Using molecular classification to predict gains in maximal aerobic capacity following endurance exercise training in humans

A low maximal oxygen consumption (V̇o 2max ) is a strong risk factor for premature mortality. Supervised endurance exercise training increases V̇o 2max with a very wide range of effectiveness in humans. Discovering the DNA variants that contribute to this heterogeneity typically requires substantial...

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Published inJournal of applied physiology (1985) Vol. 108; no. 6; pp. 1487 - 1496
Main Authors Timmons, James A., Knudsen, Steen, Rankinen, Tuomo, Koch, Lauren G., Sarzynski, Mark, Jensen, Thomas, Keller, Pernille, Scheele, Camilla, Vollaard, Niels B. J., Nielsen, Søren, Åkerström, Thorbjörn, MacDougald, Ormond A., Jansson, Eva, Greenhaff, Paul L., Tarnopolsky, Mark A., van Loon, Luc J. C., Pedersen, Bente K., Sundberg, Carl Johan, Wahlestedt, Claes, Britton, Steven L., Bouchard, Claude
Format Journal Article
LanguageEnglish
Published Bethesda, MD American Physiological Society 01.06.2010
Subjects
Biological and medical sciences
Biomarkers
Deoxyribonucleic acid
DNA
endurance training
Exercise
Family studies
Fundamental and applied biological sciences. Psychology
Gene expression
Gene mapping
Genetic diversity
Genetic variance
genotype
Genotype & phenotype
Heterogeneity
Humans
Male
Mortality
Muscle Proteins - genetics
NATURAL SCIENCES
NATURVETENSKAP
Oxygen consumption
Oxygen Consumption - genetics
personalized medicine
Phenotype
Physical Endurance - genetics
Physical Exertion - physiology
Physical Fitness - physiology
Physical training
Polymorphism
Polymorphism, Single Nucleotide - genetics
Regression analysis
Ribonucleic acid
Risk factors
RNA
Young Adult
Physical exercise
Human
Vertebrata
Physical training
Mammalia
Classification
endurance training
Genotype
Endurance
personalized medicine
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Abstract A low maximal oxygen consumption (V̇o 2max ) is a strong risk factor for premature mortality. Supervised endurance exercise training increases V̇o 2max with a very wide range of effectiveness in humans. Discovering the DNA variants that contribute to this heterogeneity typically requires substantial sample sizes. In the present study, we first use RNA expression profiling to produce a molecular classifier that predicts V̇o 2max training response. We then hypothesized that the classifier genes would harbor DNA variants that contributed to the heterogeneous V̇o 2max response. Two independent preintervention RNA expression data sets were generated ( n = 41 gene chips) from subjects that underwent supervised endurance training: one identified and the second blindly validated an RNA expression signature that predicted change in V̇o 2max (“predictor” genes). The HERITAGE Family Study ( n = 473) was used for genotyping. We discovered a 29-RNA signature that predicted V̇o 2max training response on a continuous scale; these genes contained ∼6 new single-nucleotide polymorphisms associated with gains in V̇o 2max in the HERITAGE Family Study. Three of four novel candidate genes from the HERITAGE Family Study were confirmed as RNA predictor genes (i.e., “reciprocal” RNA validation of a quantitative trait locus genotype), enhancing the performance of the 29-RNA-based predictor. Notably, RNA abundance for the predictor genes was unchanged by exercise training, supporting the idea that expression was preset by genetic variation. Regression analysis yielded a model where 11 single-nucleotide polymorphisms explained 23% of the variance in gains in V̇o 2max , corresponding to ∼50% of the estimated genetic variance for V̇o 2max . In conclusion, combining RNA profiling with single-gene DNA marker association analysis yields a strongly validated molecular predictor with meaningful explanatory power. V̇o 2max responses to endurance training can be predicted by measuring a ∼30-gene RNA expression signature in muscle prior to training. The general approach taken could accelerate the discovery of genetic biomarkers, sufficiently discrete for diagnostic purposes, for a range of physiological and pharmacological phenotypes in humans.
