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Background: Increased protein intake has been suggested to improve gains in muscle mass and strength in adults. Furthermore, the timing of protein intake has been discussed as a margin of opportunity for improved prevention measures.Objective: This systematic review investigated the effect of protein supplementation on body composition and muscle function (strength and synthesis) in healthy adults, with an emphasis on the timing of protein intake.Methods: Randomized controlled trials were identified using PubMed, Web of Science, CINAHL, and Embase, up to March 2019. For meta-analyses, data on lean body mass (LBM), handgrip strength, and leg press strength were pooled by age group (mean age 18-55 or >55 y) and timing of protein intake. The quality of evidence was assessed using the Grading of Recommendations, Assessment, Development and Evaluations approach.Results: Data from 65 studies with 2907 participants (1514 men and 1380 women, 13 unknown sex) were included in the review. Twenty-six, 8, and 24 studies were used for meta-analysis on LBM, handgrip strength, and leg press strength, respectively. The protein supplementation was effective in improving (mean difference; 95% CI) LBM in adults (0.62 kg; 0.36, 0.88) and older adults (0.46 kg; 0.23, 0.70), but not handgrip strength (older adults: 0.26 kg; -0.51, 1.04) and leg press strength (adults: 5.80 kg; -0.33, 11.93; older adults: 1.97 kg; -2.78, 6.72). Sensitivity analyses removing studies without exercise training had no impact on the outcomes. Data regarding muscle synthesis were scarce and inconclusive. Subgroup analyses showed no beneficial effect of a specific timing of protein intake on LBM, handgrip strength, and leg press strength.Conclusion: Overall, the results support the positive impact of protein supplementation on LBM of adults and older adults, independently of intake timing. Effects on muscle strength and synthesis are less clear and need further investigation. This systematic review was registered on PROSPERO as CRD42019126742. [ABSTRACT FROM AUTHOR]

Background: Dietary modifications can contribute to improved pancreatic β cell function and enhance glycemic control.Objectives: The objectives of this study were as follows: 1) to investigate the potential of milk protein hydrolysates to modulate postprandial glucose response; 2) to assess individual responses; and 3) to explore the inter- and intraindividual reproducibility of the response.Methods: A 14-d randomized crossover study investigated interstitial glucose levels of participants in response to 12% w/v milk protein drinks (intact caseinate and casein hydrolysate A and B) consumed in random order with a 2-d washout between treatments. Milk protein drinks were consumed immediately prior to study breakfast and evening meals. Twenty participants (11 men, 9 women) aged 50 ± 8 y with a body mass index (in kg/m2) of 30.2 ± 3.1 were recruited. Primary outcome was glucose levels assessed at 15-min intervals with the use of glucose monitors.Results: Repeated-measures ANOVA revealed that for breakfast there was a significant difference across the 3 treatment groups (P = 0.037). The ability to reduce postprandial glucose was specific to casein hydrolysate B in comparison with intact caseinate (P = 0.039). However, despite this significant difference, further examination revealed that only 3 out of 18 individuals were classified as responders (P < 0.05). High intraclass correlation coefficients were obtained for glucose response to study meals (intraclass correlation coefficient: 0.892 for breakfast with intact caseinate). The interindividual CVs were higher than the intraindividual CVs. Mean inter- and intraindividual CVs were 19.4% and 5.7%, respectively, for breakfast with intact caseinate.Conclusion: Ingestion of a specific casein hydrolysate successfully reduced the postprandial glucose response; however, at an individual level only 3 participants were classified as responders, highlighting the need for precision nutrition. Exploration of high interindividual responses to nutrition interventions is needed, in combination with the development of precision nutrition, potentially through an n-of-1 approach. This clinical trial was registered as ISRCTN61079365 (https://www.isrctn.com/). [ABSTRACT FROM AUTHOR]

