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Objective: Type 2 diabetes mellitus (T2DM) is a metabolic disease characterized by insulin resistance and progressively impaired insulin secretion resulting in dynamic fluctuations in glucose levels.High blood urea nitrogen (BUN) levels have been linked to decreased insulin sensitivity, suppressed insulin synthesis and increased risk of incident diabetes mellitus in humans as well as insulin use in patients with T2DM.This study characterize the association between BUN levels and short-term and long-term glycemic variability(GV) in the elderly patients with T2DM who were hospitalized. Methods: A total of 927 elderly patients with T2DM were included in the study. The short-term GV was quantified using parameters such as standard deviation (SD), coefficient of variation (CV), time in range (TIR), and mean amplitude of glycemic excursions (MAGE), based on multi-point fingertip blood glucose monitoring. The long-term GV was quantified using parameters such as SD, CV, variation independent of the mean (VIM), and average successive variability (ARV), based on fasting blood glucose(FPG). The relationship between BUN levels and short-term and long-term GV in elderly T2DM who were hospitalized was explored using methods such as Spearman correlation coefficient, linear regression analysis, logistic regression analysis, and interaction tests. Results: In elderly patients with T2DM were hospitalized, there is a significant correlation between BUN levels and both short-term and long-term GV. BUN is negatively correlated with the GV parameter TIR (r=-0.12, P=0.000), and positively correlated with SD (r=0.12, P=0.000), CV (r=0.07, P=0.026), MAGE (r=0.11, P=0.001), FPG-SD (r=0.08, P=0.013), and FPG-CV (r=0.08, P=0.014).Furthermore, the association remains consistent across different age, gender, BMI, and haemoglobin A1c (HbA1c) subgroups (P interaction > 0.05). Conclusion: In elderly patients with T2DM were hospitalized, BUN levels were positively associated with GV.Therefore, monitoring BUN levels were beneficial in assessing the degree of GV.
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This large-scale prospective study showed that a significant association between longer duration of daily outdoor walking and reduced osteoporosis risk was found among older adults, particularly among those with a low genetic predisposition to osteoporosis, which highlighted the importance of outdoor walking as a simple, cost-effective adjunct for preventing osteoporosis. PURPOSE: The available cross-sectional data and small-scale studies indicate that outdoor walking benefits bone metabolism. Nevertheless, there is a scarcity of comprehensive prospective research investigating the enduring correlation between outdoor walking and osteoporosis. This study aims to conduct a prospective analysis of the correlation between outdoor walking and osteoporosis while also examining potential variations influenced by genetic susceptibility to osteoporosis. METHODS: 24,700 older adults without osteoporosis at baseline were enrolled. These individuals were followed up until December 31, 2021, during which data on outdoor walking was gathered. The genetic risk score for osteoporosis was comprised of 14 single-nucleotide polymorphisms. RESULTS: 4,586 cases of osteoporosis were identified throughout a median follow-up period of 37.3 months. Those who walked outside for > 30 but ≤ 60 min per day had a hazard ratio (HR) of 0.83 (95% confidence interval (CI): 0.72-0.95) for incident osteoporosis, whereas those who walked outside for > 60 min per day had an HR of 0.60 (95% CI: 0.39-0.92). We found that osteoporosis risk exhibited a declining trend in individuals with low genetic risk. Individuals walking outside for > 60 min per day tended to have the lowest overall osteoporosis risk among those with high genetic risk. CONCLUSIONS: A significant negative correlation exists between an extended period of daily outdoor walking and osteoporosis incidence risk. This correlation is particularly pronounced among individuals with low genetic risk. The results above underscore the significance of outdoor walking as a simple and economical adjunct to public health programs to prevent osteoporosis.
