Exercise-induced improvement of glycemic fluctuation and its relationship with fat and muscle distribution in type 2 diabetes.

AIMS: Management of blood glucose fluctuation is essential for diabetes. Exercise is a key therapeutic strategy for diabetes patients, although little is known about determinants of glycemic response to exercise training. We aimed to investigate the effect of combined aerobic and resistance exercise training on blood glucose fluctuation in type 2 diabetes patients and explore the predictors of exercise-induced glycemic response. MATERIALS AND METHODS: Fifty sedentary diabetes patients were randomly assigned to control or exercise group. Participants in the control group maintained sedentary lifestyle for 2 weeks, and those in the exercise group specifically performed combined exercise training for 1 week. All participants received dietary guidance based on a recommended diet chart. Glycemic fluctuation was measured by flash continuous glucose monitoring. Baseline fat and muscle distribution were accurately quantified through magnetic resonance imaging (MRI). RESULTS: Combined exercise training decreased SD of sensor glucose (SDSG, exercise-pre vs exercise-post, mean 1.35 vs 1.10 mmol/L, p = .006) and coefficient of variation (CV, mean 20.25 vs 17.20%, p = .027). No significant change was observed in the control group. Stepwise multiple linear regression showed that baseline MRI-quantified fat and muscle distribution, including visceral fat area (ß = -0.761, p = .001) and mid-thigh muscle area (ß = 0.450, p = .027), were significantly independent predictors of SDSG change in the exercise group, as well as CV change. CONCLUSIONS: Combined exercise training improved blood glucose fluctuation in diabetes patients. Baseline fat and muscle distribution were significant factors that influence glycemic response to exercise, providing new insights into personalized exercise intervention for diabetes.

Contributions of Hyperactive Mutations in Mpro from SARS-CoV-2 to Drug Resistance.

The appearance and spread of mutations that cause drug resistance in rapidly evolving diseases, including infections by the SARS-CoV-2 virus, are major concerns for human health. Many drugs target enzymes, and resistance-conferring mutations impact inhibitor binding or enzyme activity. Nirmatrelvir, the most widely used inhibitor currently used to treat SARS-CoV-2 infections, targets the main protease (Mpro) preventing it from processing the viral polyprotein into active subunits. Our previous work systematically analyzed resistance mutations in Mpro that reduce binding to inhibitors; here, we investigate mutations that affect enzyme function. Hyperactive mutations that increase Mpro activity can contribute to drug resistance but have not been thoroughly studied. To explore how hyperactive mutations contribute to resistance, we comprehensively assessed how all possible individual mutations in Mpro affect enzyme function using a mutational scanning approach with a fluorescence resonance energy transfer (FRET)-based yeast readout. We identified hundreds of mutations that significantly increased the Mpro activity. Hyperactive mutations occurred both proximal and distal to the active site, consistent with protein stability and/or dynamics impacting activity. Hyperactive mutations were observed 3 times more than mutations which reduced apparent binding to nirmatrelvir in recent studies of laboratory-grown viruses selected for drug resistance. Hyperactive mutations were also about three times more prevalent than nirmatrelvir binding mutations in sequenced isolates from circulating SARS-CoV-2. Our findings indicate that hyperactive mutations are likely to contribute to the natural evolution of drug resistance in Mpro and provide a comprehensive list for future surveillance efforts.

Associations of change in body fat percentage with baseline body composition and diabetes remission after bariatric surgery.

