Metabolomics Analysis of Serum and Urine After Bean Consumption by Patients with Peripheral Arterial Disease
Notice bibliographique
Résumé
Peripheral artery disease (PAD) is symptomatic of systemic atherosclerosis, and is characterized by the presence of plaques that occlude the arteries of the lower extremities. PAD substantially increases the incidence for myocardial infarction, ischemic stroke, and cardiovascular death. The risk factors for PAD include advanced age (over 40 years), smoking, hypertension, diabetes mellitus, hyperlipidemia, and hyperhomocysteinemia. Treatments for PAD can be divided into three categories: risk‐factor modification, drug therapy, and catheter‐based endovascular intervention. Bean consumption has been shown to reduce risk factors of PAD (e.g. blood pressure, LDL‐cholesterol), however, beans have not been directly investigated as a dietary intervention in the PAD population. Given that common beans (pinto, red kidney, black and navy) are rich in dietary fibre, this component has been assumed to be responsible for the cholesterol‐lowering effects. However, beans also contain phenolic acids, which may explain their risk factor lowering actions. Objective To determine whether 8 weeks of bean consumption affects the profile of metabolites in serum and urine of individuals with PAD. A non‐targetted metabolomics approach was therefore employed to profile compounds in serum and urine associated with bean consumption and with PAD. Participants PAD patients (n=75) were randomly assigned to 3 groups (n=25/group): i. pulse‐free foods (rice instead of beans = control), ii. 1.5 cups/week, or iii. 3 cups/week of mixed cooked beans (pinto, red kidney, black and navy) for 8 weeks. Urine and serum were collected at baseline and week 8. Extraction Procedure Urine (250 μL) and serum (100 μL) were extracted with 500 μL and 250 μL of acetonitrile, respectively, and centrifuged (10,000 g, 10 min at 4ºC). The supernatants were dried under vacuum and kept at −20ºC. Dried samples were reconstituted in 200 μL of 1:4 acetonitrile:deionized water (urine) and in 100 μL of 4:1 acetonitrile:deionized water (serum) using glass inserts and brown Gas Chromatography vials for Liquid chromatography‐Quadrupole Time Of Flight‐Mass Spectrometry analysis. Results Several endogenous metabolites in serum and urine were significantly affected (P<0.05; ≥2‐fold change) by the consumption of beans relative to the comparator study foods. Specific alterations were detected in several class of compounds, including amino acids (His, Arg, Ser, Pro, Leu, Glu), peptides (Lys‐Ala‐His), glutathione, bile salts (glycocholic acid), phospholipids (PE, PS, PI, LysoPE) and products of arachidonic acid metabolism by cyclooxygenase (prostaglandin E2 p‐acetamidophenyl ester). Additionally, this approach detected a number of pharmaceuticals and their corresponding metabolites. In a subset of participants, the decrease in the metabolites of several anti‐hypertensive drugs in urine after 8 weeks of bean consumption suggested the existence of potential drug‐diet interactions that could affect the required dosage of certain anti‐hypertensive medications. Conclusion The use of a non‐targeted metabolomics approach in our study was invaluable as a screening tool to obtain insight into the biochemical pathways that are affected by bean consumption. Furthermore, by conducting these analyses on samples from individuals with PAD, it was revealed that bean consumption might influence management strategies for hypertension. Support or Funding Information Pulse Science Cluster, and Agriculture and Agri‐Food Canada, Natural Sciences and Engineering Research Council of Canada and Canada Foundation for Innovation.
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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi 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.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 0,000 |
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.
score_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 découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».