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Predisposición Genética a la Enfermedad , Osteoporosis , Polimorfismo de Nucleótido Simple , Caminata , Humanos , Femenino , Anciano , Masculino , Caminata/fisiología , Estudios Prospectivos , Osteoporosis/genética , Osteoporosis/epidemiología , Incidencia , Persona de Mediana Edad , Factores de Riesgo , Medición de Riesgo/métodos , Anciano de 80 o más Años , Densidad Ósea/genética , Densidad Ósea/fisiologíaRESUMEN
Background: Predicting hypoglycemia while maintaining a low false alarm rate is a challenge for the wide adoption of continuous glucose monitoring (CGM) devices in diabetes management. One small study suggested that a deep learning model based on the long short-term memory (LSTM) network had better performance in hypoglycemia prediction than traditional machine learning algorithms in European patients with type 1 diabetes. However, given that many well-recognized deep learning models perform poorly outside the training setting, it remains unclear whether the LSTM model could be generalized to different populations or patients with other diabetes subtypes. Objective: The aim of this study was to validate LSTM hypoglycemia prediction models in more diverse populations and across a wide spectrum of patients with different subtypes of diabetes. Methods: We assembled two large data sets of patients with type 1 and type 2 diabetes. The primary data set including CGM data from 192 Chinese patients with diabetes was used to develop the LSTM, support vector machine (SVM), and random forest (RF) models for hypoglycemia prediction with a prediction horizon of 30 minutes. Hypoglycemia was categorized into mild (glucose=54-70 mg/dL) and severe (glucose<54 mg/dL) levels. The validation data set of 427 patients of European-American ancestry in the United States was used to validate the models and examine their generalizations. The predictive performance of the models was evaluated according to the sensitivity, specificity, and area under the receiver operating characteristic curve (AUC). Results: For the difficult-to-predict mild hypoglycemia events, the LSTM model consistently achieved AUC values greater than 97% in the primary data set, with a less than 3% AUC reduction in the validation data set, indicating that the model was robust and generalizable across populations. AUC values above 93% were also achieved when the LSTM model was applied to both type 1 and type 2 diabetes in the validation data set, further strengthening the generalizability of the model. Under different satisfactory levels of sensitivity for mild and severe hypoglycemia prediction, the LSTM model achieved higher specificity than the SVM and RF models, thereby reducing false alarms. Conclusions: Our results demonstrate that the LSTM model is robust for hypoglycemia prediction and is generalizable across populations or diabetes subtypes. Given its additional advantage of false-alarm reduction, the LSTM model is a strong candidate to be widely implemented in future CGM devices for hypoglycemia prediction.
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Context: Selecting appropriate individuals for genetic testing is essential due to the optimal treatment for maturity-onset diabetes of the young (MODY). However, how to effectively screen for MODY in China remains unclear. Objective: To validate the performance of current screening strategies in selecting patients with MODY based on a nationwide type 2 diabetes cohort. Methods: A panel of 14 MODY genes was analyzed from 1911 type 2 diabetes patients who were ages 15 to 35 years. Variants were evaluated according to the American College of Medical Genetics and Genomics guidelines. Based on this cohort, we simulated the 2 most frequently used screening strategies, including the traditional MODY criteria and the MODY probability calculator (MPC), to assess their ability to select patients with MODY. Results: From a total of 1911 participants, 42 participants harbored pathogenic/likely pathogenic variants. The performance of the traditional criteria was sensitivity: 19.0%, specificity: 72.9%, positive predictive value (PPV): 1.6%, and missing rate: 81.0%. The optimal cut-off for MPC was 40.7%. Based on this cut-off value, the performance was sensitivity: 54.8%, specificity: 81.0%, PPV: 6.1%, and missing rate: 45.2%. Moreover, hemoglobin A1c, insulin treatment, and family history of diabetes have poor discrimination between MODY and young-onset type 2 diabetes. Conclusion: The MPC is better than traditional criteria in terms of both sensitivity and PPV. To ensure more MODY patients benefit from optimal treatment, we therefore suggest that routine genetic testing be performed on all type 2 diabetes patients who are between the ages of 15 and35 years and have MPC probability value over 40.7%.
