Overburden of rare ALMS1 deleterious variants in Chinese early-onset type 2 diabetes with severe insulin resistance.

AIMS: Alström syndrome (AS) is a rare recessive disorder characterised by diabetes, obesity, insulin resistance (IR), and visual and hearing impairments. Mutations in the ALMS1 gene have been identified as the causative agents of AS. This study aimed to explore the relationship between rare ALMS1 variants and clinical features in Chinese patients with early-onset type 2 diabetes (age at diagnosis ≤40 years; EOD). MATERIALS AND METHODS: ALMS1 gene sequencing was performed in 611 Chinese individuals with EOD, 36 with postprandial hyperinsulinemia, and 47 with pre-diabetes and fasting IR. In-silico prediction algorithm and the American College of Medical Genetics Guidelines (ACMG) were used to evaluate the deleteriousness and pathogenicity of the variants. RESULTS: Sixty-two rare ALMS1 variants (frequency <0.005) were identified in 82 patients with EOD. Nineteen variants were predicted to be deleterious (pD). Patients with EOD carrying pD variants had higher fasting C-peptide, postprandial C-peptide, and HOMA2-IR levels than those without variants. The frequency of ALMS1 pD variants in the subgroup with more insulin-resistant EOD was higher than that in other EOD subgroups. Two patients with EOD, obesity, and IR who carried one heterozygous pathogenic/likely pathogenic rare variant of ALMS1 according to ACMG were identified. Moreover, rare heterozygous pD variants of ALMS1 were found in participants from cohorts of postprandial hyperinsulinemia as well as in pre-diabetes with fasting IR. CONCLUSIONS: ALMS1 rare pD variants are enriched in the populations with significant IR, which is a major hallmark of diabetes pathogenesis. Accordingly, our exploratory study provides insights and hypotheses for further studies of gene function.

Clinical and genetic characteristics of CEL-MODY (MODY8): a literature review and screening in Chinese individuals diagnosed with early-onset type 2 diabetes.

OBJECTIVE: CEL-related maturity-onset diabetes of the young (CEL-MODY, MODY8) is a special type of monogenetic diabetes caused by mutations in the carboxyl-ester lipase (CEL) gene. This study aimed to summarize the genetic and clinical characteristics of CEL-MODY patients and to determine the prevalence of the disease among Chinese patients with early-onset type 2 diabetes (EOD). METHODS: We systematically reviewed the literature associated with CEL-MODY in PubMed, Embase, Web of Science, China National Knowledge Infrastructure and Wanfang Data to analyze the features of patients with CEL-MODY. We screened and evaluated rare variants of the CEL gene in a cohort of 679 Chinese patients with EOD to estimate the prevalence of CEL-MODY in China. RESULTS: In total, 21 individuals reported in previous studies were diagnosed with CEL-MODY based on the combination of diabetes and pancreatic exocrine dysfunction as well as frameshift mutations in exon 11 of the CEL gene. CEL-MODY patients were nonobese and presented with exocrine pancreatic affection (e.g., chronic pancreatitis, low fecal elastase levels, pancreas atrophy and lipomatosis) followed by insulin-dependent diabetes. No carriers of CEL missense mutations were reported with exocrine pancreatic dysfunction. Sequencing of CEL in Chinese EOD patients led to the identification of the variant p.Val736Cysfs*22 in two patients. However, these patients could not be diagnosed with CEL-MODY because there were no signs that the exocrine pancreas was afflicted. CONCLUSION: CEL-MODY is a very rare disease caused by frameshift mutations affecting the proximal VNTR segments of the CEL gene. Signs of exocrine pancreatic dysfunction provide diagnostic clues for CEL-MODY, and genetic testing is vital for proper diagnosis. Further research in larger cohorts is needed to investigate the characteristics and prevalence of CEL-MODY in the Chinese population.

Association between frequency of self-monitoring of blood glucose and glycemic control in patients with type 2 diabetes.

AIM: To determine the association between the frequency of self-monitoring of blood glucose (SMBG) and glycemic control in patients with type 2 diabetes. METHODS: The retrospective study analyzed data from 3,630 patients with type 2 diabetes who were enrolled between August 2020 and July 2021. 2456 non-insulin-treated patients were divided into SMBG ≤ 6 times/week and > 6 times/week groups. 1174 insulin-treated patients were divided into SMBG ≤ 9 times/week and > 9 times/week groups. Propensity score matching was used to generate groups with well-balanced covariates. Primary outcomes were changes in fasting blood glucose (FBG) and postprandial blood glucose (PBG) from baseline to 6 months. RESULTS: In the non-insulin-treated group, a comparison in FBG and PBG reduction between the SMBG > 6 times/week and ≤ 6 times/week groups was statistically significant (-0.59 vs. -0.18 mmol/l, P < 0.001; -0.91 vs. -0.36 mmol/l, P < 0.001). In the insulin-treated group, there was no statistically significant reduction in FBG or PBG, patients with baseline FBG > 8 or 9 mmol/l and SMBG > 9 times/week had a significant FBG reduction than the ≤ 9 times/week group. CONCLUSION: Frequent SMBG was associated with better glycemic control in non-insulin-treated patients. Insulin-treated patients with poor glycemic control may benefit from frequent SMBG.

