MétaCan
Menu
Back to cohort
Record W2336317376 · doi:10.1097/ede.0000000000000490

Goat’s Milk, Plant-based Milk, Cow’s Milk, and Serum 25-hydroxyvitamin D Levels in Early Childhood

2016· letter· en· W2336317376 on OpenAlexafffundabout
Grace J. Lee, Catherine S. Birken, Patricia C. Parkin, Gerald Lebovic, Yang Chen, Mary R. L’Abbé, Jonathon L. Maguire

Bibliographic record

VenueEpidemiology · 2016
Typeletter
Languageen
FieldEnvironmental Science
TopicAgriculture Sustainability and Environmental Impact
Canadian institutionsSickKids FoundationUniversity of TorontoHospital for Sick ChildrenCanadian Institutes of Health ResearchSt. Michael's Hospital
FundersCanadian Institutes of Health Research
KeywordsBreast milkMedicineVitamin D and neurologyFortificationFood scienceCow milkSkimmed milkAnthropometryAnimal scienceEnvironmental healthBiologyInternal medicine

Abstract

fetched live from OpenAlex

To the Editor Commercial availability and parental interest in alternative milk beverages for children have been increasing. We previously identified a relationship between higher consumption of alternative milk beverages and lower vitamin D levels in early childhood.1 Based on existing research, it is unclear whether this is true for both animal-based (goat’s milk) and plant-based (soy, almond, rice, etc.) milk beverages. Vitamin D fortification of alternative milk beverages is voluntary in both the United States and Canada.2–4 Our objective was to determine whether the relationship between alternative milk beverage consumption and children’s 25-hydroxyvitamin D is different for goat’s milk, plant-based milk beverages, and cow’s milk. In this cross-sectional study, children 1–6 years old seen for routine primary healthcare were recruited between 2008 and 2013 in Toronto, Canada (latitude 43.4°N).5 A parent-completed questionnaire based on the Canadian Community Health Survey, anthropometric and laboratory measurements were collected by trained research assistants and phlebotomists using standardized methods during the primary healthcare visit.1,5,6 We measured serum 25-hydroxyvitamin D concentration using the DiaSorin LIAISION 25-hydroxyvitmain D TOTAL chemiluminescence assay, with an interassay imprecision of 4.9 nmol/L using DEQAS (www.mountsinaiservices.com). Consumption of cow’s milk, goat’s milk, and plant-based milk beverages were measured as cups per day.1 We used multiple linear regression to test the association between each milk type consumed (goat, plant, and cow) and children’s 25-hydroxyvitamin D level, adjusted for clinically relevant covariates identified a priori (age, sex, body mass index z score, vitamin D supplementation, margarine consumption [vitamin D fortified in Canada], skin pigmentation, outdoor play time, and seasonality). 25-hydroxyvitamin D was positively skewed and was log-transformed. Residual analysis indicated a good fit. We conducted multiple imputation for missing data (no variable had >12% missing data). The R Project was used for statistical analyses. This study was approved by the Hospital for Sick Children and St. Michael’s Hospital Research Ethics Boards. Parents of children consented to study participation. Of the 4,523 recruited children, 2,711 children had laboratory testing and were included in the study. The mean age was 2.9 years (SD 1.5) and 53% were male. Vitamin D supplementation was reported in 53% of children, and median 25-hydroxyvitamin D level was 80 nmol/L (interquartile range 66–99). Each cup of plant-based milk was associated with a 3.2 nmol/L (95% CI, 0.7, 5.6) lower median 25-hydroxyvitamin D level and each cup of cow’s milk was associated with a 3.0 nmol/L (95% CI, 2.1, 3.9) higher median 25-hydroxyvitamin D level. Goat’s milk consumption was not associated with children’s serum 25-hydroxyvitamin D level although the trend was similar to cow’s milk. Comparing the relationship between volume of each milk type consumed and 25-hydroxyvitamin D level revealed similar associations for goat’s milk and cow’s milk whereas plant-based milk beverage consumption was associated with lower 25-hydroxyvitamin D than both cow’s milk and goat’s milk (Figure).FIGURE: Adjusted association between milk consumption and