The Science

New technologies used by computation biologists have ushered in a new era of medicine and understanding of the inner workings of our bodies.


The complete set of small molecules found in biological organisms with a size of <1,500 Dalton, also known as metabolites [1][2]. This comprises biochemical substances such as amino acids, nucleic acids, fatty acids, vitamins, and hormones, as well as external chemicals like drugs, environmental contaminants, food additives, toxins [3][4] and metabolites produced by the gut microbiome.As of 2022, over 200,000 metabolites have been identified in nature, 40,000 of which are in blood, and over 1,500,000 are expected to still be identified (what we call the dark metabolome) [5].

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Sample collection

We use an innovative collection device used in studies at Stanford, Cornell, and various pharmaceutical companies. Users of the device report it to be nearly completely painless. The sample is collected from the comfort of your own home.

The collection device contains a stabilizing substance that allows the dried sample to be returned without the need for refrigeration.

Using mass-spectrometry technology, the analysis is performed on as little as 20ul, so the 80ul we collect is more than enough for accurate measurements [6][7].

Sample preparation

When the sample arrives at the lab, we store it at -80°C. Samples are defrosted, centrifuged to collect the desired blood extracts, and the extract is dried under liquid nitrogen.

We use a separate sterilized device for each sample to remove each sponge and place each of the sponges into their own Eppendorf tubes. It takes approximately 5 hours to prepare the sample using a 96-well plate.

Mass-Spectrometry Technology

The sample undergoes two different mass-spectrometry analysis steps — first, through an ultra-performance liquid chromatography coupled with tandem mass-spectrometry (UPLC-MS/MS), and second, through a flow injection analysis tandem mass-spectrometry (FIA-MS/MS) on the same instrument to specifically extract lipids.

The measured mass-spectra from the machines are then analyzed using specialized software to obtain quantification values of all metabolites.

Artificial Intelligence

Our machine learning methods analyze your data. Your results are then generated by comparing your personalized report to iollo’s database built from a curated list of peer-reviewed scientific research, domain knowledge from our team of scientists, previous testing, and user feedback.

Depending on the number of tests you do, our models calculate your trends after a few weeks or months, which allows you to build your own, individualized longitudinal metabolic monitoring.

Evidence-Based Recommendations

The recommendations we provide are based on published studies that are known to positively impact the metabolome and health. We match you with recommendations that could benefit your metabolome the most.

For example, some well-studied interventions that we could match you with include the DASH diet [8] (which reduces the risk for heart disease), fasting [9, 10], targeted physical activity [11], and more. As you build your metabolomic trends when you test over time, we'll also be able to train personalized ML models for you and give you better recommendations that you are more likely to respond to.

Metabolomics tells the rest of the story

Only 18.5% of the risk of developing chronic conditions is attributable to genetics [12]. The rest is attributable to environment, lifestyle, and therapeutic factors where there are known metabolites in each respective area.

Compared to genetic testing, which tells people what might happen to their health, metabolomics tells us exactly what is happening in a body right now. Recent studies have shown links between blood metabolites and the risk or presence of various systemic diseases, including diabetes, heart disease, and Alzheimer’s disease[13].

Selected Publications

Plasma metabolites to profile pathways in noncommunicable disease multimorbidity

Pietzner, Stewart, Raffler, Khaw, Michelotti, Kastenmüller, et al.

Nature Medicine

Metabolomics for Investigating Physiological and Pathophysiological Processes


Physiological Reviews

Emerging applications of metabolomics in drug discovery and precision medicine


Nature Reviews Drug Discovery

Multi-omics microsampling for the profiling of lifestyle-associated changes in health

Shen, Snyder

Nature Biomedical Engineering

HMDB 5.0: the Human Metabolome Database for 2022

Wishart, Guo, Oler, Wang, Anjum, Peters, et al.

Nucleic Acids Research

Genetic Factors Are Not the Major Causes of Chronic Diseases


Plos One

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  • Wishart DS. Metabolomics for Investigating Physiological and Pathophysiological Processes. Physiol Rev. 2019 Oct 1;99(4):1819–75.
  • Wishart DS, Tzur D, Knox C, Eisner R, Guo AC, Young N, et al. HMDB: the Human Metabolome Database. Nucleic Acids Res. 2007 Jan;35(Database issue):D521-526.
  • Wishart DS. Current progress in computational metabolomics. Brief Bioinform. 2007 Sep;8(5):279–93.
  • Nordström A, O’Maille G, Qin C, Siuzdak G. Nonlinear data alignment for UPLC-MS and HPLC-MS based metabolomics: quantitative analysis of endogenous and exogenous metabolites in human serum. Anal Chem. 2006 May 15;78(10):3289–95.
  • Wishart DS, Guo A, Oler E, Wang F, Anjum A, Peters H, et al. HMDB 5.0: the Human Metabolome Database for 2022. Nucleic Acids Research. 2022 Jan 7;50(D1):D622–31.
  • Tobin NH, Murphy A, Li F, Brummel SS, Taha TE, Saidi F, Owor M, Violari A, Moodley D, Chi B, Goodman KD, Koos B, Aldrovandi GM. Comparison of dried blood spot and plasma sampling for untargeted metabolomics. Metabolomics. 2021 Jun 23;17(7):62. doi: 10.1007/s11306-021-01813-3. PMID: 34164733; PMCID: PMC8340475.
  • Guma, M., Dadpey, B., Coras, R. et al. Xanthine oxidase inhibitor urate-lowering therapy titration to target decreases serum free fatty acids in gout and suppresses lipolysis by adipocytes. Arthritis Res Ther24, 175 (2022).
  • Casey M Rebholz, Alice H Lichtenstein, Zihe Zheng, Lawrence J Appel, Josef Coresh, Serum untargeted metabolomic profile of the Dietary Approaches to Stop Hypertension (DASH) dietary pattern, The American Journal of Clinical Nutrition, Volume 108, Issue 2, August 2018, Pages 243–255,
  • Washburn RL, Cox JE, Muhlestein JB, May HT, Carlquist JF, Le VT, Anderson JL, Horne BD. Pilot Study of Novel Intermittent Fasting Effects on Metabolomic and Trimethylamine N-oxide Changes During 24-hour Water-Only Fasting in the FEELGOOD Trial. Nutrients. 2019 Jan 23;11(2):246. doi: 10.3390/nu11020246. PMID: 30678028; PMCID: PMC6412259.
  • Kondoh H, Teruya T, Yanagida M. Metabolomics of human fasting: new insights about old questions. Open Biol. 2020 Sep;10(9):200176. doi: 10.1098/rsob.200176. Epub 2020 Sep 16. PMID: 32931723; PMCID: PMC7536077.
  • Contrepois K, Wu S, Moneghetti KJ, et al. Molecular Choreography of Acute Exercise. Cell. 2020;181(5):1112-1130.e16. doi:10.1016/j.cell.2020.04.043
  • Rappaport SM (2016) Genetic Factors Are Not the Major Causes of Chronic Diseases. PLOS ONE 11(4): e0154387.
  • Pietzner, Maik, et al. "Plasma metabolites to profile pathways in noncommunicable disease multimorbidity." Nature medicine 27.3 (2021): 471-479.
iollo is for anyone who wants to be empowered with information and data about their blood metabolome. iollo tests are intended exclusively for wellness purposes. iollo cannot provide you with medical advice or diagnose you with any disease or condition. Any information provided by iollo is not medical advice and is not intended to replace the advice of your medical professional.