Depression Cohort Research Articles

The risk of acute infection in association with first ever diagnosed depression: a cohort study.

To assess the risk of acute infections in patients with first ever diagnosed depression compared to patients with no diagnosed depression in a primary-care database. We conducted a cohort study using the UK CPRD GOLD database (2000-2019). We identified patients aged 18 years or older with a recorded Read code for depression (cohort entry date) and compared them to patients with no Read codes for depression using risk set sampling. Comparison groups were frequency-matched on age and sex, and comparison patients were required to have ≥ 1 general practitioner (GP) contact within 14 days before cohort entry. The primary outcome was a composite of outpatient diagnosed acute infections, including respiratory, gastrointestinal, urogenital infections and septicemia) within the two-years after cohort entry. We applied propensity score fine stratification and estimated incidence rates and IR ratios (IRR) using negative binomial regression. In a weighted population of 285,922 patients with diagnosed depression and 285,921 comparison patients, the IR of acute infections was 97.3/1000 person-years (py) in patients with and 83.7/1000 py in patients with no diagnosed depression. The weighted IRR of acute infection was 1.18 (95% CI 1.16-1.20) comparing those with and with no diagnosed depression. Excluding patients with baseline comorbidities yielded an IRR even closer to the null: 1.07 (95% CI, 1.04-1.09). Our results suggest that patients with diagnosed depression are not at a meaningfully increased risk of acute infections compared to patients with no diagnosed depression. Slightly increased overall relative risks of infections can be explained by residual differences in health care utilization and by the severity of comorbidities.

Assessing the contributions of gender, clinical symptoms, and psychometric traits to non-suicidal self-injury behaviors in Chinese adolescents: a nomogram approach.

Non-suicidal self-injury (NSSI) behaviors among adolescents with mood disorders represent a significant global public health concern. This study aimed to assess the prevalence and identify key predictors of NSSI behaviors in Chinese adolescents diagnosed with depressive or bipolar disorders, addressing a critical gap in the literature. Data from 2343 participants in the Chinese Adolescent Depression Cohort (CADC) were analyzed. The study employed a comprehensive approach, utilizing medical records, clinical assessments, and validated psychometric instruments. Statistical analyses included chi-square tests, logistic regression, and ROC curve analyses, culminating in the development of a predictive nomogram. The prevalence of NSSI behaviors was strikingly high at 73.32%. Significant predictors included female gender (AOR = 2.14, 95% CI [1.70, 2.68]), presence of hallucinations (AOR = 1.52, 95% CI [1.18, 1.97]), borderline personality features (AOR = 1.03, 95% CI [1.01, 1.08]), and childhood trauma (AOR = 1.02, 95% CI [1.01, 1.03]). The study's key contribution is a predictive nomogram with an AUC of 0.74, demonstrating good efficacy in predicting NSSI behaviors. This research reveals an alarmingly high prevalence of NSSI behaviors in Chinese adolescents with mood disorders and identifies critical predictors spanning demographic, clinical, and psychometric domains. The developed nomogram offers a novel approach for risk assessment, highlighting the importance of comprehensive evaluations in adolescent mental healthcare. These findings have significant implications for developing targeted interventions and improving risk assessment strategies in clinical practice.

The effect of community environment on the trajectory of depressive symptoms and cohort differences among middle-aged and older Chinese adults.

Urbanization has changed the living environment of middle-aged and older Chinese adults, but it also brings certain mental pressure to them. Few studies have explored the effect of community environment on the development trajectory and the cohort differences of depressive symptoms in Chinese individuals. Based on the longitudinal data of the China Health and Retirement Longitudinal Study (CHARLS) from 2011 to 2020, using three-level hierarchical linear growth model, this study examined the effect of community environment on the trajectory of depressive symptoms and cohort differences among middle-aged and older Chinese adults in five cohorts from 1920 to 1929, 1930-1939, 1940-1949, 1950-1959, and 1960-1966. The results of this study showed that middle-aged and older adult individuals who lived in neighborhoods with better community physical and social environments had a protective effect on depressive symptoms. There was a cohort difference in the development of depressive symptoms among middle-aged and older Chinese adults. The baseline depressive symptoms in the later birth cohorts were higher than those in the earliest cohort, and the development rate were also significantly higher. The role of community environment in the development rate of depressive symptoms among middle-aged and older adult individuals varied across the cohort. A better community social environment had a more significant moderating effect on the development rate of depressive symptoms in the early birth cohort (1930-1939), and a better physical environment had a more significant moderating effect on the development rate of depressive symptoms in the 1940-1949 cohort. Under the strategic background of healthy aging in China, the construction and improvement of community environment should become an important part of coping with the realistic challenges of the aging population, such as the expanding scale of depressed population among the middle-aged and older adult individual.

