Depressive subtypes in an elderly cohort identified using latent class analysis

Characterization of depression subtypes and their relationships to stressor profiles among middle-aged and older adults: An analysis of the canadian longitudinal study on aging (CLSA)

The current diagnostic criteria for depression do not sufficiently reflect its heterogeneous clinical presentations. Associations between adverse childhood experiences (ACEs), allostatic load (AL), and depression subtypes have not been extensively studied. Depression subtypes were determined based on clinical presentations, and their relationships to AL biomarkers and ACEs were elucidated in a sample of middle-aged and older adults. Participants from the Canadian Longitudinal Study on Aging who screened positive for depression were included (n = 3966). Depression subtypes, AL profiles and ACE profiles were determined with latent profile analyses, and associations between them were determined using multinomial logistic regression. Four depression subtypes were identified: positive affect, melancholic, typical, and atypical. Distinct associations between depression subtypes, stressor profiles and covariates were observed. Among the subtypes compared to positive affect, atypical subtype had the most numerous significant associations, and the subtypes had unique relationships to stressor profiles. Age, sex, smoking status, chronic conditions, marital status, and physical activity were significant covariates. The present study describes distinct associations between depression subtypes and measures of stress (objective and self-reported), as well as related factors that differentiate subtypes. The findings may inform more targeted and integrated clinical management strategies for depression in individuals exposed to multiple stressors.

Latent class analysis of symptoms of depression and anxiety among older women

OBJECTIVES

This cross-sectional study aims to consider the potential classification of depression and anxiety symptoms among older women, and identify the influencing factors of this classification.

METHODS

This study examines Chinese women aged 65 years and older. Latent class analysis was used to explore the mental health subgroups of older women, and multivariate logistic regression was employed to examine the influencing factors based on the health ecological model among these subgroups.

RESULTS

The results helped classify this population under three subgroups: the coexistence of depression and anxiety group, dominated depression group, and the low symptoms group. Moreover, class differences in terms of age, residence, education, income, assessment of current life and health status, sleep duration, and health behaviors, such as alcohol use and exercise were noted.

CONCLUSIONS

These findings explain the heterogeneity among older women, and help illuminate their unique aspects of mental health. Accordingly, they are significant for scholars and policymakers to understand depression and anxiety among older women.

Latent class of multidimensional dependency in community-dwelling older adults: evidence from the longitudinal ageing study in India

BACKGROUND

Existing studies have used ADL and IADL separately as measures of dependency. However, dependency is a heterogeneous and complex issue, and the dependency of each older adult is a synergistic combination of several functional activities. In this study, we assess the pattern of multidimensional dependency of older adults based on ADL, IADL, visual impairment, difficulty in climbing a flight of stairs, pushing or pulling objects, depressive symptoms, cognitive impairment, marital status, and economic distress. It is important to classify the dependency status of older adults because this will be key to evaluating the needs for care, and plan services that effectively cater for the needs of the older adults. The classification into different latent classes means that older adults within each class have the same needs of dependency but different needs between the latent classes. Our objective is to identify patterns of multidimensional dependency in older adults.

METHODS

Data from the Longitudinal Ageing Study in India (LASI) Wave-1, was used, the analytical sample consisted of 32,827 individuals of age 45 years and above. LCA was used to identify the multidimensional dependency class. LCA was conducted in R statistical package, using the poLCA package. The optimal number of classes was selected based on the comparison of model fit statistics. Independent variables were incorporated to explore the association between these variables and the latent class.

RESULTS

Based on nine indicator variables, three latent classes were identified: "Active Older adults", "Moderately independent" and "Psychological and physically impaired". The "Active older adults" profile is comprised of older adults who have a very low probability of needing help for any ADL, IADL and other activities. The "Moderately independent" class were characterized as those older adults who were visually impaired but less likely to need help for IADL activities. The "Psychological and physically impaired", the smallest of all classes, comprised of older adults with poor dependency status.

CONCLUSIONS

In this study, we found that the dependency status of older adults which is based on several domains of functional activity has been classified into three distinct classes. These three classes have distinct physical, psychological, economic, and socio-demographic characteristics in terms of activities in which help is required.

A mitochondrial nexus in major depressive disorder: Integration with the psycho-immune-neuroendocrine network

Nervous system processes, including cognition and affective state, fundamentally rely on mitochondria. Impaired mitochondrial function is evident in major depressive disorder (MDD), reflecting cumulative detrimental influences of both extrinsic and intrinsic stressors, genetic predisposition, and mutation. Glucocorticoid 'stress' pathways converge on mitochondria; oxidative and nitrosative stresses in MDD are largely mitochondrial in origin; both initiate cascades promoting mitochondrial DNA (mtDNA) damage with disruptions to mitochondrial biogenesis and tryptophan catabolism. Mitochondrial dysfunction facilitates proinflammatory dysbiosis while directly triggering immuno-inflammatory activation via released mtDNA, mitochondrial lipids and mitochondria associated membranes (MAMs), further disrupting mitochondrial function and mitochondrial quality control, promoting the accumulation of abnormal mitochondria (confirmed in autopsy studies). Established and putative mechanisms highlight a mitochondrial nexus within the psycho-immune neuroendocrine (PINE) network implicated in MDD. Whether lowering neuronal resilience and thresholds for disease, or linking mechanistic nodes within the MDD pathogenic network, impaired mitochondrial function emerges as an important risk, a functional biomarker, providing a therapeutic target in MDD. Several treatment modalities have been demonstrated to reset mitochondrial function, which could benefit those with MDD.

Age-related subtypes of late life depression and mortality: A prospective clinical cohort study

OBJECTIVES

Late Life Depression (LLD) is associated with increased mortality rates, but it remains unclear which depressed patients are at increased risk. This study examined the mortality risk of previously identified subgroups of depressed older patients based on age-related clinical features (the presence of physical and cognitive frailty).

METHODS

A six-year follow-up of a clinical cohort study including 375 depressed older patients and 132 non-depressed persons (NESDO). Depressed patients were diagnosed with the Composite International Diagnostic Interview (CIDI) according to DSM-IV criteria and classified by latent profile analysis on depressive symptom severity, cognitive domains and physical frailty. We estimated the hazard rate of mortality for the four depressed subgroups compared to non-depressed persons by applying Cox-regression analyses. Models were adjusted for age, sex and education as confounders and for explanatory variables per pathway in separate models: somatic burden, lifestyle characteristics, vascular burden or inflammation markers.

