The ALDH2 gene rs671 polymorphism is associated with cardiometabolic risk factors in East Asian population: an updated meta-analysis.

Introduction: Acetaldehyde dehydrogenase 2 (ALDH2) had reported as a prominent role in the development of cardiometabolic diseases among Asians. Our study aims to investigate the relationship between ALDH2 polymorphism and cardiometabolic risk factors in East Asian population. Method: We searched databases of PubMed, Web of Science, and Embase updated to Oct 30th, 2023. We extracted data of BMI, Hypertension, SBP, DBP, T2DM, FBG, PPG, HbA1c, TG, TC, LDL-C and HDL-C. Result: In total, 46 studies were finally included in our meta-analysis, containing, 54068 GG and, 36820 GA/AA participants. All outcomes related to blood pressure revealed significant results (hypertension OR=0.83 [0.80, 0.86]; SBP MD=-1.48 [-1.82, -1.14]; DBP MD=-1.09 [-1.58, -0.61]). FBG showed a significant difference (MD=-0.10 [-0.13, -0.07]), and the lipid resulted significantly in some outcomes (TG MD=-0.07 [-0.09, -0.04]; LDL-C MD=-0.04 [-0.05, -0.02]). As for subgroups analysis, we found that in populations without severe cardiac-cerebral vascular diseases (CCVDs), GG demonstrated a significantly higher incidence of T2DM (T2DM OR=0.88 [0.79, 0.97]), while the trend was totally opposite in population with severe CCVDs (T2DM OR=1.29 [1.00, 1.66]) with significant subgroup differences. Conclusion: Our updated meta-analysis demonstrated that ALDH2 rs671 GG populations had significantly higher levels of BMI, blood pressure, FBG, TG, LDL-C and higher risk of hypertension than GA/AA populations. Besides, to the best of our knowledge, we first report GG had a higher risk of T2DM in population without severe CCVDs, and GA/AA had a higher risk of T2DM in population with severe CCVDs.Systematic Review Registration: https://www.crd.york.ac.uk/PROSPERO, identifier CRD42023389242.

Advancements in culture technology of adipose-derived stromal/stem cells: implications for diabetes and its complications.

Stem cell-based therapies exhibit considerable promise in the treatment of diabetes and its complications. Extensive research has been dedicated to elucidate the characteristics and potential applications of adipose-derived stromal/stem cells (ASCs). Three-dimensional (3D) culture, characterized by rapid advancements, holds promise for efficacious treatment of diabetes and its complications. Notably, 3D cultured ASCs manifest enhanced cellular properties and functions compared to traditional monolayer-culture. In this review, the factors influencing the biological functions of ASCs during culture are summarized. Additionally, the effects of 3D cultured techniques on cellular properties compared to two-dimensional culture is described. Furthermore, the therapeutic potential of 3D cultured ASCs in diabetes and its complications are discussed to provide insights for future research.

Development and validation of machine learning-augmented algorithm for insulin sensitivity assessment in the community and primary care settings: a population-based study in China.

Objective: Insulin plays a central role in the regulation of energy and glucose homeostasis, and insulin resistance (IR) is widely considered as the "common soil" of a cluster of cardiometabolic disorders. Assessment of insulin sensitivity is very important in preventing and treating IR-related disease. This study aims to develop and validate machine learning (ML)-augmented algorithms for insulin sensitivity assessment in the community and primary care settings. Methods: We analyzed the data of 9358 participants over 40 years old who participated in the population-based cohort of the Hubei center of the REACTION study (Risk Evaluation of Cancers in Chinese Diabetic Individuals). Three non-ensemble algorithms and four ensemble algorithms were used to develop the models with 70 non-laboratory variables for the community and 87 (70 non-laboratory and 17 laboratory) variables for the primary care settings to screen the classifier of the state-of-the-art. The models with the best performance were further streamlined using top-ranked 5, 8, 10, 13, 15, and 20 features. Performances of these ML models were evaluated using the area under the receiver operating characteristic curve (AUROC), the area under the precision-recall curve (AUPR), and the Brier score. The Shapley additive explanation (SHAP) analysis was employed to evaluate the importance of features and interpret the models. Results: The LightGBM models developed for the community (AUROC 0.794, AUPR 0.575, Brier score 0.145) and primary care settings (AUROC 0.867, AUPR 0.705, Brier score 0.119) achieved higher performance than the models constructed by the other six algorithms. The streamlined LightGBM models for the community (AUROC 0.791, AUPR 0.563, Brier score 0.146) and primary care settings (AUROC 0.863, AUPR 0.692, Brier score 0.124) using the 20 top-ranked variables also showed excellent performance. SHAP analysis indicated that the top-ranked features included fasting plasma glucose (FPG), waist circumference (WC), body mass index (BMI), triglycerides (TG), gender, waist-to-height ratio (WHtR), the number of daughters born, resting pulse rate (RPR), etc. Conclusion: The ML models using the LightGBM algorithm are efficient to predict insulin sensitivity in the community and primary care settings accurately and might potentially become an efficient and practical tool for insulin sensitivity assessment in these settings.

