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Taghdiri A. Cardiovascular biomarkers: exploring troponin and BNP applications in conditions related to carbon monoxide exposure. Egypt Heart J 2024; 76:9. [PMID: 38282021 PMCID: PMC10822827 DOI: 10.1186/s43044-024-00446-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2023] [Accepted: 01/25/2024] [Indexed: 01/30/2024] Open
Abstract
BACKGROUND The diagnosis and prognosis of cardiovascular disorders are greatly aided by cardiovascular biomarkers. The uses of troponin and B-type natriuretic peptide in situations involving carbon monoxide exposure are examined in this narrative review. These biomarkers are important because they help predict outcomes in cardiovascular disorders, track the effectiveness of therapy, and influence therapeutic choices. MAIN BODY Clinical practice makes considerable use of B-type natriuretic peptide (BNP), which has diuretic and vasodilatory effects, and troponin, a particular marker for myocardial injury. Carbon monoxide (CO) poisoning is a major worldwide health problem because CO, a "silent killer," has significant clinical consequences. Higher risk of cardiac problems, poorer clinical outcomes, and greater severity of carbon monoxide poisoning are all linked to elevated troponin and B-type natriuretic peptide levels. BNP's adaptability in diagnosing cardiac dysfunction and directing decisions for hyperbaric oxygen therapy is complemented by troponin's specificity in identifying CO-induced myocardial damage. When combined, they improve the accuracy of carbon monoxide poisoning diagnoses and offer a thorough understanding of cardiac pathophysiology. CONCLUSIONS To sum up, this review emphasizes the importance of troponin and B-type natriuretic peptide (BNP) as cardiac indicators during carbon monoxide exposure. While BNP predicts long-term cardiac problems, troponin is better at short-term morbidity and death prediction. When highly sensitive troponin I (hsTnI) and B-type natriuretic peptide are combined, the diagnostic accuracy of carbon monoxide poisoning patients is improved. One of the difficulties is evaluating biomarker levels since carbon monoxide poisoning symptoms are not always clear-cut. Accurate diagnosis and treatment depend on the investigation of new biomarkers and the use of standardized diagnostic criteria. The results advance the use of cardiovascular biomarkers in the intricate field of carbon monoxide exposure.
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Affiliation(s)
- Andia Taghdiri
- Faculty of Medicine, Ivane Javakhishvili Tbilisi State University, Tbilisi, Georgia.
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Margaritelis NV, Nastos GG, Vasileiadou O, Chatzinikolaou PN, Theodorou AA, Paschalis V, Vrabas IS, Kyparos A, Fatouros IG, Nikolaidis MG. Inter-individual variability in redox and performance responses after antioxidant supplementation: A randomized double blind crossover study. Acta Physiol (Oxf) 2023; 238:e14017. [PMID: 37401190 DOI: 10.1111/apha.14017] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/13/2023] [Revised: 06/14/2023] [Accepted: 06/19/2023] [Indexed: 07/05/2023]
Abstract
AIM We aimed to investigate the inter-individual variability in redox and physiological responses of antioxidant-deficient subjects after antioxidant supplementation. METHODS Two hundred individuals were sorted by plasma vitamin C levels. A low vitamin C group (n = 22) and a control group (n = 22) were compared in terms of oxidative stress and performance. Subsequently, the low vitamin C group received for 30 days vitamin C (1 g) or placebo, in randomized, double-blind, crossover fashion, and the effects were examined through a mixed-effects model, while individual responses were calculated. RESULTS The low vitamin C group exhibited lower vitamin C (-25 μmol/L; 95%CI[-31.7, -18.3]; p < 0.001), higher F2 -isoprostanes (+17.1 pg/mL; 95%CI[6.5, 27.7]; p = 0.002), impaired VO2max (-8.2 mL/kg/min; 95%CI[-12.8, -3.6]; p < 0.001) and lower isometric peak torque (-41.5 Nm; 95%CI[-61.8, -21.2]; p < 0.001) compared to the control group. Regarding antioxidant supplementation, a significant treatment effect was found in vitamin C (+11.6 μmol/L; 95%CI[6.8, 17.1], p < 0.001), F2 -isoprostanes (-13.7 pg/mL; 95%CI[-18.9, -8.4], p < 0.001), VO2max (+5.4 mL/kg/min; 95%CI[2.7, 8.2], p = 0.001) and isometric peak torque (+18.7; 95%CI[11.8, 25.7 Nm], p < 0.001). The standard deviation for individual responses (SDir) was greater than the smallest worthwhile change (SWC) for all variables indicating meaningful inter-individual variability. When a minimal clinically important difference (MCID) was set, inter-individual variability remained for VO2max , but not for isometric peak torque. CONCLUSION The proportion of response was generally high after supplementation (82.9%-95.3%); however, a few participants did not benefit from the treatment. This underlines the potential need for personalized nutritional interventions in an exercise physiology context.
