Obesity is associated with alterations in anatomical connectivity of frontal-corpus callosum.

Obesity has been linked to abnormal frontal function, including the white matter fibers of anterior portion of the corpus callosum, which is crucial for information exchange within frontal cortex. However, alterations in white matter anatomical connectivity between corpus callosum and cortical regions in patients with obesity have not yet been investigated. Thus, we enrolled 72 obese and 60 age-/gender-matched normal weight participants who underwent clinical measurements and diffusion tensor imaging. Probabilistic tractography with connectivity-based classification was performed to segment the corpus callosum and quantify white matter anatomical connectivity between subregions of corpus callosum and cortical regions, and associations between corpus callosum-cortex white matter anatomical connectivity and clinical behaviors were also assessed. Relative to normal weight individuals, individuals with obesity exhibited significantly greater white matter anatomical connectivity of corpus callosum-orbitofrontal cortex, which was positively correlated with body mass index and self-reported disinhibition of eating behavior, and lower white matter anatomical connectivity of corpus callosum-prefrontal cortex, which was significantly negatively correlated with craving for high-calorie food cues. The findings show that alterations in white matter anatomical connectivity between corpus callosum and frontal regions involved in reward and executive control are associated with abnormal eating behaviors.

White adipose tissue distribution and amount are associated with increased white matter connectivity.

Obesity represents a significant public health concern and is linked to various comorbidities and cognitive impairments. Previous research indicates that elevated body mass index (BMI) is associated with structural changes in white matter (WM). However, a deeper characterization of body composition is required, especially considering the links between abdominal obesity and metabolic dysfunction. This study aims to enhance our understanding of the relationship between obesity and WM connectivity by directly assessing the amount and distribution of fat tissue. Whole-body magnetic resonance imaging (MRI) was employed to evaluate total adipose tissue (TAT), visceral adipose tissue (VAT), and subcutaneous adipose tissue (SAT), while MR liver spectroscopy measured liver fat content in 63 normal-weight, overweight, and obese males. WM connectivity was quantified using microstructure-informed tractography. Connectome-based predictive modeling was used to predict body composition metrics based on WM connectomes. Our analysis revealed a positive dependency between BMI, TAT, SAT, and WM connectivity in brain regions involved in reward processing and appetite regulation, such as the insula, nucleus accumbens, and orbitofrontal cortex. Increased connectivity was also observed in cognitive control and inhibition networks, including the middle frontal gyrus and anterior cingulate cortex. No significant associations were found between WM connectivity and VAT or liver fat. Our findings suggest that altered neural communication between these brain regions may affect cognitive processes, emotional regulation, and reward perception in individuals with obesity, potentially contributing to weight gain. While our study did not identify a link between WM connectivity and VAT or liver fat, further investigation of the role of various fat depots and metabolic factors in brain networks is required to advance obesity prevention and treatment approaches.

Associations between adiposity and white matter hyperintensities: Cross-sectional and longitudinal analyses of 34,653 participants.

OBJECTIVES: White matter hyperintensities (WMH) increase the risk of stroke and cognitive impairment. This study aims to determine the cross-sectional and longitudinal associations between adiposity and WMH. METHODS: Participants were enrolled from the UK Biobank cohort. Associations of concurrent, past, and changes in overall and central adiposity with WMH were investigated by linear and nonlinear regression models. The association of longitudinal adiposity and WMH volume changes was determined by a linear mixed model. Mediation analysis investigated the potential mediating effect of blood pressure. RESULTS: In 34,653 participants with available adiposity measures and imaging data, the concurrent obese group had a 25.3% (ß [95% CI] = 0.253 [0.222-0.284]) higher WMH volume than the ideal weight group. Increment in all adiposity measures was associated with a higher WMH volume. Among them, waist circumference demonstrated the strongest effect (ß [95% CI] = 0.113 [0.101-0.125]). Past adiposity also demonstrated similar effects. Among the subset of 2664 participants with available WMH follow-up data, adiposity measures were predictive of WMH change. Regarding changes of adiposity, compared with ideal weight stable group, those who turned from ideal weight to overweight/obese had a 8.1% higher WMH volume (ß [95% CI] = 0.081 [0.039-0.123]), while participants who turned from overweight/obese to ideal weight demonstrated no significant WMH volume change. Blood pressure partly meditates the associations between adiposity and WMH. CONCLUSIONS: Both concurrent and past adiposity were associated with a higher WMH volume. The detrimental effects of adiposity on WMH occurred throughout midlife and in the elderly and may still exist after changes in obesity status.

Deep learning-based BMI inference from structural brain MRI reflects brain alterations following lifestyle intervention.