AbstractList A low maximal oxygen consumption (V̇ o 2max ) is a strong risk factor for premature mortality. Supervised endurance exercise training increases V̇ o 2max with a very wide range of effectiveness in humans. Discovering the DNA variants that contribute to this heterogeneity typically requires substantial sample sizes. In the present study, we first use RNA expression profiling to produce a molecular classifier that predicts V̇ o 2max training response. We then hypothesized that the classifier genes would harbor DNA variants that contributed to the heterogeneous V̇ o 2max response. Two independent preintervention RNA expression data sets were generated ( n = 41 gene chips) from subjects that underwent supervised endurance training: one identified and the second blindly validated an RNA expression signature that predicted change in V̇ o 2max (“predictor” genes). The HERITAGE Family Study ( n = 473) was used for genotyping. We discovered a 29-RNA signature that predicted V̇ o 2max training response on a continuous scale; these genes contained ∼6 new single-nucleotide polymorphisms associated with gains in V̇ o 2max in the HERITAGE Family Study. Three of four novel candidate genes from the HERITAGE Family Study were confirmed as RNA predictor genes (i.e., “reciprocal” RNA validation of a quantitative trait locus genotype), enhancing the performance of the 29-RNA-based predictor. Notably, RNA abundance for the predictor genes was unchanged by exercise training, supporting the idea that expression was preset by genetic variation. Regression analysis yielded a model where 11 single-nucleotide polymorphisms explained 23% of the variance in gains in V̇ o 2max , corresponding to ∼50% of the estimated genetic variance for V̇ o 2max . In conclusion, combining RNA profiling with single-gene DNA marker association analysis yields a strongly validated molecular predictor with meaningful explanatory power. V̇ o 2max responses to endurance training can be predicted by measuring a ∼30-gene RNA expression signature in muscle prior to training. The general approach taken could accelerate the discovery of genetic biomarkers, sufficiently discrete for diagnostic purposes, for a range of physiological and pharmacological phenotypes in humans.
A low maximal oxygen consumption (VO2max) is a strong risk factor for premature mortality. Supervised endurance exercise training increases VO2max with a very wide range of effectiveness in humans. Discovering the DNA variants that contribute to this heterogeneity typically requires substantial sample sizes. In the present study, we first use RNA expression profiling to produce a molecular classifier that predicts VO2max training response. We then hypothesized that the classifier genes would harbor DNA variants that contributed to the heterogeneous VO2max response. Two independent preintervention RNA expression data sets were generated (n=41 gene chips) from subjects that underwent supervised endurance training: one identified and the second blindly validated an RNA expression signature that predicted change in VO2max ("predictor" genes). The HERITAGE Family Study (n=473) was used for genotyping. We discovered a 29-RNA signature that predicted VO2max training response on a continuous scale; these genes contained approximately 6 new single-nucleotide polymorphisms associated with gains in VO2max in the HERITAGE Family Study. Three of four novel candidate genes from the HERITAGE Family Study were confirmed as RNA predictor genes (i.e., "reciprocal" RNA validation of a quantitative trait locus genotype), enhancing the performance of the 29-RNA-based predictor. Notably, RNA abundance for the predictor genes was unchanged by exercise training, supporting the idea that expression was preset by genetic variation. Regression analysis yielded a model where 11 single-nucleotide polymorphisms explained 23% of the variance in gains in VO2max, corresponding to approximately 50% of the estimated genetic variance for VO2max. In conclusion, combining RNA profiling with single-gene DNA marker association analysis yields a strongly validated molecular predictor with meaningful explanatory power. VO2max responses to endurance training can be predicted by measuring a approximately 30-gene RNA expression signature in muscle prior to training. The general approach taken could accelerate the discovery of genetic biomarkers, sufficiently discrete for diagnostic purposes, for a range of physiological and pharmacological phenotypes in humans.A low maximal oxygen consumption (VO2max) is a strong risk factor for premature mortality. Supervised endurance exercise training increases VO2max with a very wide range of effectiveness in humans. Discovering the DNA variants that contribute to this heterogeneity typically requires substantial sample