Inhibition of bone turnover by milk intake in postmenopausal women

Increased postmenopausal bone turnover leads to bone loss and fragility fracture risk. In the absence of osteoporosis, risk preventive measures, particularly those modifying nutritional lifestyle, are appropriate. We tested the hypothesis that milk supplementation affects bone turnover related to biochemical markers in a direction that, in the long term, may be expected to reduce postmenopausal bone loss. Thirty healthy postmenopausal women aged 59·3 (sd 3·3) years were enrolled in a prospective crossover trial of 16 weeks. After a 4-week period of adaptation with diet providing 600mg calcium plus 300mg ingested as 250ml semi-skimmed milk, participants were maintained during 6 weeks under the same 600mg calcium diet and randomized to receive either 500ml semi-skimmed milk, thus providing a total of 1200mg calcium, or no milk supplement. In the next 6 weeks they were switched to the alternative regimen. At the end of the each period, i.e. after 4, 10 and 16 weeks, blood and urinary samples were collected. The changes in blood variables between the periods of 6 weeks without and with milk supplementation were: for parathyroid hormone, −3·2pg/ml (P=0·0054); for crosslinked telopeptide of type I collagen, −624pg/ml (P<0·0001); for propeptide of type I procollagen, −5·5ng/ml (P=0·0092); for osteocalcin, −2·8ng/ml (P=0·0014). In conclusion, a 6-week period of milk supplementation induced a decrease in several biochemical variables compatible with diminished bone turnover mediated by reduction in parathyroid hormone secretion. This nutritional approach to postmenopausal alteration in bone metabolism may be a valuable measure in the primary prevention of osteoporosi

Therapeutic paracetamol treatment in older persons induces dietary and metabolic modifications related to sulfur amino acids

Sulfur amino acids are determinant for the detoxification of paracetamol (N-acetyl-p-aminophenol) through sulfate and glutathione conjugations. Long-term paracetamol treatment is common in the elderly, despite a potential cysteine/glutathione deficiency. Detoxification could occur at the expense of anti-oxidative defenses and whole body protein stores in elderly. We tested how older persons satisfy the extra demand in sulfur amino acids induced by long-term paracetamol treatment, focusing on metabolic and nutritional aspects. Effects of 3 g/day paracetamol for 14 days on fasting blood glutathione, plasma amino acids and sulfate, urinary paracetamol metabolites, and urinary metabolomic were studied in independently living older persons (five women, five men, mean (+/- SEM) age 74 +/- 1 years). Dietary intakes were recorded before and at the end of the treatment and ingested sulfur amino acids were evaluated. Fasting blood glutathione, plasma amino acids, and sulfate were unchanged. Urinary nitrogen excretion supported a preservation of whole body proteins, but large-scale urinary metabolomic analysis revealed an oxidation of some sulfur-containing compounds. Dietary protein intake was 13% higher at the end than before paracetamol treatment. Final sulfur amino acid intake reached 37 mg/kg/day. The increase in sulfur amino acid intake corresponded to half of the sulfur excreted in urinary paracetamol conjugates. In conclusion, older persons accommodated to long-term paracetamol treatment by increasing dietary protein intake without any mobilization of body proteins, but with decreased anti-oxidative defenses. The extra demand in sulfur amino acids led to a consumption far above the corresponding population-safe recommendation

Diététicien en recherche clinique interventionnelle en nutrition

Initialement médical, le métier de diététicien s'est étendu peu à peu à d'autres domaines dont celui de la recherche biomédicale interventionnelle. Ce milieu est soumis à une législation révisée régulièrement qui indique les procédures à respecter, et définit les différents acteurs qui interviennent (leurs responsabilités et leurs qualifications respectives). En pratique, les diététiciens interviennent à toutes les étapes allant de la préparation de l'étude à la communication des résultats, ce qui demande l'acquisition de savoirs, savoir-faire et savoir-être un peu différents de ceux des diététiciens du secteur paramédical. D'ailleurs, l'implication des diététiciens dédiés à la recherche interventionnelle est variable en fonction des types d'études et de volontaires (sains ou malades). Elle comprend parfois la formation de collègues hospitaliers en vue d'une collaboration ou de leur soutien. Dans les années à venir, la loi de 2012 et la création des Programmes Hospitaliers de Recherche Infirmière et Paramédicale (PHRIP), pourraient peut-être inciter davantage de diététiciens à initier leurs propres études que ce soit dans un but scientifique ou d'amélioration de leurs pratiques professionnelles

Plsda versus pca on barycenters applied to metabolomics in a context of discrimination

