Pub Date : 2024-12-02DOI: 10.1093/clinchem/hvae143
Christina C Pierre, Dina N Greene, Daniel S Herman, Octavia M Peck Palmer, Shani Delaney
{"title":"Beyond the Screen-Positive Rate: Racial Equity Considerations for Serum Screening for Open Neural Tube Defects.","authors":"Christina C Pierre, Dina N Greene, Daniel S Herman, Octavia M Peck Palmer, Shani Delaney","doi":"10.1093/clinchem/hvae143","DOIUrl":"10.1093/clinchem/hvae143","url":null,"abstract":"","PeriodicalId":10690,"journal":{"name":"Clinical chemistry","volume":" ","pages":"1494-1495"},"PeriodicalIF":7.1,"publicationDate":"2024-12-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142371217","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-12-02DOI: 10.1093/clinchem/hvae179
David L Murray, Maria A V Willrich
Background: Immunoglobulin (Ig) measurements in the clinical laboratory have been traditionally performed by nephelometry, turbidimetry, electrophoresis, and ELISA assays. Mass spectrometry (MS) measurements have the potential to provide deeper insights on the nature of these markers.
Content: Different approaches-top-down, middle-down, or bottom-up-have been described for measuring specific Igs for endogenous monoclonal immunoglobulins (M-proteins) and exogenous therapeutic monoclonal antibody therapies (t-mAbs). Challenges arise in distinguishing the Ig of interest from the polyclonal Ig background. MS is emerging as a practical method to provide quantitative analysis and information about structural and clonal features that are not easily determined by current clinical laboratory methods. This review discusses clinically implemented examples, including isotyping and quantification of M-proteins and quantitation of t-mAbs within the polyclonal Ig background, as examples of how MS can enhance our detection and characterization of Igs.
Summary: This review of current clinically available MS proteomic tests for Igs highlights both analytical and nonanalytical challenges for implementation. Given the new insight into Igs from these methods, it is hoped that vendors, laboratorians, healthcare providers, and payment systems can work to overcome these challenges and advance the care of patients.
背景：在临床实验室中，免疫球蛋白（Ig）的测定传统上是通过浊度法、浊度测定法、电泳法和酶联免疫吸附试验（ELISA）来进行的。质谱（MS）测量有可能更深入地揭示这些标记物的本质：在测量内源性单克隆免疫球蛋白（M-蛋白）和外源性治疗性单克隆抗体疗法（t-mAbs）的特异性 Igs 时，采用了自上而下、自中而下或自下而上的不同方法。从多克隆 Ig 背景中区分相关 Ig 是一项挑战。MS 正在成为一种实用的方法，可提供定量分析以及有关目前临床实验室方法难以确定的结构和克隆特征的信息。本综述讨论了临床应用的实例，包括 M 蛋白的同型和定量以及多克隆 Ig 背景中 t-mAbs 的定量，作为 MS 如何增强 Igs 检测和定性的范例。鉴于这些方法对 Igs 有了新的认识，我们希望供应商、实验室人员、医疗服务提供者和支付系统能够努力克服这些挑战，促进对患者的护理。
{"title":"Applications of Mass Spectrometry Proteomic Methods to Immunoglobulins in the Clinical Laboratory.","authors":"David L Murray, Maria A V Willrich","doi":"10.1093/clinchem/hvae179","DOIUrl":"https://doi.org/10.1093/clinchem/hvae179","url":null,"abstract":"<p><strong>Background: </strong>Immunoglobulin (Ig) measurements in the clinical laboratory have been traditionally performed by nephelometry, turbidimetry, electrophoresis, and ELISA assays. Mass spectrometry (MS) measurements have the potential to provide deeper insights on the nature of these markers.</p><p><strong>Content: </strong>Different approaches-top-down, middle-down, or bottom-up-have been described for measuring specific Igs for endogenous monoclonal immunoglobulins (M-proteins) and exogenous therapeutic monoclonal antibody therapies (t-mAbs). Challenges arise in distinguishing the Ig of interest from the polyclonal Ig background. MS is emerging as a practical method to provide quantitative analysis and information about structural and clonal features that are not easily determined by current clinical laboratory methods. This review discusses clinically implemented examples, including isotyping and quantification of M-proteins and quantitation of t-mAbs within the polyclonal Ig background, as examples of how MS can enhance our detection and characterization of Igs.</p><p><strong>Summary: </strong>This review of current clinically available MS proteomic tests for Igs highlights both analytical and nonanalytical challenges for implementation. Given the new insight into Igs from these methods, it is hoped that vendors, laboratorians, healthcare providers, and payment systems can work to overcome these challenges and advance the care of patients.</p>","PeriodicalId":10690,"journal":{"name":"Clinical chemistry","volume":"70 12","pages":"1422-1435"},"PeriodicalIF":7.1,"publicationDate":"2024-12-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142817272","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-12-02DOI: 10.1093/clinchem/hvae149
Hao Ma, Xuan Wang, Yoriko Heianza, JoAnn E Manson, Lu Qi
Background: Obesity, defined by body mass index (BMI) alone, is a metabolically heterogeneous disorder with distinct cardiovascular manifestations across individuals. This study aimed to investigate the associations of a proteomic signature of BMI with risk of major subtypes of cardiovascular disease (CVD).