Obesity is associated with negative effects on the brain. We exploit Artificial Intelligence (AI) tools to explore whether differences in clinical measurements following lifestyle interventions in overweight population could be reflected in brain morphology. In the DIRECT-PLUS clinical trial, participants with criterion for metabolic syndrome underwent an 18-month lifestyle intervention. Structural brain MRIs were acquired before and after the intervention. We utilized an ensemble learning framework to predict Body-Mass Index (BMI) scores, which correspond to adiposity-related clinical measurements from brain MRIs. We revealed that patient-specific reduction in BMI predictions was associated with actual weight loss and was significantly higher in active diet groups compared to a control group. Moreover, explainable AI (XAI) maps highlighted brain regions contributing to BMI predictions that were distinct from regions associated with age prediction. Our DIRECT-PLUS analysis results imply that predicted BMI and its reduction are unique neural biomarkers for obesity-related brain modifications and weight loss.

Principal component analysis as an efficient method for capturing multivariate brain signatures of complex disorders-ENIGMA study in people with bipolar disorders and obesity.

Multivariate techniques better fit the anatomy of complex neuropsychiatric disorders which are characterized not by alterations in a single region, but rather by variations across distributed brain networks. Here, we used principal component analysis (PCA) to identify patterns of covariance across brain regions and relate them to clinical and demographic variables in a large generalizable dataset of individuals with bipolar disorders and controls. We then compared performance of PCA and clustering on identical sample to identify which methodology was better in capturing links between brain and clinical measures. Using data from the ENIGMA-BD working group, we investigated T1-weighted structural MRI data from 2436 participants with BD and healthy controls, and applied PCA to cortical thickness and surface area measures. We then studied the association of principal components with clinical and demographic variables using mixed regression models. We compared the PCA model with our prior clustering analyses of the same data and also tested it in a replication sample of 327 participants with BD or schizophrenia and healthy controls. The first principal component, which indexed a greater cortical thickness across all 68 cortical regions, was negatively associated with BD, BMI, antipsychotic medications, and age and was positively associated with Li treatment. PCA demonstrated superior goodness of fit to clustering when predicting diagnosis and BMI. Moreover, applying the PCA model to the replication sample yielded significant differences in cortical thickness between healthy controls and individuals with BD or schizophrenia. Cortical thickness in the same widespread regional network as determined by PCA was negatively associated with different clinical and demographic variables, including diagnosis, age, BMI, and treatment with antipsychotic medications or lithium. PCA outperformed clustering and provided an easy-to-use and interpret method to study multivariate associations between brain structure and system-level variables. PRACTITIONER POINTS: In this study of 2770 Individuals, we confirmed that cortical thickness in widespread regional networks as determined by principal component analysis (PCA) was negatively associated with relevant clinical and demographic variables, including diagnosis, age, BMI, and treatment with antipsychotic medications or lithium. Significant associations of many different system-level variables with the same brain network suggest a lack of one-to-one mapping of individual clinical and demographic factors to specific patterns of brain changes. PCA outperformed clustering analysis in the same data set when predicting group or BMI, providing a superior method for studying multivariate associations between brain structure and system-level variables.

Metabolic interactions between organs in overweight and obesity using total-body positron emission tomography.

BACKGROUND AND OBJECTIVES: Overweight and obesity is a complex condition resulting from unbalanced energy homeostasis among various organs. However, systemic abnormalities in overweight and obese people are seldom explored in vivo by metabolic imaging techniques. The aim of this study was to determine metabolic abnormities throughout the body in overweight and obese adults using total-body positron emission tomography (PET) glucose uptake imaging. METHODS: Thirty normal weight subjects [body mass index (BMI) < 25 kg/m2, 55.47 ± 13.94 years, 16 men and 14 women], and 26 overweight and obese subjects [BMI ≥ 25 kg/m2, 52.38 ± 9.52 years, 21 men and 5 women] received whole-body 18F-fluorodeoxyglucose PET imaging using the uEXPLORER. Whole-body standardized uptake value normalized by lean body mass (SUL) images were calculated. Metabolic networks were constructed based on the whole-body SUL images using covariance network approach. Both group-level and individual-level network differences between normal weight and overweight/obese subjects were evaluated. Correlation analysis was conducted between network properties and BMI for the overweight/obese subjects. RESULTS: Compared with normal weight subjects, overweight/obese subjects exhibited altered network connectivity strength in four network nodes, namely the pancreas (p = 0.033), spleen (p = 0.021), visceral adipose tissue (VAT) (p = 1.12 × 10-5) and bone (p = 0.021). Network deviations of overweight/obese subjects from the normal weight were positively correlated with BMI (r = 0.718, p = 3.64 × 10-5). In addition, overweight/obese subjects experienced altered connections between organs, and some of the altered connections, including pancreas-right lung and VAT-bilateral lung connections were significantly correlated with BMI. CONCLUSION: Overweight/obese individuals exhibit metabolic alterations in organ level, and altered metabolic interactions at the systemic level. The proposed approach using total-body PET imaging can reveal individual metabolic variability and metabolic deviations between organs, which would open up a new path for understanding metabolic alterations in overweight and obesity.