RESULTS

A total of 61/375 (16.3%) depressed patients and 8/132 (6.1%) non-depressed persons died during the 6-year follow-up. Two of the four subgroups (n = 186/375 (50%) of the depressed sample) had a higher hazard rate (HR) for mortality compared to non-depressed participants, that is, frail-depressed patients (HR = 5.25, [95%-CI: 2.13-13.0]) and pure mild depressed patients (HR = 3.32 [95%-CI: 1.46-7.58]) adjusted for confounders. Adding possible underlying pathways did not explain these associations.

CONCLUSIONS

Age-related features (the presence of physical and cognitive frailty) contribute to the increased mortality risk in late-life depression. Future studies in depressed older patients should study the additional value of a clinical geriatric assessment and integrated treatment aimed to at reduce frailty and ameliorate their mortality risk.

Mental health profiles and the related socio-demographic predictors in Hong Kong university students under the COVID-19 pandemic: A latent class analysis

While the COVID-19 pandemic has brought about significant challenges to mental health of university students, there is limited research in this area. Particularly, few studies examined the person-centered mental health symptom profiles such as depression and anxiety and the related socio-demographic predictors. Using Latent Class Analysis (LCA), this study investigated the symptom profiles of depression and anxiety in university students in Hong Kong under the COVID-19 pandemic and the socio-demographic predictors. A total of 978 undergraduate students completed an online questionnaire including socio-demographic factors and measures of depression and anxiety during the summer of 2022. The LCA identified three latent classes: "normal" group, "moderate comorbid depression and anxiety" group and "severe comorbid depression and anxiety" group. Multinominal logistic regression showed that comparing with the "normal" group and the "moderate symptom" group, the "severe symptom" group had higher personal financial difficulties and individual/family member unemployment during the pandemic. In contrast, other socio-demographic factors (age, gender, year of study, living status, and COVID-19 infection status) had no significant association with group status. The study contributes to understanding of person-centered depression and anxiety symptom profiles and the risk role of personal financial difficulty in mental health of university students under the pandemic.

Racial Differences in Older Adult's Mental Health and Cognitive Symptomatology: Identifying Subgroups Using Multiple-Group Latent Class Analysis

INTRODUCTION

Little is known on the potential racial differences in latent subgroup membership based on mental health and cognitive symptomatology among older adults.

METHODS

This is a secondary data analysis of Wave 2 data from the National Social Life, Health, and Aging Project (N = 1819). Symptoms were depression, anxiety, loneliness, happiness, and cognition. Multiple-group latent class analysis was conducted to identify latent subgroups based on mental health and cognitive symptoms and to compare these differences between race.

RESULTS

Class 1: "Severe Cognition & Mild-Moderate Mood Impaired," Class 2: "Moderate Cognition & Mood Impaired," and Class 3: "Mild Cognition Impaired & Healthy Mood" were identified. Black older adults were more likely to be in Class 1 while White older adults were more likely to be in Class 2 and Class 3.

DISCUSSION

Clinicians need to provide culturally-sensitive care when assessing and treating symptoms across different racial groups.

Depression Subtypes in Systolic Heart Failure: A Secondary Analysis From a Randomized Controlled Trial

BACKGROUND

Heart failure (HF) is associated with an elevated risk of morbidity, mortality, hospitalization, and impaired quality of life. One potential contributor to these poor outcomes is depression. Yet the effectiveness of treatments for depression in patients with HF is mixed, perhaps due to the heterogeneity of depression.

METHODS

This secondary analysis applied latent class analysis (LCA) to data from a clinical trial to classify patients with systolic HF and comorbid depression into LCA subtypes based on depression symptom severity, and then examined whether these subtypes predicted treatment response and mental and physical health outcomes at 12 months follow-up.

RESULTS

In LCA of 629 participants (mean age 63.6 ± 12.9; 43% females), we identified 4 depression subtypes: mild (prevalence 53%), moderate (30%), moderately severe (12%), and severe (5%). The mild subtype was characterized primarily by somatic symptoms of depression (e.g., energy loss, sleep disturbance, poor appetite), while the remaining LCA subtypes additionally included nonsomatic symptoms of depression (e.g., depressed mood, anhedonia, worthlessness). At 12 months, LCA subtypes with more severe depressive symptoms reported significantly greater improvements in mental quality of life and depressive symptoms compared to the LCA mild subtype, but the incidence of cardiovascular- and noncardiovascular-related readmissions, and mortality was similar among all subtypes.

CONCLUSIONS

In patients with depression and systolic heart failure those with the LCA mild depression subtype may not meet full criteria for major depressive disorder, given the overlap between HF and somatic symptoms of depression. We recommend requiring depressed mood or anhedonia as a necessary symptom for major depressive disorder in patients with HF.

Brain morphometric features predict depression symptom phenotypes in late-life depression using a deep learning model

Objectives

Our objective was to use deep learning models to identify underlying brain regions associated with depression symptom phenotypes in late-life depression (LLD).

Participants

Diagnosed with LLD (N = 116) and enrolled in a prospective treatment study.

Design

Cross-sectional.

Measurements

Structural magnetic resonance imaging (sMRI) was used to predict five depression symptom phenotypes from the Hamilton and MADRS depression scales previously derived from factor analysis: (1) Anhedonia, (2) Suicidality, (3) Appetite, (4) Sleep Disturbance, and (5) Anxiety. Our deep learning model was deployed to predict each factor score via learning deep feature representations from 3D sMRI patches in 34 a priori regions-of-interests (ROIs). ROI-level prediction accuracy was used to identify the most discriminative brain regions associated with prediction of factor scores representing each of the five symptom phenotypes.

Results

Factor-level results found significant predictive models for Anxiety and Suicidality factors. ROI-level results suggest the most LLD-associated discriminative regions in predicting all five symptom factors were located in the anterior cingulate and orbital frontal cortex.

Conclusions

We validated the effectiveness of using deep learning approaches on sMRI for predicting depression symptom phenotypes in LLD. We were able to identify deep embedded local morphological differences in symptom phenotypes in the brains of those with LLD, which is promising for symptom-targeted treatment of LLD. Future research with machine learning models integrating multimodal imaging and clinical data can provide additional discriminative information.

