Cargando…

Using data mining technology to explore homocysteine at low levels

A high homocysteine level is known to be an independent risk factor for cardiovascular diseases; however, whether or not low homocysteine level contributes to any damage to the body has not been extensively studied. Furthermore, acquiring healthy subject databases from domestic studies on homocystei...

Descripción completa

Detalles Bibliográficos
Autores principales: Tseng, Fei-Ching, Huang, Tin-Chung
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Lippincott Williams & Wilkins 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8376364/
https://www.ncbi.nlm.nih.gov/pubmed/34414944
http://dx.doi.org/10.1097/MD.0000000000026893
_version_ 1783740479381176320
author Tseng, Fei-Ching
Huang, Tin-Chung
author_facet Tseng, Fei-Ching
Huang, Tin-Chung
author_sort Tseng, Fei-Ching
collection PubMed
description A high homocysteine level is known to be an independent risk factor for cardiovascular diseases; however, whether or not low homocysteine level contributes to any damage to the body has not been extensively studied. Furthermore, acquiring healthy subject databases from domestic studies on homocysteine is not trivial. Therefore, we aimed to investigate the causality between serum homocysteine levels and health status and lifestyle factors, particularly with a focus on low serum homocysteine levels. Additionally, we discussed a systematic methodical platform for data collection and statistical analysis, using the descriptive analysis of the chi-square test, t test, multivariate analysis of variance, and logistic regression. This study was a cross-sectional analysis of 5864 subjects (i.e., clients of a health examination clinic) in Taipei, Taiwan during a general health check-up in 2017. The patients’ personal information and associated links were excluded. A sample group was selected as per the health criteria defined for this research whose data were processed using SPSS for descriptive statistical analysis using chi-square test, t test, multivariate analysis of variance, and logistic regression analysis. Those working for >12 hours/day had a higher homocysteine level than those working for <12 hours/day (P < .001). The average serum homocysteine level was 7.9 and 8.6 mol/L for people with poor sleep quality and good sleep quality, respectively (P = .003). The homocysteine value of people known to have cancer was analyzed using the logistic regression analysis, revealing a Δodds value of 0.898. The percentage of subjects with a homocysteine value of ≤6.3 μmol/L, who perceived their health status as “not very good” or “very bad,” was higher than those with a higher homocysteine level. The number of subjects who perceived their health as poor was higher than expected. The results suggest that the homocysteine level could be an effective health management indicator. We conclude that normal homocysteine level should not be ≤6.3 μmol/L. Moreover, homocysteine should not be considered as harmful and its fluctuations from the normal range could be utilized to infer a person's physical status for health management.
format Online
Article
Text
id pubmed-8376364
institution National Center for Biotechnology Information
language English
publishDate 2021
publisher Lippincott Williams & Wilkins
record_format MEDLINE/PubMed
spelling pubmed-83763642021-08-21 Using data mining technology to explore homocysteine at low levels Tseng, Fei-Ching Huang, Tin-Chung Medicine (Baltimore) 5700 A high homocysteine level is known to be an independent risk factor for cardiovascular diseases; however, whether or not low homocysteine level contributes to any damage to the body has not been extensively studied. Furthermore, acquiring healthy subject databases from domestic studies on homocysteine is not trivial. Therefore, we aimed to investigate the causality between serum homocysteine levels and health status and lifestyle factors, particularly with a focus on low serum homocysteine levels. Additionally, we discussed a systematic methodical platform for data collection and statistical analysis, using the descriptive analysis of the chi-square test, t test, multivariate analysis of variance, and logistic regression. This study was a cross-sectional analysis of 5864 subjects (i.e., clients of a health examination clinic) in Taipei, Taiwan during a general health check-up in 2017. The patients’ personal information and associated links were excluded. A sample group was selected as per the health criteria defined for this research whose data were processed using SPSS for descriptive statistical analysis using chi-square test, t test, multivariate analysis of variance, and logistic regression analysis. Those working for >12 hours/day had a higher homocysteine level than those working for <12 hours/day (P < .001). The average serum homocysteine level was 7.9 and 8.6 mol/L for people with poor sleep quality and good sleep quality, respectively (P = .003). The homocysteine value of people known to have cancer was analyzed using the logistic regression analysis, revealing a Δodds value of 0.898. The percentage of subjects with a homocysteine value of ≤6.3 μmol/L, who perceived their health status as “not very good” or “very bad,” was higher than those with a higher homocysteine level. The number of subjects who perceived their health as poor was higher than expected. The results suggest that the homocysteine level could be an effective health management indicator. We conclude that normal homocysteine level should not be ≤6.3 μmol/L. Moreover, homocysteine should not be considered as harmful and its fluctuations from the normal range could be utilized to infer a person's physical status for health management. Lippincott Williams & Wilkins 2021-08-20 /pmc/articles/PMC8376364/ /pubmed/34414944 http://dx.doi.org/10.1097/MD.0000000000026893 Text en Copyright © 2021 the Author(s). Published by Wolters Kluwer Health, Inc. https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the Creative Commons Attribution License 4.0 (CCBY), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. http://creativecommons.org/licenses/by/4.0 (https://creativecommons.org/licenses/by/4.0/)
spellingShingle 5700
Tseng, Fei-Ching
Huang, Tin-Chung
Using data mining technology to explore homocysteine at low levels
title Using data mining technology to explore homocysteine at low levels
title_full Using data mining technology to explore homocysteine at low levels
title_fullStr Using data mining technology to explore homocysteine at low levels
title_full_unstemmed Using data mining technology to explore homocysteine at low levels
title_short Using data mining technology to explore homocysteine at low levels
title_sort using data mining technology to explore homocysteine at low levels
topic 5700
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8376364/
https://www.ncbi.nlm.nih.gov/pubmed/34414944
http://dx.doi.org/10.1097/MD.0000000000026893
work_keys_str_mv AT tsengfeiching usingdataminingtechnologytoexplorehomocysteineatlowlevels
AT huangtinchung usingdataminingtechnologytoexplorehomocysteineatlowlevels