Recent Developments of Gramine: Chemistry and Biological Activity.

The natural alkaloid gramine has attracted significant attention in both academic and industrial circles because of its potential and diverse biological activities, including antiviral, antibacterial, antifungal, anti-inflammatory and antitumor activities; application in therapy for Alzheimer's disease; serotonin-receptor-related activity; insecticidal activity; and application as an algicide. In this review, we focus on the research advances that have been made for gramine-based molecules since their discovery, providing key information on their extraction and separation, chemical synthesis and diverse biological activities. Data regarding their mechanisms of action are also presented. This comprehensive and critical review will serve as a guide for developing more drug candidates based on gramine skeletons.

Role of Circulating Exosomal miRNA-3976 in Early Diabetic Retinopathy.

Background: Diabetic retinopathy (DR) remains as the most frequent complication of diabetes, and is the major cause of vision loss for middle-aged to elderly people. With longer life expectancies for people with diabetes, there is a significant rise in diabetic retinopathy worldwide. The treatment of DR is limited; and therefore, our study aimed to investigate the possibilities of circulating exosomal miRNAs in the early screening and prevention of DR; and to explore the function of the exosomal miRNAs in DR. Materials and Methods: Eighteen participants were recruited and divided into two groups: the diabetes mellitus (DM) group and the DR group. We analyzed the expression profile of exosomal miRNAs derived from serum using RNA sequencing. Additionally, we conducted co-culture experiments of RGC-5 and HUVEC cells with DR-derived exosomes to examine the role of highly expressed exosomal miRNA-3976 in DR. Furthermore, we transfected RGC-5 and HUVEC cells with miRNA-3976 to investigate its effects. Results: Among the 1059 miRNAs analyzed, we identified eighteen up-regulated exosomal miRNAs. Treatment with DR-derived exosomes resulted in increased proliferation and reduced apoptosis of RGC-5 cells, and these effects were partially reversed by the miRNA-3976 inhibitor. Moreover, over-expression of miRNA-3976 led to increased apoptosis of RGC-5 cells and indirectly reduced the abundance of NFκB1. Conclusion: Serum-derived exosomal miRNA-3976 has the potential to serve as a biomarker for DR, primarily exerting its effects in the early stages of DR through the regulation of NFκB-associated mechanisms.

Framingham risk score conventional risk factors are potent to predict all-cause mortality using machine learning algorithms: a population-based prospective cohort study over 40 years in China.

Predicting all-cause mortality using available or conveniently modifiable risk factors is potentially crucial in reducing deaths precisely and efficiently. Framingham risk score (FRS) is widely used in predicting cardiovascular diseases, and its conventional risk factors are closely pertinent to deaths. Machine learning is increasingly considered to improve the predicting performances by developing predictive models. We aimed to develop the all-cause mortality predictive models using five machine learning (ML) algorithms (decision trees, random forest, support vector machine (SVM), XgBoost, and logistic regression) and determine whether FRS conventional risk factors are sufficient for predicting all-cause mortality in individuals over 40 years. Our data were obtained from a 10-year population-based prospective cohort study in China, including 9143 individuals over 40 years in 2011, and 6879 individuals followed-up in 2021. The all-cause mortality prediction models were developed using five ML algorithms by introducing all features available (182 items) or FRS conventional risk factors. The area under the receiver operating characteristic curve (AUC) was used to evaluate the performance of the predictive models. The AUC and 95% confidence interval of the all-cause mortality prediction models developed by FRS conventional risk factors using five ML algorithms were 0.75 (0.726-0.772), 0.78 (0.755-0.799), 0.75 (0.731-0.777), 0.77 (0.747-0.792), and 0.78 (0.754-0.798), respectively, which is close to the AUC values of models established by all features (0.79 (0.769-0.812), 0.83 (0.807-0.848), 0.78 (0.753-0.798), 0.82 (0.796-0.838), and 0.85 (0.826-0.866), respectively). Therefore, we tentatively put forward that FRS conventional risk factors were potent to predict all-cause mortality using machine learning algorithms in the population over 40 years.

