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Eating behavior is highly heterogeneous across individuals and cannot be fully explained using only the degree of obesity. We utilized unsupervised machine learning and functional connectivity measures to explore the heterogeneity of eating behaviors measured by a self-assessment instrument using 424 healthy adults (mean ± standard deviation [SD] age = 47.07 ± 18.89 years; 67% female). We generated low-dimensional representations of functional connectivity using resting-state functional magnetic resonance imaging and estimated latent features using the feature representation capabilities of an autoencoder by nonlinearly compressing the functional connectivity information. The clustering approaches applied to latent features identified three distinct subgroups. The subgroups exhibited different levels of hunger traits, while their body mass indices were comparable. The results were replicated in an independent dataset consisting of 212 participants (mean ± SD age = 38.97 ± 19.80 years; 35% female). The model interpretation technique of integrated gradients revealed that the between-group differences in the integrated gradient maps were associated with functional reorganization in heteromodal association and limbic cortices and reward-related subcortical structures such as the accumbens, amygdala, and caudate. The cognitive decoding analysis revealed that these systems are associated with reward- and emotion-related systems. Our findings provide insights into the macroscopic brain organization of eating behavior-related subgroups independent of obesity., (© 2024 The Authors. Human Brain Mapping published by Wiley Periodicals LLC.)

Dysregulated neural mechanisms in reward and somatosensory circuits result in an increased appetitive drive for and reduced inhibitory control of eating, which in turn causes obesity. Despite many studies investigating the brain mechanisms of obesity, the role of macroscale whole-brain functional connectivity remains poorly understood. Here, we identified a neuroimaging-based functional connectivity pattern associated with obesity phenotypes by using functional connectivity analysis combined with machine learning in a large-scale (n ~ 2,400) dataset spanning four independent cohorts. We found that brain regions containing the reward circuit positively associated with obesity phenotypes, while brain regions for sensory processing showed negative associations. Our study introduces a novel perspective for understanding how the whole-brain functional connectivity correlates with obesity phenotypes. Furthermore, we demonstrated the generalizability of our findings by correlating the functional connectivity pattern with obesity phenotypes in three independent datasets containing subjects of multiple ages and ethnicities. Our findings suggest that obesity phenotypes can be understood in terms of macroscale whole-brain functional connectivity and have important implications for the obesity neuroimaging community., (© 2020 The Authors. Human Brain Mapping published by Wiley Periodicals LLC.)

Many studies have linked dysfunction in cognitive control-related brain regions with obesity and the burden of white matter hyperintensities (WMHs). This study aimed to explore how functional connectivity differences in the brain are associated with WMH burden and degree of obesity using resting-state functional magnetic resonance imaging (fMRI) in 182 participants. Functional connectivity measures were compared among four different groups: (1) low WMH burden, non-obese; (2) low WMH burden, obese; (3) high WMH burden, non-obese; and (4) high WMH burden, obese. At a large-scale network-level, no networks showed significant interaction effects, but the frontoparietal network showed a main effect of degree of obesity. At a finer node level, the orbitofrontal cortex showed interaction effects between periventricular WMH burden and degree of obesity. Higher functional connectivity was observed when the periventricular WMH burden and degree of obesity were both high. These results indicate that the functional connectivity of the orbitofrontal cortex is affected by the mutual interaction between the periventricular WMHs and degree of obesity. Our results suggest that this region links obesity with WMHs in terms of functional connectivity.

Aims: We conducted a 4-week randomized, sham-controlled, single-blind, parallel-group trial to examine the effect of repetitive transcranial magnetic stimulation (rTMS) delivered to the left dorsolateral prefrontal cortex (DLPFC) on functional brain connectivity and body weight in adults with obesity., Materials and Methods: Of the 45 volunteers with obesity, aged between 18 and 70 years (body mass index [BMI] ≥25 kg/m 2 according to the obesity criterion for an Asian population), 36 participants (54.1 ± 11.0 years, BMI 30.2 ± 3.5 kg/m 2 , 77.8% female) completed the 4 weeks of follow-up, undergoing two resting state fMRI scans (20 in the real stimulation group and 16 in the sham stimulation group). A total of eight sessions of high-frequency rTMS targeting the left DLPFC were provided over a period of 4 weeks (5-second trains with 25-second inter-train intervals, 10 Hz, 110% motor threshold; 2000 pulses over 20 minutes)., Results: Participants in the real stimulation group showed significantly greater weight loss from baseline following the eight session of rTMS (-2.53 ± 2.41 kg vs 0.38 ± 1.13 kg, P < 0.01). For intrinsic brain connectivity comparisons, the between-ness centrality values within the right frontoparietal network tended to increase with rTMS, and a significant interaction effect was identified for time (pre vs post) × rTMS (real vs sham) in the right frontoparietal network (P = 0.031, FDR corrected)., Conclusions: We observed that rTMS selectively increased resting state functional connectivity within the right frontoparietal network. Our findings suggest that high-frequency rTMS to the left DLPFC might strengthen the frontoparietal network that orchestrates top-down inhibitory control to reduce food intake., (© 2019 John Wiley & Sons Ltd.)

