Changes in Plasma Metabolite Concentrations after a Low‐Glycemic Index Diet Intervention

Hernández‐Alonso, Pablo; Giardina, S; Cañueto, Daniel; Salas‐Salvadó, Jordi; Cañellas, Nicolau; Bulló, Mònica

doi:10.1002/mnfr.201700975

Cited by 27 publications

(22 citation statements)

References 50 publications

(62 reference statements)

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“…The third block comprises three studies that used metabolomics to predict weight loss (Geidenstam et al 2017a , b ; Stroeve et al 2016 ) and 11 randomized clinical trials (RCTs) aimed to assess the effects of weight loss interventions (both hypocaloric diet programs and exercise interventions) on the metabolomic profiles of overweight or obese human subjects (Table 2 ) (Almanza-Aguilera et al 2018 ; Duft et al 2017 ; Kang et al 2018 ; Leal-Witt et al 2018 ; Meucci et al 2017 ; Mills et al 2019 ; Munukka et al 2018 ; Palau-Rodriguez et al 2019 ; Perez-Cornago et al 2014 ; Zheng et al 2016a , b ). Additionally, the fourth block includes 11 articles that studied the effects of specific dietary patterns or dietary compounds on obesity-related metabolic alterations in humans, such as inflammation or oxidative stress (Table 3 ) (Baldrick et al 2018 ; Gu et al 2013 ; Hernández-Alonso et al 2019 ; Hibberd et al 2019 ; Kim et al 2013 ; Kim et al 2017 ; Mayengbam et al 2019 ; Nieman et al 2012a , b ; Romo-Hualde et al 2018 ; Xu et al 2018 ).…”

Section: Resultsmentioning

confidence: 99%

“…At 120 min, nine metabolites (i.e., orotate, leucine/isoleucine, mesoxalate, asparagine, citrulline, methionine, allantoin, ornithine, and tyrosine) were significantly altered in the low-fat milk versus the rice beverage group. The evaluation of a low-glycemic index diet in overweight/obese adults for 6 months (Table 3 ) (Hernández-Alonso et al 2019 ) revealed several changes. The plasma serine levels were significantly increased following the low glycemic index diet compared to both the high glycemic index and low-fat diets.…”

Section: Resultsmentioning

confidence: 99%

“…Stroeve et al ( 2016 ) detected higher concentrations of BCAAs in MO women than in obese women. By contrary, decreased levels of BCAAs have been associated with weight loss in obese subjects (Geidenstam et al 2017a ), after weight loss through diet (Kim et al 2013 ; Zheng et al 2016b ; Hernández-Alonso et al 2019 ) or physical activity in pregnant women (Mills et al 2019 ). The increase, or decrease during weight loss, of BCAAs, reflect the status of the protein breakdown, which is a consequence of IR, thus pointing towards to metabolic complications.…”

Section: Discussionmentioning

confidence: 99%

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Are we close to defining a metabolomic signature of human obesity? A systematic review of metabolomics studies

2019

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Introduction Obesity is a disorder characterized by a disproportionate increase in body weight in relation to height, mainly due to the accumulation of fat, and is considered a pandemic of the present century by many international health institutions. It is associated with several non-communicable chronic diseases, namely, metabolic syndrome, type 2 diabetes mellitus (T2DM), cardiovascular diseases (CVD), and cancer. Metabolomics is a useful tool to evaluate changes in metabolites due to being overweight and obesity at the body fluid and cellular levels and to ascertain metabolic changes in metabolically unhealthy overweight and obese individuals (MUHO) compared to metabolically healthy individuals (MHO). Objectives We aimed to conduct a systematic review (SR) of human studies focused on identifying metabolomic signatures in obese individuals and obesity-related metabolic alterations, such as inflammation or oxidative stress. Methods We reviewed the literature to identify studies investigating the metabolomics profile of human obesity and that were published up to May 7th, 2019 in SCOPUS and PubMed through an SR. The quality of reporting was evaluated using an adapted of QUADOMICS. Results Thirty-three articles were included and classified according to four types of approaches. (i) studying the metabolic signature of obesity, (ii) studying the differential responses of obese and non-obese subjects to dietary challenges (iii) studies that used metabolomics to predict weight loss and aimed to assess the effects of weight loss interventions on the metabolomics profiles of overweight or obese human subjects (iv) articles that studied the effects of specific dietary patterns or dietary compounds on obesity-related metabolic alterations in humans. Conclusion The present SR provides state-of-the-art information about the use of metabolomics as an approach to understanding the dynamics of metabolic processes involved in human obesity and emphasizes metabolic signatures related to obesity phenotypes.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

