Literature DB >> 23713866

Metabolic phenotype modulation by caloric restriction in a lifelong dog study.

Selena E Richards1, Yulan Wang, Sandrine P Claus, Dennis Lawler, Sunil Kochhar, Elaine Holmes, Jeremy K Nicholson.   

Abstract

Modeling aging and age-related pathologies presents a substantial analytical challenge given the complexity of gene-environment influences and interactions operating on an individual. A top-down systems approach is used to model the effects of lifelong caloric restriction, which is known to extend life span in several animal models. The metabolic phenotypes of caloric-restricted (CR; n = 24) and pair-housed control-fed (CF; n = 24) Labrador Retriever dogs were investigated by use of orthogonal projection to latent structures discriminant analysis (OPLS-DA) to model both generic and age-specific responses to caloric restriction from the ¹H NMR blood serum profiles of young and older dogs. Three aging metabolic phenotypes were resolved: (i) an aging metabolic phenotype independent of diet, characterized by high levels of glutamine, creatinine, methylamine, dimethylamine, trimethylamine N-oxide, and glycerophosphocholine and decreasing levels of glycine, aspartate, creatine and citrate indicative of metabolic changes associated largely with muscle mass; (ii) an aging metabolic phenotype specific to CR dogs that consisted of relatively lower levels of glucose, acetate, choline, and tyrosine and relatively higher serum levels of phosphocholine with increased age in the CR population; (iii) an aging metabolic phenotype specific to CF dogs including lower levels of liproprotein fatty acyl groups and allantoin and relatively higher levels of formate with increased age in the CF population. There was no diet metabotype that consistently differentiated the CF and CR dogs irrespective of age. Glucose consistently discriminated between feeding regimes in dogs (≥312 weeks), being relatively lower in the CR group. However, it was observed that creatine and amino acids (valine, leucine, isoleucine, lysine, and phenylalanine) were lower in the CR dogs (<312 weeks), suggestive of differences in energy source utilization. ¹H NMR spectroscopic analysis of longitudinal serum profiles enabled an unbiased evaluation of the metabolic markers modulated by a lifetime of caloric restriction and showed differences in the metabolic phenotype of aging due to caloric restriction, which contributes to longevity studies in caloric-restricted animals. Furthermore, OPLS-DA provided a framework such that significant metabolites relating to life extension could be differentiated and integrated with aging processes.

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Year:  2013        PMID: 23713866     DOI: 10.1021/pr301097k

Source DB:  PubMed          Journal:  J Proteome Res        ISSN: 1535-3893            Impact factor:   4.466


  7 in total

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Journal:  Free Radic Biol Med       Date:  2017-02-14       Impact factor: 7.376

Review 2.  The dog aging project: translational geroscience in companion animals.

Authors:  Matt Kaeberlein; Kate E Creevy; Daniel E L Promislow
Journal:  Mamm Genome       Date:  2016-05-03       Impact factor: 2.957

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Journal:  Metabolomics       Date:  2019-09-06       Impact factor: 4.290

4.  Decreased fasting serum glucogenic amino acids with a higher compared to normal protein diet during energy restriction in women: a randomized controlled trial.

Authors:  A R Ogilvie; M Watford; G Wu; D Sukumar; J Kwon; S A Shapses
Journal:  Amino Acids       Date:  2021-08-02       Impact factor: 3.520

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Journal:  Aging Cell       Date:  2015-06-18       Impact factor: 9.304

6.  1H-NMR-based metabolomic analysis of cerebrospinal fluid from adult bilateral moyamoya disease: comparison with unilateral moyamoya disease and atherosclerotic stenosis.

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Journal:  Medicine (Baltimore)       Date:  2015-05       Impact factor: 1.889

7.  Predominant gut Lactobacillus murinus strain mediates anti-inflammaging effects in calorie-restricted mice.

Authors:  Fengwei Pan; Liying Zhang; Min Li; Yingxin Hu; Benhua Zeng; Huijuan Yuan; Liping Zhao; Chenhong Zhang
Journal:  Microbiome       Date:  2018-03-21       Impact factor: 14.650

  7 in total

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