OBJECTIVE: The objective of this study was to determine the role of body fat percentage (BFP) changes in diabetes remission (DR) and the association between baseline body composition and its changes after bariatric surgery. METHODS: We analyzed 203 patients with type 2 diabetes who underwent Roux-en-Y gastric bypass. Body composition was measured using a gold-standard-derived predictive equation and magnetic resonance imaging. Body composition changes were calculated as 100 × (baseline value - follow-up value)/baseline value. We verified the results in a laparoscopic sleeve gastrectomy cohort with 311 patients. RESULTS: Compared with non-remission patients in the Roux-en-Y gastric bypass cohort, those who achieved DR showed a higher baseline fat-free mass index (FFMI) and experienced the most significant changes in BFP (p < 0.001). In comparative analyses, BFP changes were significantly better than BMI changes in identifying short- and long-term DR. Linear regression analysis identified FFMI as the most significant baseline variable correlated with BFP changes (p < 0.001). Baseline BMI was positively correlated with changes in BFP but negatively correlated with changes in FFMI. These findings were replicated in the laparoscopic sleeve gastrectomy cohort. CONCLUSIONS: BFP changes determine DR after bariatric surgery, and baseline FFMI is crucial for BFP changes. A low initial BMI is associated with a smaller BFP reduction and greater FFMI loss after bariatric surgery.

DRAC 2022: A public benchmark for diabetic retinopathy analysis on ultra-wide optical coherence tomography angiography images.

We described a challenge named "DRAC - Diabetic Retinopathy Analysis Challenge" in conjunction with the 25th International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI 2022). Within this challenge, we provided the DRAC datset, an ultra-wide optical coherence tomography angiography (UW-OCTA) dataset (1,103 images), addressing three primary clinical tasks: diabetic retinopathy (DR) lesion segmentation, image quality assessment, and DR grading. The scientific community responded positively to the challenge, with 11, 12, and 13 teams submitting different solutions for these three tasks, respectively. This paper presents a concise summary and analysis of the top-performing solutions and results across all challenge tasks. These solutions could provide practical guidance for developing accurate classification and segmentation models for image quality assessment and DR diagnosis using UW-OCTA images, potentially improving the diagnostic capabilities of healthcare professionals. The dataset has been released to support the development of computer-aided diagnostic systems for DR evaluation.

Resistant starch intake facilitates weight loss in humans by reshaping the gut microbiota.

Emerging evidence suggests that modulation of gut microbiota by dietary fibre may offer solutions for metabolic disorders. In a randomized placebo-controlled crossover design trial (ChiCTR-TTRCC-13003333) in 37 participants with overweight or obesity, we test whether resistant starch (RS) as a dietary supplement influences obesity-related outcomes. Here, we show that RS supplementation for 8 weeks can help to achieve weight loss (mean -2.8 kg) and improve insulin resistance in individuals with excess body weight. The benefits of RS are associated with changes in gut microbiota composition. Supplementation with Bifidobacterium adolescentis, a species that is markedly associated with the alleviation of obesity in the study participants, protects male mice from diet-induced obesity. Mechanistically, the RS-induced changes in the gut microbiota alter the bile acid profile, reduce inflammation by restoring the intestinal barrier and inhibit lipid absorption. We demonstrate that RS can facilitate weight loss at least partially through B. adolescentis and that the gut microbiota is essential for the action of RS.

Benefits of 12-month peer support for diabetes self management sustained at 18 months.

AIMS: To evaluate sustainability of peer support (PS) benefits in diabetes management. METHODS: Supporting a Peer Leader program through Community Health Centers (CHCs) included trainings and consultations from baseline to 12 months. Evaluation at baseline, 12-month, and 18-month follow-up included primary outcome, HbA1c, and other outcomes of SBP, DBP, LDLc, PHQ-8, diabetes distress, and EQ-5D. RESULTS: 1284 participants with type 2 diabetes mellitus were recruited from 9 CHCs. Mean (SD) for age = 68.00 (7.55) years, 43.07 % male, mean (SD) for diabetes duration = 11.79 (7.34) years. Across 18-months, linear mixed model analyses controlling for confounders found the least square mean (SE) of HbA1c improved significantly from 7.62 % (0.06 %) to 7.53 % (0.06 %) for all, and from 9.25 % (0.09 %) to 8.52 % (0.11 %) among those ≥8 % at baseline. Parallel improvements were found among all for SBP, DBP, PHQ-8, diabetes distress, and, among those elevated at baseline for all outcomes. EQ-5D showed significant but modest increase from baseline to 18 months. No significant reversals between 12 and 18 months were found except for LDLc. Supporting robustness of findings, patterns were similar across age, diabetes duration, and gender. CONCLUSIONS: Relative to the fundamentally progressive nature of diabetes, it is striking that improvements associated with PS were generally sustained after program support ended.

