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.

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

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

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.

Review: Machine learning in precision pharmacotherapy of type 2 diabetes-A promising future or a glimpse of hope?

Precision pharmacotherapy of diabetes requires judicious selection of the optimal therapeutic agent for individual patients. Artificial intelligence (AI), a swiftly expanding discipline, holds substantial potential to transform current practices in diabetes diagnosis and management. This manuscript provides a comprehensive review of contemporary research investigating drug responses in patient subgroups, stratified via either supervised or unsupervised machine learning approaches. The prevalent algorithmic workflow for investigating drug responses using machine learning involves cohort selection, data processing, predictor selection, development and validation of machine learning methods, subgroup allocation, and subsequent analysis of drug response. Despite the promising feature, current research does not yet provide sufficient evidence to implement machine learning algorithms into routine clinical practice, due to a lack of simplicity, validation, or demonstrated efficacy. Nevertheless, we anticipate that the evolving evidence base will increasingly substantiate the role of machine learning in molding precision pharmacotherapy for diabetes.

Differential effects of PGAM5 knockout on high fat high fructose diet and methionine choline-deficient diet induced non-alcoholic steatohepatitis (NASH) in mice.

BACKGROUND: Phosphoglycerate mutase 5 (PGAM5), a phosphatase involved in mitochondrial homeostasis, is reported to be closely related to the metabolic stress induced by high-fat diet (HFD) or cold. In this study, we aimed to investigate the effects of PGAM5 on hepatic steatosis, inflammation and fibrosis in nonalcoholic steatohepatitis (NASH). METHODS AND RESULTS: We generated PGAM5 global knockout (GKO) mice and their wildtype (WT) littermates using CRISPR/CAS9. The mice were fed with a high fat high fructose (HFHF) diet for 12 weeks or a methionine choline-deficient (MCD) diet (methionine choline supplemented (MCS) as control) for 6 weeks. Hepatic PGAM5 expression was up-regulated in humans with NASH and WT mice fed with HFHF and MCS, and reduced in WT mice fed with MCD diet. In HFHF-fed mice, GKO had reduced body weight, hepatic triglyceride (TG) content and serum transaminase along with decreased hepatic pro-inflammatory and pro-fibrotic responses compared with their WT control. GKO had increased expression of antioxidative gene glutathione peroxidase-6 (GPX6) and activation of mammalian target of rapamycin (mTOR). In mice fed with MCS diet, GKO significantly increased serum TNF-α and IL-6 and decreased hepatic GPX6 mRNA expression. There was no difference in hepatic steatosis, inflammation or fibrosis between GKO and WT mice fed with MCD diet. We investigated the role of PGAM5 deficiency in a variety of cell types. In differentiated THP-1 cells, PGAM5 silencing significantly increased pro-inflammatory cytokine secretion and decreased antioxidative proteins, including nuclear factor erythroid 2- related factors (NRF2), heme oxygenase-1 (HO-1) and GPX6 without affecting mTOR activity. In HepG2 cells with steatosis, PGAM5 knockdown reduced insulin sensitivity, increased mTOR phosphorylation and reduced the expression of NRF2, catalase (CAT), HO-1 and GPX6. Conversely, PGAM5 knockdown reduced TG accumulation, increased insulin sensitivity, and increased antioxidative genes in 3T3-L1 cells, despite the up-regulation in mTOR phosphorylation. CONCLUSIONS: PGAM5-KO relieved hepatic steatosis and inflammation in HFHF model, promoted inflammation in MCS-fed mice and had no effects on the MCD-fed model. The distinct effects may be owing to the different effects of PGAM5-KO on anti-oxidative pathways in energy-dependent, possible involves mTOR, and/or cell type-dependent manner. Our findings suggest that PGAM5 can be a potential therapeutic target for NASH.

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 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/ ).

The Association between Non-Alcoholic Fatty Liver Disease (NAFLD) and Advanced Fibrosis with Serological Vitamin B12 Markers: Results from the NHANES 1999-2004.