Course of Subtypes of Late-Life Depression Identified by Bipartite Network Analysis During Psychosocial Interventions

Importance

Approximately half of older adults with depression remain symptomatic at treatment end. Identifying discrete clinical profiles associated with treatment outcomes may guide development of personalized psychosocial interventions.

Objective

To identify clinical subtypes of late-life depression and examine their depression trajectory during psychosocial interventions in older adults with depression.

Design, Setting, and Participants

This prognostic study included older adults aged 60 years or older who had major depression and participated in 1 of 4 randomized clinical trials of psychosocial interventions for late-life depression. Participants were recruited from the community and outpatient services of Weill Cornell Medicine and the University of California, San Francisco, between March 2002 and April 2013. Data were analyzed from February 2019 to February 2023.

Interventions

Participants received 8 to 14 sessions of (1) personalized intervention for patients with major depression and chronic obstructive pulmonary disease, (2) problem-solving therapy, (3) supportive therapy, or (4) active comparison conditions (treatment as usual or case management).

Main Outcomes and Measures

The main outcome was the trajectory of depression severity, assessed using the Hamilton Depression Rating Scale (HAM-D). A data-driven, unsupervised, hierarchical clustering of HAM-D items at baseline was conducted to detect clusters of depressive symptoms. A bipartite network analysis was used to identify clinical subtypes at baseline, accounting for both between- and within-patient variability across domains of psychopathology, social support, cognitive impairment, and disability. The trajectories of depression severity in the identified subtypes were compared using mixed-effects models, and time to remission (HAM-D score ≤10) was compared using survival analysis.

Results

The bipartite network analysis, which included 535 older adults with major depression (mean [SD] age, 72.7 [8.7] years; 70.7% female), identified 3 clinical subtypes: (1) individuals with severe depression and a large social network; (2) older, educated individuals experiencing strong social support and social interactions; and (3) individuals with disability. There was a significant difference in depression trajectories (F2,2976.9 = 9.4; P < .001) and remission rate (log-rank χ22 = 18.2; P < .001) across clinical subtypes. Subtype 2 had the steepest depression trajectory and highest likelihood of remission regardless of the intervention, while subtype 1 had the poorest depression trajectory.

Conclusions and Relevance

In this prognostic study, bipartite network clustering identified 3 subtypes of late-life depression. Knowledge of patients' clinical characteristics may inform treatment selection. Identification of discrete subtypes of late-life depression may stimulate the development of novel, streamlined interventions targeting the clinical vulnerabilities of each subtype.

The cardiometabolic depression subtype and its association with clinical characteristics: The Maastricht Study

BACKGROUND

Individuals with depression often show an adverse cardiometabolic risk profile and might represent a distinct depression subtype. The aim of this study was to investigate whether a cardiometabolic depression subtype could be identified and to investigate its association with demographics and clinical characteristics (severity, symptomatology, anti-depressant use, persistence and cognitive functioning).

METHODS

We used data from The Maastricht Study, a population-based cohort in the southern part of The Netherlands. A total of 248 participants with major depressive disorder were included (mean [SD] age, 58.8 ± 8.5 years; 121 [48.8 %] were men). Major depressive disorder was assessed at baseline by the Mini-International Neuropsychiatric Interview. Cardiometabolic risk factors were defined as indicators of the metabolic syndrome according to the National Cholesterol Education Program Adult Treatment Panel III guidelines. We measured severity and persistence of depressive symptoms by use of the 9-item Patient Health Questionnaire.

RESULTS

Latent class analysis resulted in two subtypes, one with cardiometabolic depression (n = 145) and another with non-cardiometabolic depression (n = 103). The cardiometabolic depression subtype was characterized by being male, low education, more severe depressive symptoms, less symptoms of depressed mood and more symptoms of loss of energy, more use of antidepressant medication and lower cognitive functioning.

LIMITATIONS

No conclusions can be made about causality.

CONCLUSIONS

Latent class analysis suggested a distinct cardiometabolic depression subtype. Participants with cardiometabolic depression differed from participants with non-cardiometabolic depression in terms of demographics and clinical characteristics. The existence of a cardiometabolic depression subtype may indicate the need for prevention and treatment targeting cardiometabolic risk management.

Clinical and biological subtypes of late-life depression

BACKGROUND

Late-life depression (LDD) results from multiple psychosocial and neurobiological changes occurring in later life. The current study investigated how patterns of clinical symptoms and brain structural features are classified into LDD subtypes.

METHOD

Self-report scale of depression, behavioral rating of affective symptoms, and brain structural imaging of white matter change and cortical thickness were assessed in 541 older adults with no cognitive impairment or mild cognitive impairment. Latent profile analysis was used to identify distinct subtypes of depression.

RESULTS

The latent profile analysis identified four classes with mild to severe depressive symptoms and two classes with minimal symptoms. While the classes primarily differed in the overall severity, the combinatory patterns of clinical symptoms and neuropathological signature distinguished the classes with similar severity. The classes were distinguished in terms of whether or not neurodegenerative risk accompanied the corresponding depressive symptoms. The presence of the negative self-scheme and cortical thinning pattern notably characterized the subtypes of LDD.

LIMITATIONS

The underlying etiologies of the biological subtypes are still speculative, and the current study lacks clinical history that differentiates late- and early-onset depression.

CONCLUSIONS

Our finding provides insight in identifying heterogeneities of depressive disorder in later life and suggests that self-report and behavioral symptom profile in combination with white matter lesion and cortical thickness effectively characterizes distinct subtypes of LDD.

Network structure of time-varying depressive symptoms through dynamic time warp analysis in late-life depression

OBJECTIVES

Late-life major depressive disorder (MDD) can be conceptualized as a complex dynamic system. However, it is not straightforward how to analyze the covarying depressive symptoms over time in case of sparse panel data. Dynamic time warping (DTW) analysis may yield symptom networks and dimensions both at the patient and group level.

METHODS

In the Netherlands Study of Depression in Older People (NESDO) depressive symptoms were assessed every 6 months using the 30-item Inventory of Depressive Symptomatology (IDS) with up to 13 assessments per participant. Our sample consisted of 182 persons, aged ≥ 60 years, with an IDS total score of 26 or higher at baseline. Symptom networks dimensions, and centrality metrics were analyzed using DTW and Distatis analyses.

