BackgroundObesity is a major health concern linked to chronic conditions such as diabetes and cardiovascular disease. However, most neurological studies have focused on specific metabolic states, limiting understanding of how brain function changes from fasting to satiety. Furthermore, hypothesis-driven approaches may introduce bias and fail to capture complex neural interactions. This study aimed to identify brain connectivity patterns associated with obesity across different metabolic states using a data-driven approach.MethodsElectroencephalography data were collected from 30 women with obesity and 30 women without obesity over a four-hour period encompassing fasting and post-meal states. All subjects were aged 20 to 65 years. Functional connectivity was calculated from source-localized signals, and a machine learning framework incorporating a feature selection method was applied to identify the most discriminative connectivity features between groups.ResultsHere we show that six connectivity features classify obesity with 95% accuracy across metabolic states. Reduced connectivity are observed within food-reward processing regions in the obese group, with the dorsal anterior cingulate cortex emerging as a central hub. This pattern reflects a persistent alteration in energy prediction and craving regulation that is independent of metabolic state.ConclusionsThese findings demonstrate that disrupted brain connectivity is a fundamental characteristic of obesity. The results highlight the dorsal anterior cingulate cortex as a key region underlying maladaptive reward processing and suggest that targeting this area through neuromodulation therapies may offer a promising intervention for obesity treatment.

Obesity is a complex metabolic disease characterized by systemic metabolic and inflammatory dysregulation, yet the molecular signatures underlying these processes remain incompletely understood. Circulating microRNAs (miRNAs) have emerged as promising biomarkers capable of capturing systemic regulatory changes associated with obesity. In this study, we investigated whether machine learning (ML) could identify obesity-discriminative circulating miRNA signatures and assess their persistence following weight loss. Circulating miRNA profiles from lean individuals and individuals with obesity before and after a weight-loss intervention were analysed using ML-based classification frameworks combined with feature selection and multiple classifier models. Comparative analyses of miRNA signatures were further integrated with target gene interaction networks and pathway enrichment analyses to explore the biological processes associated with obesity and weight-loss responses. The ML models identified a small set of circulating miRNAs capable of distinguishing individuals with obesity from lean individuals both before and after weight loss. Comparative analyses revealed that some miRNAs showed partial normalization after weight reduction, whereas others remained persistently dysregulated. Network and pathway analyses suggested that persistent miRNA signals are linked to regulatory processes involved in immune-metabolic interactions and systemic metabolic control. These findings indicate that circulating miRNAs capture both reversible and persistent molecular components of obesity and may serve as informative biomarkers of obesity-related dysregulation. Overall, this work demonstrates the utility of ML for uncovering biologically meaningful miRNA signatures and provides new insight into the molecular complexity of obesity and its response to weight-loss interventions. KEY POINTS: Machine learning (ML) identified a minimal circulating microRNA (miRNA) signature that robustly discriminates obesity (baseline and following weight loss) from lean status, with performance comparable to transcriptomic models. Several miRNAs remained persistently dysregulated after weight loss, suggesting core obesity-related pathways and potential predisposition to weight regain. Other miRNAs normalized following weight loss, indicating reversible, metabolically responsive mechanisms (e.g. glucose regulation).

Obesity presents a critical public health challenge and is commonly associated with a marked decline in quality of life. Existing research exploring the neurological correlates of obesity through electroencephalography (EEG) has predominantly employed traditional statistical approaches, which rely on prior assumptions about brain networks and are limited in their ability to capture complex interactions between neural features. In this study, we conducted a machine learning (ML) analysis using a novel incremental wrapper-based feature selection method (DLI-WFS) to identify neural signatures of obesity in female individuals through functional connectivity-based resting-state EEG classification. Our proposed model demonstrated its efficiency by outperforming other benchmark models in the classification task with only a minimal number of features selected. Neurologically, our results indicate that obesity is associated with a disrupted network, where regions involved in processing self-referential and contextual environmental information exhibit functional impairments. By exercising targeted intervention in the relevant brain connections, it is possible to enhance neurological behaviours associated with obesity.

Obesity is a common issue in modern societies today that can lead to various diseases and significantly reduced quality of life. Currently, research has been conducted to investigate resting state EEG (electroencephalogram) signals with an aim to identify possible neurological characteristics associated with obesity. In this study, we propose a deep learning-based framework to extract the resting state EEG features for obese and lean subject classification. Specifically, a novel variational autoencoder framework is employed to extract subject-invariant features from the raw EEG signals, which are then classified by a 1-D convolutional neural network. Comparing with conventional machine learning and deep learning methods, we demonstrate the superiority of using VAE for feature extraction, as reflected by the significantly improved classification accuracies, better visualizations and reduced impurity measures in the feature representations. Future work can be directed to gaining an in-depth understanding regarding the spatial patterns that have been learned by the proposed model from a neurological view, as well as improving the interpretability of the proposed model by allowing it to uncover any temporal-related information.

