The aim of this study was to evaluate the effect and mechanism of miR‐122‐5p on hepatic injury induced by hyperthyroidism. Male Sprague Dawley rats were divided into three groups: a normal thyroid function group (sham), a levothyroxine (LT4)–induced hyperthyroidism group (T4 sodium), and a hyperthyroidism group treated with a miR‐122‐5p inhibitor (T4 sodium + antagomir‐122‐5p). Liver tissue pathology, ferroptosis and inflammatory markers, and miR‐122‐5p expression were assessed. Finally, rat hepatocytes (BRL‐3A) were treated with carbon tetrachloride (CCL4) to explore the role of the miR‐122‐5p/CCDC6 axis in hepatocyte injury. We found that miR‐122‐5p was expressed at low levels in liver tissues from hyperthyroid rats. Inhibition of miR‐122‐5p aggravated liver tissue injury. Additionally, in hyperthyroid rats, inflammatory factors, iron content, and markers of ferroptosis were upregulated by miR‐122‐5p inhibitors. Mechanistically, miR‐122‐5p was found to bind to CCDC6 and negatively regulate CCDC6 expression. Furthermore, miR‐122‐5p deletion induced hepatocyte injury by exacerbating the ferroptosis process and mitochondrial damage in CCL4‐treated hepatocytes, a process dependent on CCDC6 expression. The miR‐122‐5p/CCDC6 axis plays a protective role in hyperthyroidism‐induced liver injury and serves as a potential target for the clinical treatment of hyperthyroidism‐associated liver disease.

[This retracts the article DOI: 10.1155/2010/841090.].

BackgroundHepatic steatosis (HS) is a common liver condition characterized by excessive fat accumulation in hepatocytes. In the present study, we developed a machine learning (ML)‐based predictive model for HS defined by vibration‐controlled transient elastography (VCTE). Key predictive factors were further interpreted using SHapley Additive exPlanations (SHAP).MethodsThis study used data from two NHANES cycles, 2017–2020 and 2021–2023. The Boruta algorithm and LASSO regression were applied for feature selection. Six ML models, including logistic regression (LR), random forest (RF), multilayer perceptron (MLP), support vector machine (SVM), K‐nearest neighbor (KNN), and extreme gradient boosting (XGBoost), were used to build prediction models. SHAP was used to interpret model predictions and identify the most influential predictors of HS. Model performance was evaluated using area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, specificity, positive predictive value, and negative predictive value.ResultsA total of 12,177 participants were included in the current study. Boruta and LASSO feature selection identified 14 significant predictors, comprising age, BMI, gender, glycohemoglobin, HDL cholesterol, hemoglobin, hypertension, lymphocyte, manganese, monocyte, race, segmented neutrophils, selenium, and total cholesterol. XGBoost achieved the highest AUC (0.832), followed by LR (0.826), RF (0.825), SVM (0.818), MLP (0.792), and KNN (0.737). SHAP analysis of the XGBoost model highlighted BMI, glycohemoglobin, age, and HDL cholesterol as key contributors to HS prediction.ConclusionThe XGBoost model showed the highest discriminative ability for predicting HS, with BMI, glycohemoglobin, age, and HDL cholesterol identified as the most important predictors. This interpretable model may support early identification and risk stratification of HS.

ObjectiveTo study the expression of peripheral helper T (Tph)‐cells in the blood and thyroid of Graves’ disease (GD) patients and their correlation with B‐cells and clinical indicators.MethodsA total of 25 cases of newly diagnosed GD and 25 cases of healthy controls were recruited. Peripheral blood mononuclear cells were isolated. The frequency and proliferative activity of follicular helper T (Tfh)‐cells, Tph cells, and B‐cells were detected by flow cytometry. Thyroid tissues of 4 patients with GD and 4 normal thyroid tissues were selected for multiple immunofluorescence staining. The number of Tfh cells and Tph cells in the thyroid tissue was observed.ResultsTph and Tfh cells were significantly increased in the blood and thyroid tissue of GD patients compared with healthy controls. Both cells were positively correlated with the proliferative activity of B‐cells. The proportion of Tph cells correlated with serum TT4 (r = 0.501) and TR‐Ab (r = 0.472) levels. Tph cell levels were significantly increased in the TR‐Ab (high) group compared to the TR‐Ab (low) group. Tph is localized in areas beyond lymphoid follicles and may extensively promote B‐cells in the inflamed thyroid gland.ConclusionThis study demonstrates for the first time that Tph cells are significantly expanded in the blood and thyroid of GD patients. Similar to Tfh cells, Tph cells correlated with GD severity and B‐cell differentiation. Therefore, it may be a new target for GD immunotherapy.