Improving our understanding of the paradoxical protective effect of obesity on radiographic damage: a large magnetic resonance imaging-study in early arthritis.

OBJECTIVE: Obesity conveys a risk for RA development, while paradoxically, associating with less radiographic progression after RA diagnosis. Using MRI we can study this surprising association in detail from MRI-detected synovitis and osteitis to MRI-detected erosive progression, which precedes radiographic progression. Previous research suggested obesity associates with less osteitis and synovitis. We therefore aimed to (i) validate the previously suggested association between BMI and MRI-detected osteitis/synovitis; (ii) study whether this is specific for ACPA-positive or ACPA-negative RA or also present in other arthritides; (iii) study whether MRI-detected osteitis associates with MRI-detected erosive progression; and (iv) study whether obesity associates with MRI-detected erosive progression. METHODS: We studied 1029 early arthritis patients (454 RA, 575 other arthritides), consecutively included in Leiden Early Arthritis Clinic. At baseline patients underwent hand-and-foot MRI that were RAMRIS-scored, and 149 RA patients underwent follow-up MRIs. We studied associations between baseline BMI and MRI-detected osteitis/synovitis (using linear regression), and erosive progression (using Poisson mixed models). RESULTS: In RA, higher BMI associated with less osteitis at disease onset (ß = 0.94; 95% CI: 0.93, 0.96) but not with synovitis. Higher BMI associated with less osteitis in ACPA-positive RA (ß = 0.95; 95% CI: 0.93, 0.97), ACPA-negative RA (ß = 0.97; 95% CI: 0.95, 0.99) and other arthritides (ß = 0.98; 95% CI: 0.96, 0.99). Over 2 years, overweight and obesity associated with less MRI-detected erosive progression (P = 0.02 and 0.03, respectively). Osteitis also associated with erosive progression over 2 years (P < 0.001). CONCLUSIONS: High BMI relates to less osteitis at disease onset, which is not confined to RA. Within RA, high BMI and less osteitis associated with less MRI-detected erosive progression. This suggests that the protective effect of obesity on radiographic progression is exerted via a path of less osteitis and subsequently fewer MRI-detected erosions.

CT attenuation correction improves quantitative risk prediction by cardiac SPECT in obese patients.

PURPOSE: This study aimed to compare the predictive value of CT attenuation-corrected stress total perfusion deficit (AC-sTPD) and non-corrected stress TPD (NC-sTPD) for major adverse cardiac events (MACE) in obese patients undergoing cadmium zinc telluride (CZT) SPECT myocardial perfusion imaging (MPI). METHODS: The study included 4,585 patients who underwent CZT SPECT/CT MPI for clinical indications (chest pain: 56%, shortness of breath: 13%, other: 32%) at Yale New Haven Hospital (age: 64 ± 12 years, 45% female, body mass index [BMI]: 30.0 ± 6.3 kg/m2, prior coronary artery disease: 18%). The association between AC-sTPD or NC-sTPD and MACE defined as the composite end point of mortality, nonfatal myocardial infarction or late coronary revascularization (> 90 days after SPECT) was evaluated with survival analysis. RESULTS: During a median follow-up of 25 months, 453 patients (10%) experienced MACE. In patients with BMI ≥ 35 kg/m2 (n = 931), those with AC-sTPD ≥ 3% had worse MACE-free survival than those with AC-sTPD < 3% (HR: 2.23, 95% CI: 1.40 - 3.55, p = 0.002) with no difference in MACE-free survival between patients with NC-sTPD ≥ 3% and NC-sTPD < 3% (HR:1.06, 95% CI:0.67 - 1.68, p = 0.78). AC-sTPD had higher AUC than NC-sTPD for the detection of 2-year MACE in patients with BMI ≥ 35 kg/m2 (0.631 versus 0.541, p = 0.01). In the overall cohort AC-sTPD had a higher ROC area under the curve (AUC, 0.641) than NC-sTPD (0.608; P = 0.01) for detection of 2-year MACE. In patients with BMI ≥ 35 kg/m2 AC sTPD provided significant incremental prognostic value beyond NC sTPD (net reclassification index: 0.14 [95% CI: 0.20 - 0.28]). CONCLUSIONS: AC sTPD outperformed NC sTPD in predicting MACE in patients undergoing SPECT MPI with BMI ≥ 35 kg/m2. These findings highlight the superior prognostic value of AC-sTPD in this patient population and underscore the importance of CT attenuation correction.

