An intergenerational life-course approach to address early childhood obesity and adiposity: the Healthy Life Trajectories Initiative (HeLTI)
Bibliographic record
Abstract
BACKGROUND: Interactions between genes and early-life exposures during conception, fetal life, infancy, and early childhood have been shown to affect an individual's health later in life. Maternal undernutrition and obesity, gestational diabetes, and impaired growth in utero and in early life are associated with adiposity and overweight and obesity in childhood, which are risk factors for poor health trajectories and non-communicable diseases. In Canada, China, India, and South Africa, 10-30% of children aged 5-16 years are overweight or obese. METHODS: The application of developmental origins of health and disease principles offers a novel approach to prevention of overweight and obesity and reduction of adiposity by delivering integrated interventions across the life course, starting before conception and continuing through early childhood. The Healthy Life Trajectories Initiative (HeLTI) was established in 2017 through a unique collaboration between national funding agencies in Canada, China, India, South Africa, and WHO. The aim of HeLTI is to evaluate the effect of an integrated four-phase intervention starting preconceptionally and continuing through pregnancy, infancy, and early childhood on reducing childhood adiposity (fat mass index) and overweight and obesity, and optimising early child development, nutrition, and other healthy behaviours. FINDINGS: Approximately 22 000 women are being recruited in Shanghai (China), Mysore (India), Soweto (South Africa), and across various provinces of Canada. Women who conceive (an expected 10 000) and their children will be followed up until the child reaches the age of 5 years. INTERPRETATION: HeLTI has harmonised the intervention, measures, tools, biospecimen collection, and analysis plans for the trial to be run across four countries. HeLTI will help establish whether an intervention aimed at addressing maternal health behaviours, nutrition, and weight; providing psychosocial support to reduce maternal stress and prevent mental illness; optimising infant nutrition, physical activity, and sleep; and promoting parenting skills can reduce the intergenerational risk of excess childhood adiposity and overweight and obesity across diverse settings. FUNDING: Canadian Institutes of Health Research; National Science Foundation of China; Department of Biotechnology, India; and South African Medical Research Council.
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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 | 0.015 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.001 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 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; 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".