Development and external validation of a prediction rule for an unfavorable course of late-life depression: A multicenter cohort study

High-density lipoprotein cholesterol abnormalities correlate with severe fatigue in major depressive disorder: A cross-sectional study

OBJECTIVES

This cross-sectional study aimed to characterize the differences of metabolic profiles and atherogenicity between various levels of fatigue severity in patients with major depressive disorder (MDD), and examine the extent to which metabolic abnormality correlates with fatigue severity.

METHODS

We recruited 119 patients with MDD and assessed fatigue severity using Krupp's Fatigue Severity Scale. Blood samples were collected to determine plasma levels of fasting glucose, high-density lipoprotein cholesterol (HDL-C), triglycerides, total cholesterol and low-density lipoprotein cholesterol. The atherogenic index of plasma (AIP) was calculated as log10 (triglycerides/HDL-C).

RESULTS

MDD with severe fatigue were more likely to be younger (43.3 ± 10.3 years vs. 49.4 ± 8.5 years, p = 0.001), had a younger age of onset (34.7 ± 9.7 years vs. 40.7 ± 9.5 years, p = 0.001), demonstrated higher HAMD scores (18.0 ± 7.6 vs. 10.9 ± 7.5, p < 0.001), as well as lower HDL-C levels (48.5 ± 10.8 vs. 55.3 ± 13.9, p = 0.003), a greater prevalence of low HDL-C (43.9% vs. 22.6%, p = 0.015) and higher AIP levels (0.4 ± 0.3 vs. 0.3 ± 0.3, p = 0.046). Both a decreased plasma HDL-C level (OR = 0.95, 95% CI = 0.91-0.99, p = 0.009) and a diagnosis of low HDL-C (OR = 3.29, 95% CI = 1.27-8.57, p = 0.015) were significantly correlated with an increased risk of fatigue severity.

CONCLUSION

HDL-C could potentially protect patients with MDD from severe fatigue and the associated risk of cardiovascular disease.

Program of algorithm for pharmacological treatment of major depressive disorder in China: Benefits or not?

Background

This research was designed to investigate Algorithm Guided Treatment (AGT) and clinical traits for the prediction of antidepressant treatment outcomes in Chinese patients with major depressive disorder (MDD).

Methods

This study included 581 patients who had reached treatment response and 406 patients remained non-responded observed after three months of treatment. Sociodemographic factors, clinical traits, and psychiatric rating scales for evaluating therapeutic responses between the two groups were compared. Logistic regression analysis was adopted to determine the risk factors of unresponsive to antidepressant (URA) in MDD. Kaplan-Meier survival analysis was utilized to compare the therapeutic response between AGT and treatment as usual (TAU).

Results

Compared to the MDD responsive to antidepressant (RA) group, the URA group had significantly lower rates of the following clinical traits: married status, anxious distress, moderate to severe depressive symptoms, and higher rates of comorbidity (p-value < 0.05). Logistic Regression Analysis showed that eight clinical traits from psychiatric rating scales, such as anxious characteristics, were correlated positively with URA, while the other eight symptoms, such as autonomic symptoms, were negatively correlated. Time to symptomatic remission was longer in TAU without statistically significant (p-value = 0.11) by log-rank testing.

Conclusions

The factors may affect the therapeutic responses and compliance of patients, increasing the non-response risk for antidepressants. Therapeutic responses might be improved by increasing the clarification and elucidation of different symptom clusters of patients. Benefits on treatment response to AGT were not found in our study, indicating a one-size-fits-all approach may not work.Trial Registration: We registered as a clinical trial at the International Clinical Trials Registry Platform (No. NCT01764867) and obtained ethical approval 2012-42 from SMHC.

Protocol of a prospective community-based study about the onset and course of depression in a nationally representative cohort of adults in China: the China Depression Cohort Study-I

BACKGROUND

Depression is the second most important cause of disability worldwide. Reducing this major burden on global health requires a better understanding of the etiology, risk factors, and course of the disorder. With the goal of improving the prevention, recognition, and appropriate management of depressive disorders in China, the China Depression Cohort Study will establish a nationally representative sample of at least 85,000 adults (the China Depression Cohort Study-I) and 15,000 middle school students (the China Depression Cohort Study-II) and follow them over time to identify factors that influence the onset, characteristics, and course of depressive disorders. This protocol describes the China Depression Cohort Study-I.