Myocardial tissue remodeling in early adult obesity and its association with regional adipose tissue distribution and ectopic fat deposits: a prospective study.

OBJECTIVES: To evaluate the left ventricular (LV) myocardial tissue characteristics in early adult obesity and its association with regional adipose tissue and ectopic fat deposition. METHODS: Forty-nine obese adults (mean body mass index: 29.9 ± 2.0 kg/m2) and 44 healthy controls were prospectively studied. LV native and post-contrast T1 values, extracellular volume fraction (ECV), regional adipose tissue (epicardial, visceral, and subcutaneous adipose tissue (EAT, VAT, and SAT)), and ectopic fat deposition (hepatic and pancreatic proton density fat fractions (H-PDFF and P-PDFF)) based on magnetic resonance imaging were compared. The association was assessed by multivariable linear regression. RESULTS: The obese participants showed reduced global ECV compared to the healthy controls (p < 0.05), but there was no significant difference in global native or post-contrast T1 values between the two groups. Additionally, the obese individuals exhibited higher EAT, VAT, SAT, H-PDFF, and P-PDFF than the controls (p < 0.05). ECV was associated with insulin resistance, dyslipidemia, and systolic blood pressure (SBP) (p < 0.05). Multiple linear regression demonstrated that H-PDFF and SAT were independently associated with ECV in entire population (ß = - 0.123 and - 0.012; p < 0.05). CONCLUSIONS: Reduced myocardial ECV in patients with mild-to-moderate obesity and its relationship to SBP may indicate that cardiomyocyte hypertrophy, rather than extracellular matrix expansion, is primarily responsible for myocardial tissue remodeling in early adult obesity. Our findings further imply that H-PDFF and SAT are linked with LV myocardial tissue remodeling in this cohort beyond the growth difference and cardiovascular risk factors. CLINICAL TRIALS REGISTRATION: Effect of lifestyle intervention on metabolism of obese patients based on smart phone software (ChiCTR1900026476). CLINICAL RELEVANCE STATEMENT: Myocardial fibrosis in severe obesity predicts poor prognosis. We showed that cardiomyocyte hypertrophy, not myocardial fibrosis, is the main myocardial tissue characteristic of early obesity. This finding raises the possibility that medical interventions, like weight loss, may prevent cardiac fibrosis. KEY POINTS: • Myocardial tissue characteristics in early adult obesity are unclear. • Myocardial extracellular volume fraction (ECV) can be quantitatively evaluated using T1 mapping based on cardiac magnetic resonance imaging (MRI). • Cardiac MRI-derived ECV may noninvasively evaluate myocardial tissue remodeling in early adult obesity.

Multivariate resting-state functional connectomes predict and characterize obesity phenotypes.

The univariate obesity-brain associations have been extensively explored, while little is known about the multivariate associations between obesity and resting-state functional connectivity. We therefore utilized machine learning and resting-state functional connectivity to develop and validate predictive models of 4 obesity phenotypes (i.e. body fat percentage, body mass index, waist circumference, and waist-height ratio) in 3 large neuroimaging datasets (n = 2,992). Preliminary evidence suggested that the resting-state functional connectomes effectively predicted obesity/weight status defined by each obesity phenotype with good generalizability to longitudinal and independent datasets. However, the differences between resting-state functional connectivity patterns characterizing different obesity phenotypes indicated that the obesity-brain associations varied according to the type of measure of obesity. The shared structure among resting-state functional connectivity patterns revealed reproducible neuroimaging biomarkers of obesity, primarily comprising the connectomes within the visual cortex and between the visual cortex and inferior parietal lobule, visual cortex and orbital gyrus, and amygdala and orbital gyrus, which further suggested that the dysfunctions in the perception, attention and value encoding of visual information (e.g. visual food cues) and abnormalities in the reward circuit may act as crucial neurobiological bases of obesity. The recruitment of multiple obesity phenotypes is indispensable in future studies seeking reproducible obesity-brain associations.

Noradrenergic control of neurobehavior in human binge-eating disorder and obesity (NOBEAD): A smartphone-supported behavioral emotion regulation intervention study protocol integrating molecular brain imaging.

