Glycometabolic reprogramming in cementoblasts: A vital target for enhancing cell mineralization.

Cementum, a constituent part of periodontal tissues, has important adaptive and reparative functions. It serves to attach the tooth to alveolar bone and acts as a barrier delimit epithelial growth and bacteria evasion. A dynamic and highly responsive cementum is essential for maintaining occlusal relationships and the integrity of the root surface. It is a thin layer of mineralized tissue mainly produced by cementoblasts. Cementoblasts are osteoblast-like cells essential for the restoration of periodontal tissues. In recent years, glucose metabolism has been found to be critical in bone remodeling and osteoblast differentiation. However, the glucose metabolism of cementoblasts remains incompletely understood. First, immunohistochemistry staining and in vivo tracing with 18 F-fluorodeoxyglucose (18 F-FDG) revealed significantly higher glucose metabolism in cementum formation. To test the bioenergetic pathways of cementoblast differentiation, we compared the bioenergetic profiles of mineralized and unmineralized cementoblasts. As a result, we observed a significant increase in the consumption of glucose and production of lactate, coupled with the higher expression of glycolysis-related genes. However, the expression of oxidative phosphorylation-related genes was downregulated. The verified results were consistent with the RNA sequencing results. Likewise, targeted energy metabolomics shows that the levels of glycolytic metabolites were significantly higher in the mineralized cementoblasts. Seahorse assays identified an increase in glycolytic flux and reduced oxygen consumption during cementoblast mineralization. Apart from that, we also found that lactate dehydrogenase A (LDHA), a key glycolysis enzyme, positively regulates the mineralization of cementoblasts. In summary, cementoblasts mainly utilized glycolysis rather than oxidative phosphorylation during the mineralization process.

CXXC5 mitigates P. gingivalis-inhibited cementogenesis by influencing mitochondrial biogenesis.

BACKGROUND: Cementoblasts on the tooth-root surface are responsible for cementum formation (cementogenesis) and sensitive to Porphyromonas gingivalis stimulation. We have previously proved transcription factor CXXC-type zinc finger protein 5 (CXXC5) participates in cementogenesis. Here, we aimed to elucidate the mechanism in which CXXC5 regulates P. gingivalis-inhibited cementogenesis from the perspective of mitochondrial biogenesis. METHODS: In vivo, periapical lesions were induced in mouse mandibular first molars by pulp exposure, and P. gingivalis was applied into the root canals. In vitro, a cementoblast cell line (OCCM-30) was induced cementogenesis and submitted for RNA sequencing. These cells were co-cultured with P. gingivalis and examined for osteogenic ability and mitochondrial biogenesis. Cells with stable CXXC5 overexpression were constructed by lentivirus transduction, and PGC-1α (central inducer of mitochondrial biogenesis) was down-regulated by siRNA transfection. RESULTS: Periapical lesions were enlarged, and PGC-1α expression was reduced by P. gingivalis treatment. Upon apical inflammation, Cxxc5 expression decreased with Il-6 upregulation. RNA sequencing showed enhanced expression of osteogenic markers, Cxxc5, and mitochondrial biogenesis markers during cementogenesis. P. gingivalis suppressed osteogenic capacities, mitochondrial biogenesis markers, mitochondrial (mt)DNA copy number, and cellular ATP content of cementoblasts, whereas CXXC5 overexpression rescued these effects. PGC-1α knockdown dramatically impaired cementoblast differentiation, confirming the role of mitochondrial biogenesis on cementogenesis. CONCLUSIONS: CXXC5 is a P. gingivalis-sensitive transcription factor that positively regulates cementogenesis by influencing PGC-1α-dependent mitochondrial biogenesis. Video Abstract.

Expression profile analysis of long noncoding RNA and messenger RNA during mouse cementoblast mineralization.