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Integrative and conjugative elements (ICEs) are important vectors of lateral gene transfer and contribute to the evolution of bacterial pathogens. However, studies on the transfer among species and the physiological consequences of ICEs are rare. The objective of this study was to investigate the cross-species transferability of newly identified erm(B)-carried ICE in Streptococcus anginosus San95 and its physiological consequences after transfer. The erm(B)-carried ICE, characterized by a triple serine integrase module, integrated into hsdM genes, thus designated ICESan95_hsdM. Analysis of ICESan95_hsdM revealed 32 additional ICESan95-like ICEs in the available NCBI genome (n = 24) and sequence of clinical isolates (n = 8). Polymerase chain reaction (PCR) was used to evaluate the 467 clinical isolates, of which 84 were positive for core genes (integrase, relaxase, and T4SS genes) of ICESan95_hsdM. Cross-species transfer experiments demonstrated that ICESan95_hsdM could transfer from S. anginosus to different streptococcal and enterococcal recipients. Growth and competitive culture assays showed acquisition of ICESan95_hsdM incurred no fitness cost. Our work discovered a group of ICEs in Streptococci and Enterococci. For the first time, we demonstrated the broad cross-species transferability to different species or genera of ICEs with no fitness cost that enables commensal S. anginosus to deliver antimicrobial resistance genes to other streptococci and enterococci.
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Antibacterianos , Conjugación Genética , Enterococcus , Transferencia de Gen Horizontal , Streptococcus anginosus , Streptococcus anginosus/genética , Streptococcus anginosus/efectos de los fármacos , Antibacterianos/farmacología , Enterococcus/genética , Enterococcus/efectos de los fármacos , Streptococcus/genética , Streptococcus/efectos de los fármacos , Pruebas de Sensibilidad Microbiana , Humanos , Proteínas Bacterianas/genética , Farmacorresistencia Bacteriana/genéticaRESUMEN
AIMS: To investigate the sex-specific associations between predicted skeletal muscle mass index (pSMI) and incident type 2 diabetes in a retrospective longitudinal cohort of Chinese men and women. MATERIALS AND METHODS: We enrolled Chinese adults without diabetes at baseline from WATCH (West chinA adulT health CoHort), a large health check-up-based database. We calculated pSMI to estimate skeletal muscular mass, and measured blood glucose variables and assessed self-reported history to identify new-onset diabetes. The nonlinear association between pSMI and incident type 2 diabetes was modelled using the penalized spline method. The piecewise association was estimated using segmented linear splines in weighted Cox proportional hazards regression models. RESULTS: Of 47 885 adults (53.2% women) with a median age of 40 years, 1836 developed type 2 diabetes after a 5-year median follow-up. In women, higher pSMI was associated with a lower risk of incident type 2 diabetes (Pnonlinearity = 0.09, hazard ratio [HR] per standard deviation increment in pSMI: 0.79 [95% confidence interval {CI} 0.68, 0.91]). A nonlinear association of pSMI with incident type 2 diabetes was detected in men (Pnonlinearity < 0.001). In men with pSMI lower than 8.1, higher pSMI was associated with a lower risk of incident type 2 diabetes (HR 0.58 [95% CI 0.40, 0.84]), whereas pSMI was not significantly associated with incident diabetes in men with pSMI equal to or greater than 8.1 (HR 1.08 [95% CI 0.93, 1.25]). CONCLUSIONS: In females, a larger muscular mass is associated with a lower risk of type 2 diabetes. For males, this association is significant only among those with diminished muscle mass.
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Diabetes Mellitus Tipo 2 , Adulto , Masculino , Humanos , Femenino , Diabetes Mellitus Tipo 2/epidemiología , Estudios de Cohortes , Estudios Retrospectivos , Músculo Esquelético , China/epidemiología , Factores de Riesgo , IncidenciaRESUMEN
BACKGROUND: To subgroup Chinese patients with newly diagnosed type 2 diabetes (T2D) by K-means cluster analysis on clinical indicators, and to explore whether these subgroups represent different genetic features and calculated cardiovascular risks. METHODS: The K-means clustering analysis was performed on two cohorts (n = 590 and 392), both consisting of Chinese participants with newly diagnosed T2D. To assess genetic risks, multiple polygenic risk scores (PRSs) and mitochondrial DNA copy numbers (mtDNA-CN) were calculated for all participants. Furthermore, Framingham risk scores (FRS) of cardiovascular diseases in two cohorts were also calculated to verify the genetic risks. RESULTS: Four clusters were identified including the mild age-related diabetes (MARD)(35.08%), mild obesity-related diabetes (MOD) (34.41%), severe autoimmune diabetes (SAID) 19.15%, and severe insulin-resistant diabetes (SIRD) 11.36% subgroups in the MARCH (metformin, and acarbose in Chinese patients as the initial hypoglycemic treatment) cohort. There was a significant difference in PRS for cardiovascular diseases (CVD) across four subgroups in the MARCH cohort (p < 0.05). Compared with the SIDD and SIRD subgroups, patients in the MOD subgroup had a relatively lower PRS for CVD (p < 0.05) in the MARCH cohort. Females had a higher PRS compared to males, with no significant difference in FRS across the four clusters. The MOD subgroup had a significantly lower FRS which was consistent with the results of PRS. Similar results of PRS and FRS were also replicated in the CONFIDENCE (comparison of glycemic control and b-cell function among newly diagnosed patients with type 2 diabetes treated with exenatide, insulin or pioglitazone) cohort. CONCLUSION: There are different CVD risks in diabetic subgroups based on clinical and genetic evidence which may promote precision medicine.