OBJECTIVE: The neurobehavioral underpinnings of binge-eating disorder (BED), co-occurring with obesity (OB), are largely unknown. This research project conceptualizes BED as a disorder with dysfunctional emotion regulation (ER) linked with changes in central noradrenaline (NA) transmission and NA-modulated neuronal networks. METHODS: We expect abnormalities in NA activity in both BED and OB, but most pronounced in BED. We expect these abnormalities to be modifiable through state-of-the-art ER intervention, specifically in BED. To assess the role of NA transmission, we will quantify changes in NA transporter (NAT) availability using the highly NAT-specific [11 C]methylreboxetin (MRB) and positron emission tomography-magnetic resonance imaging (PET-MRI) that allows measuring molecular and neuronal changes before and after an ER intervention. Individual 12-session smartphone-supported acceptance-based behavioral therapy will be conducted to improve ER. Thirty individuals with OB and BED (OB + BED), 30 individuals with OB without BED (OB - BED), and 20 individuals with normal weight will undergo assessments of NAT availability and neuronal network activity under rest and stimulated conditions, clinical interviews, self-report questionnaires on eating behavior, ER, mental and physical health, and quality of life, and neuropsychological tests on executive function. Afterwards, in an experimental randomized-controlled design, individuals with OB + BED and OB - BED will be allocated to smartphone-supported ER intervention versus a waitlist and re-assessed after 10 weeks. DISCUSSION: By obtaining biological and behavioral markers, the proposed study will disentangle the involvement of NAT and the central NA system in the modulation of emotion-supporting neuronal networks that influence eating behavior. Neurobehavioral mechanisms of change during an ER intervention will be determined. TRIAL REGISTRATION: German Clinical Trials Register (DRKS): DRKS00029367. PUBLIC SIGNIFICANCE: This study investigates the central noradrenaline system by using hybrid brain imaging in conjunction with emotion regulation as a putative core biological mechanism in individuals with obesity with or without binge-eating disorder that is targeted by emotion regulation intervention. The results will provide a molecular signature beyond functional imaging biomarkers as a predictive biomarker toward precision medicine for tailoring treatments for individuals with binge-eating disorders and obesity.

Hypothalamic subregion alterations in anorexia nervosa and obesity: Association with appetite-regulating hormone levels.

OBJECTIVE: Anorexia nervosa (AN) and obesity are weight-related disorders with imbalances in energy homeostasis that may be due to hormonal dysregulation. Given the importance of the hypothalamus in hormonal regulation, we aimed to identify morphometric alterations to hypothalamic subregions linked to these conditions and their connection to appetite-regulating hormones. METHODS: Structural magnetic resonance imaging (MRI) was obtained from 78 patients with AN, 27 individuals with obesity and 100 normal-weight healthy controls. Leptin, ghrelin, and insulin blood levels were measured in a subsample of each group. An automated segmentation method was used to segment the hypothalamus and its subregions. Volumes of the hypothalamus and its subregions were compared between groups, and correlational analysis was employed to assess the relationship between morphometric measurements and appetite-regulating hormone levels. RESULTS: While accounting for total brain volume, patients with AN displayed a smaller volume in the inferior-tubular subregion (ITS). Conversely, obesity was associated with a larger volume in the anterior-superior, ITS, posterior subregions (PS), and entire hypothalamus. There were no significant volumetric differences between AN subtypes. Leptin correlated positively with PS volume, whereas ghrelin correlated negatively with the whole hypothalamus volume in the entire cohort. However, appetite-regulating hormone levels did not mediate the effects of body mass index on volumetric measures. CONCLUSION: Our results indicate the importance of regional structural hypothalamic alterations in AN and obesity, extending beyond global changes to brain volume. Furthermore, these alterations may be linked to changes in hormonal appetite regulation. However, given the small sample size in our correlation analysis, further analyses in a larger sample size are warranted. PUBLIC SIGNIFICANCE: Using an automated segmentation method to investigate morphometric alterations of hypothalamic subregions in AN and obesity, this study provides valuable insights into the complex interplay between hypothalamic alterations, hormonal appetite regulation, and body weight, highlighting the need for further research to uncover underlying mechanisms.

Clinical and imaging findings associated with preservation of knee joint health over 8 years in individuals aged 65 and over: data from the OAI.

