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.

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.

DNA methylation profiling reveals novel pathway implicated in cardiovascular diseases of diabetes.

Objective: Epigenetics was reported to mediate the effects of environmental risk factors on disease pathogenesis. We intend to unleash the role of DNA methylation modification in the pathological process of cardiovascular diseases in diabetes. Methods: We screened differentially methylated genes by methylated DNA immunoprecipitation chip (MeDIP-chip) among the enrolled participants. In addition, methylation-specific PCR (MSP) and gene expression validation in peripheral blood of participants were utilized to validate the DNA microarray findings. Results: Several aberrantly methylated genes have been explored, including phospholipase C beta 1 (PLCB1), cam kinase I delta (CAMK1D), and dopamine receptor D5 (DRD5), which participated in the calcium signaling pathway. Meanwhile, vascular endothelial growth factor B (VEGFB), placental growth factor (PLGF), fatty acid transport protein 3 (FATP3), coagulation factor II, thrombin receptor (F2R), and fatty acid transport protein 4 (FATP4) which participated in vascular endothelial growth factor receptor (VEGFR) signaling pathway were also found. After MSP and gene expression validation in peripheral blood of participants, PLCB1, PLGF, FATP4, and VEGFB were corroborated. Conclusion: This study revealed that the hypomethylation of VEGFB, PLGF, PLCB1, and FATP4 might be the potential biomarkers. Besides, VEGFR signaling pathway regulated by DNA methylation might play a role in the cardiovascular diseases' pathogenesis of diabetes.

Corrigendum: Case Report: A Chinese Family of Woodhouse-Sakati Syndrome With Diabetes Mellitus, With a Novel Biallelic Deletion Mutation of the DCAF17 Gene.

[This corrects the article DOI: 10.3389/fendo.2021.770871.].

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.

Integrative Analyses of Genes Associated with Hashimoto's Thyroiditis.

OBJECTIVE: Hashimoto's thyroiditis, also known as chronic lymphocytic thyroiditis, is a common autoimmune thyroiditis, which mostly occurs in young and middle-aged women. It can be manifested as hyperthyroidism in the early stage; hypothyroidism may appear with the progression of the disease. Studies have shown that multiple factors such as heredity, environment, and autoimmunity are involved in the pathogenesis, but the specific mechanism is not clear. In our study, we tried to find key genes and potential molecular mechanisms of Hashimoto's thyroiditis to provide new ideas for the therapeutic targets of Hashimoto's thyroiditis. METHOD: GSE138198 and GSE54958 were downloaded from the GEO database, and two datasets were combined for analysis. The combined data were normalized to identify the differentially expressed genes (DEGs), and GO and KEGG enrichment analyses were performed. Protein-protein interaction (PPI) networks and hub genes between DEGs were identified. We also used the miRWalk database to identify regulatory miRNAs associated with expressions of DEGs. RESULT: We identified 182 DEGs (160 upregulated and 22 downregulated) between Hashimoto's disease patients and the healthy control group. GO analysis showed that DEGs were mostly concentrated in detection of chemical stimulus involved in sensory perception, intermediate filament cytoskeleton, and olfactory receptor activity. KEGG pathway analysis showed that DEGs were mainly related to olfactory transduction. Some members of the KRTAP family and HTR5A, KNG1, DRD3, HTR1D, TAS2R16, INSL5, TAS2R42, and GRM7 are the most important hub genes in the PPI network. In addition, we recognized that OTUD4, LLPH, and ECHDC1 were the most important hub genes in the miRNA-target gene network. CONCLUSION: In this study, a series of bioinformatics analyses of DEGs were performed to identify the key genes and pathways associated with Hashimoto's thyroiditis. These genes and pathways provide a more detailed understanding of the pathogenesis of Hashimoto's disease and provide new ideas for the therapeutic targets of Hashimoto's thyroiditis.

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.

Case Report: A Chinese Family of Woodhouse-Sakati Syndrome With Diabetes Mellitus, With a Novel Biallelic Deletion Mutation of the DCAF17 Gene.

Woodhouse-Sakati syndrome (WSS) (OMIM#241080) is a rare multi-system autosomal recessive disease with homozygous mutation of the DCAF17 gene. The main features of WSS include diabetes, hypogonadism, alopecia, deafness, intellectual disability and progressive extrapyramidal syndrome. We identified a WSS family with a novel DCAF17 gene mutation type in China. Two unconsanguineous siblings from the Chinese Han family exhibiting signs and symptoms of Woodhouse-Sakati syndrome were presented for evaluation. Whole-exome sequencing revealed a homozygous deletion NM_025000.4:c.1488_1489delAG in the DCAF17 gene, which resulted in a frameshift mutation that led to stop codon formation. We found that the two patients exhibited low insulin and C-peptide release after glucose stimulation by insulin and C-peptide release tests. These findings indicate that the DCAF17 gene mutation may cause pancreatic ß cell functional impairment and contribute to the development of diabetes.

Identification of Inflammation-Related Biomarker Pro-ADM for Male Patients With Gout by Comprehensive Analysis.

