Why Quality Matters
Learn more how to empower you research with Bevital’s targeted metabolomics
Quality vs Quantity
In many metabolomics research scenarios, data quality can be unequivocally more important than the sheer quantity of data points or samples. While large sample sizes are generally desirable for statistical power, if the underlying data is noisy, inaccurate, or irreproducible, even a massive dataset can lead to misleading conclusions.
| Scenario | Why Quality Matters |
|---|---|
| Biomarker Discovery & Validation for Diagnostics/Prognostics | High precision, accuracy, and reproducibility are critical for identifying reliable biomarkers. Low quality leads to false positives or missed true biomarkers, hindering clinical utility and regulatory approval. |
| Pharmacometabolomics & Drug Efficacy/Toxicity Studies | Even subtle metabolic changes can indicate drug effects. Inaccurate or noisy data can obscure true drug mechanisms, efficacy, or adverse events, leading to flawed drug development decisions. |
| Mechanism of Action Studies | Accurate and specific quantification of pathway intermediates and end products is essential to correctly map metabolic perturbations to specific biochemical pathways and understand underlying biological processes. |
| Clinical Trials (Later Phases: II/III) | Data quality, reproducibility, and standardization are paramount for regulatory scrutiny and informing critical decisions about drug development, patient stratification, and treatment monitoring. Results directly impact patient care. |
| Studies with Limited, Precious Samples | When samples are scarce (e.g., rare diseases, specific tissues), maximizing high-quality data from each individual sample is crucial. Poor quality would waste invaluable biological material and lead to insufficient insights. |
| Quantitative Pathway Modeling & Flux Analysis | These advanced computational analyses rely on highly accurate and precise quantification of metabolites to build reliable mathematical models and determine metabolic fluxes. Inaccurate input data generates fundamentally flawed models. |
| Multi-Center Studies | Consistency and comparability across sites are critical. Low data quality (e.g., high technical variability, different instrument performance, inconsistent protocols) across different laboratories will introduce significant batch effects and confound real biological variations, making results incomparable and invalidating conclusions despite large sample numbers. |
| Translational Research (Bench to Bedside) | Reliable translation of findings from basic research (e.g., animal models, cell lines) to human clinical applications requires robust and consistent data. Poor quality or irreproducible data at any stage can invalidate promising preclinical discoveries upon clinical testing. |
More Power. Real Correlations.
Bevital’s use of authentic isotope-labelled internal standards for each single analyte enhances both study power by reducing assay variation, and assay specificity by minimizing false-positive correlations. When metabolites are quantified using structurally identical, isotope-labelled internal standard, our methods achieve median between-run CVs as low as 2.7–5.9%, whereas using non-matching internal standards, as used in semi-targeted approaches, increases CVs up to 10.7 percentage points. Furthermore, quantifying each metabolites by its own authentic standard reduces the risk of spurious correlations between analytes. As a result, studies using this approach benefit from greater reproducibility and more reliable biomarker identification.
Calculations below illustrate the benefit of high precision on sample size (left) and the introduction of spurious correlation by sharing the same internal standard (right).
Study size
Sample size (N) as function of % change between means (x: ratio of means) for different assay precision (CV: 5%, 10%, 20% and 30%). Calculations are based on “Statistical Rules of Thumb” by Gerald van Belle using Two Sample Tests, Two-sided Alternatives with Type I Error 0.05 and Power 0.90.
Significant detection of e.g. a 3% change between two means requires 57 subjects per group (two samples) using a method with of 5% CV, while a sample size of 226 or 905 subjects per group are required for assays with 10 or 20% precision, respectively.
Spurious correlation
Spurious correlation, particularly concerning ratios, refers to the misleadingly high correlation that can appear between two variables x1 and x2Â (analyte peak area) when they share a common component x3 (internal standard peak area), even if the underlying original variables (analytes) are statistically independent or truly unrelated. This phenomenon can lead researchers to infer a genuine relationship where none exists.
The calculation shows the correlation coefficient between x1/x3 and x2/x3 of the two independent variables x1 and x2 (variations set to 1). For the realistic case in which variations are equal both for analytes and internal standards (v=1) the correlation coefficient between both two analytes becomes 0.5. In the situation where several analytes are quantified via a lower performing standard, the problem of spurious correlation increases, turning what might be a subtle statistical artifact into a dominant and misleading pattern.
Sample size (N) as function of % change between means (x: ratio of means) for different assay precision (CV: 5%, 10%, 20% and 30%). Calculations are based on “Statistical Rules of Thumb” by Gerald van Belle using Two Sample Tests, Two-sided Alternatives with Type I Error 0.05 and Power 0.90.
Significant detection of e.g. a 3% change between two means requires 57 subjects per group (two samples) using a method with of 5% CV, while a sample size of 226 or 905 subjects per group are required for assays with 10 or 20% precision, respectively.
Spurious correlation, particularly concerning ratios, refers to the misleadingly high correlation that can appear between two variables x1 and x2Â (analyte peak area) when they share a common component x3 (internal standard peak area), even if the underlying original variables (analytes) are statistically independent or truly unrelated. This phenomenon can lead researchers to infer a genuine relationship where none exists.
The calculation shows the correlation coefficient between x1/x3 and x2/x3 of the two independent variables x1 and x2 (variations set to 1). For the realistic case in which variations are equal both for analytes and internal standards (v=1) the correlation coefficient between both two analytes becomes 0.5. In the situation where several analytes are quantified via a lower performing standard, the problem of spurious correlation increases, turning what might be a subtle statistical artifact into a dominant and misleading pattern.