Methods: A total of 40 089 participants from UK Biobank, free of CVD at baseline, had complete data on proteomic data measured by the Olink assay. A BMI-proteomic score (pro-BMI score) was calculated from 67 pre-identified plasma proteins associated with BMI.
Results: A higher pro-BMI score was significantly associated with higher risks of ischemic heart disease (IHD) and heart failure (HF), but not with risk of stroke. Comparing the highest with the lowest quartiles, the adjusted hazard ratio (HR) for IHD was 1.49 (95% CI, 1.32-1.67) (P-trend < 0.001), and the adjusted HR for HF was 1.52 (95% CI, 1.25-1.85) (P-trend < 0.001). Further analyses showed that the association of pro-BMI score with HF risk was largely driven by the actual BMI, whereas the association of the pro-BMI score with IHD risk was independent of actual BMI and waist-to-hip ratio (WHR). The association between pro-BMI score and IHD risk appeared to be stronger in the normal BMI group than other BMI groups (P-interaction = 0.004) and stronger in the normal WHR group than the high WHR group (P-interaction = 0.049).
Conclusions: Higher pro-BMI score is significantly associated with higher IHD risk, independent of actual BMI levels. Our findings suggest that plasma proteins hold promise as complementary markers for diagnosing obesity and may facilitate personalized interventions.
{"title":"Proteomic Signature of BMI and Risk of Cardiovascular Disease.","authors":"Hao Ma, Xuan Wang, Yoriko Heianza, JoAnn E Manson, Lu Qi","doi":"10.1093/clinchem/hvae149","DOIUrl":"10.1093/clinchem/hvae149","url":null,"abstract":"<p><strong>Background: </strong>Obesity, defined by body mass index (BMI) alone, is a metabolically heterogeneous disorder with distinct cardiovascular manifestations across individuals. This study aimed to investigate the associations of a proteomic signature of BMI with risk of major subtypes of cardiovascular disease (CVD).</p><p><strong>Methods: </strong>A total of 40 089 participants from UK Biobank, free of CVD at baseline, had complete data on proteomic data measured by the Olink assay. A BMI-proteomic score (pro-BMI score) was calculated from 67 pre-identified plasma proteins associated with BMI.</p><p><strong>Results: </strong>A higher pro-BMI score was significantly associated with higher risks of ischemic heart disease (IHD) and heart failure (HF), but not with risk of stroke. Comparing the highest with the lowest quartiles, the adjusted hazard ratio (HR) for IHD was 1.49 (95% CI, 1.32-1.67) (P-trend < 0.001), and the adjusted HR for HF was 1.52 (95% CI, 1.25-1.85) (P-trend < 0.001). Further analyses showed that the association of pro-BMI score with HF risk was largely driven by the actual BMI, whereas the association of the pro-BMI score with IHD risk was independent of actual BMI and waist-to-hip ratio (WHR). The association between pro-BMI score and IHD risk appeared to be stronger in the normal BMI group than other BMI groups (P-interaction = 0.004) and stronger in the normal WHR group than the high WHR group (P-interaction = 0.049).</p><p><strong>Conclusions: </strong>Higher pro-BMI score is significantly associated with higher IHD risk, independent of actual BMI levels. Our findings suggest that plasma proteins hold promise as complementary markers for diagnosing obesity and may facilitate personalized interventions.</p>","PeriodicalId":10690,"journal":{"name":"Clinical chemistry","volume":" ","pages":"1474-1484"},"PeriodicalIF":7.1,"publicationDate":"2024-12-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142399651","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-12-02DOI: 10.1093/clinchem/hvae165
Frederick G Strathmann, Susan Burden, Jenna Hua, Andrew Patterson, Robert Middleberg
{"title":"Forever Chemicals, Endless Testing? Expert Advice to Be Prepared for Per- and Polyfluoroalkyl Substances.","authors":"Frederick G Strathmann, Susan Burden, Jenna Hua, Andrew Patterson, Robert Middleberg","doi":"10.1093/clinchem/hvae165","DOIUrl":"10.1093/clinchem/hvae165","url":null,"abstract":"","PeriodicalId":10690,"journal":{"name":"Clinical chemistry","volume":" ","pages":"1402-1410"},"PeriodicalIF":7.1,"publicationDate":"2024-12-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142544201","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-12-02DOI: 10.1093/clinchem/hvae158
Ravinder Sodi
