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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Alcohol consumption and diabetes risk in a Chinese population: a Mendelian randomization analysis.

AIM: To assess the causality between alcohol intake, diabetes risk and related traits. DESIGN: Mendelian randomization (MR) study. Subgroup analysis, standard instrumental variable analysis and local average treatment effect (LATE) methods were applied to assess linear and non-linear causality. SETTING: China. PARTICIPANTS: A total of 4536 participants, including 721 diabetes cases. FINDINGS: Carriage of an ALDH2 rs671 A allele reduced alcohol consumption by 44.63% [95% confidence interval (CI) = -49.44%, -39.37%]. In males, additional carriage of an A allele was significantly connected to decreased diabetes risk for the overall population [odds ratio (OR) = 0.716, 95% CI = 0.567-0.904, P = 0.005] or moderate drinkers (OR = 0.564, 95% CI = 0.355-0.894, P = 0.015). In instrumental variable (IV) analysis, increasing alcohol consumption by 1.7-fold was associated with an incidence-rate ratio of 1.32 (95% CI = 1.06-1.67, P = 0.014) for diabetes risk, and elevated alcohol intake was causally connected to natural log-transformed fasting, 2-hour post-load plasma glucose (ß = 0.036, 95% CI = 0.018-0.054; ß = 0.072, 95% CI = 0.035-0.108) and insulin resistance [homeostatic model assessment for IR (HOMA-IR] (ß = 0.104, 95% CI = 0.039-0.169), but was not associated with beta-cell function (HOMA-beta). In addition, the LATE method did not identify significant U-shaped causality between alcohol consumption and diabetes-related traits. In females, the effects of alcohol intake on all the outcomes were non-significant. CONCLUSION: Among men in China, higher alcohol intake appears to be causally associated with increased diabetes risk and worsened related traits, even for moderate drinkers. This study found no significant U-shaped causality between alcohol consumption and diabetes-related traits.

Not performing an OGTT results in underdiagnosis, inadequate risk assessment and probable cost increases of (pre)diabetes in Han Chinese over 40 years: a population-based prospective cohort study.

OBJECTIVE: To explore the influence by not performing an oral glucose tolerance test (OGTT) in Han Chinese over 40 years. DESIGN: Overall, 6682 participants were included in the prospective cohort study and were followed up for 3 years. METHODS: Fasting plasma glucose (FPG), 2-h post-load plasma glucose (2h-PG), FPG and 2h-PG (OGTT), and HbA1c testing using World Health Organization (WHO) or American Diabetes Association (ADA) criteria were employed for strategy analysis. RESULTS: The prevalence of diabetes is 12.4% (95% CI: 11.6-13.3), while the prevalence of prediabetes is 34.1% (95% CI: 32.9-35.3) and 56.5% (95% CI: 55.2-57.8) using WHO and ADA criteria, respectively. 2h-PG determined more diabetes individuals than FPG and HbA1c. The testing cost per true positive case of OGTT is close to FPG and less than 2h-PG or HbA1c. FPG, 2h-PG and HbA1c strategies would increase costs from complications for false-positive (FP) or false-negative (FN) results compared with OGTT. Moreover, the least individuals identified as normal by OGTT at baseline developed (pre)diabetes, and the most prediabetes individuals identified by HbA1c or FPG using ADA criteria developed diabetes. CONCLUSIONS: The prevalence of isolated impaired glucose tolerance and isolated 2-h post-load diabetes were high, and the majority of individuals with (pre)diabetes were undetected in Chinese Han population. Not performing an OGTT results in underdiagnosis, inadequate developing risk assessment and probable cost increases of (pre)diabetes in Han Chinese over 40 years and great consideration should be given to OGTT in detecting (pre)diabetes in this population. Further population-based prospective cohort study of longer-term effects is necessary to investigate the risk assessment and cost of (pre)diabetes.