BACKGROUND: There is evidence that vitamin B12 and associated metabolite levels are changed in non-alcoholic fatty liver disease (NAFLD) and non-alcoholic steatohepatitis (NASH); however, their association has been in dispute. METHODS: We included 8397 individuals without previous liver condition or excess alcohol intake from the National Health and Nutrition Examination Survey (NHANES) 1999-2004. NAFLD was diagnosed with Fatty Liver Index (FLI) ≥ 60 or USFLI ≥ 30, and participants with advanced fibrosis risks were identified with elevated non-alcoholic fatty liver disease fibrosis score (NFS), fibrosis 4 index (FIB-4), or aspartate aminotransferase (AST)/platelet ratio index (APRI). Step-wide logistic regression adjusting for confounders was used to detect the association between NAFLD or advanced fibrosis with serum vitamin B12, folate, red blood cell folate (RBC folate), homocysteine (HCY), and methylmalonic acid (MMA). RESULTS: The weighted prevalence of NAFLD was 44.2%. Compared with non-NAFLD participants, patients with NAFLD showed significantly increased RBC folate level and RBC counts, decreased serum vitamin B12 and folate, and similar HCY and MMA levels. NAFLD with advanced fibrosis risk had higher MMA and HCY, reduced serum vitamin B12, and similar serum folate and RBC folate levels than NAFLD with low fibrosis risk. Only RBC folate was independently associated with an increased risk of NAFLD (OR (95% CI): 2.24 (1.58, 3.18)). In all participants, MMA (OR: 1.41 (1.10, 1.80)) and HCY (OR: 2.76 (1.49, 5.11)) were independently associated with increased risk for advanced fibrosis. In participants with NAFLD, this independent association still existed (OR: 1.39 (1.04, 1.85) for MMA and 1.95 (1.09, 3.46) for HCY). In all participants, the area under the receiver operating characteristic curve (ROC AUC) on fibrosis was 0.6829 (0.6828, 0.6831) for MMA and 0.7319 (0.7318, 0.7320) for HCY; in participants with NAFLD, the corresponding ROC AUC was 0.6819 (0.6817, 0.6821) for MMA and 0.6926 (0.6925, 0.6928) for HCY. CONCLUSION: Among vitamin B12-associated biomarkers, RBC folate was independently associated with elevated NAFLD risk, whereas MMA and HCY were associated with increased risk for advanced fibrosis in the total population and NAFLD participants. Our study highlighted the clinical diagnostic value of vitamin B12 metabolites and the possibility that vitamin B12 metabolism could be a therapeutic target for NASH. Further studies using recent perspective data with biopsy proven NASH could be conducted to validate our results.

Do East Asians With Normal Glucose Tolerance Have Worse ß-Cell Function? A Meta-Analysis of Epidemiological Studies.

Background: The difference in the relationship between ß-cell function and insulin resistance among Africans, Caucasians and East Asians with normal glucose tolerance (NGT) was not well investigated. Methods: We searched PubMed and Web of Science with keywords and identified studies that used the homeostasis model assessment (HOMA) model to evaluate ß-cell function (HOMA-B) and insulin sensitivity/resistance (HOMA-S/HOMA-IR) in certain ethnic groups. We used random-effect model to pool data of HOMAs and compared the combined data among the three ethnic groups using subgroup analysis. Linear regression analysis was used to estimate the coefficient of HOMA-S on HOMA-B in these ethnic groups. Results: We evaluated pooled data of HOMAs in eight African, 26 Caucasian, and 84 East Asian cohorts with NGT, and also 2,392, 6,645 and 67,317 individuals, respectively. The three ethnic groups had distinct HOMA-B but similar HOMA-IR. The regression coefficient of lnHOMA-B on lnHOMA-S was different between Africans and Caucasians (-1.126 vs -0.401, P = 0.0006) or East Asian (-1.126 vs -0.586, P = 0.0087), but similar between Caucasians and East Asians (-0.401 vs -0.586, P = 0.1282). The coefficient in all ethnic groups was similar when age, BMI, and gender were adjusted (African vs Caucasian P = 0.0885, African vs East Asian P = 0.1092, and Caucasian vs East Asian P = 0.6298). Conclusions: In subjects with NGT, East Asians had lower HOMA-B but similar ß-cell response relative to insulin resistance with Caucasians and Africans when age, BMI, and gender were controlled. This result may challenge the allegation that there was an Asian-specific diabetes phenotype with worse ß-cell function.

NAFLD or MAFLD: Which Has Closer Association With All-Cause and Cause-Specific Mortality?-Results From NHANES III.

