Tannerella forsythia and coating color on the tongue dorsum, and fatty food liking associate with fat accumulation and insulin resistance in adult catch-up fat

Oral Microbiome Dysbiosis as a Risk Factor for Stroke: A Comprehensive Review

Stroke represents a significant global health burden, with a substantial impact on mortality, morbidity, and long-term disability. The examination of stroke biomarkers, particularly the oral microbiome, offers a promising avenue for advancing our understanding of the factors that contribute to stroke risk and for developing strategies to mitigate that risk. This review highlights the significant correlations between oral diseases, such as periodontitis and caries, and the onset of stroke. Periodontal pathogens within the oral microbiome have been identified as a contributing factor in the exacerbation of risk factors for stroke, including obesity, dyslipidemia, atherosclerosis, hypertension, and endothelial dysfunction. The alteration of the oral microbiome may contribute to these conditions, emphasizing the vital role of oral health in the prevention of cardiovascular disease. The integration of dental and medical health practices represents a promising avenue for enhancing stroke prevention efforts and improving patient outcomes.

Noninvasive Diagnostic Technique for Nonalcoholic Fatty Liver Disease Based on Features of Tongue Images

OBJECTIVE

To investigate a new noninvasive diagnostic model for nonalcoholic fatty liver disease (NAFLD) based on features of tongue images.

METHODS

Healthy controls and volunteers confirmed to have NAFLD by liver ultrasound were recruited from China-Japan Friendship Hospital between September 2018 and May 2019, then the anthropometric indexes and sampled tongue images were measured. The tongue images were labeled by features, based on a brief protocol, without knowing any other clinical data, after a series of corrections and data cleaning. The algorithm was trained on images using labels and several anthropometric indexes for inputs, utilizing machine learning technology. Finally, a logistic regression algorithm and a decision tree model were constructed as 2 diagnostic models for NAFLD.

RESULTS

A total of 720 subjects were enrolled in this study, including 432 patients with NAFLD and 288 healthy volunteers. Of them, 482 were randomly allocated into the training set and 238 into the validation set. The diagnostic model based on logistic regression exhibited excellent performance: in validation set, it achieved an accuracy of 86.98%, sensitivity of 91.43%, and specificity of 80.61%; with an area under the curve (AUC) of 0.93 [95% confidence interval (CI) 0.68-0.98]. The decision tree model achieved an accuracy of 81.09%, sensitivity of 91.43%, and specificity of 66.33%; with an AUC of 0.89 (95% CI 0.66-0.92) in validation set.

CONCLUSIONS

The features of tongue images were associated with NAFLD. Both the 2 diagnostic models, which would be convenient, noninvasive, lightweight, rapid, and inexpensive technical references for early screening, can accurately distinguish NAFLD and are worth further study.

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.

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

BACKGROUND

Catch-up fat in adult (CUFA) caused by rapid nutrition promotion after undernutrition plays an important role in the epidemic of insulin resistance-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 of 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 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 [IQRs], 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 (AUC) 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 insulin resistance-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. This article is protected by copyright. All rights reserved.

A Nonlinear Association between Tongue Fur Thickness and Tumor Marker Abnormality: A Cross-Sectional Study

Background

Many associations between tongue fur and different physiological and biochemical indexes have been revealed. However, the relationship between tongue fur and tumor markers remains unexplored.

Methods

We collected the medical examination reports of 1625 participants. Participants were residents of Chengdu, China, undergoing routine health checkups at the health management center of the Affiliated Hospital of Chengdu University of Traditional Chinese Medicine between December 2018 and September 2020. The participants' tongue fur thickness was measured using the DAOSH four-diagnostic instrument. Tumor marker levels, including t-PSA, AFP, CEA, CA125, and CA199, were measured in the clinical laboratory. Curve-fitting and multivariable logistic regression were used to analyze the association between tongue fur thickness and tumor marker abnormality.

