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Taking care of a cat and a baby brother turns into a series of comic adventures when George wakes up to find himself shrunk to the size of a mouse.

BackgroundDespite increasing knowledge on the neuroimaging patterns of eating disorder (ED) symptoms in non-clinical populations, studies using whole-brain machine learning to identify connectome-based neuromarkers of ED symptomatology are absent. This study examined the association of connectivity within and between large-scale functional networks with specific symptomatic behaviors and cognitions using connectome-based predictive modeling (CPM).MethodsCPM with ten-fold cross-validation was carried out to probe functional networks that were predictive of ED-associated symptomatology, including body image concerns, binge eating, and compensatory behaviors, within the discovery sample of 660 participants. The predictive ability of the identified networks was validated using an independent sample of 821 participants.ResultsThe connectivity predictive of body image concerns was identified within and between networks implicated in cognitive control (frontoparietal and medial frontal), reward sensitivity (subcortical), and visual perception (visual). Crucially, the set of connections in the positive network related to body image concerns identified in one sample was generalized to predict body image concerns in an independent sample, suggesting the replicability of this effect.ConclusionsThese findings point to the feasibility of using the functional connectome to predict ED symptomatology in the general population and provide the first evidence that functional interplay among distributed networks predicts body shape/weight concerns.

Marine habitats vary widely in structure, from incredibly complex coral reefs to simpler deep water and open ocean habitats. Hydromechanical models of swimming kinematics and microevolutionary studies suggest that these habitats select for different body shape characteristics. Fishes living in simple habitats are predicted to experience selection for energy-efficient sustained swimming, which can be achieved by fusiform body shapes. In contrast, fishes living in complex habitats are predicted to be under selection for maneuverability, which can be enhanced by deep-bodied and laterally compressed forms. To look for a signature of these processes at a broad macroevolutionary scale, we quantified the body shapes of 3322 species of marine teleostean fishes using a series of linear measurements. We scored each species for whether they were reef-associated or not and tested for morphological differences using a phylogenetic framework. Our results confirmed significant overall shape differences between reef-associated teleosts and those occupying structurally simpler marine habitats. Reef-associated species have, on average, deeper bodies and higher depth-to-width ratios, while non-reef species are more streamlined with narrower and shallower caudal peduncles. Despite the numerous evolutionary forces that may influence body shapes on a broad macroevolutionary scale, our results reveal differences in body shapes between reef-associated and non-reef species that are consistent with hydromechanical models of swimming kinematics as well as with microevolutionary patterns.

The use of anthropometric indices is one of the new and low-cost diagnostic methods of metabolic syndrome (MetS). The present study aimed to determine optimal cutoff points for the visceral adiposity index (VAI), body roundness index (BRI), and a body shape index (ABSI) in the prediction of MetS. This cross-sectional study was performed on 10,000 individuals aged from 35 to 65 years, recruited in Ravansar Non-Communicable Diseases (RaNCD) cohort study, in the west region of Iran, in 2019. MetS was defined according to International Diabetes Federation (IDF) criteria. The receiver operating characteristic (ROC) curve analysis was used to assess predictive anthropometric indices and determine optimal cutoff values. The optimal cutoff points for VAI were 4.11 (AUC: 0.82; 95% CI: 0.81-0.84) in men and 4.28 (AUC: 0.86; 95% CI: 0.85-0.87) in women to prediction of MetS. The optimal cutoff points for BRI were 4.75 (AUC: 0.75; 95% CI: 0.74-0.77) in men and 6.17 (AUC: 0.62; 95% CI: 0.61-0.64) in women to prediction of MetS. The optimal cutoff points for ABSI were 0.12 (AUC: 0.49; 95% CI: 0.47-0.51) in men and 0.13 (AUC: 0.49; 95% CI: 0.47-0.51) in women to prediction of MetS. The risk of MetS in men and women with a VAI higher than the optimal cutoff point was, respectively, 9.82 and 11.44 times higher than that in those with a VAI lower than the cutoff point. Although VAI might not be very cost-beneficial compared to IDF, our study showed VAI is a better predictor of MetS than BRI in adults. ABSI was not a suitable predictor for MetS.