EEG classification is a challenging task due to the nonstationary nature of EEG data and the covariance shift induced by cross-subject variance. Recently, various machine learning and deep learning models have been developed to learn robust features for inter-subject EEG classification tasks. However, current existing models are designed based on active task-related EEG, with a lack of investigation into learning robust feature representation from resting-state EEG data. Given the differences in the nature of brain activities captured by resting-state and active task-related EEG, existing models might not be applicable to resting-state EEG. This study proposed an unsupervised hybrid deep feature encoder to learn robust feature representation in resting-state EEG data. It involves using a Variational Autoencoder (VAE) to learn latent feature representation, followed by a further feature selection conducted through a non-task-related sample-level proximity classification using K-means clustering. We demonstrate the efficiency of our proposed model through significantly improved classification accuracies compared to benchmark models, as well as the high between-subject separability manifested by the learned feature representation.

Objective:The objective of this study is to report results from the open-label extension (OLE) of the OPTIMAL trial of oral octreotide capsules (OOC) in adults with acromegaly, evaluating the long-term durability of therapeutic response.Design:The study design is an OLE of a double-blind placebo-controlled (DPC) trial.Methods:Patients completing the 36-week DPC period on the study drug (OOC or placebo) or meeting predefined withdrawal criteria were eligible for OLE enrollment at 60 mg/day OOC dose, with the option to titrate to 40 or 80 mg/day. The OLE is ongoing; week 48 results are reported.Results:Forty patients were enrolled in the OLE, 20 each having received OOC or placebo, with 14 and 5 patients completing the DPC period as responders, respectively. Ninety percent of patients completing the DPC period on OOC and 70% of those completing on placebo completed 48 weeks of the OLE. Maintenance of response in the OLE (i.e. insulin-like growth factor I (IGF1) ≤ 1.0 × upper limit of normal (ULN)) was achieved by 92.6% of patients who responded to OOC during the DPC period. Mean IGF1 levels were maintained between the end of the DPC period (0.91 × ULN; 95% CI: 0.784, 1.045) and week 48 of the OLE (0.90 × ULN; 95% CI: 0.750, 1.044) for those completing the DPC period on OOC. OOC safety was consistent with previous findings, with no increased adverse events (AEs) associated with the higher dose and improved gastrointestinal tolerability observed over time.Conclusions:Patients with acromegaly maintained long-term biochemical response while receiving OOC, with no new AEs observed with prolonged OOC exposure.

Obesity is a serious issue in the modern society and is often associated to significantly reduced quality of life. Current research conducted to explore obesity-related neurological evidences using electroencephalography (EEG) data are limited to traditional approaches. In this study, we developed a novel machine learning model to identify brain networks of obese females using alpha band functional connectivity features derived from EEG data. An overall classification accuracy of 0.937 is achieved. Our finding suggests that the obese brain is characterized by a dysfunctional network in which the areas that responsible for processing self-referential information and environmental context information are impaired.

Background Although excess visceral fat (VAT) is associated with numerous cardio-metabolic risk factors, measurement of this fat depot has historically been difficult. Recent dual X-ray absorptiometry approaches have provided an accessible estimate of VAT that has shown acceptable validity against gold standard methods. The aims of this study were to (i) evaluate DXA measured VAT as a predictor of elevated blood lipids and blood pressure and (ii) calculate thresholds associated with these cardio-metabolic risk factors. Subjects/methods The sample comprised 1482 adults (56.4% women) aged 18–66 years. Total body scans were performed using a GE Lunar Prodigy, and VAT analyses were enabled through Corescan software (v 16.0). Blood pressure and blood lipids were measured by standard procedures. Regression models assessed how VAT mass was associated with each cardio-metabolic risk factor compared to other body composition measures. Measures of sensitivity and specificity were used to determine age- and sex-specific cut points for VAT mass associated with high cardio-metabolic risk. Results Similar to waist circumference, VAT mass was a strong predictor of cardio-metabolic risk especially in men over age 40. Four cut-offs for VAT mass were proposed, above which the cardio-metabolic risk increased: 700 g in women <40 yrs; 800 g in women 40+ yrs; 1000 g in men <40 yrs; and 1200 g in men 40+ yrs. In general, these cut-offs discriminated well between those with high and low cardio-metabolic risk. Conclusions In both sexes, DXA measured VAT was associated with traditional cardio-metabolic risk factors, particularly high blood pressure in those 40+ yrs and low HDL < 40 yrs. These reference values provide a simple, accessible method to assess cardio-metabolic risk in adults.

