Low-frequency electroacupuncture improves disordered hepatic energy metabolism in insulin-resistant Zucker diabetic fatty rats via the AMPK/mTORC1/p70S6K signaling pathway

Background and aim: Disordered hepatic energy metabolism is found in obese rats with insulin resistance (IR). There are insufficient experimental studies of electroacupuncture (EA) for IR and type 2 diabetes mellitus (T2DM). The aim of this study was to probe the effect of EA on disordered hepatic energy metabolism and the adenosine monophosphate (AMP)-activated protein kinase (AMPK)/mammalian target of rapamycin complex 1 (mTORC1)/ribosomal protein S6 kinase, 70-kDa (p70S6K) signaling pathway. Methods: Zucker Diabetic Fatty (ZDF) rats were randomly divided into three groups: EA group receiving EA treatment; Pi group receiving pioglitazone gavage; and ZF group remaining untreated (n = 8 per group). Inbred non-insulin-resistant Zucker lean rats formed an (untreated) healthy control group (ZL, n = 8). Fasting plasma glucose (FPG), fasting insulin (FINS), C-peptide, C-reactive protein (CRP) and homeostatic model assessment of insulin resistance (HOMA-IR) indices were measured. Hematoxylin–eosin (H&E) staining was used to investigate the liver morphologically. The mitochondrial structure of hepatocytes was observed by transmission electron microscopy (TEM). Western blotting was adopted to determine protein expression of insulin receptor substrate 1 (IRS-1), mTOR, mTORC1, AMPK, tuberous sclerosis 2 (TSC2) and p70S6K, and their phosphorylation. RT-PCR was used to quantify IRS-1, mTOR, mTORC1, AMPK and p70S6K mRNA levels. Results: Compared with the ZF group, FPG, FINS, C-peptide, CRP and HOMA-IR levels were significantly reduced in the EA group ( p < 0.05, p < 0.01). Evaluation of histopathology showed improvement in liver appearances following EA. Phosphorylation levels of AMPK, mTOR and TSC2 decreased, and IRS-1 and p70S6K increased, in hepatocytes of the ZF group, while these negative effects appeared to be alleviated by EA. Conclusions: EA can effectively ameliorate IR and regulate energy metabolism in the ZDF rat model. AMPK/mTORC1/p70S6K and related molecules may represent a potential mechanism of action underlying these effects.

Effects of Sambiloto (Andrographis paniculata) on GLP-1 and DPP-4 Concentrations between Normal and Prediabetic Subjects: A Crossover Study

Background. The extract of Andrographis paniculata (Burm. F.) Wall. Ex. Nees. (sambiloto) (穿心蓮 chuān xīn lián) has been reported to have an antidiabetic effect on mice models and has been used traditionally in the community. The exact mechanism of sambiloto extract in decreasing plasma glucose is unclear, so we investigated the role of sambiloto extract in the incretin pathway in healthy and prediabetic subjects. Methods. This study was a randomized, placebo-controlled, crossover, double-blind trial. It included 38 people who were healthy and 35 people who had prediabetes. All subjects were randomly assigned to receive either the intervention sambiloto extract or a placebo. All subjects were randomly assigned to receive the first intervention for 14 days. There was a washout period between subsequent interventions. The primary outcome was glucagon-like peptide 1 (GLP-1) concentration, and secondary outcomes were fasting insulin, 2-hour postprandial insulin, homeostasis model assessment of insulin resistance (HOMA-IR), fasting blood glucose, 2-hour postprandial blood glucose, dipeptidyl peptidase-4 (DPP-4), and glycated albumin before and after the intervention. Result. After the intervention, GLP-1 concentration significantly increased in prediabetes by 19.6% compared to the placebo ( p = 0.043 ). There were no significant differences in the changes of fasting insulin, 2-hour postprandial insulin, HOMA-IR, fasting blood glucose, 2-hour postprandial blood glucose, DPP-4, and glycated albumin levels after the intervention. Sambiloto extract did not inhibit the DPP-4 enzyme in healthy and prediabetic subjects. Conclusion. Sambiloto extract increased GLP-1 concentration without inhibiting the DPP-4 enzyme in prediabetic subjects. This trial is registered with ClinicalTrials.gov (ID: NCT03455049), registered on 6 March 2018—retrospectively registered (https://clinicaltrials.gov/ct2/show/NCT03455049).

Isolated systolic hypertension and insulin resistance assessment tools in young and middle-aged Chinese men with normal fasting glucose: a cross-sectional study

The vital role of insulin resistance (IR) in the pathogenesis of isolated systolic hypertension (ISH) has been expounded at the theoretical level. However, research on the correlation between some specific IR indicators and ISH is still rare, especially at different glycemic statuses. We conducted this study to explore the association between three IR indicators and ISH among young and middle-aged adults with normal fasting plasma glucose (NFG). This large cross-sectional study included 8246 young and middle-aged men with NFG and diastolic blood pressure < 90 mmHg. The homeostasis model assessment for IR (HOMA-IR) index, triglyceride glucose (TyG) index, and the metabolic score for IR (METS-IR) were calculated with the corresponding formula. The proportions of ISH among young and middle-aged men were 6.7% and 4.4%, respectively. After fully adjusting, only HOMA-IR rather than TyG and METS-IR was significantly associated with ISH. Moreover, fully adjusted smooth curve fitting showed that the association between HOMA-IR and ISH were approximately linear in both two age groups (P for non-linearity were 0.047 and 0.430 in young and middle-aged men, respectively). Among young and middle-aged men with NFG, using HOMA-IR instead of noninsulin-dependent IR indicators may have advantages in the hierarchical management of ISH. Further longitudinal research may be needed to determine their potential causal relationship.

