Subjective mental health, incidence of depressive symptoms in later life, and the role of epigenetics: results from two longitudinal cohort studies

Perna, Laura; Zhang, Yan; Matías‐García, Pamela R.; Ladwig, Karl‐Heinz; Wiechmann, Tobias; Wild, Beate; Waldenberger, Mélanie; Schöttker, Ben; Mons, Ute; Ihle, Andreas; Kliegel, Matthias; Schwettmann, Lars; Peters, Annette; Brenner, Hermann

doi:10.1038/s41398-020-00997-x

Cited by 5 publications

(3 citation statements)

References 40 publications

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“…Selfrated health was the second most important predictor of presence of depression in this sample during the pandemic, with prior literature also suggesting a strong association between subjective well-being and symptoms of depression. 29,30 Among the rest of the factors, sleep medications and sleep quality were found to be among the strongest predictors of depression, which has also been suggested in previous findings. 31,32 Furthermore, we assessed the importance of each lifestyle variable on the SMILE scale as independent predictors of anxiety in the Spanish training sample.…”

Section: Discussionsupporting

confidence: 82%

Lifestyle predictors of depression and anxiety during COVID-19: a machine learning approach

Simjanoski¹,

Ballester²,

Mota³

et al. 2022

Trends Psychiatry Psychother

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Introduction:Recent research has suggested an increase in the global prevalence of psychiatric symptoms during the COVID-19 pandemic. This study aimed to assess whether lifestyle behaviors can predict the presence of depression and anxiety in the Brazilian general population, using a model developed in Spain. Methods: A web survey was conducted during April-May 2020, which included the Short Multidimensional Inventory Lifestyle Evaluation (SMILE) scale, assessing lifestyle behaviors during the COVID-19 pandemic. Depression and anxiety were examined using the PHQ-2 and the GAD-7, respectively. Elastic net, random forest, and gradient tree boosting were used to develop predictive models. Each technique used a subset of the Spanish sample to train the models, which were then tested internally (vs. the remainder of the Spanish sample) and externally (vs. the full Brazilian sample), evaluating their effectiveness. Results:The study sample included 22,562 individuals (19,069 from Brazil, and 3,493 from Spain). The models developed performed similarly and were equally effective in predicting depression and anxiety in both tests, with internal test AUC-ROC values of 0.85 (depression) and 0.86 (anxiety), and external test AUC-ROC values of 0.85 (depression) and 0.84 (anxiety). Meaning of life was the strongest predictor of depression, while sleep quality was the strongest predictor of anxiety during the COVID-19 epidemic. Conclusions: Specific lifestyle behaviors during the early COVID-19 epidemic successfully predicted the presence of depression and anxiety in a large Brazilian sample using machine learning models developed on a Spanish sample. Targeted interventions focused on promoting healthier lifestyles are encouraged.

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Section: Discussionsupporting

confidence: 82%

Lifestyle predictors of depression and anxiety during COVID-19: a machine learning approach

Simjanoski¹,

Ballester²,

Mota³

et al. 2022

Trends Psychiatry Psychother

View full text Add to dashboard Cite

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“…Both physical and mental health aspects could be considered by participants when they rated their health. It is either they considered one of these two aspects or both at the same time (31).Therefore, this could be the reason for selfrated health to be one of the factors that contributed to depression because there was a longitudinal study conducted among people who had no prior history of depression showed that poor self-perceived mental health was associated with future depressive symptoms but could be influenced by epigenetic and inflammatory mechanisms (63). Additionally, since our study used the general self-rated health which also described that our samples used their own perception and judgement in rating their overall health, future studies are encouraged to be more specific which physical aspect or mental aspect that want to measure.…”

Section: Discussionmentioning

confidence: 99%

Self-Rated Health, Demographic Factors and Depressive Symptoms Among Malaysian Adults