Poster 146: Higher Reoperation Rates Following Primary Rotator Cuff Repair in Patients Screening Positive for Depression

Objectives: Recent studies have identified an association between preoperative depression and worse outcomes following arthroscopic rotator cuff repair (RCR) surgery, including lower patient-reported outcomes, increased pain and impairment, and higher rates of complications. Patient Health Questionnaire-2 (PHQ-2) is a commonly administered screening tool for measuring depressive symptoms; however, the relationship between PHQ-2 and postoperative outcomes after RCR is not yet established. The purpose of this study is to investigate the association between depression and reoperation rates in patients undergoing primary RCR. Methods: This retrospective chart review evaluated data from all patients who underwent elective primary RCR at 1 health system between March 2016 and December 2021 and had a PHQ-2 score at least 6 months prior to their surgery. The PHQ-2 is a validated tool used to screen for depression with scores ranging from 0-6. Patients were categorized as either depressed (PHQ-2 ≥ 2) or nondepressed (PHQ-2 < 2). Patients <18 years old, history of prior RCR, and patients without PHQ-2 scores within 6 months of surgery were excluded from the study. The primary outcome was to compare reoperation rate, defined as the need for any subsequent surgery related to the primary RCR between depressed and non-depressed patients. The secondary outcome was a comparison of postoperative health care utilization, which included emergency department visits and hospital readmissions within 90 days. Depressed patients were also propensity matched 1:1 to nondepressed patients via age, body mass index (BMI), and tear size for a subanalysis. Results: A total of 238 patients who underwent primary RCR were included with 84 depressed patients and 154 nondepressed patients. Significantly more patients were female (67% versus 46%; P = 0.002) in the depressed cohort compared to nondepressed. There was a significantly increased incidence of comorbid depression (63% vs 28%; P < 0.001), anxiety (52% vs 17%; P < 0.001), and substance use disorder (20% vs 6%; P < 0.001) as well as a lower median household income (MHI) in the depressed cohort versus the nondepressed cohort ($58,451.93 ± $21,024.93 vs. $66,751.93 ± $22,654.45; P < 0.001). Fifteen (17.9%) depressed patients underwent reoperation versus 10 (6.5%) nondepressed patients (P = 0.006). Mean time to reoperation in the depressed cohort was 11.7 months (0.8-35 months). In the subanalysis, 80 patients in the depressed cohort and 80 patients in the nondepressed cohort were matched. Depressed patients had significantly higher rates of comorbid depression (63% vs 18%; P < 0.001), anxiety (52% vs 17%; P < 0.001), and substance use disorder (20% vs 6%; P < 0.001) and a lower MHI ($58,615.20 ± $21,455.12 vs $66,470.73 ± $22,996.49; P = 0.04). Fewer patients were employed full time (22% vs. 44%) and more were disabled (30% vs 8%) amongst the depressed cohort (P = 0.003). Amongst depressed patients, 14 patients (17.5%) versus 4 (5%) underwent reoperation (P = 0.01). No significant differences in postoperative emergency department visits, postoperative complications, or hospital readmissions were found between the depressed and nondepressed cohorts in either analysis. Conclusions: We found patients screening positive for depression preoperatively, as measured by PHQ-2, had a significantly higher reoperation rate following primary RCR compared to nondepressed patients. These patients were more likely to be female, have comorbid mental health diagnoses, no full-time employment, and a lower household income. Screening for depression preoperatively could be a useful adjunct for providers to better identify patients at risk for worse outcomes.

Poster 385: Incidence of Preoperative Depression in Knee and Shoulder Arthroscopy Using Patient Health Questionnaire-2 (PHQ-2)