Background: Improved assessment of meat intake with the use of metabolomics-derived markers can provide objective data and could be helpful in clarifying proposed associations between meat intake and health.Objective: The objective of this study was to identify novel markers of chicken intake using a metabolomics approach and use markers to determine intake in an independent cohort.Methods: Ten participants [age: 62 y; body mass index (in kg/m2): 28.25] in the NutriTech food intake study consumed increasing amounts of chicken, from 88 to 290 g/d, in a 3-wk span. Urine and blood samples were analyzed by nuclear magnetic resonance and mass spectrometry, respectively. A multivariate data analysis was performed to identify markers associated with chicken intake. A calibration curve was built based on dose-response association using NutriTech data. A Bland-Altman analysis evaluated the agreement between reported and calculated chicken intake in a National Adult Nutrition Survey cohort.Results: Multivariate data analysis of postprandial and fasting urine samples collected in participants in the NutriTech study revealed good discrimination between high (290 g/d) and low (88 g/d) chicken intakes. Urinary metabolite profiles showed differences in metabolite levels between low and high chicken intakes. Examining metabolite profiles revealed that guanidoacetate increased from 1.47 to 3.66 mmol/L following increasing chicken intakes from 88 to 290 g/d (P < 0.01). Using a calibration curve developed from the NutriTech study, chicken intake was calculated through the use of data from the National Adult Nutrition Survey, in which consumers of chicken had a higher guanidoacetate excretion (0.70 mmol/L) than did nonconsumers (0.47 mmol/L; P < 0.01). A Bland-Altman analysis revealed good agreement between reported and calculated intakes, with a bias of -30.2 g/d. Plasma metabolite analysis demonstrated that 3-methylhistidine was a more suitable indicator of chicken intake than 1-methylhistidine.Conclusions: Guanidoacetate was successfully identified and confirmed as a marker of chicken intake, and its measurement in fasting urine samples could be used to determine chicken intake in a free-living population. This trial was registered at clinicaltrials.gov as NCT01684917. [ABSTRACT FROM AUTHOR]

Background: Studies examining vitamin E intake and the percentage of the population meeting dietary guidelines do not distinguish between natural (RRR-α-tocopherol) and synthetic (all-rac-α-tocopherol) intake, even though these different isomeric forms differ in bioactivity. Objective: This study aimed to determine the effect of RRR-α-tocopherol vs. all-rac-α-tocopherol intake on the percentage of the population meeting the vitamin E recommendation and on plasma α-tocopherol stereoisomer distribution. Methods: With the use of data from the Irish National Adult Nutrition Survey (NANS), this study examined the percentage of the Irish population meeting the European Union (EU) RDA for vitamin E of 12 mg/d, correcting for a bioactivity difference in all-rac- vs. RRR-α-tocopherol, where 1 mg of all-rac-α-tocopherol is considered to be equivalent to 1:1.36 (0.74) mg in the EU RDA. In a subcohort of supplement users and nonusers, plasma α- and γ-tocopherol concentrations and α-tocopherol stereoisomer distribution were measured. Receiver operating characteristic (ROC) curve analysis was conducted to determine ability to discriminate supplement user types. Results: Analysis of the NANS showed that 100% of participants still met the recommended intake of 12 mg/d, after all- rac-α-tocopherol intake was corrected for α-tocopherol equivalent bioactivity. In the subcohort analysis, the percentage of plasma RRR-α-tocopherol was significantly lower in high all-rac-α-tocopherol supplement (>11 mg/d) users (82%) compared with nonusers and with high RRR-α-tocopherol supplement (>35 mg/d) users (91% and 93% respectively, P < 0.01). High RRR-α-tocopherol supplement users had a significantly higher plasma α-tocopherol than low all-rac- α-tocopherol supplement (<2.5 mg/d) users (34 vs. 25 µmol/L, P = 0.01). ROC analysis demonstrated an ability to distinguish between RRR- and all-rac-α-tocopherol consumers, which may be useful in investigating the potential effect of RRR- and all-rac-α-tocopherol intake on health. Conclusions: This study demonstrated that the percentage of the population meeting the vitamin E recommendation was unaffected when all-rac-α-tocopherol intake was corrected for α-tocopherol equivalent bioactivity. all-rac-α-Tocopherol intake led to a decrease in the percentage of plasma RRR-α-tocopherol relative to RRR-α-tocopherol intake. [ABSTRACT FROM AUTHOR]

A review is presented of the article "Untargeted metabolomics identifies novel potential biomarkers of habitual food intake in a cross-sectional study of postmenopausal women" by Y. Wang, T.J. Hartman, M.L. McCullough and others which was published in the same issue concerning dietary intake biomarkers using metabolomics.