RESULTS

The mean age was 69.8 years (SD 7.1), with 69.0% females, and a mean IDS score of 38.0 (SD = 8.7). DTW enabled visualization of an idiographic symptom network in a single NESDO participant. In the group-level nomothetic approach, four depressive symptom dimensions were identified: "core symptoms", "lethargy/somatic", "sleep", and "appetite/atypical". Items of the "internalizing symptoms" dimension had the highest centrality, whose symptom changes over time were most similar to those changes of other symptoms.

CONCLUSIONS

DTW revealed symptom networks and dimensions based on the within-person symptom changes in older MDD patients. Its centrality metrics signal the most influential symptoms, which may aid personalized care.

Subtyping late-life depression according to inflammatory and metabolic dysregulation: a prospective study

BACKGROUND

Inflammation and metabolic dysregulation are age-related physiological changes and are associated with depressive disorder. We tried to identify subgroups of depressed older patients based on their metabolic-inflammatory profile and examined the course of depression for these subgroups.

METHODS

This clinical cohort study was conducted in a sample of 364 depressed older (⩾60 years) patients according to DSM-IV criteria. Severity of depressive symptoms was monitored every 6 months and a formal diagnostic interview repeated at 2-year follow-up. Latent class analyses based on baseline metabolic and inflammatory biomarkers were performed. Adjusted for confounders, we compared remission of depression at 2-year follow-up between the metabolic-inflammatory subgroups with logistic regression and the course of depression severity over 2-years by linear mixed models.

RESULTS

We identified a 'healthy' subgroup (n = 181, 49.7%) and five subgroups characterized by different profiles of metabolic-inflammatory dysregulation. Compared to the healthy subgroup, patients in the subgroup with mild 'metabolic and inflammatory dysregulation' (n = 137, 37.6%) had higher depressive symptom scores, a lower rate of improvement in the first year, and were less likely to be remitted after 2-years [OR 0.49 (95% CI 0.26-0.91)]. The four smaller subgroups characterized by a more specific immune-inflammatory dysregulation profile did not differ from the two main subgroups regarding the course of depression.

CONCLUSIONS

Nearly half of the patients with late-life depressions suffer from metabolic-inflammatory dysregulation, which is also associated with more severe depression and a worse prognosis. Future studies should examine whether these depressed older patients benefit from a metabolic-inflammatory targeted treatment.

Heterogenous Subtypes of Late-Life Depression and Their Cognitive Patterns: A Latent Class Analysis

BACKGROUND

Late-life depression (LLD), characterized by cognitive deficits, is considered heterogeneous across individuals. Previous studies have identified subtypes with diverse symptom profiles, but their cognitive patterns are unknown. This study aimed to investigate the subtypes of LLD and the cognitive profile of each group.

METHODS

In total, 109 depressed older adults were enrolled. We performed latent class analysis using Geriatric Depression Scale items as indicators to generate latent classes. We compared the sociodemographic and clinical characteristics with cognitive functions between groups and conducted regression analysis to investigate the association between class membership and variables with significant differences.

RESULTS

Two classes were identified: the "pessimistic" group was characterized by pessimistic thoughts and the "worried" group with a relatively high prevalence of worry symptoms. The two groups did not differ in sociodemographic characteristics. The "pessimistic" group showed a higher rate of past history of depression and lower age of onset. The "worried" group had more physical comorbidities and a higher rate of past history of anxiety. The "pessimistic" group was more impaired in general cognitive function, executive function, information processing speed, and attention. Lower general and executive functions were associated with the membership in the "pessimistic" group.

CONCLUSIONS

Subjects with pessimistic symptoms and subjects with a propensity to worry may form two distinct subtypes of late-life depression with different cognitive profiles. Further, the cognitive evaluation of subjects with pessimistic symptoms is of utmost importance.

Depressive symptom heterogeneity among older adults after hip fracture

OBJECTIVE

to evaluate patterns of depressive symptoms after hip fracture and examine their impact on functional recovery.

METHODS

participants (n = 304) included older adults from the Baltimore Hip Studies 7th cohort who experienced a hip fracture. Depressive symptoms were measured at baseline or 2-, 6- or 12-month post-hip fracture using the 20-item Center for Epidemiologic Studies Depression scale. Gait speed was measured after hip fracture at 2-, 6- or 12-month follow-up. Latent class analysis was used to identify individuals with similar patterns of depressive symptoms after hip fracture. Item response probabilities characterised symptom profiles, and posterior probability estimates were used to assign participants to a baseline depressive symptom subtype. Weighted estimated equations compared post-fracture gait speed between baseline symptomatic and asymptomatic groups.

RESULTS

four patterns of depressive symptoms were identified: asymptomatic (50.8%), somatic (28.6%), melancholic (11.4%) and anhedonic (9.2%). The somatic subtype was characterised by difficultly concentrating and reduced energy and movement, whereas anhedonic symptoms were associated with the inability to experience pleasure. Melancholic symptoms corresponded to anhedonia, decreased physical activity and other psychological and somatic complaints. Compared with the asymptomatic group, somatic symptoms were consistently associated with slower gait speed, -0.03 metres per second (m/s) and between-group differences for melancholic symptomology were as large as -0.05 m/s, but the associations were not statistically significant.

CONCLUSION

findings demonstrate unique depressive symptom subtypes in older adults after hip fracture and provide confirmatory evidence of unique clinical phenotypes; however, their impact on functional recovery after hip fracture remains unclear.

Subtypes of Late-Life Depression: A Data-Driven Approach on Cognitive Domains and Physical Frailty

BACKGROUND

With increasing age, symptoms of depression may increasingly overlap with age-related physical frailty and cognitive decline. We aim to identify late-life-related subtypes of depression based on measures of depressive symptom dimensions, cognitive performance, and physical frailty.

METHODS

A clinical cohort study of 375 depressed older patients with a DSM-IV depressive disorder (acronym NESDO). A latent profile analysis was applied on the three subscales of the Inventory of Depressive Symptomatology, as well as performance in five cognitive domains and two proxies for physical frailty. For each class, we investigated remission, dropout, and mortality at 2-year follow-up as well as change over time of depressive symptom severity, cognitive performance, and physical frailty.

RESULTS

A latent profile analysis model with five classes best described the data, yielding two subgroups suffering from pure depression ("mild" and "severe" depression, 55% of all patients) and three subgroups characterized by a specific profile of cognitive and physical frailty features, labeled as "amnestic depression," "frail-depressed, physically dominated," and "frail-depressed, cognitively dominated." The prospective analyses showed that patients in the subgroup of "mild depression" and "amnestic depression" had the highest remission rates, whereas patients in both frail-depressed subgroups had the highest mortality rates.