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This study aims to determine whether CYP2C19 loss-of-function (LoF) variants were associated with long-term ischemic stroke risk in Chinese primary care patients treated with clopidogrel. Patients treated with clopidogrel were ascertained from Chinese electronic medical records linked with a biobank for a retrospective cohort study. Their medical information was examined for the period from January 2018 to December 2021. Two CYP2C19 major loss of function variants (*2:rs4244285 and *3: rs4986893) were genotyped. The clinical outcome was ischemic stroke event. Cox regression analysis was used to evaluate the association between the occurrence of ischemic stroke events and CYP2C19 LoF variants. Covariates included age, gender, body mass index, prior ischemic stroke, transient ischemic attack, hypertension, diabetes mellitus, hyperlipoidemia, smoke status, aspirin use, proton-pump inhibitor use, and statin use. Of the 1,141 patients included in the clopidogrel therapy cohort, 61.9% carried at least one CYP2C19 LoF variant. During a median follow-up period of 12 months, 103 patients (9.0%) had an ischemic stroke. After adjusting for other risk factors, carriers of CYP2C19 LoF variants had significantly higher risk of ischemic stroke compared with non-carriers (hazard ratio: 1.64, 95% confidence interval: 1.06-2.53, P = 0.025). This pharmacogenetic study of clopidogrel provides novel insights into the association between the CYP2C19 LoF variant and long-term stroke risk. We established that there is still a need for CYP2C19 genotype-guided personalized antiplatelet therapy in those who have returned to the primary care setting for clopidogrel prescription.
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BACKGROUND: Mitochondrial dysfunction in kidney cells has been implicated in the pathogenesis of chronic kidney disease (CKD). Estimation of mitochondrial DNA copy number (mtDNA-CN) is considered a convenient method for representing mitochondrial function in large samples. However, no study has investigated the association between mtDNA-CN and CKD in older adults with the highest prevalence. The objective is to examine cross-sectional and prospective associations between mtDNA-CN values and CKD risk in older adults to determine whether mtDNA-CN represents a novel potential biomarker for the recognition of CKD risk. PATIENTS AND METHODS: In a Chinese community-based cohort of over 65-year-olds, we included 14,467 participants (52.6% females). CKD was defined by eGFR < 60 mL/min/1.73 m2 or ICD-10 codes (patients = 3831 (26.5%)). Participants had peripheral blood levels of mtDNA-CN calculated from probe intensities of the Axiom CAS Array. RESULTS: The risk of CKD prevalence decreased with mtDNA-CN per 1-SD increment, independent of established risk factors for older CKD (odds ratio [OR] per SD 0.90, 95% confidence interval [CI] 0.86, 0.93, P < 0.001), and has comparable strength of association with these established risk factors. Furthermore, the progression of kidney function was stratified according to the worsening of eGFR categories. The risk of kidney function progression to a more severe stage gradually decreased as the mtDNA-CN increased (P trend < 0.001). Non-CKD participants in the highest quartile of mtDNA-CN had a lower risk of developing CKD compared to the lowest quartile within 2 years of follow-up, reducing the risk of CKD by 36% (95% CI 0.42, 0.97; P = 0.037). CONCLUSIONS: Based on the analysis of the largest sample to date investigating the association between mtDNA-CN and CKD in older adults, higher levels of mtDNA-CN were found to be associated with a lower risk of CKD, suggesting that a reduced level of mtDNA-CN is a potential risk factor for CKD.
