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Risk adjustment model for tuberculosis compared to non-tuberculosis mycobacterium or latent tuberculosis infection: Center for Personalized Precision Medicine of Tuberculosis (cPMTb) cohort database
BACKGROUND: The Center for Personalized Precision Medicine of Tuberculosis (cPMTb) was constructed to develop personalized pharmacotherapeutic systems for tuberculosis (TB). This study aimed to introduce the cPMTb cohort and compare the distinct characteristics of patients with TB, non-tuberculosis...
Autores principales: | , , , , , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10675857/ https://www.ncbi.nlm.nih.gov/pubmed/38001469 http://dx.doi.org/10.1186/s12890-023-02646-7 |
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author | Seo, Woo Jung Koo, Hyeon-Kyoung Kang, Ji Yeon Kang, Jieun Park, So Hee Kang, Hyung Koo Park, Hye Kyeong Lee, Sung-Soon Choi, Sangbong Jang, Tae Won Shin, Kyeong-Cheol Oh, Jee Youn Choi, Joon Young Min, Jinsoo Choi, Young-Kyung Shin, Jae-Gook Cho, Yong-Soon |
author_facet | Seo, Woo Jung Koo, Hyeon-Kyoung Kang, Ji Yeon Kang, Jieun Park, So Hee Kang, Hyung Koo Park, Hye Kyeong Lee, Sung-Soon Choi, Sangbong Jang, Tae Won Shin, Kyeong-Cheol Oh, Jee Youn Choi, Joon Young Min, Jinsoo Choi, Young-Kyung Shin, Jae-Gook Cho, Yong-Soon |
author_sort | Seo, Woo Jung |
collection | PubMed |
description | BACKGROUND: The Center for Personalized Precision Medicine of Tuberculosis (cPMTb) was constructed to develop personalized pharmacotherapeutic systems for tuberculosis (TB). This study aimed to introduce the cPMTb cohort and compare the distinct characteristics of patients with TB, non-tuberculosis mycobacterium (NTM) infection, or latent TB infection (LTBI). We also determined the prevalence and specific traits of polymorphisms in N-acetyltransferase-2 (NAT2) and solute carrier organic anion transporter family member 1B1 (SLCO1B1) phenotypes using this prospective multinational cohort. METHODS: Until August 2021, 964, 167, and 95 patients with TB, NTM infection, and LTBI, respectively, were included. Clinical, laboratory, and radiographic data were collected. NAT2 and SLCO1B1 phenotypes were classified by genomic DNA analysis. RESULTS: Patients with TB were older, had lower body mass index (BMI), higher diabetes rate, and higher male proportion than patients with LTBI. Patients with NTM infection were older, had lower BMI, lower diabetes rate, higher previous TB history, and higher female proportion than patients with TB. Patients with TB had the lowest albumin levels, and the prevalence of the rapid, intermediate, and slow/ultra-slow acetylator phenotypes were 39.2%, 48.1%, and 12.7%, respectively. The prevalence of rapid, intermediate, and slow/ultra-slow acetylator phenotypes were 42.0%, 44.6%, and 13.3% for NTM infection, and 42.5%, 48.3%, and 9.1% for LTBI, respectively, which did not differ significantly from TB. The prevalence of the normal, intermediate, and lower transporter SLCO1B1 phenotypes in TB, NTM, and LTBI did not differ significantly; 74.9%, 22.7%, and 2.4% in TB; 72.0%, 26.1%, and 1.9% in NTM; and 80.7%, 19.3%, and 0% in LTBI, respectively. CONCLUSIONS: Understanding disease characteristics and identifying pharmacokinetic traits are fundamental steps in optimizing treatment. Further longitudinal data are required for personalized precision medicine. TRIAL REGISTRATION: This study registered ClinicalTrials.gov NO. NCT05280886. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12890-023-02646-7. |
format | Online Article Text |
id | pubmed-10675857 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-106758572023-11-24 Risk adjustment model for tuberculosis compared to non-tuberculosis mycobacterium or latent tuberculosis infection: Center for Personalized Precision Medicine of Tuberculosis (cPMTb) cohort database Seo, Woo Jung Koo, Hyeon-Kyoung Kang, Ji Yeon Kang, Jieun Park, So Hee Kang, Hyung Koo Park, Hye Kyeong Lee, Sung-Soon Choi, Sangbong Jang, Tae Won Shin, Kyeong-Cheol Oh, Jee Youn Choi, Joon Young Min, Jinsoo Choi, Young-Kyung Shin, Jae-Gook Cho, Yong-Soon BMC Pulm Med Research BACKGROUND: The Center for Personalized Precision Medicine of Tuberculosis (cPMTb) was constructed to develop personalized pharmacotherapeutic systems for tuberculosis (TB). This study aimed to introduce the cPMTb cohort and compare the distinct characteristics of patients with TB, non-tuberculosis mycobacterium (NTM) infection, or latent TB infection (LTBI). We also determined the prevalence and specific traits of polymorphisms in N-acetyltransferase-2 (NAT2) and solute carrier organic anion transporter family member 1B1 (SLCO1B1) phenotypes using this prospective multinational cohort. METHODS: Until August 2021, 964, 167, and 95 patients with TB, NTM infection, and LTBI, respectively, were included. Clinical, laboratory, and radiographic data were collected. NAT2 and SLCO1B1 phenotypes were classified by genomic DNA analysis. RESULTS: Patients with TB were older, had lower body mass index (BMI), higher diabetes rate, and higher male proportion than patients with LTBI. Patients with NTM infection were older, had lower BMI, lower diabetes rate, higher previous TB history, and higher female proportion than patients with TB. Patients with TB had the lowest albumin levels, and the prevalence of the rapid, intermediate, and slow/ultra-slow acetylator phenotypes were 39.2%, 48.1%, and 12.7%, respectively. The prevalence of rapid, intermediate, and slow/ultra-slow acetylator phenotypes were 42.0%, 44.6%, and 13.3% for NTM infection, and 42.5%, 48.3%, and 9.1% for LTBI, respectively, which did not differ significantly from TB. The prevalence of the normal, intermediate, and lower transporter SLCO1B1 phenotypes in TB, NTM, and LTBI did not differ significantly; 74.9%, 22.7%, and 2.4% in TB; 72.0%, 26.1%, and 1.9% in NTM; and 80.7%, 19.3%, and 0% in LTBI, respectively. CONCLUSIONS: Understanding disease characteristics and identifying pharmacokinetic traits are fundamental steps in optimizing treatment. Further longitudinal data are required for personalized precision medicine. TRIAL REGISTRATION: This study registered ClinicalTrials.gov NO. NCT05280886. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12890-023-02646-7. BioMed Central 2023-11-24 /pmc/articles/PMC10675857/ /pubmed/38001469 http://dx.doi.org/10.1186/s12890-023-02646-7 Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data. |
spellingShingle | Research Seo, Woo Jung Koo, Hyeon-Kyoung Kang, Ji Yeon Kang, Jieun Park, So Hee Kang, Hyung Koo Park, Hye Kyeong Lee, Sung-Soon Choi, Sangbong Jang, Tae Won Shin, Kyeong-Cheol Oh, Jee Youn Choi, Joon Young Min, Jinsoo Choi, Young-Kyung Shin, Jae-Gook Cho, Yong-Soon Risk adjustment model for tuberculosis compared to non-tuberculosis mycobacterium or latent tuberculosis infection: Center for Personalized Precision Medicine of Tuberculosis (cPMTb) cohort database |
title | Risk adjustment model for tuberculosis compared to non-tuberculosis mycobacterium or latent tuberculosis infection: Center for Personalized Precision Medicine of Tuberculosis (cPMTb) cohort database |
title_full | Risk adjustment model for tuberculosis compared to non-tuberculosis mycobacterium or latent tuberculosis infection: Center for Personalized Precision Medicine of Tuberculosis (cPMTb) cohort database |
title_fullStr | Risk adjustment model for tuberculosis compared to non-tuberculosis mycobacterium or latent tuberculosis infection: Center for Personalized Precision Medicine of Tuberculosis (cPMTb) cohort database |
title_full_unstemmed | Risk adjustment model for tuberculosis compared to non-tuberculosis mycobacterium or latent tuberculosis infection: Center for Personalized Precision Medicine of Tuberculosis (cPMTb) cohort database |
title_short | Risk adjustment model for tuberculosis compared to non-tuberculosis mycobacterium or latent tuberculosis infection: Center for Personalized Precision Medicine of Tuberculosis (cPMTb) cohort database |
title_sort | risk adjustment model for tuberculosis compared to non-tuberculosis mycobacterium or latent tuberculosis infection: center for personalized precision medicine of tuberculosis (cpmtb) cohort database |
topic | Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10675857/ https://www.ncbi.nlm.nih.gov/pubmed/38001469 http://dx.doi.org/10.1186/s12890-023-02646-7 |
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