METHODS

A multistage stratified random sampling method will be used to identify a nationally representative community-based cohort of at least 85,000 adults (i.e., ≥ 18 years of age) from 34 communities in 17 of mainland China's 31 provincial-level administrative regions. Baseline data collection includes 1) demographic, social and clinical data, 2) diagnostic information, 3) biological samples (i.e., blood, urine, hair), 4) brain MRI scans, and 5) environmental data (e.g., community-level metrics of climate change, air pollution, and socio-economic characteristics). Baseline findings will identify participants with or without depressive disorders. Annual reassessments will monitor potential risk factors for depression and identify incident cases of depression. Cox Proportional-Hazards Regression, Network analysis, Disease trajectory modelling, and Machine learning prediction models will be used to analyze the collected data. The study's main outcomes are the occurrence of depressive disorders; secondary outcomes include adverse behaviors (e.g., self-harm, suicide), the recurrence of depression and the incidence other mental disorders.

DISCUSSION

The China Depression Cohort Study-I will collect a comprehensive, nationally representative set of individual-level and community-level variables over time. The findings will reframe the understanding of depression from a 'biology-psychology-society' perspective. This perspective will improve psychiatrists' understanding of depression and, thus, promote the development of more effective subgroup-specific antidepressant drugs and other interventions based on the new biomarkers and relationships identified in the study.

TRAIL REGISTRATION

The protocol has been registered on the Chinese Clinical Trial Registry (No. ChiCTR2200059016).

Loneliness, Depression, and Genetics in the Elderly: Prognostic Factors of a Worse Health Condition?

Loneliness is considered a prognostic factor for poorer health status in the elderly. It is proposed to analyze the role of loneliness in health status in terms of various factors. A total of 1747 individuals from the pilot survey of the Aging in Spain Longitudinal Study (ELES-PS) were reviewed. ELES is a cross-sectional study for collecting health variables, food habits, socioeconomic data, and cognitive and functional capacities, which was carried out on a Spanish representative sample of noninstitutionalized persons of 50 years of age or older. Moreover, since telomere shortening is associated with cellular senescence, 35 telomere-related SNPs and cognitive impairments were analyzed. The results characterize the "solos" as males of 50-60 years, who were overweight and had lower levels of hemoglobin and neutrophils. There is also an association between five SNPs related to telomere length and BDNF. A group of people with loneliness and depression was identified with poorer health and cognitive status, poorer perception of their quality of life, poorer quality of sleep, and lower physical activity. Therefore, it follows that telomeres and BDNF play a role as intermediaries between loneliness and depression and their relationship with a worse state of health.

Abnormalities in the default mode network in late-life depression: A study of resting-state fMRI

Background/Objective

Neuroimaging studies have reported abnormalities in the examination of functional connectivity in late-life depression (LLD) in the default mode network (DMN). The present study aims to study resting-state functional connectivity within the DMN in people diagnosed with late-life major depressive disorder (MDD) compared to healthy controls (HCs). Moreover, we would like to differentiate these same connectivity patterns between participants with high vs. low anxiety levels.

Method

The sample comprised 56 participants between the ages of 60 and 75; 27 of them were patients with a diagnosis of MDD. Patients were further divided into two samples according to anxiety level: the four people with the highest anxiety level and the five with the lowest anxiety level. Clinical aspects were measured using psychological questionnaires. Each participant underwent functional magnetic resonance imaging (fMRI) acquisition in different regions of interest (ROIs) of the DMN.

Results

There was a greater correlation between pairs of ROIs in the control group than in patients with LLD, being this effect preferentially observed in patients with higher anxiety levels.

Conclusions

There are differences in functional connectivity within the DMN depending on the level of psychopathology. This can be reflected in these correlations and in the number of clusters and how the brain lateralizes (clustering).

