Literature DB >> 19106317

Lipid metabolism predicts changes in body composition during energy restriction in overweight humans.

Jennifer T Smilowitz1, Michelle M Wiest, Steven M Watkins, Dorothy Teegarden, Michael B Zemel, J Bruce German, Marta D Van Loan.   

Abstract

Dietary weight loss regimens could be more effective by selectively targeting adipose while sparing lean mass (LM) if predictive information about individuals' lipid metabolic responses to an intervention were available. The objective of this study was to examine the relationships among changes in 4 anthropometric outcomes, weight, waist circumference (WC), percent body fat (BF), and percent LM, and comprehensive circulating lipid metabolites in response to energy reduction in overweight participants. This was a cohort study (n = 46) from a larger multi-center (n = 105) weight loss trial. We used stepwise regression to examine relationships among baseline plasma fatty acids of 7 lipid classes, biochemical metabolites, and diet to explain the variance of 4 anthropometric outcomes after intervention. No predictor variables explained the variance in the percent change in body weight. The circulating concentration of FFA 18:1(n-9) at baseline explained 31% of the variance in percent change of WC, with adjustment for energy intake at 12 wk. Circulating concentrations of phosphatidylcholine 18:0 and FFA 18:1(n-9) at baseline together explained 33% of the variance in percent LM change. The circulating concentration of phosphatidylcholine 18:0 at baseline explained 23% of the variance in the change in percent BF. This study determined relationships among comprehensive and quantitative measurements of complex lipid metabolites and metabolic outcomes as changes in body composition. Measurements of plasma circulating metabolites explained 20-30% of the variance in changes in body composition after a weight loss intervention. Thus, circulating lipids reflect lipid metabolism in relation to changes in body composition.

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Year:  2008        PMID: 19106317     DOI: 10.3945/jn.108.095364

Source DB:  PubMed          Journal:  J Nutr        ISSN: 0022-3166            Impact factor:   4.798


  7 in total

1.  Metabolomics identifies changes in fatty acid and amino acid profiles in serum of overweight older adults following a weight loss intervention.

Authors:  A Perez-Cornago; L Brennan; I Ibero-Baraibar; H H M Hermsdorff; A O'Gorman; M A Zulet; J Alfredo Martínez
Journal:  J Physiol Biochem       Date:  2014-01-09       Impact factor: 4.158

Review 2.  Nutrigenomics and personalized diets: What will they mean for food?

Authors:  J Bruce German; Angela M Zivkovic; David C Dallas; Jennifer T Smilowitz
Journal:  Annu Rev Food Sci Technol       Date:  2011

Review 3.  Calorie restriction and its impact on gut microbial composition and global metabolism.

Authors:  Xiaojiao Zheng; Shouli Wang; Wei Jia
Journal:  Front Med       Date:  2018-11-16       Impact factor: 4.592

Review 4.  Nutritional lipidomics: molecular metabolism, analytics, and diagnostics.

Authors:  Jennifer T Smilowitz; Angela M Zivkovic; Yu-Jui Yvonne Wan; Steve M Watkins; Malin L Nording; Bruce D Hammock; J Bruce German
Journal:  Mol Nutr Food Res       Date:  2013-07-01       Impact factor: 5.914

5.  Dietary fat and not calcium supplementation or dairy product consumption is associated with changes in anthropometrics during a randomized, placebo-controlled energy-restriction trial.

Authors:  Jennifer T Smilowitz; Michelle M Wiest; Dorothy Teegarden; Michael B Zemel; J Bruce German; Marta D Van Loan
Journal:  Nutr Metab (Lond)       Date:  2011-10-05       Impact factor: 4.169

6.  Metabolomics identifies increases in the acylcarnitine profiles in the plasma of overweight subjects in response to mild weight loss: a randomized, controlled design study.

Authors:  Miso Kang; Hye Jin Yoo; Minjoo Kim; Minkyung Kim; Jong Ho Lee
Journal:  Lipids Health Dis       Date:  2018-10-15       Impact factor: 3.876

7.  Genetic association-based functional analysis detects HOGA1 as a potential gene involved in fat accumulation.

Authors:  Myungsuk Kim; Kye Won Park; Yeongseon Ahn; Eun Bi Lim; Soo Heon Kwak; Ahmad Randy; No Joon Song; Kyong Soo Park; Chu Won Nho; Yoon Shin Cho
Journal:  Front Genet       Date:  2022-08-12       Impact factor: 4.772

  7 in total

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