Introduction: The kinetics of protein oxidation, monitored in breath, and its contribution to the whole body protein status is not well established. Objectives: To analyze protein oxidation in various metabolic conditions we developed/validated a 13C-protein oxidation breath test using low enriched milk proteins. Method/Design: 30 g of naturally labeled 13C-milk proteins were consumed by young healthy volunteers. Breath samples were taken every 10 min and 13CO2 was measured by Isotope Ratio Mass Spectrometry. To calculate the amount of oxidized substrate we used: substrate dose, molecular weight and 13C enrichment of the substrate, number of carbon atoms in a substrate molecule, and estimated CO2-production of the subject based on body surface area. Results: We demonstrated that in 255 min 20% ± 3% (mean ± SD) of the milk protein was oxidized compared to 18% ± 1% of 30 g glucose. Postprandial kinetics of oxidation of whey (rapidly digestible protein) and casein (slowly digestible protein) derived from our breath test were comparable to literature data regarding the kinetics of appearance of amino acids in blood. Oxidation of milk proteins was faster than that of milk lipids (peak oxidation 120 and 290 minutes, respectively). After a 3-day protein restricted diet (~10 g of protein/day) a decrease of 31% ± 18% in milk protein oxidation was observed compared to a normal diet. Conclusions: Protein oxidation, which can be easily monitored in breath, is a significant factor in protein metabolism. With our technique we are able to characterize changes in overall protein oxidation under various meta-bolic conditions such as a protein restricted diet, which could be relevant for defining optimal protein intake under various conditions. Measuring protein oxidation in new-born might be relevant to establish its contribution to the protein status and its age-dependent development.
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BACKGROUND & AIMS: Sufficient protein intake is of great importance in hemodialysis (HD) patients, especially for maintaining muscle mass. Daily protein needs are generally estimated using bodyweight (BW), in which individual differences in body composition are not accounted for. As body protein mass is best represented by fat free mass (FFM), there is a rationale to apply FFM instead of BW. The agreement between both estimations is unclear. Therefore, the aim of this study is to compare protein needs based on either FFM or BW in HD patients.METHODS: Protein needs were estimated in 115 HD patients by three different equations; FFM, BW and BW adjusted for low or high BMI. FFM was measured by multi-frequency bioelectrical impedance spectroscopy and considered the reference method. Estimations of FFM x 1.5 g/kg and FFM x 1.9 g/kg were compared with (adjusted)BW x 1.2 and x 1.5, respectively. Differences were assessed with repeated measures ANOVA and Bland-Altman plots.RESULTS: Mean protein needs estimated by (adjusted)BW were higher compared to those based on FFM, across all BMI categories (P < 0.01) and most explicitly in obese patients. In females with BMI >30, protein needs were 69 ± 17.4 g/day higher based on BW and 45 ± 9.3 g/day higher based on BMI adjusted BW, compared to FFM. In males with BMI >30, protein needs were 51 ± 20.4 g/day and 23 ± 20.9 g/day higher compared to FFM, respectively.CONCLUSIONS: Our data show large differences and possible overestimations of protein needs when comparing BW to FFM. We emphasize the importance of more research and discussion on this topic.
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Under- and overfeeding in Intensive Care Units (ICUs) are linked to prolonged hospitalisation, increased morbidity, and elevated mortality. This study investigates whether ICU patients were optimally nourished according to the European Society for Clinical Nutrition and Metabolism (ESPEN) guidelines. Methods: A cohort of 158 COVID-19 patients requiring intensive care for severe respiratory failure, necessitating a nuanced approach to nutritional support, was analysed. Nutritional status was determined regarding kilocalories and protein using the Energy Expenditure derived from ventilator-measured VCO2 and the adjusted Weir equation, and data on intake through enteral feeding was used. The study included ventilated patients hospitalised for over five days without Extra Corporeal Life Support (ECLS) and receiving enteral nutrition. Associations between mortality and (i) calorie intake and (ii) protein intake were examined using Chi-Square statistics. Results: Conforming to the ESPEN guidelines, 45% of patients were malnourished, and 21% were over-nourished in kilocalories. Additionally, 61% were malnourished, and 16% were over-nourished in protein. The distribution between the groups of survivors and deceased relative to each of the groups well nourished, malnourished, and over-nourished was not statistically different (p = 0.21). The protein distribution among survivors and deceased groups was not statistically different (p = 0.67) regarding correct, insufficient, or excessive protein intake. Conclusions: Based on ESPEN guidelines, most ICU patients were inadequately nourished in kilocalories and protein. However, no significant survival differences were observed across groups with varying nutritional adequacy. Further research is recommended to explore the implications of nutritional interventions in critically ill patients.
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Cell-based production processes in bioreactors and fermenters need to be carefully monitored due to the complexity of the biological systems and the growth processes of the cells. Critical parameters are identified and monitored over time to guarantee product quality and consistency and to minimize over-processing and batch rejections. Sensors are already available for monitoring parameters such as temperature, glucose, pH, and CO2, but not yet for low-concentration substances like proteins and nucleic acids (DNA). An interesting critical parameter to monitor is host cell DNA (HCD), as it is considered an impurity in the final product (downstream process) and its concentration indicates the cell status (upstream process). The Molecular Biosensing group at the Eindhoven University of Technology and Helia Biomonitoring are developing a sensor for continuous biomarker monitoring, based on Biosensing by Particle Motion. With this consortium, we want to explore whether the sensor is suitable for the continuous measurement of HCD. Therefore, we need to set-up a joint laboratory infrastructure to develop HCD assays. Knowledge of how cells respond to environmental changes and how this is reflected in the DNA concentration profile in the cell medium needs to be explored. This KIEM study will enable us to set the first steps towards continuous HCD sensing from cell culture conditions controlling cell production processes. It eventually generates input for machine learning to be able to automate processes in bioreactors and fermenters e.g. for the production of biopharmaceuticals. The project entails collaboration with new partners and will set a strong basis for subsequent research projects leading to scientific and economic growth, and will also contribute to the human capital agenda.
