Haiqing Zhang

Background: To evaluate the relationship between thyroid function and thyroid homeostasis parameters with the prevalence of chronic kidney disease (CKD) and furtherly explore the all-cause and cardiovascular mortality among individuals with CKD using data from the National Health and Nutrition Examination Survey (NHANES) 2007–2012. Methods: This study included 8,526 adults, including 1,625 patients with CKD. Thyroid function included serum free triiodothyronine (FT3), free thyroxine (FT4) and thyroid-stimulating hormone (TSH). The thyroid homeostasis parameters, including FT3/FT4, thyroid feedback quantile-based index (TFQI_FT4, TFQI_FT3), thyrotrophic thyroxine resistance index (TT4RI, TT3RI) and thyroid-stimulating hormone index (TSHI) were calculated. Weighted multivariate logistic regression models to explore the association between thyroid function and thyroid homeostasis parameters and the prevalence of CKD. Cox proportional hazards models were used to investigate the association of thyroid function and thyroid homeostasis parameters with all-cause and cardiovascular mortality among CKD patients. Kaplan–Meier curves compared survival across the quartiles of the thyroid function and thyroid homeostasis parameters among CKD patients. Furthermore, the restricted cubic splines were used to explore the non‑linear relationships. Results: The weighted multivariate logistic regression models showed that FT4 was positively correlated with the prevalence of CKD, FT3/FT4 and TFQI_FT3 were negatively correlated with mortality in patients with CKD. The Cox regression models 3 shows that the multivariate-adjusted hazard ratios (HRs) and 95% confidence intervals (CIs) of FT3, FT4 and TSH with the all-cause mortality were 0.66(0.47,0.93), 1.07(1.04,1.10) and 1.01(0.98,1.04). At the same time, FT3/FT4 and TFQI_FT3 were significantly associated with all-cause mortality after multivariate adjustment. And we further converted thyroid function indicators and thyroid homeostasis parameters from a continuous variable to a categorical variable (quartiles) to conduct the sensitivity analysis. There was no difference in cardiovascular mortality. In crude Kaplan–Meier analyses, there was a U-shaped nonlinear relationship between FT3, TSH, FT3/FT4, TT4RI, TT3RI and TSHI with all-cause mortality, but not FT4, TFQI_FT4 and TFQI_FT3. There was an inverted U-shaped relationship between TFQI_FT3 and TT4RI with cardiovascular mortality, but not FT3, FT4, TSH, FT3/FT4, TFQI_FT4, TT3RI and TSHI. Conclusions: Thyroid function and thyroid parameters are closely related to the prevalence of the CKD and all-cause and cardiovascular mortality among individuals with CKD, and the specific mechanisms still required further in-depth research in the future.

Background: The non-HDL-C to HDL-C ratio (NHHR) is a dependable lipid marker linked to atherosclerotic traits. This study examines the potential relationship between NHHR and both metabolic dysfunction-associated steatotic liver disease (MASLD) and advanced liver fibrosis.Methods: This study investigated the relationship between NHHR levels and both MASLD and advanced liver fibrosis using data from the 2017–2020 National Health and Nutrition Examination Survey (NHANES) in the United States. First, we conducted a baseline characteristics analysis of the population based on NHHR quartiles. Second, we employed multivariable weighted linear regression models to examine the associations between NHHR and MASLD, as well as advanced liver fibrosis. Third, we utilized restricted cubic splines (RCS) to assess potential non-linear relationships. Fourth, we performed subgroup analyses. Finally, ROC curve analysis was conducted to evaluate the effectiveness of NHHR.Results: In the main analysis, this study included a total of 9,864 participants. Following multivariable logistic regression and comprehensive adjustments, elevated NHHR levels in the Q3 and Q4 groups were significantly linked to MASLD, with odds ratios of 1.59 (95% CI: 1.20–2.11) and 1.83 (95% CI: 1.40–2.39), respectively (P for trend < 0.0001). Elevated NHHR levels in the Q2 and Q3 groups remained significantly linked to a decreased risk of advanced liver fibrosis, with odds ratios of 0.61 (95% CI 0.40–0.94, P = 0.03) and 0.64 (95% CI 0.47–0.89, P = 0.01), respectively. RCS analysis revealed a U-shaped nonlinear association between NHHR and both MASLD (P = 0.000; P for nonlinear = 0.029) and advanced liver fibrosis (P = 0.0001; P for nonlinear = 0.000). In the subgroup analysis, we found that this relationship was significant only in certain subgroups. The ROC curve analysis revealed that NHHR exhibited the best predictive performance for diagnosing MASLD based on the fatty liver index (FLI). The optimal cutoff point for NHHR in predicting MASLD using FLI was determined to be 2.476, with sensitivity and specificity values of 0.589 and 0.698, respectively.Conclusion: NHHR may serve as a predictive marker for MASLD and advanced liver fibrosis, highlighting its potential significance in risk assessment and prevention strategies.