International audienceIntroduction. Untargeted metabolomics is a powerful phenotyping tool to better understand the biological mechanisms involved in the physiopathological processes, and identify biomarkers of metabolic status. The complex data need dedicated preparation and treatments to extract meaningful information. The major specificity of metabolomics data is the large number of variables compared to the number of samples, as well as their high degree of correlation. The common analysis strategy consists in performing univariate and multivariate statistics to highlight variables of interest. In a discriminant context, partial least squares-discriminant analysis (PLSDA) is one of the most effective multivariate tools currently used, because of its ability to analyze collinear and noisy data. Another multivariate method that could be used is the Principal Component Analysis (PCA) of the matrix of barycenters of the observation groups (called here “PCAC”). The objective of our study is to compare these approaches in terms of explained variances and important variables.Material and methods. Published data from a project on the impact of genetic mutations in mice (ProMetIS) were used as a case study (Imbert, 2021). Mice (n=42), males and females, belonged to one of the three genotype groups (wild type, lacking the linker for activation of T cells gene, or lacking the MX dynamin-like GTPase 2 gene). The metabolomics dataset we used, was obtained from the analysis of plasma samples using a mass spectrometry-based untargeted approach (LC-MS), and contained 6104 variables after preparation. In the present work, data analysis was performed with the R-package “rchemo”. Six atypical mice were removed to have a balanced experiment design, before Pareto scaling. Due to the sex effect, without interaction with the genotype, the data were centered by sex before applying PLSDA, and PCAC, to discriminate genotype groups. On one hand, the optimal number of PLS components was determined according to the error in repeated cross-validation (30 repetitions of 10-fold cross-validation) and application of the one-standard-error-rule. On the other hand, using PCAC model, subjects were projected onto the components. For each PLSDA or PCAC component, the total, inter- and intra-group variances were then calculated, and the group effect was assessed by ANOVA. ANOVA were also performed for metabolomics variables becoming important in the discrimination in the PLSDA model with the optimal number of components (called here “PLSDAopt”), compared to the one with 2 components.Results. The optimal number of PLS components was 3, and the number of PCAC components was 2. Only these components had a significant p-value in the ANOVA. As expected, the 1st component of both methods were the same, and, as shown below, in our study, the 2nd components were closely similar. The 3rd component of the PLSDA model was also of interest because it still significantly explained intergroup variability and highlighted other important discriminant variables.Discussion and conclusion. The PLSDA and the PCAC components maximize the inter-group variability. When 3 groups are to be discriminated, the PCAC finds 2 components while the PLSDA can find more. Presumably, each component of the PLSDAopt can discriminate one group from one or both others, and would thus allow a better discrimination

Input of sequential and orthogonalized partial least squares regression method in the exploration of metabolic syndrome

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Input of sequential and orthogonalized partial least squares regression method in the exploration of metabolic syndrome

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A new tool for multi-block PLS discriminant analysis: application to metabolomic data in systems epidemiology

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Higher calcium urinary loss induced by a calcium sulphate-rich mineral water intake than by milk in young women

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A new tool for multi-block PLS discriminant analysis of metabolomic data: application to systems epidemiology

Metabolomics is a powerful phenotyping tool in nutrition and health research, generating massive and complex data that need dedicated treatments to enrich our knowledge of biological systems. In particular, to deeper investigate relations between environmental factors, phenotypes and metabolism, discriminant statistical analyses performed separately on metabolomic datasets, are often complemented by associations with metadata (anthropometric, clinical, nutritional and physical activity data…). Another relevant strategy is to perform a multi-block partial least squares discriminant analysis (MBPLSDA) that simultaneously analyses data available from different sources, allowing determining the importance of variables and variable blocks in discriminating groups of subjects, taking into account data structure in thematic blocks.In order to propose a full open-source standalone tool, the present objective was to develop an R package allowing all steps of MBPLSDA analysis for the joint analysis of metabolomic and additional data.The tool was based on the mbpls function of the ade4 R package, enriched with different functionalities, including some dedicated to discriminant analysis. Provided indicators help to determine the optimal number of components, to check the MBPLSDA model validity, and to evaluate the variability of its parameters and predictions. To illustrate the potential of the proposed tool and the associated procedure, MBPLSDA was applied to a real case study involving metabolomics, nutritional and clinical data from a human cohort.The availability of the different functionalities in a single R package allowed optimizing parameters for an efficient joint analysis of metabolomics and epidemiological data to obtain new insights into multidimensional phenotypes. In particular, we highlighted the impact of filtering the metabolomic variables beforehand, and the relevance of a MBPLSDA approach in comparison to a standard PLS-discriminant analysis method