The relationship between autoimmune thyroid disease, thyroid nodules and sleep traits: a Mendelian randomization study

BackgroundPrevious studies have suggested a potential association between Autoimmune thyroid disease Thyroid nodules and Sleep Traits, but the evidence is limited and controversial, and the exact causal relationship remains uncertain.ObjectiveTherefore, we employed a MR analysis to investigate the causal relationship between Autoimmune thyroid disease, Thyroid nodules and Sleep Traits.MethodsTo explore the interplay between Autoimmune thyroid disease Thyroid nodules and Sleep Traits, we employed MR studies utilizing summary statistics derived from GWAS in individuals of European ancestry. To ensure robustness, multiple techniques were employed to assess the stability of the causal effect, including random-effect inverse variance weighted, weighted median, MR-Egger regression, and MR-PRESSO. Heterogeneity was evaluated using Cochran’s Q value. Additionally, we investigated the presence of horizontal pleiotropy through MR-Egger regression and MR-PRESSO.ResultsThe IVW method indicates a significant causal relationship between “Getting up” and autoimmune hypothyroidism, as revealed by the IVW method (OR: 0.59, 95% CI: 0.45 to 0.78, P-value = 1.99e-4). Additionally, there might be a potential correlation between sleep duration and autoimmune hypothyroidism (OR: 0.76, 95% CI: 0.60 to 0.79, P-value = 0.024). Moreover, the observed potential positive link between daytime nap and thyroid nodules (OR: 1.66, 95% CI: 1.07 to 2.58, P-value = 0.023) is subject to caution, as subsequent MR PRESSO testing reveals the presence of horizontal pleiotropy, raising concerns about the reliability of the findings. The findings suggested a potential inverse association between Autoimmune hypothyroidism and Getting up (OR: 0.99, 95% CI: 0.98 to 1.00, P-value = 6.66e-3).As the results of MR-Egger method(OR: 1.00, 95% CI: 0.98 to 1.02, P-value = 0.742) exhibited an opposing trend to that observed with the IVW method and the results did not reach significance after P-value correction.ConclusionThe results of our study reveal a notable cause-and-effect relationship between Getting up and Autoimmune hypothyroidism, indicating its potential role as a protective factor against this condition. However, no causal connection was observed between sleep traits and Graves’ disease or Thyroid nodule

DataSheet_2_Autoimmune thyroid disease and myasthenia gravis: a study bidirectional Mendelian randomization.pdf

BackgroundPrevious studies have suggested a potential association between AITD and MG, but the evidence is limited and controversial, and the exact causal relationship remains uncertain.ObjectiveTherefore, we employed a Mendelian randomization (MR) analysis to investigate the causal relationship between AITD and MG.MethodsTo explore the interplay between AITD and MG, We conducted MR studies utilizing GWAS-based summary statistics in the European ancestry. Several techniques were used to ensure the stability of the causal effect, such as random-effect inverse variance weighted, weighted median, MR-Egger regression, and MR-PRESSO. Heterogeneity was evaluated by calculating Cochran’s Q value. Moreover, the presence of horizontal pleiotropy was investigated through MR-Egger regression and MR-PRESSOResultsThe IVW method indicates a causal relationship between both GD(OR 1.31,95%CI 1.08 to 1.60,P=0.005) and autoimmune hypothyroidism (OR: 1.26, 95% CI: 1.08 to 1.47, P =0.002) with MG. However, there is no association found between FT4(OR 0.88,95%CI 0.65 to 1.18,P=0.406), TPOAb(OR: 1.34, 95% CI: 0.86 to 2.07, P =0.186), TSH(OR: 0.97, 95% CI: 0.77 to 1.23, P =0.846), and MG. The reverse MR analysis reveals a causal relationship between MG and GD(OR: 1.50, 95% CI: 1.14 to 1.98, P =3.57e-3), with stable results. On the other hand, there is a significant association with autoimmune hypothyroidism(OR: 1.29, 95% CI: 1.04 to 1.59, P =0.019), but it is considered unstable due to the influence of horizontal pleiotropy (MR PRESSO Distortion Test P ConclusionAITD patients are more susceptible to developing MG, and MG patients also have a higher incidence of GD.</p

DataSheet_1_Autoimmune thyroid disease and myasthenia gravis: a study bidirectional Mendelian randomization.pdf

