Seasonal affective disorder and seasonal changes in weight and sleep duration are inversely associated with plasma adiponectin levels

Akram, Faisal; Gragnoli, Claudia; Raheja, Uttam K.; Snitker, Søren; Lowry, Christopher A.; Stearns‐Yoder, Kelly A.; Hoisington, Andrew J.; Brenner, Lisa A.; Saunders, Erika F.H.; Stiller, John W.; Ryan, Kathleen A.; Rohan, Kelly J.; Mitchell, Braxton D.; Postolache, Teodor T.

doi:10.1016/j.jpsychires.2019.12.016

Cited by 6 publications

(3 citation statements)

References 92 publications

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“…For example, bipolar disorder has been linked to impairment of glucose metabolism (de Melo et al, 2017 ), seasonal affective disorder with dysregulations of major metabolic regulator (i.e. adiponectin) (Akram et al, 2020 ), and sickness behavior with immuno-metabolic alterations (Capuron & Miller, 2011 ). Also, in a small study that combined neuroimaging and biochemical approaches, hyperphagia during depression was strongly associated with endocrine dysregulation and inflammation (Simmons et al, 2016 ).…”

Section: Discussionmentioning

confidence: 99%

Metabolomics dissection of depression heterogeneity and related cardiometabolic risk

et al. 2021

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Background A recent hypothesis postulates the existence of an ‘immune-metabolic depression’ (IMD) dimension characterized by metabolic dysregulations. Combining data on metabolomics and depressive symptoms, we aimed to identify depressions associated with an increased risk of adverse metabolic alterations. Method Clustering data were from 1094 individuals with major depressive disorder in the last 6 months and measures of 149 metabolites from a 1H-NMR platform and 30 depressive symptoms (IDS-SR30). Canonical correlation analyses (CCA) were used to identify main independent metabolite-symptom axes of variance. Then, for the replication, we examined the association of the identified dimensions with metabolites from the same platform and cardiometabolic diseases in an independent population-based cohort (n = 6572). Results CCA identified an overall depression dimension and a dimension resembling IMD, in which symptoms such as sleeping too much, increased appetite, and low energy level had higher relative loading. In the independent sample, the overall depression dimension was associated with lower cardiometabolic risk, such as (i.e. per s.d.) HOMA-1B −0.06 (95% CI −0.09 – −0.04), and visceral adipose tissue −0.10 cm2 (95% CI −0.14 – −0.07). In contrast, the IMD dimension was associated with well-known cardiometabolic diseases such as higher visceral adipose tissue 0.08 cm2 (95% CI 0.04–0.12), HOMA-1B 0.06 (95% CI 0.04–0.09), and lower HDL-cholesterol levels −0.03 mmol/L (95% CI −0.05 – −0.01). Conclusions Combining metabolomics and clinical symptoms we identified a replicable depression dimension associated with adverse metabolic alterations, in line with the IMD hypothesis. Patients with IMD may be at higher cardiometabolic risk and may benefit from specific treatment targeting underlying metabolic dysregulations.

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Section: Discussionmentioning

confidence: 99%

Metabolomics dissection of depression heterogeneity and related cardiometabolic risk

et al. 2021

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“…While contradictory findings have been reported in a few, majority of the studies showed high risk of SAD among females and younger age groups. 12,15,[16][17][18][19][20] SAD sex ratios as reported in epidemiological studies range from 2.7:1 upto 9:1. 17 The results from a community health survey in Alaska by Booker…”

Section: Comparison With Previous Literaturementioning

confidence: 99%

Prevalence of seasonal affective disorder among primary health care attendees in eastern Riyadh-a cross-sectional study

Alqahtani,

Sayeed,

Marie

et al. 2023

Int J Community Med Public Health

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Background: Seasonal affective disorder (SAD), a type of mood disorder, is defined by seasonal depressive symptoms that appear at a particular time of a year. Its burden in Saudi Arabia is poorly understood. In this study, we aimed to estimate the prevalence of and factors associated with seasonal depression among attendees of primary health care centres (PHC) in Eastern Riyadh. Methods: We conducted a cross-sectional study in the PHC of Eastern Riyadh. We systematically sampled participants attending the PHCs the required sample size was achieved. We measured seasonal depression and beliefs using the validated seasonal pattern assessment questionnaire (SPAQ) and seasonal beliefs questionnaire. We presented the mean and standard deviation (SD) of scores obtained and used Student’s t test ANOVA test to compare differences across explanatory variables. We demonstrated internal consistency using Cronbach’s alpha. Results: Among 232 participants, majority were aged 18-35 years (67.2%) and males (60.8%). The mean (SD) score on SPAQ was 1.00 (0.71) and that on the seasonal beliefs was 3.03 (0.69). With a cut-off of 11 on SPAQ, prevalence of SAD was found to be 33.5%. SPAQ score was significantly higher among younger ages, females, single/divorced, Saudis, those with secondary/lower level education and those who earn<50,000 Saudi Riyals (p<0.001). Seasonal beliefs score was significantly higher among younger ages (p=0.013) and Saudis (p=0.001). Both the scales used had high internal consistency (Cronbach’s alpha of 0.901 for SPAQ and 0.847 for seasonal beliefs questionnaire). Conclusions: SAD was a prevalent mental health issue among primary care attendees in Eastern Riyadh. Screening for such conditions at the primary level among high-risk groups should be considered by family physicians.

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“…As emotional eating and weight gain are associated with SAD (112), our finding seems to be unexpected. However, it was shown recently that the self reported seasonal changes in weight is related to lower plasma adiponectin levels, an indicator for metabolic dysregulation (113). Therefore, it might be that the difference in weight between summer and winter is more relevant, and a lower BMI in summer might be indicative for a larger weight gain.…”

Section: Self-reported Characteristics and Cognitive Vulnerabilitiesmentioning

confidence: 99%

Predictability of Seasonal Mood Fluctuations Based on Self-Report Questionnaires and EEG Biomarkers in a Non-clinical Sample

et al. 2022

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Induced by decreasing light, people affected by seasonal mood fluctuations may suffer from low energy, have low interest in activities, experience changes in weight, insomnia, difficulties in concentration, depression, and suicidal thoughts. Few studies have been conducted in search for biological predictors of seasonal mood fluctuations in the brain, such as EEG oscillations. A sample of 64 participants was examined with questionnaires and electroencephalography in summer. In winter, a follow-up survey was recorded and participants were grouped into those with at least mild (N = 18) and at least moderate (N = 11) mood decline and those without self-reported depressive symptoms both in summer and in winter (N = 46). A support vector machine was trained to predict mood decline by either EEG biomarkers alone, questionnaire data from baseline alone, or a combination of the two. Leave-one-out-cross validation with lasso regularization was used with logistic regression to fit a model. The accuracy for classification for at least mild/moderate mood decline was 77/82% for questionnaire data, 72/82% for EEG alone, and 81/86% for EEG combined with questionnaire data. Self-report data was more conclusive than EEG biomarkers recorded in summer for prediction of worsening of depressive symptoms in winter but it is advantageous to combine EEG with psychological assessment to boost predictive performance.

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Seasonal affective disorder and seasonal changes in weight and sleep duration are inversely associated with plasma adiponectin levels

Cited by 6 publications

References 92 publications

Metabolomics dissection of depression heterogeneity and related cardiometabolic risk

Metabolomics dissection of depression heterogeneity and related cardiometabolic risk

Prevalence of seasonal affective disorder among primary health care attendees in eastern Riyadh-a cross-sectional study

Predictability of Seasonal Mood Fluctuations Based on Self-Report Questionnaires and EEG Biomarkers in a Non-clinical Sample

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