Background: Pregnancy and postpartum are periods of intense changes in women's metabolism. The knowledge of the metabolites and maternal factors underlying these changes is limited., Objectives: We aimed to investigate the maternal factors that could influence serum metabolome changes from late pregnancy to the first months of postpartum., Methods: Sixty-eight healthy women from a Brazilian prospective cohort were included. Maternal blood and general characteristics were collected during pregnancy (28-35 wk) and postpartum (27-45 d). A targeted metabolomics approach was applied to quantify 132 serum metabolites, including amino acids, biogenic amines, acylcarnitines, lysophosphatidylcholines (LPC), diacyl phosphatidylcholines (PC), alkyl:acyl phosphatidylcholines (PC-O), sphingomyelins with (SM) and without hydroxylation [SM(OH)], and hexoses. Metabolome changes from pregnancy to postpartum were measured as log 2 fold change (log 2 FC), and simple linear regressions were employed to evaluate associations between maternal variables and metabolite log 2 FC. Multiple comparison-adjusted P values of < 0.05 were considered significant., Results: Of 132 metabolites quantified in serum, 90 changed from pregnancy to postpartum. Most metabolites belonging to PC and PC-O classes decreased, whereas most LPC, acylcarnitines, biogenic amines, and a few amino acids increased in postpartum. Maternal prepregnancy body mass index (ppBMI) showed positive associations with leucine and proline. A clear opposite change pattern was observed for most metabolites across ppBMI categories. Few phosphatidylcholines were decreased in women with normal ppBMI, while an increase was observed in women with obesity. Similarly, women with high postpartum levels of total cholesterol, LDL cholesterol, and non-HDL cholesterol showed increased sphingomyelins, whereas a decrease was observed for women with lower levels of those lipoproteins., Conclusions: The results revealed several maternal serum metabolomic changes from pregnancy to postpartum, and the maternal ppBMI and plasma lipoproteins were associated with these changes. We highlight the importance of the nutritional care of women prepregnancy to improve their metabolic risk profile., (Copyright © 2022 American Society for Nutrition. Published by Elsevier Inc. All rights reserved.)

Background: Improved assessment of meat intake with the use of metabolomics-derived markers can provide objective data and could be helpful in clarifying proposed associations between meat intake and health., Objective: The objective of this study was to identify novel markers of chicken intake using a metabolomics approach and use markers to determine intake in an independent cohort., Methods: Ten participants [age: 62 y; body mass index (in kg/m2): 28.25] in the NutriTech food intake study consumed increasing amounts of chicken, from 88 to 290 g/d, in a 3-wk span. Urine and blood samples were analyzed by nuclear magnetic resonance and mass spectrometry, respectively. A multivariate data analysis was performed to identify markers associated with chicken intake. A calibration curve was built based on dose-response association using NutriTech data. A Bland-Altman analysis evaluated the agreement between reported and calculated chicken intake in a National Adult Nutrition Survey cohort., Results: Multivariate data analysis of postprandial and fasting urine samples collected in participants in the NutriTech study revealed good discrimination between high (290 g/d) and low (88 g/d) chicken intakes. Urinary metabolite profiles showed differences in metabolite levels between low and high chicken intakes. Examining metabolite profiles revealed that guanidoacetate increased from 1.47 to 3.66 mmol/L following increasing chicken intakes from 88 to 290 g/d (P < 0.01). Using a calibration curve developed from the NutriTech study, chicken intake was calculated through the use of data from the National Adult Nutrition Survey, in which consumers of chicken had a higher guanidoacetate excretion (0.70 mmol/L) than did nonconsumers (0.47 mmol/L; P < 0.01). A Bland-Altman analysis revealed good agreement between reported and calculated intakes, with a bias of -30.2 g/d. Plasma metabolite analysis demonstrated that 3-methylhistidine was a more suitable indicator of chicken intake than 1-methylhistidine., Conclusions: Guanidoacetate was successfully identified and confirmed as a marker of chicken intake, and its measurement in fasting urine samples could be used to determine chicken intake in a free-living population. This trial was registered at clinicaltrials.gov as NCT01684917., (© 2017 American Society for Nutrition.)

