Cargando…
Connections between cross-tissue and intra-tissue biomarkers of aging biology in older adults
BACKGROUND: Saliva measures are generally more accessible than blood, especially in vulnerable populations. However, connections between aging biology biomarkers in different body tissues remain unknown. METHODS: The present study included individuals (N = 2406) who consented for saliva and blood dr...
Autores principales: | , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
2023
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10688599/ https://www.ncbi.nlm.nih.gov/pubmed/38037563 http://dx.doi.org/10.1186/s43682-023-00022-4 |
_version_ | 1785152197533630464 |
---|---|
author | Waziry, R. Gu, Y. Williams, O. Hägg, S. |
author_facet | Waziry, R. Gu, Y. Williams, O. Hägg, S. |
author_sort | Waziry, R. |
collection | PubMed |
description | BACKGROUND: Saliva measures are generally more accessible than blood, especially in vulnerable populations. However, connections between aging biology biomarkers in different body tissues remain unknown. METHODS: The present study included individuals (N = 2406) who consented for saliva and blood draw in the Health and Retirement Telomere length study in 2008 and the Venous blood study in 2016 who had complete data for both tissues. We assessed biological aging based on telomere length in saliva and DNA methylation and physiology measures in blood. DNA methylation clocks combine information from CpGs to produce the aging measures representative of epigenetic aging in humans. We analyzed DNA methylation clocks proposed by Horvath (353 CpG sites), Hannum (71 CpG sites), Levine or PhenoAge, (513 CpG sites), GrimAge, (epigenetic surrogate markers for select plasma proteins), Horvath skin and blood (391 CpG sites), Lin (99 CpG sites), Weidner (3 CpG sites), and VidalBralo (8 CpG sites). Physiology measures (referred to as phenotypic age) included albumin, creatinine, glucose, [log] C-reactive protein, lymphocyte percent, mean cell volume, red blood cell distribution width, alkaline phosphatase, and white blood cell count. The phenotypic age algorithm is based on parametrization of Gompertz proportional hazard models. Average telomere length was assayed using quantitative PCR (qPCR) by comparing the telomere sequence copy number in each patient’s sample (T) to a single-copy gene copy number (S). The resulting T/S ratio was proportional to telomere length, mean. Within individual, relationships between aging biology measures in blood and saliva and variations according to sex were assessed. RESULTS: Saliva-based telomere length showed inverse associations with both physiology-based and DNA methylation-based aging biology biomarkers in blood. Longer saliva-based telomere length was associated with 1 to 4 years slower biological aging based on blood-based biomarkers with the highest magnitude being Weidner (β = − 3.97, P = 0.005), GrimAge (β = − 3.33, P < 0.001), and Lin (β = − 3.45, P = 0.008) biomarkers of DNA methylation. CONCLUSIONS: There are strong connections between aging biology biomarkers in saliva and blood in older adults. Changes in telomere length vary with changes in DNA methylation and physiology biomarkers of aging biology. We observed variations in the relationship between each body system represented by physiology biomarkers and biological aging, particularly at the DNA methylation level. These observations provide novel opportunities for integration of both blood-based and saliva-based biomarkers in clinical care of vulnerable and clinically difficult to reach populations where either or both tissues would be accessible for clinical monitoring purposes. |
format | Online Article Text |
id | pubmed-10688599 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
record_format | MEDLINE/PubMed |
spelling | pubmed-106885992023-11-30 Connections between cross-tissue and intra-tissue biomarkers of aging biology in older adults Waziry, R. Gu, Y. Williams, O. Hägg, S. Epigenetics Commun Article BACKGROUND: Saliva measures are generally more accessible than blood, especially in vulnerable populations. However, connections between aging biology biomarkers in different body tissues remain unknown. METHODS: The present study included individuals (N = 2406) who consented for saliva and blood draw in the Health and Retirement Telomere length study in 2008 and the Venous blood study in 2016 who had complete data for both tissues. We assessed biological aging based on telomere length in saliva and DNA methylation and physiology measures in blood. DNA methylation clocks combine information from CpGs to produce the aging measures representative of epigenetic aging in humans. We analyzed DNA methylation clocks proposed by Horvath (353 CpG sites), Hannum (71 CpG sites), Levine or PhenoAge, (513 CpG sites), GrimAge, (epigenetic surrogate markers for select plasma proteins), Horvath skin and blood (391 CpG sites), Lin (99 CpG sites), Weidner (3 CpG sites), and VidalBralo (8 CpG sites). Physiology measures (referred to as phenotypic age) included albumin, creatinine, glucose, [log] C-reactive protein, lymphocyte percent, mean cell volume, red blood cell distribution width, alkaline phosphatase, and white blood cell count. The phenotypic age algorithm is based on parametrization of Gompertz proportional hazard models. Average telomere length was assayed using quantitative PCR (qPCR) by comparing the telomere sequence copy number in each patient’s sample (T) to a single-copy gene copy number (S). The resulting T/S ratio was proportional to telomere length, mean. Within individual, relationships between aging biology measures in blood and saliva and variations according to sex were assessed. RESULTS: Saliva-based telomere length showed inverse associations with both physiology-based and DNA methylation-based aging biology biomarkers in blood. Longer saliva-based telomere length was associated with 1 to 4 years slower biological aging based on blood-based biomarkers with the highest magnitude being Weidner (β = − 3.97, P = 0.005), GrimAge (β = − 3.33, P < 0.001), and Lin (β = − 3.45, P = 0.008) biomarkers of DNA methylation. CONCLUSIONS: There are strong connections between aging biology biomarkers in saliva and blood in older adults. Changes in telomere length vary with changes in DNA methylation and physiology biomarkers of aging biology. We observed variations in the relationship between each body system represented by physiology biomarkers and biological aging, particularly at the DNA methylation level. These observations provide novel opportunities for integration of both blood-based and saliva-based biomarkers in clinical care of vulnerable and clinically difficult to reach populations where either or both tissues would be accessible for clinical monitoring purposes. 2023 2023-10-12 /pmc/articles/PMC10688599/ /pubmed/38037563 http://dx.doi.org/10.1186/s43682-023-00022-4 Text en 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 | Article Waziry, R. Gu, Y. Williams, O. Hägg, S. Connections between cross-tissue and intra-tissue biomarkers of aging biology in older adults |
title | Connections between cross-tissue and intra-tissue biomarkers of aging biology in older adults |
title_full | Connections between cross-tissue and intra-tissue biomarkers of aging biology in older adults |
title_fullStr | Connections between cross-tissue and intra-tissue biomarkers of aging biology in older adults |
title_full_unstemmed | Connections between cross-tissue and intra-tissue biomarkers of aging biology in older adults |
title_short | Connections between cross-tissue and intra-tissue biomarkers of aging biology in older adults |
title_sort | connections between cross-tissue and intra-tissue biomarkers of aging biology in older adults |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10688599/ https://www.ncbi.nlm.nih.gov/pubmed/38037563 http://dx.doi.org/10.1186/s43682-023-00022-4 |
work_keys_str_mv | AT waziryr connectionsbetweencrosstissueandintratissuebiomarkersofagingbiologyinolderadults AT guy connectionsbetweencrosstissueandintratissuebiomarkersofagingbiologyinolderadults AT williamso connectionsbetweencrosstissueandintratissuebiomarkersofagingbiologyinolderadults AT haggs connectionsbetweencrosstissueandintratissuebiomarkersofagingbiologyinolderadults |