Background: The recent change of terminology from non-alcoholic fatty liver disease (NAFLD) to metabolic dysfunction-associated fatty liver disease (MAFLD) has raised heated discussion. We aim to investigate the association of MAFLD or NAFLD with all-cause and cause-specific mortality to compare the outcomes of the two diagnostic criteria in population-based study. Methods: We recruited 12,480 participants from the Third National Health and Nutrition Examination Survey (NHANES III) with matched mortality data in 2015. Participants were divided into four groups for survival analysis: without NAFLD or MAFLD, with only NAFLD, only MAFLD. Cox proportional hazard regression was used to estimate multivariable-adjusted hazard ratios (HRs) and 95% confidence intervals (CIs) for all-cause and cause-specific mortality. Subgroup analysis were applied in MAFLD patients. Results: The weighted prevalence of MAFLD and NAFLD was relatively 27.4 and 27.9%. Participants with NAFLD or MAFLD were largely overlapped (weighted Cohen's kappa coefficient 0.76). MAFLD increased the overall risk for total mortality in a greater magnitude than NAFLD [HR 2.07 (95% CI 1.86, 2.29) vs. 1.47 (1.20, 1.79)], However, the difference was non-significant after metabolic parameters were adjusted. Risks for cardiovascular, neoplasm, and diabetes-related mortality were similar between MAFLD and NAFLD. Referring to individuals without both NAFLD and MAFLD, individuals with only NAFLD showed reduced total mortality [HR 0.48 (0.34, 0.68)] and neoplasm mortality [HR 0.46 (0.24, 0.89)] in crude. Nevertheless, individuals with only MAFLD independently increased the risk for total mortality [adjusted HR 1.47 (1.22, 1.77)] and neoplasm mortality [aHR 1.58 (1.09, 2.28)]. The risk for overall mortality in MAFLD was consistent between subgroups except for race-ethnicity and whether secondary to viral hepatitis. Conclusions: Participants with MAFLD or NAFLD were highly concordant. MAFLD showed greater risk for all-cause mortality and equal risk for cause-specific mortality referring to NAFLD. The new terminology excluded participants with lower mortality risk and included participants with higher risk. Drug development for MAFLD should consider ethnic differences.

Urinary C-peptide/creatinine ratio: A useful biomarker of insulin resistance and refined classification of type 2 diabetes mellitus.

BACKGROUND: The urinary C-peptide/creatinine ratio (UCPCR) is low in patients with type 1 diabetes mellitus, but it has not been well characterized in patients with type 2 diabetes mellitus (T2DM). We aimed to measure the UCPCRs in patients with T2DM and explore the relationships among UCPCR, insulin resistance (IR), and chronic vascular complications of diabetes. METHODS: A cross-sectional study was performed of 1299 Chinese hospitalized patients with T2DM. Binary logistic regression was used to evaluate the relationships between the chronic vascular complications of diabetes and UCPCR. K-means analysis was used to allocate participants to subgroups with five to six variables (age at diagnosis, body mass index [BMI], glycosylated hemoglobin, homoeostasis model assessment 2-estimated beta-cell function (HOMA2-B), and HOMA2-insulin resistance (HOMA2-IR), with or without UCPCR). RESULTS: UCPCR positively correlated with HOMA2-IR (r = 0.448, P < .001). After adjustment for sex, age, duration of diabetes, and other cardiovascular risk factors, UCPCR was positively associated with diabetic kidney disease (DKD) (odds ratio [OR] = 1.198, 95% CI 1.019-1.408, P = .029) and coronary heart disease (CHD) (OR = 1.312, 95% CI 1.079-1.594, P = .006). When UCPCR was added, cluster analysis using the six variables identified five subgroups of T2DM, characterized by differing age at diagnosis, BMI, beta-cell function, IR, and prevalence of vascular complications. CONCLUSIONS: UCPCR is positively associated with IR, DKD, and CHD and represents a promising biomarker that could refine the classification of T2DM.

FOXO1 inhibition prevents renal ischemia-reperfusion injury via cAMP-response element binding protein/PPAR-γ coactivator-1α-mediated mitochondrial biogenesis.