Purpose: Thyroid-stimulating hormone (TSH) has been identified as an independent risk factor for non-alcoholic fatty liver disease (NAFLD), TSH binds to the TSH receptor (TSHR) to exert its function. However, the underlying mechanisms by which TSHR influences NAFLD development remain unclear. This study investigates the role of miR374b in NAFLD progression. Methods: Firstly, a rat model of non-alcoholic fatty liver was constructed and divided into a normal group and a model group. The liver tissue pathology and fat accumulation were detected by Oil Red O staining and hematoxylin-eosin staining. Western blot and Real time PCR were used to detect for the impact of TSHR/miR-374b/C/EBP β/ FoxO1 pathway in the NAFLD model, and the expression of relevant inflammatory factors in each group was detected by ELISA assay. A NAFLD cell model was constructed using HepG2 cells, TSHR overexpression and interference, combined with miR-374b inhibitor and mimics, were transfected simultaneously to demonstrate TSHR/miR-374b/C/EBP β/ The mechanism of FoxO1 adipogenesis in vitro. Results: TSHR stimulates miR374b secretion in human liver cancer cells (HepG2) and promotes lipid accumulation in the liver. Deficiency of miR374b in HepG2 cells attenuated NAFLD progression. Mechanistically, TSH increases miR374b expression, which then suppresses the transcription of its target genes, CCAAT/enhancer binding protein-b (C/EBP β) and Forkhead Box Protein O1 (FOXO1). This suppression influences the expression of downstream lipid metabolism proteins, including PPARγ, SREBP2, and SREBP1c. Additionally, miR374b directly targets the 3′UTR of C/EBP β and FOXO1, establishing a negative feedback loop in lipid metabolism. Conclusion: These findings suggest that TSHR-induced upregulation of miR374b accelerates NAFLD progression by modulating lipid metabolism pathways through C/EBP β and FOXO1.

Lipids and thyroid hormones (TH) are closely interrelated. However, previous studies have not mentioned the linkage encompassing the non-high-density lipoprotein cholesterol to high-density lipoprotein cholesterol ratio (NHHR) alongside TH level, as well as sensitivity indices. This cross-sectional study leverages expansive datasets from the National Health and Nutrition Examination Survey (NHANES) spanning 2007 to 2012. Weighted multivariate linear regression, smoothed curve fitting and sensitivity analyses were used to investigate the associations of the NHHR with the thyroid. Subgroup analyses and interaction tests were conducted to determine the robustness of the findings across diverse segments of the population, ensuring the consistency and generalizability of the observed associations. The NHHR was significantly positively correlated with free triiodothyronine (FT3) levels, thyroid-stimulating hormone (TSH) levels, the FT3 to FT4 ratio (FT3/FT4), and the quantile-based thyroid feedback index for FT3 (TFQIFT3) and negatively correlated with free thyroxin (FT4) levels [0.17(0.07–0.27), P = 0.001; 0.60 (0.03–1.17), P = 0.040; 0.06 (0.04–0.08), P < 0.0001; 0.23 (0.16–0.30), P < 0.0001; and -0.65 (-1.05–-0.24), P = 0.002]. Smoothed curve fitting revealed nonlinear correlations of the NHHR with thyroid function and thyroid hormone sensitivity indices. In subgroup analyses, interaction tests, and smoothed curve fitting analyses, different populations presented largely consistent statistical differences. Among American adults, the NHHR was significantly positively correlated with FT3 levels, TSH levels, the FT3/FT4 and the TFQIFT3. Conversely, a negative association was noted between the NHHR and FT4 levels.

