Tracking of physical activity during adolescence: The 1993 Pelotas Birth Cohort, Brazil

Objective: To analyze physical activity during adolescence in participants of the 1993 Pelotas Birth Cohort Study, Brazil. Methods: Data on leisure time physical activity at 11, 15, and 18 years of age were analyzed. At each visit, a cut-off point of 300 min/week was used to classify adolescents as active or inactive. A total of 3,736 participants provided data on physical activity at each of the three age points. Results: A significant decline in the proportion of active adolescents was observed from 11 to 18 years of age, particularly among girls (from 32.9% to 21.7%). The proportions of girls and boys who were active at all three age points were 28.0% and 55.1%, respectively. After adjustment for sex, economic status, and skin color, participants who were active at 11 and 15 years of age were 58.0% more likely to be active at 18 years of age compared with those who were inactive at 11 and 15 years of age. Conclusions: Physical activity declined during adolescence and inactivity tended to track over time. Our findings reinforce the need to promote physical activity at early stages of life, because active behavior established early tends to be maintained over time.

Health-related quality of life in the Cambridge City over-75s Cohort (CC75C): development of a dementia-specific scale and descriptive analyses.

BACKGROUND: The assessment of Health Related Quality of Life (HRQL) is important in people with dementia as it could influence their care and support plan. Many studies on dementia do not specifically set out to measure dementia-specific HRQL but do include related items. The aim of this study is to explore the distribution of HRQL by functional and socio-demographic variables in a population-based setting. METHODS: Domains of DEMQOL's conceptual framework were mapped in the Cambridge City over 75's Cohort (CC75C) Study. HRQL was estimated in 110 participants aged 80+ years with a confirmed diagnosis of dementia with mild/moderate severity. Acceptability (missing values and normality of the total score), internal consistency (Cronbach's alpha), convergent, discriminant and known group differences validity (Spearman correlations, Wilcoxon Mann-Whitney and Kruskal-Wallis tests) were assessed. The distribution of HRQL by socio-demographic and functional descriptors was explored. RESULTS: The HRQL score ranged from 0 to 16 and showed an internal consistency Alpha of 0.74. Validity of the instrument was found to be acceptable. Men had higher HRQL than women. Marital status had a greater effect on HRQL for men than it did for women. The HRQL of those with good self-reported health was higher than those with fair/poor self-reported health. HRQL was not associated with dementia severity. CONCLUSIONS: To our knowledge this is the first study to examine the distribution of dementia-specific HRQL in a population sample of the very old. We have mapped an existing conceptual framework of dementia specific HRQL onto an existing study and demonstrated the feasibility of this approach. Findings in this study suggest that whereas there is big emphasis in dementia severity, characteristics such as gender should be taken into account when assessing and implementing programmes to improve HRQL.

Nutrimetabolomics fingerprinting to identify biomarkers of bread exposure in a free-living population from the PREDIMED study cohort

Bread is one of the most widely consumed foods. Its impact on human health is currently of special interest for researchers. We aimed to identify biomarkers of bread consumption by applying a nutrimetabolomic approach to a free-living population. An untargeted HPLC q-TOF-MS and multivariate analysis was applied to human urine from 155 subjects stratified by habitual bread consumption in three groups: non-consumers of bread (n = 56), white-bread consumers (n = 48) and whole-grain bread consumers (n = 51). The most differential metabolites (variable importance for projection ≥1.5) included compounds originating from cereal plant phytochemicals such as benzoxazinoids and alkylresorcinol metabolites, and compounds produced by gut microbiota (such as enterolactones, hydroxybenzoic and dihydroferulic acid metabolites). Pyrraline, riboflavin, 3-indolecarboxylic acid glucuronide, 2,8-dihydroxyquinoline glucuronide and N-α-acetylcitrulline were also tentatively identified. In order to combine multiple metabolites in a model to predict bread consumption, a stepwise logistic regression analysis was used. Receiver operating curves were constructed to evaluate the global performance of individual metabolites and their combination. The area under the curve values [AUC (95 % CI)] of combined models ranged from 77.8 % (69.1 86.4 %) to 93.7 % (89.4 98.1 %), whereas the AUC for the metabolites included in the models had weak values when they were evaluated individually: from 58.1 % (46.6 69.7 %) to 78.4 % (69.8 87.1 %). Our study showed that a daily bread intake significantly impacted on the urinary metabolome, despite being examined under uncontrolled free-living conditions. We further concluded that a combination of several biomarkers of exposure is better than a single biomarker for the predictive ability of discriminative analysis.

Nutrimetabolomics fingerprinting to identify biomarkers of bread exposure in a free-living population from the PREDIMED study cohort

Bread is one of the most widely consumed foods. Its impact on human health is currently of special interest for researchers. We aimed to identify biomarkers of bread consumption by applying a nutrimetabolomic approach to a free-living population. An untargeted HPLC q-TOF-MS and multivariate analysis was applied to human urine from 155 subjects stratified by habitual bread consumption in three groups: non-consumers of bread (n = 56), white-bread consumers (n = 48) and whole-grain bread consumers (n = 51). The most differential metabolites (variable importance for projection ≥1.5) included compounds originating from cereal plant phytochemicals such as benzoxazinoids and alkylresorcinol metabolites, and compounds produced by gut microbiota (such as enterolactones, hydroxybenzoic and dihydroferulic acid metabolites). Pyrraline, riboflavin, 3-indolecarboxylic acid glucuronide, 2,8-dihydroxyquinoline glucuronide and N-α-acetylcitrulline were also tentatively identified. In order to combine multiple metabolites in a model to predict bread consumption, a stepwise logistic regression analysis was used. Receiver operating curves were constructed to evaluate the global performance of individual metabolites and their combination. The area under the curve values [AUC (95 % CI)] of combined models ranged from 77.8 % (69.1 86.4 %) to 93.7 % (89.4 98.1 %), whereas the AUC for the metabolites included in the models had weak values when they were evaluated individually: from 58.1 % (46.6 69.7 %) to 78.4 % (69.8 87.1 %). Our study showed that a daily bread intake significantly impacted on the urinary metabolome, despite being examined under uncontrolled free-living conditions. We further concluded that a combination of several biomarkers of exposure is better than a single biomarker for the predictive ability of discriminative analysis.