Objectives: Preoperative depression has been associated with worse outcomes following orthopaedic surgery including lower patient-reported outcomes, increased pain and impairment, and a higher rate of complications. Patient Health Questionnaire-2 (PHQ-2) is a commonly administered screening tool for measuring depressive symptoms, however the relationship between PHQ-2 and preoperative characteristics of patients presenting for arthroscopic knee and shoulder surgery have not been established. The purpose of this study was to investigate the association between depression and preoperative demographics and patient recorded outcomes of patients presenting for arthroscopic knee and shoulder surgery. Methods: This retrospective chart review evaluated data from all patients who underwent elective primary arthroscopic shoulder and knee surgery at 1 health system. Current Procedural Terminology codes were used to identify patients undergoing anterior cruciate ligament reconstruction (ACLR) (29888), meniscectomy (29880/29881), meniscus repair (29882/29883), knee chondroplasty (29877), labral repair (29807), rotator cuff repair (29827), and capsulorrhaphy (29806). Patients were included if they had completed Patient-Reported Outcomes Measurement Information System - Pain Interference (PROMIS-PI) and a PHQ-2 score at least 3 months prior to their surgery. The PHQ-2 is a validated tool used to screen for depression with scores ranging from 0-6. Patients were categorized as either depressed (PHQ-2 ≥ 3) or non-depressed (PHQ-2 < 3). Demographic characteristics and PROMIS scores were compared and a univariate and multivariate regression analysis was performed. Results: A total of 979 patients were included with 128 (13%) depressed patients and 851 nondepressed patients. There was no significant differences between the depressed and nondepressed group for age (49.18 ± 16.13 vs. 49.01 ± 15.71; P=0.926), sex (53.1% vs. 47.8% female; P = 0.263), median household income ($65,230 ± $24,684 vs. $66,810 ± $22,414; P=0.463), area deprivation index national percentile (62.48 ± 24.31 vs. 64.12 ± 24.58; P = 0.494), and depression diagnosis (4.7% vs. 3.1%; P = 0.333). Preoperative PROMIS-PI was significantly worse in the depressed cohort versus non-depressed (66.1 ± 5.9 vs. 63.6 ± 6.3; P < 0.001). Preoperative PROMIS-Depression score (R2 = 0.34) was the highest correlated variable to preoperative PHQ2 score in the univariate analysis. In the multivariate regression analysis, higher preoperative PROMIS-PI scores were found to be a risk factor for a PHQ2 score >3 (P < 0.001). Patients undergoing knee chondroplasty were less likely to be in the depressed cohort (odds ratio: 0.092 (0.009-0.914); P = 0.042). No other surgeries were more or less likely to be depressed. There was no difference between groups for age, sex, smoking status, insurance type, Area Deprivation Index, Metabolic Health Index, comorbid depression, and anxiety diagnosis. Conclusions: Overall, a larger number of patients screened positive for depression amongst patients undergoing knee and shoulder arthroscopy than were formally diagnosed. Demographic and socioeconomic variables were similar indicating these patients may be hard to identify without a screening tool like PHQ-2. Preoperative pain scores were higher amongst the depressed patients, indicating higher pain scores may be associated with increased risk of depression. This study highlights the need for further research on preoperative mood screening before arthroscopic surgery.

ABCB1 C1236T, G2677TA and C3435T Genetic Polymorphisms and Antidepressant Response Phenotypes: Results from a Portuguese Major Depressive Disorder Cohort.

P-glycoprotein (P-GP) is a transporter molecule expressed on the apical surface of capillary endothelial cells of the Blood-Brain Barrier (BBB), whose activity heavily influences drug distribution, including antidepressants. This transporter is encoded by ABCB1 gene, and genetic variations within ABCB1 gene have been proposed to affect drug efflux and have been previously associated with depression. In this context, we aimed to evaluate the role of C1236T, G2677TA and C3435T ABCB1 genetic polymorphisms in antidepressant treatment phenotypes from a cohort of patients harboring Major Depressive Disorder. Patients enrolled in the study consisted of 80 individuals with Major Depressive Disorder, who took part in a 27-month follow-up study at HML, Portugal. To investigate the correlation between ABCB1 polymorphisms and antidepressant response phenotypes, DNA was extracted from peripheral blood, and C1236T, C3435T and G2677TA polymorphisms were genotyped with TaqMan® SNP Genotyping Assays. Despite the fact that the evaluated polymorphisms (C1236T, C3435T and G2677TA) were not associated with treatment resistant depression, or relapse, we observed that patients carrying TT genotype of the C3435T polymorphism remit earlier than the ones carrying CC or CT genotypes (10.2 weeks vs. 14.9 and 21.3, respectively, p = 0.028, Log-rank test). Since we found an association with C3435T and time to remission, and not to the absence of remission, we suggest that this polymorphism could have an impact on antidepressant drug distribution, and thus influence on the time to remission will occur, without influencing the risk of remission itself.

Study on plasma metabolomics profiling of depression in Chinese community-dwelling older adults based on untargeted LC/GC‒MS

