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de Figueiredo M, Saugy J, Saugy M, Faiss R, Salamin O, Nicoli R, Kuuranne T, Rudaz S, Botrè F, Boccard J. A new multimodal paradigm for biomarkers longitudinal monitoring: a clinical application to women steroid profiles in urine and blood. Anal Chim Acta 2023; 1267:341389. [PMID: 37257979 DOI: 10.1016/j.aca.2023.341389] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2023] [Revised: 05/12/2023] [Accepted: 05/16/2023] [Indexed: 06/02/2023]
Abstract
BACKGROUND Most current state-of-the-art strategies to generate individual adaptive reference ranges are designed to monitor one clinical parameter at a time. An innovative methodology is proposed for the simultaneous longitudinal monitoring of multiple biomarkers. The estimation of individual thresholds is performed by applying a Bayesian modeling strategy to a multivariate score integrating several biomarkers (compound concentration and/or ratio). This multimodal monitoring was applied to data from a clinical study involving 14 female volunteers with normal menstrual cycles receiving testosterone via transdermal route, as to test its ability to detect testosterone administration. The study samples consisted of urine and blood collected during 4 weeks of a control phase and 4 weeks with a daily testosterone gel application. RESULTS Integrating multiple biomarkers improved the detection of testosterone gel administration with substantially higher sensitivity compared with the distinct follow-up of each biomarker, when applied to selected urine and serum steroid biomarkers, as well as the combination of both. Among the 175 known positive samples, 38% were identified by the multimodal approach using urine biomarkers, 79% using serum biomarkers and 83% by combining biomarkers from both biological matrices, whereas 10%, 67% and 64% were respectively detected using standard unimodal monitoring. SIGNIFICANCE AND NOVELTY The detection of abnormal patterns can be improved using multimodal approaches. The combination of urine and serum biomarkers reduced the overall number of false-negatives, thus evidencing promising complementarity between urine and blood sampling for doping control, as highlighted in the case of the use of transdermal testosterone preparations. The generation in a multimodal setting of adaptive and personalized reference ranges opens up new opportunities in clinical and anti-doping profiling. The integration of multiple parameters in a longitudinal monitoring is expected to provide a more complete evaluation of individual profiles generating actionable intelligence to further guide sample collection, analysis protocols and decision-making in clinics and anti-doping.
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Affiliation(s)
- Miguel de Figueiredo
- School of Pharmaceutical Sciences, University of Geneva, Geneva, Switzerland; Institute of Pharmaceutical Sciences of Western Switzerland, University of Geneva, Geneva, Switzerland
| | - Jonas Saugy
- Center of Research and Expertise in Anti-Doping Sciences, Institute of Sport Sciences, University of Lausanne, Lausanne, Switzerland
| | - Martial Saugy
- Center of Research and Expertise in Anti-Doping Sciences, Institute of Sport Sciences, University of Lausanne, Lausanne, Switzerland
| | - Raphaël Faiss
- Center of Research and Expertise in Anti-Doping Sciences, Institute of Sport Sciences, University of Lausanne, Lausanne, Switzerland
| | - Olivier Salamin
- Center of Research and Expertise in Anti-Doping Sciences, Institute of Sport Sciences, University of Lausanne, Lausanne, Switzerland; Swiss Laboratory for Doping Analyses, University Center of Legal Medicine, Lausanne and Geneva, Lausanne University, Hospital and University of Lausanne, Switzerland
| | - Raul Nicoli
- Swiss Laboratory for Doping Analyses, University Center of Legal Medicine, Lausanne and Geneva, Lausanne University, Hospital and University of Lausanne, Switzerland
| | - Tiia Kuuranne
- Swiss Laboratory for Doping Analyses, University Center of Legal Medicine, Lausanne and Geneva, Lausanne University, Hospital and University of Lausanne, Switzerland
| | - Serge Rudaz
- School of Pharmaceutical Sciences, University of Geneva, Geneva, Switzerland; Institute of Pharmaceutical Sciences of Western Switzerland, University of Geneva, Geneva, Switzerland
| | - Francesco Botrè
- Center of Research and Expertise in Anti-Doping Sciences, Institute of Sport Sciences, University of Lausanne, Lausanne, Switzerland
| | - Julien Boccard
- School of Pharmaceutical Sciences, University of Geneva, Geneva, Switzerland; Institute of Pharmaceutical Sciences of Western Switzerland, University of Geneva, Geneva, Switzerland.
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Roshan D, Ferguson J, Pedlar CR, Simpkin A, Wyns W, Sullivan F, Newell J. A comparison of methods to generate adaptive reference ranges in longitudinal monitoring. PLoS One 2021; 16:e0247338. [PMID: 33606821 PMCID: PMC7894906 DOI: 10.1371/journal.pone.0247338] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/12/2020] [Accepted: 02/05/2021] [Indexed: 11/18/2022] Open
Abstract
In a clinical setting, biomarkers are typically measured and evaluated as biological indicators of a physiological state. Population based reference ranges, known as 'static' or 'normal' reference ranges, are often used as a tool to classify a biomarker value for an individual as typical or atypical. However, these ranges may not be informative to a particular individual when considering changes in a biomarker over time since each observation is assessed in isolation and against the same reference limits. To allow early detection of unusual physiological changes, adaptation of static reference ranges is required that incorporates within-individual variability of biomarkers arising from longitudinal monitoring in addition to between-individual variability. To overcome this issue, methods for generating individualised reference ranges are proposed within a Bayesian framework which adapts successively whenever a new measurement is recorded for the individual. This new Bayesian approach also allows the within-individual variability to differ for each individual, compared to other less flexible approaches. However, the Bayesian approach usually comes with a high computational cost, especially for individuals with a large number of observations, that diminishes its applicability. This difficulty suggests that a computational approximation may be required. Thus, methods for generating individualised adaptive ranges by the use of a time-efficient approximate Expectation-Maximisation (EM) algorithm will be presented which relies only on a few sufficient statistics at the individual level.
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Affiliation(s)
- Davood Roshan
- School of Mathematics, Statistics and Applied Mathematics, National University of Ireland Galway, Galway, Ireland.,CÚRAM, SFI Research Centre for Medical Devices, National University of Ireland, Galway, Ireland.,Prostate Cancer Institute, National University of Ireland Galway, Galway, Ireland
| | - John Ferguson
- HRB Clinical Research Facility, National University of Ireland Galway, Galway, Ireland
| | - Charles R Pedlar
- Faculty of Sport, Health and Applied Science, St Mary's University, Twickenham, United Kingdom
| | - Andrew Simpkin
- School of Mathematics, Statistics and Applied Mathematics, National University of Ireland Galway, Galway, Ireland
| | - William Wyns
- The Lambe Institute for Translational Medicine, National University of Ireland, Galway, Ireland
| | - Frank Sullivan
- Prostate Cancer Institute, National University of Ireland Galway, Galway, Ireland
| | - John Newell
- School of Mathematics, Statistics and Applied Mathematics, National University of Ireland Galway, Galway, Ireland.,CÚRAM, SFI Research Centre for Medical Devices, National University of Ireland, Galway, Ireland
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