Plasma proteome profiling reveals the therapeutic effects of the PPAR pan-agonist chiglitazar on insulin sensitivity, lipid metabolism, and inflammation in type 2 diabetes.

Chiglitazar is a novel peroxisome proliferator-activated receptor (PPAR) pan-agonist, which passed phase III clinical trials and was newly approved in China for use as an adjunct to diet and exercise in glycemic control in adult patients with Type 2 Diabetes (T2D). To explore the circulating protein signatures associated with the administration of chiglitazar in T2D patients, we conducted a comparative longitudinal study using plasma proteome profiling. Of the 157 T2D patients included in the study, we administered chiglitazar to a specific group, while the controls were given either placebo or sitagliptin. The plasma proteomes were profiled at baseline and 12 and 24 weeks post-treatment using data-independent acquisition mass spectrometry (DIA-MS). Our study indicated that 13 proteins were associated with chiglitazar treatment in T2D patients, including 10 up-regulated proteins (SHBG, TF, APOA2, APOD, GSN, MBL2, CFD, PGLYRP2, A2M, and APOA1) and 3 down-regulated proteins (PRG4, FETUB, and C2) after treatment, which were implicated in the regulation of insulin sensitivity, lipid metabolism, and inflammation response. Our study provides insight into the response of chiglitazar treatment from a proteome perspective and demonstrates the multi-faceted effects of chiglitazar in T2D patients, which will help the clinical application of chiglitazar and further study of its action mechanism.

Effect of Magnetic Field on Maskless Localized Electrodepositing Three-Dimensional Microstructure of Nano Nickel Crystals.

In the intricate process of maskless localized electrodeposition (MLED) for fabricating three-dimensional microstructures, specifically nickel micro-columns with an aspect ratio of 7:1, magnetic fields of defined strength were employed, oriented both parallel and anti-parallel to the electric field. The aim was to achieve nanocrystalline microstructures and elevated deposition rates. A detailed comparative analysis was conducted to examine the volumetric deposition rate, surface morphology, and grain size of the MLED nickel crystal 3D microstructures, both in the absence and presence of the two magnetic field directions, facilitated by a self-assembled experimental setup. The results indicate that the anti-parallel magnetic field significantly boosts the volumetric deposition rate to a notable 19,050.65 µm3/s and refines the grain size, achieving an average size of 24.82 nm. Conversely, the parallel magnetic field is found to enhance the surface morphology of the MLED nickel crystal 3D microstructure.

Improved Antitumor Efficacy of a Dextran-based Docetaxel-coupled Conjugate against Triple-Negative Breast Cancer.

BACKGROUND: Most chemotherapeutic agents are characterized by poor water solubility and non-specific distribution. Polymer-based conjugates are promising strategies for overcoming these limitations. OBJECTIVE: This study aims to fabricate a polysaccharide, dextran-based, dual-drug conjugate by covalently grafting docetaxel (DTX) and docosahexaenoic acid (DHA) onto the bifunctionalized dextran through a long linker, and to investigate the antitumor efficacy of this conjugate against breast cancer. METHODS: DTX was firstly coupled with DHA and covalently bounded with the bifunctionalized dextran (100 kDa) through a long linker to produce a conjugate dextran-DHA-DTX (termed C-DDD). Cytotoxicity and cellular uptake of this conjugate were measured in vitro. Drug biodistribution and pharmacokinetics were investigated through liquid chromatography/mass spectrometry analysis. The inhibitory effects on tumor growth were evaluated in MCF-7- and 4T1-tumor-bearing mice. RESULTS: The loading capacity of the C-DDD for DTX was 15.90 (weight/weight). The C-DDD possessed good water solubility and was able to self-assemble into nanoparticles measuring 76.8 ± 5.5 nm. The maximum plasma concentration and area under the curve (0-∞) for the released DTX and total DTX from the C-DDD were significantly enhanced compared with the conventional DTX formulation. The C-DDD selectively accumulated in the tumor, with limited distribution was observed in normal tissues. The C-DDD exhibited greater antitumor activity than the conventional DTX in the triplenegative breast cancer model. Furthermore, the C-DDD nearly eliminated all MCF-7 tumors in nude mice without leading to systemic adverse effects. CONCLUSION: This dual-drug C-DDD has the potential to become a candidate for clinical application through the optimization of the linker.

