Chemical Composition of Commercial Cow’s Milk

Foroutan, Aidin; Guo, An Chi; Vázquez-Fresno, Rosa; Lipfert, Matthias; Zhang, Lun; Zheng, Jiamin; Badran, Hasan; Budinski, Zachary; Mandal, Rupasri; Ametaj, Burim N.; Wishart, David S.

doi:10.1021/acs.jafc.9b00204

Cited by 173 publications

(168 citation statements)

References 66 publications

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“…A targeted quantitative metabolomics approach was used to analyze the samples using a combination of direct injection (DI) MS with a reverse-phase LC-MS/MS custom assay. This custom assay, in combination with an ABSciex 4000 QTrap (Applied Biosystems, Foster City, CA/MDS Sciex, Foster City, CA) mass spectrometer, can be used for the targeted identification and quantification of up to 150 different endogenous metabolites including amino acids, acylcarnitines, biogenic amines and derivatives, uremic toxins, glycerophospholipids, sphingolipids, and sugars ( 23 , 24 ). The method combines the derivatization and extraction of analytes, and the selective mass-spectrometric detection using multiple reaction monitoring pairs.…”

Section: Methodsmentioning

confidence: 99%

Metabolomics Profiling of Critically Ill Coronavirus Disease 2019 Patients: Identification of Diagnostic and Prognostic Biomarkers

Fraser¹,

Slessarev

Martin

et al. 2020

Critical Care Explorations

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104

101

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Objectives: Coronavirus disease 2019 continues to spread rapidly with high mortality. We performed metabolomics profiling of critically ill coronavirus disease 2019 patients to understand better the underlying pathologic processes and pathways, and to identify potential diagnostic/prognostic biomarkers. Design: Blood was collected at predetermined ICU days to measure the plasma concentrations of 162 metabolites using both direct injection-liquid chromatography-tandem mass spectrometry and proton nuclear magnetic resonance. Setting: Tertiary-care ICU and academic laboratory. Subjects: Patients admitted to the ICU suspected of being infected with severe acute respiratory syndrome coronavirus 2, using standardized hospital screening methodologies, had blood samples collected until either testing was confirmed negative on ICU day 3 (coronavirus disease 2019 negative) or until ICU day 10 if the patient tested positive (coronavirus disease 2019 positive). Interventions: None. Measurements and Main Results: Age- and sex-matched healthy controls and ICU patients that were either coronavirus disease 2019 positive or coronavirus disease 2019 negative were enrolled. Cohorts were well balanced with the exception that coronavirus disease 2019 positive patients suffered bilateral pneumonia more frequently than coronavirus disease 2019 negative patients. Mortality rate for coronavirus disease 2019 positive ICU patients was 40%. Feature selection identified the top-performing metabolites for identifying coronavirus disease 2019 positive patients from healthy control subjects and was dominated by increased kynurenine and decreased arginine, sarcosine, and lysophosphatidylcholines. Arginine/kynurenine ratio alone provided 100% classification accuracy between coronavirus disease 2019 positive patients and healthy control subjects ( p = 0.0002). When comparing the metabolomes between coronavirus disease 2019 positive and coronavirus disease 2019 negative patients, kynurenine was the dominant metabolite and the arginine/kynurenine ratio provided 98% classification accuracy ( p = 0.005). Feature selection identified creatinine as the top metabolite for predicting coronavirus disease 2019-associated mortality on both ICU days 1 and 3, and both creatinine and creatinine/arginine ratio accurately predicted coronavirus disease 2019-associated death with 100% accuracy ( p = 0.01). Conclusions: Metabolomics profiling with feature classification easily distinguished both healthy control subjects and coronavirus disease 2019 negative patients from coronavirus disease 2019 positive patients. Arginine/kynurenine ratio accurately identified coronavirus disease 2019 status, whereas creatinine/arginine ratio accurately predicted coronavirus disease 2019-associated death. Administrat...

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Section: Methodsmentioning

confidence: 99%

Metabolomics Profiling of Critically Ill Coronavirus Disease 2019 Patients: Identification of Diagnostic and Prognostic Biomarkers

Fraser¹,

Slessarev

Martin

et al. 2020

Critical Care Explorations

Self Cite

104

101

View full text Add to dashboard Cite

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“…Most studies report the carbon footprint per unit (e.g., kg) of final product. However, the nutritional composition of products and their importance for human nutritional requirements are important and particularly so for a food such as milk, which provides a rich source of a range of nutrients including calcium, vitamins B 2 and B 12 , EAA, and lipids (Foroutan et al, 2019). Two key constituents are the energy and protein content of the product and most dairy studies now use fat-and protein-corrected milk (FPCM) so that results are compared on a common energy and protein basis (IDF, 2015).…”

Section: Introductionmentioning

confidence: 99%

Temporal, spatial, and management variability in the carbon footprint of New Zealand milk