Genetic Regulation of the Thymic Stromal Lymphopoietin (TSLP)/TSLP Receptor (TSLPR) Gene Expression and Influence of Epistatic Interactions Between IL-33 and the TSLP/TSLPR Axis on Risk of Coronary Artery Disease.

The thymic stromal lymphopoietin (TSLP)/TSLP receptor (TSLPR) axis is involved in multiple inflammatory immune diseases, including coronary artery disease (CAD). To explore the causal relationship between this axis and CAD, we performed a three-stage case-control association analysis with 3,628 CAD cases and 3,776 controls using common variants in the genes TSLP, interleukin 7 receptor (IL7R), and TSLPR. Three common variants in the TSLP/TSLPR axis were significantly associated with CAD in a Chinese Han population [rs3806933T in TSLP, Padj = 4.35 × 10-5, odds ratio (OR) = 1.18; rs6897932T in IL7R, Padj = 1.13 × 10-7, OR = 1.31; g.19646A>GA in TSLPR, Padj = 2.04 × 10-6, OR = 1.20]. Reporter gene analysis demonstrated that rs3806933 and rs6897932 could influence TSLP and IL7R expression, respectively. Furthermore, the "T" allele of rs3806933 might increase plasma TSLP levels (R2 = 0.175, P < 0.01). In a stepwise procedure, the risk for CAD increased by nearly fivefold compared with the maximum effect of any single variant (Padj = 6.99 × 10-4, OR = 4.85). In addition, the epistatic interaction between TSLP and IL33 produced a nearly threefold increase in the risk of CAD in the combined model of rs3806933TT-rs7025417TT (Padj = 3.67 × 10-4, OR = 2.98). Our study illustrates that the TSLP/TSLPR axis might be involved in the pathogenesis of CAD through upregulation of mRNA or protein expression of the referenced genes and might have additive effects on the CAD risk when combined with IL-33 signaling.

Correction: A Chinese version of the Language Screening Test (CLAST) for early-stage stroke patients.

[This corrects the article DOI: 10.1371/journal.pone.0196646.].

A Chinese version of the Language Screening Test (CLAST) for early-stage stroke patients.

There is a severe lack of aphasia screening tools for bedside use in Chinese. A number of aphasia assessment tools have recently been developed abroad, but some of these scales were not suitable for patients with acute stroke. The Language Screening Test (which includes two parallel versions [a/b]) in French has been proven to be an effective and time-saving aphasia screening scale for early-stage stroke patients. Therefore, we worked out a Chinese version of the LAST taking into consideration Chinese language and culture. Two preliminary parallel versions (a/b) were tested on 154 patients with stroke at acute phase and 107 patients with stroke at non-acute phase, with the Western Aphasia Battery serving as a gold standard. The equivalence between the two parallel versions and the reliability/validity of each version were assessed. The median time to complete one preliminary Chinese version (each had some item redundancy) was 98 seconds. Two final parallel versions were established after adjustment/elimination of the redundant items and were found to be equivalent (intra-class correlation coefficient: 0.991). Internal consistency is(Cronbach α for each version [a/b] was 0.956 and 0.965, respectively) good. Internal validity was fine: (a) no floor or ceiling effect/item redundancy; (b) construct validity revealed a 1-dimension structure, just like the French version. The higher educated subjects scored higher than their lower educated counterparts (p<0.01). The external validity: at the optimum cut-off point where the score of version a/b <14 in higher educated group(<13 in lower): the specificity of each version was 0.878/0.902(1/1 in lower) and sensitivity was 0.972/0.944(0.944/0.944 in lower). Inter-rater equivalence (intra-class correlation coefficient) was 1. The Chinese version of the Language Screening Test was proved to be an efficient and time-saving bedside aphasia screening tool for stroke patients at acute phase and can be used by an average medical physician.

Upregulation of pyruvate kinase M2 expression by fatty acid synthase contributes to gemcitabine resistance in pancreatic cancer.