CONCLUSIONS

Late-life depression can be subtyped by specific combinations of age-related clinical features, which seems to have prospective relevance. Subtyping according to the cognitive profile and physical frailty may be relevant for studies examining underlying disease processes as well as to stratify treatment studies on the effectiveness of antidepressants, psychotherapy, and augmentation with geriatric rehabilitation.

Associations between depression subtype and functional impairment and treatment utilization in a national sample of adolescents

BACKGROUND

Prior studies have characterized distinct major depressive episode (MDE) subtypes among adults, yet limited evidence exists regarding variation in MDE during adolescence.

METHODS

Using 2008-2016 National Survey of Drug Use and Health data, latent class analysis (LCA) was used to characterize depression subtypes (based on symptom presentation) among 9,896 youth ages 12-17 with recent first-onset MDE. Logistic regression was used to estimate associations of MDE subtype with functional outcomes and treatment utilization, adjusting for demographic characteristics and depression severity (i.e., number of MDE diagnostic criteria and recurrence status) RESULTS: A 5-class LCA model provided optimal fit. Three distinct categories of MDE symptoms generally clustered together, which we termed "somatic," "cognitive," and "self-worth;" classes were differentiated by distinct combinations of symptoms across these 3 categories. Subtypes were characterized as: Highly Symptomatic (39% of youth); Somatic & Cognitive (24%), Somatic (22%), Diffuse Symptoms (8%), and Somatic & Self-Worth (6%). The majority of youth reported at least moderate impairment across multiple domains; subtype was a significant predictor of functional impairment. Only 34% of youth received any past-year depression-related treatment; treatment utilization was significantly higher for MDE subtypes with the highest prevalences of suicidal ideation.

LIMITATIONS

Due to cross-sectional data, we cannot establish causal directionality.

CONCLUSIONS

Subtype was significantly predictive of functional impairment and treatment utilization, above and beyond number of MDE diagnostic criteria or recurrence status. Understanding distinct profiles of adolescent depression, as well as potential differential associations with impairment, can inform prevention, diagnosis, and treatment of depression among youth.

Health profiles and socioeconomic characteristics of nonagenarians residing in Mugello, a rural area in Tuscany (Italy)

Background

Health, as defined by the WHO, is a multidimensional concept that includes different aspects. Interest in the health conditions of the oldest-old has increased as a consequence of the phenomenon of population aging. This study investigates whether (1) it is possible to identify health profiles among the oldest-old, taking into account physical, emotional and psychological information about health, and (2) there are demographic and socioeconomic differences among the health profiles.

Methods

Latent Class Analysis with covariates was applied to the Mugello Study data to identify health profiles among the 504 nonagenarians residing in the Mugello district (Tuscany, Italy) and to evaluate the association between socioeconomic characteristics and the health profiles resulting from the analysis.

Results

This study highlights four groups labeled according to the posterior probability of determining a certain health characteristic: “healthy”, “physically healthy with cognitive impairment”, “unhealthy”, and “severely unhealthy”. Some demographic and socioeconomic characteristics were found to be associated with the final groups: older nonagenarians are more likely to be in worse health conditions; men are in general healthier than women; more educated individuals are less likely to be in extremely poor health conditions, while the lowest-educated are more likely to be cognitively impaired; and office or intellectual workers are less likely to be in poor health conditions than are farmers.

Conclusions

Considering multiple dimensions of health to determine health profiles among the oldest-old could help to better evaluate their care needs according to their health status.

Depression symptoms and cognitive impairment in older nursing home residents in the USA: A latent class analysis

OBJECTIVES

To identify subgroups of nursing home (NH) residents in the USA experiencing homogenous depression symptoms and evaluate if subgroups vary by cognitive impairment.

METHODS

We identified 104 465 newly admitted, long-stay residents with depression diagnosis at NH admission in 2014 using the Minimum Data Set 3.0. The Patient Health Questionnaire-9 was used to measure depression symptoms and the Brief Interview of Mental Status for cognitive impairment (intact; moderately impaired; severely impaired). Latent class analysis (LCA) with logistic regression was used to: (a) construct the depression subgroups and (b) estimate adjusted odds ratios (aOR) and 95% confidence intervals (CI) of the associations between the subgroups and cognitive impairment level, adjusting for demographic and clinical characteristics.

RESULTS

The best-fitted LCA model suggested four subgroups of depression: minimal symptoms (latent class prevalence: 42.4%), fatigue (32.0%), depressed mood (14.5%), and multiple symptoms (11.2%). Odds of subgroup membership varied by cognitive impairment. Compared to residents with intact cognition, those with moderate or severe cognitive impairment were less likely to belong to the fatigue subgroup [aOR(95% CI): moderate: 0.75 (0.71-0.80); severe: 0.26 (0.23-0.29)] and more likely to belong to the depressed mood subgroup [aOR (95% CI): moderate: 4.54 (3.55-5.81); severe: 6.41 (4.86-8.44)]. Residents with moderate cognitive impairment had increased odds [aOR (95% CI): 1.19 (1.12-1.27)] while those with severe impairment had reduced odds of being in the multiple symptoms subgroup [aOR (95% CI): 0.63 (0.58-0.68)].

CONCLUSIONS

Findings provide a basis for improving depression management with consideration of both subgroups of depression symptoms and levels of cognitive function.

Characterization of the signature of peripheral innate immunity in women with later-life major depressive disorder

The prevalence of depression in later life is higher in women than in men. However, the sex difference in the pathophysiology of depression in elderly patients is not fully understood. Here, we performed gene expression profiling in leukocytes of middle-aged and elderly patients with major depressive disorder, termed later-life depression (LLD) in this context, and we characterized the sex-dependent pathophysiology of LLD. A microarray dataset obtained from leukocytes of patients (aged ≥50 years) with LLD (32 males and 39 females) and age-matched healthy individuals (20 males and 24 females) was used. Differentially expressed probes were determined by comparing the expression levels between patients and healthy individuals, and then functional annotation analyses (Ingenuity Pathway Analysis, Reactome pathway analysis, and cell-type enrichment analysis) were performed. A total of 1656 probes were differentially expressed in LLD females, but only 3 genes were differentially expressed in LLD males. The differentially expressed genes in LLD females were relevant to leukocyte extravasation signaling, Tec kinase signaling and the innate immune response. The upregulated genes were relevant to myeloid lineage cells such as CD14⁺ monocytes. In contrast, the downregulated genes were relevant to CD4⁺ and CD8⁺ T cells. Remarkable innate immune signatures are present in the leukocytes of LLD females but not males. Because inflammation is involved in the pathophysiology of depression, the altered inflammatory activity may be involved in the pathophysiology of LLD in women. In contrast, abnormal inflammation may be an uncommon feature in LLD males.

