Chemical structures and descriptions

NAD metabolome

NAD strippet

What is measured?

Nicotinic acid riboside (NAR), nicotinic acid adenine dinucleotide (NAAD), nicotinic acid (NA), nicotinamide riboside (NR), nicotinamide N-oxide (NAMO), nicotinamide mononucleotide (NMN), nicotinamide adenine dinucleotide phosphate (reduced form) (NADPH), nicotinamide adenine dinucleotide phosphate (oxidized form) (NADP), nicotinamide adenine dinucleotide (reduced form) (NADH), nicotinamide adenine dinucleotide (oxidized form) (NAD), nicotinamide (NAM), 4-methylpyridine (m4PY), 2-methylpyridine (m2PY), and 1-methylnicotinamide (mNAM).
Method(s): LC-MS/MS

The NAD metabolome

The profile includes NAD/NADH and NADP/NADPH pairs that are essential for electron transfer in biochemical pathways, with the former reflecting NAD status. NR and NMN serve as NAD precursors that are given as supplements. The profile also includes mNAM which is a NAD catabolite that is further oxidized to m2PY, and m4PY (mice), and NAMO that is formed as a result of the oxidation of NAM.

For years BEVITAL has developed and expanded analysis of the kynurenine pathway and cofactors involved. This pathway (blue) serves as a route of de novo NAD synthesis, and includes several metabolites with immunomodulatory and neuroactive effects. The kynurenine pathway and NAD+ metabolism are tightly interconnected: changes in one directly impact the other, with significant implications for cellular health and disease processes.

Indication(s)

The NAD metabolome is currently investigated in relation to  mitochondrial function, energy metabolism, cellular senescence, epigenetic regulation, and is relevant to research investigating neurodegenerative diseases, type 2 diabetes, insulin resistance and obesity.

Specimen, collection and processing

Whole blood should be sampled in cold EDTA tubes, preferentially put in liquid nitrogen, and stored at –80°C. Freeze-thawing causes preanalytical distortion of the metabolite profile, including rapid degradation of NADH (and NADPH), and a substantial formation of NAM.

Transportation

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

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Reported values, interpretation

The reported values for the NAD metabolome vary substantially across publications, partly related to preanalytical degradation and metabolite interconversion in hemolyzed blood. After supplementation with NR, NAAD in particularly, but also NAMO, m2PYand mNAM may increase more than 10-fold from values close to LOD to several micromolar. Also, NAD and NADH may increase more that 2-fold following supplementation.
Reported values (µmol/L): NAR, 3-15; NAAD, 0.005-1; NA, 0-0.6; NR, 0-9; NAMO, 0.005-1.5; NMN, 2-10; NADPH, 1-4; NADP, 10-40; NADH, 0.5-3; NAD, 10-50; NAM, 5-25; m2PY, 0-20; mNAM, 0.1-3.

Literature

Media

1. Iqbal, T., & Nakagawa, T. (2024). The therapeutic perspective of NAD+ precursors in age-related diseases. Biochem Biophys Res Commun, 702, 149590.
2. Migaud, M. E., Ziegler, M., & Baur, J. A. (2024). Regulation of and challenges in targeting NAD+ metabolism. Nat Rev Mol Cell Biol, 25(10), 822-840.
3. Gindri, I. M., Ferrari, G., Pinto, L. P. S., Bicca, J., Dos Santos, I. K., Dallacosta, D. et al. (2024). Evaluation of safety and effectiveness of NAD in different clinical conditions: a systematic review. Am J Physiol Endocrinol Metab, 326(4), E417-E427.

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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 in Bergen. At Bevital she works with LC-MS/MS anlyses 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.

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. At Bevital, she works with GC-MS/MS analyses, focusing on 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 holds a PhD in physics from the University of Münster in Germany. He has over three decades of experience in Time-of-Flight mass spectrometry. He leverages his extensive expertise to provide customers with cutting-edge MALDI-MS analysis and the newest Olink Proteomics services.

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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