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Healthy aging and muscle function are positively associated with NAD+ abundance in humans

Nature Aging volume 2, pages 254–263 (2022)Cite this article

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Abstract

Skeletal muscle is greatly affected by aging, resulting in a loss of metabolic and physical function. However, the underlying molecular processes and how (lack of) physical activity is involved in age-related metabolic decline in muscle function in humans is largely unknown. Here, we compared, in a cross-sectional study, the muscle metabolome from young to older adults, whereby the older adults were exercise trained, had normal physical activity levels or were physically impaired. Nicotinamide adenine dinucleotide (NAD+) was one of the most prominent metabolites that was lower in older adults, in line with preclinical models. This lower level was even more pronounced in impaired older individuals, and conversely, exercise-trained older individuals had NAD+ levels that were more similar to those found in younger individuals. NAD+ abundance positively correlated with average number of steps per day and mitochondrial and muscle functioning. Our work suggests that a clear association exists between NAD+ and health status in human aging.

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Fig. 1: The metabolome of human muscle aging.
Fig. 2: Muscle NAD+ levels are related to muscle health during aging.
Fig. 3: Major NAD+ metabolites and changes in healthy muscle aging.
Fig. 4: Molecular–physiological and NAD+-related healthy aging network.

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Data availability

Metabolomics data are available as supplementary materials accompanying this article as both summary statistics and processed abundance values per individual (statistical Source Data). Physiological data from this cohort have been reported in our previous study as part of a different analysis25. All other data supporting the findings of this study are available from the corresponding author upon reasonable request.

Code availability

Code supporting the findings of this study are available from the corresponding author upon reasonable request

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Acknowledgements

L.G., J.H. and P.S. are financially supported by the TI Food and Nutrition (TIFN) research program Mitochondrial Health (ALWTF.2015.5) and the Netherlands Organization for Scientific Research. Work in the Houtkooper group is financially supported by the European Research Council (starting grant 638290), ZonMw (Vidi grant 91715305) and Velux Stiftung (grant 1063). G.E.J. is supported by a Veni grant from ZonMw and an Amsterdam Gastroenterology Endocrinology Metabolism (AGEM) Talent grant. R.Z.P. is supported by a postdoctoral grant from the European Union’s Horizon 2020 research and innovation program under Marie Skłodowska-Curie grant agreement 840110. The funders had no role in data collection and analysis or decision to publish.

Author information

Author notes

  1. These authors contributed equally: Georges E. Janssens, Lotte Grevendonk.

Authors and Affiliations

  1. Laboratory Genetic Metabolic Diseases, Amsterdam UMC, University of Amsterdam, Amsterdam Gastroenterology, Endocrinology, and Metabolism, Amsterdam Cardiovascular Sciences, Amsterdam, The Netherlands

    Georges E. Janssens, Ruben Zapata Perez, Bauke V. Schomakers, Michel van Weeghel & Riekelt H. Houtkooper

  2. Department of Nutrition and Movement Sciences, NUTRIM School of Nutrition and Translational Research in Metabolism, Maastricht University, Maastricht, the Netherlands

    Lotte Grevendonk, Patrick Schrauwen & Joris Hoeks

  3. TI Food and Nutrition, Wageningen, the Netherlands

    Lotte Grevendonk, Patrick Schrauwen & Joris Hoeks

  4. Core Facility Metabolomics, Amsterdam UMC, University of Amsterdam, Amsterdam, the Netherlands

    Bauke V. Schomakers & Michel van Weeghel

  5. Danone Nutricia Research, Utrecht, the Netherlands

    Johan de Vogel-van den Bosch

  6. FrieslandCampina, Amersfoort, the Netherlands

    Jan M. W. Geurts

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Contributions

G.E.J., L.G., P.S., J.H. and R.H.H. conceived the study. L.G. designed and performed the human cohort characterization and experiments. G.E.J. designed and performed the bioinformatics analyses. R.Z.P., B.V.S., and M.v.W. performed the metabolomics analyses. J.M.W.G. and J.d.V.-v.d.B. reviewed the manuscript. G.E.J., L.G., R.Z.P., B.V.S., P.S., R.H.H. and J.H. interpreted the results and wrote the manuscript with contributions from all other authors.

Corresponding authors

Correspondence to Riekelt H. Houtkooper or Joris Hoeks.

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Competing interests

J.M.W.G. and J.d.V.-v.d.B. are affiliated with FrieslandCampina and Danone Nutricia Research, respectively, which sponsored the TI Food and Nutrition (TIFN) program and partly financed the project that led to human sample collection. They had no role in data collection, analysis, or decision to publish. The remaining authors declare no competing interests.

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Nature Aging thanks Nicholas Rattray, Paul Coen, and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Extended data

Extended Data Fig. 1 Global metabolomics changes of human muscle aging.

(a) Principal Component Analysis (PCA) of metabolomes of young and older individuals possessing equal physical activity levels (‘young’ vs ‘normal older adults’). (b) Volcano plot of fold change (x axis, log2 scale) versus p value (y axis, -log10 scale) for older adults (n = 17) compared to young individuals (n = 12) with equal physical activity levels, illustrating significantly lower (blue) or higher (red) metabolites with age. The horizontal line indicates significance (p < 0.05). Significance was determined using an empirical Bayes moderated t test (two-sided, p values adjusted for multiple comparisons between groups). Source data: Statistical_Source_Data.csv, all exact p values for comparison between groups are listed therein.

