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A novel multi-tissue RNA diagnostic of healthy ageing relates to cognitive health status.

Sood S, Gallagher IJ, Lunnon K, Rullman E, Keohane A, Crossland H, Phillips BE, Cederholm T, Jensen T, van Loon LJ, Lannfelt L, Kraus WE, Atherton PJ, Howard R, Gustafsson T, Hodges A, Timmons JA - Genome Biol. (2015)

Bottom Line: Using the Uppsala Longitudinal Study of Adult Men birth-cohort (n = 108) we demonstrate that the RNA classifier is insensitive to confounding lifestyle biomarkers, while greater gene score at age 70 years is independently associated with better renal function at age 82 years and longevity.We identify a novel and statistically robust multi-tissue RNA signature of human healthy ageing that can act as a diagnostic of future health, using only a peripheral blood sample.This RNA signature has great potential to assist research aimed at finding treatments for and/or management of AD and other ageing-related conditions.

View Article: PubMed Central - PubMed

Affiliation: XRGenomics Ltd, London, UK.

ABSTRACT

Background: Diagnostics of the human ageing process may help predict future healthcare needs or guide preventative measures for tackling diseases of older age. We take a transcriptomics approach to build the first reproducible multi-tissue RNA expression signature by gene-chip profiling tissue from sedentary normal subjects who reached 65 years of age in good health.

Results: One hundred and fifty probe-sets form an accurate classifier of young versus older muscle tissue and this healthy ageing RNA classifier performed consistently in independent cohorts of human muscle, skin and brain tissue (n = 594, AUC = 0.83-0.96) and thus represents a biomarker for biological age. Using the Uppsala Longitudinal Study of Adult Men birth-cohort (n = 108) we demonstrate that the RNA classifier is insensitive to confounding lifestyle biomarkers, while greater gene score at age 70 years is independently associated with better renal function at age 82 years and longevity. The gene score is 'up-regulated' in healthy human hippocampus with age, and when applied to blood RNA profiles from two large independent age-matched dementia case-control data sets (n = 717) the healthy controls have significantly greater gene scores than those with cognitive impairment. Alone, or when combined with our previously described prototype Alzheimer disease (AD) RNA 'disease signature', the healthy ageing RNA classifier is diagnostic for AD.

Conclusions: We identify a novel and statistically robust multi-tissue RNA signature of human healthy ageing that can act as a diagnostic of future health, using only a peripheral blood sample. This RNA signature has great potential to assist research aimed at finding treatments for and/or management of AD and other ageing-related conditions.

No MeSH data available.


Related in: MedlinePlus

Development, validation and clinical application of ageing diagnostic. Overview of the selection process and use of RNA probe-sets for the development and validation of the healthy physiological age classifier. We identified useful probe-sets from a possible starting number of ~54,000 during step one [e.g. probe-sets with leave-one-out cross-validation (LOOCV) performance ≥ 90 %]. We then evaluated the performance of the top-ranked 150 probe-sets in a number of independent muscle, brain, and skin samples, demonstrating that the signature was diagnostic for age. We then applied the 150-probe-set healthy ageing signature to several clinical studies, as illustrated at the end of the workflow. Key features included discarding the training data set immediately after selecting the 150 probe-sets and relying on LOOCV and full external validation processes
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Fig1: Development, validation and clinical application of ageing diagnostic. Overview of the selection process and use of RNA probe-sets for the development and validation of the healthy physiological age classifier. We identified useful probe-sets from a possible starting number of ~54,000 during step one [e.g. probe-sets with leave-one-out cross-validation (LOOCV) performance ≥ 90 %]. We then evaluated the performance of the top-ranked 150 probe-sets in a number of independent muscle, brain, and skin samples, demonstrating that the signature was diagnostic for age. We then applied the 150-probe-set healthy ageing signature to several clinical studies, as illustrated at the end of the workflow. Key features included discarding the training data set immediately after selecting the 150 probe-sets and relying on LOOCV and full external validation processes

Mentions: Our objective was to discover a pattern of RNA expression that could be reliably used as a biomarker for ‘health status’ in older subjects — one that differed substantially in terms of ability to stratify health, and one that was more informative than chronological age. We applied machine-learning methods to RNA expression data to distinguish between healthy 25-year-old and healthy 65-year-old individuals. We took a simple classifier approach [43] without ad hoc a priori filtering to identify a consistent set of RNA markers of ageing across tissue types because standard differential expression is unable to provide a common multi-tissue set of discriminatory RNA molecules [9]. We selected muscle tissue gene-chip profiles from 15 sedentary young and 15 sedentary older subjects with good aerobic fitness (Gene Expression Omnibus (GEO) accession [GSE59880]) [31, 44] and who were free of diabetes [42, 44]. Specifically, we utilized a k-nearest neighbor (kNN) classification approach because this captures data features that share non-linear interactions with robust performance [45] and is a method consistent with strategies recommended by the Microarray Quality Control consortium [43]. This first data set — called the ‘training data-set’ — was used only once to select genes (Affymetrix probe-sets) and direction of gene expression change, and was then discarded from the project (Fig. 1). Expression differences of ~54,000 probe-sets were ranked using an empirical Bayesian statistic and a leave-one-out cross-validation (LOOCV) process (see “Materials and methods”). Probe-sets that targeted multiple genomic loci were removed and a 150 probe-set list, each gene having a nominal performance of 90 % or better, was selected for further study (Additional file 1). The extended list of probe-sets with a 70 % or better performance is also included in Additional file 1.Fig. 1


A novel multi-tissue RNA diagnostic of healthy ageing relates to cognitive health status.