BACKGROUND AND PURPOSE: Growing evidence indicates targeting mitochondrial dynamics and biogenesis could accelerate recovery from renal ischemia-reperfusion (I/R) injury, but the underlying mechanisms remain elusive. Transcription factor forkhead box O1 (FOXO1) is a key regulator of mitochondrial homeostasis and plays a pathological role in the progression of renal disease. EXPERIMENTAL APPROACH: A mouse model of renal I/R injury and a hypoxia/reoxygenation (H/R) injury model for human renal tubular epithelial cells were used. KEY RESULTS: I/R injury up-regulated renal expression of FOXO1 and treatment with FOXO1-selective inhibitor AS1842856 prior to I/R injury decreased serum urea nitrogen, serum creatinine and the tubular damage score after injury. Post-I/R injury AS1842856 treatment could also ameliorate renal function and improve the survival rate of mice following injury. AS1842856 administration reduced mitochondrial-mediated apoptosis, suppressed the overproduction of mitochondrial ROS and accelerated recovery of ATP both in vivo and in vitro. Additionally, FOXO1 inhibition improved mitochondrial biogenesis and suppressed mitophagy. Expression of PPAR-γ coactivator 1α (PGC-1α), a master regulator of mitochondrial biogenesis, was down-regulated in both I/R and H/R injury, which could be abrogated by FOXO1 inhibition. Experiments using integrated bioinformatics analysis and coimmunoprecipitation established that FOXO1 inhibited PGC-1α transcription by competing with cAMP-response element binding protein (CREB) for its binding to transcriptional coactivators CREBBP/EP300 (CBP/P300). CONCLUSION AND IMPLICATIONS: These findings suggested that FOXO1 was critical to maintain mitochondrial function in renal tubular epithelial cells and FOXO1 may serve as a therapeutic target for pharmacological intervention in renal I/R injury.

A Decision-Support Software to Improve the Standard Care in Chinese Type 2 Diabetes.

BACKGROUND: To develop a decision-support software according to the Chinese Diabetes Society guideline in order to improve the standard care in type 2 diabetes. METHODS: Firstly, we developed a decision-support software for healthcare professionals. It was an independent software on a tablet to record the data of patients and treatments given by their physicians. A major function of the software was to remind doctors when and how they should implement the standard care as recommended by the Chinese Diabetes Society guideline. Secondly, we compared the baseline data of standard care including statin and aspirin usage with data from a previous "3B study" to see whether there was an improvement of these standard cares. Finally, we further compared the data during four quarters of the whole year to evaluate whether there was a continuous improvement. RESULTS: During the first quarter, 27,291 cases and 27,352 cases were collected with complete information about statin and aspirin usage, respectively. The percentage of patients treated with statins and aspirin in our study was significantly higher than that reported in the 3B study (59.6% vs. 19.9% and 59.8% vs. 18.5%, P < 0.001). There were no significant differences among the four quarters for the percentage of the patients who were taking statin or aspirin (P > 0.05). CONCLUSION: Our decision-support software has been shown to be effective in continuously improving the standardization of comprehensive treatment in type 2 diabetes.

11Beta-hydroxysteroid dehydrogenase-1 deficiency or inhibition enhances hepatic myofibroblast activation in murine liver fibrosis.

A hallmark of chronic liver injury is fibrosis, with accumulation of extracellular matrix orchestrated by activated hepatic stellate cells (HSCs). Glucocorticoids limit HSC activation in vitro, and tissue glucocorticoid levels are amplified by 11beta-hydroxysteroid dehydrogenase-1 (11ßHSD1). Although 11ßHSD1 inhibitors have been developed for type 2 diabetes mellitus and improve diet-induced fatty liver in various mouse models, effects on the progression and/or resolution of liver injury and consequent fibrosis have not been characterized. We have used the reversible carbon tetrachloride-induced model of hepatocyte injury and liver fibrosis to show that in two models of genetic 11ßHSD1 deficiency (global, Hsd11b1-/- , and hepatic myofibroblast-specific, Hsd11b1fl/fl /Pdgfrb-cre) 11ßHSD1 pharmacological inhibition in vivo exacerbates hepatic myofibroblast activation and liver fibrosis. In contrast, liver injury and fibrosis in hepatocyte-specific Hsd11b1fl/fl /albumin-cre mice did not differ from that of controls, ruling out 11ßHSD1 deficiency in hepatocytes as the cause of the increased fibrosis. In primary HSC culture, glucocorticoids inhibited expression of the key profibrotic genes Acta2 and Col1α1, an effect attenuated by the 11ßHSD1 inhibitor [4-(2-chlorophenyl-4-fluoro-1-piperidinyl][5-(1H-pyrazol-4-yl)-3-thienyl]-methanone. HSCs from Hsd11b1-/- and Hsd11b1fl/fl /Pdgfrb-cre mice expressed higher levels of Acta2 and Col1α1 and were correspondingly more potently activated. In vivo [4-(2-chlorophenyl-4-fluoro-1-piperidinyl][5-(1H-pyrazol-4-yl)-3-thienyl]-methanone administration prior to chemical injury recapitulated findings in Hsd11b1-/- mice, including greater fibrosis. CONCLUSION: 11ßHSD1 deficiency enhances myofibroblast activation and promotes initial fibrosis following chemical liver injury; hence, the effects of 11ßHSD1 inhibitors on liver injury and repair are likely to be context-dependent and deserve careful scrutiny as these compounds are developed for chronic diseases including metabolic syndrome and dementia. (Hepatology 2018;67:2167-2181).