AimsWaist‐to‐hip ratio (WHR) directly reflects body fat distribution compared to other obesity indicators such as body mass index (BMI), waist circumference (WC), and waist‐to‐height ratio (WHtR). However, WHR may misclassify Type 2 diabetes mellitus (T2DM) risk by failing to account for the individual contribution of WC and hip circumference (HC). The study aimed to evaluate the discriminatory ability of WHR for T2DM and to identify its misclassification bias.Materials and MethodsThis prospective cohort study included 15,211 Chinese participants aged 50+ years without T2DM at baseline (2003–08), with follow‐up conducted from 2008 to 2015. The Cox proportional hazard regression model was used to examine associations between obesity indicators and T2DM. Discriminatory ability was evaluated using the C‐statistic, net reclassification index (NRI), and integrated discrimination improvement (IDI).ResultsDuring a median follow‐up of 5.4 years, 2238 participants (14.71%) developed T2DM. Participants with large WC and large HC had a higher T2DM risk compared to those with small WC and small HC in the low or high WHR category (adjusted hazard ratios (95% confidence intervals [CIs]): 2.33 [1.98, 2.74], 1.73 [1.47, 2.03]). WHR showed lower discrimination (C‐statistic 0.632, 95% CI 0.619, 0.645) than WC (0.645, 95% CI 0.632, 0.658) and WHtR (0.657, 95% CI 0.644, 0.670). Combining WHR with BMI significantly improved discrimination compared to WHR alone (△C‐statistic, NRI, IDI, and 95% CIs were 0.028 [0.020, 0.035], 0.015 [0.012, 0.020], and 0.141 [0.115, 0.165]), and it showed better discriminatory ability compared to the combination of BMI and WC (△C‐statistic 0.007, 95% CI 0.004, 0.011).ConclusionsWHR alone underestimates T2DM risk in individuals with large WC and large HC. Combining WHR with BMI addresses misclassification bias.

BackgroundAdult growth hormone deficiency (AGHD) is often diagnosed after surgery for nonfunctioning pituitary neuroendocrine tumor (NF‐PitNET). While a growth hormone stimulation test (GHST) is required to establish its diagnosis, it is associated with a large burden on patients and healthcare providers alike. Currently, the recommended indicators for the diagnosis of AGHD include the IGF‐1 standard deviation score (IGF‐1SDS) from a random blood sample and the number of deficiencies in pituitary hormones other than GH; however, there are also some challenges associated with these indicators. Therefore, to predict the need for GHST, this study focused on the interrelationship between GH and pituitary hormones to investigate whether postoperative thyroid‐stimulating hormone (TSH), free triiodothyronine (FT3), and free thyroxine (FT4) levels measured before GHST could represent novel predictors of AGHD.MethodsAmong 166 patients who underwent GHST following surgical treatment for NF‐PitNET, 60 patients who were diagnosed with NF‐PitNET based on clinical and pathological criteria were included in this analysis. Exclusion criteria were as follows: missing data (n = 16), data collection outside the specified timeframe (n = 4), positive thyroid autoantibodies (n = 2), and oral levothyroxine administration during GHST (n = 3). Clinical parameters of the 35 patients were compared between the AGHD group (n = 11) and the non‐AGHD group (n = 24) using Fisher’s exact test and Mann–Whitney U test.ResultsPatients with AGHD showed significantly lower postoperative levels of FT3 and FT4 prior to GHST, with median values of 2.10 pg/mL (1.97–2.34) and 0.98 ng/dL (0.77–1.19), respectively, than those without postoperative AGHD, who showed median levels of 2.45 pg/mL (2.28–2.67) and 1.18 ng/dL (1.03–1.26) (p < 0.001 and p = 0.008, respectively). In contrast, no significant difference in TSH levels was observed between the two groups. Receiver operating characteristic (ROC) analysis identified the optimal cutoff values for predicting AGHD as 2.125 pg/mL for FT3 and 1.010 ng/dL for FT4.ConclusionThese findings suggest the potential utility of FT3 and FT4 as novel predictors for the diagnosis of postoperative AGHD.

