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Enregistrement W2803142519 · doi:10.1113/jp276274

Reducing branched‐chain amino acid intake to reverse metabolic complications in obesity and type 2 diabetes

2018· letter· en· W2803142519 sur OpenAlexaff
Dana-Rae Reguis Yadao, Stephanie Mackenzie, Andreas Bergdahl

Notice bibliographique

RevueThe Journal of Physiology · 2018
Typeletter
Langueen
DomaineMedicine
ThématiqueAdipose Tissue and Metabolism
Établissements canadiensConcordia University
Organismes subventionnairesnon disponible
Mots-clésObesityType 2 diabetesDiabetes mellitusMedicineAmino acidInternal medicineEndocrinologyChemistryBiochemistry

Résumé

récupéré en direct d'OpenAlex

The Western diet (WD) is defined as the high intake of calories, saturated fats and sugars, and has been linked with the alarming increase of obesity and Type 2 diabetes (T2D) in North America and Europe. Chronic consumption of the Western diet induces obesity, which originates as a positive energy balance and subsequent disruption of regulatory and metabolic processes leading to excess fat mass. T2D is characterized by insulin resistance, which results in the abnormal cellular uptake of carbohydrates and lipids. Ramifications of insulin resistance include hyperglycaemia, dyslipidaemia and oxidative stress. While caloric restriction has been considered an effective strategy for shedding excess weight and restoring metabolic function, many patients find it difficult to implement as a long-term solution. Recent studies have highlighted a different approach towards weight loss and metabolic health, one exclusively through the modification of protein composition. Dietary proteins generally have an insulinotropic effect, consequently leading to the increased uptake of glucose and subsequent glycaemic control (Rietman et al. 2014). However, long-term consumption of a high-protein (HP) diet is correlated with a greater risk of developing conditions related to metabolic dysfunction. Recent observational and experimental evidence suggest that excessive consumption of a prominent group of amino acids (AAs), the branched-chain AAs (BCAAs), may elicit insulin resistance and glucose intolerance (Chen &Yang, 2015). Proteins are composed of both essential and non-essential AAs, with the three BCAAs – leucine, isoleucine and valine – constituting up to 20% of total dietary protein intake (Chen & Yang, 2015). Augmented levels of BCAAs have garnered much research attention as potential biomarkers of T2D and obesity (Rietman et al. 2014; Chen & Yang, 2015). Greater BCAA serum levels obtained from a HP diet result in poor insulin action and increased mortality in both rodents and humans, whereas a low-protein (LP) diet promotes metabolic health via enhanced insulin sensitivity as well as lower white adipose tissue (WAT) mass (Fontana et al. 2016). Additionally, it was found that a reduction in BCAA content can elicit positive metabolic effects similar to a normal LP diet (Fontana et al. 2016). Consequently, the potential to alter the pathophysiology of metabolic conditions through the specific modification of dietary proteins merits further investigation. In a recent article published in The Journal of Physiology, Cummings et al. (2018) effectively filled this gap and helped to further our overall understanding of the positive correlation between augmented BCAA serum levels and metabolic dysfunction. Specifically, the authors challenged this relationship by determining whether mice already conditioned with obesity could achieve restored metabolic health in less than 4 weeks after switching from a high-fat Western diet to either a LP or a low-BCAA diet. A reduction in adiposity, lowered insulin resistance, adequate glycaemic control and greater energy expenditure (EE) served as indicators for overall metabolic health. Mice underwent diet-induced obesity (DIO) through the consumption of a WD for 12 weeks and were then randomized into one of five experimental groups: Control AA, ExLow AA (contains a formulation of AAs that are all significantly lower than the Control AA diet), ExLow BCAA (specifically has lowered amounts of L-Isoleucine, L-Leucine and L-Valine compared to the Control AA diet (Table 1, Cummings et al. 2018)), WD supplemented with BCAAs, or WD. All diets contained the same energy density and macronutrient composition as typical rodent chow. Body composition, EE, activity levels and thickness of dermal white adipose tissue (dWAT) were noted over a period of 15 weeks. The two WD groups continued to gain weight throughout the intervention while the ExLow AA and ExLow BCAA groups sustained rapid weight loss. Moreover, the ExLow AA group lost 25% of their body weight in 2 weeks and later settled at a lower weight compared to non-WD mice. Control AA mice normalized their weight but at a much slower pace, which required two additional months. Glucose tolerance and insulin sensitivity improved in all normal calorie diets compared to the WD group. However, ExLow BCAA and ExLow AA diets exhibited better glucose tolerance relative to all groups, and ExLow AA displayed the greatest correction with regards to insulin sensitivity. Interestingly, the experienced weight loss was not due to calorie restriction, but rather from a LP and low AA diet which subsequently increased EE, as confirmed via indirect calorimetry (P < 0.05). All DIO mice that were switched to a non-WD had thinner dWAT measurements (ExLow BCAA specifically), whereas DIO mice on the WD or WD + BCAAs demonstrated larger fat droplets. To further verify the relationship between decreased BCAAs and energy