Chemical structures and descriptions

Short-chain fatty acids (SCFAs)

SCFAs

What is measured?

Formate (Frm), acetate (Ace), propionate (Pro), butyrate (But), isobutyrate (iBut), valerate (Vlr), isovalerate (iVlr) and α-methylbutyrate (aMB) .
Method(s): GC-MS/MS.

What are short-chain fatty acids (SCFAs)?

Formate holds a unique position among the SCFAs since has a critical role in one-carbon metabolism, primarily as a source of one-carbon groups for the synthesis of 10-formyl-tetrahydrofolate (10-fTHF). 10-fTHF is a substrate for purine synthesis, and after further reduction of the one-carbon group, may serve as a substrate for thymidylate synthesis and for homocysteine remethylation. Serum formate shows a positive association with serum folate and with potential metabolic precursors (serine, methionine, tryptophan, choline) and an inverse association with vitamin B12 (1).
Acetate, propionate and butyrate are SCFAs produced from non-digestible complex carbohydrates (dietary fibre) in the gut during microbial fermentation, whereas the branched-chain SCFAs, isobutyrate , isovalerate and α-methylbutyrate, are bacterial-produced metabolites of amino acids. Acetate is the most abundant (50-70 %) followed by propionate and butyrate. They are absorbed into the systemic circulation, and each SCFA exerts specific metabolic effects, and may mediate beneficial but also adverse health effects.
SCFAs, in particular butyrate, are anti-inflammatory, expand the pool of intestinal regulatory T cells, protect against allergic sensitization, mitigate production of reactive oxygen species, are essential for gut integrity, and exert antiproliferative effects on cancer cells. Butyrate’s effects on the immune system are mediated through the inhibition of class I histone deacetylases and activation of G-protein coupled receptors: GPR109A, GPR41 and GPR43. SCFAs increase insulin secretion (via GPR41/43), and low gut-derived SCFAs have been suggested to be associated with type II diabetes, insulin resistance, obesity and NAFLD (2).
In a recent seminal review article, the authors recommend measurement of circulating SCFAs in human studies, since the SCFAs in faeces may not be an accurate measure of SCFA production or exposure (3).

Important note on contamination

Contamination from chemicals, additives and blood collection tubes with formate and acetate in particular is widespread. For example, we observed contamination of sealing O-rings in one brand of cryopreservation tubes with formate (1). Others have reported on the presence of acetate and propionate in EDTA K2 tubes, and propionate and butyrate in tubes containing polyacrylamide gel (4).

Indication(s)

To investigate the metabolomic signature of human diseases.

Specimen, collection and processing

Matrix: Serum.
Volume: Minimum volume is 60 µL, but 200 µL is optimal and allows reanalysis.
Preparation and stability: Samples should be put on ice immediately after collection and stored at -80 °C.

Transportation

Frozen, on dry ice. (for general instruction on transportation, click here)

Reported values, interpretation

Frm: 8-100 µmol/L; Ace: 5-300 µmol/L; Pro: 1-13 µmol/L; But: 1-30 µmol/L; iBut: 0.7-6 µmol/L; Vlr: 0.2-1 µmol/L; iVlr: 0.3-3 µmol/L; aMB: 0.3-3 µmol/L.
Intraclass correlation coefficient (ICC): na.

Literature

1. Brosnan, J. T., Mills, J. L., Ueland, P. M., Shane, B., Fan, R., Chiu, C.-Y., Pangilinan, F., Brody, L. C., Brosnan, M. E., Pongnopparat, T., & Molloy, A. M. (2018). Lifestyle, metabolite, and genetic determinants of formate concentrations in a cross-sectional study in young, healthy adults. Am J Clin Nutr, 107, 345-354.
2. Blaak, E. E., Canfora, E. E., Theis, S., Frost, G., Groen, A. K., Mithieux, G., Nauta, A., Scott, K., Stahl, B., van Harsselaar, J., van Tol, R., Vaughan, E. E., & Verbeke, K. (2020). Short chain fatty acids in human gut and metabolic health. Benef Microbes, 11, 411-455.
3. Eslick, S., Thompson, C., Berthon, B., & Wood, L. (2021). Short-chain fatty acids as anti-inflammatory agents in overweight and obesity: a systematic review and meta-analysis. Nutr Rev, nuab059.
4. Deroover, L., Boets, E., Tie, Y., Vandermeulen, G., & Verbeke, K. (2017). Quantification of plasma or serum short-chain fatty acids: Choosing the correct blood tube. J Nutrition Health Food Sci, 5, 1-6.

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Per Christian Eriksen

Øivind

Per Magne Ueland has been Professor at the University of Bergen 1987-2018. He is one of the founders of Bevital AS and the scientific advisor in Bevital since 2023. His interests includes biomarkers related to nutrition, inflammation, ageing and life-style related chronic diseases. Per is committed to the development of precise, high-throughput mass spectrometry methods, tailored for metabolic profiling of biobank specimens from large cohorts.