BackgroundBlood glucose fluctuations in patients with brittle diabetes have been a problem for clinicians. A recent study found that transplanting the intestinal flora of healthy people to patients with brittle diabetes can improve their blood glucose fluctuations. However, the underlying mechanism remains unclear.MethodsStreptozotocin‐induced diabetic rats were assigned to receive fecal microbiota transplantation (FMT) from healthy donors or to remain untreated, while normal rats received FMT from brittle diabetes donors or remained untreated. Groups included the normal control group (NC group), the diabetic group (DM group), normal rats with FMT from brittle diabetic patients (NC‐DMFMT group), and diabetic rats with FMT from normal individuals (DM‐NCFMT group). Blood glucose variability, rat liver glucokinase, and glycogen levels, as well as intestinal short‐chain fatty acid content, were detected in each group of rats. Gut microbiota composition was analyzed using 16S rDNA sequencing.ResultsCompared with the standard deviation of blood glucose (SDBG) (1.664 ± 0.427 mmol/L) in the NC group, that of the DM group (6.879 ± 1.475 mmol/L) was higher. However, the DM‐NCFMT group reduced SDBG (4.387 ± 0.619 mmol/L) vs. the DM group (p < 0.05). Hepatic glycogen (27.57 ± 5.254 mg/L) was lower in the DM group than in the NC group (55.48 ± 9.467 mg/L) but increased in the DM‐NCFMT group (37.59 ± 1.283 mg/L) vs. the DM group. The abundance of Bifidobacteria in the DM group was decreased compared to that in the NC group. In contrast, Bifidobacterium abundance in the DM‐NCFMT group increased after standard human flora transplants (p < 0.05). Correlation and stepwise regression analysis indicated that Bifidobacteria reduced SDBG partly by promoting hepatic glycogen synthesis, with an effect share of 23.01%.ConclusionNormal individual fecal microbiota transplantation improves glucose variability in DM rats, potentially mediated by enhanced hepatic glycogen synthesis.

AimsNumerous preclinical studies suggested that targeted inhibition of mammalian target of rapamycin (mTOR) may be beneficial for the treatment of osteoporosis. However, the relevance of these findings to human populations remains unclear. We hypothesized that lifelong genetically higher mTOR expression leads to reduced bone mineral density (BMD), and we use Mendelian randomization to estimate its causal effect.BackgroundWe aimed to utilize existing genome‐wide association studies (GWAS) and expression quantitative trait locus (eQTL) data to comprehensively evaluate whether genetically proxied mTOR expression is associated with BMD using the Mendelian randomization method.MethodsIndependent cis‐eQTLs for the gene‐encoding mTOR were selected as instrumental variables (IVs). Two large GWAS meta‐analysis cohorts were used for BMD outcomes. Summary data–based Mendelian randomization (SMR), inverse variance–weighted Mendelian randomization (IVW‐MR), and multivariable Mendelian randomization (MVMR) were applied to estimate the association between genetically proxied mTOR expression and BMD. Phenome‐wide association study (PheWAS) analysis was conducted to explore potential horizontal pleiotropy and genetically associated phenotypes related to mTOR. Bayesian colocalization was performed to assess whether mTOR expression and BMD shared a common causal single‐nucleotide variant (SNV). Blood transcriptome data were analyzed to further evaluate expression patterns of mTOR in relation to BMD status. In addition, molecular docking was conducted to explore potential interactions between mTOR and representative fatty acids. Analyses followed STROBE‐MR guidance.ResultsSMR analysis indicated that increased genetically proxied mTOR expression in blood was associated with reduced BMD (OR = 0.8130–0.9340), except in individuals younger than 30 years. Colocalization analysis suggested that mTOR expression and BMD may share a common genetic locus (posterior probability for H4 = 77.7%). PheWAS identified an association between mTOR and basal metabolic rate (Beta = −16.769, FDR = 2.21E − 25), indicating potential metabolic implications. Further analyses evaluated associations between mTOR‐related genetic variation, circulating fatty acids, hormone levels, and BMD. MVMR results suggested that in individuals aged 30–45 years, the association between genetically proxied mTOR expression and BMD may be influenced by blood fatty acid levels (OR = 0.8823–0.9020). In older age groups (45–60 years and ≥ 60 years), genetically predicted insulin levels were associated with attenuation of these metabolic effects. Blood transcriptome analysis showed no significant difference in mTOR expression between high‐ and low‐BMD groups, indicating that peripheral transcript levels were not detectably associated with BMD status. Arachidonic acid demonstrated the highest predicted binding affinity to mTOR (−5.9 kcal/mol), followed by palmitic acid (−5.0 kcal/mol), oleic acid (−4.9 kcal/mol), linoleic acid (−4.8 kcal/mol), and stearic acid (−4.4 kcal/mol). Given the modest binding energies and lack of experimental validation, these findings should be interpreted cautiously and are intended to provide preliminary mechanistic hypotheses rather than confirm functional interactions.ConclusionsGenetic evidence indicates that mTOR‐related pathways may contribute to variation in BMD, with age‐dependent metabolic associations. The molecular docking results provide exploratory structural hypotheses that may inform future mechanistic studies but do not constitute functional or translational validation. Although these findings do not establish the clinical efficacy of pharmacological mTOR inhibition, they offer human genetic evidence supporting further experimental and pharmacological investigation of mTOR as a potential therapeutic target in osteoporosis.