Sex- and age-specific associations between abdominal fat and non-alcoholic fatty liver disease: a prospective cohort study.

Obesity is closely related to non-alcoholic fatty liver disease (NAFLD). Although sex differences in body fat distribution have been well demonstrated, little is known about the sex-specific associations between adipose tissue and the development of NAFLD. Using community-based cohort data, we evaluated the associations between magnetic resonance imaging-quantified areas of abdominal adipose tissue, including visceral adipose tissue (VAT) and subcutaneous adipose tissue (SAT), and incident NAFLD in 2830 participants (1205 males and 1625 females) aged 55-70 years. During a 4.6-year median follow-up, the cumulative incidence rates of NAFLD increased with areas of VAT and SAT both in males and females. Further analyses showed that the abovementioned positive associations were stronger in males than in females, especially in participants under 60 years old. In contrast, these sex differences disappeared in those over 60 years old. Furthermore, the risk of developing NAFLD increased nonlinearly with increasing fat area in a sex-specific pattern. Additionally, sex-specific potential mediators, such as insulin resistance, lipid metabolism, inflammation, and adipokines, may exist in the associations between adipose tissue and NAFLD. This study showed that the associations between abdominal fat and the risk of NAFLD were stratified by sex and age, highlighting the potential need for sex- and age-specific management of NAFLD.

Personalized glucose-lowering effect of chiglitazar in type 2 diabetes.

Chiglitazar (carfloglitazar) is a peroxisome proliferator-activated receptor pan-agonist presenting non-inferior glucose-lowering efficacy with sitagliptin in patients with type 2 diabetes. To delineate the subgroup of patients with greater benefit from chiglitazar, we conducted a machine learning-based post-hoc analysis in two randomized controlled trials. We established a character phenomap based on 13 variables and estimated HbA1c decline to the effects of chiglitazar in reference to sitagliptin. Out of 1,069 patients, 63.3% were found to have greater reduction in HbA1c levels with chiglitazar, while 36.7% showed greater reduction with sitagliptin. This distinction in treatment response was statistically significant between groups (pinteraction<0.001). To identify patients who would gain the most glycemic control benefit from chiglitazar, we developed a machine learning model, ML-PANPPAR, which demonstrated robust performance using sex, BMI, HbA1c, HDL, and fasting insulin. The phenomapping-derived tool successfully identified chiglitazar responders and enabled personalized drug allocation in patients with drug-naïve diabetes.

Influence of unsaturated fatty acids on the antitumor activity of polymeric conjugates grafted with cabazitaxel against prostate cancer.

Cabazitaxel (CTX) is a medication used for treating metastatic prostate cancer. However, its effectiveness is majorly limited by its poor water solubility and lack of tumor targeting. In this study, three unsaturated fatty acids, GLA, ALA and DHA, were separately connected with CTX and then covalently attached to bifunctionalized dextran through a linker to produce three dual drug conjugates named dextran-GLA-CTX, dextran-ALA-CTX and dextran-DHA-CTX. The three conjugates displayed enhanced solubility of CTX in water and improved antitumor effects compared to the conventional CTX formulation. The results also confirmed that dextran-GLA-CTX exhibited the strongest antitumor activity, while dextran-DHA-CTX displayed less efficacy, as evaluated through xenografted nude mice bearing PC-3 and DU145 prostate cancer cells. Additionally, dextran-GLA-CTX showed greater inhibition of tumor growth than dextran-CTX. Moreover, the dextran-GLA-CTX conjugate was found to prolong the half-life of CTX in plasma and selectively accumulate in tumors. This study revealed that unsaturated fatty acids can enhance the antitumor activity of dextran-based conjugates grafted with CTX.