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ADN Mitocondrial , Insuficiencia Renal Crónica , Femenino , Humanos , Anciano , Masculino , ADN Mitocondrial/genética , Estudios Transversales , Variaciones en el Número de Copia de ADN/genética , Mitocondrias , Insuficiencia Renal Crónica/diagnóstico , Insuficiencia Renal Crónica/epidemiología , Insuficiencia Renal Crónica/genéticaRESUMEN
Aim: This study examined intronic gene variants for their association with metformin intolerance in a Chinese population, focusing on the plasma monoamine transporter (PMAT) cis-protein expression quantitative trait loci (cis-eQTL) variant rs3889348. Methods: We recruited Type 2 diabetes patients from two hospitals and identified 111 metformin-intolerant patients using a questionnaire, and selected 206 metformin-tolerant patients from 2180 Type 2 diabetes mellitus patients. Genetic testing revealed an association between adverse gastrointestinal (GI) effects and SLC22A1 and PMAT. Results: The single-nucleotide polymorphism rs3889348 is associated with metformin-induced adverse GI effects. Each additional copy of the G allele increases the score by 5.23 (95% CI: 1.82-8.64; p = 0.003). Patients taking more transporter inhibitors were more likely to respond to metformin-induced GI intolerance (p = 0.042). Conclusion: PMAT cis-eQTL rs3889348 was significantly associated with metformin-induced adverse GI effects.
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Diabetes Mellitus Tipo 2 , Hipoglucemiantes , Metformina , Humanos , Diabetes Mellitus Tipo 2/tratamiento farmacológico , Diabetes Mellitus Tipo 2/genética , Pueblos del Este de Asia/genética , Hipoglucemiantes/efectos adversos , Metformina/efectos adversos , Polimorfismo de Nucleótido Simple/genética , Enfermedades Gastrointestinales/inducido químicamente , Enfermedades Gastrointestinales/genéticaRESUMEN
Diabetes mellitus is a metabolic disorder with a complex etiology in which glycemic dynamics are disturbed and the body is unable to maintain the process of glucose homeostasis through the pancreas. Persistent symptoms of high blood glucose or low blood glucose may lead to diabetic complications, such as neuropathy, nephropathy, retinopathy, and cardiovascular diseases. Glycemic variability which can represent the presence of excessive glycemic excursions is an indicator for evaluating glucose homoeostasis. Limiting glycemic variability has gradually become an emerging therapeutic target in improve diabetes metabolism and prevent associated complications. This article reviews the progress of research on the various quantifiable parameters of glycemic variability and their relationships with vascular lesions and mechanisms.
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Hyperglycemia is a key risk factor for death and disability worldwide. To better inform prevention strategies, we aimed to delineate and predict the temporal, spatial, and demographic patterns in mean fasting plasma glucose (FPG) levels and their related disease burden globally. Based on the Global Burden of Disease Study 2019, we estimated the distributions of mean FPG levels and high FPG-related disease burden by age, sex, year, socioeconomic status (SES), and geographical region from 1990 to 2050. We also investigated the possible associations of demographic, behavioral, dietary, metabolic, and environmental factors with FPG levels and high FPG-related disease burden. In 2019, the global mean FPG level was 5.40 mmol/L (95% uncertainty interval [UI]: 4.86-6.00), and high FPG contributed to 83.0 deaths (95% UI, 64.5-107.1) and 2,104.3 DALYs (95% UI: 1,740.7-2,520.7) per 100,000 people. For both historical (1990-2019) and future (2020-2050) periods, the mean FPG levels and the high FPG-related disease burden increased globally, with greater increases among the middle-aged and elderly, and people in low-to-middle SES countries, relative to their counterparts. Aging, unhealthy lifestyles, elevated body mass index, and lower air temperatures were potential risk factors for high FPG levels and the high FPG-related disease burden. This study demonstrates that high FPG continues to contribute to the global disease burden and is expected to do so for at least the next 30 years. Older people and those living in low-to-middle SES countries should receive more attention in glycemic management health interventions. In addition, effective interventions that target identified risk factors should be adopted to handle the increasingly large disease burden of high FPG.