Source data

Extended Data Fig. 2 Comparison of age-related changes in each age group.

(a) Volcano plots of fold change (x axis, log2 scale) versus p value (y axis, -log10 scale) for trained older adults (top; n = 17), and physically impaired older adults (bottom; n = 6) compared to young individuals (n = 12), illustrating significantly depleted (blue) or accumulated (red) metabolites with age. Line indicates significance (p < 0.05). Significance was determined using an empirical Bayes moderated t test (two-sided, p values adjusted for multiple comparisons between groups). (b) Venn diagram of the overlap of significantly higher or lower abundances of metabolites in each aged group (trained older adults, older adults with normal physical activity levels, physically impaired older adults) compared to young individuals (p < 0.05). Source data: Statistical_Source_Data.csv, all exact p values for comparison between groups are listed therein.

Source data

Extended Data Fig. 3 NAD+-related metabolites of healthy aging groups.

(a-l) Abundance levels of NAD + -related metabolites in the four muscle health groups, for (A) ADP-ribose, (B) Kynurenic acid, (C) Kynurenine, (D) Methyl-NAM, (E) NAD + , (F) NADH, (G) NADP + , (H) NADPH, (I) Nicotinamide mononucleotide (NMN), (J) Dihydronicotinamide riboside (NRH), (K) Ribose-5P, and (L) Tryptophan. Sample sizes are: young n = 12, older adults; trained n = 17, Normal=17, impaired=6. Significance was determined using an empirical Bayes moderated t test (two-sided, p values adjusted for multiple comparisons between groups, * p < 0.05, ** p < 0.01, *** p < 0.001, n.s. = not significant). Boxplots: Inner line within the box is the median of the data, the box extends to the upper and lower quartile of the dataset (25% of the data above and below the median), whiskers (dashed lines) represent up to 1.5 times the upper or lower quartiles, circles beyond the whisker represent individual data points outside this range. Source data: Statistical_Source_Data.csv, all exact p values for comparison between groups are listed therein.

Source data

Extended Data Fig. 4 NAD+/ NADH ratio, and the glutathione/oxiglutathion oxidative stress pathway.

(a) NAD + to NADH ratio (log2 scale) in young and older adults belonging to trained, normal, and impaired aging groups. (b) The glutathione – oxliglutathione oxidative stress pathway for metabolites measured in this study. Glutamate and glycine feed into glutathione production. Conversion of glutathione to oxiglutathione results in quenching of free radicals, whereby the ratio of glutathione to oxiglutathione is indicative of this process. A byproduct of this pathway is ophthalmic acid (data presented in Fig. 2c). Data suggests an increase of the oxidative milieu in the aged groups relative to young. Sample sizes are: young n = 12, older adults; trained n = 17, Normal=17, impaired=6. Significance was determined using an empirical Bayes moderated t test (two-sided, p values adjusted for multiple comparisons between groups, * p < 0.05, ** p < 0.01, n.s. = not significant). Boxplots: Inner line within the box is the median of the data, the box extends to the upper and lower quartile of the dataset (25% of the data above and below the median), whiskers (dashed lines) represent up to 1.5 times the upper or lower quartiles, circles beyond the whisker represent individual data points outside this range. Source data: Statistical_Source_Data.csv, all exact p values for comparison between groups are listed therein.

Source data

Extended Data Fig. 5 Correlation of molecular–physiological phenotypes with metabolites involved in NAD+ synthesis.

(a) Correlation matrix comparing the paired metabolome (dark blue) and muscle health parameters (light blue) in the older adults (Pearson’s product-moment correlation coefficient, sample size is all older adults, n = 40). The scale ranges from blue (negative correlation), to yellow (no correlation), to red (positive correlation). Inset and cartoon: correlation between metabolites and muscle health parameters are used to reconstruct the network. (b) Abbreviations and measurements of the molecular-physiological phenotypes assessed in this analysis. Note: MOGS3, state 3 respiration upon malate + octanoyl-carnitine + glutamate + succinate, MGS3, state 3 respiration upon malate + glutamate + succinate. Source data: Statistical_Source_Data.csv and physiological data is available in our previous study25.

Source data

Supplementary information

Source data

Source Data Fig. 1

CSV file of raw metabolomics data and group statistics underlying the figure.

Source Data Fig. 2

CSV file of raw metabolomics data and group statistics underlying the figure.

Source Data Fig. 3

CSV file of raw metabolomics data and group statistics underlying the figure.

Source Data Fig. 4

CSV file of raw metabolomics data and group statistics underlying the figure (the original population characteristics publication is available at 10.1038/s41467-021-24956-2).

Source Data Extended Data Fig. 1

CSV file of raw metabolomics data and group statistics underlying the figure.

Source Data Extended Data Fig. 2

CSV file of raw metabolomics data and group statistics underlying the figure.

Source Data Extended Data Fig. 3

CSV file of raw metabolomics data and group statistics underlying the figure.

Source Data Extended Data Fig. 4

CSV file of raw metabolomics data and group statistics underlying the figure.

Source Data Extended Data Fig. 5

CSV file of raw metabolomics data and group statistics underlying the figure.

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Janssens, G.E., Grevendonk, L., Perez, R.Z. et al. Healthy aging and muscle function are positively associated with NAD+ abundance in humans. Nat Aging 2, 254–263 (2022). https://doi.org/10.1038/s43587-022-00174-3

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