The weight–length relationship (WLR) of fish is a crucial tool in fish biology research and has received extensive attention. However, fish growth is influenced by many factors, and the WLR also changes accordingly. Our aim was to investigate how fish body shape is affected by various factors by analyzing the existing parameters of the relationship between fish body length and weight. We analyzed 198,354 fish specimens belonging to 402 species of 82 families in China and investigated the change of fish body shape in the function of their ecology. Herbivorous fish tended to be shorter and fatter than carnivorous fish, and omnivorous fish fall somewhere in between. This difference could be due to variations in feeding habits and the availability of food sources. Additionally, fish living in lentic waters tended to have a shorter and fatter body shape compared to those living in lotic waters. This could be attributed to differences in swimming behavior in these environments. Furthermore, our results showed that the b value decreased as altitude increased, and fish tended to be thinner and longer due to lower oxygen and temperature levels in high-altitude waters. Overall, our study provides valuable insights into the WLR of fish and the impact of multiple factors on fish body shape.

A body shape index (ABSI) is a recently introduced index of abdominal adiposity, relative to body mass index and height, and represents an alternative to body mass index and waist circumference. We aimed to determine whether ABSI is associated with osteoporosis and the ability of ABSI to predict osteoporosis, to investigate the relationship between obesity and osteoporosis In total, 6717 Korean participants (3151 men and 3566 women; 63.6 ± 8.5 years) were recruited and placed into the Normal, Osteopenia, or Osteoporosis groups on the basis of the minimum T-scores of the lumbar spine, proximal femur, and femoral neck. The T-scores of each region and ABSI were compared among the groups and odds ratios and cut-off values of ABSI for osteoporosis were calculated. In participants of both sexes, ABSI tended to increase as bone health deteriorated. The men and women in the highest quartile of ABSI were 1.887 and 2.808 times more likely to have osteoporosis, respectively, and the potential ABSI cut-off values for osteoporosis were 0.0813 and 0.0874 for male and female participants, respectively. These findings suggest that augmentation of ABSI and obesity is associated with a higher risk of osteoporosis and that ABSI may predict the risk of osteoporosis.

Purpose This study examined the psychometric properties and provided normative data of the Dutch Body Shape Questionnaire (BSQ34) and its shortened BSQ8C among patients with binge-eating disorder. Methods The two versions of the BSQ were administered to patients with binge-eating disorder ( N  = 155) enrolled for treatment, and to a community sample ( N  = 333). The translation and back-translation of the BSQ were performed by translators with and without eating-disorder expertise. Internal consistency, concurrent validity, test–retest reliability, incremental validity, and sensitivity to change were determined. A receiver-operating-characteristic curve-analysis was used to establish criterion-related validity, for which the Eating Disorder Examination—Shape concern subscale, was used. Uni-dimensionality of the instrument was investigated with confirmatory factor analysis. Norms (population-based T -scores and clinical percentile-scores) were determined. Results The psychometric properties of the BSQs were satisfactory. The BSQ34 discriminated well in body-shape dissatisfaction between patients with binge-eating disorder and the community sample (area-under-the-curve value = 0.91–0.98) and had a unidimensional factor structure. Comparing structural invariance between both samples revealed that scaler invariance was not supported, indicating that items may be interpreted differently by patients with binge-eating disorder and subjects from the community. Analyses were repeated for the BSQ8C, which yielded similar results. Conclusion The results indicated that both versions of the BSQ appeared suitable to screen for body-shape dissatisfaction among patients with binge-eating disorder. The BSQ34 supplies valuable information on the various types of concerns respondents have, which are critical to consider in clinical settings; the BSQ8C is recommended as a short screening tool. Level of evidence : Level III: Evidence obtained from well-designed cohort or case–control analytic studies.