Metabolic dysregulation in older adults with depression and loneliness: The ATHLOS study

This study aimed to examine how loneliness contributes to metabolic dysregulation among older adults with depression and determine the relative contribution of loneliness to the development of chronic diseases in late adulthood. Harmonised data from the Ageing Trajectories of Health: Longitudinal Opportunities and Synergies (ATHLOS) project were used. Concretely, the sample comprised 6195 participants (53.95% women; M = 61.30 years, SD = 7.11) from three European cohorts. Three groups were considered: control group (CG); depressive symptom episode group (DEP); and a group with depression and loneliness (DEP + LONE). A metabolic score was estimated using anthropometric and blood indicators, by means of multi-indicator multi-causes (MIMIC) modelling and after controlling for sociodemographic and health-related covariates. Group-comparison was based on measurement-invariance procedures. Multimorbidity development was predicted at follow-up considering the study group and relevant covariates. All the analyses were sex-specific. As a result, measurement invariance revealed the influence of group (ΔCFI = -0.017 for male participants and ΔCFI = -0.009 for female ones) on metabolic scores in both sexes. Metabolic scores were significantly lower (i.e., they had more metabolic risk) in DEP + LONE women in comparison to women from the other groups. DEP men showed the lowest metabolic scores but those from the DEP + LONE group showed meaningfully lower scores than CG men (d = 1.35). In terms of multimorbidity prediction, DEP + LONE group membership significantly predicted the outcome in both sexes; DEP group membership significantly predicted multimorbidity at follow-up in women. In summary, these results highlight the relevant contribution of loneliness in depression-related metabolic dysregulation in the short- (concurrent metabolic risk) and long-term (chronic condition development). Moreover, sex-specific mechanisms seem to be involved in metabolic alterations of depressed people showing loneliness feelings. This study calls for action to reduce the impact of loneliness in old age and to promote healthy ageing.

Implementing Precision Psychiatry: A Systematic Review of Individualized Prediction Models for Clinical Practice

BACKGROUND

The impact of precision psychiatry for clinical practice has not been systematically appraised. This study aims to provide a comprehensive review of validated prediction models to estimate the individual risk of being affected with a condition (diagnostic), developing outcomes (prognostic), or responding to treatments (predictive) in mental disorders.

METHODS

PRISMA/RIGHT/CHARMS-compliant systematic review of the Web of Science, Cochrane Central Register of Reviews, and Ovid/PsycINFO databases from inception until July 21, 2019 (PROSPERO CRD42019155713) to identify diagnostic/prognostic/predictive prediction studies that reported individualized estimates in psychiatry and that were internally or externally validated or implemented. Random effect meta-regression analyses addressed the impact of several factors on the accuracy of prediction models.

FINDINGS

Literature search identified 584 prediction modeling studies, of which 89 were included. 10.4% of the total studies included prediction models internally validated (n = 61), 4.6% models externally validated (n = 27), and 0.2% (n = 1) models considered for implementation. Across validated prediction modeling studies (n = 88), 18.2% were diagnostic, 68.2% prognostic, and 13.6% predictive. The most frequently investigated condition was psychosis (36.4%), and the most frequently employed predictors clinical (69.5%). Unimodal compared to multimodal models (β = .29, P = .03) and diagnostic compared to prognostic (β = .84, p < .0001) and predictive (β = .87, P = .002) models were associated with increased accuracy.

INTERPRETATION

To date, several validated prediction models are available to support the diagnosis and prognosis of psychiatric conditions, in particular, psychosis, or to predict treatment response. Advancements of knowledge are limited by the lack of implementation research in real-world clinical practice. A new generation of implementation research is required to address this translational gap.

