Thyroiditis

The following types of data were extracted from electronic medical records using a data collection table: epidemiological data, demographic characteristics, medical history, contact history, signs and symptoms, comorbidities [hypertension, diabetes, coronary heart disease, cerebrovascular disease (cerebral haemorrhage, subarachnoid haemorrhage, ischemic stroke), chronic obstructive pulmonary disease, chronic kidney disease, chronic liver disease (chronic viral hepatitis, nonalcoholic fatty liver disease, alcoholic liver disease, primary and secondary cholestasis, liver cirrhosis), malignant tumors, thyroid diseases (hyperthyroidism, hypothyoidism, thyroiditis)], laboratory results [complete blood count, coagulation function, arterial blood gas, cellular immune, humoral immune, liver and renal functions, lactate dehydrogenase (LDH), electrolytes, osmotic pressure, lactic acid, C-reactive protein (CRP), myocardial markers, and procalcitonin (PCT)] on ICU admission, chest CT scans, time from onset to visit, time from onset to ICU admission, duration of SARS-CoV-2 positivity, and outcome. Clinical treatment measures (e.g., oxygen therapy, mechanical ventilation, kidney replacement therapy, antiviral therapy, antibiotics, glucocorticoid usage, traditional Chinese medicine, and nutritional support) were also collected. These treatments were based on the recommendations for COVID-19 diagnosis and treatment program (Fifth Edition) issued by the National Health Commission of the People’s Republic of China on 8 February 2020¹⁴ . Sequential Organ Failure Assessment (SOFA) score was calculated on ICU admission based on age, medical history, vital signs, and laboratory results. All data were checked by two physicians (YJ and DC), and a third researcher (PG) adjudicated any differences in interpretation between the two primary reviewers.

The MGI is a repository of electronic health and genetic data collected from patients at Michigan Medicine during pre-surgical encounters, who have consented to linking of genetic and clinical data for research purposes^{26 (link)}. Participants were genotyped using Illumina Infinium CoreExome-24 bead arrays and genotype data were imputed to the HRC using the Michigan Imputation Server. Unrelated individuals with European ancestry were used for the GWAS on TSH levels. MGI participants (16,003) have at least one TSH measurement. Then, 5918 of these individuals with any thyroid disorders were excluded based on the ICD9 and ICD10 codes mapped to PheCodes^{52 (link)} 193 (thyroid cancer), 244 (hypothyroidism), 245 (thyroiditis), and 246 (other disorders of thyroid), leaving 10,085 samples in the association analysis (53.4% are females). If more than one TSH measurement for an individual was available in the electronic health records, we used the average of the TSH levels for the individual in the analysis. The mean and SD for TSH in MGI are 1.914 and 1.175 mU/L, respectively. The mean age at TSH measurement is 55.90 years. We performed single-variant association testing of the inverse-normalized TSH levels on 17 million variants using a linear regression model as implemented in EPACTS with first four PCs, age, and sex as covariates. Later, we also performed single-variant association testing of thyroid cancer on the TSH-associated index variants using a logistic mixed model as implemented in SAIGE^{28 (link)} with first 4 PCs, age and sex as covariates.

We divided the study group into two subgroups according to diagnosed thyroiditis. Results are presented as median (interquartile range 25–75%, IQR) for continuous variables or number (proportion) of patients for categorical variables. Correlation analysis was used to study relationships between various variables. Mann-Whitney U test was used to analyze the differences between subgroups according to the presence of thyroiditis. The chi-square test was used to compare frequencies.
There were no differences in bleeding (SBI) between subgroups; still, the significant differences in plaque accumulation (API), we chose to use API in the further analysis. Stepwise multiple regression was used to assess the effect of thyroid function (expressed as TSH level) on dental plaque accumulation. Based on clinical judgment, we included the following confounders in the stepwise models: age, gender, cigarette smoking, BMI, HbA1c, and T-Chol.
A p-value of less than 0.05 was considered statistically significant. Statistical analysis was performed using STATISTICA 10 (StatSoft, Tulsa, Oklahoma, USA).

This was a cross-sectional study. We included patients with a diagnosis of IgG4-RD according to the Comprehensive Diagnostic Criteria for IgG4-RD and/or the Consensus Statement on Pathology [9 (link),10 (link)] who attended a referral center in Mexico City from August 2018 to October 2019. The 2019 American College of Rheumatology (ACR)/European League Against Rheumatism (EULAR) classification criteria for IgG4-RD were retrospectively applied as they were published after the end of the recruitment of our cohort [11 (link)]. According to them, the patients were classified as definitive, probable, or atypical IgG4-RD, as suggested by Sanders et al. [12 (link)]. We excluded patients with concomitant diagnoses of another systemic autoimmune/inflammatory disease, active infection, or malignancy. As a control group, we recruited healthy subjects from the blood bank of our center. None had an autoimmune disease, a malignant disease, or a current/chronic infection. We obtained approval from the Institutional Review Board (IRE-2549-18-20-1), and the study complied with the Declaration of Helsinki. Patients and controls gave written informed consent.
We retrospectively collected patient information from the clinical records, such as age at diagnosis, the number of organs involved, IgG4 serum levels at disease onset, biopsy results, and the use of glucocorticoids and immunosuppressive therapy at recruitment.
Patients were classified according to the clinical phenotypes described by Wallace et al. in pancreato-hepato-biliary, retroperitoneal/aortic, head and neck-limited, and Mikulicz/systemic phenotypes [2 (link)]. Patients were also classified according to the clinical phenotypes described by Zhang et al. in proliferative and fibrotic phenotypes [1 (link)]. Patients in the proliferative phenotype were those with involvement of glandular and epithelial organs (e.g., lacrimal and major salivary glands, pancreas, biliary tract, kidney, lung); patients in the fibrotic phenotype were those with involvement of extra-glandular sites or body regions rather than a specific organ (e.g., retroperitoneal fibrosis, mediastinal fibrosis, sclerosing mesenteritis) and those with pachymeningitis and Riedel’s thyroiditis [1 (link)]. We assessed the number of involved organs and the IgG4-RD Responder Index (IgG4-RD RI) at recruitment [13 (link)]. As a consensus definition of active and inactive disease is not available, we defined active disease as the presence of clinical signs and symptoms, laboratory abnormalities, or radiological findings attributable to IgG4-RD and an IgG4-RD RI ≥ 2. We defined inactive disease as the absence of clinical signs and symptoms, laboratory abnormalities, or radiological findings attributable to IgG4-RD and an IgG4-RD RI of 0, regardless of the use of immunosuppressive drugs. Patients were defined as atopic according to the definitions of the European Academy of Allergy and Clinical Immunology [14 (link)].