Comparative efficacy of aromatase inhibitors and gonadotropin-releasing hormone analogue in increasing final height of idiopathic short stature boys: a network meta-analysis.

Objective: To investigate the efficacy of monotherapy with AIs or GnRHa in improving the height of boys with idiopathic short stature (ISS). Method: We performed a systematic search in Pubmed, The Cochrane Library, Chinese National Knowledge Infrastructure databases, and Wanfang Database for eligible studies. The network meta-analysis was conducted using STATA software. Results: We identified a total of four studies that included 136 individuals. We used FAH/PAH as the main outcome of final height. The results revealed a statistically higher final height after treatment with AI or GnRHa in idiopathic short stature children(MD= 4.63, 95% CI[3.29,5.96]). In network meta-analysis, the direct and indirect comparison between AI and GnRHa was presented in the forest plot. Compared with control group, both AI and GnRHa were effective in increasing the final height, with the mean effect of 4.91(95%CI:1.10,8.17) and 5.55(95%CI:1.12,9.98) respectively. However, there was no statistical difference between the GnRHa and AI treatment, of which the mean effect was 0.65(95%CI: -4.30,5.60). Conclusion: Both AIs and GnRHa monotherapy were effective in augmenting the final height of boys with idiopathic short stature when compared to placebo groups. However, there was no statistical difference between the GnRHa and AI treatments.

Stomach-derived human insulin-secreting organoids restore glucose homeostasis.

Gut stem cells are accessible by biopsy and propagate robustly in culture, offering an invaluable resource for autologous cell therapies. Insulin-producing cells can be induced in mouse gut, but it has not been possible to generate abundant and durable insulin-secreting cells from human gut tissues to evaluate their potential as a cell therapy for diabetes. Here we describe a protocol to differentiate cultured human gastric stem cells into pancreatic islet-like organoids containing gastric insulin-secreting (GINS) cells that resemble ß-cells in molecular hallmarks and function. Sequential activation of the inducing factors NGN3 and PDX1-MAFA led human gastric stem cells onto a distinctive differentiation path, including a SOX4High endocrine and GalaninHigh GINS precursor, before adopting ß-cell identity, at efficiencies close to 70%. GINS organoids acquired glucose-stimulated insulin secretion in 10 days and restored glucose homeostasis for over 100 days in diabetic mice after transplantation, providing proof of concept for a promising approach to treat diabetes.

Comparative Efficacy of Novel Antidiabetic Drugs on Albuminuria Outcomes in Type 2 Diabetes: A Systematic Review.

INTRODUCTION: Albuminuria, or elevated urinary albumin-to-creatine ratio (UACR), is a biomarker for chronic kidney disease that is routinely monitored in patients with type 2 diabetes (T2D). Head-to-head comparisons of novel antidiabetic drugs on albuminuria outcomes remain limited. This systematic review qualitatively compared the efficacy of novel antidiabetic drugs on improving albuminuria outcomes in patients with T2D. METHODS: We searched the MEDLINE database until December 2022 for Phase 3 or 4 randomized, placebo-controlled trials that evaluated the effects of sodium-glucose co-transporter-2 (SGLT2) inhibitors, glucagon-like peptide-1 receptor agonists (GLP-1 RAs) and dipeptidyl peptidase-4 (DPP-4) inhibitors on changes in UACR and albuminuria categories in patients with T2D. RESULTS: Among 211 records identified, 27 were included, which reported on 16 trials. SGLT2 inhibitors and GLP-1 RAs decreased UACR by 19-22% and 17-33%, respectively, versus placebo (P < 0.05 for all studies) over median follow-up of ≥ 2 years; DPP-4 inhibitors showed varying effects on UACR. Compared with placebo, SGLT2 inhibitors decreased the risk for albuminuria onset by 16-20% and for albuminuria progression by 27-48% (P < 0.05 for all studies) and promoted albuminuria regression (P < 0.05 for all studies) over median follow-up of ≥ 2 years. Evidence on changes in albuminuria categories with GLP-1 RA or DPP-4 inhibitor treatment were limited with varying outcome definitions across studies and potential drug-specific effects within each class. The effect of novel antidiabetic drugs on UACR or albuminuria outcomes at ≤ 1 year remains poorly studied. CONCLUSION: Among the novel antidiabetic drugs, SGLT2 inhibitors consistently improved UACR and albuminuria outcomes in patients with T2D, with continuous treatment showing long-term benefit.