Adrenal limb thickness is associated with metabolism profiles in patients with diabetes: A cross-sectional study.

BACKGROUND: The association between adrenal size and metabolic profiles in patients with diabetes mellitus (DM) is unclear. This study was conducted to determine whether the adrenal thickness measured by computed tomography (CT) is correlated with the metabolic profiles of patients with DM. METHODS: This was a cross-sectional study including 588 Chinese hospitalized patients with DM without comorbidities or medications known to affect adrenal morphology or hormone secretion. Adrenal limb thickness was measured on unenhanced chest CT. Participants were stratified into tertiles according to their total adrenal limb thickness. Linear and logistic regression models were used to estimate the correlations. RESULTS: After adjustment for sex and age, the adrenal thickness was positively associated with body mass index (BMI), waist circumference (WC), urinary albumin/creatinine ratio, and 24-h urinary free cortisol (UFC) and negatively correlated with high-density lipoprotein cholesterol. The sequential equation model (SEM) suggested UFC partially mediated the effect of adrenal limb thickness on WC by 12%. Adrenal thickness, but not UFC, was associated with a higher risk of existing hypertension (odds ratio [OR] = 3.78, 95% confidence interval [CI] 1.58, 9.02) and hyperlipidemia (OR = 2.76, 95% CI 1.03, 7.38), independent of age, gender, BMI, and WC. CONCLUSIONS: The adrenal thickness is independently associated with BMI, WC, cortisol levels, urinary albumin/creatinine ratio, hypertension, and dyslipidemia but not glycemic parameters in patients with diabetes. Our study encourages further studies to investigate the role of adrenal physiology in patients with diabetes.

Differential effect of interventions in patients with prediabetes stratified by a machine learning-based diabetes progression prediction model.

AIM: To investigate whether stratifying participants with prediabetes according to their diabetes progression risks (PR) could affect their responses to interventions. METHODS: We developed a machine learning-based model to predict the 1-year diabetes PR (ML-PR) with the least predictors. The model was developed and internally validated in participants with prediabetes in the Pinggu Study (a prospective population-based survey in suburban Beijing; n = 622). Patients from the Beijing Prediabetes Reversion Program cohort (a multicentre randomized control trial to evaluate the efficacy of lifestyle and/or pioglitazone on prediabetes reversion; n = 1936) were stratified to low-, medium- and high-risk groups using ML-PR. Different effect of four interventions within subgroups on prediabetes reversal and diabetes progression was assessed. RESULTS: Using least predictors including fasting plasma glucose, 2-h postprandial glucose after 75 g glucose administration, glycated haemoglobin, high-density lipoprotein cholesterol and triglycerides, and the ML algorithm XGBoost, ML-PR successfully predicted the 1-year progression of participants with prediabetes in the Pinggu study [internal area under the curve of the receiver operating characteristic curve 0.80 (0.72-0.89)] and Beijing Prediabetes Reversion Program [external area under the curve of the receiver operating characteristic curve 0.80 (0.74-0.86)]. In the high-risk group pioglitazone plus intensive lifestyle therapy significantly reduced diabetes progression by about 50% at year l and the end of the trial in the high-risk group compared with conventional lifestyle therapy with placebo. In the medium- or low-risk group, intensified lifestyle therapy, pioglitazone or their combination did not show any benefit on diabetes progression and prediabetes reversion. CONCLUSIONS: This study suggests personalized treatment for prediabetes according to their PR is necessary. ML-PR model with simple clinical variables may facilitate personal treatment strategies in participants with prediabetes.

A Novel Risk Score Model for the Differential Diagnosis of Type 2 Diabetic Nephropathy: A Multicenter Study.

Introduction: DN is a common complication of diabetes. However, diabetes combined with renal injury may involve DN or NDKD, with different treatment schemes. The purpose of our study was to determine the independent risk factors of DN and establish a risk score model to help differentiate DN and NDKD, providing a reference for clinical treatment. Methods: A total of 678 T2D patients who had undergone renal biopsy in four affiliated hospitals of Peking University were consecutively enrolled. Patients were assigned to the DN group and NDKD group according to histopathological results. Seventy percent of patients from PKUFH were randomly assigned to the training group, and the remaining 30% were assigned to the internal validation group. Patients from the other three centers were assigned to the external validation group. We used univariate and multivariate logistic regression analyses to identify independent risk factors of DN in the training group and conducted multivariate logistic regression analysis with these independent risk factors in the training group to find regression coefficients "ß" to establish a risk score model. Finally, we conducted internal and external validation of the model with ROC curves. Results: Diabetic retinopathy, diabetes duration ≥ 5 years, eGFR < 30 ml/min/1.73 m2, 24 h UTP ≥ 3 g, and no hematuria were independent risk factors (P < 0.05), and each factor scored 2, 1, 1, 1, and 1. We assigned the patients to a low-risk group (0-1 points), a medium-risk group (2-3 points), and a high-risk group (4-6 points), representing unlikely DN, possibly DN, and a high probability of DN, respectively. The AUCs were 0.860, 0.924, and 0.855 for the training, internal validation, and external validation groups, respectively. Conclusion: The risk score model could help differentiate DN and NDKD in a noninvasive manner, reduce the number of renal biopsies, and provide a reference for clinical treatment.