The characteristics of newly diagnosed adult early-onset diabetes: a population-based cross-sectional study.

To investigate the characteristics of newly diagnosed early-onset diabetes in the Chinese population, 2801 newly diagnosed diabetes participants without known diabetes or pre-diabetes in a national cross-sectional survey were analysed. Participants were divided into quartiles (22-43, 44-52, 53-61 and >61 years) according to age of diabetes onset and the first group were defined as early-onset diabetes group. Early-onset diabetes group had lower systolic blood pressure (SBP), total cholesterol, low density lipoprotein cholesterol, 2-hour post prandial blood glucose and urine albumin creatinine ratio. There was no difference in body mass index, Homeostasis model assessment (HOMA) of beta cell function and diabetes family history between early-onset diabetes participants and any other age groups. HOMA of insulin resistance (IR) scores and disposition index 30 minutes after glucose load (DI30) were increased in early-onset diabetes participants. The beta cell function declination was more deteriorated in early-onset diabetes participants. Male gender, triglycerides, HOMA-IR and DI30 were positively associated with an earlier age at diagnosis. In conclusion, patients diagnosed with diabetes at a younger age are characterized by a similar cardiovascular risk profile with increased insulin resistance and more severe beta cell failure than patients diagnosed at a later age.

Decreased Glycemic Difference Between Diabetes and Nondiabetes in the Elderly Leads to the Reduced Diagnostic Accuracy of Hemoglobin A1c for Diabetes Screening in an Aged Chinese Population.

BACKGROUND: This study investigated the impact of age on the accuracy of glycated hemoglobin (HbA1c) for diabetes screening and explored the possible cause(s). MATERIALS AND METHODS: Data from 3,050 Chinese participants 25-75 years of age without known diabetes in a population-based cross-sectional survey were analyzed. Diabetes was diagnosed by the oral glucose tolerance test (OGTT). The performance of HbA1c for detecting OGTT-defined diabetes in tertile groups (divided by age) was evaluated by the area under the curve (AUC) of the receiver operating characteristic curve (ROC). The effect of age on the difference in glucose levels between participants with and without diabetes and the impact of this difference on the performance of HbA1c were evaluated. RESULTS: In young (25-41 years old), middle-aged (41-53 years old), and old (55-72 years old) participants, the ROC AUC (95% confidence interval) of HbA1c for detecting OGTT-defined diabetes was 0.958 (0.915, 1.000), 0.891 (0.852, 0.930), and 0.861 (0.821, 0.901), respectively (P = 0.005). The difference of fasting plasma glucose between participants with diabetes and those without diabetes decreased with increasing age: 3.01 (2.80, 3.22) mmol/L, 2.90 (2.71, 3.09) mmol/L, and 2.33 (2.16, 2.50) mmol/L in the three consecutive age groups, respectively. A similar pattern was found in 2-h postprandial plasma glucose. The impact of age on the diagnostic power of HbA1c diminished after data were rearranged to artificially increase the difference between participants without diabetes and those with diabetes. CONCLUSIONS: The accuracy of HbA1c for detecting OGTT-defined diabetes declines with age. This is largely due to the decreased separation in glycemic levels between participants with diabetes and without diabetes in the elderly.