Obesity causes critical health problems including cardiovascular disease, diabetes, and stroke. Various neuroimaging methods including diffusion tensor imaging (DTI) are used to explore white matter (WM) alterations in obesity. The functional correlation tensor (FCT) is a method to simulate DTI in WM using resting-state functional magnetic resonance imaging (rs-fMRI). In this study, we enhanced the FCT with additional anatomical information from T1-weighted data in a regression framework. The goal was to 1) develop a spatially guided enhanced FCT (s-eFCT) and to 2) use it to identify imaging biomarkers for obesity. We computed fractional anisotropy (FA) and the mean diffusivity (MD) from the s-eFCT. The regional FA and MD values that can explain body mass index (BMI) well were chosen. The identified regional FA and MD values were used to predict BMI values. The correlation between real and predicted BMIs was 0.57. There was no significant correlation between real and predicted DTI using the MD. The BMI predicted using FA was used to classify participants into three obesity subgroups. The classification accuracy was 57.20%. In summary, we found potential imaging biomarkers of obesity based on the s-eFCT., (Copyright © 2019 Elsevier Ltd. All rights reserved.)

Purpose: To estimate the risk of stillbirth associated with excessive and inadequate weight gain during pregnancy., Methods: Retrospective cohort study using the Texas vital records database between 2006 and 2011, with 2,230,310 births (5502 stillbirths) was included for analysis. Pregnancies were categorized as adequate weight gain, excessive weight gain, inadequate weight gain, or weight loss based on the Institute of Medicine 2009 recommendations. Hazard ratios (HRs) for stillbirth were estimated for each gestational weight-gain stratum using adequate weight gain as the comparison group. The analysis was performed separately for each body mass index (BMI) class., Results: Both inadequate weight gain and weight loss were associated with an increased risk of stillbirth for all BMI classes except the morbidly obese group. Highest risk was seen in weight-loss groups after 36 completed weeks (normal weight: HR = 18.85 [8.25-43.09]; overweight: HR = 5.87 [2.99-11.55]; obese: HR = 3.44 [2.34-5.05]). Weight loss was associated with reduced stillbirth risk in morbidly obese women between 24 and 28 weeks (HR = 0.56 [0.34-0.95]). Excess weight gain was associated with an increased risk of stillbirth among obese and morbidly obese women, with highest risk after 36 completed weeks (obese: HR = 2.00 [1.55-2.58]; morbidly obese: HR = 3.16 [2.17-4.62]). In contrast, excess weight gain was associated with reduced risk of stillbirth in normal-weight women between 24 and 28 weeks (HR = 0.57 [0.44-0.70]) and in overweight women between 29 and 33 weeks (HR = 0.62 [0.45-0.85]). Analysis for the underweight group was limited by sample size. Both excessive weight gain and inadequate weight gain were not associated with stillbirth in this group., Conclusions: Stillbirth risk increased with inadequate weight gain and weight loss in all BMI classes except the morbidly obese group, where weight demonstrated a protective effect. Conversely, excessive weight gain was associated with higher risk of stillbirth among obese and morbidly obese women but was protective against stillbirth in lower weight women., (Copyright © 2017 Elsevier Inc. All rights reserved.)

Obesity is a serious medical condition highly associated with health problems such as diabetes, hypertension, and stroke. Obesity is highly associated with negative emotional states, but the relationship between obesity and emotional states in terms of neuroimaging has not been fully explored. We obtained 196 emotion task functional magnetic resonance imaging (t-fMRI) from the Human Connectome Project database using a sampling scheme similar to a bootstrapping approach. Brain regions were specified by automated anatomical labeling atlas and the brain activity (z-statistics) of each brain region was correlated with body mass index (BMI) values. Regions with significant correlation were identified and the brain activity of the identified regions was correlated with emotion-related clinical scores. Hippocampus, amygdala, and inferior temporal gyrus consistently showed significant correlation between brain activity and BMI and only the brain activity in amygdala consistently showed significant negative correlation with fear-affect score. The brain activity in amygdala derived from t-fMRI might be good neuroimaging biomarker for explaining the relationship between obesity and a negative emotional state.