Prevalence and predisposing factors of depressive symptoms in patients with stable coronary artery disease: a cross-sectional single-center study

The incidence of depressive symptoms in patients with stable coronary artery disease (SCAD) has significantly increased. However, its pathogenesis and treatment mechanisms are still incompletely understood. In this study, 144 patients with SCAD were recruited. Depressive symptoms of patients with SCAD were evaluated using Zung Self-Rating Depression Scale during hospitalization, and the patients were categorized into two subgroups: the non-depressive and depressive groups (further divided into mild and moderate/severe depressive groups). The rate of moderate/severe depressive symptoms in patients with SCAD was 18.8%. The mean age of patients in the depressive and mild depressive groups was older than that of those in the non-depressive group, and patients in the moderate/severe depressive group had higher high-density lipoprotein (HDL) and lower creatinine (Cr) levels. Binary logistic regression analysis showed that lower low-density lipoprotein (LDL) levels were significantly associated with increased risks of mild depressive symptoms, whereas higher HDL and lower Cr levels were significantly associated with moderate/severe depressive symptoms, suggesting that patients with SCAD were prone to experience depressive symptoms, especially in the elderly. Abnormality in LDL, HDL, and Cr levels might contribute to the depressive symptoms.

Serum Levels of FGF21, β-Klotho, and BDNF in Stable Coronary Artery Disease Patients With Depressive Symptoms: A Cross-Sectional Single-Center Study

Background: The incidence of depressive symptoms (DS) in patients with stable coronary artery disease (SCAD) is significantly higher than those in healthy population, and that DS are independent risk factors for cardiovascular events. Previous studies have reported that fibroblast growth factor 21 (FGF21), β-klotho, mature brain-derived neurotrophic factor (mBDNF), and BDNF precursor (proBDNF) play important roles in the pathogenesis and treatment of coronary heart disease and depression. With this in mind, the present study aimed to clarify the relationship between FGF21, β-klotho, mBDNF, and proBDNF and SCAD with comorbid depression, in addition to also exploring the underlying mechanisms of these disease processes. Methods: A total of 116 patients with SCAD and 45 healthy controls were recruited. Patients with SCAD were further divided into two subgroups based on the Zung Self-Rating Depression Scale (SDS), which were characterized as those with no DS (NDS) and those with DS. Baseline data were collected, and serum levels of FGF21, β-klotho, mBDNF, and proBDNF were determined. Results: In SCAD patients, Gensini scores-denoting the degree of coronary arteriostenosis-were significantly greater in the DS group than in the NDS group. There was also a positive correlation between the Gensini scores and the SDS scores. Patients in the SCAD group demonstrated a lower serum FGF21. Serum β-klotho, mBDNF, and mBDNF/proBDNF were also significantly lower in the DS group than in the NDS group. Furthermore, β-klotho and mBDNF were negatively correlated with the SDS scores. Additionally, SCAD patients were divided into lower- and higher-level groups using hierarchical cluster analysis, with the results highlighting that patients in the lower mBDNF group had a higher incidence of DS. Conclusions: The depression score was positively correlated with the severity of coronary artery stenosis, and serum FGF21, β-klotho, mBDNF, and proBDNF were closely related to the development of DS in patients with SCAD. These observations suggest FGF21, β-klotho, mBDNF, and proBDNF as potential diagnostic and/or therapeutic targets for SCAD with co-morbid depression.

Cognitive behavioral therapy improves diverse profiles of depressive symptoms in Parkinson's disease

OBJECTIVE

Depression is among the most common and debilitating nonmotor complaints in Parkinson's disease (PD), yet there is a paucity of controlled research to guide treatment. Little research has focused on the extent to which specific depressive symptom profiles may dictate unique clinical recommendations to ultimately improve treatment outcomes. The current study examined the impact of cognitive behavioral therapy (CBT) on different types of depressive symptoms in PD. It was hypothesized that the cognitive (eg, guilt, rumination, and negative attitudes towards self) and behavioral (eg, avoidance and procrastination) symptoms targeted most intensively by the treatment protocol would show the most robust response. The extent to which stabilized antidepressant use moderated specific symptom change was examined on an exploratory basis.

METHOD

Eighty depressed people with PD participated in a randomized controlled trial of CBT plus clinical management, versus clinical management only. Hamilton Depression Rating Scale (HAMD) and Beck Depression Inventory (BDI) subscale scores, reflecting depressive symptom heterogeneity in PD, were the focus of this investigation.

