Nutrition Research and Human Disease: A Critical Appraisal of Mechanistic Research, Cohort Studies, and Randomized Trials
Notice bibliographique
Résumé
For many decades researchers have followed the strategy that in order to achieve a fuller understanding of nutrition, it is necessary to study the biochemical and physiological action of the huge numbers of chemicals in food, and thence learn the mechanisms by which they protect health or increase risk of disease [1][2][3].This strategy, often known as reductionism, has revealed a great deal about the role played in the body by vitamins, minerals, and many other substances, and why deficiencies of them lead to specific symptoms.However, this mechanistic strategy has achieved little success in recent decades in terms of generating information that is of practical value with regard to nutrition and human health [1-3].The reason for this is that because of the great complexity of the human body it is extraordinarily difficult to properly understand the exact details of the pathways leading to disease.Even with relatively "simple" disorders, such as hypertension, obesity, and type 2 diabetes, there are multiple pathways involved and the story of each disorder becomes steadily more complex as new discoveries are made.There is an additional reason for the poor success of mechanistic research as applied to nutrition: foods contain thousands of separate substances and this leads to vast numbers of possible interactions.A major part of nutrition research consists of the investigation of how food components affect the biochemical and physiological processes within the body.The rationale is that this mechanistic research will lead to a fuller understanding of disease etiology thereby generating information of practical value for the treatment and prevention of disease.More direct approaches to understanding dietdisease relationships are based on cohort studies and randomized controlled trials (RCTs).This paper critically examines examples of diet-disease relationships so as to determine which research approaches have been most productive.Areas covered include several foods (such as sugar-sweetened beverages, fish, meat, and fruit), several nutrients (such as fat, sodium, and selenium), and several diseases/disorders (hypertension, obesity, cancer, and coronary heart disease).This analysis reveals that most of our information of practical value has come from cohort studies and RCTs but relatively little has come from mechanistic research.It follows, therefore, that top priorities for nutrition research should be the carrying out of more cohort studies and RCTs.This is then discussed with reference to research on phytochemicals.However, mechanistic research has been of value in particular areas.This occurs where disease processes involve simple mechanisms; examples include several metabolic disorders with a genetic basis (such as lactose intolerance) and deficiencies of various vitamins and minerals.
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 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 | 0,351 | 0,567 |
| Méta-épidémiologie (sens strict) | 0,004 | 0,003 |
| Méta-épidémiologie (sens large) | 0,021 | 0,013 |
| Bibliométrie | 0,012 | 0,010 |
| Études des sciences et des technologies | 0,002 | 0,009 |
| Communication savante | 0,013 | 0,013 |
| Science ouverte | 0,007 | 0,004 |
| Intégrité de la recherche | 0,009 | 0,012 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,006 | 0,001 |
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.
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; l’étiquette directe de Gemma et le classifieur distillé Codex s’accordent sur ce qui est montré ici.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».