AbstractIntroduction: Several anthropometric indices reflecting cardiometabolic risks have been developed, but the relationship of body composition with arterial stiffness remains unclear. We aimed to determine the interaction between age-related anthropometric changes and progression of arterial stiffness. Methods: This research analyzed cross-sectional data (N = 13,672) and 4-year longitudinal data (N = 5,118) obtained from a healthy Japanese population without metabolic disorders. The relationship of age with anthropometric indices comprising estimated lean body mass index (eLBMI), body mass index (BMI), waist circumference (WC), and a body shape index (ABSI) was examined. The mediating effects of the indices on the association between age and arterial stiffness assessed by cardio-ankle vascular index (CAVI) were analyzed. Results: Unlike BMI and WC, ABSI (Rs = 0.284) and CAVI (Rs = 0.733) showed a positive linear relationship with aging in stratified analyses. Especially in the middle-older age groups, eLBMI showed a declining trend with aging. An increase in ABSI was associated with a decrease in eLBMI, whereas increase in BMI or WC was related to increased eLBMI. In cross-sectional analyses, age was associated with CAVI, partially mediated by ABSI or eLBMI after adjusting confounders. Baseline CAVI correlated negatively with 4-year change in (Δ)eLBMI (Rs = −0.120 in men, −0.161 in women). ΔCAVI correlated negatively with ΔeLBMI (Rs = −0.031). Conclusion: ABSI is a modifiable index that well reflects age-related changes in arterial stiffness and body composition including lean body mass. Since arterial stiffening may cause skeletal muscle loss, potentially creating a vicious cycle, prioritizing CAVI and anthropometric indices in clinical practice may be a useful strategy. Plain Language SummaryThis retrospective cohort study examined the relationship between age and several anthropometric indices in a healthy Japanese population. The results of cross-sectional data analyses revealed that body mass index (BMI) and waist circumference (WC) did not necessarily show a linear relationship with aging. A body shape index (ABSI) showed a positive linear correlation with age, while estimated lean body mass index (eLBMI) showed a declining trend with aging, especially in the middle-to-older age groups. In addition, ABSI correlated inversely with eLBMI, whereas BMI and WC correlated directly with eLBMI. Furthermore, age was associated linearly with cardio-ankle vascular index (CAVI) as a systemic arterial stiffness parameter, and this association was partially mediated by ABSI or eLBMI. In the longitudinal data analysis, baseline CAVI correlated negatively with ΔeLBMI, and a negative correlation between ΔCAVI and ΔeLBMI was observed. These findings suggest that ABSI is an indicator that reflects not only abdominal obesity but also loss of lean body mass with aging. This feature of ABSI may contribute to its superior predictive ability for mortality. Furthermore, vascular function and lean body mass may be related to each other, especially because CAVI increase precedes skeletal muscle loss. Taken together, when managing individuals with high ABSI or high CAVI, it would be appropriate to target imbalance of body composition as a potential cause.

Classifying and recognizing the human body shape during human body measurements based on 2D images helps to improve measurement accuracy. In this paper, 430 young women's 2D body images were selected to establish 2D body datasets. The characteristic indices used to represent the body shape in 2D images were extracted by computer vision technology, namely the body height pixel value, projected unit area, and projected area ratio of the front and side of the body. The two-step cluster model was used to classify the body shape into three clusters: the tall, flat, and medium fatness type; the short, thin, and medium roundness type; and the round, fat, and medium height type. Then, the decision tree model and AdaBoost algorithm, an ensemble learning algorithm with the decision tree as the weak classifier, were used to recognize the body shape. The results show that the recognition accuracy of the decision tree recognition model was 93.19%. The body shape recognition method using AdaBoost achieved a better recognition effect than the decision tree model, and the recognition accuracy of the test set reached 97.35%. Through comparative study, we found that the recognition accuracy of the 2D body shape recognition method based on AdaBoost was improved and that the recognition accuracy was relatively stable. This study provides a new method for the recognition of human body shape in clothing customization and online shopping.

Objective Recent evidence suggests a strong association between triglyceride glucose-a body shape index (TyG-ABSI) and cardiovascular mortality. However, the association between TyG-ABSI and incident stroke has not been explored. Our study utilized a prospective cohort design to explore the potential association between TyG-ABSI and incident stroke within a Chinese population. Methods A total of 8,715 subjects from the China Health and Retirement Longitudinal Study were included in this analysis, categorized into stroke ( n  = 805) and non-stroke ( n  = 7,910) groups. To assess the association between TyG-ABSI and incident stroke, this study employed Cox proportional hazards regression, restricted cubic spline models, subgroup analyses, and sensitivity analyses. Results The mean age of participants was significantly higher in the stroke group (61.26 ± 8.42 years) compared to the non-stroke group (59.14 ± 9.27 years). Multivariate analysis revealed that higher TyG-ABSI was significantly associated with increased stroke risk, with an HR of 1.22 (95% CI: 1.09–1.37, P  = 0.0004). Notably, this association between TyG-ABSI and incident stroke was non-linear, and a significant inflection point was identified at a TyG-ABSI of 6.42. The HR to the left and right of this inflection point were 2.44 (95%CI: 1.19–5.02, P  = 0.0151) and 1.16 (95%CI: 1.02–1.31, P  = 0.0239), respectively. Subgroup and sensitivity analyses further validated the robustness of the association between TyG-ABSI and incident stroke. Conclusion Our analysis revealed a non-linear positive association between TyG-ABSI and incident stroke. These findings emphasize the necessity of incorporating TyG-ABSI monitoring into clinical practice to identify individuals at greater risk for stroke.