The Repeatable Battery for the Assessment of Neuropsychological Status (RBANS) is a quick assessment of cognitive function with four equivalent forms, validated in the United States. This permits assessment of cognitive decline or improvement. Equivalent forms reduce some repeat testing effects in longitudinal assessments. An important incidental finding in a New Zealand controlled trial utilising the RBANS as a primary outcome measure, was that form A and form B were different in immediate memory scores. The controlled trial was negative for changes in all RBANS items. Although validating the RBANS in our cohort was not the purpose of this study, the difference found between form A and B was significant. The RBANS form A 'story memory' item contains a phrase that is unusual in New Zealand speech, and could explain the observed discrepancy between the forms. Although the forms have been validated previously, different English language regions should check for any phrasing that is unusual if not previously validated in the local population.

Obesity is a risk factor for coronavirus disease 2019 (COVID-19) infection, with studies demonstrating the prevalence of individuals with obesity admitted with COVID-19 ranging between 30 and 60%. We determined whether early changes in microRNAs (miRNAs) are associated with dysregulation of angiotensin-converting enzyme 2 (ACE2), the specific functional receptor for severe acute respiratory syndrome coronavirus 2. ACE2 is a membrane-bound enzyme that catalyzes the conversion of angiotensin II to angiotensin 1-7 the latter having cardioprotective and vasorelaxation effects. Quantitative real-time PCR analysis of plasma samples for circulating miRNAs showed upregulation of miR-200c and miR-let-7b in otherwise healthy individuals with obesity. This was associated with significant downregulation of ACE2, a direct target for both miRNAs, in individuals with obesity. Correlation analysis confirmed a significant negative correlation between ACE2 and both the miRNAs. Studies showed that despite being the functional receptor, inhibition/downregulation of ACE2 did not reduce the severity of COVID-19 infection. In contrast, increased angiotensin II following inhibition of ACE2 may increase the severity of the disease. Taken together, our novel results identify that upregulation of miR-200c may increase the susceptibility of individuals with obesity to COVID-19. Considering miRNA are the earliest molecular regulators, the level of circulating miR-200c could be a potential biomarker in the early identification of those at the risk of severe COVID-19.

Obesity is a risk factor for coronavirus disease 2019 (COVID-19) infection, the prevalence of obese individuals admitted with COVID-19 ranging between 30 and 60%. Herein we determined whether early changes in microRNAs (miRNAs) could be the underlying molecular mechanism increasing the risk of obese individuals to COVID-19 infection. Quantitative real-time PCR analysis of plasma samples for circulating miRNAs showed a significant upregulation of miR-200c and a small increase in miR-let-7b obese individuals. This was associated with significant downregulation of angiotensin-converting enzyme 2 (ACE2). Both the miRNAs are the direct targets of ACE2, the specific functional receptor for severe acute respiratory syndrome coronavirus 2. Correlation analysis confirmed a significant negative correlation between ACE2 and both the miRNAs. Recent studies showed that despite being the functional receptor, inhibition/downregulation of ACE2 did not reduce the severity of COVID-19 infection. In contrast, increased angiotensin II following inhibition of ACE2 may increase the severity of the disease. Taken together, our novel results identify that upregulation of miR-200c may increase the susceptibility of obese individuals to COVID-19. Considering miRNA are the earliest molecular regulators, circulating miR-200c could be a potential biomarker in the early identification of those at the risk of severe COVID-19.

Abnormal neural activity, particularly in the rostrodorsal anterior cingulate cortex (rdACC), appears to be responsible for intense alcohol craving. Neuromodulation of the rdACC using cortical implants may be an option for individuals with treatment-resistant alcohol dependence. This study assessed the effectiveness and feasibility of suppressing alcohol craving using cortical implants of the rdACC using a controlled one-group pre- and post-test study design. Eight intractable alcohol-dependent participants (four males and four females) were implanted with two Lamitrode 44 electrodes over the rdACC bilaterally connected to an internal pulse generator (IPG). The primary endpoint, self-reported alcohol craving reduced by 60.7% ( p = 0.004) post- compared to pre-stimulation. Adverse events occurred in four out of the eight participants. Electrophysiology findings showed that among responders, there was a post-stimulation decrease ( p = 0.026) in current density at the rdACC for beta 1 band (13–18 Hz). Results suggest that rdACC stimulation using implanted electrodes may potentially be a feasible method for supressing alcohol craving in individuals with severe alcohol use disorder. However, to further establish safety and efficacy, larger controlled clinical trials are needed.

