Search

Accurate dietary assessment is crucial for nutrition and health research. Traditional methods, such as food records, food frequency questionnaires, and 24-hour dietary recalls (24HR), have limitations, such as the need for trained interviewers, time-consuming procedures, and inaccuracies in estimations. Novel technologies, such as image-based dietary assessment apps, have been developed to overcome these limitations. SNAQ is a novel image-based food-recognition app which, based on computer vision, assesses food type and volume, and provides nutritional information about dietary intake. This cross-sectional observational study aimed to investigate the validity of SNAQ as a dietary assessment tool for measuring energy and macronutrient intake in adult women with normal body weight ( n  = 30), compared to doubly labeled water (DLW), a reference method for total daily energy expenditure (TDEE). Energy intake was also estimated using a one-day 24HR for direct comparison. Bland-Altman plots, paired difference tests, and Pearson's correlation coefficient were used to assess agreement and relationships between the methods. SNAQ showed a slightly higher agreement (bias = -329.6 kcal/day) with DLW for total daily energy intake (TDEI) compared to 24HR (bias = -543.0 kcal/day). While both SNAQ and 24HR tended to underestimate TDEI, only 24HR significantly differed from DLW in this regard ( p  < 0.001). There was no significant relationship between estimated TDEI and TDEE using SNAQ ( R 2  = 27%, p  = 0.50) or 24HR ( R 2  = 34%, p  = 0.20) and there were no significant differences in energy and macronutrient intake estimates between SNAQ and 24HR (Δ = 213.4 kcal/day). In conclusion, these results indicate that SNAQ provides a closer representation of energy intake in adult women with normal body weight than 24HR when compared to DLW, but no relationship was found between the energy estimates of DLW and of the two dietary assessment tools. Further research is needed to determine the clinical relevance and support the implementation of SNAQ in research and clinical settings. Clinical trial registration : This study is registered on ClinicalTrials.gov with the unique identifier NCT04600596 (https://clinicaltrials.gov/ct2/show/NCT04600596)., Competing Interests: SNAQ AG developed the customized version of the app, SNAQ Study, in exchange for financial compensation. RS is employed by DSM Nutritional Products. AS is a member of the scientific advisory board of Gila Therapeutics. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2023 Serra, Alceste, Hauser, Hulshof, Meijer, Thalheimer, Steinert, Gerber, Spector, Gero and Bueter.)

Background: Studies do not show consistent relationships between self-reported intake of sugar and outcome of disease. To overcome the drawbacks of self-reported intake methods, we investigated whether there is an agreement in ranking of individuals between their self-reported sugar intake and urinary sucrose and fructose., Methods: We used data of 198 Dutch adults (106 women) from the DUPLO study. Sugar intake of all foods and drinks consumed over 24-hour period was estimated by collecting duplicate portions (DP) and 24-hour recalls (24hR), telephone (24hRT) and Web-based (24hRW), while sugar excretion was based on 24-hour urine samples. Sugar content of 24hR was calculated using a newly developed sugar database and sugar content of DPs and urine samples was calculated using high-performance liquid chromatography-atomic emission spectrometry and LC/MS-MS, respectively. Measurement error models assessed validity coefficients (VC) and attenuation factors (AF). Coefficients were compared with those of protein biomarker., Results: The VC for the marker, using DP as reference, showed comparability with substantially better ranking of participants (0.72 for women and 0.93 for men), than 24hRT (0.57 and 0.78) or 24hRW (0.70 and 0.78) as reference in the sucrose models. The VC of the sucrose models was within 10% of the protein models, except for the model with 24hRT as reference, among women. The AF started at higher values and increased by a greater factor compared with the VC., Conclusions: Repeated measurements of urinary sucrose and fructose as a marker of daily sucrose intake had a ranking performance comparable to urinary nitrogen as marker of protein intake in free-living Dutch adults., Impact: The validation of the sugar biomarker in a free-living population with three different dietary assessment methods and its comparable ranking ability with a good recovery biomarker (i.e., protein biomarker) have important research applications. The biomarker may be used for validating dietary assessment methods, for monitoring compliance in human feeding studies, for monitoring the effect of public health interventions, and as a surrogate for ranking subjects according to sucrose intake when information on sucrose in food composition databases is lacking., (©2020 American Association for Cancer Research.)

Saliva and urine are the two main body fluids sampled when breast milk intake is measured with the 2H oxide dose-to-mother technique. However, these two body fluids may generate different estimates of breast milk intake due to differences in isotope enrichment. Therefore, we aimed to assess how the estimated amount of breast milk intake differs when based on saliva and urine samples and to explore whether the total energy expenditure of the mothers is related to breast milk output. We used a convenience sample of thirteen pairs of mothers and babies aged 2-4 months, who were exclusively breastfed and apparently healthy. To assess breast milk intake, we administered doubly labelled water to the mothers and collected saliva samples from them, while simultaneously collecting both saliva and urine from their babies over a 14-d period. Isotope ratio MS was used to analyse the samples for 2H and 18O enrichments. Mean breast milk intake based on saliva samples was significantly higher than that based on urine samples (854·5 v. 812·8 g/d, P = 0·029). This can be attributed to slightly higher isotope enrichments in saliva and to a poorer model fit for urine samples as indicated by a higher square root of the mean square error (14·6 v. 10·4 mg/kg, P = 0·001). Maternal energy expenditure was not correlated with breast milk output. Our study suggests that saliva sampling generates slightly higher estimates of breast milk intake and is more precise as compared with urine and that maternal energy expenditure does not influence breast milk output.