children’s serum 25-hydroxyvitamin D levels, by milk type.In summary, we identified a dose-dependent association between plant-based milk beverage consumption and lower 25-hydroxyvitamin D level in early childhood. This association was in the opposite direction to the relationship between consumption of animal-based milks and 25-hydroxyvitamin D. One explanation for the lower 25-hydroxyvitamin D levels among children who consume plant-based milk beverages may be a difference in the biological potency of vitamin D2, found in plant-based milk, relative to vitamin D3, found in animal-based milk. There has been considerable debate about whether vitamin D2 is as effective as vitamin D3 in raising serum 25-hydroxyvitamin D concentration.7,8 Another explanation may be differences in regulatory requirements for vitamin D fortification of animal- and plant-based milk. Future investigations are needed to elucidate the differences between the effects of plant-based milk beverage consumption and animal-based milk consumption on children’s 25-hydroxyvitamin D levels. ACKNOWLEDGMENTS We thank Azar Azad, PhD, Tonya D’Amour, Julie DeGroot, MSc, Sharmilaa Kandasamy, Kanthi Kavikondala, Tarandeep Malhi, Magda Melo, MSc, Subitha Rajakumaran, Juela Sejdo, and Laurie Thompson for administrative and technical support for TARGetKids!. The following clinical site investigators participated in the TARGetKids! Collaboration: Tony Barozzino, MD, Gary Bloch, MD, Ashna Bowry, MD, Douglas Campbell, MD, Sohail Cheema, MD, Brian Chisamore, MD, Karoon Danayan, MD, Anh Do, MD, Michael Evans, MD, Mark Feldman, MD, Sloane Freeman, MD, Moshe Ipp, MD, Sheila Jacobson, MD, Tara Kiran, MD, Holly Knowles, MD, Eddy Lau, MD, Fok-Han Leung, MD, Muhammad Mamdani, PharmD, MA, MPH, Julia Morinis, MD, MSc, Sharon Naymark, MD, Patricia Neelands, MD, Michael Peer, MD, Marty Perlmutar, MD, Michelle Porepa, MD, Noor Ramji, MD, Alana Rosenthal, MD, Janet Saunderson, MD, Michael Sgro, MD, Susan Shepherd, MD, Carolyn Taylor, MD, Sheila Wijayasinghe, MD, Ethel Ying, MD, and Elizabeth Young, MD. Grace J. Lee Department of Nutritional Sciences University of Toronto Toronto, ON, Canada Department of Pediatrics St. Michael’s Hospital Toronto, ON, Canada Li Ka Shing Knowledge Institute of St. Michael’s Hospital Toronto, ON, Canada Catherine S. Birken Patricia C. Parkin Division of Paediatric Medicine and the Paediatric Outcomes Research Team The Hospital for Sick Children Toronto, ON, Canada Department of Paediatrics University of Toronto Toronto, ON, Canada Child Health Evaluative Sciences The Hospital for Sick Children Research Institute Toronto, ON, Canada Gerald Lebovic Li Ka Shing Knowledge Institute of St. Michael’s Hospital Toronto, ON, Canada Institute of Health Policy Management and Evaluation University of Toronto Toronto, ON, Canada Yang Chen Li Ka Shing Knowledge Institute of St. Michael’s Hospital Toronto, ON, Canada Mary R. L’Abbe Jonathon L. Maguire Department of Nutritional Sciences University of Toronto Toronto, ON, Canada Jonathon L. Maguire Department of Pediatrics St. Michael’s Hospital Toronto, ON, Canada Li Ka Shing Knowledge Institute of St. Michael’s Hospital Toronto, ON, Canada Division of Paediatric Medicine and the Paediatric Outcomes Research Team The Hospital for Sick Children Toronto, ON, Canada Department of Paediatrics University of Toronto Toronto, ON, Canada Child Health Evaluative Sciences The Hospital for Sick Children Research Institute Toronto, ON, Canada Institute of Health Policy Management and Evaluation University of Toronto Toronto, ON, Canada [email protected] for the TARGet Kids! Collaboration

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.295
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0020.001

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.

Opus teacher head0.021
GPT teacher head0.240
Teacher spread0.219 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; both teacher heads agree on what is shown here.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations2
Published2016
Admission routes3
Has abstractyes

Explore more

Same venueEpidemiologySame topicAgriculture Sustainability and Environmental ImpactFrench-language works237,207