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Enregistrement W4389220320 · doi:10.1182/blood-2023-182878

A Whole Foods Plant-Based Weight Loss Intervention Improves Quality of Life, Metabolic, Microbiome and Immune Profile in MGUS/SMM As Well As Progression Trajectory in a Subset - the Nutrivention Trial

2023· article· en· W4389220320 sur OpenAlexaff
Urvi A. Shah, Francesca Castro, Andriy Derkach, Teng Fei, Kinga Hosszu, Devin McAvoy, Susan DeWolf, Justin R. Cross, Ana Gradíssimo, Alexis Nwankwo, Aishwarya Anuraj, Miranda Burge, Blaslov Jenna, Elizabeth M. Raynor, Jenna Wosleger, Peter Adintori, Michaël Pollak, Jenny Paredes Sanchez, Thomas M. Atkinson, Kylee Maclachlan, Sham Mailankody, Neha Korde, Carlyn Tan, Malin Hultcrantz, Hani Hassoun, Jonathan U. Peled, Gunjan L. Shah, Michael Scordo, Oscar Lahoud, David J. Chung, Heather Landau, Anita D’Souza, Ola Landgren, Sergio Giralt, Neil M. Iyengar, Saad Z. Usmani, Alexander M. Lesokhin, Marcel R.M. van den Brink

Notice bibliographique

RevueBlood · 2023
Typearticle
Langueen
DomaineBiochemistry, Genetics and Molecular Biology
ThématiqueNutrition, Genetics, and Disease
Établissements canadiensMcGill University
Organismes subventionnairesnon disponible
Mots-clésMedicineAdiponectinInternal medicineMicrobiomeBody mass indexImmunologyObesityBiologyInsulin resistanceBioinformatics

Résumé

récupéré en direct d'OpenAlex

Introduction Obesity, low adiponectin, high leptin, high insulin, and diets lacking plant-based foods are risk factors for plasma cell disorders (PCDs). Patients (pts) with monoclonal gammopathy (MGUS) and smoldering myeloma (SMM) and an elevated body mass index (BMI) are twice as likely to progress to myeloma (MM). There is evidence of microbiome dysbiosis in pts with PCDs and dietary changes can induce large microbial shifts. Therefore, there is a rationale to study a whole food plant-based dietary (WFPBD) intervention to improve modifiable biomarkers and disease trajectory. Methods This was a pilot, single-arm trial of WFPBD (self-selected prepared frozen lunch/dinner provided by Plantable with breakfast/snack guidance and no calorie restriction) for 12-weeks (w) and health coaching for 24w (by Plantable coach and a research dietitian) in pts with MGUS/SMM and BMI ≥25 (NCT04920084). The primary endpoint was feasibility (mean BMI reduction ≥5% and mean dietary adherence [kcal % unprocessed plant food intake] ≥70%) at 12w; secondary endpoints included quality of life (QoL) (EORTC QLQ C30 survey using a linear-mixed effect model), metabolic markers (plasma insulin, adiponectin leptin (AL) ratio via ELISA), gut microbiome (inverse Simpson index α-diversity and relative abundance of butyrate producers (RABP) via stool 16S sequencing), peripheral blood immune profile (leukocyte subsets via 36 color flow cytometry), secreted biomarkers (plasma via Olink inflammation panel) and change in monoclonal (M)-spike concentrations. Rate of change of M-spike/year (y) measured by slope with 95% CI was calculated for up to 20 months (m) pre-intervention and 20m post intervention start (baseline (BL)). A p-value for difference in M-spike rates was calculated. Results The study enrolled 23 pts of which 20 completed the 12w intervention and 16 completed 1y. The median age was 62y with 43% male, 43% non-White, 52% MGUS, 74% obese, and 26% prediabetic/diabetic. The study met feasibility endpoints with 90% mean and 92% median adherence during 12w intervention and 77% median at 24w. There was an 8.3% mean and 6.6% median BMI reduction at 12w and 8.6% median at 24w. There was an improvement in global health status/QoL (median increase 16.7 points, p=0.03), a reduction in dyspnea (median decrease -33.3 points, p=0.001), fatigue (median decrease -11.1 points, p=0.06), and insulin (median decrease 0.791 mU/L; p=0.01), and an increase in AL ratio (median increase 0.09; p=0.0002), α-diversity (median increase 6.18 to 8.42; p= 0.03) and RABP (median increase 0.03 to 0.08; p=0.042) at 12w. Prelim results at 12w suggest a measurable, albeit not significant change in immune subsets such as an increase in monocytes, regulatory T cells, and a decrease in dendritic cells, trends previously associated with anti-inflammatory diets, as well as increase in butyrate. A decrease in proinflammatory cytokines IL8 (p=0.065), IL12B (p=0.068), and TNFB (p=0.082) and an increase in FGF21 (p=0.043) (promoter of insulin sensitivity) was also observed. Butyrate is known to reduces pro-inflammatory cytokines and increase anti-inflammatory proteins, including FGF21. Further immune and microbiome analysis is ongoing. Of 16 pts followed for 1y, 2 with significant BMI reduction had significant improvement in M-spike trajectory: Case 1: Mayo Int Risk IgGκ/IgGλ MGUS achieved a 1y BMI reduction of 19%. Pre-intervention M-spike changed +0.28g/dL/y (6 M-spike values), and during intervention M-spike changed +0.03g/dL/y (11 M-spike values); p=0.008. BL M-spike 1.2g/dL and bone marrow plasma cells (BM PC) <5%; 1.5y BM PC 5-9%. Case 2: IMWG Int Risk IgGκ SMM achieved a 1y BMI reduction of 13%. Pre-intervention M-spike changed +0.11g/dL/y (8 M-spike values), and during intervention M-spike changed -0.05g/dL/y (8 M-spike values); p=0.04. BL M-spike 1.2g/dL and BM PC 20-30%; 1y BM PC 10-15%. Two pts without significant BMI reduction (-4% and -1% at 1y) had rising M-spikes. The remaining 12 pts had median 7.5% BMI reduction at 1y with stable M-spike. Conclusions This is the first dietary intervention trial in PCDs with insights into the mechanisms by which a WFPBD may delay progression. The intervention improves QoL, metabolic (BMI, insulin resistance), microbiome (α-diversity and butyrate producers) as well as immune (reduced inflammation) profile with potentially slowing progression trajectory in a subset. A larger trial is underway (NCT05640843).

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 distillée sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.

score de la tête « metaresearch » (Codex)0,001
score de la tête « metaresearch » (Gemma)0,000
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Expérimental (laboratoire) · Signal consensuel: Expérimental (laboratoire)
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,123
Score d'incertitude au seuil0,502

Scores Codex et Gemma par catégorie

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

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 Opus0,011
Tête enseignante GPT0,285
Écart entre enseignants0,274 · 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 tête enseignante, pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeExpérimental (laboratoire)
Domainenon disponible
GenreEmpirique

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

En bref

Citations6
Publié2023
Routes d'admission1
Résumé présentoui

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