Serum Thyrotropin and Triiodothyronine Levels in Levothyroxine-treated Patients

CONTEXT

Small adjustments in levothyroxine (LT4) dose do not appear to provide clinical benefit despite changes in thyrotropin (TSH) levels within the reference range. We hypothesize that the accompanying changes in serum total triiodothyronine (T3) levels do not reflect the magnitude of the changes in serum TSH.

OBJECTIVE

This work aims to characterize the relationships of serum free thyroxine (FT4) vs T3, FT4 vs TSH, and FT4 vs the T3/FT4 ratio.

METHODS

This cross-sectional, observational study comprised 9850 participants aged 18 years and older treated with LT4 from a large clinical database from January 1, 2009, to December 31, 2019. Patients had been treated with LT4, subdivided by serum FT4 level. Main outcome measures included model fitting of the relationships between serum FT4 vs TSH, FT4 vs T3, and FT4 vs T3/FT4. Mean and median values of TSH, T3, and T3/FT4 were calculated.

RESULTS

The relationships T3 vs FT4 and TSH vs FT4 were both complex and best represented by distinct, segmented regression models. Increasing FT4 levels were linearly associated with T3 levels until an inflection point at an FT4 level of 0.7 ng/dL, after which a flattening of the slope was observed following a convex quadratic curve. In contrast, increasing FT4 levels were associated with steep declines in TSH following 2 negative sigmoid curves. The FT4 vs T3/FT4 relationship was fit to an asymptotic regression curve supporting less T4 to T3 activation at higher FT4 levels.

CONCLUSION

In LT4-treated patients, the relationships between serum FT4 vs TSH and FT4 vs T3 across a range of FT4 levels are disproportionate. As a result, dose changes in LT4 that robustly modify serum FT4 and TSH values may only minimally affect serum T3 levels and result in no significant clinical benefit.

ASSOCIAÇÃO ENTRE TIPO SANGUÍNEO ABO E RESULTADO DO RT-PCR PARA SARS-COV-2

Introdução

Estudos apontam relação entre grupos sanguíneos e diversas condições clínicas como, por exemplo, eventos trombóticos, doença de von Willebrande doenças infecciosas como SARS-CoV-1, H. pylori entre outros. Associação entre maior susceptibilidade à COVID-19 e pior evolução em indivíduos do grupo sanguíneo A vem sendo estudada por diversos grupos.

Objetivo

Neste estudo, analisamos a correlação entre os grupos sanguíneos ABO e resultado do teste para o vírus SARS-CoV-2 por RT-PCR em um laboratório de análises clínicas com grande fluxo de amostras representativas das cinco regiões do Brasil.

Material e métodos

Os dados foram obtidos do banco de dados do laboratório referente a 20 anos de registro, analisados de forma anonimizada e de acordo com as regras que regem a lei geral de proteção de dados (LGPD). Os registros possuem um identificador numérico único que leva em conta CPF e data de nascimento e todas as análises foram feitas considerando-se esta identificação. Foram obtidas as tipagem sanguínea ABO/Rh e os resultados de RT-PCR para SARS-CoV-2. Para este último, os indivíduos foram classificados “covid-positivo”quando ao menos um exame resultou positivo. Os demais foram considerados “covid-negativo”. As análises foram realizadas no banco de dados obtido do cruzamento do banco de indivíduos únicos com tipagem sanguínea e ao menos um teste para COVID-19. Análise estatística foi realizada do teste chi-quadrado de Pearson e V-quadrado de Cramér.

Resultados e discussão

Foram identificados 66.181 indivíduos que realizaram tipagem sanguínea e ao menos um teste para SARS-CoV-2. A distribuição global dos grupos ABO é a que segue: grupo O 44%, grupo A 41%, grupo B 11% e grupo AB 4%, compatível com a distribuição dos grupos no Brasil (O 45%, A 42%, B 10% e AB 3%).Do total de indivíduos estudado, 21% (13.617) apresentou ao menos um resultado positivo para SARS-CoV-2, distribuídos da seguinte forma por grupo sanguíneo: tipo O 42%, A 42%, B 12% e AB 4%. A distribuição dos grupos sanguíneos entre os indivíduos negativos para SARS-CoV-2 foi: tipo O 44%, A 41%, B 11% e AB 4%. As análises consideraram grupo ABO e Rh. Foi realizado um teste chi-quadrado de independência de variáveis e verificou-se associação positiva entre o tipo sanguíneo e a infecção por COVID-19 (χ2 = 27,273, df = 7, p = 0,0002975).O teste pós-hoc de comparação entre status de COVID-19 e tipo sanguíneo sugere associação entre o tipo B- e infecção por COVID-19 (p = 0.0407700).Entretanto, este dado não foi confirmado por análise adicional pelo teste V de Cramér, sugerindo que o efeito observado está provavelmente associado ao grande número de amostras do que uma associação real entre as variáveis.Sabe-se que a testagem para COVID-19 pode resultar em falso negativo quando realizada fora da janela de maior sensibilidade para detecção do vírus. Deve-se considerar que, na presente análise, a informação sobre início dos sintomas e data de realização do teste não está disponível. Entretanto, o grande número de casos analisados diminui o impacto da ausência desta informação. Outro ponto importante a ser considerado é que não foram avaliados parâmetros de evolução clínica dos pacientes, sendo os dados restritos à presença ou não de diagnóstico positivo para COVID-19.