Type 2 diabetes has a strong association with the development of cardiovascular disease, which is grouped as diabetic heart disease (DHD). DHD is associated with the progressive loss of cardiovascular cells through the alteration of molecular signalling pathways associated with cell death. In this study, we sought to determine whether diabetes induces dysregulation of miR-532 and if this is associated with accentuated apoptosis. RT-PCR analysis showed a significant increase in miR-532 expression in the right atrial appendage tissue of type 2 diabetic patients undergoing coronary artery bypass graft surgery. This was associated with marked downregulation of its anti-apoptotic target protein apoptosis repressor with caspase recruitment domain (ARC) and increased TUNEL positive cardiomyocytes. Further analysis showed a positive correlation between apoptosis and miR-532 levels. Time-course experiments in a mouse model of type 2 diabetes showed that diabetes-induced activation of miR-532 occurs in the later stage of the disease. Importantly, the upregulation of miR-532 preceded the activation of pro-apoptotic caspase-3/7 activity. Finally, inhibition of miR-532 activity in high glucose cultured human cardiomyocytes prevented the downregulation of ARC and attenuated apoptotic cell death. Diabetes induced activation of miR-532 plays a critical role in accelerating cardiomyocytes apoptosis. Therefore, miR-532 may serve as a promising therapeutic agent to overcome the diabetes-induced loss of cardiomyocytes.

In this paper, we propose novel second-order cone programming formulations for binary classification, by extending the Minimax Probability Machine (MPM) approach. Inspired by Support Vector Machines, a regularization term is included in the MPM and Minimum Error Minimax Probability Machine (MEMPM) methods. This inclusion reduces the risk of obtaining ill-posed estimators, stabilizing the problem, and, therefore, improving the generalization performance. Our approaches are first derived as linear methods, and subsequently extended as kernel-based strategies for nonlinear classification. Experiments on well-known binary classification datasets demonstrate the virtues of the regularized formulations in terms of predictive performance.

The posterior cingulate cortex (PCC) is involved in food craving in obese food addicted individuals. This randomised, double-blind, placebo-controlled parallel study explored the potential therapeutic effects of infraslow neurofeedback (ISF-NF) on food craving targeting the PCC in obese women with symptoms of food addiction. Participants received six sessions of either ISF-NF (n = 11) or placebo (n = 10) over a three-week period. There were no reported adverse effects. Electrophysiologically, there were significant increases in infraslow activity (p = 0.0002) and infraslow/beta nesting (p < 0.001) in the PCC in the ISF-NF group (mean r = 0.004 ± 0.002) compared to placebo (mean r = 0.02 ± 0.002) two days after the last intervention. Also, there was a significant decrease in different dimensions of state food craving compared to baseline and to placebo. Findings suggest that source localized IFS-NF results in electrophysiological changes and may be associated with reduced food craving. This trial is registered at www.anzctr.org.au , identifier, ACTRN12617000601336. This study was funded by the Otago Medical Research Grant: CT375.

CONTEXT:Obesity is a global epidemic and an independent risk factor for several diseases. miRNAs are gaining interest as early molecular regulators of various pathological processes.OBJECTIVE:To examine the miRNA signatures in women who are obese and determine the response of miRNAs to acute weight loss.METHODS:Plasma samples were collected from women who are obese (n = 80) before and after acute weight loss (mean, 7.2%). Plasma samples from age-matched lean volunteers (n = 80) were used as controls. Total RNA was extracted from the plasma samples and subjected to NanoString analysis of 822 miRNAs. The expression level of candidate miRNAs was validated in all participants using quantitative real-time PCR analysis.RESULTS:NanoString analysis identified substantial dysregulation of 21 miRNAs in women who are obese that were associated with impaired glucose tolerance, senescence, cardiac hypertrophy, angiogenesis, inflammation, and cell death. Acute weight loss reversed the expression pattern of 18 of these miRNAs toward those seen in the lean control group. Furthermore, real-time PCR validation of all the samples for 13 miRNAs with at least twofold upregulation or downregulation confirmed substantial dysregulation of all the chosen miRNAs in women who are obese at baseline. After acute weight loss, the levels of seven miRNAs in women who are obese and who are lean were comparable, with no statistically significant evidence for differences between the two groups.CONCLUSIONS:Our study has provided evidence that the circulating miRNAs associated with various disorders are dysregulated in women who are obese. We also found that seven of these miRNAs showed levels comparable to those in lean controls after acute weight loss in women who are obese.

