Developmental origins of metabolic diseases
Bibliographic record
Abstract
Almost 2 billion adults in the world are overweight, and more than half of them are classified as obese, while nearly one-third of children globally experience poor growth and development. Given the vast amount of knowledge that has been gleaned from decades of research on growth and development, a number of questions remain as to why the world is now in the midst of a global epidemic of obesity accompanied by the "double burden of malnutrition," where overweight coexists with underweight and micronutrient deficiencies. This challenge to the human condition can be attributed to nutritional and environmental exposures during pregnancy that may program a fetus to have a higher risk of chronic diseases in adulthood. To explore this concept, frequently called the developmental origins of health and disease (DOHaD), this review considers a host of factors and physiological mechanisms that drive a fetus or child toward a higher risk of obesity, fatty liver disease, hypertension, and/or type 2 diabetes (T2D). To that end, this review explores the epidemiology of DOHaD with discussions focused on adaptations to human energetics, placental development, dysmetabolism, and key environmental exposures that act to promote chronic diseases in adulthood. These areas are complementary and additive in understanding how providing the best conditions for optimal growth can create the best possible conditions for lifelong health. Moreover, understanding both physiological as well as epigenetic and molecular mechanisms for DOHaD is vital to most fully address the global issues of obesity and other chronic diseases.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".