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BACKGROUND: Observational studies suggested a close link between type 2 diabetes (T2D), metabolic factors and depression, while the causal relationships remained poorly understood. OBJECTIVE: To determine the causality between T2D and depression, and to investigate the roles of metabolic factors in mediating the relationship between T2D and depression in East Asians. METHODS: Using summary statistics from the largest and most up-to-date genome-wide association studies of depression (12,588 cases and 85,914 controls) and T2D (36,614 cases and 155,150 controls) among East Asians, two-step and two-sample MR analyses were performed to estimate the causal mediation effects of metabolic factors including lipid profiles, blood pressure (BP) and fasting insulin (FI) on the relationship between T2D and depression. RESULTS: Genetically predicted T2D was significantly associated with depression (OR [95 % CI]:1.06 [1.01, 1.11], P = 0.043), but not vice versa. T2D was causally associated with lower levels of HDL-C and higher levels of LDL-C, triglycerides (TG), BP and FI. Furthermore, the causal effects of T2D on depression were significantly mediated by LDL-C (ß [95 % CI]: -0.003 [-0.005, -0.001], P = 0.007), and suggestively mediated by TG (0.001 [0.001, 0.003], P = 0.049) and FI (0.006 [0.001, 0.012], P = 0.049). LIMITATIONS: First, depression was defined by several methods, like symptom questionnaires or self-completed surveys. Second, two-sample MR approach is unable to detect the non-linear causal relationships. Third, independent data sets were not available for replication of our findings. CONCLUSION: T2D was causally associated with the risk of depression, and LDL-C, TG, and FI were potential causal mediators of the effect of T2D on depression. Understanding the causality among T2D, metabolic factors and depression is crucial for identifying potential targets for early intervention.
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Diabetes Mellitus Tipo 2 , Humanos , Diabetes Mellitus Tipo 2/epidemiología , Diabetes Mellitus Tipo 2/genética , Factores de Riesgo , Pueblos del Este de Asia , Estudio de Asociación del Genoma Completo , Análisis de la Aleatorización Mendeliana/métodos , LDL-Colesterol , Depresión/epidemiología , Depresión/genética , Insulina , Triglicéridos , Polimorfismo de Nucleótido SimpleRESUMEN
Inter-site interaction in densely populated single-atom catalysts has been demonstrated to have a crucial role in regulating the electronic structure of metal atoms, and consequently their catalytic performances. We herein report a general and facile strategy for the synthesis of several densely populated single-atom catalysts. Taking cobalt as an example, we further produce a series of Co single-atom catalysts with varying loadings to investigate the influence of density on regulating the electronic structure and catalytic performance in alkene epoxidation with O2. Interestingly, the turnover frequency and mass-specific activity are significantly enhanced by 10 times and 30 times with increasing Co loading from 5.4 wt% to 21.2 wt% in trans-stilbene epoxidation, respectively. Further theoretical studies reveal that the electronic structure of densely populated Co atoms is altered through charge redistribution, resulting in less Bader charger and higher d-band center, which are demonstrated to be more beneficial for the activation of O2 and trans-stilbene. The present study demonstrates a new finding about the site interaction in densely populated single-atom catalysts, shedding insight on how density affects the electronic structure and catalytic performance for alkene epoxidation.
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Background: Chronic diseases are becoming a critical challenge to the aging Chinese population. Biobanks with extensive genomic and environmental data offer opportunities to elucidate the complex gene-environment interactions underlying their aetiology. Genome-wide genotyping array remains an efficient approach for large-scale genomic data collection. However, most commercial arrays have reduced performance for biobanking in the Chinese population. Materials and methods: Deep whole-genome sequencing data from 2 641 Chinese individuals were used as a reference to develop the CAS array, a custom-designed genotyping array for precision medicine. Evaluation of the array was performed by comparing data from 384 individuals assayed both by the array and whole-genome sequencing. Validation of its mitochondrial copy number estimating capacity was conducted by examining its association with established covariates among 10 162 Chinese elderly. Results: The CAS Array adopts the proven Axiom technology and is restricted to 652 429 single-nucleotide polymorphism (SNP) markers. Its call rate of 99.79% and concordance rate of 99.89% are both higher than for commercial arrays. Its imputation-based genome coverage reached 98.3% for common SNPs and 63.0% for low-frequency SNPs, both comparable to commercial arrays with larger SNP capacity. After validating its mitochondrial copy number estimates, we developed a publicly available software tool to facilitate the array utility. Conclusion: Based on recent advances in genomic science, we designed and implemented a high-throughput and low-cost genotyping array. It is more cost-effective than commercial arrays for large-scale Chinese biobanking.