It is suggested that nutrient densities are less affected by measurement errors than absolute intake estimates of dietary exposure. We compared the validity of absolute intakes and densities of protein (kJ from protein/total energy (kJ)), potassium, and sodium (potassium or sodium (in mg)/total energy (kJ)) assessed by different dietary assessment methods. For 69 Dutch subjects, two duplicate portions (DPs), five to fifteen 24-h dietary recalls (24 hRs, telephone-based and web-based) and two food frequency questionnaires (FFQs) were collected and compared to duplicate urinary biomarkers and one or two doubly labelled water measurements. Multivariate measurement error models were used to estimate validity coefficients (VCs) and attenuation factors (AFs). This research showed that group bias diminished for protein and sodium densities assessed by all methods as compared to the respective absolute intakes, but not for those of potassium. However, the VCs and AFs for the nutrient densities did not improve compared to absolute intakes for all four methods; except for the AF of sodium density (0.71) or the FFQ which was better than that of the absolute sodium intake (0.51). Thus, using nutrient densities rather than absolute intakes does not necessarily improve the performance of the DP, FFQ, or 24 hR., Competing Interests: The authors declare no conflict of interest.

Nuts and vegetable oils are important sources of fat and of a wide variety of micronutrients and phytochemicals. Following their intake, several of their constituents, as well as their derived metabolites, are found in blood circulation and in urine. As a consequence, these could be used to assess the compliance to a dietary intervention or to determine habitual intake of nuts and vegetable oils. However, before these metabolites can be widely used as biomarkers of food intake (BFIs), several characteristics have to be considered, including specificity, dose response, time response, stability, and analytical performance. We have, therefore, conducted an extensive literature search to evaluate current knowledge about potential BFIs of nuts and vegetable oils. Once identified, the strengths and weaknesses of the most promising candidate BFIs have been summarized. Results from selected studies have provided a variety of compounds mainly derived from the fatty fraction of these foods, but also other components and derived metabolites related to their nutritional composition. In particular, α-linolenic acid, urolithins, and 5-hydroxyindole-3-acetic acid seem to be the most plausible candidate BFIs for walnuts, whereas for almonds they could be α-tocopherol and some catechin-derived metabolites. Similarly, several studies have reported a strong association between selenium levels and consumption of Brazil nuts. Intake of vegetable oils has been mainly assessed through the measurement of specific fatty acids in different blood fractions, such as oleic acid for olive oil, α-linolenic acid for flaxseed (linseed) and rapeseed (canola) oils, and linoleic acid for sunflower oil. Additionally, hydroxytyrosol and its metabolites were the most promising distinctive BFIs for (extra) virgin olive oil. However, most of these components lack sufficient specificity to serve as BFIs. Therefore, additional studies are necessary to discover new candidate BFIs, as well as to further evaluate the specificity, sensitivity, dose-response relationships, and reproducibility of these candidate biomarkers and to eventually validate them in other populations. For the discovery of new candidate BFIs, an untargeted metabolomics approach may be the most effective strategy, whereas for increasing the specificity of the evaluation of food consumption, this could be a combination of different metabolites., Competing Interests: Not applicable.Not applicable.JMG received unrestricted research grants from Unilever for studies of fatty acids in the Alpha Omega Cohort. The other authors declare that they have no competing interests.Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Background & Aims: Screening populations for skeletal muscle mass (SMM) is important for early detection of sarcopenia. Our aim was to develop an age specific bio-impedance (BI) prediction equation for the assessment of appendicular skeletal muscle mass (ASMM) in (pre-) frail elderly people aged 65 and older., Methods: Anthropometric, BI and dual-energy X-ray absorptiometry (DEXA) measurements from 106 (pre-) frail elderly subjects (61 females and 45 males, aged 65-96 years) were used to derive three ASMM prediction equations using multiple regression analysis: one for single frequency BI measurements at 50 kHz (ASMM 50kHz ), one for measurements at the characteristic frequency (ASMM Fc ) and one for bioelectrical impedance spectroscopy (ASMM BIS ). The same data was used to evaluate an existing prediction equation., Results: ASMM 50kHz had the best fitting model (r 2 adj  = 0.923, SEE = 1.19 and a PRESS value = 163.4), followed by ASMM Fc (r 2 adj  = 0.915, SEE = 1.25 and a PRESS value = 175.9) and ASMM BIS (r 2 adj  = 0.915, SEE = 1.26 and a PRESS value = 177.1). Average ASMM measured by DEXA and both ASMM 50kHz and ASMM Fc were comparable. ASMM BIS tended to underestimate ASMM slightly. An existing prediction equation had a tendency to underestimate ASMM in people with a lower amount of ASMM and overestimate ASMM in people with a higher amount of ASMM., Conclusions: ASMM 50kHz was able to measure average ASMM within our population of (pre-) frail elderly in a valid way. However, its predictive power on both individual and population level needs to be confirmed in an independent and larger (pre-) frail elderly population and across multiple institutions and ethnic groups., (Copyright © 2015 European Society for Clinical Nutrition and Metabolism. Published by Elsevier Ltd. All rights reserved.)