Challenging the “jolly fat” hypothesis among older adults: High body mass index predicts increases in depressive symptoms over a 5-year period

Dearborn, Peter J; Robbins, Michael A.; Elias, Merrill F.

doi:10.1177/1359105316675209

Cited by 17 publications

(10 citation statements)

References 38 publications

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“…We also found a significant association between body weight status assessed by BMI and occurrence of depression, suggesting that obese elderly people had higher odds of being depressed. Similarly, a longitudinal study conducted by Dearborn et al [25] among community-dwelling older adults residing in New York demonstrated that both objectively measured and self-reported body mass index were positively associated with concurrent and future depressive symptoms. In another multicenter study, which included baseline data from 17 European countries, the percentage of obesity among depressed and non-depressed elderly people was 29% and 23%, respectively (p < 0.001) [15].…”

Section: Discussionmentioning

confidence: 85%

Predictors of Depression and Insomnia in Community-Dwelling Elderly People: A Cross-Sectional Evidence of Their Bidirectional Relationship

Tsaras¹,

Tsiantoula²,

Papathanasiou³

et al. 2021

Cureus

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The aim of this study was to examine associated factors of depression and insomnia in community-dwelling elderly people in order to identify independent predictors. Materials and methods A cross-sectional study was conducted among 250 older people aged 60 years and over living in an urban area. A stratified random sampling method was used for recruiting samples from five Open Care Centers for Elderly People of the Municipality of Larissa, Greece. Data were obtained through a questionnaire that included demographic, socioeconomic, and health-related characteristics, 15-item Geriatric Depression Scale (GDS-15), and Athens Insomnia Scale (AIS). Simple and multiple logistic regression analysis was performed. Results The prevalence of depression and insomnia was 28.4% (95% CI: 22.9-33.9) and 39.2% (95% CI: 33.0-45.4), respectively. Our findings showed that the overall GDS-15 score was positively related to the overall AIS score (r = 0.405; p < 0.001). The best-fit regression analysis demonstrated four significant predictors (marital status, monthly income, body weight status, and insomnia) explaining 31.6% of the variance in depression risk. Moreover, community-dwelling elderly Greek people with insomnia symptoms were females, had a lower monthly income, and more likely to suffer from chronic diseases and depression. Conclusion These findings point to the importance of recognizing risk factors for both depression and insomnia in attempting to apply preventive interventions in the elderly and optimize their quality of life.

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Section: Discussionmentioning

confidence: 85%

Predictors of Depression and Insomnia in Community-Dwelling Elderly People: A Cross-Sectional Evidence of Their Bidirectional Relationship

Tsaras¹,

Tsiantoula²,

Papathanasiou³

et al. 2021

Cureus

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“…This association was named 'the jolly fat hypothesis' (Crisp and McGuiness 1976). Unfortunately, other studies have not confirmed it (Dearborn, Robbins and Elias 2018).…”

Section: Resultsmentioning

confidence: 97%

Body weight status is not a predictive factor of depressive symptoms in Polish adolescents aged 13-18 years

Trambacz-Oleszak

Krzyżaniak

Szafrańska-Komarowska³

et al. 2018

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Depression and unhealthy weight status are among the most common concerns in adolescent boys and girls. Research results on the relationship between depression and weight status are contradictory. The aim of the present study was to assess the association between body weight status and depressive symptoms among Polish adolescent boys and girls. The cross-sectional survey was carried out on adolescents attending lower and higher secondary schools located in three medium-sized cities and one big agglomeration in Poland. Anthropometric measurements, such as body height and weight, were performed. Beck Depression Inventory-II (BDI-II) was used to assess depressive symptoms. The total sample consisted of 624 boys and 910 girls aged 13-18 (the mean age was 16.4 ± 1.8) participated in the study. Of them, 73.3% had a healthy weight status, 12.9% were classified as thin, 11.4% were overweight and 2.5% were obese. The prevalence of depression based on BDI-II was 20.2% in boys and 34.9% in girls. The Kruskal–Wallis test revealed a lack of association between the body weight status and total scores of depressive symptoms after controlling for age (p=0.98 and p=0.4 for boys and girls, respectively). The multiple regression analysis revealed that gender and age differentiated the depressive symptoms (for gender: ß=0.19; SE=0.38; p<0.001 and for age: ß=0.05; SE=0.1; p<0.05). Age and gender explained 3.5% of the total variance of depressive symptoms. The study did not confirm the association between the body weight status and depression in adolescence.

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“…It is quite popular to analyze the effect of a predictor measured at baseline on an outcome measured at follow-up while controlling for the outcome at baseline. Studies employing this method have, for example, concluded that while controlling for degree of depression symptoms at baseline, a higher degree of depression symptoms at follow-up can be predicted from higher levels of C-reactive protein and interleukin-6 (Gimeno et al, 2009), underweight (Kim, Noh, Park, & Kwon, 2014), higher body mass index (Dearborn, Robbins, & Elias, 2018), lower self-esteem (Johnson, Meyer, Winett, & Small, 2000), less cognitive control over sad stimuli (Vanderlind et al, 2014), more life stressors (Moos, Schutte, Brennan, & Moos, 2005), higher job demands (Paterniti, Niedhammer, Lang, & Consoli, 2002), poorer relationship quality (Eberhart & Hammen, 2006), and separation (O’Connor, Cheng, Dunn, & Golding, 2005).…”

Section: Introductionmentioning

confidence: 99%

Predicting the Effect of a Predictor When Controlling for Baseline

Sorjonen

Melin

Ingre

2019

Educational and Psychological Measurement

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The present simulation study indicates that a method where the regression effect of a predictor (X) on an outcome at follow-up (Y1) is calculated while adjusting for the outcome at baseline (Y0) can give spurious findings, especially when there is a strong correlation between X and Y0 and when the test–retest correlation between Y0 and Y1 is relatively weak. Researchers wishing to avoid spurious findings and Type 1 errors should be aware of this phenomenon and are recommended to verify found effects by an unadjusted effect of X on the Y1–Y0 difference.

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Challenging the “jolly fat” hypothesis among older adults: High body mass index predicts increases in depressive symptoms over a 5-year period

Cited by 17 publications

References 38 publications

Predictors of Depression and Insomnia in Community-Dwelling Elderly People: A Cross-Sectional Evidence of Their Bidirectional Relationship

Predictors of Depression and Insomnia in Community-Dwelling Elderly People: A Cross-Sectional Evidence of Their Bidirectional Relationship

Body weight status is not a predictive factor of depressive symptoms in Polish adolescents aged 13-18 years

Predicting the Effect of a Predictor When Controlling for Baseline

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