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Metformin is the first-line drug for type 2 diabetes (T2D) while acarbose is suggested as a viable alternative in Chinese patients with newly diagnosed T2D. However, few biomarkers have been established to guide the choice between these two agents. Mitochondrial DNA (mtDNA) copy number (mtDNA-CN) is a biomarker of mitochondrial function, which is associated with various metabolic outcomes. Using data from the trial of Metformin and Acarbose in Chinese as the Initial Hypoglycaemic Treatment (MARCH) (metformin n = 214; acarbose n = 198), we examined whether mtDNA-CN was associated with response to the drugs in terms of glycemic response and ß-cell function protection response. The glycemic response is defined as the maximum glucose reduction of glycated hemoglobin A1c , fasting plasma glucose, or postprandial blood glucose during 48 weeks. ß-cell function protection response is defined as the maximum increment of insulinogenic index (IGI) or disposition index (DI). For all three glycemic responses, mtDNA-CN was not significantly associated with either metformin or acarbose. Importantly, for ß-cell function protection response, we found the increased mtDNA-CN was significantly associated with more IGI increment (beta: 0.84; 95% confidence interval (CI), 0.02 to 1.66) in the metformin group, but less IGI increment (beta: -1.38; 95% CI, -2.52 to -0.23) in the acarbose group. A significant interaction (P = 0.008) between mtDNA-CN and the treatment group was observed. Consistent results were also obtained when DI increment was used as a measure of ß-cell function response. This study demonstrated the potential application of mtDNA-CN in guiding the treatment choice between metformin and acarbose based on ß-cell protection.
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Diabetes Mellitus Tipo 2 , Metformina , Humanos , Metformina/uso terapéutico , Acarbosa/uso terapéutico , Diabetes Mellitus Tipo 2/diagnóstico , Diabetes Mellitus Tipo 2/tratamiento farmacológico , Diabetes Mellitus Tipo 2/genética , ADN Mitocondrial/genética , Variaciones en el Número de Copia de ADN , Hemoglobina Glucada , Hipoglucemiantes/uso terapéutico , Glucemia/metabolismo , Biomarcadores , Mitocondrias/metabolismoRESUMEN
BACKGROUND: Maturity-onset diabetes of the young (MODY) is the most common monogenic diabetes. The aim of this study was to assess the prevalence of MODY in phenotypic type 2 diabetes (T2DM) among Chinese young adults. METHODS: From April 2015 to October 2017, this cross-sectional study involved 2429 consecutive patients from 46 hospitals in China, newly diagnosed between 15 years and 45 years, with T2DM phenotype and negative for standardized glutamic acid decarboxylase antibody at the core laboratory. Sequencing using a custom monogenic diabetes gene panel was performed, and variants of 14 MODY genes were interpreted as per current guidelines. RESULTS: The survey determined 18 patients having genetic variants causing MODY (6 HNF1A , 5 GCK , 3 HNF4A , 2 INS , 1 PDX1 , and 1 PAX4 ). The prevalence of MODY was 0.74% (95% confidence interval [CI]: 0.40-1.08%). The clinical characteristics of MODY patients were not specific, 72.2% (13/18) of them were diagnosed after 35 years, 47.1% (8/17) had metabolic syndrome, and only 38.9% (7/18) had a family history of diabetes. No significant difference in manifestations except for hemoglobin A1c levels was found between MODY and non-MODY patients. CONCLUSION: The prevalence of MODY in young adults with phenotypic T2DM was 0.74%, among which HNF1A -, GCK -, and HNF4A -MODY were the most common subtypes. Clinical features played a limited role in the recognition of MODY.