Rare Variant in Metallothionein 1E Increases the Risk of Type 2 Diabetes in a Chinese Population.

OBJECTIVE: To uncover novel targets for the treatment of type 2 diabetes (T2D) by investigating rare variants with large effects in monogenic forms of the disease. RESEARCH DESIGN AND METHODS: We performed whole-exome sequencing in a family with diabetes. We validated the identified gene using Sanger sequencing in additional families and diabetes- and community-based cohorts. Wild-type and variant gene transgenic mouse models were used to study the gene function. RESULTS: Our analysis revealed a rare variant of the metallothionein 1E (MT1E) gene, p.C36Y, in a three-generation family with diabetes. This risk allele was associated with T2D or prediabetes in a community-based cohort. MT1E p.C36 carriers had higher HbA1c levels and greater BMI than those carrying the wild-type allele. Mice with forced expression of MT1E p.C36Y demonstrated increased weight gain, elevated postchallenge serum glucose and liver enzyme levels, and hepatic steatosis, similar to the phenotypes observed in human carriers of MT1E p.C36Y. In contrast, mice with forced expression of MT1E p.C36C displayed reduced weight and lower serum glucose and serum triglyceride levels. Forced expression of wild-type and variant MT1E demonstrated differential expression of genes related to lipid metabolism. CONCLUSIONS: Our results suggest that MT1E could be a promising target for drug development, because forced expression of MT1E p.C36C stabilized glucose metabolism and reduced body weight, whereas MT1E p.C36Y expression had the opposite effect. These findings highlight the importance of considering the impact of rare variants in the development of new T2D treatments.

Association between remnant cholesterol level and severity of chronic kidney disease in patients with type 2 diabetes.

BACKGROUND: Studies on whether remnant cholesterol (RC) affects the progression of chronic kidney disease (CKD) remain insufficient. This study aimed to determine whether RC level was associated with the severity of CKD in patients with type 2 diabetes mellitus (T2DM). METHODS: In total, 3383 individuals diagnosed with T2DM were enrolled in this cross-sectional study from China. The severity of CKD was defined as no, moderate, severe, and very severe CKD based on the urinary albumin-to-creatinine ratio and estimated glomerular filtration rate. Because RC was non-normally distributed, it was log-transformed and categorized into quantiles. Multivariate logistic regression and multivariate ordinal logistic regression analysis were performed to investigate whether RC was independently associated with CKD and its severity. RESULTS: The median RC level was 25.9 mg/dL. The number of patients with no, moderate, severe, and very severe CKD was 2587 (76.5 %), 520 (15.4 %), 189 (5.6 %), and 87 (2.5 %), respectively. After adjusting for confounding factors, the prevalence of CKD increased 1.67-fold when the log-transformed RC level was elevated by one unit (OR [95 % CI], 1.67 [1.43-1.95]). The likelihood of CKD severity increasing by one degree was 1.76-fold for each one-unit increase in log-transformed RC level (OR [95 % CI], 1.76 [1.52-2.05]). When RC was incorporated as a categorical variable, it still correlated with CKD severity compared with quantile 1 (Q1) (Q2, 1.30 [1.01-1.68]; Q3, 1.60 [1.23-2.07]; Q4, 2.39 [1.86-3.09]). The association remained regardless of whether the patient's traditional lipid profiles achieved the target range. CONCLUSION: RC level was associated with CKD severity even when traditional lipid profiles were within the target range in patients with T2DM.

Sensitivity to thyroid hormone and risk of components of metabolic syndrome in a Chinese euthyroid population.