OBJECTIVE: While risk factors for osteoarthritis (OA) are well known, it is not well understood why certain individuals maintain high mobility and joint health throughout their life while others demonstrate OA at older ages. The purpose of this study was to assess which demographic, clinical and MRI quantitative and semi-quantitative factors are associated with preserving healthy knees in older individuals. METHODS: This study analyzed data from the OA Initiative (OAI) cohort of individuals at the age of 65 years or above. Participants without OA at baseline (BL) (Kellgren-Lawrence (KL) ≤ 1) were followed and classified as incident cases (KL ≥ 2 during follow-up; n = 115) and as non-incident (KL ≤ 1 over 96-month; n = 391). Associations between the predictor-variables sex, age, BMI, race, clinical scoring systems, T2 relaxation times and Whole-Organ Magnetic Resonance Imaging-Score (WORMS) readings at BL and the preservation of healthy knees (KL ≤ 1) during a 96-month follow-up period were assessed using logistic regression models. RESULTS: Obesity and presence of pain showed a significant inverse association with maintaining radiographically normal joints in patients aged 65 and above. T2 relaxation times of the lateral femur and tibia as well as the medial femur were also significantly associated with maintaining radiographically normal knee joints. Additionally, absence of lesions of the lateral meniscus and absence of cartilage lesions in the medial and patellofemoral compartments were significantly associated with maintaining healthy knee joints. CONCLUSION: Overall, this study provides protective clinical parameters as well as quantitative and semi-quantitative MR-imaging parameters associated with maintaining radiographically normal knee joints in an older population over 8 years.

Effect of weight loss on knee joint synovitis over 48 months and mediation by subcutaneous fat around the knee: data from the Osteoarthritis Initiative.

BACKGROUND: Obesity influences the development of osteoarthritis via low-grade inflammation. Progression of local inflammation (= synovitis) increased with weight gain in overweight and obese women compared to stable weight. Synovitis could be associated with subcutaneous fat (SCF) around the knee. Purpose of the study was to investigate the effect of weight loss on synovitis progression and to assess whether SCF around the knee mediates the relationship between weight loss and synovitis progression. METHODS: We included 234 overweight and obese participants (body mass index [BMI] ≥ 25 kg/m2) from the Osteoarthritis Initiative (OAI) with > 10% weight loss (n = 117) or stable overweight (< ± 3% change, n = 117) over 48 months matched for age and sex. In magnetic resonance imaging (MRI) at baseline and 48 months, effusion-synovitis and Hoffa-synovitis using the MRI Osteoarthritis Knee Score (MOAKS) and average joint-adjacent SCF (ajSCF) were assessed. Odds-ratios (ORs) for synovitis progression over 48 months (≥ 1 score increase) were calculated in logistic regression models adjusting for age, sex, baseline BMI, Physical Activity Scale for the Elderly (PASE), and baseline SCF measurements. Mediation of the effect of weight loss on synovitis progression by local SCF change was assessed. RESULTS: Odds for effusion-synovitis progression decreased with weight loss and ajSCF decrease (odds ratio [OR] = 0.61 and 0.56 per standard deviation [SD] change, 95% confidence interval [CI] 0.44, 0.83 and 0.40, 0.79, p = 0.002 and 0.001, respectively), whereas odds for Hoffa-synovitis progression increased with weight loss and ajSCF decrease (OR = 1.47 and 1.48, CI 1.05, 2.04 and 1.02, 2.13, p = 0.024 and 0.038, respectively). AjSCF decrease mediated 39% of the effect of weight loss on effusion-synovitis progression. CONCLUSIONS: Effusion-synovitis progression was slowed by weight loss and decrease in local subcutaneous fat. Hoffa-synovitis characterized by fluid in the infrapatellar fat pad increased at the same time, suggesting a decreasing fat pad rather than active synovitis. Decrease in local subcutaneous fat partially mediated the systemic effect of weight loss on synovitis.

Development of a Non-Contact Sensor System for Converting 2D Images into 3D Body Data: A Deep Learning Approach to Monitor Obesity and Body Shape in Individuals in Their 20s and 30s.

This study demonstrates how to generate a three-dimensional (3D) body model through a small number of images and derive body values similar to the actual values using generated 3D body data. In this study, a 3D body model that can be used for body type diagnosis was developed using two full-body pictures of the front and side taken with a mobile phone. For data training, 400 3D body datasets (male: 200, female: 200) provided by Size Korea were used, and four models, i.e., 3D recurrent reconstruction neural network, point cloud generative adversarial network, skinned multi-person linear model, and pixel-aligned impact function for high-resolution 3D human digitization, were used. The models proposed in this study were analyzed and compared. A total of 10 men and women were analyzed, and their corresponding 3D models were verified by comparing 3D body data derived from 2D image inputs with those obtained using a body scanner. The model was verified through the difference between 3D data derived from the 2D image and those derived using an actual body scanner. Unlike the 3D generation models that could not be used to derive the body values in this study, the proposed model was successfully used to derive various body values, indicating that this model can be implemented to identify various body types and monitor obesity in the future.