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Affiliation(s)
- Nikos V Margaritelis
- Department of Physical Education and Sports Science at Serres, Aristotle University of Thessaloniki, Serres, Greece
| | - George G Nastos
- Department of Physical Education and Sports Science at Serres, Aristotle University of Thessaloniki, Serres, Greece
| | - Olga Vasileiadou
- Department of Physical Education and Sports Science at Serres, Aristotle University of Thessaloniki, Serres, Greece
| | - Panagiotis N Chatzinikolaou
- Department of Physical Education and Sports Science at Serres, Aristotle University of Thessaloniki, Serres, Greece
| | - Anastasios A Theodorou
- Department of Life Sciences, School of Sciences, European University Cyprus, Nicosia, Cyprus
| | - Vassilis Paschalis
- School of Physical Education and Sport Science, National and Kapodistrian University of Athens, Athens, Greece
| | - Ioannis S Vrabas
- Department of Physical Education and Sports Science at Serres, Aristotle University of Thessaloniki, Serres, Greece
| | - Antonios Kyparos
- Department of Physical Education and Sports Science at Serres, Aristotle University of Thessaloniki, Serres, Greece
| | - Ioannis G Fatouros
- Department of Physical Education and Sport Sciences, University of Thessaly, Trikala, Greece
| | - Michalis G Nikolaidis
- Department of Physical Education and Sports Science at Serres, Aristotle University of Thessaloniki, Serres, Greece
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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] [What about the content of this article? (0)] [Affiliation(s)] [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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Lucagbo MD, Mathew T. Rectangular tolerance regions and multivariate normal reference regions in laboratory medicine. Biom J 2023; 65:e2100180. [PMID: 36284498 DOI: 10.1002/bimj.202100180] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2021] [Revised: 07/27/2022] [Accepted: 08/04/2022] [Indexed: 11/06/2022]
Abstract
Reference intervals are widely used in the interpretation of results of biochemical and physiological tests of patients. When there are multiple biochemical analytes measured from each subject, a multivariate reference region is needed. Because of their greater specificity against false positives, such reference regions are more desirable than separate univariate reference intervals that disregard the cross-correlations between variables. Traditionally, under multivariate normality, reference regions have been constructed as ellipsoidal regions. This approach suffers from a major drawback: it cannot detect component-wise extreme observations. In the present work, procedures are developed to construct rectangular reference regions in the multivariate normal setup. The construction is based on the criteria for tolerance intervals. The problems addressed include the computation of a rectangular tolerance region and simultaneous tolerance intervals. Also addressed is the computation of mixed reference intervals that include both two-sided and one-sided limits, simultaneously. A parametric bootstrap approach is used in the computations, and the accuracy of the proposed methodology is assessed using estimated coverage probabilities. The problem of sample size determination is also addressed, and the results are illustrated using examples that call for the computation of reference regions.
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Affiliation(s)
- Michael Daniel Lucagbo
- Department of Mathematics & Statistics, University of Maryland Baltimore County, Baltimore, Maryland, USA
- School of Statistics, University of the Philippines Diliman, Quezon City, Philippines
| | - Thomas Mathew
- Department of Mathematics & Statistics, University of Maryland Baltimore County, Baltimore, Maryland, USA
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Shieh G. Determining reference ranges and sample sizes in parallel-group studies. PLoS One 2022; 17:e0278447. [PMID: 36449490 PMCID: PMC9710766 DOI: 10.1371/journal.pone.0278447] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2022] [Accepted: 11/17/2022] [Indexed: 12/02/2022] Open
Abstract
BACKGROUND AND OBJECTIVES Reference ranges are widely used to locate the major range of the target probability distribution. When future measurements fall outside the reference range, they are classified as atypical and require further investigation. The fundamental principles and statistical properties of reference ranges are closely related to those of tolerance interval procedures. Existing investigations of reference ranges and tolerance intervals mainly devoted to the primitive cases of one- and paired-sample designs. Although reference ranges hold considerable promise for parallel group designs, the corresponding methodological and computational issues for determining reference limits and sample sizes have not been adequately addressed. METHODS This paper describes a complete collection of one- and two-sided reference ranges for assessing measurement differences in parallel-group studies that assume variance homogeneity. RESULTS The problem of sample size determination for precise reference ranges is also examined under the expected half-width and assurance probability considerations. Unlike the current methods, the suggested sample size criteria explicitly accommodate desired interval width in precise interval estimation. CONCLUSIONS Theoretical examinations and empirical assessments are presented to validate the usefulness of the proposed reference range and sample size procedures. To enhance the usages of the recommended techniques in practical applications, computer programs are developed for efficient calculation and exact analysis. A real data example regarding tablet absorption rate and extent is presented to illustrate the suggested assessments between two drug formulations.