BACKGROUND AND OBJECTIVE: Emerging evidence has uncovered that long noncoding RNAs (lncRNAs) and messenger RNAs (mRNAs) exert biofunctions on cellular mineralization and bone formation. In this study, we aimed to identify lncRNA-mRNA expression profiles and expression patterns, and explore their underlying biofunctions during cementoblast mineralization. MATERIALS AND METHODS: Cementoblasts were cultured in mineralized medium for 0, 7, and 14 days. We used quantitative real-time polymerase chain reaction (qRT-PCR) and western blot (WB) to detect expression levels of osteocalcin (OCN), bone sialoprotein (BSP), and Osterix (Osx). Alkaline phosphatase (ALP) staining and alizarin red staining (ARS) were conducted to detect ALP activity and number of mineralized nodule. Total RNA was extracted from cells and used for high-throughput sequencing. EBSeq package was applied to analyze differentially expressed genes. Mfuzz R package was used to identify gene expression patterns. The weighted gene co-expression network analysis (WGCNA) was performed to explore co-expressed mRNAs of differentially expressed lncRNAs (DElncRNAs). Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses were adopted by Clusterprofile R package. RESULTS: Cementoblasts were successfully induced by osteogenic medium. Compared with those on day 0, 384 DElncRNAs and 4255 differentially expressed mRNAs (DEmRNAs), respectively, were found on day 7. Meanwhile, 645 DElncRNAs and 4717 DEmRNAs were detected on day 14. Both DElncRNAs and DEmRNAs were classified into six clusters with different expression patterns. DEmRNAs and co-expressed mRNA of DElncRNAs were predominantly related to cell process, binding, phosphatidylinositol-3 kinase (PI3K)-Akt signaling pathway, hypoxia-inducible factor-1 (HIF-1) signaling pathway, mitogen-activated protein kinase (MAPK) signaling pathway, and hippo signaling pathway. CONCLUSION: The results demonstrated that both noncoding and coding RNAs were involved in the process of mineralization in cementoblasts, which may provide a new database for further study.

Platr3/NUDT21/NF-κB Axis Mediates P. gingivalis-Suppressed Cementoblast Mineralization.

Porphyromonas gingivalis (P. gingivalis) is one of the major pathogens causing periodontitis and apical periodontitis (AP). Long noncoding RNA (lncRNA) can regulate cellular mineralization and inflammatory diseases. The aim of this study was to investigate the role and mechanism of lncRNA in P. gingivalis-stimulated cementoblast mineralization. In vivo, C57BL/6 mice were divided into the healthy, the AP, and AP + P. gingivalis groups (n = six mice per group). Micro computed tomography, immunohistochemistry staining, and fluorescence in situ hybridization were used to observe periapical tissue. In vitro, cementoblasts were treated with osteogenic medium or P. gingivalis. Pluripotency associated transcript 3 (Platr3), interleukin 1 beta (IL1B), and osteogenic markers were analyzed by quantitative real-time polymerase chain reaction and western blot. RNA pull-down and RNA immunoprecipitation assays were used to detect proteins that bind to Platr3. RNA sequencing was performed in Platr3-silenced cementoblasts. In vivo, P. gingivalis promoted periapical tissue destruction and IL1B expression, but inhibited Platr3 expression. In vitro, P. gingivalis facilitated IL1B expression (P < 0.001), whereas suppressed the expression of Platr3 (P < 0.001) and osteogenic markers (P < 0.01 or 0.001). In contrast, Platr3 overexpression alleviated the repressive effect of P. gingivalis on cementoblast mineralization (P < 0.01 or 0.001). Furthermore, Platr3 bound to nudix hydrolase 21 (NUDT21) and regulated the nuclear factor-κB (NF-κB) signaling pathway. Knocking down NUDT21 suppressed osteogenic marker expression and activated the above signaling pathway. Collectively, the results elucidated that Platr3 mediated P. gingivalis-suppressed cementoblast mineralization through the NF-κB signaling pathway by binding to NUDT21.

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.

CKIP-1 Promotes P. gingivalis-Induced Inflammation of Periodontal Soft Tissues by Inhibiting Autophagy.