Comparative study of abdominal CT enhancement in overweight and obese patients based on different scanning modes combined with different contrast medium concentrations.

PURPOSE: To compare image quality, iodine intake, and radiation dose in overweight and obese patients undergoing abdominal computed tomography (CT) enhancement using different scanning modes and contrast medium. METHODS: Ninety overweight and obese patients (25 kg/m2≤body mass index (BMI)< 30 kg/m2 and BMI≥30 kg/m2) who underwent abdominal CT-enhanced examinations were randomized into three groups (A, B, and C) of 30 each and scanned using gemstone spectral imaging (GSI) +320 mgI/ml, 100 kVp + 370 mgI/ml, and 120 kVp + 370 mgI/ml, respectively. Reconstruct monochromatic energy images of group A at 50-70 keV (5 keV interval). The iodine intake and radiation dose of each group were recorded and calculated. The CT values, contrast-to-noise ratios (CNRs), and subjective scores of each subgroup image in group A versus images in groups B and C were by using one-way analysis of variance or Kruskal-Wallis H test, and the optimal keV of group A was selected. RESULTS: The dual-phase CT values and CNRs of each part in group A were higher than or similar to those in groups B and C at 50-60 keV, and similar to or lower than those in groups B and C at 65 keV and 70 keV. The subjective scores of the dual-phase images in group A were lower than those of groups B and C at 50 keV and 55 keV, whereas no significant difference was seen at 60-70 keV. Compared to groups B and C, the iodine intake in group A decreased by 12.5% and 13.3%, respectively. The effective doses in groups A and B were 24.7% and 25.8% lower than those in group C, respectively. CONCLUSION: GSI +320 mgI/ml for abdominal CT-enhanced in overweight patients satisfies image quality while reducing iodine intake and radiation dose, and the optimal keV was 60 keV.

Functional imaging and neurochemistry identify in vivo neuroprotection mechanisms counteracting excitotoxicity and neurovascular changes in the hippocampus and visual cortex of obese and type 2 diabetic animal models.

Functional MRI (fMRI) with 1 H-MRS was combined on the hippocampus and visual cortex of animal models of obesity (high-fat diet, HFD) and type 2 diabetes (T2D) to identify the involved mechanisms and temporal evolution of neurometabolic changes in these disorders that could serve as potentially reliable clinical biomarkers. HFD rats presented elevated levels of N-acetylaspartylglutamate (NAAG) (p = 0.0365 vs. standard diet, SD) and glutathione (GSH) (p = 0.0494 vs. SD) in the hippocampus. NAAG and GSH levels in this structure proved to be correlated (r = 0.4652, p = 0.0336). This mechanism was not observed in diabetic rats. Combining MRS and fMRI-evaluated blood-oxygen-level-dependent (BOLD) response, elevated taurine (p = 0.0326 vs. HFD) and GABA type A receptor (GABAA R) (p = 0.0211 vs. SD and p = 0.0153 vs. HFD) were observed in the visual cortex of only diabetic rats, counteracting the elevated BOLD response and suggesting an adaptative mechanism against hyperexcitability observed in the primary visual cortex (V1) (p = 0.0226 vs. SD). BOLD amplitude was correlated with the glutamate levels (r = 0.4491; p = 0.0316). Therefore, here we found evidence for several biological dichotomies regarding excitotoxicity and neuroprotection in different brain regions, identifying putative markers of their different susceptibility and response to the metabolic and vascular insults of obesity and diabetes.

Brain dopamine receptor system is not altered in obesity: Bayesian and frequentist meta-analyses.