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Affiliation(s)
- Gwowen Shieh
- Department of Management Science, National Yang Ming Chiao Tung University, Hsinchu, Taiwan
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Iqbal T, Simpkin AJ, Roshan D, Glynn N, Killilea J, Walsh J, Molloy G, Ganly S, Ryman H, Coen E, Elahi A, Wijns W, Shahzad A. Stress Monitoring Using Wearable Sensors: A Pilot Study and Stress-Predict Dataset. Sensors (Basel) 2022; 22:s22218135. [PMID: 36365837 PMCID: PMC9654418 DOI: 10.3390/s22218135] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/03/2022] [Revised: 10/15/2022] [Accepted: 10/20/2022] [Indexed: 05/14/2023]
Abstract
With the recent advancements in the field of wearable technologies, the opportunity to monitor stress continuously using different physiological variables has gained significant interest. The early detection of stress can help improve healthcare and minimizes the negative impact of long-term stress. This paper reports outcomes of a pilot study and associated stress-monitoring dataset, named the "Stress-Predict Dataset", created by collecting physiological signals from healthy subjects using wrist-worn watches with a photoplethysmogram (PPG) sensor. While wearing these watches, 35 healthy volunteers underwent a series of tasks (i.e., Stroop color test, Trier Social Stress Test and Hyperventilation Provocation Test), along with a rest period in-between each task. They also answered questionnaires designed to induce stress levels compatible with daily life. The changes in the blood volume pulse (BVP) and heart rate were recorded by the watch and were labelled as occurring during stress-inducing tasks or a rest period (no stress). Additionally, respiratory rate was estimated using the BVP signal. Statistical models and personalised adaptive reference ranges were used to determine the utility of the proposed stressors and the extracted variables (heart rate and respiratory rate). The analysis showed that the interview session was the most significant stress stimulus, causing a significant variation in heart rate of 27 (77%) participants and respiratory rate of 28 (80%) participants out of 35. The outcomes of this study contribute to the understanding the role of stressors and their association with physiological response and provide a dataset to help develop new wearable solutions for more reliable, valid, and sensitive physio-logical stress monitoring.
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Affiliation(s)
- Talha Iqbal
- Smart Sensor Laboratory, Lambe Institute of Translational Research, College of Medicine, Nursing Health Sciences, University of Galway, H91 TK33 Galway, Ireland
- Correspondence:
| | - Andrew J. Simpkin
- School of Mathematical and Statistical Sciences, University of Galway, H91 TK33 Galway, Ireland
| | - Davood Roshan
- School of Mathematical and Statistical Sciences, University of Galway, H91 TK33 Galway, Ireland
- CÚRAM Center for Research in Medical Devices, University of Galway, H91 W2TY Galway, Ireland
| | - Nicola Glynn
- Smart Sensor Laboratory, Lambe Institute of Translational Research, College of Medicine, Nursing Health Sciences, University of Galway, H91 TK33 Galway, Ireland
| | - John Killilea
- Smart Sensor Laboratory, Lambe Institute of Translational Research, College of Medicine, Nursing Health Sciences, University of Galway, H91 TK33 Galway, Ireland
| | - Jane Walsh
- School of Psychology, University of Galway, H91 TK33 Galway, Ireland
| | - Gerard Molloy
- School of Psychology, University of Galway, H91 TK33 Galway, Ireland
| | - Sandra Ganly
- Smart Sensor Laboratory, Lambe Institute of Translational Research, College of Medicine, Nursing Health Sciences, University of Galway, H91 TK33 Galway, Ireland
| | - Hannah Ryman
- Smart Sensor Laboratory, Lambe Institute of Translational Research, College of Medicine, Nursing Health Sciences, University of Galway, H91 TK33 Galway, Ireland
| | - Eileen Coen
- Smart Sensor Laboratory, Lambe Institute of Translational Research, College of Medicine, Nursing Health Sciences, University of Galway, H91 TK33 Galway, Ireland
| | - Adnan Elahi
- Electrical and Electronic Engineering, University of Galway, H91 TK33 Galway, Ireland
| | - William Wijns
- Smart Sensor Laboratory, Lambe Institute of Translational Research, College of Medicine, Nursing Health Sciences, University of Galway, H91 TK33 Galway, Ireland
- CÚRAM Center for Research in Medical Devices, University of Galway, H91 W2TY Galway, Ireland
| | - Atif Shahzad
- Smart Sensor Laboratory, Lambe Institute of Translational Research, College of Medicine, Nursing Health Sciences, University of Galway, H91 TK33 Galway, Ireland
- Centre for Systems Modelling and Quantitative Biomedicine (SMQB), University of Birmingham, Birmingham B15 2TT, UK
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