RESULTS

CBT response was associated with significant improvements in mood, sleep, anxiety, and somatic symptoms (HAMD), and negative attitudes toward self, performance impairment, and somatic symptoms (BDI). As hypothesized, the largest effect sizes were observed for cognitive and behavioral (vs somatic) symptoms of depression. Stabilized antidepressant use moderated the effect of CBT on somatic complaints (HAMD and BDI).

CONCLUSIONS

CBT may improve a diverse array of depressive symptoms in PD. Cognitive and behavioral (vs somatic) symptoms showed the greatest change. Combining CBT with antidepressants may help optimize the management of somatic complaints in depression in PD (dPD).

The Science of Prognosis in Psychiatry: A Review

IMPORTANCE

Prognosis is a venerable component of medical knowledge introduced by Hippocrates (460-377 BC). This educational review presents a contemporary evidence-based approach for how to incorporate clinical risk prediction models in modern psychiatry. The article is organized around key methodological themes most relevant for the science of prognosis in psychiatry. Within each theme, the article highlights key challenges and makes pragmatic recommendations to improve scientific understanding of prognosis in psychiatry.

OBSERVATIONS

The initial step to building clinical risk prediction models that can affect psychiatric care involves designing the model: preparation of the protocol and definition of the outcomes and of the statistical methods (theme 1). Further initial steps involve carefully selecting the predictors, preparing the data, and developing the model in these data. A subsequent step is the validation of the model to accurately test its generalizability (theme 2). The next consideration is that the accuracy of the clinical prediction model is affected by the incidence of the psychiatric condition under investigation (theme 3). Eventually, clinical prediction models need to be implemented in real-world clinical routine, and this is usually the most challenging step (theme 4). Advanced methods such as machine learning approaches can overcome some problems that undermine the previous steps (theme 5). The relevance of each of these themes to current clinical risk prediction modeling in psychiatry is discussed and recommendations are given.

CONCLUSIONS AND RELEVANCE

Together, these perspectives intend to contribute to an integrative, evidence-based science of prognosis in psychiatry. By focusing on the outcome of the individuals, rather than on the disease, clinical risk prediction modeling can become the cornerstone for a scientific and personalized psychiatry.

Symptomatic and functional recovery in depression in later life

OBJECTIVES

Functional limitations give an indication of the total impact of diseases, such as depression, on individuals health and recovery. This study examines the change in several domains of functioning over 2 years in older persons depressed at baseline (non-remitted group and remitted group after 2 years) and in a non-depressed comparison group.

METHODS

Data were used from a cohort study (Netherlands Study of Depression in Older persons [NESDO]) consisting of depressed older persons ≥ 60 years (N = 378) and a non-depressed comparison group (N = 132) with 2 years of follow-up (attrition rate 24%). Functional limitations (outcome) were assessed with the World Health Organization Disability Assessment Schedule 2.0 (WHODAS 2.0) questionnaire every 6 months. Total scores and domain scores were used. Depression was classified according to the Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition (DSM-IV) criteria at baseline and at 2-year follow-up. Severity of depression (predictor) was assessed with the Inventory of Depressive Symptomatology (IDS) at 6-month intervals.

RESULTS

Linear mixed models showed that the level of functional limitations differed between the three groups during 2 years of follow-up. The non-remitted group had the highest level of functional limitations during 2 years, followed by the remitted group. Stable low levels of functional limitations were found for the non-depressed group. Remission from depression was accompanied by improvements in functioning, however, compared to the non-depressed comparison group significant functional limitations remained. Higher severity of depression appeared as risk factor for a declining course of functioning, especially the social aspects of functioning.

METHODOLOGICAL CONSIDERATIONS

Participants that were more severely depressed and more functionally impaired at baseline had higher attrition rates than the participants that were included in the analytical sample.

CONCLUSION

This study showed that depression in later life has long-term debilitating effects on functioning, enduring even after remission from depression. This implies that depression treatment in later life should aim broader than just symptomatic recovery, but also include functional recovery.