Background: Patients with gallstone disease (GSD) often have highly co-occurrence with metabolic syndrome (MetS) and Nonalcoholic fatty liver disease (NAFLD) both associated with insulin resistance (IR). Meanwhile, highly prevalence of NAFLD was found in patients who received cholecystectomy. However, the associations of GSD with MetS, NAFLD is inconsistent in the published literature. And risk of cholecystectomy on NAFLD is unclear.Methods: We searched the Medline EMBASE and WOS databases for literature that met our study topic. To be specific, studies with focus on associations between GSD and MetS/NAFLD, and risk evaluation on cholecystectomy and NAFLD incidence were enrolled for further analysis. The random effect model was used to calculate the combined relative ratio (RR) and odds ratio (OR)and 95% confidence interval (CI).Results: Seven and six papers with focus on connections between GSD and NAFLD/MetS prevalence. Correspondingly, seven papers with focus on risk of cholecystectomy on NAFLD occurrence were also enrolled into meta-analysis. After pooling the results from individual study, patients with GSD had higher risk of MetS (OR:1.45, 95%CI: 1.23-1.67, I² = 41.1%, P=0.165). Risk of GSD was increased by 52% in NAFLD patients (pooled OR:1.52, 95%CI:1.24-1.80). And about 32% of increment on NAFLD prevalence was observed in patients with GSD (pooled OR: 1.32, 95%CI:1.14-1.50). With regard to individual MetS components, patients with higher systolic blood pressure were more prone to develop GSD, with combined SMD of 0.29 (96%CI: 0.24-0.34, P<0.05). Dose-response analysis found the GSD incidence was significantly associated with increased body mass index (BMI) (pooled OR: 1.02, 95%CI:1.01-1.03) in linear trends. Patients who received cholecystectomy had a higher risk of post-operative NAFLD (OR:2.14, 95%CI: 1.43-2.85), P<0.05). And this impact was amplified in obese patients (OR: 2.51, 95%CI: 1.95-3.06, P<0.05).Conclusion: Our results confirmed that controls on weight and blood pressure might be candidate therapeutic strategy for GSD prevention. And concerns should be raised on de-novo NAFLD after cholecystectomy.

Hashimoto’s thyroiditis (HT) and papillary thyroid carcinoma (PTC) co-occurrence raises significant questions regarding the immune microenvironment and molecular mechanisms in thyroid tumor development. This review synthesizes recent literature to explore the immune microenvironment and molecular characteristics of PTC patients with HT, and to analyze how these characteristics influence disease onset, progression, and treatment. We focused on the immunological and molecular biological mechanisms underlying the interaction between HT and PTC, particularly the recruitment and activation of immune cells and alterations in key signaling pathways. Studies indicate that PTC with HT exhibits distinctive immune microenvironmental features, such as the role of regulatory T cells (Tregs), activation of the IFN-γ-mediated CXCR3A-CXCL10 signaling axis, and NF-κB pathway activation. Additionally, thyroid-stimulating hormone (TSH) stimulation, RET/PTC gene rearrangements, and changes in STAT6 and DMBT1 gene expression levels also play significant roles in PTC development. Notably, while HT may increase the risk of PTC, patients with concurrent HT tend to have better prognoses. Future research should further elucidate the complex interplay between these two diseases to prevent the transformation of HT into PTC and offer more personalized treatment plans for PTC patients, including considerations for preoperative thyroidectomy and lymph node dissection strategies, as well as postoperative TSH suppression therapy risk assessment. This review underscores the importance of a deeper understanding of HT and PTC interactions and offers new perspectives for future research directions and therapeutic strategies.

Background: Liver transplantation (LT) is one of the most effective surgical treatment for patients with end-stage liver disease. Steatosis is a contributor for inferior graft quality. But its impact and safety on transplantation was less assessed in Chinese patients. Methods: Graft steatosis and related information involved in recipients, donors and surgical procedures were retrospectively collected from 239 patients. Results: Donor macrosteatosis (MaS) caused about 2.14 and 2.80 folds of increment on patient and graft mortality. Dose-response analysis revealed prominent risk of grafts on overall patient/organ mortality when MaS content exceeded 10% (P<0.05). Noteworthy, deaths were only observed in MaS group when concurrent with extremely higher post-transplant alanine aminotransferase (ALT, 64%). However, microsteatosis (MiS) grafts didn’t affect outcomes after LT. In a cohort of Chinese patients, MaS had comprehensive effects on post-transplant outcomes with relatively lower safety threshold at 10%. Mortality gap caused by MaS grafts was observed in patients with severer ischemia reperfusion injury. Conclusions: Our study revealled the graft MaS affected the post-transplant outcomes in lower risk cutoff in Chinese patients. Further study is worthy to validate these results and investigate inner mechanism under the phenomenon.