Depression is a serious psychiatric illness that causes great inconvenience to the lives of elderly individuals. However, the diagnosis of depression is somewhat subjective. Nontargeted gas chromatography (GC)/liquid chromatography (LC)–mass spectrometry (MS) was used to study the plasma metabolic profile and identify objective markers for depression and metabolic pathway variation. We recruited 379 Chinese community-dwelling individuals aged ≥ 65. Plasma samples were collected and detected by GC/LC‒MS. Orthogonal partial least squares discriminant analysis and a heatmap were utilized to distinguish the metabolites. Receiver operating characteristic curves were constructed to evaluate the diagnostic value of these differential metabolites. Additionally, metabolic pathway enrichment was performed to reveal metabolic pathway variation. According to our standard, 49 people were included in the depression cohort (DC), and 49 people age- and sex-matched individuals were included in the non-depression cohort (NDC). 64 metabolites identified via GC‒MS and 73 metabolites identified via LC‒MS had significant contributions to the differentiation between the DC and NDC, with VIP values > 1 and p values < 0.05. Three substances were detected by both methods: hypoxanthine, phytosphingosine, and xanthine. Furthermore, 1-(sn-glycero-3-phospho)-1D-myo-inositol had the largest area under the curve (AUC) value (AUC = 0.842). The purine metabolic pathway is the most important change in metabolic pathways. These findings show that there were differences in plasma metabolites between the depression cohort and the non-depression cohort. These identified differential metabolites may be markers of depression and can be used to study the changes in depression metabolic pathways.

Physical Fitness and Risk of Mental Disorders in Children and Adolescents

With the rising prevalence of mental disorders among children and adolescents, identifying modifiable associations is critical. To examine the association between physical fitness and mental disorder risks. This nationwide cohort study used data from the Taiwan National Student Fitness Tests and National Health Insurance Research Databases from January 1, 2009 to December 31, 2019. Participants were divided into 2 cohorts targeting anxiety and depression (1 996 633 participants) and attention-deficit/hyperactivity disorder (ADHD; 1 920 596 participants). Participants were aged 10 to 11 years at study entry and followed up for at least 3 years, had a nearly equal gender distribution, and an average follow-up of 6 years. Data were analyzed from October 2022 to February 2024. Assessments of physical fitness included cardiorespiratory fitness (CF), muscular endurance (ME), muscular power (MP), and flexibility, measured through an 800-m run time, bent-leg curl-ups, standing broad jump, and sit-and-reach test, respectively. Kaplan-Meier method calculated the cumulative incidence of anxiety, depression, and ADHD across fitness quartiles. Additionally, multivariable Cox proportional hazards models were used that included all 4 fitness components and explored sex and income as modifiers. The anxiety and depression cohort had 1 996 633 participants (1 035 411 participants were male [51.9%], and the median [IQR] age was 10.6 [10.3-11.0] years), while the ADHD cohort had 1 920 596 (975 568 participants were male [51.9%], and the median [IQR] age was 10.6 [10.3-11.0] years). Cumulative incidence of mental disorders was lower among participants in better-performing fitness quartiles, suggesting a dose-dependent association. Gender-specific analyses, controlling for confounders, revealed that improved CF, indicated by a 30-second decrease in run times, was associated with reduced risks of anxiety, depression, and ADHD in female participants, and lower risks of anxiety and ADHD in male participants (adjusted hazard ratio [aHR] for ADHD risk for female participants, 0.92; 95% CI, 0.90-0.94; P < .001; for male participants, 0.93; 95% CI, 0.92-0.94; P < .001). Enhanced ME, marked by an increase of 5 curl-ups per minute, was associated with decreased risks of depression and ADHD in female participants, and lower anxiety and ADHD risks in male participants (aHR for ADHD risk for female participants, 0.94; 95% CI, 0.92-0.97; P < .001; for male participants, 0.96; 95% CI, 0.95-0.97; P < .001). Improved MP, reflected by a 20-cm increase in jump distance, was associated with reduced risks of anxiety and ADHD in female participants and reduced anxiety, depression, and ADHD in male participants (aHR for ADHD risk for female participants, 0.95; 95% CI, 0.91-1.00; P = .04; for male participants, 0.96; 95% CI, 0.94-0.99; P = .001). This study highlights the potential protective role of cardiorespiratory fitness, muscular endurance, and muscular power in preventing the onset of mental disorders. It warrants further investigation of the effectiveness of physical fitness programs as a preventive measure for mental disorders among children and adolescents.

Depression State Correlates with Functional Recovery Following Elective Lumbar Spine Fusion