INTRODUCTION: To evaluate the association of sensitivity to thyroid hormone with metabolic syndrome (MetS) and its components in a Chinese euthyroid population. METHODS: A total of 3573 participants from Pinggu Metabolic Disease Study were analyzed. Serum-free triiodothyronine (FT3), free thyroxine (FT4), thyrotropin (TSH), total adipose tissue (TAT), visceral adipose tissue (VAT), subcutaneous adipose tissue (SAT) area of abdominal, and lumbar skeletal muscle area (SMA) were measured. Central thyroid hormone resistance was calculated by the Thyroid Feedback Quantile-based Index (TFQI) and Chinese-referenced Parametric TFQI (PTFQI), Thyrotroph T4 Resistance Index (TT4RI) and TSH Index (TSHI). Peripheral thyroid hormone resistance was assessed by FT3/FT4 ratio. RESULTS: Higher values of TSHI (odds ratio [OR] = 1.167, 95% confidence interval [CI]: 1.079-1.262, p < .001), TT4RI (OR = 1.115, 95% CI: 1.031-1.206, p = .006), TFQI (OR = 1.196, 95% CI: 1.106-1.294, p < .001), PTFQI (OR = 1.194, 95% CI: 1.104-1.292, p < .001), and lower values of FT3/FT4 ratio (OR = 0.914, 95% CI: 0.845-0.990, p = .026) were associated with MetS. Increased levels of TFQI and PTFQI were associated with abdominal obesity, hypertriglyceridemia, and hypertension. Increased levels of TSHI and TT4RI were associated with hypertriglyceridemia, abdominal obesity, low high-density lipoprotein cholesterol. Reduced levels of FT3/FT4 ratio were associated with hyperglycemia, hypertension, and hypertriglyceridemia. The levels of TSHI, TFQI, and PTFQI were negatively related to SMA and positively related to VAT, SAT, and TAT (all p < .05). CONCLUSIONS: Reduced thyroid hormone sensitivity was associated with MetS and its components. Impaired thyroid hormone sensitivity might affect the distribution of adipose tissue and muscle.

Prevalence and Clinical Characteristics of PDX1 Variant Induced Diabetes in Chinese Early-Onset Type 2 Diabetes.

CONTEXT: Maturity-onset diabetes of the young 4 (MODY4) is caused by mutations of PDX1; its prevalence and clinical features are not well known. OBJECTIVE: This study aimed to investigate the prevalence and clinical characteristics of MODY4 in Chinese people clinically diagnosed with early-onset type 2 diabetes (EOD), and to evaluate the relationship between the PDX1 genotype and the clinical phenotype. METHOD: The study cohort consisted of 679 patients with EOD. PDX1 mutations were screened by DNA sequencing, and their pathogenicity was evaluated by functional experiments and American College of Medical Genetics and Genomics guidelines. MODY4 was diagnosed in individuals with diabetes who carry a pathogenic or likely pathogenic PDX1 variant. All reported cases were reviewed for analyzing the genotype-phenotype relationship. RESULT: 4 patients with MODY4 were identified, representing 0.59% of this Chinese EOD cohort. All the patients were diagnosed before 35 years old, either obese or not obese. Combined with previously reported cases, the analysis revealed that the carriers of homeodomain variants were diagnosed earlier than those with transactivation domain variants (26.10 ± 11.00 vs 41.85 ± 14.66 years old, P < .001), and the proportions of overweight and obese individuals with missense mutation were higher than those with nonsense or frameshift mutations (27/34 [79.4%] vs 3/8 [37.5%], P = .031). CONCLUSION: Our study suggested that MODY4 was prevalent in 0.59% of patients with EOD in a Chinese population. It was more difficult to identify clinically than other MODY subtypes owning to its clinical similarity to EOD. Also, this study revealed that there is some relationship between genotype and phenotype.

The genetic and clinical characteristics of WFS1 related diabetes in Chinese early onset type 2 diabetes.

Diabetes is one of the most common phenotypes of Wolfram syndrome owing to the presence of the variants of the WFS1 gene and is often misdiagnosed as other types of diabetes. We aimed to explore the prevalence of WFS1-related diabetes (WFS1-DM) and its clinical characteristics in a Chinese population with early-onset type 2 diabetes (EOD). We sequenced all exons of the WFS1 gene in 690 patients with EOD (age at diagnosis ≤ 40 years) for rare variants. Pathogenicity was defined according to the standards and guidelines of the American College of Medical Genetics and Genomics. We identified 33 rare variants predicted to be deleterious in 39 patients. The fasting [1.57(1.06-2.22) ng/ml] and postprandial C-peptide levels [2.8(1.75-4.46) ng/ml] of the patients with such WFS1 variations were lower than those of the patients without WFS1 variation [2.09(1.43-3.05) and 4.29(2.76-6.07) respectively, ng/ml]. Six (0.9%) patients carried pathogenic or likely pathogenic variants; they met the diagnostic criteria for WFS1-DM according to the latest guidelines, but typical phenotypes of Wolfram syndrome were seldom observed. They were diagnosed at an earlier age and usually presented with an absence of obesity, impaired beta cell function, and the need for insulin treatment. WFS1-DM is usually mistakenly diagnosed as type 2 diabetes, and genetic testing is helpful for individualized treatment.

Gender-specific data-driven adiposity subtypes using deep-learning-based abdominal CT segmentation.