Novel N1 or N9 modified α-carboline analogues as potential ligands in Alzheimer's disease therapy: Synthesis and neurobiological activity evaluation.

A series of new α-carboline analogues modified at N1 or N9 positions by alkyl, benzyl and phenyl were synthesized and characterized as potential ligands for AD therapy. These compounds exhibited multifunctional neurobiological activities including anti-neuroinflammatory, neuroprotective and cholinesterase inhibition. Among them, compound 5d with good drug-like properties and no cytotoxicity, showed potent inhibitory activity against NO production (IC50 = 1.45 µM), which could suppress the expression levels of iNOS and COX-2 in a dose-dependent manner. Further mechanism exploration indicated that compound 5d could regulate the NF-κB signaling pathway by decreasing the phosphorylation of IκB-α and p65. Notably, compound 5d could effectively decrease the LPS-induced aberrations in zebrafish. Compounds 3b, 4f, 5c, 5g, 5m and 6i exhibited potential neuroprotective activity (cell viability > 70 %) in the H2O2-induced PC-12 neuronal death model and rescued the SOD activity. In particular, compounds 3b, 4f, and 5g activated the Nrf2 signaling pathway, and improved the expressions of antioxidant proteins NQO-1 and HO-1, which alleviated the head cell apoptosis in zebrafish. Additionally, compound 6i exhibited potential inhibitory activity against BuChE with IC50 of 0.77 µM. Overall, this work provided some lead compounds based on α-carboline used for AD therapy.

Development and validation of a machine learning-augmented algorithm for diabetes screening in community and primary care settings: A population-based study.

Background: Opportunely screening for diabetes is crucial to reduce its related morbidity, mortality, and socioeconomic burden. Machine learning (ML) has excellent capability to maximize predictive accuracy. We aim to develop ML-augmented models for diabetes screening in community and primary care settings. Methods: 8425 participants were involved from a population-based study in Hubei, China since 2011. The dataset was split into a development set and a testing set. Seven different ML algorithms were compared to generate predictive models. Non-laboratory features were employed in the ML model for community settings, and laboratory test features were further introduced in the ML+lab models for primary care. The area under the receiver operating characteristic curve (AUC), area under the precision-recall curve (auPR), and the average detection costs per participant of these models were compared with their counterparts based on the New China Diabetes Risk Score (NCDRS) currently recommended for diabetes screening. Results: The AUC and auPR of the ML model were 0·697and 0·303 in the testing set, seemingly outperforming those of NCDRS by 10·99% and 64·67%, respectively. The average detection cost of the ML model was 12·81% lower than that of NCDRS with the same sensitivity (0·72). Moreover, the average detection cost of the ML+FPG model is the lowest among the ML+lab models and less than that of the ML model and NCDRS+FPG model. Conclusion: The ML model and the ML+FPG model achieved higher predictive accuracy and lower detection costs than their counterpart based on NCDRS. Thus, the ML-augmented algorithm is potential to be employed for diabetes screening in community and primary care settings.

Neutrophil-to-lymphocyte Ratio is Associated with LV Diastolic Dysfunction in the Overt Hyperthyroid Patients.

Background: Recent studies have shown that the neutrophil-to-lymphocyte ratio (NLR) has gradually been identified as a more reliable marker of inflammation, with predictive value for the development of many diseases. However, its association with left ventricular (LV) diastolic dysfunction in overt hyperthyroid patients is unclear. Here, we aimed to explore the relationship between NLR and LV diastolic dysfunction in overt hyperthyroid patients. Methods: For this study, we retrospected the consecutive medical files of 350 overt hyperthyroid patients. Their medical data and laboratory findings were recorded. According to the presence or absence of LV diastolic dysfunction, the patients with overt hyperthyroidism were divided into two groups. One group with LV diastolic dysfunction included 104 patients and another group with non-LV diastolic dysfunction included 246 patients. The NLR values between the two groups were compared, and the relationship between NLR levels and the prevalence of LV diastolic dysfunction was also explored. Results: The NLR value in LV diastolic dysfunction group in the overt hyperthyroid subjects was significantly higher than that in non-LV diastolic dysfunction group [1.100 (0.907-1.580) vs 1.000 (0.761-1.405), P=0.016]. The prevalence of LV diastolic dysfunction in Low- (NLR<0.879), Medium- (0.879< NLR<1.287), and High- (NLR >1.287) NLR level groups were 20.9%, 32.5% and 35.7% respectively. Moreover, increased NLR is associated with increased prevalence of LV diastolic dysfunction, and after adjustment for potential associated factors, NLR remained significantly associated with LV diastolic dysfunction. (OR = 11.753, 95%CI = 1.938-71.267, P = 0.007). Conclusions: Our findings demonstrated that the NLR was associated with LV diastolic dysfunction in the overt hyperthyroid patients, and the prevalence of LV diastolic dysfunction may be positively correlated with NLR levels.