Incidence rate and risk factors for hypoglycemia among individuals with type 1 diabetes or type 2 diabetes in China receiving insulin treatment.

AIMS: We investigated the real-world incidence of hypoglycemic events among patients with type 1 or type 2 diabetes (T1DM or T2DM) receiving insulin in routine clinical practice in China. METHODS: In this observational study, data were collected electronically via the Lilly Connected Care Program (LCCP) electronic system from adults with T1DM or T2DM who had registered on LCCP between 1 February 2019 and 31 January 2022, had used insulin for a full 12-week period following registration. The following outcomes were assessed during the 12 weeks following registration: incidence of level 1 and level 2 hypoglycemia. RESULTS: In total, 22,752 patients were enrolled. Among patients with monitoring data, over the 12-week study period, level 1 and 2 hypoglycemia were experienced by 48.8% and 25.9% of patients with T1DM and 26.5% and 13.9% of patients with T2DM. The proportion of patients treated with oral anti-diabetes drugs (OADs) capable of producing hypoglycemia (sulfonylurea or glinide) was 1.3% in T1DM and 1.6% in T2DM, respectively. Questionnaire data revealed that up to 92.5% of hypoglycemic events occurred outside of hospital and 18.6% were serious. CONCLUSIONS: These real-world data collected from Chinese patients with diabetes receiving insulin treatment reveal a relatively high percentage of patients experiencing hypoglycemia, with around one quarter of these events classified as severe and as many as 92.5% occurring outside of a hospital or clinic.

Artificial intelligence in diabetes management: Advancements, opportunities, and challenges.

The increasing prevalence of diabetes, high avoidable morbidity and mortality due to diabetes and diabetic complications, and related substantial economic burden make diabetes a significant health challenge worldwide. A shortage of diabetes specialists, uneven distribution of medical resources, low adherence to medications, and improper self-management contribute to poor glycemic control in patients with diabetes. Recent advancements in digital health technologies, especially artificial intelligence (AI), provide a significant opportunity to achieve better efficiency in diabetes care, which may diminish the increase in diabetes-related health-care expenditures. Here, we review the recent progress in the application of AI in the management of diabetes and then discuss the opportunities and challenges of AI application in clinical practice. Furthermore, we explore the possibility of combining and expanding upon existing digital health technologies to develop an AI-assisted digital health-care ecosystem that includes the prevention and management of diabetes.

Effect of virtual reality-based exercise and physical exercise on adolescents with overweight and obesity: study protocol for a randomised controlled trial.

INTRODUCTION: Obesity is a complex and multifactorial disease that has affected many adolescents in recent decades. Clinical practice guidelines recommend exercise as the key treatment option for adolescents with overweight and obesity. However, the effects of virtual reality (VR) exercise on the physical and brain health of adolescents with overweight and obese remain unclear. This study aims to evaluate the effects of physical and VR exercises on physical and brain outcomes and explore the differences in benefits between them. Moreover, we will apply a multiomics analysis to investigate the mechanism underlying the effects of physical and VR exercises on adolescents with overweight and obesity. METHODS AND ANALYSIS: This randomised controlled clinical trial will include 220 adolescents with overweight and obesity aged between 11 and 17 years. The participants will be randomised into five groups after screening. Participants in the exercise groups will perform an exercise programme by adding physical or VR table tennis or soccer classes to routine physical education classes in schools three times a week for 8 weeks. Participants in the control group will maintain their usual physical activity. The primary outcome will be the change in body fat mass measured using bioelectrical impedance analysis. The secondary outcomes will include changes in other physical health-related parameters, brain health-related parameters and multiomics variables. ETHICS AND DISSEMINATION: This study was approved by the Ethics Committee of Shanghai Sixth People's Hospital and registered in the Chinese Clinical Trial Registry. Dissemination of the findings will include peer-reviewed publications, conference presentations and media releases. TRIAL REGISTRATION NUMBER: ChiCTR2300068786.

