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Association Between Dietary Calcium Intake, Dairy Product Consumption and Body Composition Indices in Healthy Postmenopausal Women: A Cross‐Sectional Analysis

2017· article· en· W4389020023 on OpenAlexafffundabout
Angel M. Ong, Hope A. Weiler, Michelle Wall, Stella S. Daskalopoulou, David Goltzman, Suzanne N. Morin

Bibliographic record

VenueThe FASEB Journal · 2017
Typearticle
Languageen
FieldMedicine
TopicNutritional Studies and Diet
Canadian institutionsMcGill UniversityMcGill University Health Centre
FundersCanadian Institutes of Health ResearchDairy Farmers of Canada
KeywordsBioelectrical impedance analysisMedicineBody mass indexVitamin D and neurologyFood frequency questionnaireAnimal scienceCalciumComposition (language)Environmental healthInternal medicine

Abstract

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Evidence on the inverse association between dietary calcium (dCa) and body composition has been inconsistent. The objective of this study was to examine the association between dCa intake, dairy product consumption and body composition indices (BCI) in healthy postmenopausal women. Baseline data of 91 women participating in a randomized controlled trial (RCT) that aims to evaluate the effect of dCa as compared to supplemental Ca intake on vascular health (ClinicalTrial.Gov NCT0173140) were analyzed. All were ≥50 y, non‐users of Ca or vitamin D supplements at baseline, with a BMI >19 and <35 kg/m 2 . A validated 51‐item semi‐quantitative food frequency questionnaire was administered to assess usual dCa, dietary vitamin D and dairy product intakes over the preceding month. Body mass index (BMI) was calculated from measured height and weight. Body fat mass (FM), percent body fat (%BF), and fat‐free mass (FFM) were measured by bioelectrical impedance analysis using a body composition analyzer in “standard mode” (Tanita TBF‐310). FM index (FMI) and FFM index (FFMI) were calculated. Physical activity level (PAL) was assessed using the International Physical Activity Questionnaire. Intakes were categorized into two groups as below or above the observed median. Differences in means of BCI between groups of dCa (<850 or ≥850 mg/d), total dairy products (<1.5 or ≥1.5 servings/d), milk (<0.5 or ≥0.5 serving/d), yogurt (<0.5 or ≥0.5 serving/d), and cheese (<0.5 or ≥0.5 serving/d) consumption were analyzed using a one‐way analysis of covariance after adjustment for age, dietary vitamin D intake and PAL. Participants were 60±6 y with a mean BMI of 25.5±3.9 kg/m 2 and a median PAL of 2487 MET‐mins/week (interquartile range [IQR] 1535–4753). Median dCa intake was 846 mg/d (IQR 605–1072). Daily median servings of total dairy products, milk, yogurt and cheese were 1.59 (IQR 0.97–2.48), 0.5 (IQR 0.07–0.93), 0.43 (IQR 0.12–0.74), and 0.51 (IQR 0.29–0.84), respectively. There were no differences in BCI between groups of dCa intake or total dairy product consumption (). No differences were found between groups of milk or yogurt consumption and BCI (). However, there was a lower mean %BF (33.0% vs 35.8%, p=0.03) and FM (21.8% vs 25.0%, p=0.045) in cheese intake ≥0.5 serving/d compared to the group with <0.5 serving/d of cheese intake. Although dCa and total dairy product intakes were not associated with body composition, our results suggest that cheese intake may be inversely associated with adiposity in healthy postmenopausal women. Our ongoing RCT with a larger sample will enable a better evaluation of the association between dCa and BCI as compared to dairy and supplemental Ca in this population. Support or Funding Information This work was supported by funding from the Canadian Institutes of Health Research and the Dairy Farmers of Canada. The funding agencies did not have a role in design, implementation, analysis or interpretation. Adjusted means (95% confidence interval) of body composition indices between dietary calcium intake groups and groups of dairy consumption Dietary calcium Dairy <850 mg/d (n=48) ≥850 mg/d (n=43) p‐value <1.5 servings/d (n=43) ≥1.5 servings/d (n=48) p‐value BMI (kg/m 2 ) 25.8 (24.6, 27.0) 25.2 (23.9, 26.5) 0.52 26.2 (25.0, 27.4) 25.0 (23.8, 26.1) 0.17 %BF 34.9 (32.9, 36.8) 33.9 (31.8, 35.9) 0.53 35 (33.0, 36.9) 33.9 (32.0, 35.7) 0.43 FM (kg) 24.1 (21.8, 26.4) 22.6 (20.1, 25.0) 0.41 24.5 (22.2, 26.8) 22.4 (20.2, 24.6) 0.22 FFM (kg) 42.6 (41.7, 43.6) 43 (41.9, 44.0) 0.67 43 (42.0, 44.0) 42.6 (41.7, 43.6) 0.60 FMI 9.3 (8.4, 10.2) 8.7 (7.8, 9.7) 0.43 9.5 (8.6, 10.4) 8.6 (7.8, 9.5) 0.20 FFMI 16.6 (16.1, 17.0) 16.5 (16.0, 16.9) 0.81 16.7 (16.3, 17.2) 16.3 (15.9, 16.7) 0.18 BMI, body mass index; %BF, percent body fat; FM, body fat mass; FFM, fat‐free mass; FMI, fat mass index; FFMI, fat‐free mass index. Analysis of covariance adjusted for age, dietary vitamin D intake and physical activity level. Adjusted means (95% confidence interval) of body composition indices between groups of milk, yogurt and cheese consumption Milk Yogurt Cheese <0.5 servings/day (n=45) ≥0.5 servings/day (n=46) <0.5 servings/day (n=49) ≥0.5 servings/day (n=42) <0.5 se

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.015

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.004
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.048
GPT teacher head0.344
Teacher spread0.296 · 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; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
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".

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Citations0
Published2017
Admission routes3
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