To determine how depression state impacts postoperative Patient-Reported Outcomes Measurement Information System (PROMIS) scores and achievement of minimum clinically important difference (MCID) following lumbar fusion. Depression has been shown to negatively impact outcomes following numerous orthopedic surgeries. Situational and major clinical depression can differentially affect postoperative outcomes. Adult patients undergoing elective 1-3 level lumbar fusion were reviewed. Patients with a formal diagnosis of major depression were classified as "clinically depressed" whereas patients with at least "mild" PROMIS Depression scores in the absence of formal depression diagnosis were deemed "situationally depressed." analysis of variance testing was used to assess differences within and between groups. Multivariate regression was used to identify features associated with the achievement of MCID. Two hundred patients were included. The average age was 65.9±12.2years. 75 patients (37.5%) were nondepressed, 66 patients (33.0%) were clinically depressed, and 59 patients (29.5%) were situationally depressed. Situationally depressed patients had worse preoperative physical function (PF) and pain interference (PI) scores and were more likely to have severe symptoms (P= 0.001, P= 0.001). All groups improved significantly from preoperative baseline scores. All groups met MCID PF at different rates, with highest proportion of situationally depressed reaching this metric (P=0.03). Rates of achieving MCID PI were not significantly different between groups (P= 0.47). Situational depression was predictive of achieving MCID PF (P= 0.002) but not MCID PI. Our study investigated the relationship between depression and postoperative PROMIS scores and identified situationally depressed patients as having the worst preoperative impairment. Despite this, the situationally depressed cohort had the highest likelihood of achieving MCID PF, suggestive of a bidirectional relationship between lumbar degenerative disease and subclinical, situational depression. These findings may help guide preoperative counseling on expectations, and patient selection.

Identifying depression-related topics in smartphone-collected free-response speech recordings using an automatic speech recognition system and a deep learning topic model

BackgroundPrior research has associated spoken language use with depression, yet studies often involve small or non-clinical samples and face challenges in the manual transcription of speech. This paper aimed to automatically identify depression-related topics in speech recordings collected from clinical samples. MethodsThe data included 3919 English free-response speech recordings collected via smartphones from 265 participants with a depression history. We transcribed speech recordings via automatic speech recognition (Whisper tool, OpenAI) and identified principal topics from transcriptions using a deep learning topic model (BERTopic). To identify depression risk topics and understand the context, we compared participants' depression severity and behavioral (extracted from wearable devices) and linguistic (extracted from transcribed texts) characteristics across identified topics. ResultsFrom the 29 topics identified, we identified 6 risk topics for depression: ‘No Expectations’, ‘Sleep’, ‘Mental Therapy’, ‘Haircut’, ‘Studying’, and ‘Coursework’. Participants mentioning depression risk topics exhibited higher sleep variability, later sleep onset, and fewer daily steps and used fewer words, more negative language, and fewer leisure-related words in their speech recordings. LimitationsOur findings were derived from a depressed cohort with a specific speech task, potentially limiting the generalizability to non-clinical populations or other speech tasks. Additionally, some topics had small sample sizes, necessitating further validation in larger datasets. ConclusionThis study demonstrates that specific speech topics can indicate depression severity. The employed data-driven workflow provides a practical approach for analyzing large-scale speech data collected from real-world settings.

Just a gut feeling: Faecal microbiota transplant for treatment of depression - A mini-review.

The microbiota-gut-brain axis (MGBA) allows bidirectional crosstalk between the brain and gut microbiota (GM) and is believed to contribute to regulating mood/cognition/behaviour/metabolism/health and homeostasis. Manipulation of GM through faecal microbiota transplant (FMT) is a new, exciting and promising treatment for major depressive disorder (MDD). This mini-review examines current research into GM and FMT as a therapy for depression. Original research articles published in Medline/Cochrane Library/PubMed/EMBASE/PsycINFO databases/National Institute of Health website Clinicaltrials.gov/controlled-trials.com were searched. Full articles included in reference lists were evaluated. We summarise current data on GM and depression and discuss communication through the MGBA and the interaction of antidepressants and GM through this. We review compositions of dysbiosis in depressed cohorts, focusing on future directions in the treatment of MDD. Studies have demonstrated significant gut dysbiosis in depressed patients compared to healthy cohorts, with overgrowth of pro-inflammatory microbiota, reduction in anti-inflammatory species and reduced overall stability and taxonomic richness. FMT allows the introduction of healthy microbiota into the gastrointestinal tract, facilitating the restoration of eubiosis. The GM plays an integral role in human health and disease through its communication with the rest of the body via the MGBA. FMT may provide a means to transfer the healthy phenotype into the recipient and this concept in humans is attracting enormous attention as a prospective treatment for psychopathologies, such as MDD, in the future. It may be possible to manipulate the GM in a number of ways, but further research is needed to determine the exact likelihood and profiles involved in the development and amelioration of MDD in humans, as well as the long-term effects and potential risks of this procedure.