Comparison of insulin sensitivity between healthy neonatal foals and horses using minimal model analysis

The equine neonate is considered to have impaired glucose tolerance due to delayed maturation of the pancreatic endocrine system. Few studies have investigated insulin sensitivity in newborn foals using dynamic testing methods. The objective of this study was to assess insulin sensitivity by comparing the insulin-modified frequently sampled intravenous glucose tolerance test (I-FSIGTT) between neonatal foals and adult horses. This study was performed on healthy neonatal foals (n = 12), 24 to 60 hours of age, and horses (n = 8), 3 to 14 years of age using dextrose (300 mg/kg IV) and insulin (0.02 IU/kg IV). Insulin sensitivity (SI), acute insulin response to glucose (AIRg), glucose effectiveness (Sg), and disposition index (DI) were calculated using minimal model analysis. Proxy measurements were calculated using fasting insulin and glucose concentrations. Nonparametric statistical methods were used for analysis and reported as median and interquartile range (IQR). SI was significantly higher in foals (18.3 L·min-1· μIU-1 [13.4–28.4]) compared to horses (0.9 L·min-1· μIU-1 [0.5–1.1]); (p < 0.0001). DI was higher in foals (12 × 103 [8 × 103−14 × 103]) compared to horses (4 × 102 [2 × 102−7 × 102]); (p < 0.0001). AIRg and Sg were not different between foals and horses. The modified insulin to glucose ratio (MIRG) was lower in foals (1.72 μIUinsulin2/10·L·mgglucose [1.43–2.68]) compared to horses (3.91 μIU insulin2/10·L·mgglucose [2.57–7.89]); (p = 0.009). The homeostasis model assessment of beta cell function (HOMA-BC%) was higher in horses (78.4% [43–116]) compared to foals (23.2% [17.8–42.2]); (p = 0.0096). Our results suggest that healthy neonatal foals are insulin sensitive in the first days of life, which contradicts current literature regarding the equine neonate. Newborn foals may be more insulin sensitive immediately after birth as an evolutionary adaptation to conserve energy during the transition to extrauterine life.

Increased occurrence of high impact compound events under climate change

While compound weather and climate events (CEs) can lead to significant socioeconomic consequences, their response to climate change is mostly unexplored. We report the first multi-model assessment of future changes in return periods for the co-occurrence of heatwaves and drought, and extreme winds and precipitation based on the Coupled Model Intercomparison Project (CMIP6) and three emission scenarios. Extreme winds and precipitation CEs occur more frequently in many regions, particularly under higher emissions. Heatwaves and drought occur more frequently everywhere under all emission scenarios examined. For each CMIP6 model, we derive a skill score for simulating CEs. Models with higher skill in simulating historical CEs project smaller increases in the number of heatwaves and drought in Eurasia, but larger numbers of strong winds and heavy precipitation CEs everywhere for all emission scenarios. This result is partly masked if the whole CMIP6 ensemble is used, pointing to the considerable value in further improvements in climate models.

An Empirical Study on the Segmentation of Potential Users of Shared Parking Spaces considering Individual Heterogeneity

Shared parking has become the most effective way to utilize existing parking resources. Little attention has been focused on drivers’ intention to use shared parking spaces in residential areas considering individual heterogeneity. To fill this gap, this paper explores the influencing factors and mechanism of shared parking use intention (SPUI) and further studies the preferences for the shared parking of different types of drivers. Firstly, based on the extended unified theory of acceptance and use of technology that includes psychological factors, personal attributes, and travel characteristics, the multiple indicator multiple cause (MIMIC) model was employed for parameter estimation and model assessment. Secondly, using MIMIC’s output results as input variables, the segmentation method of the latent class model (LCM) was adopted to explore drivers’ preferences regarding SPUI. Finally, a quantitative study was carried out through questionnaire data. The empirical results show that: (a) the extended unified theory of acceptance and use of technology has good explanatory power for SPUI. SPUI is directly affected by perceived risk (PR), behavioral habit (BH), social influence (SI), facilitating conditions (FCs), and effort expectancy (EE), while performance expectancy (PE) have no significant effect on SPUI. In addition, some factors of personal attributes and travel characteristics affect SPUI through psychological factors. (b) According to individual heterogeneity, the surveyed driver groups are divided into four segments: sensitive type (36%), conservative type (29.6%), neutral type (24.5%), and approved type (9.9%), respectively. There are significant differences in psychological observation variables such as EE, PE, FC, and SI among the four segments of drivers. According to the influence mechanism of psychological factors and preferences analysis of different types of drivers, the shared parking promotion strategy can be formulated from the aspects of management, operation, and technology.