Results

Curve-fitting showed that the relationship between tongue fur thickness and abnormal tumor marker rate was nonlinear, similar to a U shape. As the tongue fur thickness value increased, the abnormal tumor marker probability initially decreased and then increased. Logistic regression showed that, in the crude model, compared with the thin tongue fur group, the odds ratios (ORs) and 95% confidence intervals (CIs) of the less or peeling tongue fur group and thick tongue fur group for tumor marker abnormality were 1.79 (1.02–3.17) and 1.70 (1.13–2.54), respectively. After adjusting gender, age, body mass index (BMI), smoking history, drinking history, tongue color, the form of the tongue, and fur color, the ORs and 95% CIs of the less or peeling tongue fur group and thick tongue fur group were 1.93 (1.04–3.57) and 1.82 (1.17–2.81), respectively.

Conclusions

Excessive or very little tongue fur is associated with tumor marker abnormality. Further cross-sectional studies are needed to evaluate the clinical value of tongue fur for cancer diagnosis and screening.

Variations of tongue coating microbiota in children with Henoch-Schönlein purpura nephritis

BACKGROUND

Variations in the oral microbiota have been significantly correlated with the progress of autoimmune diseases, such as immunoglobulin A nephropathy and Henoch-Schönlein purpura (HSP). However, there is no report outlining the character of tongue coating microbiota variations in children with Henoch-Schönlein purpura nephritis (HSPN).

METHOD

A total of 20 children with HSPN and 14 healthy controls were recruited for this research. Tongue coating samples of two groups were collected for 16S rRNA gene sequencing. The diversity, principal component analysis (PCA), nonmetric multidimensional scaling (NMDS), partial least squares discriminant analysis (PLS-DA), and linear discriminant analysis (LDA) effect size (LEfSe) were performed. Microbial function was assessed using the PICRUST.

RESULTS

The ACE and Chao indices were greatly lower in the HSPN group than in the HG (P = 0.001). The Shannon and Simpson indices were dramatically reduced in children with HSPN compared with those in the healthy controls (P = 0.005). Bacteroidales, Selenomonadales, Lactobacillales, Fusobacteriales, Neisseriales, and Actinomycetales composed more than 80% of all sequences, while Bacteroidales was the most generous order in both groups. PCA, NMDS and PLS-DA showed a marked difference between the control and HSPN groups. LEfSe analysis showed alteration of tongue coating microbiota in the HSPN group. There were 30 metabolic functions significantly differed between the two groups.

CONCLUSIONS

Children with HSPN have substantially various tongue coating microbiota compared to healthy controls. Even though this research does not indicate causality, it is beneficial to enhance the possibility for coming microbial-based treatments to enhance the clinical effects of HSPN in children.

TLR4/AP-1-Targeted Anti-Inflammatory Intervention Attenuates Insulin Sensitivity and Liver Steatosis

Insulin resistance has been shown to be the common pathogenesis of many metabolic diseases. Metainflammation is one of the important characteristics of insulin resistance. Macrophage polarization mediates the production and development of metainflammation. Toll-like receptor 4 (TLR4) mediates macrophage activity and is probably the intersection of immunity and metabolism, but the detailed mechanism is probably not fully understood. Activated protein 1 (AP1) signaling pathway is very important in macrophage activation-mediated inflammation. However, it is unclear whether AP1 signaling pathway mediates metabolic inflammation in the liver. We aimed to investigate the effects of macrophage TLR4-AP1 signaling pathway on hepatocyte metabolic inflammation, insulin sensitivity, and lipid deposition, as well as to explore the potential of TLR4-AP1 as new intervention targets of insulin resistance and liver steatosis. TLR4 and AP1 were silenced in the RAW264.7 cells by lentiviral siRNA transfection. In vivo transduction of lentivirus was administered in mice fed with high-fat diet. Insulin sensitivity and inflammation were evaluated in the treated cells or animals. Our results indicated that TLR4/AP-1 siRNA transfection alleviated high-fat diet-induced systemic and hepatic inflammation, obesity, and insulin resistance in mice. Additionally, TLR4/AP-1 siRNA transfection mitigated palmitic acid- (PA-) induced inflammation in RAW264.7 cells and metabolic abnormalities in cocultured AML hepatocytes. Herein, we propose that TLR4-AP1 signaling pathway activation plays a crucial role in high fat- or PA-induced metabolic inflammation and insulin resistance in hepatocytes. Intervention of the TLR4 expression regulates macrophage polarization and metabolic inflammation and further alleviates insulin resistance and lipid deposition in hepatocytes.