Investigating Risk of Suboptimal Macro and Micronutrient Intake and Their Determinants in Older Danish Adults with Specific Focus on Protein Intake-A Cross-Sectional Study.

Suboptimal intake of nutrients is associated with adverse health outcomes. The current study investigated the risk of suboptimal macro and micronutrient intake and their potential determinants in a cross-sectional study of community-dwelling older Danish adults (65⁻81 years). Nutrient intake was obtained through a 3-day weighted dietary record and information on personal characteristics and attitudes towards specific foods and dietary habits and nutrition through questionnaires. Dietary Reference Values (DRV) from the Nordic Nutrition Recommendations were used for the assessment. Among 157 participants, 68% and 66% had risk of suboptimal intake of dietary fiber and saturated fatty acids (SFA). For mono-unsaturated fatty acids (MUFA) and poly-unsaturated fatty acids (PUFA), the numbers were 47% and 62%, respectively. Increased risk of suboptimal protein intake was estimated in 3 to 45% of the participants, depending on the criteria used for the DRV and of the mode of expressing protein intake. Fifty percent had intakes of alcohol above the maximum recommended intake. Risk of micronutrient inadequacy was particularly high for vitamin D and thiamine (80 and 45%, respectively). Total energy intake and attitude regarding healthy eating were associated with lower nutrient intake. The current study illustrates that there is room for improvements in the dietary quality of community dwelling older Danish adults.

Quantification of benzoxazinoids and their metabolites in Nordic breads.

Benzoxazinoids (Bx) and their metabolites are molecules with suggested health effects in humans, found in cereal grains and consequently in cereal foods. However, to date little is known about the amount of Bx in our diet. In this study, deuterated standards 2-hydroxy-1,4-benzoxazin-3-one (HBOA-d4) and 2-hydroxy-N-(2-hydroxyphenyl) acetamide (HHPAA-d4) were synthesized, to allow quantification of nine Bx and their metabolites in 30 breads and flours from Nordic countries by UHPLC-MS/MS. Samples containing rye had larger amounts of Bx (143-3560µg/g DM) than the ones containing wheat (11-449µg/g DM). More Bx were found in whole grain wheat (57-449µg/g DM) compared to refined wheat (11-92µg/g DM) breads. Finnish sourdough rye breads were notably high in their 2-hydroxy-N-(2-hydroxyphenyl) acetamide (HHPAA) concentration (40-48µg/g DM). This new information on Bx content in flours and breads available in the Nordic countries will be useful for future work on determining dietary exposure to Bx.

Biomarkers for predicting type 2 diabetes development-Can metabolomics improve on existing biomarkers?

AIM: The aim was to determine if metabolomics could be used to build a predictive model for type 2 diabetes (T2D) risk that would improve prediction of T2D over current risk markers. METHODS: Gas chromatography-tandem mass spectrometry metabolomics was used in a nested case-control study based on a screening sample of 64-year-old Caucasian women (n = 629). Candidate metabolic markers of T2D were identified in plasma obtained at baseline and the power to predict diabetes was tested in 69 incident cases occurring during 5.5 years follow-up. The metabolomics results were used as a standalone prediction model and in combination with established T2D predictive biomarkers for building eight T2D prediction models that were compared with each other based on their sensitivity and selectivity for predicting T2D. RESULTS: Established markers of T2D (impaired fasting glucose, impaired glucose tolerance, insulin resistance (HOMA), smoking, serum adiponectin)) alone, and in combination with metabolomics had the largest areas under the curve (AUC) (0.794 (95% confidence interval [0.738-0.850]) and 0.808 [0.749-0.867] respectively), with the standalone metabolomics model based on nine fasting plasma markers having a lower predictive power (0.657 [0.577-0.736]). Prediction based on non-blood based measures was 0.638 [0.565-0.711]). CONCLUSIONS: Established measures of T2D risk remain the best predictor of T2D risk in this population. Additional markers detected using metabolomics are likely related to these measures as they did not enhance the overall prediction in a combined model.