Beyond BMI: Personality traits’ associations with adiposity and metabolic rate

Arumäe, Kadri; Mõttus, René; Vainik, Uku

doi:10.1016/j.physbeh.2022.113703

Cited by 11 publications

(11 citation statements)

References 51 publications

(83 reference statements)

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“…An additional limitation is one that this study shares with any other study that relies on BMI: This index of body weight reflects lean mass in addition to fat mass and can therefore conflate the two. Yet, BMI broadly relates to the same personality traits as skinfold thickness, waist circumference, and an estimate of fat mass based on waist circumference (Arumäe, Mõttus, et al, 2022; Sutin et al, 2011). But still, relying on self‐reported height and weight data in two of the three study waves may have additionally suppressed BMI's associations with personality traits (see Roehling et al, 2008).…”

Section: Discussionmentioning

confidence: 99%

Body mass predicts personality development across 18 years in middle to older adulthood

Arumäe

Mõttus

Vainik

2023

Journal of Personality

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Objective: Various personality traits have longitudinal relations with body mass index (BMI), a measure of body weight and a risk factor for numerous health concerns. We tested these associations' compatibility with causality in either direction. Method: Using three waves of the Wisconsin Longitudinal Study (N = 12,235, M age = 53.33 at baseline), we tested how accurately the Five-Factor Model personality domains and their items could collectively predict BMI and change in it with elastic net models. With multilevel models, we tested (a) bidirectional and (b) within-person associations between BMI and personality traits. Results: The five domains were able to predict concurrent (r = 0.08), but not future BMI. Twenty-nine personality items predicted concurrent and future BMI at r = 0.21 and r = 0.16 to 0.25, respectively. Neither the domains nor items could collectively predict change in BMI. Similarly, no individual trait predicted change in BMI, but BMI predicted changes in Conscientiousness, Agreeableness, and several items (|b*| = 0.03 to 0.08). BMI had within-person correlations with these same traits; time-invariant third factors like genetics or childhood environments therefore could not (fully) account for their relations. Conclusions: Body weight may contribute to adults' personality development, but the reverse appears less likely.

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Section: Discussionmentioning

confidence: 99%

Body mass predicts personality development across 18 years in middle to older adulthood

Arumäe

Mõttus

Vainik

2023

Journal of Personality

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“…We explored relative fat mass as an alternative obesity indicator but discarded due to lack of additional information (see ref. 71). The PPS method limits the results by ignoring causal relationships of individual features within a factor, but this was offset by gain in statistical power.…”

Section: Discussionmentioning

confidence: 99%

“…The results are limited by the use of %BMI p95 as a proxy for obesity and the assumption of obesity being continuous while the majority of participants were not obese. We explored relative fat mass as an alternative obesity indicator but discarded due to lack of additional information 71 . The PPS method limits the results by ignoring causal relationships of individual features within a factor, but this was offset by gain in statistical power.…”

Section: Discussionmentioning

confidence: 99%

“…The results are limited using the percentage of the sex- and age-specific 95th BMI percentile as a proxy for obesity and the assumption of obesity being continuous while most participants did not have obesity. Relative fat mass was explored as an alternative approach to obesity (Arumäe et al, 2022). However, correlations were highly similar and relative fat mass was dropped.…”

Section: Discussionmentioning

confidence: 99%

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Triangulating Causality Between Childhood Obesity and Neurobehavior: Twin and Longitudinal Evidence

Kulisch

Arumäe

Briley

et al. 2022

Preprint

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Childhood obesity is a serious health concern that is not yet fully understood. Previous research has linked obesity with neurobehavioral factors such as behavior, cognition, and brain morphology. The causal directions of these relationships remain mostly untested. Here, we filled this gap by using the Adolescent Brain Cognitive Development study cohort comprising 11,875 children aged nine to ten. Cross-sectionally, body mass percentile correlated with measures of impulsivity, motivation, psychopathology, eating behavior, and cognitive tests (executive functioning, language, memory, perception, working memory). Obesity was associated with reduced cortical thickness in areas of the frontal and temporal lobe but with increased thickness in parietal and occipital brain areas. Similar although weaker patterns emerged for cortical surface area and volume. Effects for each neurobehavioral factor (personality/psychopathology, eating behavior, cognition, cortical thickness, cortical surface area, and cortical volume) were then aggregated for causal analyses. Direction of Causation twin modeling was used to test the direction of each relationship. Findings were validated by longitudinal analyses. Twin modeling suggested causal effects of childhood obesity on eating behavior (β=.26), cognition (β=.05), cortical thickness (β=.15), and cortical surface area (β=.07). Personality/psychopathology (β=.09) and eating behavior (β=.16) appeared to causally influence childhood obesity. Longitudinal evidence broadly supported these findings. Results regarding cortical volume were inconsistent. In summary, childhood obesity had considerable causal effects on brain functioning and morphology, supporting the brain inflammation theory. The present study highlights the importance of physical health for brain development during childhood and may inform interventions aimed at preventing or reducing pediatric obesity.

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“…In clinical and epidemiological studies, simple and practical techniques such as anthropometric-based measurements, which include waist circumference, body mass index (BMI), and skinfold thickness, are the cheapest and most commonly used methods for the estimation of body composition [9]. However, there are some significant drawbacks to each, for instance, BMI tends to overestimate body fat levels in individuals with high muscle mass [10]. For more details, refer to Chung et al [11].…”

Section: Introductionmentioning

confidence: 99%

Probabilistic prediction of segmental body composition in Iranian children and adolescents

et al. 2022

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Background Adolescents' body composition is considered an important measure to evaluate health status. An examination of any of the segmental compartments by anthropometric indices is a more usable method than direct methods. Objectives To propose a method based on the network approach for predicting segmental body composition components in adolescent boys and girls using anthropometric measurements. Methods A dual-energy X-ray absorptiometry (DXA) dataset in the south of Iran, including 476 adolescents (235 girls and 241 boys) with a range of 9–18 years, was obtained. Several anthropometric prediction models based on the network approach were fitted to the training dataset (TRD 80%) using bnlearn, an R add-in package. The best fitted models were applied to the validation dataset (VAD 20%) to assess the prediction accuracy. Results Present equations consisting of age, weight, height, body mass index (BMI), and hip circumference accounted for 0.85 (P < 0.001) of the variability of DXA values in the corresponding age groups of boys. Similarly, reasonable estimates of DXA values could be obtained from age, weight, height, and BMI in girls over 13 years, and from age, weight, height, BMI, and waist circumference in girls under 13 years, respectively, of 0.77 and 0.83 (P < 0.001). Correlations between robust Gaussian Bayesian network (RGBN) predictions and DXA measurements were highly significant, averaging 0.87 for boys and 0.82 for girls (P < 0.001). Conclusions The results revealed that, based on the present study’s predictive models, adolescents' body composition might be estimated by input anthropometric information. Given the flexibility and modeling of the present method to test different motivated hypotheses, its application to body compositional data is highly appealing.

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Beyond BMI: Personality traits’ associations with adiposity and metabolic rate

Cited by 11 publications

References 51 publications

Body mass predicts personality development across 18 years in middle to older adulthood

Body mass predicts personality development across 18 years in middle to older adulthood

Triangulating Causality Between Childhood Obesity and Neurobehavior: Twin and Longitudinal Evidence

Probabilistic prediction of segmental body composition in Iranian children and adolescents

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