Depression Subtypes in Individuals With or at Risk for Symptomatic Knee Osteoarthritis

OBJECTIVE

The present study was undertaken to identify depression subtypes in individuals with or at risk for symptomatic knee osteoarthritis (OA) and to evaluate differences in pain and disability trajectories between groups.

METHODS

Participants (n = 4,486) were enrolled in the Osteoarthritis Initiative. Latent class analysis was applied to the 20-item Center for Epidemiologic Studies Depression Scale measured at baseline to identify groups with similar patterns of depressive symptoms, and subtypes were assigned using posterior probability estimates. The relationships between depression subtypes and Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) pain and disability subscales were modeled over 4 years and stratified by baseline knee OA status (symptomatic [n = 1,626] or at risk [n = 2,860]).

RESULTS

Four subtypes were identified: asymptomatic (80.6%), catatonic (5.3%), anhedonic (10.6%), and melancholic (3.5%). Catatonic and anhedonic subtypes were differentiated by symptoms corresponding to psychomotor agitation and the inability to experience pleasure, respectively. The melancholic subtype expressed symptoms related to reduced energy and movement, anhedonia, and other somatic symptoms. Detectable mean differences in pain and disability compared to the asymptomatic group were observed for the anhedonic (1.5-2.3 WOMAC units) and melancholic (4.8-6.6 WOMAC units) subtypes, and associations were generally larger in individuals with symptomatic knee OA relative to those at risk.

CONCLUSION

Among individuals with or at risk for symptomatic knee OA, there is evidence of depression subtypes characterized by distinct clusters of depressive symptoms that have differential effects on reports of pain and disability over time. Our findings thus imply that depression interventions could be optimized by targeting the specific symptomology that these subtypes exhibit.

Effects of age on depressive symptomatology and response to antidepressant treatment in patients with major depressive disorder aged 18 to 65 years

BACKGROUND

There is evidence that symptomatology in patients with major depressive disorder (MDD) changes with age. However, studies comparing depressive symptomatology between different age groups during antidepressant therapy are rare. We compared demographic and clinical characteristics in depressed patients of different age groups at baseline and during treatment.

METHODS

889 MDD inpatients were divided into four age groups (18-29, 30-39, 40-49, 50-65 yrs.). Demographic and clinical characteristics including depressive symptomatology (assessed by the Inventory of Depressive Symptoms) were assessed at baseline and weekly during treatment.

RESULTS

At baseline, young patients (18-29 years) significantly more often reported cognitive symptoms like irritability, suicidality, negative self-concept and interpersonal sensitivity and more often suffered from drug abuse and comorbid personality disorders. Late middle aged patients (50-65 years) significantly more often suffered from neuro-vegetative symptoms such as reduced general interest, sexual interest and sleep disturbances and more often showed a recurrent MDD and comorbid physical disorders. During therapy, symptoms such as interpersonal sensitivity in young patients and low interest in sex in late middle aged patients persisted until the end of treatment while all other symptoms declined until day 56.

LIMITATIONS

The herein presented age differences in depressive symptomatology only hold true for the study medication and are not generalizable to other antidepressants agents.

CONCLUSION

There are substantial differences in the clinical presentation of depression between age groups. Whereas many of these differences disappear during treatment, some differences persisted until the end of treatment. These findings my help to more specifically tailor the treatment of depressed patients.

Neurovegetative symptom subtypes in young people with major depressive disorder and their structural brain correlates

Depression is a leading cause of burden of disease among young people. Current treatments are not uniformly effective, in part due to the heterogeneous nature of major depressive disorder (MDD). Refining MDD into more homogeneous subtypes is an important step towards identifying underlying pathophysiological mechanisms and improving treatment of young people. In adults, symptom-based subtypes of depression identified using data-driven methods mainly differed in patterns of neurovegetative symptoms (sleep and appetite/weight). These subtypes have been associated with differential biological mechanisms, including immuno-metabolic markers, genetics and brain alterations (mainly in the ventral striatum, medial orbitofrontal cortex, insular cortex, anterior cingulate cortex amygdala and hippocampus). K-means clustering was applied to individual depressive symptoms from the Quick Inventory of Depressive Symptoms (QIDS) in 275 young people (15-25 years old) with MDD to identify symptom-based subtypes, and in 244 young people from an independent dataset (a subsample of the STAR*D dataset). Cortical surface area and thickness and subcortical volume were compared between the subtypes and 100 healthy controls using structural MRI. Three subtypes were identified in the discovery dataset and replicated in the independent dataset; severe depression with increased appetite, severe depression with decreased appetite and severe insomnia, and moderate depression. The severe increased appetite subtype showed lower surface area in the anterior insula compared to both healthy controls. Our findings in young people replicate the previously identified symptom-based depression subtypes in adults. The structural alterations of the anterior insular cortex add to the existing evidence of different pathophysiological mechanisms involved in this subtype.

Stability and transition of depression subtypes in late life

BACKGROUND

The heterogeneity of late-life depression hampers diagnosis and treatment. Data-driven methods have identified several subtypes of depression in older persons, but the longitudinal stability of these subtypes remains unknown.

METHODS

In total 111 older persons with a major depressive disorder both at baseline and 2-year follow-up from the Netherlands Study of Depression in Older persons (NESDO) were included. Latent class analysis was performed to identify subtypes of depression at baseline and at 2-year follow-up, and latent transition analysis was used to examine the stability of these subtypes over time. Transition rates between subtypes and characteristics of groups were examined.