Associations of rumination with suicidal ideation and suicide attempts amongst individuals with major depressive disorder: A 12-month longitudinal study in China

BackgroundThis study aimed to explore the longitudinal associations of rumination with suicidal ideation and suicide attempts in individuals with major depressive disorder (MDD). MethodsParticipants were derived from the Depression Cohort in China study (DCC). Those who completed at least one follow-up visit during the 12 months were included in the analysis. Dimensions of rumination including brooding and reflection were each measured using five items of the Ruminative Responses Scale. Suicidal ideation was assessed using the Beck Scale for Suicide Ideation. Suicide attempts were also assessed and all were analyzed with generalized estimating equations. ResultsOur final sample included 532 participants aged 18 to 59 years (mean [SD], 26.91 [6.94] years) consisting of 148 (27.8%) males and 384 (72.2%) females. After adjusting for temporal trend and potential confounders, individuals with higher levels of reflection were more likely to report suicidal ideation (AOR =1.11, 95% CI:1.01–1.22). However, no statistically significant association was found between brooding and suicidal ideation (AOR =1.06, 95% CI:0.96–1.17). Conversely, individuals with higher levels of brooding were more likely to report suicide attempts (AOR =1.13, 95% CI:1.02–1.24), while no statistically significant association was observed between reflection and suicide attempts (AOR =0.91, 95% CI:0.82–1.01). ConclusionRumination reflects a disturbance in cognitive emotional processing and manifests in different dimensions. Our findings suggest that high levels of reflection and brooding may be associated with a higher likelihood of having suicidal ideation and suicide attempts, respectively. However, it should be interpreted with caution, given that effect sizes are small.

Pharmacogenomic overlap between antidepressant treatment response in major depression & antidepressant associated treatment emergent mania in bipolar disorder

There is increasing interest in individualizing treatment selection for more than 25 regulatory approved treatments for major depressive disorder (MDD). Despite an inconclusive efficacy evidence base, antidepressants (ADs) are prescribed for the depressive phase of bipolar disorder (BD) with oftentimes, an inadequate treatment response and or clinical concern for mood destabilization. This study explored the relationship between antidepressant response in MDD and antidepressant-associated treatment emergent mania (TEM) in BD. We conducted a genome-wide association study (GWAS) and polygenic score analysis of TEM and tested its association in a subset of BD-type I patients treated with SSRIs or SNRIs. Our results did not identify any genome-wide significant variants although, we found that a higher polygenic score (PGS) for antidepressant response in MDD was associated with higher odds of TEM in BD. Future studies with larger transdiagnostic depressed cohorts treated with antidepressants are encouraged to identify a neurobiological mechanism associated with a spectrum of depression improvement from response to emergent mania.

Predictive modeling of neuroticism in depressed and non-depressed cohorts using voice features

BackgroundNeuroticism's impact on psychopathological and physical health issues has significant public health implications. Multiple studies confirm its predictive effect on suicide risk among depressed patients. However, previous research lacks a standardized criterion for assessing neuroticism through speech, often relying on simple features (such as pitch, loudness and MFCCs). This study aims to improve upon this by extracting features using advanced pre-trained speaker embedding models (i-vector and x-vector extractors). Additionally, unlike prior studies utilizing general population data, we explore neuroticism prediction in depressed and non-depressed subgroups. MethodsWe collected edited discourse data from clinical interviews of 3580 depressed individuals and 4016 healthy individuals from the CONVERGE study. Instead of solely extracting Low-Level Acoustic Descriptors, we incorporated i-vector and x-vector features. We compared the performance of three different features in predicting neuroticism and explored their combination to enhance model accuracy. ResultsThe SVR model, combining three speech features with downscaled features to 300, exhibited the highest performance in predicting neuroticism scores. It achieved a coefficient of determination (R-squared) of 0.3 or higher and a correlation of 0.56 between predicted and actual values. The predictive classification accuracy of speech features for neuroticism in specific populations (healthy and depressed) exceeded 60 %. LimitationsThis study included only women. ConclusionCombining diverse speech features enhances the predictive capacity of models using speech features to assess neuroticism, particularly in specific populations. This study lays the foundation for future exploration of speech features in neuroticism prediction.

What is the effect of preoperative depression on outcomes after minimally invasive surgery for adult spinal deformity? A prospective cohort analysis.