OBJECTIVE: The aim of this study was to quantify abdominal adiposity and generate data-driven adiposity subtypes with different diabetes risks. METHODS: A total of 3817 participants from the Pinggu Metabolic Disease Study were recruited. A deep-learning-based recognition model on abdominal computed tomography (CT) images (A-CT model) was developed and validated in 100 randomly selected cases. The volumes and proportions of subcutaneous fat, visceral fat, liver fat, and muscle fat were automatically recognized in all cases. K-means clustering was used to identify subgroups using the proportions of the four fat components. RESULTS: The Dice indices among the measurements assessed by the A-CT model and manual evaluation to detect liver fat, muscle fat, and subcutaneous fat areas were 0.96, 0.95, and 0.92, respectively. Three subtypes were generated separately in men and women: visceral fat dominant type (VFD); subcutaneous fat dominant type (SFD); and intermuscular fat dominant type (MFD). Compared with the SFD group, the MFD group had similar diabetes risk, and the VFD group had a 60% higher diabetes risk when age and BMI were adjusted for in men. The adjusted odds ratio for diabetes was 1.92 (95% CI: 1.32-2.78) in the MFD group and 6.14 (95% CI: 4.18-9.03) in the VFD group in women. CONCLUSIONS: This study identified gender-specific abdominal adiposity subgroups, which may help clinicians to distinguish diabetes risk quickly and automatically.

How does obesity affect mortality through blood pressure and blood glucose in Chinese and US citizens? Insights from a causal mediation analysis of two large cohorts.

Background: Obesity, which has reached the scale of a global pandemic, is a leading cause of premature death. It is unclear to what extent its effect on mortality was driven by blood pressure or glucose levels in people of different ethnicities. Methods: We conducted a causal mediation analysis to estimate the mediation effect of blood pressure and glucose between body mass index (BMI) or waist-hip ratio (WHR) on mortality based on data from the China Kadoorie Biobank (CKB) (n = 458 385) and US National Health and Nutrition Examination Survey (NHANES) (1999-2008, n = 20 726). Results: The WHR's effect on mortality was mediated by blood pressure and glucose in the CKB data set by 38.7% (95% confidence interval (CI) = 34.1, 43.2) and 36.4% (95% CI = 31.6, 42.8), whereas in NHANES by 6.0% (95% CI = 2.3, 8.3) and 11.2% (95% CI = 4.7, 22.7), respectively. For associations between BMI and mortality in subjects with overweight or obesity, the mediator proportion of blood glucose and pressure was 49.4% (95% CI = 40.1, 62.5) and 16.9% (95% CI = 13.6, 22.9) in CKB and 9.10% (95% CI = 2.2, 25.9) and 16.7% (95% CI = 7.3, 49.0) in NHANES, respectively. We stratified the patients by their blood glucose, blood pressure level, or both into four groups. The effect of WHR on mortality was comparable across subgroups in either cohort. The associations between BMI and mortality were stronger in patients with higher blood pressure in CKB (P = 0.011) and blood glucose in NHANES (P = 0.035) in patients with overweight and obesity. Conclusions: The relationship between WHR and mortality in the CKB data set was potentially caused by blood pressure and glucose to a much greater extent than in the NHANES one. The effect of BMI influenced by blood pressure was significantly higher among Chinese individuals with overweight and obesity. These results implicate a different intervention strategy is required for blood pressure and blood glucose in China and US to prevent obesity and obesity-related premature death.

The association between living altitude and serum leptin concentrations in native women.

Background: Lower diabetes prevalence and cardiovascular mortality have been observed in residents at a higher altitude. Leptin is associated with incident diabetes and adverse cardiovascular outcomes, and our aim was to investigate the association of living altitude with serum leptin concentrations. Methods: Two cross-sectional surveys were used in this study, including native populations living at Tibet (high altitude) and Beijing (low altitude). A propensity score was conducted for matching age and body mass index (BMI) between native women at high and low altitude. Pearson's correlation analysis was performed to evaluate the correlation of leptin with other variables. Results: A total of 1414 native women were included in this study, including 594 at high altitude and 820 at low altitude. The serum leptin concentrations of native women living at high altitude were 13.74 ± 11.03 ng/ml, which was significantly lower than that of native women living at low altitude (20.90 ± 12.91 ng/ml). After matching age and BMI, women living at the high altitude still had lower serum leptin concentrations. After adjusting for the potential confounding factors, the correlation coefficient between Ln (leptin) and BMI of women at high altitude was significantly lower than that of women at low altitude (0.228 versus 0.559; P <0.0001). The serum leptin concentrations of each BMI subgroup (<18.5, 18.5 to <25, 25 to <30, ≥ 30 kg/m2) in women at high altitude were lower than that in women at low altitude. Conclusions: Serum leptin concentrations were significantly decreased in native women living at high altitude, and living altitude may alter the correlation of BMI and leptin. The findings of our study support that residents at high altitude have a protective effect with regards to improving cardiovascular and metabolic outcomes.

The association between self-monitoring of blood glucose and HbA1c in type 2 diabetes.