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Diabetes Mellitus Tipo 2 , Humanos , Diabetes Mellitus Tipo 2/epidemiología , Diabetes Mellitus Tipo 2/genética , Diabetes Mellitus Tipo 2/diagnóstico , Estudios Transversales , Mutación , Prevalencia , FenotipoRESUMEN
BACKGROUND: Considerable variability exists in response to metformin with few effective biomarkers to guide the treatment. Here we evaluated whether whole blood derived mitochondrial DNA copy number (mtDNA-CN) is a biomarker of metformin response as measured by glucose reduction or weight loss. METHODS: Using data from the trial of Metformin (n = 304) and AcaRbose (n = 300) in Chinese as the initial Hypoglycaemic treatment (MARCH), we examined the association between mtDNA-CN and two metformin response outcomes of HbA1c reduction and weight loss. The acarbose arm was used as a comparator group. Whole blood mtDNA-CN was estimated by deep whole genome sequencing with adjustments for confounders. Multiple linear regression and repeated measurement analyses were used to evaluate the association between mtDNA-CN and drug response outcomes. RESULTS: Here we show that glucose reduction is not significantly associated with mtDNA-CN and in either treatment arm. In the metformin arm, each increase of 1 SD in mtDNA-CN is significantly (P = 0.006) associated with a 0.43 kg more weight loss. Repeated measurement analysis shows that after 16 weeks of metformin monotherapy, patients in the top tertile of mtDNA-CN consistently lost 1.21 kg more weight than those in the bottom tertile (P < 0.001). In comparison, mtDNA-CN is not significantly associated with acarbose-induced weight loss. CONCLUSIONS: Patients with higher mtDNA-CN are likely to lose more weight upon metformin treatment, suggesting mtDNA-CN as a potential novel biomarker for more effective weight management in type 2 diabetes.
Treatment of diabetes with the drug metformin can lead to beneficial weight loss. However, there is considerable variability in how patients respond to metformin and few markers or tests are available to guide prescribing. Here, we look at data from patients who took part in a trial comparing metformin with another diabetes drug and determine whether a particular markermitochondrial DNA copy number (mtDNA-CN)is associated with weight loss with these treatments. mtDNA-CN is a proxy for the function of the mitochondria, an important organelle for the generation of metabolic energy in eukaryotic cells. Our results show that patients with diabetes with a higher mtDNA-CN lost more weight upon metformin treatment. This marker could potentially be used to guide treatment with metformin. Our findings warrant further exploration of mtDNA-CN as a marker of response to other drugs.
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The R package Continuous Glucose Monitoring Time Series Data Analysis (CGMTSA) was developed to facilitate investigations that examine the continuous glucose monitoring (CGM) data as a time series. Accordingly, novel time series functions were introduced to (1) enable more accurate missing data imputation and outlier identification; (2) calculate recommended CGM metrics as well as key time series parameters; (3) plot interactive and three-dimensional graphs that allow direct visualizations of temporal CGM data and time series model optimization. The software was designed to accommodate all popular CGM devices and support all common data processing steps. The program is available for Linux, Windows, and Mac at GitHub.
Asunto(s)
Automonitorización de la Glucosa Sanguínea , Glucemia , Glucemia/análisis , Automonitorización de la Glucosa Sanguínea/métodos , Factores de Tiempo , Programas InformáticosRESUMEN
Protein arginine methyltransferase 5 (PRMT5) is a promising target for the treatment of malignant tumors. The discovery of nucleoside-derived inhibitors against PRMT5 with novel scaffold has been challenging. Herein, we report our effort on the design and synthesis of nucleoside derivatives bearing sulfonamide scaffold as potent PRMT5 inhibitors. The representative compound 23n was identified as a potent and selective PRMT5 inhibitor with an IC50 value of 8 nM. Molecular docking study demonstrated the binding mode of compound 23n and illustrated its inhibitory activity to PRMT5. The Trimethyl Lock prodrug strategy was used to afford prodrug 36 with lower polarity which could rapidly release the active compound 23n after entering the tumor cells. Cell-based assays revealed that the prodrug 36 restrained the proliferation of Z-138 and MOLM-13 cells and suppressed methylation of PRMT5 substrate more potently than 23n. Additionally, both compound 23n and 36 exerted antiproliferative effects against Z-138 cells mainly by inducing apoptosis effectively rather than arresting cell cycle. Thus, compounds 23n and 36 represent a series of potent PRMT5 inhibitor with novel scaffold.