The systemic immunity-inflammation index (SII) is a newly developed biomarker that provides an integrated measure of inflammation in the body. We aim to evaluate the relationship between SII and body fat distribution. Adults from the National Health and Nutrition Examination Survey (NHANES) 2011–2018 were included. The SII was computed using lymphocyte (LC), neutrophil (NC), and platelet (PC) counts as its components. Body fat distribution was assessed by (total, android, gynoid) percentage fat, total abdominal fat area, subcutaneous adipose tissue area, visceral adipose tissue area, and the ratio of visceral to subcutaneous adipose tissue area (V/S ratio). Multivariable weighted linear regression and subgroup analysis were use to examine the relationships between fat distribution and SII. Restricted cubic splines (RCS) and threshold effect analysis were used to examine analyze nonlinear associations. After exclusions, a total of 11,192 adults with a weighted mean age of 38.46 ± 0.26 years were studied. In multivariable weighted linear regression, each level increase in log2SII was associated with increased of 0.23 SDs total percentage fat (95% CI = 0.03, 0.43) and 0.26 SDs android percentage fat (95% CI = 0.06, 0.47). Besides, the subgroup analysis showed that the positive association between SII and android percentage fat was mainly among obese individuals (BMI > 30 kg/m2) and non-obese individuals without DM or hypertension. Meanwhile, the relationship between SII and the V/S ratio was found to be significant in the female subgroup, the obese subgroup, individuals with non-alcoholic fatty liver disease (NAFLD), and those without diabetes mellitus. Finally, SII exhibited an inverted U-shaped relationship with total percentage fat, android percent fat and total abdominal fat. Accordingly, threshold effect analysis indicated a positive association between lower SII levels and total percentage fat, android percentage fat and total abdominal fat area. In the nationwide study, it was observed that the SII exhibited a significant correlation with higher levels of body fat, specifically android fat. This association was particularly noticeable within specific subgroups of the population.

Context: Evidence on the link between iron status markers and insulin resistance (IR) is limited. Objective: We aimed to explore the relationship between iron status and IR among US adults. Methods: This study involved 2993 participants from the National Health and Nutrition Examination Survey (NHANES) 2003-2006, 2017-2020. IR is characterized by a homeostatic model assessment (HOMA)-IR value of ≥2.5. Weighted linear and multivariable logistic regression analyses were used to examine the linear relationships between iron status and IR. Furthermore, restricted cubic splines (RCS) were used to identify the nonlinear dose–response associations. Stratified analyses by age, sex, body mass index, and physical activity were also performed. Last, a receiver operating characteristic (ROC) curve was used to evaluate the predictive value of iron status in IR. Results: In weighted linear analyses, serum iron (SI) exhibited a negative correlation with HOMA-IR (β −0.03, 95% CI −0.05, −0.01, P = .01). In weighted multivariate logistic analyses, iron intake and the serum transferrin receptor (sTfR) were positively correlated with IR (OR 1.02, 95% CI 1.00-1.04, P = .04; OR 1.07, 95% CI 1.02-1.13, P = .01). Also, SI and transferrin saturation (TSAT) were negatively correlated with IR (OR 0.96, 95% CI 0.94-0.98, P < .0001; OR 0.98, 95% CI 0.97-0.99, P < .001) after adjusting for confounding factors. RCS depicted a nonlinear dose–response relationship between sTfR and TSAT and IR. This correlation remained consistent across various population subgroups. The ROC curve showed that TSAT performed better than iron intake, SI and sTfR in ROC analyses for IR prediction. Conclusion: All biomarkers demonstrated significantly lower risk of IR with increasing iron levels, which will contribute to a more comprehensive and in-depth understanding of the relationship between the 2 and provide a solid foundation for future exploration of the mechanisms underlying their relationship.

Objective: It is well known that macro-thyroid-stimulating hormone (macro-TSH) could interfere with the detection of TSH. The anti-TSH autoantibody is an essential component of macro-TSH. However, the epidemiological characteristics and the clinical interference of the anti-TSH autoantibody are unclear.Methods: In this study, the radioimmunoprecipitation technique was used to detect the anti-TSH autoantibody. Platforms with different detection mechanisms were applied to measure the TSH in patients with the anti-TSH autoantibody. Polyethylene glycol (PEG) precipitation was used to determine the immunoassay interference.Results: The prevalence of the anti-TSH autoantibody in patients with mild subclinical hypothyroidism (SCH) and autoimmune thyroiditis, but normal thyroid function, was 4.78%. All 10 patients with anti-TSH antibodies had autoimmune diseases, with five of them having significant clinical test interference.Conclusion: The appearance of the anti-TSH antibody is not associated with thyroid autoantibodies. The presence of the anti-TSH autoantibody can interfere with the detection of TSH and can affect clinical diagnosis and treatment.