A Novel Diabetes Healthcare Disease Prediction Framework Using Machine Learning Techniques

Diabetes is a chronic disease that continues to be a significant and global concern since it affects the entire population’s health. It is a metabolic disorder that leads to high blood sugar levels and many other problems such as stroke, kidney failure, and heart and nerve problems. Several researchers have attempted to construct an accurate diabetes prediction model over the years. However, this subject still faces significant open research issues due to a lack of appropriate data sets and prediction approaches, which pushes researchers to use big data analytics and machine learning (ML)-based methods. Applying four different machine learning methods, the research tries to overcome the problems and investigate healthcare predictive analytics. The study’s primary goal was to see how big data analytics and machine learning-based techniques may be used in diabetes. The examination of the results shows that the suggested ML-based framework may achieve a score of 86. Health experts and other stakeholders are working to develop categorization models that will aid in the prediction of diabetes and the formulation of preventative initiatives. The authors perform a review of the literature on machine models and suggest an intelligent framework for diabetes prediction based on their findings. Machine learning models are critically examined, and an intelligent machine learning-based architecture for diabetes prediction is proposed and evaluated by the authors. In this study, the authors utilize our framework to develop and assess decision tree (DT)-based random forest (RF) and support vector machine (SVM) learning models for diabetes prediction, which are the most widely used techniques in the literature at the time of writing. It is proposed in this study that a unique intelligent diabetes mellitus prediction framework (IDMPF) is developed using machine learning. According to the framework, it was developed after conducting a rigorous review of existing prediction models in the literature and examining their applicability to diabetes. Using the framework, the authors describe the training procedures, model assessment strategies, and issues associated with diabetes prediction, as well as solutions they provide. The findings of this study may be utilized by health professionals, stakeholders, students, and researchers who are involved in diabetes prediction research and development. The proposed work gives 83% accuracy with the minimum error rate.

Breath biomarkers of insulin resistance in pre-diabetic Hispanic adolescents with obesity

Insulin resistance (IR) affects a quarter of the world’s adult population and is a major factor in the pathogenesis of cardio-metabolic disease. In this pilot study, we implemented a non-invasive breathomics approach, combined with random forest machine learning, to investigate metabolic markers from obese pre-diabetic Hispanic adolescents as indicators of abnormal metabolic regulation. Using the ReCIVA breathalyzer device for breath collection, we have identified a signature of 10 breath metabolites (breath-IR model), which correlates with Homeostatic Model Assessment for Insulin Resistance (HOMA-IR) (R = 0.95, p < 0.001). A strong correlation was also observed between the breath-IR model and the blood glycemic profile (fasting insulin R = 0.91, p < 0.001 and fasting glucose R = 0.80, p < 0.001). Among tentatively identified metabolites, limonene, undecane, and 2,7-dimethyl-undecane, significantly cluster individuals based on HOMA-IR (p = 0.003, p = 0.002, and p < 0.001, respectively). Our breath-IR model differentiates between adolescents with and without IR with an AUC-ROC curve of 0.87, after cross-validation. Identification of a breath signature indicative of IR shows utility of exhaled breath metabolomics for assessing systemic metabolic dysregulation. A simple and non-invasive breath-based test has potential as a diagnostic tool for monitoring IR progression, allowing for earlier detection of IR and implementation of early interventions to prevent onset of type 2 diabetes mellitus.

Development and validation of dietary and lifestyle insulinemic indices among Iranian adult population

Background There is no study regarding developing a valid index to predict insulin-related disorders in the Iranian population based on their dietary habits and lifestyle. In the current study, we aimed to develop and validate insulinemic potential indices of diet and lifestyle in Iranian adults. Methods In this cross-sectional study, we analysed data of 1063 men and women aged ≥ 25 years among participants of the examination three of Tehran lipid and glucose study (TLGS) (2006–2008). Dietary intakes were assessed using a valid semi-quantitative food frequency questionnaire. Dietary and lifestyle indices were developed using stepwise linear regression analysis based on dietary intakes, body mass index, and physical activity data. Fasting serum insulin concentration and homeostatic model assessment for insulin resistance (HOMA-IR) were used as biomarkers of hyperinsulinemia (HI) and insulin resistance (IR). Validation analyses were performed in examination four of TLGS. Results We developed four indices related to insulin homeostasis, including the dietary index for HI (DIH), the dietary index for IR (DIR), the lifestyle index for HI (LIH), and the lifestyle index for IR (LIR). Based on multivariable-adjusted models, the relative values of the biomarker in subjects in the highest quartile of indices were 45% for LIH (95% CI 1.36–1.55, Ptrend < 0.001), 28% for DIR (95% CI 1.13–1.42, Ptrend = 0.019), and 51% for LIR (95% CI 1.41–1.61, Ptrend < 0.001), higher than those in the reference quartile, respectively. Conclusion We designed and validated indices to determine the insulin potential of diet and lifestyle for the Iranian population, according to Iran’s demographic and dietary intake characteristics.