Association Between Dietary Calcium Intake, Dairy Product Consumption and Body Composition Indices in Healthy Postmenopausal Women: A Cross‐Sectional Analysis
Notice bibliographique
Résumé
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
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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,003 | 0,004 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,001 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».