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Are we close to defining a metabolomic signature of human obesity? A systematic review of metabolomics studies

2019

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“…In addition to the possible effects intrinsic to pea and barley compounds making them functional foods, there is still the possibility that the lipid-lowering effect of pea and barley is associated to the diet's glycemic index. A recent metabolomic study with obese humans found differential regulation of certain amino acids and phospholipids depending on the diet's glycemic index [46], which could contribute to explain the lipid-lowering effects attributed to low-glycemic index diet on diabetic or hyperlipidemic humans reported previously [47,48]. It was not measured in our study, but we have already shown that a pea and barley-based diet generate lower glycemic variables in diabetic dogs when compared to a maizebased diet [29].…”

Section: Discussionmentioning

confidence: 50%

Starch sources influence lipidaemia of diabetic dogs

et al. 2020

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Background: Hyperlipidaemia is considered a cause of other diseases that are clinically important and potentially life threatening. Combination of pea and barley as exclusive starch sources is known to interfere with glycemic control in diabetic dogs, but their effect on lipid profile of hiperlipidaemic dogs is yet to be evaluated. Twelve adult diabetic dogs were fed three dry extruded diets with different starch sources and different fat levels: peas and barley (PB), maize (Mi), and peas, barley and rice (Ba) with 15.7, 15.6 and 9.0% of their dry matter as fat, respectively. Plasmatic cholesterol and triglycerides concentration curves over 10 h were obtained after 60 days on each diet and with the same NPH insulin dose. ANOVA test or Friedman test were used to compare the dietary effects on triglycerides and cholesterol variables among the diets. Results: Dogs presented lower mean (p = 0.05), fasting (p = 0.03), and time 8-h postprandial (p = 0.05) triglyceridemia after PB diet period than Ba diet period and time 4-h postprandial (p = 0.02) lower after PB than Mi diet. Cholesterolemia mean, minimum, maximum, area under the cholesterol curve and times points: 2, 4, 6, 8 and 10-h postprandial, had lower values after PB ingestion in comparison to Mi, without difference to Ba diet. Conclusion: Inclusion of pea and barley, as exclusive starch sources, in therapeutic diets for diabetic dogs can minimize plasmatic triglycerides and cholesterol concentration at fasting and at different postprandial time, compared to the maize diet or diet with lesser fat content.

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“…Bucketing (6e-04 ppm as bucket width), referencing to TSP at 0 ppm and probabilistic quotient normalization 17 were performed through rDolphin. 18 In the serum dataset, sample collection details are available in Hernández-Alonso, P. et al 19 For each sample, 300 μ l aliquots were mixed with 300 μ l of sodium phosphate buffer. Carr…”

Section: Datasetsmentioning

confidence: 99%

Maximizing the quality of NMR automatic metabolite profiling by a machine learning based prediction of signal parameters

Cañueto

Navarro²,

Bulló³

et al. 2018

Preprint

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The quality of automatic metabolite profiling in NMR datasets in complex matrices can be compromised by the multiple sources of variability in the samples. These sources cause uncertainty in the metabolite signal parameters and the presence of multiple low-intensity signals. Lineshape fitting approaches might produce suboptimal resolutions or distort the fitted signals to adapt them to the complex spectrum lineshape. As a result, tools tend to restrict their use to specific matrices and strict protocols to reduce this uncertainty.However, the analysis and modelling of the signal parameters collected during a first profiling iteration can further reduce the uncertainty by the generation of narrow and accurate predictions of the expected signal parameters. In this study, we show that, thanks to the predictions generated, better profiling quality indicators can be outputted and the performance of automatic profiling can be maximized. Thanks to the ability of our workflow to learn and model the sample properties, restrictions in the matrix or protocol and limitations of lineshape fitting approaches can be overcome.

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Changes in Plasma Metabolite Concentrations after a Low‐Glycemic Index Diet Intervention

Abstract: These results suggest that an LGI diet modulates certain circulating amino acids and lipid levels. These findings may explain the health benefits attributed to LGI diets in metabolic diseases such as type 2 diabetes.

Cited by 27 publications

References 50 publications

Are we close to defining a metabolomic signature of human obesity? A systematic review of metabolomics studies

Are we close to defining a metabolomic signature of human obesity? A systematic review of metabolomics studies

Starch sources influence lipidaemia of diabetic dogs

Maximizing the quality of NMR automatic metabolite profiling by a machine learning based prediction of signal parameters

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