Revealing the mechanisms of Weishi Huogu I capsules used for treating osteonecrosis of the femoral head based on systems pharmacology with one mechanism validated with in vitro experiments.

ETHNOPHARMACOLOGICAL RELEVANCE: Weishi Huogu I (WH I) capsules, developed through traditional Chinese medicine, have been used to treat clinical osteonecrosis of the femoral head (ONFH) for decades. However, the mechanisms have not been systematically studied. AIM OF THE STUDY: In this study, the mechanisms of WH I capsules used in treating ONFH were examined through a systems pharmacology strategy, and one mechanism was validated with in vitro experiments. MATERIALS AND METHODS: WH I capsules compounds were identified by screening databases; then, a database of the potential active compounds was constructed after absorption, distribution, metabolism and excretion (ADME) evaluation. The compounds were identified through a systematic approach in which the probability of an interaction of every candidate compound with each corresponding target in the DrugBank database was calculated. Gene Ontology (GO) and pathway enrichment analyses of the targets was performed with the Metascape and KEGG DISEASE databases. Then, a compound-target network (C-T) and target-pathway network (T-P) of WH I capsule components were constructed, and network characteristics and related information were used for systematically identifying WH I capsule multicomponent-target interactions. Furthermore, the effects of WH I capsule compounds identified through the systematic pharmacology analysis of the osteogenic transformation of human umbilical mesenchymal stem cells (HUMSCs) were validated in vitro. RESULTS: In total, 152 potentially important compounds and 176 associated targets were identified. Twenty-two crucial GO biological process (BP) or pathways were related to ONFH, mainly in regulatory modules regulating blood circulation, modulating growth, and affecting pathological processes closely related to ONFH. Furthermore, the GO enrichment analysis showed that corydine, isorhamnetin, and bicuculline were enriched in "RUNX2 regulates osteoblast differentiation", significantly increased alkaline phosphatase activity and calcium deposition and upregulated runt-related transcription factor 2 mRNA and protein expression and osteocalcin mRNA expression in HUMSCs, suggesting that these compounds promoted the mesenchymal stem cell (MSC) osteogenic transformation. CONCLUSIONS: The study showed that the pharmacological mechanisms of WH I capsule attenuation of ONFH mainly involve three therapeutic modules: blood circulation, modulating growth, and regulating pathological processes. The crosstalk between GOBPs/pathways may constitute the basis of the synergistic effects of the compounds in WH I capsules in attenuating ONFH. One of the pharmacological mechanisms in the WH I capsule effect on ONFH involves enhancement of the osteogenic transformation of MSCs, as validated in experiments performed in vitro; however, more mechanisms should be validated in further studies.

Catch-up fat in male adults induces low testosterone and consequently promotes metabolic abnormalities and cognitive impairment.