Aims: Fasting capillary blood glucose (FCG) and postprandial capillary blood glucose (PCG) both contribute to HbA1c in diabetes. Due to the collinearity between FCG and PCG, the HbA1c prediction model could not be developed with both FCG and PCG by linear regression. The study aimed to develop an HbA1c prediction model with both FCG and PCG to estimate HbA1c in type 2 diabetes. Methods: A total of 1,642 patients with type 2 diabetes who had at least three FCG and three PCG measurements in the past 3 months were enrolled in the study. The mean of FCG (MEANFCG) and PCG (MEANPCG) were calculated for each patient. The patients were randomized into exploratory and validation groups. The former was used for developing HbA1c prediction models and the latter for performance evaluation. Results: The new HbA1c prediction model using ridge regression expressed as HbA1c (%) = 0.320×MEANFCG (mmol/L) + 0.187×MEANPCG (mmol/L) + 2.979, R2 = 0.668. Compared to linear regression models developed with FCG, PCG, fasting plasma glucose (FPG), and 2-hour postprandial plasma glucose (2-h PPG), respectively, the new HbA1c prediction model showed the smallest mean square error, root mean square error, mean absolute error. The concordance correlation coefficient of the new HbA1c prediction model and the linear regression models with MEANFCG, MEANPCG, FPG or 2-h PPG were 0.810,0.773,0.749,0.715,0.672. Conclusion: We have developed a new HbA1c prediction model with both FCG and PCG, which showed better prediction ability and good agreement.

Contribution of mitochondrial gene variants in diabetes and diabetic kidney disease.

Objectives: Mitochondrial DNA (mtDNA) plays an important role in the pathogenesis of diabetes. Variants in mtDNA have been reported in diabetes, but studies on the whole mtDNA variants were limited. Our study aims to explore the association of whole mtDNA variants with diabetes and diabetic kidney disease (DKD). Methods: The whole mitochondrial genome was screened by next-generation sequencing in cohort 1 consisting of 50 early-onset diabetes (EOD) patients with a maternally inherited diabetes (MID) family history. A total of 42 variants possibly associated with mitochondrial diseases were identified according to the filtering strategy. These variants were sequenced in cohort 2 consisting of 90 EOD patients with MID. The association between the clinical phenotype and these variants was analyzed. Then, these variants were genotyped in cohort 3 consisting of 1,571 type 2 diabetes mellitus patients and 496 subjects with normal glucose tolerance (NGT) to analyze the association between variants with diabetes and DKD. Results: Patients with variants in the non-coding region had a higher percentage of obesity and levels of fasting insulin (62.1% vs. 24.6%, P = 0.001; 80.0% vs. 26.5% P < 0.001). The patients with the variants in rRNA had a higher prevalence of obesity (71.4% vs. 30.3%, P = 0.007), and the patients with the variants in mitochondrial complex I had a higher percentage of the upper tertile of FINS (64.3% vs. 34.3%, P = 0.049). Among 20 homogeneous variants successfully captured, two known variants (m.A3943G, m.A10005G) associated with other mitochondrial diseases were only in the diabetic group, but not in the NGT group, which perhaps indicated its possible association with diabetes. The prevalence of DKD was significantly higher in the group with the 20 variants than those without these variants (18.7% vs. 14.6%, P = 0.049) in the participants with diabetes of cohort 3. Conclusion: MtDNA variants are associated with MID and DKD, and our findings advance our understanding of mtDNA in diabetes and DKD. It will have important implications for the individual therapy of mitochondrial diabetes.

Clinical evaluation of an automated TSI bridge immunoassay in the diagnosis of Graves' disease and its relationship to the degree of hyperthyroidism.

