Limits...
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

ROC curves showing predictive performance of the healthy ageing classifier based on LOOCV (kNN = 5) for muscle, brain, and skin. Using only the 150 probe-sets identified in the first stage of the project, this ‘healthy ageing classifier’ was able to correctly classify young and old samples across independent data sets with an accuracy of ~96 %, 91 %, 85 %, and 78 %. We present two examples of independent muscle data [48, 50] and one example each for human brain [50] and skin data [11] with areas under the curve of 0.99, 0.94, 0.78, and 0.85, respectively, reflecting excellent separation of the age groups and hence accurate multi-tissue performance
© Copyright Policy - OpenAccess
Related In: Results  -  Collection

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

Fig2: ROC curves showing predictive performance of the healthy ageing classifier based on LOOCV (kNN = 5) for muscle, brain, and skin. Using only the 150 probe-sets identified in the first stage of the project, this ‘healthy ageing classifier’ was able to correctly classify young and old samples across independent data sets with an accuracy of ~96 %, 91 %, 85 %, and 78 %. We present two examples of independent muscle data [48, 50] and one example each for human brain [50] and skin data [11] with areas under the curve of 0.99, 0.94, 0.78, and 0.85, respectively, reflecting excellent separation of the age groups and hence accurate multi-tissue performance

Mentions: We checked that the 150 RNAs were not differentially expressed to any measurable extent in human muscle by exercise or a number of other common diseases that impact on skeletal muscle, using our previously published gene-chip data [8, 31, 44, 46]. We later confirmed this lack of association with lifestyle disease using a sensitive gene-set approach. Use of fully independent training and validation data sets allows for genuine external validation to be demonstrated (see “Materials and methods”). Using the ‘Campbell’ muscle data set [GEO:GSE9419] [47] as the samples of known identity, we demonstrated that additional young and old muscle samples selected from four additional muscle data sets (‘Trappe’ [GEO:GSE28422] [48], ‘Hoffman’ [GEO:GSE38718] [49], and ‘Kraus’ [GEO:GSE47969] and ‘Derby’ [GEO:GSE47881] [8]) could be classified with an average ~93 % accuracy (70–100 %) using only the 150 probe-sets selected at the start of the project. Substitution of the Campbell data set with the other muscle data sets worked equally as well. These data shared a common microarray platform (Affymetrix HGU133plus2) but, as we demonstrate below, the classifier remains robust in the face of alternative platforms. Receiver operating characteristic (ROC) curves for kNN = 5 demonstrating classifier performance for a number of tissue types are presented in Fig. 2.Fig. 2


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)

ROC curves showing predictive performance of the healthy ageing classifier based on LOOCV (kNN = 5) for muscle, brain, and skin. Using only the 150 probe-sets identified in the first stage of the project, this ‘healthy ageing classifier’ was able to correctly classify young and old samples across independent data sets with an accuracy of ~96 %, 91 %, 85 %, and 78 %. We present two examples of independent muscle data [48, 50] and one example each for human brain [50] and skin data [11] with areas under the curve of 0.99, 0.94, 0.78, and 0.85, respectively, reflecting excellent separation of the age groups and hence accurate multi-tissue performance
© Copyright Policy - OpenAccess
Related In: Results  -  Collection

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

Fig2: ROC curves showing predictive performance of the healthy ageing classifier based on LOOCV (kNN = 5) for muscle, brain, and skin. Using only the 150 probe-sets identified in the first stage of the project, this ‘healthy ageing classifier’ was able to correctly classify young and old samples across independent data sets with an accuracy of ~96 %, 91 %, 85 %, and 78 %. We present two examples of independent muscle data [48, 50] and one example each for human brain [50] and skin data [11] with areas under the curve of 0.99, 0.94, 0.78, and 0.85, respectively, reflecting excellent separation of the age groups and hence accurate multi-tissue performance
Mentions: We checked that the 150 RNAs were not differentially expressed to any measurable extent in human muscle by exercise or a number of other common diseases that impact on skeletal muscle, using our previously published gene-chip data [8, 31, 44, 46]. We later confirmed this lack of association with lifestyle disease using a sensitive gene-set approach. Use of fully independent training and validation data sets allows for genuine external validation to be demonstrated (see “Materials and methods”). Using the ‘Campbell’ muscle data set [GEO:GSE9419] [47] as the samples of known identity, we demonstrated that additional young and old muscle samples selected from four additional muscle data sets (‘Trappe’ [GEO:GSE28422] [48], ‘Hoffman’ [GEO:GSE38718] [49], and ‘Kraus’ [GEO:GSE47969] and ‘Derby’ [GEO:GSE47881] [8]) could be classified with an average ~93 % accuracy (70–100 %) using only the 150 probe-sets selected at the start of the project. Substitution of the Campbell data set with the other muscle data sets worked equally as well. These data shared a common microarray platform (Affymetrix HGU133plus2) but, as we demonstrate below, the classifier remains robust in the face of alternative platforms. Receiver operating characteristic (ROC) curves for kNN = 5 demonstrating classifier performance for a number of tissue types are presented in Fig. 2.Fig. 2

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