BACKGROUND: Catch-up fat in adults (CUFA) caused by rapid nutrition promotion after undernutrition plays an important role in the epidemic of insulin resistance (IR)-related diseases in developing societies. Insulin resistance is considered to be closely associated with reduced testosterone levels and cognitive function. However, the effects of CUFA on testosterone levels and cognitive function are unclear in males. OBJECTIVES: To investigate the changes in testosterone levels and cognitive function in CUFA in male humans and rats, and explore their probable relationship and mechanisms in rats. MATERIALS AND METHODS: The blood testosterone levels, fasting glucose, and blood insulin (FINS) were measured in subpopulation 1 (27 CUFA individuals, 61 controls without CUFA) aged 40-50 years to show the characteristics of sex hormone levels and the metabolic status in CUFA men. Cognitive Flexibility Inventory was conducted in subpopulation 2 (54 CUFA individuals, 214 controls) over 20 years to investigate the associations between sex hormone levels, cognitive function, and CUFA. Male rats (n = 27) were randomly allocated to the NC group (normal chow controls), RN group (CUFA, refeeding after caloric restriction), and RT group (RN with testosterone intramuscular injected while refeeding). The blood testosterone levels, intraperitoneal insulin tolerance test (IPITT), and FINS were measured, and the attentional set-shifting task test (ASST) for the assessment of cognitive function was performed in these animals. Insulin signaling pathway, N-methyl-d-aspartate receptors subtype 2A (NR2A) and 2B (NR2B) expression levels were determined in the rat cerebral cortex. RESULTS: The total testosterone levels decreased (medium [inter-quartile ranges], 13.43 [9.87-18.96] vs. 15.58 [13.37-24.96], p = 0.036), and HOMA-IR (Homeostatic Model Assessment for Insulin Resistance) elevated (1.61 [1.08-2.33] vs. 1.24 [0.87-1.87], P = 0.037) in CUFA men in subpopulation 1. Additionally, cognitive impairment was observed in CUFA men in subpopulation 2. Moreover, our results indicated decreases in total and free testosterone levels, elevations in visceral lipid accumulation, FINS, HOMA-IR, blood glucose, and the area under the curve after IPITT, increases in the number of trials required to achieve the criterion of the first reversal of discrimination (R1) in ASST, and downregulation of IRS-1 mRNA expression, AKT phosphorylation, and the NR2A and NR2B expression in brain tissue in male CUFA rats. Notably, testosterone supplementation improved visceral lipid accumulation and IR-related metabolic disorders, cognitive dysfunction, decreases in IRS-1 mRNA expression, Akt phosphorylation, and NR2A and NR2B expression in brain tissue in male CUFA rodents. DISCUSSION AND CONCLUSION: CUFA was characterized by reduced testosterone levels, metabolic abnormalities, and cognitive dysfunction in males, and testosterone supplementation attenuated these changes, as well as the alteration in insulin signaling and NR2A and NR2B expression in male CUFA rodents. Herein, we tentatively put forward that CUFA in males induces low testosterone, consequently promoting metabolic abnormalities and cognitive impairment probably mediated by defects in insulin signaling and NR2A, NR2B pathway in brain tissue.

SATB2 preserves colon stem cell identity and mediates ileum-colon conversion via enhancer remodeling.

Adult stem cells maintain regenerative tissue structure and function by producing tissue-specific progeny, but the factors that preserve their tissue identities are not well understood. The small and large intestines differ markedly in cell composition and function, reflecting their distinct stem cell populations. Here we show that SATB2, a colon-restricted chromatin factor, singularly preserves LGR5+ adult colonic stem cell and epithelial identity in mice and humans. Satb2 loss in adult mice leads to stable conversion of colonic stem cells into small intestine ileal-like stem cells and replacement of the colonic mucosa with one that resembles the ileum. Conversely, SATB2 confers colonic properties on the mouse ileum. Human colonic organoids also adopt ileal characteristics upon SATB2 loss. SATB2 regulates colonic identity in part by modulating enhancer binding of the intestinal transcription factors CDX2 and HNF4A. Our study uncovers a conserved core regulator of colonic stem cells able to mediate cross-tissue plasticity in mature intestines.

Predictive modeling the probability of suffering from metabolic syndrome using machine learning: A population-based study.

Background: There is an increasing trend of Metabolic syndrome (MetS) prevalence, which has been considered as an important contributor for cardiovascular disease (CVD), cancers and diabetes. However, there is often a long asymptomatic phase of MetS, resulting in not diagnosed and intervened so timely as needed. It would be very helpful to explore tools to predict the probability of suffering from MetS in daily life or routinely clinical practice. Objective: To develop models that predict individuals' probability of suffering from MetS timely with high efficacy in general population. Methods: The present study enrolled 8964 individuals aged 40-75 years without severe diseases, which was a part of the REACTION study from October 2011 to February 2012. We developed three prediction models for different scenarios in hospital (Model 1, 2) or at home (Model 3) based on LightGBM (LGBM) technique and corresponding logistic regression (LR) models were also constructed for comparison. Model 1 included variables of laboratory tests, lifestyles and anthropometric measurements while model 2 was built with components of MetS excluded based on model 1, and model 3 was constructed with blood biochemical indexes removed based on model 2. Additionally, we also investigated the strength of association between the predictive factors and MetS, as well as that between the predictors and each component of MetS. Results: In this study, 2714 (30.3%) participants suffer from MetS accordingly. The performances of the LGBM models in predicting the probability of suffering from MetS produced good results and were presented as follows: model 1 had an area under the curve (AUC) value of 0.993 while model 2 indicated an AUC value of 0.885. Model 3 had an AUC value of 0.859, which is close to that of model 2. The AUC values of LR model 1 and 2 for the scenario in hospital and model 3 at home were 0.938, 0.839 and 0.820 respectively, which seemed lower than that of their corresponding machine learning models, respectively. In both LGBM and logistic models, gender, height and resting pulse rate (RPR) were predictors for MetS. Women had higher risk of MetS than men (OR 8.84, CI: 6.70-11.66), and each 1-cm increase in height indicated 3.8% higher risk of suffering from MetS in people over 58 years, whereas each 1- Beat Per Minute (bpm) increase in RPR showed 1.0% higher risk in individuals younger than 62 years. Conclusion: The present study showed that the prediction models developed by machine learning demonstrated effective in evaluating the probability of suffering from MetS, and presented prominent predicting efficacies and accuracies. Additionally, we found that women showed a higher risk of MetS than men, and height in individuals over 58 years was important factor in predicting the probability of suffering from MetS while RPR was of vital importance in people aged 40-62 years.