Sood S, Gallagher IJ, Lunnon K, Rullman E, Keohane A, Crossland H, Phillips BE, Cederholm T, Jensen T, van Loon LJ, Lannfelt L, Kraus WE, Atherton PJ, Howard R, Gustafsson T, Hodges A, Timmons JA - Genome Biol. (2015)

Development, validation and clinical application of ageing diagnostic. Overview of the selection process and use of RNA probe-sets for the development and validation of the healthy physiological age classifier. We identified useful probe-sets from a possible starting number of ~54,000 during step one [e.g. probe-sets with leave-one-out cross-validation (LOOCV) performance ≥ 90 %]. We then evaluated the performance of the top-ranked 150 probe-sets in a number of independent muscle, brain, and skin samples, demonstrating that the signature was diagnostic for age. We then applied the 150-probe-set healthy ageing signature to several clinical studies, as illustrated at the end of the workflow. Key features included discarding the training data set immediately after selecting the 150 probe-sets and relying on LOOCV and full external validation processes
© Copyright Policy - OpenAccess
Related In: Results  -  Collection

License 1 - License 2
Show All Figures
getmorefigures.php?uid=PMC4561473&req=5

Fig1: Development, validation and clinical application of ageing diagnostic. Overview of the selection process and use of RNA probe-sets for the development and validation of the healthy physiological age classifier. We identified useful probe-sets from a possible starting number of ~54,000 during step one [e.g. probe-sets with leave-one-out cross-validation (LOOCV) performance ≥ 90 %]. We then evaluated the performance of the top-ranked 150 probe-sets in a number of independent muscle, brain, and skin samples, demonstrating that the signature was diagnostic for age. We then applied the 150-probe-set healthy ageing signature to several clinical studies, as illustrated at the end of the workflow. Key features included discarding the training data set immediately after selecting the 150 probe-sets and relying on LOOCV and full external validation processes
Mentions: Our objective was to discover a pattern of RNA expression that could be reliably used as a biomarker for ‘health status’ in older subjects — one that differed substantially in terms of ability to stratify health, and one that was more informative than chronological age. We applied machine-learning methods to RNA expression data to distinguish between healthy 25-year-old and healthy 65-year-old individuals. We took a simple classifier approach [43] without ad hoc a priori filtering to identify a consistent set of RNA markers of ageing across tissue types because standard differential expression is unable to provide a common multi-tissue set of discriminatory RNA molecules [9]. We selected muscle tissue gene-chip profiles from 15 sedentary young and 15 sedentary older subjects with good aerobic fitness (Gene Expression Omnibus (GEO) accession [GSE59880]) [31, 44] and who were free of diabetes [42, 44]. Specifically, we utilized a k-nearest neighbor (kNN) classification approach because this captures data features that share non-linear interactions with robust performance [45] and is a method consistent with strategies recommended by the Microarray Quality Control consortium [43]. This first data set — called the ‘training data-set’ — was used only once to select genes (Affymetrix probe-sets) and direction of gene expression change, and was then discarded from the project (Fig. 1). Expression differences of ~54,000 probe-sets were ranked using an empirical Bayesian statistic and a leave-one-out cross-validation (LOOCV) process (see “Materials and methods”). Probe-sets that targeted multiple genomic loci were removed and a 150 probe-set list, each gene having a nominal performance of 90 % or better, was selected for further study (Additional file 1). The extended list of probe-sets with a 70 % or better performance is also included in Additional file 1.Fig. 1

Bottom Line: Using the Uppsala Longitudinal Study of Adult Men birth-cohort (n = 108) we demonstrate that the RNA classifier is insensitive to confounding lifestyle biomarkers, while greater gene score at age 70 years is independently associated with better renal function at age 82 years and longevity.We identify a novel and statistically robust multi-tissue RNA signature of human healthy ageing that can act as a diagnostic of future health, using only a peripheral blood sample.This RNA signature has great potential to assist research aimed at finding treatments for and/or management of AD and other ageing-related conditions.

View Article: PubMed Central - PubMed

Affiliation: XRGenomics Ltd, London, UK.

ABSTRACT

Background: Diagnostics of the human ageing process may help predict future healthcare needs or guide preventative measures for tackling diseases of older age. We take a transcriptomics approach to build the first reproducible multi-tissue RNA expression signature by gene-chip profiling tissue from sedentary normal subjects who reached 65 years of age in good health.

Results: One hundred and fifty probe-sets form an accurate classifier of young versus older muscle tissue and this healthy ageing RNA classifier performed consistently in independent cohorts of human muscle, skin and brain tissue (n = 594, AUC = 0.83-0.96) and thus represents a biomarker for biological age. Using the Uppsala Longitudinal Study of Adult Men birth-cohort (n = 108) we demonstrate that the RNA classifier is insensitive to confounding lifestyle biomarkers, while greater gene score at age 70 years is independently associated with better renal function at age 82 years and longevity. The gene score is 'up-regulated' in healthy human hippocampus with age, and when applied to blood RNA profiles from two large independent age-matched dementia case-control data sets (n = 717) the healthy controls have significantly greater gene scores than those with cognitive impairment. Alone, or when combined with our previously described prototype Alzheimer disease (AD) RNA 'disease signature', the healthy ageing RNA classifier is diagnostic for AD.

Conclusions: We identify a novel and statistically robust multi-tissue RNA signature of human healthy ageing that can act as a diagnostic of future health, using only a peripheral blood sample. This RNA signature has great potential to assist research aimed at finding treatments for and/or management of AD and other ageing-related conditions.

No MeSH data available.


Related in: MedlinePlus