Iodine deficiency affects 30% of populations worldwide. The amount of thyroglobulin (Tg) in blood increases in iodine deficiency and also in iodine excess. Tg is considered as a sensitive index of iodine status in groups of children and adults, but its usefulness for individuals is unknown. The aim of this study was to determine the diagnostic performance of Tg as an index of iodine status in individual adults.

Background Weight regain is a major limitation to successful weight maintenance following weight loss. Observational studies suggest that stimulation of dopamine receptors in the central nervous system is associated with weight loss and inhibition of weight gain. Our objective was to test the hypothesis that dopamine agonist treatment would prevent weight regain following acute weight loss in individuals with obesity. Methods We conducted a 2-year double blind randomised controlled trial comparing the effect of a dopamine agonist, cabergoline, with placebo on weight regain in obese individuals who had lost at least 5% of their body weight using an 800 kcal/day commercial meal replacement programme. The primary outcome measure was the difference in mean weight between the treatment and control groups over the 2-year period following randomisation. Results At 24 months, there was no difference in body weight between cabergoline and placebo treatment after adjustment for age, gender and baseline values (0.6 kg (95% CI: −1.5, 2.6), p = 0.58). The mean (±SD) baseline body weight of the randomised participants was 101.8 kg, the mean (±SD) weight loss with the 800 kcal/day diet was 7.1 ± 1.8 kg and the mean (±SD) weight regain at 24 months was 5.1 ± 7.5 kg. There were no significant differences in BMI, percent weight loss, waist circumference, resting energy expenditure, blood pressure or metabolic parameters at 24 months between the two groups. Conclusions Treatment with the dopamine agonist cabergoline does not prevent weight regain in obese individuals following weight loss.

Dysfunctional neural activity in the cortical reward system network has been implicated in food addiction. This is the first study exploring the potential therapeutic effects of high definition transcranial pink noise stimulation (HD-tPNS) targeted at the anterior cingulate cortex (ACC) on craving and brain activity in women with obesity who showed features of food addiction (Yale Food Addiction Scale score of >= 3). Sixteen eligible females participated in a randomized, double-blind, parallel group study. Participants received six 20-minute sessions of either 1 mA (n = 8) or sham (n = 8) stimulation with HDtPNS over two weeks. Anode was placed above the ACC (Fz) with 4 cathodes (F7, T3, F8, and T4). Food craving was assessed using the Food Cravings Questionnaire State (FCQ-S) and brain activity was measured using electroencephalogram (EEG). Assessments were at baseline, and two days, four weeks, and six weeks after stimulation. A 22% decrease (mean decrease of 1.11, 95% CI-2.09, 0.14) was observed on the 5-point 'intense desire to eat' subscale two days after stimulation in the HD-tPNS group compared to sham. Furthermore, whole brain analysis showed a significant decrease in beta 1 activity in the ACC in the stimulation group compared to sham (threshold 0.38, p = 0.04). These preliminary findings suggest HD-tPNS of the ACC transiently inhibits the desire to eat and, thus, warrants further examination as a potential tool in combating food craving. (C) 2017 Elsevier Ltd. All rights reserved.

BACKGROUND:Obsessive-compulsive disorder (OCD) is a brain disorder with a lifetime prevalence of 2.3%, causing severe functional impairment as a result of anxiety and distress, persistent and repetitive, unwanted, intrusive thoughts (obsessions), and repetitive ritualized behavior (compulsions). Approximately 40%-60% of patients with OCD fail to satisfactorily respond to standard treatments. Intractable OCD has been treated by anterior capsulotomy and cingulotomy, but more recently, neurostimulation approaches have become more popular because of their reversibility.OBJECTIVE:Implants for OCD are commonly being used, targeting the anterior limb of the internal capsula or the nucleus accumbens, but an implant on the anterior cingulate cortex has never been reported.METHODS:We describe a patient who was primarily treated for alcohol addiction, first with transcranial magnetic stimulation, then by implantation of 2 electrodes overlying the rostrodorsal part of the anterior cingulate cortex bilaterally.RESULTS:Her alcohol addiction developed as she was relief drinking to self-treat her OCD, anxiety, and depression. After the surgical implant, she underwent placebo stimulation followed by real stimulation of the dorsal anterior cingulate cortex, which dramatically improved her OCD symptoms (decrease of 65.5% on the Yale-Brown Obsessive Compulsive Drinking Scale) as well as her alcohol craving (decrease of 87.5%) after 36 weeks of treatment. Although there were improvements in all the scores, there was only a modest reduction in the patient's weekly alcohol consumption (from 50 units to 32 units).CONCLUSIONS:Based on these preliminary positive results we propose to further study the possible beneficial effect of anterior cingulate cortex stimulation for intractable OCD.