Ledgard

Falconer

Abercrombie

et al. 2020

Journal of Dairy Science

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The carbon footprint of milk from year-round grazedpasture dairy systems and its variability has had limited research. The objective of this study was to determine temporal, regional, and farm system variability in the carbon footprint of milk from New Zealand (NZ) average dairy production. Farm production and input data were collected from a national database for 2010/11 to 2017/18 across regions of NZ and weighted on relative production supplied to the major dairy cooperative Fonterra to produce an NZ-average. Total greenhouse gas emissions were calculated using a life cycle assessment methodology for the cradle-to-farm gate, covering all on-and off-farm contributing sources. The NZ-average carbon footprint of milk varied from 0.81 kg of CO 2 equivalent (CO 2 eq)/kg of fat-and proteincorrected milk (FPCM) in 2010/11 (with widespread drought) to 0.75 to 0.78 kg of CO 2 eq/kg of FPCM in 2013/14 to 2017/18, with a trend for a small decrease over time. Regional variation occurred with highest carbon footprint values for the Northland region due to greatest climatic and soil limitations on pasture production. Dairy cattle diet was approximately 85% from grazed pasture with up to 15% from brought-in feeds (mainly forages and by-products). The CO 2 emissions from direct fuel and electricity use constituted <2% of total CO 2 eq emissions, whereas enteric methane was near 70% of the total. An estimate of potential contribution from direct land use change (plantation forest to pasture) was 0.13 kg of CO 2 eq/kg of FPCM. This was not included because nationally there has been a net increase in forest land and a decrease in pasture land over the last 20 yr. Data used were highly representative, as evident by the same estimated carbon footprint from 368 farms (in 2017/18) from the national database compared with that from a direct survey of 7,146 farms. New Zealand-specific nitrous oxide emission factors were used, based on many validated field trials and as used in the NZ greenhouse gas inventory, resulting in an 18% lower carbon footprint than if default Intergovernmental Panel on Climate Change factors had been used. Evaluation of the upper and lower quartiles of farms based on per-cow milk production (6,044 vs. 3,542 kg of FPCM/cow) showed a 15% lower carbon footprint for the upper quartile of farms, illustrating the potential for further decrease in carbon footprint with improved farm management practices.

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“…В незбираному молоці корів знаходиться приблизно 972 унікальні сполуки, котрі утворюють 2,35 тисячі різних метаболічних структур (Foroutan et al, 2019). Крім протеїнів та ліпідів у молоці містяться молочний цукор, жиро-та водорозчинні вітаміни, мікро-і макроелементи, гормони й інші біологічно активні речовини.…”

Section: вступunclassified

The concentration of triiodothyronine and thyroxine in colostrum, milk and plasma of cows

Pan’kiv¹,

Simonov²

2020

SMLNUVMB

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Milk contains key nutrients in optimal proportions, which makes it to be an important component of the human diet. Despite the fact that consumers receive with milk a significant amount of readily available nutrients, milk can be a source of dangerous factors of chemical and biological origin. The level of physiological hormones found in milk is not a limiting factor and is studied solely for scientific purposes. However, milk hormones can have a negative effect on the health of consumers. Based on this, the aim of this work was to study the concentration of triiodothyronine and thyroxine in colostrum, milk and blood plasma of cows at different stages of lactation. The secretion of the mammary gland and blood plasma of cows of Ukrainian black-and-white dairy breed, in their 2nd–5th lactations with milk yield 5100–5700 kg of milk per previous lactation was the object of the study. The concentration of triiodothyronine and thyroxine using enzyme-linked immunosorbent assay was investigated in withdrawn samples. The obtained data indicates that the highest level of secretion of iodine-containing thyroid hormones with milk is observed on 1st–4th days of lactation. Thereafter, the concentration of hormones in milk statistically significant decreases by 2.8–3-fold (P ˂ 0.001) till 30–40th day of lactation. The high concentration of iodine-containing hormones in colostrum allows to enhance metabolic processes in calves. Studies of the concentration of thyroid hormones in the blood plasma of cows showed a gradual increase from the beginning of lactation till 10th–14th and then till 30th–40th days of lactation. By 10th–14th days we observed 2.2-fold growth of triiodothyronine level (P ˂ 0.001), and till 30th–40th days this parameter has increased by another 12.5 %. The level of thyroxine on 10th–14th days of lactation was higher, compared to such in the beginning of lactation by 62.2 % (P ˂ 0.001), and by 30th–40th day increase was by 40.9 %. The main reasons for such changes are the formation of lactation dominant, the discharge of hormones secreted by the breast and the stimulation of metabolic processes during lactation. The prospects of further research lie in investigation of the dependence of triiodothyronine and thyroxine levels in the secretion of the mammary gland on productivity, breed, age and diet of animals.

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Chemical Composition of Commercial Cow’s Milk

Cited by 173 publications

References 66 publications

Metabolomics Profiling of Critically Ill Coronavirus Disease 2019 Patients: Identification of Diagnostic and Prognostic Biomarkers

Metabolomics Profiling of Critically Ill Coronavirus Disease 2019 Patients: Identification of Diagnostic and Prognostic Biomarkers

Temporal, spatial, and management variability in the carbon footprint of New Zealand milk

The concentration of triiodothyronine and thyroxine in colostrum, milk and plasma of cows

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