Suppression of the hypothalamic-pituitary-thyroid axis is associated with the severity of prognosis in hospitalized patients with COVID-19.

BACKGROUND: The outbreak of severe acute respiratory syndrome novel coronavirus 2 (SARS-CoV-2) has spread rapidly worldwide. SARS-CoV-2 has been found to cause multiple organ damage; however, little attention has been paid to the damage to the endocrine system caused by this virus, and the subsequent impact on prognosis. This may be the first research on the hypothalamic-pituitary-thyroid (HPT) axis and prognosis in coronavirus disease 2019 (COVID-19). METHODS: In this retrospective observational study, 235 patients were admitted to the hospital with laboratory-confirmed SARS-CoV-2 infection from 22 January to 17 March 2020. Clinical characteristics, laboratory findings, and treatments were obtained from electronic medical records with standard data collection forms and compared among patients with different thyroid function status. RESULTS: Among 235 patients, 17 (7.23%) had subclinical hypothyroidism, 11 (4.68%) severe non-thyroidal illness syndrome (NTIS), and 23 (9.79%) mild to moderate NTIS. Composite endpoint events of each group, including mortality, admission to the ICU, and using IMV were observed. Compared with normal thyroid function, the hazard ratios (HRs) of composite endpoint events for mild to moderate NTIS, severe NTIS, subclinical hypothyroidism were 27.3 (95% confidence interval [CI] 7.07-105.7), 23.1 (95% CI 5.75-92.8), and 4.04 (95% CI 0.69-23.8) respectively. The multivariate-adjusted HRs for acute cardiac injury among patients with NTF, subclinical hypothyroidism, severe NTIS, and mild to moderate NTIS were 1.00, 1.68 (95% CI 0.56-5.05), 4.68 (95% CI 1.76-12.4), and 2.63 (95% CI 1.09-6.36) respectively. CONCLUSIONS: Our study shows that the suppression of the HPT axis could be a common complication in COVID-19 patients and an indicator of the severity of prognosis. Among the three different types of thyroid dysfunction with COVID-19, mild to moderate NTIS and severe NTIS have a higher risk of severe outcomes compared with subclinical hypothyroidism.

Prognostic Value of Leucocyte to High-Density Lipoprotein-Cholesterol Ratios in COVID-19 Patients and the Diabetes Subgroup.

Background: Blood parameters, such as neutrophil-to-lymphocyte ratio, have been identified as reliable inflammatory markers with diagnostic and predictive value for the coronavirus disease 2019 (COVID-19). However, novel hematological parameters derived from high-density lipoprotein-cholesterol (HDL-C) have rarely been studied as indicators for the risk of poor outcomes in patients with severe acute respiratory syndrome-related coronavirus 2 (SARS-CoV-2) infection. Here, we aimed to assess the prognostic value of these novel biomarkers in COVID-19 patients and the diabetes subgroup. Methods: We conducted a multicenter retrospective cohort study involving all hospitalized patients with COVID-19 from January to March 2020 in five hospitals in Wuhan, China. Demographics, clinical and laboratory findings, and outcomes were recorded. Neutrophil to HDL-C ratio (NHR), monocyte to HDL-C ratio (MHR), lymphocyte to HDL-C ratio (LHR), and platelet to HDL-C ratio (PHR) were investigated and compared in both the overall population and the subgroup with diabetes. The associations between blood parameters at admission with primary composite end-point events (including mechanical ventilation, admission to the intensive care unit, or death) were analyzed using Cox proportional hazards regression models. Receiver operating characteristic curves were used to compare the utility of different blood parameters. Results: Of 440 patients with COVID-19, 67 (15.2%) were critically ill. On admission, HDL-C concentration was decreased while NHR was high in patients with critical compared with non-critical COVID-19, and were independently associated with poor outcome as continuous variables in the overall population (HR: 0.213, 95% CI 0.090-0.507; HR: 1.066, 95% CI 1.030-1.103, respectively) after adjusting for confounding factors. Additionally, when HDL-C and NHR were examined as categorical variables, the HRs and 95% CIs for tertile 3 vs. tertile 1 were 0.280 (0.128-0.612) and 4.458 (1.817-10.938), respectively. Similar results were observed in the diabetes subgroup. ROC curves showed that the NHR had good performance in predicting worse outcomes. The cutoff point of the NHR was 5.50. However, the data in our present study could not confirm the possible predictive effect of LHR, MHR, and PHR on COVID-19 severity. Conclusion: Lower HDL-C concentrations and higher NHR at admission were observed in patients with critical COVID-19 than in those with noncritical COVID-19, and were significantly associated with a poor prognosis in COVID-19 patients as well as in the diabetes subgroup.