Shah

Hashim

Basri

et al. 2022

Int J Care Scholars

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Introduction: Depression risk has been significantly associated with sociodemographic aspects such as education levels and self-rated health. The aim was to investigate the relationship of socio-demographic characteristics particularly level of education, and self-rated health on depressive symptoms among Malaysian adults. Methods: This is a cross-sectional study among Malaysians who aged 16 to 52 years old. Respondents were recruited via social media, using convenience sampling. Sociodemographic questions include education levels and self-rated health among other questions. Depressive symptoms were measured using Beck Depression Inventory-II, Malay version. The data were analysed using Chi- Square Test. Two-Way of ANOVA was utilized to determine the association of education levels and self-rated health on depressive symptoms. Results: Chi-Square indicated that age, level of education, job categories and self-rated health had a significant relationship at p<0.05 with key variables used in this study, presence, and absence of depressive symptoms except for gender, race and marital status. Two-way of ANOVA results revealed a significant interaction between self-rated health, level of education and depressive symptoms (F = 2.711,df=5,p<0.05). Conclusion: Malaysian individuals with low education levels showed depressive symptoms and showed a significant association with low self-rated health, however some of them rated themselves as healthy. Therefore, the government needs to make health literacy a priority for everyone, particularly for those with low levels of education and individuals who perceived themselves as mentally healthy.

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“…It has been associated with mental morbidity measures, such as nonspecific psychological distress, depressive symptoms, activity limitations, and physical and emotional role functioning (24)(25)(26)(27). A recent epigenome-wide association study (EWAS) assessed the predictive value of methylation beta values of EWAS that identified CpGs (5'-C-phosphate-G-3') for incidence of depressive symptoms in later life and found that subjective mental health and hypomethylation at cg27115863 are predictive of depressive symptoms, which are thought to be due to activation of the inflammatory signaling pathway (28).…”

Section: Perceived Mental Healthmentioning

confidence: 99%

Lower Energy-Adjusted Nutrient Intakes Occur Among Food Energy Under-Reporters With Poor Mental Health

2022

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BackgroundFood energy under-reporting is differentially distributed among populations. Currently, little is known about how mental health state may affect energy-adjusted nutrient intakes among food energy under-reporters.MethodsStratified analysis of energy-adjusted nutrient intake by mental health (poor vs. good) and age/sex was conducted using data from Canadian Community Health Survey (CCHS) respondents (14–70 years; n = 8,233) who were deemed as under-reporters based on Goldberg's cutoffs.ResultsMost were experiencing good mental health (95.2%). Among those reporting poor mental health, significantly lower energy-adjusted nutrient intakes tended to be found for fiber, protein, vitamins A, B2, B3, B6, B9, B12, C, and D, and calcium, potassium, and zinc (probability measures (p) < 0.05). For women (51–70 years), all micronutrient intakes, except iron, were significantly lower among those reporting poor mental health (p < 0.05). For men (31–50 years), B vitamin and most mineral intakes, except sodium, were significantly lower among those reporting poor mental health (p < 0.05). Among women (31–50 years) who reported poor mental health, higher energy-adjusted intakes were reported for vitamin B9 and phosphorus (p < 0.05).ConclusionsAmong food energy under-reporters, poor mental health tends to lower the report of specific energy-adjusted nutrient intakes that include ones critical for mental health. Future research is needed to discern if these differences may be attributed to deviations in the accurate reports of food intakes, measurement errors, or mental health states.

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Subjective mental health, incidence of depressive symptoms in later life, and the role of epigenetics: results from two longitudinal cohort studies

Cited by 5 publications

References 40 publications

Lifestyle predictors of depression and anxiety during COVID-19: a machine learning approach

Lifestyle predictors of depression and anxiety during COVID-19: a machine learning approach

Self-Rated Health, Demographic Factors and Depressive Symptoms Among Malaysian Adults

Lower Energy-Adjusted Nutrient Intakes Occur Among Food Energy Under-Reporters With Poor Mental Health

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