Ove completed his education in Biomedical Science at the Western Norway University of Applied Sciences, supplemented by specialized training in Electrical Engineering and Electronics at the Royal Norwegian Naval Training Establishment and the National Institute of Technology. With many years of experience as a biomedical scientist in hospital laboratories—specializing primarily in microbiology—he brings a unique blend of clinical and technical expertise to his work. Ove focuses on the design and prototyping of electronics, as well as the service and maintenance of laboratory instrumentation, ensuring that technical equipment and workflows remain precise and reliable for research-focused activities.

Lena holds a master’s degree in biology from the University of Bergen, where her thesis focused on identifying whale skeletons using zooarchaeology by mass spectrometry (ZooMS). At Bevital, she works with LC‑MS/MS analyses and method development, focusing on accurate and reliable testing of biological samples. She is dedicated to ensuring precise and high‑quality results that contribute to reliable scientific outcomes and support ongoing research efforts.

Marit holds a degree in chemical engineering from Bergen Ingeniørhøyskole, which is now part of the Western Norway University of Applied Sciences. She works with quantitative analysis and method development on LC-MS/MS at the laboratory of Bevital AS.

Randi holds a Master of Science in Chemical Process Engineering from the Norwegian University of Science and Technology (NTNU). She has been part of Bevital since its very beginning, contributing her expertise primarily to the LC-MS/MS platforms, but also to the microbiological assays. In 2021, she stepped into the role of Manager/CEO, where she is dedicated to strengthening Bevital’s innovative profile and ensuring the company’s continued growth and success. She is especially motivated by advancing research that improves health insights and by fostering collaboration that drives scientific and technological progress.

Ove completed a bachelor’s degree in Biomedical Laboratory Sciences at the Western Norway University of Applied Sciences in Bergen. With extensive experience in method development and expertise in GC-MS/MS, he specializes in optimizing analytical techniques for research-focused studies. At Bevital, Ove is dedicated to advancing laboratory methods and workflows, contributing to innovative research through precise and reliable analytical solutions.

Lene holds a bachelor’s degree in Biomedical Laboratory Science from the Western Norway University of Applied Sciences, where she is also completing her master’s degree in Medical Laboratory Technology, expected to graduate in 2026. Her master’s thesis focuses on method validation in fatty acid analysis. At Bevital, she works with GC-MS/MS analyses, routinely performing SCFA measurements and emphasizing accurate and reliable testing of biological samples. With her strong laboratory background, Lene is committed to delivering high-quality results that support medical research.

Klaus earned his PhD in physics from the University of Münster in Germany. For more than thirty years he has specialized in Time‑of‑Flight mass spectrometry, contributing innovative approaches to SNP genotyping and protein quantification. Together with his colleague Lene Njåstad, he oversees Bevital’s Olink Proteomics service. He also leads Bevital’s website and media design efforts, ensuring a clear and informative public presence.

Adrian holds a PhD in diabetes research, along with bachelor’s and master’s degrees in biomedical science and public health, respectively. With over 20 years of experience in laboratory science, he leads high-precision metabolite analyses and method development at Bevital. His expertise centers on quantifying biomarkers, metabolite classes, and metabolic pathways related to nutrition, cardiovascular and neurodegenerative diseases, and cancer. Adrian is committed to advancing research quality and actively collaborates nationally and internationally, leveraging targeted metabolomics to support innovative, multidisciplinary research.

Statistical power is the probability that a statistical test will correctly reject a false null hypothesis (H0​) when a specific alternative hypothesis (H1​) is true. H0​ is the null hypothesis, which states there is no effect or no difference. H1​ is the alternative hypothesis, which states there is a real effect or difference. Alpha (α) is the probability of a Type I error (a false positive), which is the risk of incorrectly rejecting the H0​ when it is actually true. You set this value before the experiment, commonly at 0.05. Beta (β) is the probability of a Type II error (a false negative), which is the risk of failing to reject the H0​ when it is actually false.

Power is calculated as 1−β. Increasing power means you are decreasing the probability of making a Type II error.

Several factors can be adjusted to increase the power of a statistical test:

  • Effect Size: This is the magnitude of the difference you are trying to detect. A larger effect size is easier to detect, thus increasing power. 

  • Sample Size: The number of observations in a study. A larger sample size provides more information about the population, reducing the margin of error and increasing the power to detect a true effect.

  • Variation: Refers to the spread or standard deviation of the data within the population. Less variation makes it easier to distinguish a real effect from random noise, thereby increasing power.

  • Alpha (): Increasing the alpha level (e.g., from 0.05 to 0.10) also increases power, but at the cost of a higher risk of a Type I error. This trade-off is often undesirable.

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