Short-Term Changes in Metabolically Healthy Overweight/Obesity Status Impact the Susceptibility to Type 2 Diabetes in Chinese Adults.

PURPOSE: Changes in transition from metabolically healthy overweight/obesity (MHO) to metabolically unhealthy overweight/obesity (MUO) are associated with the risk for cardiometabolic complications. This study aims to investigate the effects of short-term dynamic changes in body mass index (BMI) and metabolic status on the risk of type 2 diabetes (T2D) and to identify biological predictors for the MHO-to-MUO transition. PATIENTS AND METHODS: A total of 4604 subjects from the REACTION study were included for a 3-year follow-up. Subjects were categorized based on their BMI and metabolic syndrome status. Overweight/obesity was defined as BMI ≥ 24 kg/m2. Metabolically healthy was defined as having two or fewer of the metabolic syndrome components proposed by the Chinese Diabetes Society. Thus, subjects were divided into four groups: metabolically healthy normal weight (MHNW), MHO, metabolically unhealthy normal weight (MUNW), and MUO. RESULTS: Compared with MHNW, MHO was not predisposed to an increased risk for T2D (OR 1.08, 95% CI 0.64-1.83, P = 0.762). However, a 3-year transition probability of 20.6% was identified for subjects who shifted from MHO to MUO; this conversion increased the risk of T2D by 3-fold (OR 3.04, 95% CI 1.21-7.68, P = 0.018). The fatty liver index independently predicted the MHO-to-MUO transition with an OR 3.14 (95% CI 1.56-7.46, P = 0.002) when comparing the fourth quartile to the first quartile. CONCLUSION: This study reveals that metabolic changes affect the short-term susceptibility to T2D in the overweight/obese Chinese population, and the fatty liver index is an efficient clinical parameter for identifying those with a metabolic deterioration risk.

Obesity is associated with severe COVID-19 but not death: a dose-response meta-analysis.

The coronavirus disease 2019 (COVID-19) epidemic is spreading globally. Studies revealed that obesity may affect the progression and prognosis of COVID-19 patients. The aim of the meta-analysis is to identify the prevalence and impact of obesity on COVID-19. Studies on obese COVID-19 patients were obtained by searching PubMed, Cochrane Library databases and Web of Science databases, up to date to 5 June 2020. And the prevalence rate and the odds ratio (OR) of obesity with 95% confidence interval (CI) were used as comprehensive indicators for analysis using a random-effects model. A total of 6081 patients in 11 studies were included. The prevalence of obesity in patients with COVID-19 was 30% (95% CI 21-39%). Obese patients were 1.79 times more likely to develop severe COVID-19 than non-obese patients (OR 1.79, 95% CI 1.52-2.11, P < 0.0001, I2 = 0%). However obesity was not associated with death in COVID-19 patients (OR 1.05, 95% CI 0.65-1.71, P = 0.84, I2 = 66.6%). In dose-response analysis, it was estimated that COVID-19 patients had a 16% increased risk of invasive mechanical ventilation (OR 1.16, 95% CI 1.10-1.23, P < 0.0001) and a 20% increased risk of admission to ICU (OR 1.20, 95% CI 1.11-1.30, P < 0.0001) per 5 kg/m2 increase in BMI. In conclusion, obesity in COVID-19 patients is associated with severity, but not mortality.