RESULTS

Two subtypes were identified in both baseline (T0) and follow-up data (T1), including a 'melancholic' subtype (prevalence 80.2% (T0) and 62.2% (T1)), and an 'atypical' subtype (prevalence 19.8% (T0) and 37.8% (T1)). The melancholic subtype was characterized by decreased appetite and weight and had a stability of 0.86. The atypical subtype was characterized by increased appetite and weight and had a stability of 0.93, although the discriminating power of different symptoms had decreased at T1. Mean age and education differed significantly between stable and transitioning subgroups, other characteristics did not differ between subgroups.

LIMITATIONS

Limited sample size might have hampered the analyses.

CONCLUSIONS

Subtypes of late-life depression are relatively stable, but symptoms of depression (like weight loss) seem to blur with symptoms of (patho)physiological aging. This underlines the clinical relevance of depression subtyping, but also the importance of further research into subtypes and the influence of aging.

Melancholia as Predictor of Electroconvulsive Therapy Outcome in Later Life

OBJECTIVES

In clinical practice, particularly melancholic depression benefits from electroconvulsive therapy (ECT), albeit research melancholia criteria from the Diagnostic and Statistical Manual of Mental Disorders (DSM) is not conclusive. We compared clinical characteristics and ECT outcome of melancholic and nonmelancholic depression, here defined by psychomotor symptoms.

METHODS

One hundred ten depressed older in-patients treated with ECT were included in the Mood Disorders in Elderly treated with ECT study. The CORE was used for the assessment of psychomotor symptoms, with a score of 8 or higher defining melancholic depression. Depression severity was measured before, during, and after ECT. Characteristics were compared across melancholic and nonmelancholic patients. Regression analysis was used to assess the relation between psychomotor symptoms and remission/response, and survival analysis was used to examine the difference in time.

RESULTS

Patients with melancholic depression had higher severity, lower cognitive and overall functioning, and lower prevalence of cardiovascular disease. However, no significant relations were found between CORE scores and remission/response. Because psychotic symptoms are a positive predictor of ECT response and remission, we examined whether CORE score was a predictor of response in the nonpsychotic group (n = 49). In nonpsychotic patients, remission was 62%, and the association between CORE scores and remission almost reached significance (P = 0.057).

DISCUSSION

Although melancholically and nonmelancholically depressed patients differed significantly on several clinical characteristics, ECT outcome did not differ. Analyses may be hampered by a high prevalence of psychotic features. In nonpsychotic patients, CORE scores neared significance as predictor of remission, suggesting that CORE scores might be a distinguishing characteristic of melancholia in nonpsychotic patients and a clinical useful predictor of ECT response.

Can psychomotor disturbance predict ect outcome in depression?

Psychomotor symptoms are core features of melancholic depression. This study investigates whether psychomotor disturbance predicts the outcome of electroconvulsive therapy (ECT) and how the treatment modulates psychomotor disturbance. In 73 adults suffering from major depressive disorder psychomotor functioning was evaluated before, during and after ECT using the observer-rated CORE measure and objective measures including accelerometry and a drawing task. Regression models were fitted to assess the predictive value of melancholic depression (CORE ≥ 8) and the psychomotor variables on ECT outcome, while effects on psychomotor functioning were evaluated through linear mixed models. Patients with CORE-defined melancholic depression (n = 41) had a 4.9 times greater chance of reaching response than those (n = 24) with non-melancholic depression (Chi-Square = 7.5, P = 0.006). At baseline, both higher total CORE scores (AUC = 0.76; P = 0.001) and needing more cognitive (AUC = 0.78; P = 0.001) and motor time (AUC = 0.76; P = 0.003) on the drawing task corresponded to superior ECT outcomes, as did lower daytime activity levels (AUC = 0.76) although not significantly so after Bonferroni correction for multiple testing. A greater CORE-score reduction in the first week of ECT was associated with higher ECT effectiveness. ECT reduced CORE-assessed psychomotor symptoms and improved activity levels only in those patients showing the severer baseline retardation. Although the sample was relatively small, psychomotor symptoms were clearly associated with beneficial outcome of ECT in patients with major depression, indicating that monitoring psychomotor deficits can help personalise treatment.

A model-driven approach to better identify older people at risk of depression

Depression in later life is one of the most common mental disorders. Several instruments have been developed to detect the presence or the absence of certain symptoms or emotional disorders, based on cut-off points. However, the use of a cut-off does not allow identification of depression sub-types or distinguish between mild and severe depression. As a result, depression may be under- or over-diagnosed in older people. This paper aims to apply a model-driven approach to classify individuals into distinct sub-groups, based on different combinations of depressive and emotional conditions. This approach is based on two distinct statistical solutions: first, a latent class analysis is applied to the items collected by the depression scale and, according to the final model, the probability of belonging to each class is calculated for every individual. Second, a factor analysis of these classes is performed to obtain a reduced number of clusters for easy interpretation. We use data collected through the EURO-D scale in a large sample of older individuals, participants of the sixth wave of the Survey of Health, Ageing and Retirement in Europe. We show that by using such a model-based approach it is possible to classify individuals in a more accurate way than the simple dichotomisation ‘depressed’ versus ‘non-depressed’.

Association of serum ferritin level and depression with respect to the body mass index in Korean male adults

BACKGROUND/OBJECTIVES

Obesity is globally a major public health issue. Evidence suggests that elevated ferritin levels are associated with obesity, dyslipidemia, insulin resistance, and metabolic syndrome. This study was undertaken to examine the relationship between the serum ferritin level and depression in Korean male adults with respect to classification of the prevailing obesity.

SUBJECTS/METHODS

This was a case-control study; subjects were classified into obese group (≥ 25.0 kg/m², 28 subjects) and normal group (18.5–22.9 kg/m², 27 subjects). A survey was conducted to assess the depression levels as per the guidelines suggested by the Center program for Epidemiological Studies-Depression (CES-D). Blood was collected from each group for assessing biomarkers, and isolated plasma was evaluated for fasting glucose, insulin, quantitative insulin sensitivity check index, and ferritin levels. Data were analyzed, and groups were compared with respect to Body Mass Index (BMI), depression scale and biomarkers.

RESULTS

The average depression score of the obesity group was 16.86, which was higher than the normal group (12.56). Subjects scoring more than 16 points comprised 53.6% of the population in the obese group, which was more than double that in the normal group, as assessed by the CES-D program. Furthermore, the serum ferritin level of the obesity group was 207.12 ng/mL, which was higher than that of the normal group (132.66 ng/mL). Lastly, the BMI appeared to be significantly correlated with both depression (r = 0.320, P = 0.017) and elevated ferritin levels (r = 0.352, P = 0.008).

