Objective: Weight change may be considered an effect of depression. In turn, depression may follow weight change. Deteriorations in health may mediate these associations. The objective was to examine reciprocal associations between depressed mood and weight change, and the potentially mediating role of deteriorations in health (interim hospitalizations and incident mobility imitation) in these associations.
Methods: Data were from 2406 black and white men and women, aged 70-79 from Pittsburgh, Pennsylvania and Memphis, Tennessee participating in the Health, Aging and Body composition (Health ABC) study. Depressed mood at baseline (T1) and 3-year follow-up (T4) was measured with the CES-D scale. Three weight change groups (T1-T4) were created: loss (>or=5% loss), stable (within +/-5% loss or gain), and weight gain (>or=5% gain).
Results: At T1 and T4, respectively 4.4% and 9.5% of the analysis sample had depressed mood. T1 depressed mood was associated with weight gain over the 3-year period (OR:1.91; 95%CI:1.13-3.22). Weight loss over the 3-year period was associated with T4 depressed mood (OR:1.51; 95%CI:1.05-2.16). Accounting for deteriorations in health in the reciprocal associations between weight change and depressed mood reduced effect sizes between 16-27%.
Conclusions: In this study, depressed mood predicted weight gain over three years, while weight loss over three years predicted depressed mood. These associations were partly mediated through deteriorations in health. Implications for clinical practice and prevention include increased awareness that depressed mood can cause weight change, but can also be preceded by deteriorations in health and weight change.
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http://dx.doi.org/10.1097/JGP.0b013e3181c65837 | DOI Listing |
J Voice
January 2025
Department of Surgery, UMONS Research Institute for Health Sciences and Technology, University of Mons (UMons), Mons, Belgium; Division of Laryngology and Bronchoesophagology, Department of Otolaryngology Head Neck Surgery, EpiCURA Hospital, Baudour, Belgium; Department of Otolaryngology-Head and Neck Surgery, Foch Hospital, School of Medicine, UFR Simone Veil, Université Versailles Saint-Quentin-en-Yvelines (Paris Saclay University), Paris, France; Department of Otolaryngology, Elsan Hospital, Paris, France. Electronic address:
Background: Voice analysis has emerged as a potential biomarker for mood state detection and monitoring in bipolar disorder (BD). The systematic review aimed to summarize the evidence for voice analysis applications in BD, examining (1) the predictive validity of voice quality outcomes for mood state detection, and (2) the correlation between voice parameters and clinical symptom scales.
Methods: A PubMed, Scopus, and Cochrane Library search was carried out by two investigators for publications investigating voice quality in BD according to Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statements.
J Affect Disord
January 2025
Department of Neurology, The First Affiliated Hospital of Anhui Medical University, 218 Jixi Road, Anhui Province, Hefei 230022, China; Department of Neurology, The Second Affiliated Hospital of Anhui Medical University, Hefei 230601, China; Department of Psychology and Sleep Medicine, The Second Affiliated Hospital of Anhui Medical University, Hefei 230601, China. Electronic address:
Background: Electroconvulsive therapy (ECT) is an effective treatment for patients with major depressive disorder (MDD), but the underlying neuromodulatory mechanisms remain largely unknown. Functional stability represents a newly developed method based on the dynamic functional connectivity framework. This study aimed to explore ECT-evoked changes in functional stability and their relationship with clinical outcomes.
View Article and Find Full Text PDFJ Affect Disord
January 2025
Department of Psychiatry and Psychotherapy, University of Marburg, Germany; Center for Mind, Brain and Behavior (CMBB), University of Marburg, Germany.
Background: Major depressive disorder (MDD) comes along with an increased risk of recurrence and poor course of illness. Machine learning has recently shown promise in the prediction of mental illness, yet models aiming to predict MDD course are still rare and do not quantify the predictive value of established MDD recurrence risk factors.
Methods: We analyzed N = 571 MDD patients from the Marburg-Münster Affective Disorder Cohort Study (MACS).
J Affect Disord
January 2025
Department of Epidemiology, Fukushima Medical University School of Medicine, 1 Hikariga-oka, Fukushima 960-1295, Japan; Radiation Medical Science Center for the Fukushima Health Management Survey, Fukushima Medical University, 1 Hikariga-oka, Fukushima 960-1295, Japan. Electronic address:
Background: Few studies have prospectively, comprehensively, and by sex, examined the relationship between lifestyle and depressive symptoms. This study aimed to longitudinally examine which lifestyle factors are associated with depressive symptoms in a large cohort of Japanese participants stratified by sex.
Methods: Among 9087 office and community-based residents who attended a health measurement course at the Osaka Medical Center for Health Science and Promotion between 2001 and 2002, 6629 individuals (3962 men and 2667 women) without prior depressive symptoms were followed until the end of March 2012 to observe the associations between lifestyle factors and the development of new depressive symptoms.
J Affect Disord
January 2025
Department of Psychiatry and Behavioral Sciences, Duke University, Durham, NC, USA; Department of Medicine, Duke University, Durham, NC, USA; Duke Institute of Brain Sciences, Duke University, Durham, NC, USA. Electronic address:
Metabolomics provides powerful tools that can inform about heterogeneity in disease and response to treatments. In this exploratory study, we employed an electrochemistry-based targeted metabolomics platform to assess the metabolic effects of three randomly-assigned treatments: escitalopram, duloxetine, and Cognitive-Behavioral Therapy (CBT) in 163 treatment-naïve outpatients with major depressive disorder. Serum samples from baseline and 12 weeks post-treatment were analyzed using targeted liquid chromatography-electrochemistry for metabolites related to tryptophan, tyrosine metabolism and related pathways.
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