Background: Maternal obesity has been associated with higher birth weight. Small for gestational age (SGA) neonates born to obese women may be associated with pathological growth with increased neonatal complications., Methods: This was a retrospective cohort study of all non-anomalous singleton neonates born in Texas from 2006-2011. Analyses were limited to births between 34 and 42 weeks gestation with birth weight ≤10th percentile. Results were stratified by maternal pre-pregnancy BMI class. The risk for stillbirth, neonatal death, neonatal intensive care unit (NICU) admission and five minute Apgar scores <7 were estimated for each obesity class and compared to the normal weight group. Multivariable logistic regression analyses were performed to control for potential confounding variables., Results: The rate of stillbirth was 1.4/1000 births for normal weight women, and 2.9/1000 among obese women (p < 0.001, aOR: 1.83 [1.43, 2.34]). The rate of neonatal deaths among normal weight women was 4.3/1000 births, whereas among obese women it was 4.7/1000 (p = 0.94, aOR: 1.10 [0.92, 1.30]). A dose-dependent relationship between maternal obesity and stillbirths was seen, but not for other neonatal outcomes., Conclusion: Among SGA neonates, maternal pre-pregnancy obesity was associated with increased risks for stillbirth, NICU admission and low Apgar scores but not neonatal death.

Obesity causes critical health problems including diabetes and hypertension that affect billions of people worldwide. Obesity and eating behaviors are believed to be closely linked but their relationship through brain networks has not been fully explored. We identified functional brain networks associated with obesity and examined how the networks were related to eating behaviors. Resting state functional magnetic resonance imaging (MRI) scans were obtained for 82 participants. Data were from an equal number of people of healthy weight (HW) and non-healthy weight (non-HW). Connectivity matrices were computed with spatial maps derived using a group independent component analysis approach. Brain networks and associated connectivity parameters with significant group-wise differences were identified and correlated with scores on a three-factor eating questionnaire (TFEQ) describing restraint, disinhibition, and hunger eating behaviors. Frontoparietal and cerebellum networks showed group-wise differences between HW and non-HW groups. Frontoparietal network showed a high correlation with TFEQ disinhibition scores. Both frontoparietal and cerebellum networks showed a high correlation with body mass index (BMI) scores. Brain networks with significant group-wise differences between HW and non-HW groups were identified. Parts of the identified networks showed a high correlation with eating behavior scores.

Obesity is a medical condition affecting billions of people. Various neuroimaging methods including magnetic resonance imaging (MRI) have been used to obtain information about obesity. We adopted a multi-modal approach combining diffusion tensor imaging (DTI) and resting state functional MRI (rs-fMRI) to incorporate complementary information and thus better investigate the brains of non-healthy weight subjects. The objective of this study was to explore multi-modal neuroimaging and use it to predict a practical clinical score, body mass index (BMI). Connectivity analysis was applied to DTI and rs-fMRI. Significant regions and associated imaging features were identified based on group-wise differences between healthy weight and non-healthy weight subjects. Six DTI-driven connections and 10 rs-fMRI-driven connectivities were identified. DTI-driven connections better reflected group-wise differences than did rs-fMRI-driven connectivity. We predicted BMI values using multi-modal imaging features in a partial least-square regression framework (percent error 15.0%). Our study identified brain regions and imaging features that can adequately explain BMI. We identified potentially good imaging biomarker candidates for obesity-related diseases.

Central serotonin (5-HT) is an anorexigenic neurotransmitter in the brain. However, accumulating evidence suggests peripheral 5-HT may affect organismal energy homeostasis. Here we show 5-HT regulates white and brown adipose tissue function. Pharmacological inhibition of 5-HT synthesis leads to inhibition of lipogenesis in epididymal white adipose tissue (WAT), induction of browning in inguinal WAT and activation of adaptive thermogenesis in brown adipose tissue (BAT). Mice with inducible Tph1 KO in adipose tissues exhibit a similar phenotype as mice in which 5-HT synthesis is inhibited pharmacologically, suggesting 5-HT has localized effects on adipose tissues. In addition, Htr3a KO mice exhibit increased energy expenditure and reduced weight gain when fed a high-fat diet. Treatment with an Htr2a antagonist reduces lipid accumulation in 3T3-L1 adipocytes. These data suggest important roles for adipocyte-derived 5-HT in controlling energy homeostasis.