Background: Accurate dietary assessment is key to understanding nutrition-related outcomes and is essential for estimating dietary change in nutrition-based interventions., Objective: The objective of this study was to assess the pan-European reproducibility of the Food4Me food-frequency questionnaire (FFQ) in assessing the habitual diet of adults., Methods: Participants from the Food4Me study, a 6-mo, Internet-based, randomized controlled trial of personalized nutrition conducted in the United Kingdom, Ireland, Spain, Netherlands, Germany, Greece, and Poland, were included. Screening and baseline data (both collected before commencement of the intervention) were used in the present analyses, and participants were included only if they completed FFQs at screening and at baseline within a 1-mo timeframe before the commencement of the intervention. Sociodemographic (e.g., sex and country) and lifestyle [e.g., body mass index (BMI, in kg/m(2)) and physical activity] characteristics were collected. Linear regression, correlation coefficients, concordance (percentage) in quartile classification, and Bland-Altman plots for daily intakes were used to assess reproducibility., Results: In total, 567 participants (59% female), with a mean ± SD age of 38.7 ± 13.4 y and BMI of 25.4 ± 4.8, completed both FFQs within 1 mo (mean ± SD: 19.2 ± 6.2 d). Exact plus adjacent classification of total energy intake in participants was highest in Ireland (94%) and lowest in Poland (81%). Spearman correlation coefficients (ρ) in total energy intake between FFQs ranged from 0.50 for obese participants to 0.68 and 0.60 in normal-weight and overweight participants, respectively. Bland-Altman plots showed a mean difference between FFQs of 210 kcal/d, with the agreement deteriorating as energy intakes increased. There was little variation in reproducibility of total energy intakes between sex and age groups., Conclusions: The online Food4Me FFQ was shown to be reproducible across 7 European countries when administered within a 1-mo period to a large number of participants. The results support the utility of the online Food4Me FFQ as a reproducible tool across multiple European populations. This trial was registered at clinicaltrials.gov as NCT01530139., (© 2016 American Society for Nutrition.)

Background: Studies examining vitamin E intake and the percentage of the population meeting dietary guidelines do not distinguish between natural (RRR-α-tocopherol) and synthetic (all-rac-α-tocopherol) intake, even though these different isomeric forms differ in bioactivity., Objective: This study aimed to determine the effect of RRR-α-tocopherol vs. all-rac-α-tocopherol intake on the percentage of the population meeting the vitamin E recommendation and on plasma α-tocopherol stereoisomer distribution., Methods: With the use of data from the Irish National Adult Nutrition Survey (NANS), this study examined the percentage of the Irish population meeting the European Union (EU) RDA for vitamin E of 12 mg/d, correcting for a bioactivity difference in all-rac- vs. RRR-α-tocopherol, where 1 mg of all-rac-α-tocopherol is considered to be equivalent to 1:1.36 (0.74) mg in the EU RDA. In a subcohort of supplement users and nonusers, plasma α- and γ-tocopherol concentrations and α-tocopherol stereoisomer distribution were measured. Receiver operating characteristic (ROC) curve analysis was conducted to determine ability to discriminate supplement user types., Results: Analysis of the NANS showed that 100% of participants still met the recommended intake of 12 mg/d, after all-rac-α-tocopherol intake was corrected for α-tocopherol equivalent bioactivity. In the subcohort analysis, the percentage of plasma RRR-α-tocopherol was significantly lower in high all-rac-α-tocopherol supplement (>11 mg/d) users (82%) compared with nonusers and with high RRR-α-tocopherol supplement (>35 mg/d) users (91% and 93% respectively, P < 0.01). High RRR-α-tocopherol supplement users had a significantly higher plasma α-tocopherol than low all-rac-α-tocopherol supplement (<2.5 mg/d) users (34 vs. 25 μmol/L, P = 0.01). ROC analysis demonstrated an ability to distinguish between RRR- and all-rac-α-tocopherol consumers, which may be useful in investigating the potential effect of RRR- and all-rac-α-tocopherol intake on health., Conclusions: This study demonstrated that the percentage of the population meeting the vitamin E recommendation was unaffected when all-rac-α-tocopherol intake was corrected for α-tocopherol equivalent bioactivity. all-rac-α-Tocopherol intake led to a decrease in the percentage of plasma RRR-α-tocopherol relative to RRR-α-tocopherol intake., (© 2015 American Society for Nutrition.)