balance in DIO mice, Cummings et al. created an additional series of WDs, each with varying levels of BCAAs. DIO mice were placed into one of four groups: WD Control AA, WD High BCAAs, WD Low BCAAs and WD Low AAs. Similar results were found wherein both WD Low BCAA and WD Low AA groups lost weight progressively for 3 weeks and restored their glycaemic control. No difference in spontaneous activity among all groups was noted, which indicates that the weight loss was due to augmented EE (P < 0.05). Further examination of the increased EE following the WD Low AA and WD Low BCAA diets revealed that fibroblast growth factor 21 (FGF21) blood levels were transiently elevated in the 12 days post-diet switch from DIO. FGF21 is a hormone that assists with energy balance regulation and appears to be responsible for the boost in EE. The mechanism involves the uncoupling of UCP1 through the sympathetic nervous system, thus establishing the connection between FGF21 and the browning of WAT, as well as the increased activation of brown adipose tissue (BAT), which causes the expression of mitochondrial-dense multicellular adipocytes within the WAT (Douris et al. 2015). These ‘beige’ spots are very metabolically active, and thus allow for WAT to act in the same capacity as BAT (Douris et al. 2015). Consequently, a rise in EE is coupled with the loss of adipose mass – consistent with the weight loss seen in the WD Low BCAA and WD Low AA mice. Previous work on this phenomenon has already suggested that reducing the protein:carbohydrate ratio mediates the augmentation of FGF21 levels (Solon-Biet et al. 2016). Indeed, Cummings et al. found that a WD containing a physiologically relevant 67% less BCAAs can temporarily induce FGF21, which consequently lead to the sustained increase in EE observed in DIO mice. As such, this novel finding demonstrates that selective AA reduction can directly promote metabolic health through an enhanced energy balance. Therefore, in conducting a secondary analysis of EE in groups fed with WDs of varying BCAA levels, and implementing precise physiological measures to test the hypothesis, the methods employed by Cummings et al. represent a complex and comprehensive in vivo study. Taking this into consideration, it is possible that these findings can be applied to humans, especially given that the relationship between serum BCAAs and obesity has already been established. In conclusion, existing metabolic dysfunctions resulting from a high-calorie, high-fat and high-sugar WD can potentially be reversed without caloric restriction. Additionally, given that greater BCAA serum levels have the potential to serve as a biomarker for metabolic dysfunctions, its detection in clinical settings should be considered as it may contribute to lowering the prevalence of obesity and T2D. The Cummings et al. study also highlights that both significant weight loss and improved glycaemic control observed in mice belonging to the WD low BCAA group are likely a result of an increase in EE. However, while the findings are promising, more work needs to be done to determine at what level of BCAA manipulation and overall change in protein quality can best elicit positive metabolic effects and optimize EE in humans, and whether this can be considered as a viable, long-term solution for individuals suffering from metabolic disorders. Moreover, future research should address the FGF21 mechanism seen following BCAA restricted diets in order to shed light on this specific pathway, which includes how weight is lost through the beiging of WAT and how UCP1 expression affects the process. To ascertain what is precisely behind the shift in EE will allow for a more realistic, translatable weight loss alternative in humans. Thus, by selectively moderating BCAA content, whether through individualized diet plans or eliciting AA-specific catabolism via pharmaceutical measures, Cummings et al. have revealed that it is possible to reduce the complications associated with highly prevalent metabolic diseases, while simultaneously maintaining adequate nutrition. None declared. All authors have read and approved the final version of this manuscript and agree to be accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved. All persons designated as authors qualify for authorship, and all those who qualify for authorship are listed. D.R.Y. is supported by the Concordia University Graduate Master's Fellowship.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction machine sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.

score de la tête « metaresearch » (Codex)0,000
score de la tête « metaresearch » (Gemma)0,001
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Sans objet · Signal consensuel: aucune
GenreSignal candidat: Commentaire · Signal consensuel: aucune
Score de désaccord entre enseignants0,004
Score d'incertitude au seuil0,014

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0000,001
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0010,001
Bibliométrie0,0010,000
Études des sciences et des technologies0,0000,000
Communication savante0,0010,000
Science ouverte0,0000,000
Intégrité de la recherche0,0010,002
Charge utile insuffisante (le modèle a refusé de juger)0,0040,001

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,025
Tête enseignante GPT0,284
Écart entre enseignants0,259 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeSans objet
Domainenon disponible
GenreCommentaire

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations4
Publié2018
Routes d'admission1
Résumé présentoui

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