The dilution effect of high plasma volume contributes to a lower level of specific tumor marker such as prostate-specific antigen. To date, few studies have focused on this effect of other tumor marker levels. Therefore, we evaluated the dilution effects of a higher plasma volume on lowering the serum concentration of various tumor markers such as CA125 and CA19-9. Data from patients (age ≥40 years) visiting our public health center between March 2007 and March 2013 were gathered. A total of 19,439 male and 11,655 female native Koreans were eligible to have a serum CA125 and CA19-9 screening for ovarian and pancreatic cancer, respectively. CA125 levels in 11,234 women were analyzed. There was a statistically significant trend toward a lower likelihood of having a serum CA125 level >35 U/mL with increased body mass index (BMI), with obese men having a 53.1% lower likelihood than those with a normal BMI. After CA19-9 analysis in 29,978 participants, there was also a significant trend toward a lower likelihood of having a serum CA19-9 level >35 U/mL with increased BMI, with obese men having a 27.0% and obese women having a 32.7% lower likelihood than those with a normal BMI. These results might affect tumor screening efficiency for ovarian and pancreatic cancer using serum CA125 and CA19-9, respectively. Further studies are needed to better define these results in clinical practice.

Objective: Obesity is a known risk factor for stillbirth. However, this relationship has not been characterized fully. We attempted to further examine this relationship with a focus on delivery near and at term., Study Design: We designed a retrospective cohort study of singleton nonanomalous live births and stillbirths in the states of Washington and Texas to examine the associations of maternal prepregnancy body mass index (BMI) and risk of stillbirth. Confounder-adjusted hazard ratio of stillbirth in relation to BMI was estimated through Cox proportional hazards regression model. The hazard ratio was used to estimate the population-attributable risk. We also estimated the fetuses who were at risk for stillbirth based on gestational age., Results: Among 2,868,482 singleton births, the overall stillbirth risk was 3.1 per 1000 births (n = 9030). Compared with normal-weight women, the hazard ratio for stillbirth was 1.36 for overweight women, 1.71 for class I obese women, 2.00 for class II obese women, 2.48 for class III obese women, and 3.16 for women with a BMI of ≥50 kg/m(2). The fetuses who are at risk for stillbirth increased after 39 weeks' gestation for each obesity class; however, the risk increased more rapidly with increasing BMI. Women with a BMI of ≥50 kg/m(2) were at 5.7 times greater risk than normal weight women at 39 weeks' gestation and 13.6 times greater at 41 weeks' gestation. Obesity was associated with nearly 25% of stillbirth that occurred between 37 and 42 weeks' gestation., Conclusion: There is a pronounced increase in the risk of stillbirth with increasing BMI; the association is strongest at early- and late-term gestation periods. Extreme maternal obesity is a significant risk factor for stillbirth., (Copyright © 2014 Mosby, Inc. All rights reserved.)

Functional hierarchy establishes core axes of the brain, and overweight individuals show alterations in the networks anchored on these axes, particularly in those involved in sensory and cognitive control systems. However, quantitative assessments of hierarchical brain organization in overweight individuals are lacking. Capitalizing stepwise functional connectivity analysis, we assess altered functional connectivity in overweight individuals relative to healthy weight controls along the brain hierarchy. Seeding from the brain regions associated with obesity phenotypes, we conduct stepwise connectivity analysis at different step distances and compare functional degrees between the groups. We find strong functional connectivity in the somatomotor and prefrontal cortices in both groups, and both converge to transmodal systems, including frontoparietal and default-mode networks, as the number of steps increased. Conversely, compared with the healthy weight group, overweight individuals show a marked decrease in functional degree in somatosensory and attention networks across the steps, whereas visual and limbic networks show an increasing trend. Associating functional degree with eating behaviors, we observe negative associations between functional degrees in sensory networks and hunger and disinhibition-related behaviors. Our findings suggest that overweight individuals show disrupted functional network organization along the hierarchical axis of the brain and these results provide insights for behavioral associations. Hyebin Lee et al. use a stepwise functional connectivity technique to study differences in functional connectivity between overweight and healthy weight individuals. They find disrupted functional network organization along the hierarchical axis of the brain in overweight individuals that relates to behavioral traits [ABSTRACT FROM AUTHOR]