ObjectivesTo assess body composition (BC) and quality of life (QoL) in young patients with differentiated thyroid carcinoma (DTC) after total thyroidectomy.MethodsFifty‐one patients with DTC were included. Clinical, demographic, and anthropometric data were collected. BC was assessed using dual‐energy X‐ray absorptiometry (DXA), QoL was assessed with the SF‐36 questionnaire, and DTC features and thyroid function parameters were analyzed.ResultsPatients with DTC, with a mean age of 35 years and a mean BMI of 28.5 kg/m2, showed SF‐36 physical and mental component summary (PCS; MCS) scores slightly shorter than 50 (100 being the best) and higher total fat percentage (TFP), fat mass (FM), and fat mass index (FMI). A negative correlation was observed between FMI and FM with general condition (GC) as measured by the SF‐36. Higher free T4 and better GC were predictors of lower body fat. Older age, higher levothyroxine dose, and better GC predicted lower FMI. Age, levothyroxine dose, TSH, and higher PCS score were predictors of lower TFP. A higher GC score also predicted lower FM.ConclusionsYoung thyroidectomized patients with DTC presented a compromised QoL and BC profile, with increased FM, TFP, FMI, and BMI. Alterations in BC were associated with worse QoL, suggesting a negative impact of DTC on patient well‐being. These findings emphasize the importance of a multidisciplinary management approach that considers both BC and QoL in patients with DTC under levothyroxine therapy, aiming to improve overall outcomes and QoL.

BackgroundThe impact of the triglyceride–glucose (TyG) index on mortality in diabetes patients with hypertension remains unclear.MethodsThis retrospective cohort study included 3296 participants from the National Health and Nutrition Examination Survey (2001–2018). The TyG index was calculated as Ln [fasting triglycerides (mg/dL) × fasting glucose (mg/dL)/2]. Multivariable Cox proportional hazards models adjusted for demographic and clinical confounders were used to estimate mortality risks. Restricted cubic spline analysis assessed nonlinear relationships between TyG index and mortality risk. Stratified analyses and interaction tests were conducted based on gender, age, race, body mass index, smoking status, and the presence of cardiovascular disease.ResultsA total of 3296 individuals were ultimately included in the study cohort. Over a median follow‐up of 82 months (IQR, 44–129), 912 participants (27.7%) died, with 315 (9.6%) from cardiovascular disease. A nonlinear association between TyG index and mortality was observed, with inflection points at 8.9 for all‐cause mortality and 9.0 for cardiovascular mortality. Hazard ratios (95% CI) were 0.85 (0.62–1.16) and 1.38 (1.14–1.67) for all‐cause mortality and 0.70 (0.44–1.01) and 1.78 (1.27–2.48) for cardiovascular mortality, below and above these points, respectively. In participants aged < 65 years, elevated TyG index levels showed significantly greater susceptibility to all‐cause mortality [HR = 1.69 (1.20–2.39), p < 0.003, interaction p = 0.01].ConclusionIn this national cohort of adults with diabetes and hypertension, the TyG index showed a nonlinear association with mortality: the risk of all‐cause death increased above TyG = 8.9 and cardiovascular death above TyG = 9.0; associations were stronger among participants under 65 years old.