Evaluating the Homeostasis Assessment Model Insulin Resistance and the Cardiac Autonomic System in Bariatric Surgery Patients: A Meta-Analysis

Non-Hereditary Obesity Type Networks and New Drug Targets: An In Silico Approach

Obesity, a chronic, preventable disease, has significant comorbidities that are associated with a great human and financial cost for society. The aim of the present work is to reconstruct the interactomes of non-hereditary obesity to highlight recent advances of its pathogenesis, and discover potential therapeutic targets. Obesity and biological-clock-related genes and/or gene products were extracted from the biomedical literature databases PubMed, GeneCards and OMIM. Their interactions were investigated using STRING v11.0 (a database of known and predicted physical and indirect associations among genes/proteins), and a high confidence interaction score of >0.7 was set. We also applied virtual screening to discover natural compounds targeting obesity- and circadian-clock-associated proteins. Two updated and comprehensive interactomes, the (a) stress- and (b) inflammation-induced obesidomes involving 85 and 93 gene/gene products of known and/or predicted interactions with an average node degree of 9.41 and 10.8, respectively, were produced. Moreover, 15 of these were common between the two non-hereditary entities, namely, ADIPOQ, ADRB2/3, CCK, CRH, CXCL8, FOS, GCG, GNRH1, IGF1, INS, LEP, MC4R, NPY and POMC, while phelligridin E, a natural product, may function as a potent FOX1-DBD interaction blocker. Molecular networks may contribute to the understanding of the integrated regulation of energy balance/obesity pathogenesis and may associate chronopharmacology schemes with natural products.

Soft Tissue Ewing Sarcoma Cell Drug Resistance Revisited: A Systems Biology Approach

Ewing sarcoma is a rare type of cancer that develops in the bones and soft tissues. Drug therapy represents an extensively used modality for the treatment of sarcomas. However, cancer cells tend to develop resistance to antineoplastic agents, thereby posing a major barrier in treatment effectiveness. Thus, there is a need to uncover the molecular mechanisms underlying chemoresistance in sarcomas and, hence, to enhance the anticancer treatment outcome. In this study, a differential gene expression analysis was conducted on high-throughput transcriptomic data of chemoresistant versus chemoresponsive Ewing sarcoma cells. By applying functional enrichment analysis and protein-protein interactions on the differentially expressed genes and their corresponding products, we uncovered genes with a hub role in drug resistance. Granted that non-coding RNA epigenetic regulators play a pivotal role in chemotherapy by targeting genes associated with drug response, we investigated the non-coding RNA molecules that potentially regulate the expression of the detected chemoresistance genes. Of particular importance, some chemoresistance-relevant genes were associated with the autonomic nervous system, suggesting the involvement of the latter in the drug response. The findings of this study could be taken into consideration in the clinical setting for the accurate assessment of drug response in sarcoma patients and the application of tailored therapeutic strategies.

Penehyclidine hydrochloride for treating postoperative nausea and vomiting after laparoscopic bariatric surgery: a double-blinded randomized controlled trial

BACKGROUND

Postoperative nausea and vomiting (PONV) is a common and distressing complication of laparoscopic bariatric surgery (LBS). Penehyclidine hydrochloride has been reported to be effective in preventing PONV. Considering the potential preventive effects of penehyclidine against PONV, we hypothesized that intravenous infusion of penehyclidine may alleviate PONV within the first 48 h in patients scheduled for LBS.

METHODS

Patients who underwent LBS were randomly assigned (1:2) to receive saline (Control group, n = 113) or a single intravenous dose of penehyclidine 0.5 mg (PHC group, n = 221). The primary outcome was incidence of PONV within the first 48 h postoperatively. Secondary endpoints included severity of PONV, need for rescue antiemetic therapy, volume of water intake, and time to first flatus.

RESULTS

PONV occurred in 159 (48%) patients within the first 48 h postoperatively, including 51% in the Control group and 46% in the PHC group. There was no significant difference in the incidence or severity of PONV between the two groups (P > 0.05). Within the first 24 h and 24-48 h, no significant difference was found in incidence or severity of PONV, postoperative nausea, postoperative vomiting, need for rescue antiemetic therapy, or volume of water intake (P > 0.05). Kaplan-Meier curves showed that penehyclidine was significantly associated with a prolonged time to first flatus (median onset time: 22 h vs. 21 h, P = 0.036).

CONCLUSIONS

Penehyclidine did not decrease incidence and severity of PONV in patients undergoing LBS. However, a single intravenous dose of penehyclidine (0.5 mg) was associated with a slightly prolonged time to first flatus.

TRIAL REGISTRATION

Chinese Clinical Trial Registry (ChiCTR2100052418, http://www.chictr.org.cn/showprojen.aspx?proj=134893 , date of registration: 25/10/2021).