sizes. In the present study, we first use RNA expression profiling to produce a molecular classifier that predicts VO2max training response. We then hypothesized that the classifier genes would harbor DNA variants that contributed to the heterogeneous VO2max response. Two independent preintervention RNA expression data sets were generated (n=41 gene chips) from subjects that underwent supervised endurance training: one identified and the second blindly validated an RNA expression signature that predicted change in VO2max ("predictor" genes). The HERITAGE Family Study (n=473) was used for genotyping. We discovered a 29-RNA signature that predicted VO2max training response on a continuous scale; these genes contained approximately 6 new single-nucleotide polymorphisms associated with gains in VO2max in the HERITAGE Family Study. Three of four novel candidate genes from the HERITAGE Family Study were confirmed as RNA predictor genes (i.e., "reciprocal" RNA validation of a quantitative trait locus genotype), enhancing the performance of the 29-RNA-based predictor. Notably, RNA abundance for the predictor genes was unchanged by exercise training, supporting the idea that expression was preset by genetic variation. Regression analysis yielded a model where 11 single-nucleotide polymorphisms explained 23% of the variance in gains in VO2max, corresponding to approximately 50% of the estimated genetic variance for VO2max. In conclusion, combining RNA profiling with single-gene DNA marker association analysis yields a strongly validated molecular predictor with meaningful explanatory power. VO2max responses to endurance training can be predicted by measuring a approximately 30-gene RNA expression signature in muscle prior to training. The general approach taken could accelerate the discovery of genetic biomarkers, sufficiently discrete for diagnostic purposes, for a range of physiological and pharmacological phenotypes in humans.
Timmons JA, Knudsen S, Rankinen T, Koch LG, Sarzynski M, Jensen T, Keller P, Scheele C, Vollaard NB, Nielsen S, Akerstrom T, MacDougald OA, Jansson E, Greenhaff PL, Tarnopolsky MA, van Loon LJ, Pedersen BK, Sundberg CJ, Wahlestedt C, Britton SL, Bouchard C. Using molecular classification to predict gains in maximal aerobic capacity following endurance exercise training in humans. J Appl Physiol 108: 1487-1496, 2010. First published February 4, 2010; doi:10.1152/japplphysiol.01295.2009.-A low maximal oxygen consumption ((V) over dotO(2max)) is a strong risk factor for premature mortality. Supervised endurance exercise training increases (V) over dotO(2max) with a very wide range of effectiveness in humans. Discovering the DNA variants that contribute to this heterogeneity typically requires substantial sample sizes. In the present study, we first use RNA expression profiling to produce a molecular classifier that predicts (V) over dotO(2max) training response. We then hypothesized that the classifier genes would harbor DNA variants that contributed to the heterogeneous (V) over dotO(2max) response. Two independent preintervention RNA expression data sets were generated (n = 41 gene chips) from subjects that underwent supervised endurance training: one identified and the second blindly validated an RNA expression signature that predicted change in (V) over dotO(2max) (""predictor"" genes). The HERITAGE Family Study (n = 473) was used for genotyping. We discovered a 29-RNA signature that predicted (V) over dotO(2max) training response on a continuous scale; these genes contained similar to 6 new single-nucleotide polymorphisms associated with gains in (V) over dotO(2max) in the HERITAGE Family Study. Three of four novel candidate genes from the HERITAGE Family Study were confirmed as RNA predictor genes (i.e., ""reciprocal"" RNA validation of a quantitative trait locus genotype), enhancing the performance of the 29-RNA-based predictor. Notably, RNA abundance for the predictor genes was unchanged by exercise training, supporting the idea that expression was preset by genetic variation. Regression analysis yielded a model where 11 single-nucleotide polymorphisms explained 23% of the variance in gains in (V) over dotO(2max), corresponding to similar to 50% of the estimated genetic variance for (V) over dotO(2max). In conclusion, combining RNA profiling with single-gene DNA marker association analysis yields a strongly validated molecular predictor with meaningful explanatory power. (V) over dotO(2max) responses to endurance training can be predicted by measuring a similar to 30-gene RNA expression signature in muscle prior to training. The general approach taken could accelerate the discovery of genetic biomarkers, sufficiently discrete for diagnostic purposes, for a range of physiological and pharmacological phenotypes in humans.