BackgroundPrevious studies have suggested a potential association between AITD and MG, but the evidence is limited and controversial, and the exact causal relationship remains uncertain.ObjectiveTherefore, we employed a Mendelian randomization (MR) analysis to investigate the causal relationship between AITD and MG.MethodsTo explore the interplay between AITD and MG, We conducted MR studies utilizing GWAS-based summary statistics in the European ancestry. Several techniques were used to ensure the stability of the causal effect, such as random-effect inverse variance weighted, weighted median, MR-Egger regression, and MR-PRESSO. Heterogeneity was evaluated by calculating Cochran’s Q value. Moreover, the presence of horizontal pleiotropy was investigated through MR-Egger regression and MR-PRESSOResultsThe IVW method indicates a causal relationship between both GD(OR 1.31,95%CI 1.08 to 1.60,P=0.005) and autoimmune hypothyroidism (OR: 1.26, 95% CI: 1.08 to 1.47, P =0.002) with MG. However, there is no association found between FT4(OR 0.88,95%CI 0.65 to 1.18,P=0.406), TPOAb(OR: 1.34, 95% CI: 0.86 to 2.07, P =0.186), TSH(OR: 0.97, 95% CI: 0.77 to 1.23, P =0.846), and MG. The reverse MR analysis reveals a causal relationship between MG and GD(OR: 1.50, 95% CI: 1.14 to 1.98, P =3.57e-3), with stable results. On the other hand, there is a significant association with autoimmune hypothyroidism(OR: 1.29, 95% CI: 1.04 to 1.59, P =0.019), but it is considered unstable due to the influence of horizontal pleiotropy (MR PRESSO Distortion Test P ConclusionAITD patients are more susceptible to developing MG, and MG patients also have a higher incidence of GD.</p

Cardiometabolic risk profiles associated with chronic complications in overweight and obese type 2 diabetes patients in South China.

BACKGROUND: Type 2 diabetes is often accompanied by altered cardiometabolic risk profiles, including abdominal obesity, hypertension, and dyslipidaemia. The association of altered cardiometabolic risk profiles with chronic complications of diabetes is not well investigated. METHODS: We recruited 2954 type 2 diabetes patients with a body mass index ≥25 kg/m2 who visited the diabetes clinics of 62 hospitals in 21 cities in Guangdong province of China from August 2011 to March 2012. Demographic characteristics, personal and family medical histories, and data on chronic complications of diabetes were collected. Clinical examinations and laboratory assessment were conducted. RESULTS: Abdominal obesity was found in 91.6% of the study population, elevated blood pressure in 78.3%; elevated serum triacylglycerols in 57.8%, and reduced serum HDL-C in 55.9%. Among the cardiometabolic risk factors, elevated blood pressure was significantly associated with almost all the chronic complications of diabetes. After adjusting for age, gender, duration of diabetes, and HbA1c, elevated blood pressure was significantly associated with diabetic retinopathy (OR 1.63, 95% CI: 1.22-2.19), diabetic nephropathy (OR 3.16, 95% CI: 2.25-4.46), cardiovascular disease (OR 2.71, 95% CI: 1.70-4.32), and stroke (OR 1.90, 95% CI: 1.15-3.12). Abdominal adiposity was significantly associated with diabetic nephropathy (OR 1.39, 95% CI: 1.11-1.74). Elevated triacylglycerols was significantly associated with diabetic retinopathy (OR 1.29, 95% CI: 1.05-1.58) and diabetic nephropathy (OR 1.30, 95% CI: 1.05-1.58). Reduced HDL-C was significantly associated with stroke (OR 1.41, 95% CI: 1.05-1.88). CONCLUSIONS: Altered cardiometabolic risk profiles, and elevated blood pressure in particular, were significantly associated with chronic complications in overweight and obese patients with type 2 diabetes. Future studies on the prevention of chronic complications of diabetes might make lowering blood pressure a primary target

Prevalence of diabetic chronic complications.

<p>Prevalence of diabetic chronic complications.</p

Characteristics of participants.

<p>Characteristics of participants.</p

Prevalence of diabetic chronic complications according to the number of altered cardiometabolic risk factors.

<p>1, type 2 diabetes plus any other one component; 2, type 2 diabetes plus any other two components; 3, type 2 diabetes plus any other three components; 4, type 2 diabetes plus any other four components; DR, diabetic retinopathy; DN, diabetic nephropathy; DPN, diabetic peripheral neuropathy; CVD, cardiovascular disease.</p

Multiple logistic model for cardiometabolic risk factors for diabetic chronic complications.

<p>Multiple logistic model for cardiometabolic risk factors for diabetic chronic complications.</p