Progressive Additive Benefits of Prehabilitation and Subsequent Bariatric Surgery on Cardiac Autonomic Regulation as Assessed by Means of a Simple Unitary Composite Index: Preliminary Data from an Observational Study

Obesity is associated with an increased risk of several chronic comorbidities, which may also be determined by dysfunctional autonomic nervous system (ANS). The influence of bariatric surgery (BS) on ANS balance was explored in previous studies, but with high heterogeneity in both the assessment timing and methods employed. In the present observational study, we applied a clinical protocol which considers two subsequent phases. Twenty-nine non-diabetic obese subjects were studied at baseline (T0), after one month of lifestyle modification (prehabilitation) (phase 1-T1), and after eight months following BS (phase 2-T2). ANS regulation was assessed across the three study epochs by means of ANSI, a single composite percent-ranked proxy of autonomic balance, being free of gender and age bias, economical and simple to apply in a clinical setting. The aim of the present study was to investigate the effects of the clinical protocol based on prehabilitation and subsequent BS on the ANS regulation by means of ANSI. Potential intertwined correlations with metabolic parameters were also investigated. Notably, we observed a progressive improvement in ANS control, even by employing ANSI. Moreover, the reduction in the markers of sympathetic overactivity was found to significantly correlate with the amelioration in some metabolic parameters (fasting glucose, insulin levels, and waist circumference), as well as in stress and tiredness perception. In conclusion, this study provides convincing evidence that a unitary proxy of cardiac autonomic regulation (CAR) may reflect the progressive improvement in autonomic regulation following behavioral and surgical interventions in obese patients. Intriguingly, this might contribute to reducing cardiovascular and metabolic risk.

Postoperative effects of bariatric surgery on heart rate recovery and heart rate variability

Background: Several studies have reported associations between obesity and autonomic dysfunction. However, little research has investigated the effect of bariatric surgery on heart rate recovery (HRR) in the treadmill test and heart rate variability (HRV) in 24-hour Holter monitoring. We investigated the effects of bariatric surgery on HRR and HRV, which are parameters related to autonomic dysfunction. Methods: We retrospectively investigated patients who underwent bariatric surgery in 2019. The treadmill test, 24-hour Holter monitoring, and echocardiography were performed before and 6 months after surgery. We compared the changes in HRR in the treadmill test and HRV parameters such as the time domain and spectral domain in 24-hour Holter monitoring before and after surgery. Results: Of the 40 patients who underwent bariatric surgery, 25 patients had the treadmill test or 24-hour Holter monitoring both before and after surgery. Body weight and body mass index significantly decreased after surgery (112.86±24.37 kg vs. 89.10±20.26 kg, p<0.001; 39.22±5.69 kg/m2 vs. 31.00±5.09 kg/m2, p<0.001, respectively). HRR significantly increased (n=23; 43.00±20.97 vs. 64.29±18.49, p=0.001). The time domain of HRV parameters increased (n=21; standard deviation of the N-N interval 123.57±28.05 vs. 152.57±39.49, p=0.002 and mean N-N interval 791.57±88.84 vs. 869.05±126.31, p=0.002).Conclusions: Our data showed that HRR after exercise and HRV during 24-hour Holter monitoring improved after weight reduction with bariatric surgery through improved cardiac autonomic function.

Role of the Autonomic Nervous System in Mechanism of Energy and Glucose Regulation Post Bariatric Surgery

Even though lifestyle changes are the mainstay approach to address obesity, Sleeve gastrectomy (SG) and Roux-en-Y gastric bypass (RYGB) are the most effective and durable treatments facing this pandemic and its associated metabolic conditions. The traditional classifications of bariatric surgeries labeled them as “restrictive,” “malabsorptive,” or “mixed” types of procedures depending on the anatomical rearrangement of each one of them. This conventional categorization of bariatric surgeries assumed that the “restrictive” procedures induce their weight loss and metabolic effects by reducing gastric content and therefore having a smaller reservoir. Similarly, the “malabsorptive” procedures were thought to induce their main energy homeostatic effects from fecal calorie loss due to intestinal malabsorption. Observational data from human subjects and several studies from rodent models of bariatric surgery showed that neither of those concepts is completely true, at least in explaining the multiple metabolic changes and the alteration in energy balance that those two surgeries induce. Rather, neuro-hormonal mechanisms have been postulated to underly the physiologic effects of those two most performed bariatric procedures. In this review, we go over the role the autonomic nervous system plays- through its parasympathetic and sympathetic branches- in regulating weight balance and glucose homeostasis after SG and RYGB.