A low maximal oxygen consumption (VO2max) is a strong risk factor for premature mortality. Supervised endurance exercise training increases VO2max with a very wide range of effectiveness in humans. Discovering the DNA variants that contribute to this heterogeneity typically requires substantial sample sizes. In the present study, we first use RNA expression profiling to produce a molecular classifier that predicts VO2max training response. We then hypothesized that the classifier genes would harbor DNA variants that contributed to the heterogeneous VO2max response. Two independent preintervention RNA expression data sets were generated (n=41 gene chips) from subjects that underwent supervised endurance training: one identified and the second blindly validated an RNA expression signature that predicted change in VO2max ("predictor" genes). The HERITAGE Family Study (n=473) was used for genotyping. We discovered a 29-RNA signature that predicted VO2max training response on a continuous scale; these genes contained approximately 6 new single-nucleotide polymorphisms associated with gains in VO2max in the HERITAGE Family Study. Three of four novel candidate genes from the HERITAGE Family Study were confirmed as RNA predictor genes (i.e., "reciprocal" RNA validation of a quantitative trait locus genotype), enhancing the performance of the 29-RNA-based predictor. Notably, RNA abundance for the predictor genes was unchanged by exercise training, supporting the idea that expression was preset by genetic variation. Regression analysis yielded a model where 11 single-nucleotide polymorphisms explained 23% of the variance in gains in VO2max, corresponding to approximately 50% of the estimated genetic variance for VO2max. In conclusion, combining RNA profiling with single-gene DNA marker association analysis yields a strongly validated molecular predictor with meaningful explanatory power. VO2max responses to endurance training can be predicted by measuring a approximately 30-gene RNA expression signature in muscle prior to training. The general approach taken could accelerate the discovery of genetic biomarkers, sufficiently discrete for diagnostic purposes, for a range of physiological and pharmacological phenotypes in humans.
A low maximal oxygen consumption (...) is a strong risk factor for premature mortality. Supervised endurance exercise training increases ... with a very wide range of effectiveness in humans. Discovering the DNA variants that contribute to this heterogeneity typically requires substantial sample sizes. In the present study, we first use RNA expression profiling to produce a molecular classifier that predicts ... training response. We then hypothesized that the classifier genes would harbor DNA variants that contributed to the heterogeneous ... response. Two independent preintervention RNA expression data sets were generated (n = 41 gene chips) from subjects that underwent supervised endurance training: one identified and the second blindly validated an RNA expression signature that predicted change in ...("predictor" genes). The HERITAGE Family Study (n = 473) was used for genotyping. We discovered a 29- RNA signature that predicted ... training response on a continuous scale; these genes contained 6 new single-nucleotide polymorphisms associated with gains in ... in the HERITAGE Family Study. Three of four novel candidate genes from the HERITAGE Family Study were confirmed as RNA predictor genes (i.e., "reciprocal" RNA validation of a quantitative trait locus genotype), enhancing the performance of the 29-RNA-based predictor. Notably, RNA abundance for the predictor genes was unchanged by exercise training, supporting the idea that expression was preset by genetic variation. Regression analysis yielded a model where 11 single-nucleotide polymorphisms explained 23% of the variance in gains in ..., corresponding to 50% of the estimated genetic variance for ... In conclusion, combining RNA profiling with single-gene DNA marker association analysis yields a strongly validated molecular predictor with meaningful explanatory power. ... responses to endurance training can be predicted by measuring a 30-gene RNA expression signature in muscle prior to training. The general approach taken could accelerate the discovery of genetic biomarkers, sufficiently discrete for diagnostic purposes, for a range of physiological and pharmacological phenotypes in humans. (ProQuest: ... denotes formulae/symbols omitted.)