BACKGROUND: The rapid and accurate detection of thyroid-stimulating hormone (TSH) receptor antibodies has always been an urgent need for the clinical diagnosis and management of Graves' disease (GD). We aimed to evaluate the use of an automated thyroid-stimulating immunoglobulin (TSI) bridge immunoassay in the diagnosis of GD and to analyze the relationship between TSI and the degree of hyperthyroidism. METHODS: A total of 227 new-onset GD patients, 29 Hashimoto thyroiditis, 43 non-autoimmune thyroid diseases and 37 euthyroid controls were consecutively recruited. All participants accepted the measurement of their serum thyroid function and thyroid-associated antibodies, including TSI being measured by an Immulite 2000 bridge immunoassay and TSH receptor autoantibodies (TRAb) being measured by a third-generation Roche electrochemiluminescence immunoassay. The quantitative consistency between the TSI and TRAb detection methods was analyzed by using Passing-Bablok regression and Bland-Altman plots. The diagnostic performance for GD was assessed by receiver operating characteristic (ROC) curve analysis. RESULTS: Among 227 GD patients (174 females and 53 males, with a mean age of 39 years), the quantitative TSI was positively correlated with TRAb (r = 0.8099). According to the cut-off values proposed by the manufacturers (TSI: 0.55 IU/L, TRAb: 1.75 IU/L), the positive rates of TSI and TRAb in new-onset GD patients were 96.92% and 95.15%, respectively. Both TSI and TRAb levels positively correlated with FT4 levels (TSI: r = 0.243, TRAb: r = 0.317; all P < 0.001) and FT3 levels (TSI: r = 0.288, TRAb: r = 0.360; all P < 0.001) in new-onset GD patients. The ROC analysis showed that the optimal TSI cut-off value was 0.577 IU/L for GD diagnosis in this Chinese population, with a sensitivity of 96.92% and a specificity of 97.25%, respectively. The optimal TRAb cut-off value of was 1.38 IU/L, with a sensitivity of 96.92% and a specificity of 99.08%. There were no significant differences between the cut-off values obtained through the ROC analysis and those provided by the manufacturer for both TSI and TRAb when calculating their sensitivity and specificity in diagnosing GD. Among the 8 newly diagnosed GD cases with discordant qualitative antibody results, TSI was more likely than TRAb to match the clinical diagnosis of GD (6 TSI-positive vs. 2 TRAb-positive patients). CONCLUSION: The automated TSI bridge immunoassay was positively correlated with thyroxine levels in new-onset GD patients and was more likely to be consistent with the clinical diagnosis of GD than with that of TRAb. The positive Immulite 2000 TSI cut-off value of 0.577 IU/L for GD diagnosis in the Chinese population were close to the value recommended by the manufacturer.

Metagenomic analysis reveals crosstalk between gut microbiota and glucose-lowering drugs targeting the gastrointestinal tract in Chinese patients with type 2 diabetes: a 6 month, two-arm randomised trial.

AIMS/HYPOTHESIS: The use of oral glucose-lowering drugs, particularly those designed to target the gut ecosystem, is often observed in association with altered gut microbial composition or functional capacity in individuals with type 2 diabetes. The gut microbiota, in turn, plays crucial roles in the modulation of drug efficacy. We aimed to assess the impacts of acarbose and vildagliptin on human gut microbiota and the relationships between pre-treatment gut microbiota and therapeutic responses. METHODS: This was a randomised, open-labelled, two-arm trial in treatment-naive type 2 diabetes patients conducted in Beijing between December 2016 and December 2017. One hundred participants with overweight/obesity and newly diagnosed type 2 diabetes were recruited from the Pinggu Hospital and randomly assigned to the acarbose (n=50) or vildagliptin (n=50) group using sealed envelopes. The treatment period was 6 months. Blood, faecal samples and visceral fat data from computed tomography images were collected before and after treatments to measure therapeutic outcomes and gut microbiota. Metagenomic datasets from a previous type 2 diabetes cohort receiving acarbose or glipizide for 3 months were downloaded and processed. Statistical analyses were applied to identify the treatment-related changes in clinical variables, gut microbiota and associations. RESULTS: Ninety-two participants were analysed. After 6 months of acarbose (n=44) or vildagliptin (n=48) monotherapy, both groups achieved significant reductions in HbA1c (from 60 to 46 mmol/mol [from 7.65% to 6.40%] in the acarbose group and from 59 to 44 mmol/mol [from 7.55% to 6.20%] in the vildagliptin group) and visceral fat areas (all adjusted p values for pre-post comparisons <0.05). Both arms showed drug-specific and shared changes in relative abundances of multiple gut microbial species and pathways, especially the common reductions in Bacteroidetes species. Three months and 6 months of acarbose-induced changes in microbial composition were highly similar in type 2 diabetes patients from the two independent studies. Vildagliptin treatment significantly enhanced fasting active glucagon-like peptide-1 (GLP-1) levels. Baseline gut microbiota, rather than baseline GLP-1 levels, were strongly associated with GLP-1 response to vildagliptin, and to a lesser extent with GLP-1 response to acarbose. CONCLUSIONS/INTERPRETATION: This study reveals common microbial responses in type 2 diabetes patients treated with two glucose-lowering drugs targeting the gut differently and acceptable performance of baseline gut microbiota in classifying individuals with different GLP-1 responses to vildagliptin. Our findings highlight bidirectional interactions between gut microbiota and glucose-lowering drugs. TRIAL REGISTRATION: ClinicalTrials.gov NCT02999841 FUNDING: National Key Research and Development Project: 2016YFC1304901.

Sex- and body mass index-specific reference intervals for serum leptin: a population based study in China.