Rodent ischemia-reperfusion injury (IRI) and liver transplantation (LT) models play crucial roles in mimicking graft injury and immune rejection, developing therapeutic approaches, and evaluating the efficacy of treatments. The application of integrated multi-omics data and advanced omics techniques like single-cell RNA sequencing in rodent models has expanded researchers' perspectives on pathophysiological processes in LT settings. This review summarizes key molecules and pathways associated with reperfusion injury and prognosis in LT models, highlighting the potential of omics data in understanding and improving transplant outcomes. In addition, we highlight the current challenges and future approaches for the application of omics data in rodent LT models. Cross-species validation with human data will improve therapeutic potential. Finally, further applications combining advanced single-cell, spatial omics technologies and machine learning algorithms will help to identify the key regulatory networks in specific cell populations underlying poor outcomes after LT.

Background: Our previous study revealled amplified hazardous effects of macrosteatosis (MaS) on graft failure (GF) in recipients with severe liver damage in short post-operative days, with vague mechanism inside. Aim: We aimed to uncover the molecular mechanism of donor MaS on GF, and construct the predictive model to monitor post-transplant prognosis based on “omics” perspective. Methods: Ultra-performance liquid chromatography coupled to mass spectrometry metabolomic analysis was performed in allograft tissues from 82 patients with initial poor function (IPF) from multi-liver transplant (LT) centers. Pathway analysis was performed by on-line toolkit Metaboanalyst (v 3.0). Predictive model was constructed based on combinative metabonomic and clinical data extracted by stepwised cox proportional analysis. Results: Principle component analysis (PCA) analysis revealled stratification on metabolic feature in organs classified by MaS status. Differential metabolits both associated with MaS and GF were significantly enriched on pathway of glycerophospholipid metabolism (P < 0.05). Phosphatidylcholine (PC) and phosphatidylethanolamine (PE) involved in glycerophospholipid metabolism was significantly decreased in cases with MaS donors and GF (P < 0.05). Better prediction was observed on graft survival by combinative model (area under the curve = 0.91) and confirmed by internal validation. Conclusion: Metabonomic features of allografts can be clearly distinguished by MaS status in patients with IPF. Dysfunction on glycerophospholipid metabolism was culprit to link donor MaS and final GF. Decrement on PC and PE exerted the fatal effects of MaS on organ failure. Metabonomic data might help for monitoring long-term graft survival after LT.

Aim: This study aims to assess the dose–response impact of iron load on systemic and hepatic metabolic disorders including metabolic syndrome (MetS) and non-alcoholic fatty liver disease (NAFLD).Methods: Serum ferritin (SF) and dietary iron intake were selected to represent the indicators of iron load in the general population. PubMed, EMBASE and Web of Science databases were searched for epidemiological studies assessing the impact of SF/dietary iron intake on MetS/NAFLD occurrence. All literature was published before September 1st, 2023 with no language restrictions.Results: Fifteen and 11 papers were collected with a focus on connections between SF and MetS/NAFLD, respectively. Eight papers focusing on dietary iron and MetS were included in the following meta-analysis. For the impact of SF on MetS, the pooled odds ratio (OR) of MetS was 1.88 (95% CI: 1.58–2.24) for the highest versus lowest SF categories. In males, the OR was 1.15 (95% CI: 1.10–1.21) per incremental increase in SF of 50 μg/L, while for females, each 50 μg/L increase in SF was associated with a 1.50-fold higher risk of MetS (95% CI: 1.15–1.94). For connections between SF and NAFLD, we found higher SF levels were observed in NAFLD patients compared to the control group [standardized mean difference (SMD) 0.71; 95% CI: 0.27–1.15], NASH patients against control group (SMD1.05; 95% CI:0.44–1.66), NASH patients against the NAFLD group (SMD 0.6; 95% CI: 0.31–1.00), each 50 μg/L increase in SF was associated with a 1.08-fold higher risk of NAFLD (95% CI: 1.07–1.10). For the impact of dietary iron on MetS, Pooled OR of MetS was 1.34 (95% CI: 1.10–1.63) for the highest versus lowest dietary iron categories.Conclusion: Elevated SF levels is a linear relation between the incidence of MetS/NAFLD. In addition, there is a positive association between dietary iron intake and metabolic syndrome. The association between serum ferritin and metabolic syndrome may be confounded by body mass index and C-reactive protein levels.