Feeding induces dopamine release in the striatum, and a dysfunction of the dopaminergic reward system can lead to overeating, and obesity. Studies have reported inconsistent findings of dopamine receptor (DR) positron emission tomography scans in obesity. Here we investigated the association between DR availability and overweight/obesity using Bayesian and frequentist meta-analysis. We performed a systematic search of Embase, Medline, Scopus and Web of Science for studies that compared striatal DR availability between lean subjects and overweight/obese subjects. The standardized mean difference (Hedge's g) of DR availability was calculated after extraction of data from each study. Studies were divided into two groups according to the definition of overweight/obese subjects (body mass index [BMI] cutoff of 25 and 30 kg/m2 ). Both Bayesian and frequentist meta-analysis was done in R Statistical Software version 4.2.2 (The R Foundation for Statistical Computing). Nine studies were eligible for inclusion in this study. Three studies with C11-raclopride, one with C11-PNHO, two with F18-fallypride, one with I123-IBZM, one with C11-NMB and one with both C11-raclopride and C11-PNHO were included. In Bayesian meta-analysis, the standardized mean difference of DR availability between lean and overweight/obese subjects markedly overlapped with zero regardless of BMI cutoff for obesity. In frequentist meta-analysis, the pooled standardized mean difference of DR availability did not show the significant difference between lean and overweight/obese subjects. There was an effect of the radiopharmaceutical on the standardized mean difference of DR availability in meta-analysis of BMI cutoff of 25 kg/m2 . In conclusion, brain DR availability is not different between lean and overweight/obese subjects. However, the effect is dependent on the radiopharmaceutical and the degree of obesity. Further studies with multi-radiopharmaceutical in the same individuals are needed to understand the association between DR and obesity.

Pituitary T1 signal intensity at magnetic resonance imaging is reduced in patients with obesity: results from the CHIASM study.

BACKGROUND: Despite obesity being well known to be associated with several pituitary hormone imbalances, pituitary appearance in magnetic resonance imaging (MRI) in patients with obesity is understudied. OBJECTIVE: To evaluate the pituitary volume and signal intensity at MRI in patients with obesity. METHODS: This is a prospective study performed in an endocrine Italian referral center (ClinicalTrial.gov Identifier: NCT03458533). Sixty-nine patients with obesity (BMI > 30 kg/m2) and twenty-five subjects without obesity were enrolled. Thirty-three patients with obesity were re-evaluated after 3 years of diet and lifestyle changes, of whom 17 (51.5%) achieved a > 5% loss of their initial body weight, whereas the remaining 16 (48.5%) had maintained or gained weight. Evaluations included metabolic and hormone assessments, DEXA scan, and pituitary MRI. Pituitary signal intensity was quantified by measuring the pixel density using ImageJ software. RESULTS: At baseline, no difference in pituitary volume was observed between the obese and non-obese cohorts. At the 3-year follow-up, pituitary volume was significantly reduced (p = 0.011) only in participants with stable-increased body weight. Furthermore, a significant difference was noted in the mean pituitary intensity of T1-weighted plain and contrast-enhanced sequences between the obese and non-obese cohorts at baseline (p = 0.006; p = 0.002), and a significant decrease in signal intensity was observed in the subgroup of participants who had not lost weight (p = 0.012; p = 0.017). Insulin-like growth factor-1 levels, following correction for BMI, were correlated with pituitary volume (p = 0.001) and intensity (p = 0.049), whereas morning cortisol levels were correlated with pituitary intensity (p = 0.007). The T1-weighted pituitary intensity was negatively correlated with truncal fat (p = 0.006) and fibrinogen (p = 0.018). CONCLUSIONS: The CHIASM study describes a quantitative reduction in pituitary intensity in T1-weighted sequences in patients with obesity. These alterations could be explained by changes in the pituitary stromal tissue, correlated with low-grade inflammation.

Fat mitigation strategies to improve image quality of radial 4D flow MRI in obese subjects.

PURPOSE: This study addresses the challenges in obtaining abdominal 4D flow MRI of obese patients. We aimed to evaluate spectral saturation and inner volume excitation as methods to mitigating artifacts originating from adipose signals, with the goal of enhancing image quality and improving quantification. METHODS: Radial 4D flow MRI acquisitions with fat mitigation (inner volume excitation [IVE] and intermittent fat saturation [FS]) were compared to a standard slab selective excitation (SSE) in a test-retest study of 15 obese participants. IVE selectively excited a cylindrical region of interest, avoiding contamination from peripheral adipose tissue, while FS globally suppressed fat based on spectral selection. Acquisitions were evaluated qualitatively based on expert ratings and quantitatively based on conservation of mass, test-retest repeatability, and a divergence free quality metric. Errors were evaluated statistically using the absolute and relative errors, regression, and Bland-Altman analysis. RESULTS: IVE demonstrated superior performance quantitatively in the conservation of mass analysis in the portal vein, with higher correlation and lower bias in regression analysis. IVE also produced flow fields with the lowest divergence error and was rated best in overall image quality, delineating small vessels, and producing the least streaking artifacts. Evaluation results did not differ significantly between FS and SSE. Test-retest reproducibility was similarly high for all sequences, with data suggesting biological variations dominate the technical variability. CONCLUSION: IVE improved hemodynamic assessment of radial 4D flow MRI in the abdomen of obese participants while FS did not lead to significant improvements in image quality or flow metrics.