BACKGROUND: Leptin is a peptide hormone secreted by adipose tissue and is an important determinant of obesity and its complications. The purpose of this study was to establish sex- and body mass index (BMI)-specific reference intervals for serum leptin in a Chinese population and investigate the factors influencing leptin concentrations. METHODS: Fasting serum leptin levels were assayed in 469 men and 773 women from randomly sampled Chinese residents. Blood glucose, insulin, hemoglobin A1c (HbA1c), liver enzymes, blood lipid profiles, creatinine, and uric acid (UA) levels were measured. Pearson's correlation coefficient and multiple linear regression analyses were used to estimate the relationship between serum leptin level and other variables. The reference intervals were determined by the 2.5th and 97.5th percentiles. RESULTS: The mean ± standard deviation serum leptin level was much higher in women (20.92 ± 12.96 ng/mL) than in men (6.45 ± 5.53 ng/mL). The reference interval of serum leptin was 0.33-19.85 ng/mL in men and 3.60-54.86 ng/mL in women. The specific reference intervals of serum leptin in men with BMI of 20 to < 25 and 25 to < 27.5 kg/m2 were 0.42-12.32 and 2.17-20.22 ng/ml, respectively. The specific reference intervals of serum leptin in women with BMI of 20 to < 25 and 25 to < 27.5 kg/m2 were 4.11-38.09 and 8.27-48.66 ng/ml, respectively. BMI was significantly correlated with Ln (leptin) both in men (r = 0.698, P < 0.001) and women (r = 0.626, P < 0.001). In multivariate linear regression analysis, serum leptin was correlated with BMI, homeostasis model assessment of insulin resistance (HOMA-IR), UA in women, and plus triglyceride (TG) in men. The variance in serum leptin levels could be partially explained by these variables in both women (adjusted R2 = 0.447) and men (adjusted R2 = 0.552). In participants with leptin levels higher than the reference intervals, significantly higher levels of HOMA-IR, low-density lipoprotein cholesterol (LDL-C), UA, a higher proportion of central obesity (waist circumference [WC] > 90 cm), and metabolic syndrome were found in men, and significantly higher levels of HOMA-IR, UA and a higher proportion of central obesity (WC > 85 cm) were found in women. CONCLUSION: This is the first study to establish sex- and BMI-specific reference intervals of leptin for both sexes in a large Chinese population. Serum concentration of leptin was predicted by BMI, HOMA-IR, UA in women, and TG in men.

The efficacy of canagliflozin in diabetes subgroups stratified by data-driven clustering or a supervised machine learning method: a post hoc analysis of canagliflozin clinical trial data.

AIMS/HYPOTHESIS: Data-driven diabetes subgroups have shown distinct clinical characteristics and disease progression, although there is a lack of evidence that this information can guide clinical decisions. We aimed to investigate whether diabetes subgroups, identified by data-driven clustering or supervised machine learning methods, respond differently to canagliflozin. METHODS: We pooled data from five randomised, double-blinded clinical trials of canagliflozin at an individual level. We applied the coordinates from the All New Diabetics in Scania (ANDIS) study to form four subgroups: mild age-related diabetes (MARD); severe insulin-deficient diabetes (SIDD); mild obesity-related diabetes (MOD) and severe insulin-resistant diabetes (SIRD). Machine learning models for HbA1c lowering (ML-A1C) and albuminuria progression (ML-ACR) were developed. The primary efficacy endpoint was reduction in HbA1c at 52 weeks. Concordance of a model was defined as the difference between predicted HbA1c and actual HbA1c decline less than 3.28 mmol/mol (0.3%). RESULTS: The decline in HbA1c resulting from treatment was different among the four diabetes clusters (pinteraction=0.004). In MOD, canagliflozin showed a robust glucose-lowering effect at week 52, compared with other drugs, with least-squares mean of HbA1c decline [95% CI] being 6.6 mmol/mol (4.1, 9.2) (0.61% [0.38, 0.84]) for sitagliptin, 7.1 mmol/mol (4.7, 9.5) (0.65% [0.43, 0.87]) for glimepiride, and 9.8 mmol/mol (9.0, 10.5) (0.90% [0.83, 0.96]) for canagliflozin. This superiority persisted until 104 weeks. The proportion of individuals who achieved HbA1c <53 mmol/mol (<7.0%) was highest in sitagliptin-treated individuals with MARD but was similar among drugs in individuals with MOD. The ML-A1C model and the cluster algorithm showed a similar concordance rate in predicting HbA1c lowering (31.5% vs 31.4%, p=0.996). Individuals were divided into high-risk and low-risk groups using ML-ACR model according to their predicted progression risk for albuminuria. The effect of canagliflozin vs placebo on albuminuria progression differed significantly between the high-risk (HR 0.67 [95% CI 0.57, 0.80]) and low-risk groups (HR 0.91 [0.75, 1.11]) (pinteraction=0.016). CONCLUSIONS/INTERPRETATION: Data-driven clusters of individuals with diabetes showed different responses to canagliflozin in glucose lowering but not renal outcome prevention. Canagliflozin reduced the risk of albumin progression in high-risk individuals identified by supervised machine learning. Further studies with larger sample sizes for external replication and subtype-specific clinical trials are necessary to determine the clinical utility of these stratification strategies in sodium-glucose cotransporter 2 inhibitor treatment. DATA AVAILABILITY: The application for the clinical trial data source is available on the YODA website ( http://yoda.yale.edu/ ).