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Unfavorable triglyceride-rich particle profile in subclinical thyroid disease: A cross-sectional analysis of ELSA-Brasil

Citation

Janovsky, Carolina Castro Porto Silva; Bensenor, Isabela (2021), Unfavorable triglyceride-rich particle profile in subclinical thyroid disease: A cross-sectional analysis of ELSA-Brasil, Dryad, Dataset, https://doi.org/10.5061/dryad.gqnk98skp

Abstract

Introduction: Subclinical thyroid disorders have been associated with atherosclerosis and increased cardiovascular risk. As triglyceride-rich lipoprotein particles (TRLP) have recently emerged as a casual factor for atherogenesis, the aim of this study was to evaluate the relationship between subclinical hypo and hyperthyroidism and TRLP subfractions.

Methods: We selected 5,066 participants from the ELSA-Brasil cohort with available data of thyroid function and lipid profile measured by NMR. Individuals were divided into three groups by baseline thyroid function (subclinical hypothyroidism, euthyroidism, and subclinical hyperthyroidism). TRLP subfractions were analyzed through Nuclear Magnetic Resonance (NMR) spectroscopy. To examine the association between TRLP subfractions and thyroid function, we conducted univariate and multivariate linear regression models adjusted for demographic characteristics, BMI, diabetes, smoking status and alcohol use.

Results: Of 3,304 individuals, 54% were women, with a mean age of 50.6±8,7 years, 51% white and 53% with at least college education. Of these individuals, 92% were euthyroid, whereas 6.8% had subclinical hypothyroidism and 1.2% had subclinical hyperthyroidism. The univariate linear regression showed that Very Small TRLP (p=0.026) and Very Large TRLP (p=0.008) were statistically increased in subclinical hypothyroidism when compared to euthyroidism. In subclinical hyperthyroidism, there was a reduction in total TRLP (p=0.003), seemingly driven by reduced Very Small-TRLP (p=0.067). The findings were confirmed when adjusted for demographic characteristics, as well as comorbidities.

Conclusions: This study suggests that subclinical hypothyroidism is associated to very small and very large TRLP, which are related to an unfavorable atherogenic profile. Subclinical hyperthyroidism is associated to lower very small TRLP.

Methods

ELSA-Brasil Study

The Brazilian Longitudinal Study of Adult Health (ELSA-Brasil) is a prospective cohort study following 15,105 civil servants from six cities in Brazil (Salvador, Belo Horizonte, Vitória, Rio de Janeiro, São Paulo and Porto Alegre) and has been previously described(26-28). The main objective of the study is to investigate the incidence of cardiovascular diseases and diabetes, as well as their associated risk factors(27,29). All participants in this study provided written informed consent and each enrolling site obtained approval from the Institutional Review Boards (CAAE 0016.1.198.000-06) according to the Declaration of Helsinki. We selected 5,066 participants from the Investigation Center of São Paulo with available data of thyroid function and lipid profile measured by NMR. Participants with overt thyroid disease, using statins or medications that could interfere with thyroid function and those with previous severe cardiovascular event were excluded from the analysis as described in figure 1.

Thyroid Function Definition

Thyroid-stimulating hormone (TSH) and Free Thyroxine (FT4) were measured using a third‐generation immunoenzymatic assay (Siemens, Deerfield, IL, USA) in serum obtained from centrifuged venous blood samples after overnight fasting. FT4 levels were measured in participants exhibiting altered TSH levels. In this study, reference range levels were 0.55 - 4.78 mcUI/mL for TSH and 0.89 - 1.76 ng/dL for FT4, similar to other ELSA‐Brasil studies(30-32). The intra-assay and inter-assay coefficients of variation were 3.32% and 9.65% for TSH and 1.40% and 6.40% for FT4, respectively. Biochemical data about the performed tests can be seen in table 1 of the supplemental material*.

ELSA‐Brasil participants were classified into five categories of thyroid function, according to TSH and FT4 levels and information related to the use of medication to treat thyroid disorders: overt hypothyroidism (high TSH and low FT4 levels, or use of levothyroxine to treat hypothyroidism), subclinical hypothyroidism (high TSH levels, normal FT4 levels and no use of drugs to treat thyroid diseases), euthyroidism (normal TSH and no use of thyroid drugs), subclinical hyperthyroidism (low serum TSH, normal FT4 levels and no use of drugs to treat thyroid diseases) and overt hyperthyroidism (low serum TSH and high FT4 levels or use of medication to treat hyperthyroidism). Accordingly, the definition of subclinical thyroid disease included only participants who did not use any drugs to treat thyroid disorders. Participants with overt thyroid disorders were excluded from the analyses.

Lipoprotein Measurements

Blood was collected from participants after 8-12 hours nocturnal fasting. The samples were centrifuged at the sites and stored in tubes at - 80°C. Conventional lipid concentrations (total cholesterol, HDL-cholesterol, and triglycerides) were determined by a nonprecipitated colorimetric method using ADVIA 1200 Siemens equipment. LDL cholesterol was calculated using the Friedewald equation(33), except for cases with elevated triglyceride levels (more than 400mg/dL), when an enzymatic colorimetric assay was used (ADVIA 1200, Siemens). Detailed data about the performed tests can be found at table S1 of the supplemental material(26,34)*.

