p values < .05): Optimal-Responders consistently endorsed the lowest insomnia severity, sleep effort, and unhelpful beliefs about sleep. CONCLUSIONS:Three depression symptom trajectories were observed among patients with comorbid insomnia and MDD. These trajectories were associated with insomnia-related constructs after commencing treatment. Early changes in insomnia characteristics may predict long-term depression outcomes. (PsycINFO Database Record

Project description:BackgroundWhile speech analysis holds promise for mental health assessment, research often focuses on single symptoms, despite symptom co-occurrences and interactions. In addition, predictive models in mental health do not properly assess the limitations of speech-based systems, such as uncertainty, or fairness for a safe clinical deployment.ObjectiveWe investigated the predictive potential of mobile-collected speech data for detecting and estimating depression, anxiety, fatigue, and insomnia, focusing on other factors than mere accuracy, in the general population.MethodsWe included 865 healthy adults and recorded their answers regarding their perceived mental and sleep states. We asked how they felt and if they had slept well lately. Clinically validated questionnaires measuring depression, anxiety, insomnia, and fatigue severity were also used. We developed a novel speech and machine learning pipeline involving voice activity detection, feature extraction, and model training. We automatically modeled speech with pretrained deep learning models that were pretrained on a large, open, and free database, and we selected the best one on the validation set. Based on the best speech modeling approach, clinical threshold detection, individual score prediction, model uncertainty estimation, and performance fairness across demographics (age, sex, and education) were evaluated. We used a train-validation-test split for all evaluations: to develop our models, select the best ones, and assess the generalizability of held-out data.ResultsThe best model was Whisper M with a max pooling and oversampling method. Our methods achieved good detection performance for all symptoms, depression (Patient Health Questionnaire-9: area under the curve [AUC]=0.76; F1-score=0.49 and Beck Depression Inventory: AUC=0.78; F1-score=0.65), anxiety (Generalized Anxiety Disorder 7-item scale: AUC=0.77; F1-score=0.50), insomnia (Athens Insomnia Scale: AUC=0.73; F1-score=0.62), and fatigue (Multidimensional Fatigue Inventory total score: AUC=0.68; F1-score=0.88). The system performed well when it needed to abstain from making predictions, as demonstrated by low abstention rates in depression detection with the Beck Depression Inventory and fatigue, with risk-coverage AUCs below 0.4. Individual symptom scores were accurately predicted (correlations were all significant with Pearson strengths between 0.31 and 0.49). Fairness analysis revealed that models were consistent for sex (average disparity ratio [DR] 0.86, SD 0.13), to a lesser extent for education level (average DR 0.47, SD 0.30), and worse for age groups (average DR 0.33, SD 0.30).ConclusionsThis study demonstrates the potential of speech-based systems for multifaceted mental health assessment in the general population, not only for detecting clinical thresholds but also for estimating their severity. Addressing fairness and incorporating uncertainty estimation with selective classification are key contributions that can enhance the clinical utility and responsible implementation of such systems.

Dataset Information

T2D and Depression Risk Gene Proteasome Modulator 9 is Linked to Insomnia.

Publications

T2D and Depression Risk Gene Proteasome Modulator 9 is Linked to Insomnia.

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