CONCLUSION

This study provides evidence of existing correlation between ferritin and depression with obesity.

Expression of dopamine signaling genes in the post-mortem brain of individuals with mental illnesses is moderated by body mass index and mediated by insulin signaling genes

Preclinical studies implicate insulin signaling as a modulator of dopamine transmission, but human data is currently limited. We hypothesize that changes in the expression of insulin receptor-related genes in the post-mortem brain tissue of patients with mood and psychotic disorders mediate the expression of dopamine regulation-related genes. From a database containing microarray data from the post-mortem dorsolateral prefrontal cortex (dlPFC) (healthy controls [HC]: n = 209; patients: n = 321) and hippocampus (HC: n = 180; patients: n = 196), we conducted a hypothesis-driven analysis through the a priori selection of 12 dopamine- and 3 insulin-related genes. Mediation and moderated mediation models, accounting for the role of body mass index (BMI), were used. In the dlPFC, expressions of insulin receptor- and dopamine regulation-related genes were moderated by BMI, with significantly lower expression in high BMI patients. In the hippocampus, there were significantly lower expressions of these genes, which were not moderated by BMI. Illnesses by BMI effects on expression of dopamine genes were fully mediated by expression of insulin receptor gene (INSR). Analysis of conditional indirect effects showed interactions between INSR and BMI, indicating significantly stronger indirect effects at higher BMI values. In the hippocampus we observed that expression of insulin receptor substrate 1 and 2 fully mediated the effects of illnesses on expression of dopamine genes. In conclusion, differential expression of dopamine-related genes was related to altered expression of insulin signaling genes. BMI had region-specific effects, supporting the hypothesis that metabolic systems are critical mediators of dopaminergic function.

Gender-related patterns of psychiatric disorder clustering among bariatric surgery candidates: A latent class analysis

BACKGROUND

Psychiatric disorders tend to distribute unevenly in women and men with severe obesity. The current research aimed to identify homogeneous clusters of concurrent psychiatric disorders among patients seeking bariatric surgery, by gender.

METHODS

We recruited a consecutive sample of 393 candidates with obesity (311 women and 82 men) in a university-based bariatric center. Trained clinicians assessed psychiatric disorders through the Structured Clinical Interview for DSM-IV (SCID). Latent class analysis categorized pre-surgical patients into uniform clusters of co-occurring psychiatric disorders.

RESULTS

For both genders, the 3-class psychopathological clustering was the best-fitting solution. Among women, the latent classes were: (1) "oligosymptomatic", wherein 42% of patients showed low probability of psychiatric disorders; (2) "bipolar with comorbidities", in 33%; and (3) "anxiety/depression", in 25%. Among men, (1) "bipolar with comorbidities" was found in 47% of patients; (2) "oligosymptomatic", in 40%; and (3) "anxiety/depression", in 13%. For both genders, the probability of presenting eating disorders was higher in both "bipolar" and "anxiety/depression" classes. Substance use disorders was prominent among "bipolar" men. In comparison with "oligosymptomatic" class, the likelihood of higher BMI was observed among "bipolar" men and poorer work attainment among men with "anxiety/depression".

LIMITATION

Participants was cross-sectionally drawn from a single bariatric center.

CONCLUSIONS

Pre-surgical men and women with severe obesity were distributed in three comorbidity profiles and revealed analogous psychopathological patterns. The class of "bipolar disorders" most likely presented comorbidity with eating and substance use disorder. This natural clustering of psychiatric disorders among bariatric patients suggests gender-related therapeutic approaches and surgical outcomes.

Inflammatory markers and cortisol parameters across depressive subtypes in an older cohort

BACKGROUND

There is growing evidence that inflammatory and cortisol dysregulation are underlying pathophysiological mechanisms in the aetiology of major depressive disorder, particularly in younger adults. However, findings of biological disturbances in late-life depression have been divergent, probably due to the even greater heterogeneity of depression in older adults with aging processes influencing biological factors. Using empirically derived subtypes may enable the identification of biological disturbances underlying depression in older adults.

METHODS

Data were used from the Netherlands Study of Depression in Older Persons (NESDO) of 359 persons aged 60 years or older, with a current diagnosis of major depressive disorder (MDD). Depressive subtypes (severe atypical, severe melancholic, and moderate severe subtype) that were previously identified through latent class analysis (LCA), were examined on differences in inflammatory markers including C-reactive protein (CRP), interleukin-6 (IL-6), and neutrophil gelatinase-associated lipocalin (NGAL), as well as cortisol parameters.

RESULTS

No differences in measures for inflammation and cortisol across subtypes were observed in uncorrected or for putative confounders corrected models.

LIMITATIONS

Several subjects had missing cortisol and inflammatory data, decreasing the power. However, results did not change after imputation analysis.

DISCUSSION

In this cohort of depressed older adults, no differences in inflammation and cortisol measures between depression subtypes were observed. This is probably due to the many (patho)physiological processes that are involved in aging, thereby clouding the results.

Classification of Pediatric Asthma: From Phenotype Discovery to Clinical Practice

Advances in big data analytics have created an opportunity for a step change in unraveling mechanisms underlying the development of complex diseases such as asthma, providing valuable insights that drive better diagnostic decision-making in clinical practice, and opening up paths to individualized treatment plans. However, translating findings from data-driven analyses into meaningful insights and actionable solutions requires approaches and tools which move beyond mining and patterning longitudinal data. The purpose of this review is to summarize recent advances in phenotyping of asthma, to discuss key hurdles currently hampering the translation of phenotypic variation into mechanistic insights and clinical setting, and to suggest potential solutions that may address these limitations and accelerate moving discoveries into practice. In order to advance the field of phenotypic discovery, greater focus should be placed on investigating the extent of within-phenotype variation. We advocate a more cautious modeling approach by "supervising" the findings to delineate more precisely the characteristics of the individual trajectories assigned to each phenotype. Furthermore, it is important to employ different methods within a study to compare the stability of derived phenotypes, and to assess the immutability of individual assignments to phenotypes. If we are to make a step change toward precision (stratified or personalized) medicine and capitalize on the available big data assets, we have to develop genuine cross-disciplinary collaborations, wherein data scientists who turn data into information using algorithms and machine learning, team up with medical professionals who provide deep insights on specific subjects from a clinical perspective.