Background: Adequate maternal dietary protein intake is necessary for healthy pregnancy. However, current protein intake recommendations for healthy pregnant women are based on factorial calculations of nitrogen balance data derived from nonpregnant adults. Thus, an estimate of protein requirements based on pregnancy-specific data is needed.
Objective: The objective of this study was to determine protein requirements of healthy pregnant women at 11-20 (early) and 31-38 (late) wk of gestation through use of the indicator amino acid oxidation method.
Methods: Twenty-nine healthy women (24-37 y) each randomly received a different test protein intake (range: 0.22-2.56 g · kg(-1) · d(-1)) during each study day in early (n = 35 observations in 17 women) and late (n = 43 observations in 19 women) gestation; 7 women participated in both early and late gestation studies. The diets were isocaloric and provided energy at 1.7 × resting energy expenditure. Protein was given as a crystalline amino acid mixture based on egg protein composition, except phenylalanine and tyrosine, which were maintained constant across intakes. Protein requirements were determined by measuring the oxidation rate of L-[1-(13)C]phenylalanine to (13)CO2 (F(13)CO2). Breath and urine samples were collected at baseline and isotopic steady state. Linear regression crossover analysis identified a breakpoint (requirement) at minimal F(13)CO2 in response to different protein intakes.
Results: The estimated average requirement (EAR) for protein in early and late gestation was determined to be 1.22 (R(2) = 0.60; 95% CI: 0.79, 1.66 g · kg(-1) · d(-1)) and 1.52 g · kg(-1) · d(-1) (R(2) = 0.63; 95% CI: 1.28, 1.77 g · kg(-1) · d(-1)), respectively.
Conclusions: These estimates are considerably higher than the EAR of 0.88 g · kg(-1) · d(-1) currently recommended by the Dietary Reference Intakes. To our knowledge, this study is the first to directly estimate gestational stage-specific protein requirements in healthy pregnant women and suggests that current recommendations based on factorial calculations underestimate requirements. This trial was registered at clinicaltrials.gov as NCT01784198.
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http://dx.doi.org/10.3945/jn.114.198622 | DOI Listing |
Adv Sci (Weinh)
January 2025
Department of Obstetrics and Gynecology, Zhejiang Key Laboratory of Precise Protection and Promotion of Fertility, Zhejiang Provincial Clinical Research Center for Reproductive Health and Disease, Assisted Reproduction Unit, Sir Run Run Shaw Hospital, School of Medicine, Zhejiang University, Hangzhou, 310016, China.
The developmental competence and epigenetic progression of oocytes gradually become dysregulated with increasing maternal age. However, the mechanisms underlying age-related epigenetic regulation in oocytes remain poorly understood. Zygote arrest proteins 1 and 2 (ZAR1/2) are two maternal factors with partially redundant roles in maintaining oocyte quality, mainly known by regulating mRNA stability.
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Department of Bioscience and Biotechnology, Banasthali Vidyapith, Niwai-Tonk, Rajasthan, 304022, India.
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The Queen's Medical Center, 1301 Punchbowl Street, QET 4M, Honolulu, Hawai'i, 96813, USA.
High flow nasal cannula (HFNC) can reduce the need for intubation in patients with coronavirus disease-19 (COVID-19) pneumonia induced acute hypoxemic respiratory failure (AHRF), but predictors of HFNC success could be characterized better. C-reactive protein (CRP) and D-dimer are associated with COVID-19 severity and progression. However, no one has evaluated the use of serial CRP and D-dimer ratios to predict HFNC success.
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Chaum Life Center, CHA University School of Medicine, Seoul, 06062, Korea.
No biomarker can effectively screen for early gastric cancer (EGC). Players in the A disintegrin and metalloproteinase (ADAM)-natural killer group 2 member D (NKG2D) receptor axis may have a role for that. As a proof-of-concept pilot study, the expression of ADAM8, ADAM9, ADAM10, ADAM12, ADAM17, and major histocompatibility complex (MHC) class I chain-related sequence A (MICA), a ligand for NKG2D, in gastric cancer was investigated in silico using The Cancer Genome Atlas (TCGA) database.
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January 2025
School of Physics, Engineering and Technology, University of York, Heslington, York, YO10 5DD, UK.
Prostate cancer is a disease which poses an interesting clinical question: Should it be treated? Only a small subset of prostate cancers are aggressive and require removal and treatment to prevent metastatic spread. However, conventional diagnostics remain challenged to risk-stratify such patients; hence, new methods of approach to biomolecularly sub-classify the disease are needed. Here we use an unsupervised self-organising map approach to analyse live-cell Raman spectroscopy data obtained from prostate cell-lines; our aim is to exemplify this method to sub-stratify, at the single-cell-level, the cancer disease state using high-dimensional datasets with minimal preprocessing.
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