Triglyceride-rich lipoproteins (TRLP) subfractions were measured by new nuclear magnetic resonance (NMR) spectroscopy (NMR LipoProfile® 4 test spectra, LabCorp, Raleigh, NC). This technique quantifies the size as well as the concentration (“number”) of lipoprotein particles expressed as an average particle size (in nanometers) or as lipoprotein particle concentration (in particle nmol/L). NMR can quantify the concentration of lipoprotein subclass particles because of two phenomena: lipoprotein subclasses of different size in plasma simultaneously emit distinctive NMR signals whose individual amplitudes can be accurately and reproducibly measured; and also the measured subclass signal amplitudes are directly proportional to the numbers of subclass particles emitting the signal, irrespective of variation in particle lipid composition(35). Thus, this method relies on the spectroscopic distinctness of particles’ size, and not protein composition or metabolic origin(36). Total TRLP comprises particles measuring 24 to 240nm in diameter. The mean particle size for TRLP (TRLZ) rests between 30 and 100nm. IDL-cholesterol is now named very small TRLP (VS-TRLP), comprising particles with estimate diameter from 24 to 29nm. Similarly, other NMR fractions are defined as small TRLP (S-TRLP) – 30 to 36nm, medium TRLP (M-TRLP) – 37 to 49nm, large TRLP (L-TRLP) – 50 to 89nm and very large TRLP (VL-TRLP) – 90 to 240nm within the TRLP particles(37).

Other variables

Age is presented as a continuous variable. Sex is described as percentage female. Race was self-reported and categorized as white, brown, black and other (including Asian and indigenous). Educational level was stratified as less than high school, high school, and college or higher. Anthropometric measures were obtained with standard techniques with the participant wearing light clothes. Body mass index (BMI) was calculated as weight divided by height squared (kg/m2). Hypertension was defined as the use of antihypertensive drug, systolic blood pressure ≥ 140 mm Hg or diastolic blood pressure ≥ 90 mm Hg. The diagnosis of diabetes was based on a previous medical diagnosis of diabetes self-report by the participant or use of oral antidiabetic agents or insulin therapy, fasting plasma glucose ≥ 126 mg/dL, 2-hour post-prandial 75 g glucose test ≥ 200 mg/dL, or glycosylated hemoglobin ≥ 6.5% (exams which were evaluated in all participants) (38). Smoking status was defined as never, former (withdraw for more than 2 years) or current. Participants are considered smokers if they smoked more than 100 cigarettes (or five packs) in their life before enter the study or if they answered yes to the question “Do you currently smoke cigarettes?” independently of the number of cigarettes smoked. Alcohol use was defined as never, former or current. Former alcohol users were those who stopped using alcohol for more than 2 years. We classified participants in the excessive drinking category if they intake more than 210 g per week if men and more than 140 g per week if women according to WHO criteria(39).

Statistical Analysis

Continuous variables were presented as descriptive statistics with mean and standard deviation for normal distribution and, for non-normal distributions, with median (quartiles). The data distribution’s normality was verified through Kolmorogov-Smirnov test. We showed categorical variables in absolute and relative frequency. TSH, triglycerides and TRLP subfractions were log-transformed for use in regression models. Comparison of quantitative variables across groups was performed using analysis of variance for variables with normal distribution and the Kruskal-Wallis test and Spearman correlation for those with non-normal distributions. Categorical variables were analyzed by chi-square test and Fisher’s exact test. To examine the association between TRLP subfractions and thyroid function, we conducted univariate and multivariate linear regression models. For these analyses, we standardized total TRLP, VS-TRLP, S-TRLP, M-TRLP, L-TRLP, VL-TRLP and mean size of TRLP (TRLPZ). Three multiple linear regression models were adjusted as follow: Crude model: univariate; Model 1: race, gender and educational level; Model 2: model 2 + BMI, diabetes, hypertension, smoking status and alcohol use. Statistical significance was defined as p<0.05. Analyses were performed with Stata version 13.1 (StataCorp, USA).

Usage Notes

The information in this dataset refers to the Supplemental Material of the original article.

Funding

Conselho Nacional de Desenvolvimento Científico e Tecnológico, Award: 01 06 0010.00 RS

Conselho Nacional de Desenvolvimento Científico e Tecnológico, Award: 01 06 0212.00 BA

Conselho Nacional de Desenvolvimento Científico e Tecnológico, Award: 01 06 0300.00 ES

Conselho Nacional de Desenvolvimento Científico e Tecnológico, Award: 01 06 0278.00 MG

Conselho Nacional de Desenvolvimento Científico e Tecnológico, Award: 01 06 0115.00 SP

Conselho Nacional de Desenvolvimento Científico e Tecnológico, Award: 01 06 0071.00 RJ