Depression has been implicated with worse immediate postoperative outcomes in adult spinal deformity (ASD) correction, yet the specific impact of depression on those patients undergoing minimally invasive surgery (MIS) requires further clarity. This study aimed to evaluate the role of depression in the recovery of patients with ASD after undergoing MIS. Patients who underwent MIS for ASD with a minimum postoperative follow-up of 1 year were included from a prospectively collected, multicenter registry. Two cohorts of patients were identified that consisted of either those affirming or denying depression on preoperative assessment. The patient-reported outcome measures (PROMs) compared included scores on the Oswestry Disability Index (ODI), numeric rating scale (NRS) for back and leg pain, Scoliosis Research Society Outcomes Questionnaire (SRS-22), SF-36 physical component summary, SF-36 mental component summary (MCS), EQ-5D, and EQ-5D visual analog scale. Twenty-seven of 147 (18.4%) patients screened positive for preoperative depression. The nondepressed cohort had an average of 4.83 levels fused, and the depressed cohort had 5.56 levels fused per patient (p = 0.267). At 1-year follow-up, 10 patients still reported depression, representing a 63% decrease. Postoperatively, both cohorts demonstrated improvement in their PROMs; however, at 1-year follow-up, those without depression had statistically better outcomes based on the EQ-5D, MCS, and SRS-22 scores (p < 0.05). Patients with depression continued to experience higher NRS leg scores at 1-year follow-up (3.63 vs 2.22, p = 0.018). After controlling for covariates, the authors found that depression significantly impacted only 1-year follow-up MCS scores (β = 8.490, p < 0.05). Depressed and nondepressed patients reported similar improvements after MIS surgery, except MCS scores were more likely to improve in nondepressed patients.

Finding the best predictive model for hypertensive depression in older adults based on machine learning and metabolomics research.

Depression is a common comorbidity in hypertensive older adults, yet depression is more difficult to diagnose correctly. Our goal is to find predictive models of depression in hypertensive patients using a combination of various machine learning (ML) methods and metabolomics. Methods We recruited 379 elderly people aged ≥65 years from the Chinese community. Plasma samples were collected and assayed by gas chromatography/liquid chromatography-mass spectrometry (GC/LC-MS). Orthogonal partial least squares discriminant analysis (OPLS-DA), volcano diagrams and thermograms were used to distinguish metabolites. The attribute discriminators CfsSubsetEval combined with search method BestFirst in WEKA software was used to find the best predicted metabolite combinations, and then 24 classification methods with 10-fold cross-validation were used for prediction. 34 individuals were considered hypertensive combined with depression according to our criteria, and 34 subjects with hypertension only were matched according to age and sex. 19 metabolites by GC-MS and 65 metabolites by LC-MS contributed significantly to the differentiation between the depressed and non-depressed cohorts, with a VIP value of more than 1 and a P value of less than 0.05. There were multiple metabolic pathway alterations. The metabolite combinations screened with WEKA for optimal diagnostic value included 12 metabolites. The machine learning methods with AUC values greater than 0.9 were bayesNet and random forests, and their other evaluation measures are also better. Altered metabolites and metabolic pathways are present in older adults with hypertension combined with depression. Methods using metabolomics and machine learning performed quite well in predicting depression in hypertensive older adults, contributing to further clinical research.

Acylcarnitines in depression: association with diagnostic status, symptom severity and profile in the the Netherlands study of depression and anxiety cohort

Acylcarnitines in depression: association with diagnostic status, symptom severity and profile in the the Netherlands study of depression and anxiety cohort

Classification of patients with dementia in a late life depression cohort using structural MRI

AbstractBackgroundLate life depression (LLD) is a risk factor for cognitive impairment, with up to 40% of depressed older adults showing persistent cognitive deficits even after symptom remission. Over 20% of Alzheimer’s disease patients exhibit depression‐like symptoms which can represent an early marker of disease (Tsuno et Homma., 2009). We hypothesized that automated classification algorithms trained on structural MRI could predict adjudicated diagnosis of cognitive impairment (CI) in this cohort.MethodOur study evaluated 295 patients with LLD. An adjudication committee consisting of two board‐certified psychiatrists and a clinical neuropsychologist determined ground‐truth labels for patient diagnostic results of CI (N = 145) or No CI (N = 150). Patients underwent T1‐weighted scans, which were segmented with Freesurfer 6.0.1 (freesurfer.net) packaged with fMRIPrep 1.5.8 (fmriprep.org) using the Desikan‐Killiany atlas. Cortical thickness and volumetric data were obtained along with demographic parameters. Freesurfer data was then regressed against estimated total intracranial volume. 11 classifications algorithms implemented in R were trained and tested on 209 and 88 patients, respectively, to classify if a patient has CI. All continuous‐valued data were normalized with respect to the training set mean and standard deviation.ResultTable 1 shows results from multiple classifications algorithms, including high sensitivity, specificity, and accuracy from adaptive boosting algorithms. The latter algorithms iteratively optimize a set of binary classification tree algorithms (Freund et Schapire., 1996).ConclusionVolumetric structural MRI biomarkers trained with machine learning classifiers can adequately categorize depressed older adults with CI which could assist the adjudication process. Future work incorporating structural MRI biomarkers with gold standard clinical data may improve earlier detection of cognitive impairment stemming from a neurodegenerative etiology.

