Nutrition Research and Human Disease: A Critical Appraisal of Mechanistic Research, Cohort Studies, and Randomized Trials
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.351 | 0.567 |
| Meta-epidemiology (narrow) | 0.004 | 0.003 |
| Meta-epidemiology (broad) | 0.021 | 0.013 |
| Bibliometrics | 0.012 | 0.010 |
| Science and technology studies | 0.002 | 0.009 |
| Scholarly communication | 0.013 | 0.013 |
| Open science | 0.007 | 0.004 |
| Research integrity | 0.009 | 0.012 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".