Objective: Obesity and pregestational diabetes (PGDM) may interact to further increase the risk of stillbirth than either risk factors independently. The objective of this study was to determine the risk of stillbirth in pregnancies complicated by both conditions.Method: This was a retrospective cohort analysis of singleton nonanomalous births using the updated Texas vital records database between 2006 and 2014. Gestational diabetes and hypertensive diseases were additionally excluded from analysis. Analysis was stratified into 10 strata based on BMI class: underweight, normal weight, overweight, obese and morbidly obese, and PGDM. Furthermore, gestational age was stratified into the four periods for analysis: 24-33, 34-36, 37-39, and 40-42 weeks. The rate of stillbirth per 10,000 pregnancies were calculated for each stratum. The risks of stillbirth associated with each BMI class and PGDM were compared to normal weight nondiabetic pregnancies for each gestational period using proportional hazard regression models.Result: After all exclusions, 3,097,123 births remained for analysis, including 5997 stillbirths. The overall rate of stillbirth increased from 15.0 per 10,000 pregnancies in normal weight pregnancies to 26.7 per 10,000 pregnancies in the morbidly obese group. The rate of stillbirth further increased with coexistence of PGDM to 119.9 per 10,000 pregnancies in the normal weight group and 209.8 per 10,000 pregnancies in the morbidly obese group. Compared to normal weight nondiabetic pregnancies the overall adjusted hazard ratio (aHR) of stillbirth associated with morbid obesity without PGDM was 1.57 [1.38, 1.79]. However, when further complicated by PGDM, the aHR was 6.67 [5.05, 8.81] in normal weight pregnancies and 12.86 [9.36, 17.67] in morbidly obese pregnancies. The highest risk of stillbirth was seen between 37 and 39 weeks, when the aHR in the diabetic normal weight group was 9.63 [5.65, 16.40] and the aHR in the diabetic morbidly obese group was 25.34 [15.58, 41.22].Conclusion: PGDM and obesity both independently increased the risk of stillbirth. The joint effect of obesity and PGDM is stronger than the summation or multiplication of the individual effects of each risk factor. [ABSTRACT FROM AUTHOR]

Purpose: Maternal obesity has been consistently associated with offspring risk for ASD, as well as lipid metabolism derangements. However, few ASD studies have examined maternal lipids in conjunction with maternal prepregnancy body mass index (BMI).Methods: This nested case-control study was based on the Boston Birth Cohort, a prospective cohort study of mother-child dyads recruited at the Boston Medical Center. Maternal blood samples were collected shortly after delivery and analyzed for total plasma cholesterol, HDL, and triglyceride (TG) concentrations. Low-density lipoprotein (LDL) was subsequently calculated by the Friedewald equation. Cases were identified using ASD diagnoses in children's medical records. The odds of ASD were estimated with continuous lipid levels for a linear relationship, and we further explored the nonlinear relationship using the tertile of each lipid analyte with the highest tertile as the reference group. Logistic regression was used to estimate the risk of ASD adjusting for potential confounders. The analyses were performed separately for mothers with normal weight and overweight/obese based on maternal prepregnancy BMI.Results: One standard deviation decrease in postpartum maternal LDL was associated with increased odds of ASD aOR 1.35 [1.04-1.75]. There was no association between postpartum maternal HDL and TG levels and ASD risk. Decreasing levels of LDL were not associated with ASD risk in normal-weight mothers (aOR 1.2 [0.83-1.75]), but the ASD risk was more pronounced in overweight and obese mothers (aOR 1.54 [1.03-2.27]). Follow-up analysis of nonlinear association models showed that, when compared to the highest tertile, lower maternal LDL concentrations were associated with approximately two times increased risk of ASD (first tertile: aOR 2.49 [1.27-4.87] and second tertile: aOR 2.79 [1.42-5.48]). A similar pattern was observed with overweight/obese mothers but not in normal-weight mothers.Conclusions: Lower maternal postpartum plasma LDL concentration was associated with increased odds of ASD in offspring among children born to overweight and obese mothers. Our findings suggest that both maternal BMI and lipids should be considered in assessing their role in offspring ASD risk, and additional longitudinal studies are needed to better understand maternal lipid dynamics during pregnancy among normal-weight and overweight/obese mothers. [ABSTRACT FROM AUTHOR]