Resistant starch decreases intrahepatic triglycerides in patients with NAFLD via gut microbiome alterations.

Non-alcoholic fatty liver disease (NAFLD) is a hepatic manifestation of metabolic dysfunction for which effective interventions are lacking. To investigate the effects of resistant starch (RS) as a microbiota-directed dietary supplement for NAFLD treatment, we coupled a 4-month randomized placebo-controlled clinical trial in individuals with NAFLD (ChiCTR-IOR-15007519) with metagenomics and metabolomics analysis. Relative to the control (n = 97), the RS intervention (n = 99) resulted in a 9.08% absolute reduction of intrahepatic triglyceride content (IHTC), which was 5.89% after adjusting for weight loss. Serum branched-chain amino acids (BCAAs) and gut microbial species, in particular Bacteroides stercoris, significantly correlated with IHTC and liver enzymes and were reduced by RS. Multi-omics integrative analyses revealed the interplay among gut microbiota changes, BCAA availability, and hepatic steatosis, with causality supported by fecal microbiota transplantation and monocolonization in mice. Thus, RS dietary supplementation might be a strategy for managing NAFLD by altering gut microbiota composition and functionality.

Changes of health-related quality of life after initiating basal insulin treatment among people with type 2 diabetes.

To assess the association between insulin regimens and health-related quality of life (HRQoL) after the introduction of basal insulin (BI) among people with type 2 diabetes in real-world clinical settings. 16,339 registered people with diabetes who had inadequate glycaemic control by oral agents initiated BI (either single BI or Basal-bolus) and completed a 6-month follow-up from 209 hospitals were included in the analyses. At the end of the follow-up, the switches of insulin regimens, change of HRQoL (EQ-5D-3L) and their associations were assessed. Initial insulin regimens of single BI and of basal-bolus (BI included Glargine, Detemir, and Neutral Protamine Hagedorn) accounted for 75.6% and 24.4%, respectively. At 6 months, regimens used were BI alone (65.2%), basal-bolus (10.4%), and premixed (6.4%), whereas 17.9% stopped all insulin therapy. The visual analogue scale score increased by 5.46 (P < .001), and the index value increased slightly by 0.02 (P < .001). Univariate analysis showed that people with diabetes taking basal-bolus regimen had the greatest improvement on HRQoL in all dimensions, especially in the reduction of the percentage of Pain/Discomfort (by 10.03%) and Anxiety/Depression (by 11.21%). In multivariable analysis, single BI or premixed insulin at 6 months was associated with more improvement of visual analogue scale score compared with stopping all insulin. Improved HRQoL was observed after initiating BI in people with type 2 diabetes . If the same achievement on HbA1c control can be guaranteed, single BI is preferred to other regimens from the viewpoint of HRQoL. Basal-bolus has the most significant potential to increase HRQoL, however, the people with diabetes characteristics differ from those initiating BI alone. Further longitudinal cohort study with a longer study period might be necessary to evaluate the certain effect.

Application and prospect of artificial intellingence in diabetes care.

Diabetes is one of the fastest-growing non-communicable diseases, becoming an important public health concern worldwide as well as in China. Currently, China has the largest population living with diabetes. Artificial intelligence (AI) is a fast-growing field and its applications to diabetes could enable the delivery of better management services for people with diabetes. This perspective summarized the latest findings of digital technologies and AI use in the following areas of diabetes care, mainly including screening and risk predictions of diabetes and diabetic complications, precise monitoring and intervention combined with new technologies, and mobile health application in self-management support for people with diabetes. Challenges to promote further use of AI in diabetes care included data standardization and integration, performance of AI-based medical devices, motivation of patients, and sensitivity to privacy. In summary, although the AI applications in clinical practice is still at an early stage, we are moving toward a new paradigm for diabetes care with the rapid development and emerging application of AI.