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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

Distribution of healthy ageing gene score in ULSAM samples and its relation with clinical parameters. At the date of assessment (1992), when the muscle biopsy was taken for subsequent gene-chip profiling, all subjects were considered in reasonable health for their age and remained physically active. a Distribution of gene score based on the median rank for each of the 150 genes (see “Materials and methods”). b Clinical variables were determined as previously reported for ULSAM samples (chronological age = 69–70 years) [71, 101]. Linear regression was used to examine the relationship between the healthy ageing gene score at ~70 years and a variety of clinical parameters at age ~70 years. No relationship between baseline gene score and renal function (estimated from cystatin C, r2 < 0.001), systolic blood pressure (mmHg, r2 = 0.0013), 2 h glucose concentration following a standard oral glucose tolerance test (OGTT; mmol, r2 = 0.015) or total cholesterol (mmol, r2 = 0.002) was observed. Gene score was also unrelated to resting heart rate or physical activity questionnaire, and thus habitual exercise status. In fact the healthy ageing gene score was not correlated with any conventional risk factors (as listed in Additional file 2)
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Fig3: Distribution of healthy ageing gene score in ULSAM samples and its relation with clinical parameters. At the date of assessment (1992), when the muscle biopsy was taken for subsequent gene-chip profiling, all subjects were considered in reasonable health for their age and remained physically active. a Distribution of gene score based on the median rank for each of the 150 genes (see “Materials and methods”). b Clinical variables were determined as previously reported for ULSAM samples (chronological age = 69–70 years) [71, 101]. Linear regression was used to examine the relationship between the healthy ageing gene score at ~70 years and a variety of clinical parameters at age ~70 years. No relationship between baseline gene score and renal function (estimated from cystatin C, r2 < 0.001), systolic blood pressure (mmHg, r2 = 0.0013), 2 h glucose concentration following a standard oral glucose tolerance test (OGTT; mmol, r2 = 0.015) or total cholesterol (mmol, r2 = 0.002) was observed. Gene score was also unrelated to resting heart rate or physical activity questionnaire, and thus habitual exercise status. In fact the healthy ageing gene score was not correlated with any conventional risk factors (as listed in Additional file 2)

Mentions: In order to examine specificity for ‘healthy ageing’, we examined the relationship between the classifier genes, chronological age and markers of lifestyle-associated genes. We collapsed the expression pattern of all genes into a single score for each sample (see “Materials and methods”). The distribution of scores was examined for ~70-year-old males (subjects born in Uppsala within a 1-year period) and the gene ranking score was also correlated with markers of lifestyle-associated disease (Fig. 3). The gene expression profiles from 108 muscle samples from ~70-year-old male subjects from the Uppsala Longitudinal Study of Adult Men (ULSAM) cohort [51] were produced using Affymetrix arrays (Human Exon 1.0 ST Array). We ranked each subject for each of the 150 genes, taking the direction of gene expression change from the original classifier model into account (85 % down-regulated; see “Materials and methods”). We then converted the individual gene rankings into a summed median gene score for each subject. We demonstrated that despite all subjects being ~70 years of age at the time of the RNA sample, there was a very wide distribution in gene score (Fig. 3a). Thus, the healthy ageing gene score in muscle was very distinct from chronological age. The healthy ageing gene score was regressed against a variety of continuous clinical variables (variables listed in Additional file 2). The gene score at chronological age ~70 years was unrelated to conventional lifestyle regulated biomarkers (e.g., blood pressure, glucose, cholesterol, or renal function; Fig. 3b). This confirmed that the 150 gene expression markers were not reflecting a variety of lifestyle regulated biomarkers and diseases (e.g., exercise, diabetes) and tissue ‘healthy ageing status’ could not be derived from a simpler clinical biomarker.Fig. 3


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)

Distribution of healthy ageing gene score in ULSAM samples and its relation with clinical parameters. At the date of assessment (1992), when the muscle biopsy was taken for subsequent gene-chip profiling, all subjects were considered in reasonable health for their age and remained physically active. a Distribution of gene score based on the median rank for each of the 150 genes (see “Materials and methods”). b Clinical variables were determined as previously reported for ULSAM samples (chronological age = 69–70 years) [71, 101]. Linear regression was used to examine the relationship between the healthy ageing gene score at ~70 years and a variety of clinical parameters at age ~70 years. No relationship between baseline gene score and renal function (estimated from cystatin C, r2 < 0.001), systolic blood pressure (mmHg, r2 = 0.0013), 2 h glucose concentration following a standard oral glucose tolerance test (OGTT; mmol, r2 = 0.015) or total cholesterol (mmol, r2 = 0.002) was observed. Gene score was also unrelated to resting heart rate or physical activity questionnaire, and thus habitual exercise status. In fact the healthy ageing gene score was not correlated with any conventional risk factors (as listed in Additional file 2)
© Copyright Policy - OpenAccess
Related In: Results  -  Collection

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

Fig3: Distribution of healthy ageing gene score in ULSAM samples and its relation with clinical parameters. At the date of assessment (1992), when the muscle biopsy was taken for subsequent gene-chip profiling, all subjects were considered in reasonable health for their age and remained physically active. a Distribution of gene score based on the median rank for each of the 150 genes (see “Materials and methods”). b Clinical variables were determined as previously reported for ULSAM samples (chronological age = 69–70 years) [71, 101]. Linear regression was used to examine the relationship between the healthy ageing gene score at ~70 years and a variety of clinical parameters at age ~70 years. No relationship between baseline gene score and renal function (estimated from cystatin C, r2 < 0.001), systolic blood pressure (mmHg, r2 = 0.0013), 2 h glucose concentration following a standard oral glucose tolerance test (OGTT; mmol, r2 = 0.015) or total cholesterol (mmol, r2 = 0.002) was observed. Gene score was also unrelated to resting heart rate or physical activity questionnaire, and thus habitual exercise status. In fact the healthy ageing gene score was not correlated with any conventional risk factors (as listed in Additional file 2)
Mentions: In order to examine specificity for ‘healthy ageing’, we examined the relationship between the classifier genes, chronological age and markers of lifestyle-associated genes. We collapsed the expression pattern of all genes into a single score for each sample (see “Materials and methods”). The distribution of scores was examined for ~70-year-old males (subjects born in Uppsala within a 1-year period) and the gene ranking score was also correlated with markers of lifestyle-associated disease (Fig. 3). The gene expression profiles from 108 muscle samples from ~70-year-old male subjects from the Uppsala Longitudinal Study of Adult Men (ULSAM) cohort [51] were produced using Affymetrix arrays (Human Exon 1.0 ST Array). We ranked each subject for each of the 150 genes, taking the direction of gene expression change from the original classifier model into account (85 % down-regulated; see “Materials and methods”). We then converted the individual gene rankings into a summed median gene score for each subject. We demonstrated that despite all subjects being ~70 years of age at the time of the RNA sample, there was a very wide distribution in gene score (Fig. 3a). Thus, the healthy ageing gene score in muscle was very distinct from chronological age. The healthy ageing gene score was regressed against a variety of continuous clinical variables (variables listed in Additional file 2). The gene score at chronological age ~70 years was unrelated to conventional lifestyle regulated biomarkers (e.g., blood pressure, glucose, cholesterol, or renal function; Fig. 3b). This confirmed that the 150 gene expression markers were not reflecting a variety of lifestyle regulated biomarkers and diseases (e.g., exercise, diabetes) and tissue ‘healthy ageing status’ could not be derived from a simpler clinical biomarker.Fig. 3

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