As a chronic inflammatory disease, periodontitis involves many biological processes including autophagy. At the same time, casein kinase 2 interacting protein-1 (CKIP-1) was reported to play a role in regulation of inflammation. But whether CKIP-1 and autophagy interact in periodontitis remains unclear. In this paper, our research team verified the levels of CKIP-1 expression and autophagy increase in the periodontal tissues of a ligature-induced periodontitis mouse model. And this result was also confirmed in Porphyromonas gingivalis (Pg)-induced human gingival fibroblasts (HGF) and human periodontal ligament cells (PDLC). We also showed the autophagy level in periodontal tissues is higher in Ckip-1 knockout (KO) mice than wild type (WT). At the same time, CKIP-1 knockdown lentivirus was used in PDLC and HGF, and it was found that silencing CKIP-1 significantly activated autophagy. Unfortunately, the regulatory role of autophagy in periodontitis is still unclear. Then, the autophagy agonist Rapamycin and inhibitor 3-MA were used in a periodontitis mouse model to investigate periodontal tissue destruction. We found the inflammation in periodontal tissue was reduced when autophagy activated. All these conclusions have been verified both in vivo and in vitro experiments. Finally, our research proved that silencing CKIP-1 reduces the expression of inflammatory cytokines in Pg-induced PDLC and HGF by regulating autophagy. Overall, a new role for CKIP-1 in regulating periodontal tissue inflammation was demonstrated in our study, and it is possible to treat periodontitis by targeting the CKIP-1 gene.

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.

PGC-1 alpha regulates mitochondrial biogenesis to ameliorate hypoxia-inhibited cementoblast mineralization.

Hypoxia often occurs in inflammatory tissues, such as tissues affected by periodontitis and apical periodontitis lesions. Mitochondrial biogenesis can be disrupted in hypoxia. Peroxisome proliferator-activated receptor gamma coactivator-1 alpha (PGC-1α) is a core factor required for mitochondrial biogenesis. Cementoblasts are root surface lining cells that play an integral role in cementum formation. There is a dearth of research on the effect of hypoxia on cementoblasts and underlying mechanisms, particularly in relation to mitochondrial biogenesis during the hypoxic process. In this study, we found that the expression of hypoxia inducible factor-1α was elevated in apical periodontitis tissues in vivo. In contrast, periapical lesions exhibited a reduction of PGC-1α expression. For in vitro experiments, cobalt chloride (CoCl2 ) was used to induce hypoxia. We observed that CoCl2 -induced hypoxia suppressed the mineralization ability and mitochondrial biogenesis of cementoblasts, accompanied by abnormal mitochondria morphology. Furthermore, we found that CoCl2 blocked the p38 pathway, while it activated the Erk1/2 pathway, with the former upregulating the expression of PGC-1α, while the latter reversed the effects. Overall, our findings demonstrate that mitochondrial biogenesis, especially via PGC-1α, is impaired during cementogenesis in the context of CoCl2 -induced hypoxia, dependent on the mitogen-activated protein kinase signaling pathway.

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.

LTNN: A Layerwise Tensorized Compression of Multilayer Neural Network.

An efficient deep learning requires a memory-efficient construction of a neural network. This paper introduces a layerwise tensorized formulation of a multilayer neural network, called LTNN, such that the weight matrix can be significantly compressed during training. By reshaping the multilayer neural network weight matrix into a high-dimensional tensor with a low-rank approximation, significant network compression can be achieved with maintained accuracy. An according layerwise training is developed by a modified alternating least-squares method with backward propagation for fine-tuning only. LTNN can provide the state-of-the-art results on various benchmarks with significant compression. For MNIST benchmark, LTNN shows 64 × compression rate without accuracy drop. For Imagenet12 benchmark, our proposed LTNN achieves 35.84 × compression of the neural network with around 2% accuracy drop. We have also shown 1.615 × faster on inference speed than the existing works due to the smaller tensor core ranks.

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.

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.

[Progress of researches on lysozyme and its expression in Oncomelania hupensis].

Lysozyme generally exists in animals, plants and microorganisms, and it is used as a natural anti-infection material and one of the important non-specific immune factors in organisms. This paper reviews the progress of researches on its classification, gene structure and function, and expression regulation in Oncomelania hupensis, and on the factors affecting its activities in recent years, in order to further discuss its distribution in O. hupensis.