A low maximal oxygen consumption (V̇o 2max ) is a strong risk factor for premature mortality. Supervised endurance exercise training increases V̇o 2max with a very wide range of effectiveness in humans. Discovering the DNA variants that contribute to this heterogeneity typically requires substantial sample sizes. In the present study, we first use RNA expression profiling to produce a molecular classifier that predicts V̇o 2max training response. We then hypothesized that the classifier genes would harbor DNA variants that contributed to the heterogeneous V̇o 2max response. Two independent preintervention RNA expression data sets were generated ( n = 41 gene chips) from subjects that underwent supervised endurance training: one identified and the second blindly validated an RNA expression signature that predicted change in V̇o 2max (“predictor” genes). The HERITAGE Family Study ( n = 473) was used for genotyping. We discovered a 29-RNA signature that predicted V̇o 2max training response on a continuous scale; these genes contained ∼6 new single-nucleotide polymorphisms associated with gains in V̇o 2max in the HERITAGE Family Study. Three of four novel candidate genes from the HERITAGE Family Study were confirmed as RNA predictor genes (i.e., “reciprocal” RNA validation of a quantitative trait locus genotype), enhancing the performance of the 29-RNA-based predictor. Notably, RNA abundance for the predictor genes was unchanged by exercise training, supporting the idea that expression was preset by genetic variation. Regression analysis yielded a model where 11 single-nucleotide polymorphisms explained 23% of the variance in gains in V̇o 2max , corresponding to ∼50% of the estimated genetic variance for V̇o 2max . In conclusion, combining RNA profiling with single-gene DNA marker association analysis yields a strongly validated molecular predictor with meaningful explanatory power. V̇o 2max responses to endurance training can be predicted by measuring a ∼30-gene RNA expression signature in muscle prior to training. The general approach taken could accelerate the discovery of genetic biomarkers, sufficiently discrete for diagnostic purposes, for a range of physiological and pharmacological phenotypes in humans.
Author Jensen, Thomas
Bouchard, Claude
Koch, Lauren G.
Pedersen, Bente K.
MacDougald, Ormond A.
Timmons, James A.
Knudsen, Steen
Vollaard, Niels B. J.
van Loon, Luc J. C.
Keller, Pernille
Wahlestedt, Claes
Britton, Steven L.
Nielsen, Søren
Sarzynski, Mark
Åkerström, Thorbjörn
Rankinen, Tuomo
Greenhaff, Paul L.
Tarnopolsky, Mark A.
Sundberg, Carl Johan
Scheele, Camilla
Jansson, Eva
Author_xml – sequence: 1
  givenname: James A.
  surname: Timmons
  fullname: Timmons, James A.
  organization: Section of Systems Biology Research, Panum Institutet and Center for Healthy Ageing,, Royal Veterinary College, University of London, Camden, London,, The Wenner-Gren Institute, Arrhenius Laboratories, Stockholm University
– sequence: 2
  givenname: Steen
  surname: Knudsen
  fullname: Knudsen, Steen
  organization: Medical Prognosis Institute, Hørsholm, Denmark
– sequence: 3
  givenname: Tuomo
  surname: Rankinen
  fullname: Rankinen, Tuomo
  organization: Human Genomics Laboratory, Pennington Biomedical Research Center, Baton Rouge, Louisiana
– sequence: 4
  givenname: Lauren G.
  surname: Koch
  fullname: Koch, Lauren G.
  organization: Departments of 6Physical Medicine and Rehabilitation and
– sequence: 5
  givenname: Mark
  surname: Sarzynski
  fullname: Sarzynski, Mark
  organization: Human Genomics Laboratory, Pennington Biomedical Research Center, Baton Rouge, Louisiana
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  givenname: Thomas
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  fullname: Jensen, Thomas
  organization: Medical Prognosis Institute, Hørsholm, Denmark
– sequence: 7
  givenname: Pernille
  surname: Keller
  fullname: Keller, Pernille
  organization: Centre of Inflammation and Metabolism, Faculty of Health Sciences, University of Copenhagen, Copenhagen,, Molecular and Integrative Physiology, University of Michigan Medical School, Ann Arbor, Michigan
– sequence: 8
  givenname: Camilla
  surname: Scheele
  fullname: Scheele, Camilla
  organization: Centre of Inflammation and Metabolism, Faculty of Health Sciences, University of Copenhagen, Copenhagen,, The Wenner-Gren Institute, Arrhenius Laboratories, Stockholm University
– sequence: 9
  givenname: Niels B. J.
  surname: Vollaard
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  givenname: Søren
  surname: Nielsen
  fullname: Nielsen, Søren
  organization: Centre of Inflammation and Metabolism, Faculty of Health Sciences, University of Copenhagen, Copenhagen
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  surname: Åkerström
  fullname: Åkerström, Thorbjörn
  organization: Centre of Inflammation and Metabolism, Faculty of Health Sciences, University of Copenhagen, Copenhagen
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  givenname: Ormond A.