Conclusão

Não foi demonstrada associação entre o tipo sanguíneo e a suscetibilidade a infecções por COVID-19 no grupo analisado.

Use of Statins Among Patients Taking Levothyroxine: an Observational Drug Utilization Study Across Sites

CONTEXT

Treatment with levothyroxine (LT4) that normalize serum thyrotropin (TSH) is expected to restore lipid metabolism.

OBJECTIVE

To assess statin utilization in LT4-treated patients through an observational drug utilization study.

METHODS

Three sites were involved: (1) 10 723 outpatients placed on LT4 during 2006-2019 identified from the Clinical Research Data Warehouse of the University of Chicago; (2) ~1.4 million LT4 prescriptions prepared by primary care physicians during January-December 2018, identified from the IQVIA™ database of medical prescriptions in Brazil; (30 ~5.4 million patient interviews during 2009-2019, including ~0.32 million patients on LT4, identified from the Fleury Group database in Brazil.

RESULTS

On site 1, initiation of therapy with LT4 increased the frequency of statin utilization (19.1% vs 24.6%), which occurred ~1.5 years later (median 76 weeks) and, among those patients that were on statins, increased intensity of treatment by 33%, despite normalization of serum TSH levels; on site 2, after matching for sex and age, the frequency of statins prescription was higher for those patients using LT4: females, 2.1 vs 3.4% (odds ratio [OR] 1.656 [1.639-1.673]); males, 3.1 vs 4.4% (OR 1.435 [1.409-1.462]); and, on site 3, after matching for sex and age, the frequency of statin utilization was higher in those patients using LT4: females, 10 vs 18% (OR 2.02 [2.00-2.04]); males, 15 vs 25% (OR 1.92 [1.88-1.96]); all P values were <.0001.

CONCLUSION

Prescription and utilization of statins were higher in patients taking LT4. The reasons for this association should be addressed in future studies.

Assessing the Impact of Sample Heterogeneity on Transcriptome Analysis of Human Diseases Using MDP Webtool

Transcriptome analyses have increased our understanding of the molecular mechanisms underlying human diseases. Most approaches aim to identify significant genes by comparing their expression values between healthy subjects and a group of patients with a certain disease. Given that studies normally contain few samples, the heterogeneity among individuals caused by environmental factors or undetected illnesses can impact gene expression analyses. We present a systematic analysis of sample heterogeneity in a variety of gene expression studies relating to inflammatory and infectious diseases and show that novel immunological insights may arise once heterogeneity is addressed. The perturbation score of samples is quantified using nonperturbed subjects (i.e., healthy subjects) as a reference group. Such a score allows us to detect outlying samples and subgroups of diseased patients and even assess the molecular perturbation of single cells infected with viruses. We also show how removal of outlying samples can improve the "signal" of the disease and impact detection of differentially expressed genes. The method is made available via the mdp Bioconductor R package and as a user-friendly webtool, webMDP, available at http://mdp.sysbio.tools.

webCEMiTool: Co-expression Modular Analysis Made Easy

Co-expression analysis has been widely used to elucidate the functional architecture of genes under different biological processes. Such analysis, however, requires substantial knowledge about programming languages and/or bioinformatics skills. We present webCEMiTool,¹ a unique online tool that performs comprehensive modular analyses in a fully automated manner. The webCEMiTool not only identifies co-expression gene modules but also performs several functional analyses on them. In addition, webCEMiTool integrates transcriptomic data with interactome information (i.e., protein-protein interactions) and identifies potential hubs on each network. The tool generates user-friendly html reports that allow users to search for specific genes in each module, as well as check if a module contains genes overrepresented in specific pathways or altered in a specific sample phenotype. We used webCEMiTool to perform a modular analysis of single-cell RNA-seq data of human cells infected with either Zika virus or dengue virus.

CEMiTool: a Bioconductor package for performing comprehensive modular co-expression analyses

Background

The analysis of modular gene co-expression networks is a well-established method commonly used for discovering the systems-level functionality of genes. In addition, these studies provide a basis for the discovery of clinically relevant molecular pathways underlying different diseases and conditions.

Results

In this paper, we present a fast and easy-to-use Bioconductor package named CEMiTool that unifies the discovery and the analysis of co-expression modules. Using the same real datasets, we demonstrate that CEMiTool outperforms existing tools, and provides unique results in a user-friendly html report with high quality graphs. Among its features, our tool evaluates whether modules contain genes that are over-represented by specific pathways or that are altered in a specific sample group, as well as it integrates transcriptomic data with interactome information, identifying the potential hubs on each network. We successfully applied CEMiTool to over 1000 transcriptome datasets, and to a new RNA-seq dataset of patients infected with Leishmania, revealing novel insights of the disease’s physiopathology.

Conclusion

The CEMiTool R package provides users with an easy-to-use method to automatically implement gene co-expression network analyses, obtain key information about the discovered gene modules using additional downstream analyses and retrieve publication-ready results via a high-quality interactive report.

Electronic supplementary material

The online version of this article (10.1186/s12859-018-2053-1) contains supplementary material, which is available to authorized users.