Pancreatic cancer has one of the highest mortality rates of all cancer types. Fatty acid synthase (FASN) is a multifunctional protein homodimer that can convert acetyl coenzyme A (CoA) and malonyl-CoA into palmitate, thus regulating lipogenesis. FASN overexpression has also been shown to cause resistance to gemcitabine, a chemotherapy treatment for pancreatic cancer; however, the mechanism by which this happens is unclear. Analysis of gene expression of FASN and pyruvate kinase M2 (PKM2) in pancreatic cancer was performed using Oncomine microarray gene expression datasets, which demonstrated that FASN and PKM2 were upregulated in pancreatic cancer compared with normal tissue. Specifically, it was demonstrated that FASN enabled the upregulation of PKM2 expression at the mRNA and protein levels, increasing the glucose consumption rate in pancreatic cancer cells. The present study also revealed that decreased levels of FASN reduced resistance to gemcitabine treatment, which was induced by PKM2 overexpression in pancreatic ductal adenocarcinoma cells. Therefore, FASN may represent a novel therapeutic target in pancreatic cancer.

Non-high-density lipoprotein cholesterol: High-density lipoprotein cholesterol ratio is an independent risk factor for diabetes mellitus: Results from a population-based cohort study.

BACKGROUND: Dyslipidemia predicts the development and progression of diabetes. A higher non-high-density lipoprotein cholesterol (HDL-C): HDL-C ratio is reportedly associated with metabolic syndrome and insulin resistance, but its relationship with glycemic levels and diabetes remains unclear. METHODS: In all, 4882 subjects aged ≥40 years without diabetes and not using lipid-lowering drugs were enrolled in the study. The non-HDL-C: HDL-C ratio was log10 transformed to achieve normal distribution. Multivariate logistic regression was used to investigate the association between the log10 -transformed non-HDL-C: HDL-C ratio and diabetes. Stratified analyses of the association by age, gender, and body mass index (BMI) were also performed. RESULTS: After 3 years of follow-up, 704 participants developed diabetes. After adjustment for age, gender, current smoking, current drinking, physical activity, BMI, systolic blood pressure, and family history of diabetes, each 1-SD increase in the log(non-HDL-C: HDL-C ratio) was associated with higher fasting blood glucose (FPG) levels (ß = 0.1; 95% confidence interval [CI] 0.1-0.1), 2-h postload plasma glucose levels (2-h glucose; ß = 0.2; 95% CI 0.1-0.2), and risk of diabetes (odds ratio [OR] 1.1; 95% CI 1.0-1.2). In a multivariate model, subjects in the top quartile of non-HDL-C: HDL-C ratio had higher FPG (ß = 0.2; 95% CI 0.2-0.3), 2-h glucose (ß = 0.5; 95% CI 0.3-0.7) and HbA1c (ß = 0.1; 95% CI 0.1-0.2) levels, and a 40% increased risk of diabetes (OR 1.4; 95% CI 1.1-1.8) than participants in the bottom quartile. CONCLUSIONS: The non-HDL-C: HDL-C ratio was found to be an independent risk factor for diabetes.

Histone Acetyltransferase 1 Promotes Cell Proliferation and Induces Cisplatin Resistance in Hepatocellular Carcinoma.

Hepatocellular carcinoma (HCC) is one of the most common malignant diseases in the world. Mutations, overexpression, and improper recruitment of HATs can lead to tumorigenesis. HAT1 is the first histone acetyltransferase identified and is related with developing HCC, but the mechanism is still unclear. Interestingly, we found that HAT1 was upregulated in HCC patient specimens and showed that its upregulation facilitates HCC cell growth in vitro and in vivo. Moreover, we demonstrated that HAT1 promoted glycolysis in HCC cells and knockdown of HAT1 sensitized HCC cells to apoptotic death induced by cisplatin. Our results suggest that HAT1 might act as an oncogenic protein promoting cell proliferation and inducing cisplatin resistance in HCC, and targeting HAT1 represents a viable strategy for effective treatment of advanced HCC.