  surname: MacDougald
  fullname: MacDougald, Ormond A.
  organization: Molecular and Integrative Physiology, University of Michigan Medical School, Ann Arbor, Michigan
– sequence: 13
  givenname: Eva
  surname: Jansson
  fullname: Jansson, Eva
  organization: Department of Laboratory Medicine, Division of Clinical Physiology, Karolinska University Hospital
– sequence: 14
  givenname: Paul L.
  surname: Greenhaff
  fullname: Greenhaff, Paul L.
  organization: Centre for Integrated Systems Biology Medicine, University Medical School, Nottingham, United Kingdom
– sequence: 15
  givenname: Mark A.
  surname: Tarnopolsky
  fullname: Tarnopolsky, Mark A.
  organization: Department of Paediatrics and Medicine (Neurology and Rehabilitation), McMaster University Medical Centre, Hamilton, Ontario, Canada; and
– sequence: 16
  givenname: Luc J. C.
  surname: van Loon
  fullname: van Loon, Luc J. C.
  organization: Department of Human Movement Sciences, Nutrition and Toxicology Research Institute Maastricht, Maastricht University Medical Centre, Maastricht, The Netherlands
– sequence: 17
  givenname: Bente K.
  surname: Pedersen
  fullname: Pedersen, Bente K.
  organization: Centre of Inflammation and Metabolism, Faculty of Health Sciences, University of Copenhagen, Copenhagen
– sequence: 18
  givenname: Carl Johan
  surname: Sundberg
  fullname: Sundberg, Carl Johan
  organization: Department of Physiology and Pharmacology, Karolinska Institutet, Stockholm, Sweden
– sequence: 19
  givenname: Claes
  surname: Wahlestedt
  fullname: Wahlestedt, Claes
  organization: Molecular and Integrative Neurosciences Department, The Scripps Research Institute, Jupiter, Florida
– sequence: 20
  givenname: Steven L.
  surname: Britton
  fullname: Britton, Steven L.
  organization: Departments of 6Physical Medicine and Rehabilitation and, Molecular and Integrative Physiology, University of Michigan Medical School, Ann Arbor, Michigan
– sequence: 21
  givenname: Claude
  surname: Bouchard
  fullname: Bouchard, Claude
  organization: Human Genomics Laboratory, Pennington Biomedical Research Center, Baton Rouge, Louisiana
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https://www.ncbi.nlm.nih.gov/pubmed/20133430$$D View this record in MEDLINE/PubMed
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Snippet A low maximal oxygen consumption (V̇o 2max ) is a strong risk factor for premature mortality. Supervised endurance exercise training increases V̇o 2max with a...
A low maximal oxygen consumption (VO2max) is a strong risk factor for premature mortality. Supervised endurance exercise training increases VO2max with a very...
A low maximal oxygen consumption (...) is a strong risk factor for premature mortality. Supervised endurance exercise training increases ... with a very wide...
A low maximal oxygen consumption (V̇ o 2max ) is a strong risk factor for premature mortality. Supervised endurance exercise training increases V̇ o 2max with...
Timmons JA, Knudsen S, Rankinen T, Koch LG, Sarzynski M, Jensen T, Keller P, Scheele C, Vollaard NB, Nielsen S, Akerstrom T, MacDougald OA, Jansson E,...
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SubjectTerms Biological and medical sciences
Biomarkers
Deoxyribonucleic acid
DNA
endurance training
Exercise
Family studies
Fundamental and applied biological sciences. Psychology
Gene expression
Gene mapping
Genetic diversity
Genetic variance
genotype
Genotype & phenotype
Heterogeneity
Humans
Male
Mortality
Muscle Proteins - genetics
NATURAL SCIENCES
NATURVETENSKAP
Oxygen consumption
Oxygen Consumption - genetics
personalized medicine
Phenotype
Physical Endurance - genetics
Physical Exertion - physiology
Physical Fitness - physiology
Physical training
Polymorphism
Polymorphism, Single Nucleotide - genetics
Regression analysis
Ribonucleic acid
Risk factors
RNA
Young Adult
Title Using molecular classification to predict gains in maximal aerobic capacity following endurance exercise training in humans
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