Epidemiology and biobanks

Decentralized metabolomics for the future of population health

Connecting science globally

In the last decade, the field of metabolomics has been growing at an exponential rate, with scientists coming together to build a global network of population-based studies. This offers the scientific community an enormous opportunity to pool data for meta-analysis and further collaboration. Absolute quantification of metabolites with biocrates technology supports this, by making it easier to compare, exchange and integrate omics data between studies.

Prof. Dr. Seizo Koshiba
Metabolomics in the Tohoku Medical Megabank Cohort Project

Hundreds of thousands of samples in large epidemiological cohorts have been measured with biocrates technology, resulting in hundreds of publications. Please contact us for specific questions or for a comprehensive publication list.

To leverage your study and expedite insights on a global scale, build on our growing certified laboratory network or utilize innovative resources such as our Quantitative metabolomics database (QMDB).

References

Rothwell et al., 2023 | Circulating amino acid levels and colorectal cancer risk in the European Prospective Investigation into Cancer and Nutrition and UK Biobank cohorts

Tian et al., 2023 | Metabolomic markers mediate erythrocyte anisocytosis in older adults: Results from three independent aging cohorts

TumSuden et al., 2022 | Metabolite QTL analysis of ROSMAP and ADNI prioritized sequencing data identifies C14:2 genetic locus on Chr 2

Floegel et al., 2017 | Serum metabolites and risk of myocardial infarction and ischemic stroke: a targeted metabolomic approach in two German prospective cohorts

FAIR data — biocrates democratizes metabolomics

In 2016, the “FAIR Guiding Principles for scientific data management and stewardship” were published in the journal Scientific Data. These guidelines aim to “improve the Findability, Accessibility, Interoperability, and Reuse of digital assets.” To meet the requirements of FAIR compliance, metabolomic data must fulfil the following criteria

Findable | Data need to be uploadable to data repositories

Accessible | The repository should be visible for the scientific community and publicly available

Interoperable | Uploaded data require a harmonized format and common units to enable shared data from different sources

Reusable | Data must be comparable, with long-term validity, independent of time

biocrates is committed to facilitating measurement of large numbers of samples with scientific and economic excellence. Our technology connects your results to other population-based studies globally, leveraging metabolite coverage and quantification of data with clinical relevance. All biocrates methods are standardized and quality controlled – data are quantifiable, reproducible and enable FAIR compliance.

References

Griswold et al., 2022 | FAIR sharing of the BLSA data ecosystem

Vrijheid et al., 2021 | Advancing tools for human early lifecourse exposome research and translation (ATHLETE)

Ashrafian et al., 2021 | Metabolomics: The Stethoscope for the Twenty-First Century

Metabolomics in population-based studies

Proven excellence by international ring trials

The superior lab-to-lab comparability of biocrates kits enables data integration from multiple laboratories — a key requirement for a scalable, decentralized global standard metabolomics solution. The technical excellence and global analytical reproducibility of biocrates kits have been proven in international ring trials, such as the MxP® Quant 500 ring trial. This included 14 participating laboratories from North America, Europe and Asia and demonstrated high reproducibility and sensitivity with excellent precision. The publication is currently under peer review.

References

Saigusa et al., 2021 |  Comparison of Kit-Based Metabolomics with Other Methodologies in a Large Cohort, towards Establishing Reference Values

His et al., 2019 | Prospective analysis of circulating metabolites and breast cancer in EPIC

Cavus et al., 2019 | Association of Circulating Metabolites With Risk of Coronary Heart Disease in a European Population (BiomarCARE)

Koenig et al., 2022 | Whole Exome Sequencing Enhanced Imputation Identifies 85 Metabolite Associations in the Alpine CHRIS Cohort

Wang-Sattler et al., 2008 | Metabolic Profiling Reveals Distinct Variations Linked to Nicotine Consumption in Humans — First Results from the KORA Study

John-Williams et al., 2017 | Targeted metabolomics and medication classification data from participants in the ADNI1 cohort

Lotta et al., 2021 | A cross-platform approach identifies genetic regulators of human metabolism and health

Griswold et al., 2022 | FAIR sharing of the BLSA data ecosystem