Objective: Gout is a local inflammatory disease caused by the deposition of monosodium urate (MSU) crystals in joints or adjacent tissues. When some gout occurs without hyperuricemia, or its clinical symptoms and signs are not typical, the diagnosis of gout will be delayed, so there is an urgent need to find a new biomarker to predict and diagnose of gout flare. Our research attempts to find the key genes and potential molecular mechanisms of gout through bioinformatics analysis, and collected general data and blood biochemical samples of patients with gout and healthy, then analyzed and compared the expression of factors regulated by key genes. Method: GSE160170 were downloaded from GEO database for analysis. The data were normalized to identify the differentially expressed genes (DEGs), then GO and KEGG enrichment analysis were applied. Protein-protein interaction (PPI) networks and hub genes between DEGs were identified. Then collect general information and blood samples from male patients with acute gout, hyperuricemia and healthy. ELISA method was used to detect pro-ADM levels of different groups, and the data was input into SPSS statistical software for analysis. Result: We identified 266 DEGs (179 up-regulated and 87 down-regulated) between gout patients and healthy controls. GO analysis results show that DEGs are mostly enriched in inflammatory response, growth factor activity, cytokine activity, chemokine activity, S100 protein binding and CXCR chemokine receptor binding. KEGG pathway analysis showed that DEGs are mainly related to Chemokine signaling pathway and Cytokine-cytokine receptor interaction. ADM, CXCR1, CXCR6, CXCL3, CCL3, CCL18, CCL3L3, CCL4L1, CD69, CD83, AREG, EREG, B7RP1, HBEGF, NAMPT and S100B are the most important hub genes in the PPI network. We found that the expression of pro-ADM in the gout group and hyperuricemia group was higher than that in the healthy group, and the difference was statistically significant. Conclusion: In this study, a series of bioinformatics analyses were performed on DEGs to identify key genes and pathways related to gout. Through clinical verification, we found that pro-ADM can be used as an inflammation-related biomarker for acute attacks of gout, providing new ideas for the diagnosis and treatment of gout.

Impaired Fasting Glucose and Diabetes Are Related to Higher Risks of Complications and Mortality Among Patients With Coronavirus Disease 2019.

Background: Diabetes correlates with poor prognosis in patients with COVID-19, but very few studies have evaluated whether impaired fasting glucose (IFG) is also a risk factor for the poor outcomes of patients with COVID-19. Here we aimed to examine the associations between IFG and diabetes at admission with risks of complications and mortality among patients with COVID-19. Methods: In this multicenter retrospective cohort study, we enrolled 312 hospitalized patients with COVID-19 from 5 hospitals in Wuhan from Jan 1 to Mar 17, 2020. Clinical information, laboratory findings, complications, treatment regimens, and mortality status were collected. The associations between hyperglycemia and diabetes status at admission with primary composite end-point events (including mechanical ventilation, admission to intensive care unit, or death) were analyzed by Cox proportional hazards regression models. Results: The median age of the patients was 57 years (interquartile range 38-66), and 172 (55%) were women. At the time of hospital admission, 84 (27%) had diabetes (and 36 were new-diagnosed), 62 (20%) had IFG, and 166 (53%) had normal fasting glucose (NFG) levels. Compared to patients with NFG, patients with IFG and diabetes developed more primary composite end-point events (9 [5%], 11 [18%], 26 [31%]), including receiving mechanical ventilation (5 [3%], 6 [10%], 21 [25%]), and death (4 [2%], 9 [15%], 20 [24%]). Multivariable Cox regression analyses showed diabetes was associated increased risks of primary composite end-point events (hazard ratio 3.53; 95% confidence interval 1.48-8.40) and mortality (6.25; 1.91-20.45), and IFG was associated with an increased risk of mortality (4.11; 1.15-14.74), after adjusting for age, sex, hospitals and comorbidities. Conclusion: IFG and diabetes at admission were associated with higher risks of adverse outcomes among patients with COVID-19.

Newly diagnosed diabetes is associated with a higher risk of mortality than known diabetes in hospitalized patients with COVID-19.

AIM: To evaluate the association between different degrees of hyperglycaemia and the risk of all-cause mortality among hospitalized patients with COVID-19. MATERIALS AND METHODS: In a retrospective study conducted from 22 January to 17 March 2020, 453 patients were admitted to Union Hospital in Wuhan, China, with laboratory-confirmed severe acute respiratory syndrome coronavirus 2 infection. Patients were classified into four categories: normal glucose, hyperglycaemia (fasting glucose 5.6-6.9 mmol/L and/or HbA1c 5.7%-6.4%), newly diagnosed diabetes (fasting glucose ≥7 mmol/L and/or HbA1c ≥6.5%) and known diabetes. The major outcomes included in-hospital mortality, intensive care unit (ICU) admission and invasive mechanical ventilation (IMV). RESULTS: Patients with newly diagnosed diabetes constituted the highest percentage to be admitted to the ICU (11.7%) and require IMV (11.7%), followed by patients with known diabetes (4.1%; 9.2%) and patients with hyperglycaemia (6.2%; 4.7%), compared with patients with normal glucose (1.5%; 2.3%), respectively. The multivariable-adjusted hazard ratios of mortality among COVID-19 patients with normal glucose, hyperglycaemia, newly diagnosed diabetes and known diabetes were 1.00, 3.29 (95% confidence interval [CI] 0.65-16.6), 9.42 (95% CI 2.18-40.7) and 4.63 (95% CI 1.02-21.0), respectively. CONCLUSION: We showed that COVID-19 patients with newly diagnosed diabetes had the highest risk of all-cause mortality compared with COVID-19 patients with known diabetes, hyperglycaemia and normal glucose. Patients with COVID-19 need to be kept under surveillance for blood glucose screening.