Abstract 18025: Does Co-Morbid Depression Predict Worse In-Hospital Outcomes in Elderly Patients Primarily Hospitalized With Atrial Fibrillation?: A Nationwide Sex and Race Stratified Analysis

Introduction: Depression is an established risk factor for major adverse cardiac and cerebrovascular events (MACCE). However, the association between depression (DP) and all-cause mortality, as well as stroke in atrial fibrillation (AF) patients, remains understudied. We aim to establish a relationship between depression and MACCE in AF patients. Methods: Using the National Inpatient Sample (2019) and relevant international classification of disease (ICD-10) codes, we identified hospitalizations with AF among elderly patients (age &gt;65) and categorized them into groups with and without depression (DP vs nDP). The primary outcome was all-cause inpatient mortality and stroke in AF-related hospitalizations. Results: The overall prevalence of depression (DP) among elderly patients hospitalized for AF was 10.6% (32810/310,005). The DP cohort contained less number of males (30.4% vs 44.38%), Blacks ( 2.91% vs 5.77%), Hispanics (4.11% vs 4.68%) while more females (69.59% vs 55.62%) and Whites ( 88.7% vs 83.82%). Stroke prevalence was not significantly different (0.8% vs 0.8% OR:0.97, CI:0.85-1.1 p=0.701) whereas all-cause mortality prevalence was lower in the DP cohort (0.9% vs 1%, OR: 0.85, CI:0.75-0.97 p=0.015). Surprisingly, the subgroup analysis revealed that depressed males exhibited significantly higher odds of stroke, and this association persisted even after multivariable analysis (aOR 1.184, CI: 1.12-3.02, p= 0.016)[Fig 1]. All-cause mortality was higher in depressed blacks(2.6% vs 1.4%, OR:1.84, CI:1.21-2.79, p=0.004) and lower in depressed females (0.8% vs 1.1%, OR:0.78 CI:0.67-0.91, p=0.001). Adjusted multivariable analysis revealed no significant all-cause mortality and stroke in depressed vs non-depressed except for subgroup analysis for elderly depressed males. Conclusion: Depression independently associated with stroke in elderly males hospitalized for AF.

Specifying the Efficacy of Digital Therapeutic Tools for Depression and Anxiety: Retrospective, 2-Cohort, Real-World Analysis.

Depression and anxiety are the main sources of work and social disabilities as well as health-related problems around the world. Digital therapeutic solutions using cognitive behavioral therapy have demonstrated efficacy in depression and anxiety. A common goal of digital health apps is to increase user digital engagement to improve outcomes. However, there is a limited understanding of the association between digital platform components and clinical outcomes. The aim of the study is to investigate the contribution of specific digital engagement tools to mental health conditions. We hypothesized that participation in coaching sessions and breathing exercises would be associated with a reduction in depression and anxiety. Depression and general anxiety symptoms were evaluated in real-world data cohorts using the digital health platform for digital intervention and monitoring change. This retrospective real-world analysis of users on a mobile platform-based treatment followed two cohorts of people: (1) users who started with moderate levels of depression and completed at least 2 depression assessments (n=519) and (2) users who started with moderate levels of anxiety and completed at least 2 anxiety assessments (n=474). Levels of depression (Patient Health Questionnaire-9) and anxiety (Generalized Anxiety Disorder-7) were tracked throughout the first 16 weeks. A piecewise mixed-effects model was applied to model the trajectories of the Patient Health Questionnaire-9 and the Generalized Anxiety Disorder-7 mean scores in 2 segments (1-6 weeks and 7-16 weeks). Finally, simple slope analysis was used for the interpretation of the interactions probing the moderators: coaching sessions and breathing exercises in both depression and anxiety cohorts. Analysis revealed a significant decrease in depression symptoms (β=-.37, 95% CI -0.46 to 0.28; P≤.001) during the period of weeks 1-6 of app use, which was maintained during the period of 7-16 weeks. Coach interaction significantly moderated the reduction in depression symptoms during the period of weeks 1-6 (β=-.03, 95% CI -0.05 to -0.001; P=.02). A significant decrease in anxiety symptoms (β=-.41, 95% CI -0.50 to -0.33; P≤.001) was revealed during the period of 1-6 weeks, which was maintained during the period of 7-16 weeks. Breathing exercises significantly moderated the reduction in anxiety symptoms during the period of 1-6 weeks (β=-.07, 95% CI -0.14 to -0.01; P=.04). This study demonstrated general improvement followed by a period of stability of depression and anxiety symptoms associated with cognitive behavioral therapy-based digital intervention. Interestingly, engagement with a coaching session but not a breathing exercise was associated with a reduction in depression symptoms. Moreover, breathing exercise but not engagement with a coaching session was associated with a reduction of anxiety symptoms. These findings emphasize the importance of using a personalized approach to behavioral health during digital health interventions.