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.

Background: Accumulating evidence suggests that the gut microbiota and its metabolites may be involved in autoimmune hypothyroidism. However, the causal association between gut microbiota, metabolites and autoimmune hypothyroidism remains to be determined.Methods: Instrumental variables were screened from the GWAS datasets of 211 gut microbiota taxonomic groups, gut microbiota-derived metabolites, and autoimmune hypothyroidism. Univariable Mendelian randomization (MR) and multivariable Mendelian randomization (MVMR) were used to analyse the potential causal relationship between autoimmune hypothyroidism, these metabolites, or these microbiota. During the MR analysis, we alternated multiple MR methods with different model assumptions to assess the consistency and robustness of the findings: inverse variance weighted (IVW), weighted median, MR pleiotropy residual sum and outlier (MRPRESSO) and MR−Egger methods. Reverse MR analysis was performed to assess the possibility of reverse causality. Finally, enrichment analyses were used to investigate potential biofunctions.Results: The IVW results of univariable MR showed that the phyla Actinobacteria, genus DefluviitaleaceaeUCG011, genus Eggerthella, family Defluviitaleaceae, genus Subdoligranulum, genus RuminococcaceaeUCG011, and genus Intestinimonas were associated with autoimmune hypothyroidism. After FDR adjustment, the absence of a causal relationship between gut microbiota and autoimmune hypothyroidism (P_FDR > 0.05) suggested a possible marginal association. The results on gut metabolites showed that N-(3-furoyl)glycine, pipecolate, phenylalanine, allantoin, indololactate and alanine were associated with autoimmune hypothyroidism. After FDR correction, only indololactate was associated with hypothyroidism (OR=1.592; 95% CI, 1.228-2.065; P_FDR= 0.036). Family Defluviitaleaceae and genus DefluviitaleaceaeUCG011 were suggestively significant in the MVMR. The results of reverse MR analysis showed no reverse causality between autoimmune hypothyroidism and the identified gut microbiota. Enrichment analysis revealed that several key regulatory pathways were significantly enriched.Conclusion: This study supported that there were beneficial or detrimental causal effects of gut microbiota and its metabolites on autoimmune hypothyroidism risk, which provides more theoretical support for mechanistic research on the “thyroid–gut” axis.

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.

Background: The role of thyroid hormones in cancers has been discussed in observational studies; however, the causal relationship between them remains controversial. Methods: The SNPs associated with hypothyroidism and hyperthyroidism were selected from a FinnGen biobank of 342,499 (190,879 females and 151,620 males) Finnish adult subjects. Data from the Thyroidomics Consortium on 72,167 individuals were used to assess genetically determined thyroid-stimulating hormone (TSH) and free thyroxine (FT4). Lung cancer, lung adenocarcinoma and squamous cell lung cancer GWAS data from the International Lung Cancer Consortium(ILCCO). Six different Mendelian randomization (MR) Methods, including Inverse variance weighted (IVW), MR-Egger, Simple mode, MR-Pleiotropy Residual Sum and Outlier methods (MR-PRESSO), Weighted mode and Weighted median were used to Two-Sample MR analysis. IVW was used as the primary estimate. Sensitivity analyses were examined via four aspects (Cochran’s Q-test, MR Egger intercept analysis, Funnel plot and Leave-one-out sensitivity test). Results: The OR of hypothyroidism on lung cancer was 0.918 (95% CI, 0.859–0.982; p = 0.013) in MR analysis with IVW method. No evidence for effects of hyperthyroidism, TSH and FT4 on lung cancer risk was found via six MR methods. Meanwhile, there was no evidence for effects of lung cancer on hypothyroidism through six MR methods. Lung adenocarcinoma and squamous cell lung carcinoma were further analyzed on the basis of lung cancer. The OR of hypothyroidism on lung adenocarcinoma was 0.893(95% CI, 0.813–0.981; p = 0.019), the OR of hypothyroidism on squamous cell lung cancer was 0.888(95%CI,0.797–0.990, p = 0.032) in MR analysis with IVW method. Conclusion: In summary, hypothyroidism genetically had a protective causal association with lung cancer. Furthermore, hypothyroidism had protective effects both on lung adenocarcinoma and squamous cell lung cancer. Further work is needed to elucidate the potential mechanisms.

Purpose: The aim of this study included determining the prevalence of hypothyroidism in patients with systemic lupus erythematosus (SLE), clarifying the clinical characteristics of SLE patients with hypothyroidism, and identifying the relationship between hypothyroidism and SLE-related organic damage. Another purpose was to analyze the relationship between SLE and thyroid autoantibody. We also intended to discuss the pathogenesis of hypothyroidism in SLE patients, which would provide clues for further investigation.Methods: This study recruited 856 SLE patients and 856 age- and sex-matched healthy population and compared the prevalence of hypothyroidism between the cases and controls. Univariate and multivariate logistic analyses were applied to identify risk factors for hypothyroidism in SLE patients.Results: SLE patients had higher prevalence of clinical hypothyroidism (9.10%) and TgAb+TPOAb- (10.40%) than controls. The prevalence of hypothyroidism was the highest in SLE patients aged 16-26 years (18.9%) and decreased with age. The prevalence of autoimmune hypothyroidism in SLE group was higher than that in the control group (64.4% vs. 51.5%, P=0.042), which was mainly due to TgAb; the prevalence of non-autoimmune hypothyroidism in SLE group was also significantly higher than that in the control group (67.3% vs. 47.8%, P<0.001). Based on multivariate analysis, the use of glucocorticoids/immunosuppressants, liver abnormality, lupus nephritis (LN), and cardiac insufficiency were independently associated with hypothyroidism in SLE patients.Conclusion: The prevalence of hypothyroidism in SLE patients was higher than that in controls and decreased with age. The results suggested that young SLE patients combined with LN, liver abnormality and cardiac insufficiency were at higher risk of hypothyroidism. According to the results of this study, we speculated that SLE might have impact on thyroid, and SLE might be one of the causes of hypothyroidism.

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.