Objective To identify risk factors for the development of non-thyroidal illness syndrome (NTIS) in patients with systemic lupus erythematosus (SLE). Methods A retrospective analysis of 517 SLE patients and 1034 age-and sex-matched healthy population was conducted to compare the prevalence of NTIS in these two groups, and to analyze the laboratory and clinical characteristics of SLE patients with NTIS. Finally Logistic regression analysis was used to determine the risk factors for NTIS in SLE patients. Results The prevalence of NTIS in the SLE patients was significantly higher than that in controls (39.7% vs. 1.0%, P < 0.001). In SLE patients, compared with euthyroidism patients, NTIS patients exhibited higher levels of neutrophils, hepatic enzymes, kidney damage markers, inflammatory markers and SLE disease activity index (SLEDAI). They also had a higher incidence of organ insufficiency and positive antibodies such as anti-ds-DNA antibodies and anti-SSA antibodies. However, NTIS patients had lower levels of hemoglobin, lymphocytes, platelets, serum albumin, and complement. Additionally, NTIS patients had a shorter duration of lupus and lower utilization of disease-modifying antirheumatic drugs (DMARDs) (P < 0.05). Logistic regression analysis showed that elevated SLEDAI (OR = 1.060, 95%CI 1.022–1.099, P = 0.002), elevated systemic immune-inflammation index (SII) (OR = 1.003, 95%CI 1.001–1.007, P = 0.026), elevated erythrocyte sedimentation rate (ESR) (OR = 1.019, 95%CI 1.010–1.028, P < 0.001), and hepatic insufficiency (OR = 1.916, 95% CI 1.173–3.131, P = 0.009) were independent risk factors for the development of NTIS in SLE. DMARDs treatment (OR = 0.495, 95% CI 0.306–0.799, P < 0.001) was an independent protective factor for NTIS. Conclusions Inflammatory activity in SLE patients is associated with the development of NTIS. Key Points • Inflammatory activity indexes such as SLEDAI, SII, and ESR are independent risk factors for NTIS in SLE patients.

This study aimed to evaluate whether there is a causal relationship between autoimmune thyroid disorders (AITDs) and telomere length (TL) in the European population and whether there is reverse causality. In this study, Mendelian randomization (MR) and colocalization analysis were conducted to assess the potential causal relationship between AITDs and TL using summary statistics from large‐scale genome‐wide association studies, followed by analysis of the relationship between TL and thyroid stimulating hormone and free thyroxine (FT4) to help interpret the findings. The inverse variance weighted (IVW) method was used to estimate the causal estimates. The weighted median, MR‒Egger and leave‐one‐out methods were used as sensitivity analyses. The IVW method results showed a significant causal relationship between autoimmune hyperthyroidism and TL (β = −1.93 × 10⁻²; p = 4.54 × 10⁻⁵). There was no causal relationship between autoimmune hypothyroidism and TL (β = −3.99 × 10⁻³; p = 0.324). The results of the reverse MR analysis showed that genetically TL had a significant causal relationship on autoimmune hyperthyroidism (IVW: odds ratio (OR) = 0.49; p = 2.83 × 10⁻⁴) and autoimmune hypothyroidism (IVW: OR = 0.86; p = 7.46 × 10⁻³). Both horizontal pleiotropy and heterogeneity tests indicated the validity of our bidirectional MR study. Finally, colocalization analysis suggested that there were shared causal variants between autoimmune hyperthyroidism and TL, further highlighting the robustness of the results. In conclusion, autoimmune hyperthyroidism may accelerate telomere attrition, and telomere attrition is a causal factor for AITDs.

Introduction: The relationship between thyroid function/homeostasis parameters and renal function has been extensively studied. However, the relationship between thyroid function and thyroid homeostasis parameters with albuminuria among elderly individuals remains unclear.Methods: The population was divided into an albuminuria group and a non-albuminuria group for baseline characteristic difference analysis. Multivariable logistic regression was used to test the association between thyroid function, and thyroid homeostasis parameters and albuminuria. The nonlinear relationship was explored with restricted cubic splines. Meanwhile, we investigated whether the relationship also existed in the diabetes and hypertension subgroups. Receiver operating characteristic (ROC) curves were used to assess the effectiveness of the indices.Results: FT4 and TFQI_FT4 were positively correlated with albuminuria (OR = 1.12; 95% CI = 1.02–1.23, p = 0.02; OR = 1.79; 95% CI = 1.08–2.99, p = 0.03), and FT3/FT4 was negatively correlated with albuminuria (OR = 0.03; 95% CI = 0.00–0.26, p = 0.003). Additionally, the nonlinear relationship between FT3/FT4 as well as TSHI and albuminuria was approximately U-shaped. Similar results were observed in the hypertension subgroup but not in the diabetes subgroup. There was a U-shaped nonlinear relationship between FT3 and albuminuria in the diabetes group. In addition, FT3/FT4 performed better than TFQI, TT4RI, and TSHI in ROC analyses for albuminuria prediction.Conclusion: FT4, TFQI_FT4, and a low FT3/FT4 ratio were risk factors for albuminuria in euthyroid individuals over 60 years old. However, FT3 was more associated with albuminuria in the diabetes subgroup. TSH was not associated with albuminuria in any analysis. In our study, we attempted to provide more reasonable thyroid parameters and basis for evaluating patients with underlying albuminuria. FT3/FT4 may be used as a helpful indicator to predict albuminuria and provide novel ideas for the evaluation and treatment of albuminuria.