Holter-Derived Autonomic Function, Arrhythmias and Carbohydrate Metabolism in Patients with Class III Obesity Treated with Laparoscopic Sleeve Gastrectomy

The effects of weight loss following bariatric surgery on autonomic balance, arrhythmias and insulin resistance are still of interest. We prospectively investigated 50 patients with BMI > 40 kg/m², aged 36.5 (18–56) years who underwent laparoscopic sleeve gastrectomy. Among other examinations, all subjects had 24-h Holter monitoring with heart rate variability (HRV) and heart rate turbulence (HRT) evaluation. After a median of 15 months, BMI decreased from 43.9 to 29.7 kg/m², the incidence of hypertension decreased from 54 to 32% (p = 0.04) and any carbohydrate disorders decreased from 24 to 6% (p = 0.02). Fasting insulin concentration and insulin resistance index improved significantly (p < 0.001). Improvements in HRV parameters related to the sympathetic autonomic division were also observed (p < 0.001), while HRT evaluation was not conclusive. The enhancement of autonomic tone indices was correlated with reduction of BMI (SDNN-I r = 0.281 p = 0.04; SDNN r = 0.267 p = 0.05), but not with reduction of waist circumference, and it was also associated with decrease of mean heart rate (OR 0.02, 95%CI 0.0–0.1, p < 0.001). The incidence of arrhythmias was low and similar before and after follow-up. In conclusion, improvement of homeostasis of carbohydrate metabolism and autonomic function is observed in relatively young patients after weight loss due to laparoscopic sleeve gastrectomy.

Machine learning based congestive heart failure detection using feature importance ranking of multimodal features

In this study, we ranked the Multimodal Features extracted from Congestive Heart Failure (CHF) and Normal Sinus Rhythm (NSR) subjects. We categorized the ranked features into 1 to 5 categories based on Empirical Receiver Operating Characteristics (EROC) values. Instead of using all multimodal features, we use high ranking features for detection of CHF and normal subjects. We employed powerful machine learning techniques such as Decision Tree (DT), Naïve Bayes (NB), SVM Gaussian, SVM RBF and SVM Polynomial. The performance was measured in terms of Sensitivity, Specificity, Positive Predictive Value (PPV), Negative Predictive Value (NPV), Accuracy, False Positive Rate (FPR), and area under the Receiver Operating characteristic Curve (AUC). The highest detection performance in terms of accuracy and AUC was obtained with all multimodal features using SVM Gaussian with Sensitivity (93.06%), Specificity (81.82%), Accuracy (88.79%) and AUC (0.95). Using the top five ranked features, the highest performance was obtained with SVM Gaussian yields accuracy (84.48%), AUC (0.86); top nine ranked features using Decision Tree and Naïve Bayes got accuracy (84.48%), AUC (0.88); last thirteen ranked features using SVM polynomial obtained accuracy (80.17%), AUC (0.84). The findings indicate that proposed approach with feature ranking can be very useful for automatic detection of congestive heart failure patients and can be very helpful for further decision making by the clinicians and physicians in order to decrease the mortality rate.

Novel Insights into Sensorimotor and Cardiovascular Autonomic Neuropathy from Recent-Onset Diabetes and Population-Based Cohorts

The most prevalent chronic complications of diabetes are diabetic neuropathies, among which distal sensorimotor polyneuropathy (DSPN) and cardiovascular autonomic neuropathy (CAN) are the best studied. Their major clinical sequelae such as foot ulcers, neuropathic pain, and orthostatic hypotension are associated with lower quality of life and increased risk of mortality. Here we discuss the recent insights into DSPN and CAN focusing on two prospective cohorts; that is, the German Diabetes Study (GDS) including recent-onset diabetes patients and the population-based Cooperative Health Research in the Region of Augsburg, Germany (KORA) surveys. The insights from these studies investigating novel tools for early detection and prediction of (pre)diabetic neuropathy as well as biomarkers of oxidative stress and inflammation should ultimately culminate in improving the health care of patients affected by this serious condition.

Detecting epileptic seizure with different feature extracting strategies using robust machine learning classification techniques by applying advance parameter optimization approach

Epilepsy is a neurological disorder produced due to abnormal excitability of neurons in the brain. The research reveals that brain activity is monitored through electroencephalogram (EEG) of patients suffered from seizure to detect the epileptic seizure. The performance of EEG detection based epilepsy require feature extracting strategies. In this research, we have extracted varying features extracting strategies based on time and frequency domain characteristics, nonlinear, wavelet based entropy and few statistical features. A deeper study was undertaken using novel machine learning classifiers by considering multiple factors. The support vector machine kernels are evaluated based on multiclass kernel and box constraint level. Likewise, for K-nearest neighbors (KNN), we computed the different distance metrics, Neighbor weights and Neighbors. Similarly, the decision trees we tuned the paramours based on maximum splits and split criteria and ensemble classifiers are evaluated based on different ensemble methods and learning rate. For training/testing tenfold Cross validation was employed and performance was evaluated in form of TPR, NPR, PPV, accuracy and AUC. In this research, a deeper analysis approach was performed using diverse features extracting strategies using robust machine learning classifiers with more advanced optimal options. Support Vector Machine linear kernel and KNN with City block distance metric give the overall highest accuracy of 99.5% which was higher than using the default parameters for these classifiers. Moreover, highest separation (AUC = 0.9991, 0.9990) were obtained at different kernel scales using SVM. Additionally, the K-nearest neighbors with inverse squared distance weight give higher performance at different Neighbors. Moreover, to distinguish the postictal heart rate oscillations from epileptic ictal subjects, and highest performance of 100% was obtained using different machine learning classifiers.