{"title":"Commentary on Progressive Motor Regression in a 3-Year-Old: Dietary Trends Revive an Overlooked Diagnosis.","authors":"Ravinder Sodi","doi":"10.1093/clinchem/hvae158","DOIUrl":"https://doi.org/10.1093/clinchem/hvae158","url":null,"abstract":"","PeriodicalId":10690,"journal":{"name":"Clinical chemistry","volume":"70 12","pages":"1420"},"PeriodicalIF":7.1,"publicationDate":"2024-12-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142817221","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-12-02DOI: 10.1093/clinchem/hvae133
Jorik H Amesz, Erika Huijser, Rob F M Bevers, Arjan Albersen
{"title":"\"Fireworks\" in Fatty Urine.","authors":"Jorik H Amesz, Erika Huijser, Rob F M Bevers, Arjan Albersen","doi":"10.1093/clinchem/hvae133","DOIUrl":"https://doi.org/10.1093/clinchem/hvae133","url":null,"abstract":"","PeriodicalId":10690,"journal":{"name":"Clinical chemistry","volume":"70 12","pages":"1497-1498"},"PeriodicalIF":7.1,"publicationDate":"2024-12-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142817267","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-11-21DOI: 10.1093/clinchem/hvae183
Massomeh Sheikh Hassani, Ruchi Jain, Sathishkumar Ramaswamy, Shruti Sinha, Maha El Naofal, Nour Halabi, Sawsan Alyafei, Roudha Alfalasi, Shruti Shenbagam, Alan Taylor, Ahmad Abou Tayoun
Background Exome- or genome-based panels—also known as slices or virtual panels—are now a popular approach that involves comprehensive genomic sequencing while restricting analysis to subsets of genes based on patients’ phenotypes. This flexible strategy enables frequent gene updates based on novel disease associations as well as reflexing to analyzing other genes up to the whole exome or genome. With recent improvements addressing limitations associated with virtual panels, the advantages of this approach, relative to static custom-based panels, remain to be systematically characterized. Methods Here we perform slice testing on 1014 patients (50.5% females; average age 17 years) referred from multiple pediatric clinics within a single center in the Middle East (83% Arab population). Results Initial analysis uncovered molecular diagnoses for 235 patients for a diagnostic yield of 23% (235/1014). “On the fly” focused analysis in most negative cases (N = 779) identified clinically significant variants correlating with patients’ presentations in genes outside the originally ordered panel for another 35 patients (3.5% or 35/1024) increasing the overall diagnostic yield to 27%. The pathogenic variants underlying the additional cases (13% of all positive cases) were excluded from the original “panel” gene list, mainly as result of issues related to panel selection, novel gene–disease associations, phenotype spectrum broadening, or gene lists variability. The additional findings led to changes in clinical management in most patients (94%). Conclusions Our findings support slice testing as an efficient and flexible platform that facilitates updates to gene lists to achieve high clinical sensitivity and utility.
{"title":"Virtual Gene Panels Have a Superior Diagnostic Yield for Inherited Rare Diseases Relative to Static Panels","authors":"Massomeh Sheikh Hassani, Ruchi Jain, Sathishkumar Ramaswamy, Shruti Sinha, Maha El Naofal, Nour Halabi, Sawsan Alyafei, Roudha Alfalasi, Shruti Shenbagam, Alan Taylor, Ahmad Abou Tayoun","doi":"10.1093/clinchem/hvae183","DOIUrl":"https://doi.org/10.1093/clinchem/hvae183","url":null,"abstract":"Background Exome- or genome-based panels—also known as slices or virtual panels—are now a popular approach that involves comprehensive genomic sequencing while restricting analysis to subsets of genes based on patients’ phenotypes. This flexible strategy enables frequent gene updates based on novel disease associations as well as reflexing to analyzing other genes up to the whole exome or genome. With recent improvements addressing limitations associated with virtual panels, the advantages of this approach, relative to static custom-based panels, remain to be systematically characterized. Methods Here we perform slice testing on 1014 patients (50.5% females; average age 17 years) referred from multiple pediatric clinics within a single center in the Middle East (83% Arab population). Results Initial analysis uncovered molecular diagnoses for 235 patients for a diagnostic yield of 23% (235/1014). “On the fly” focused analysis in most negative cases (N = 779) identified clinically significant variants correlating with patients’ presentations in genes outside the originally ordered panel for another 35 patients (3.5% or 35/1024) increasing the overall diagnostic yield to 27%. The pathogenic variants underlying the additional cases (13% of all positive cases) were excluded from the original “panel” gene list, mainly as result of issues related to panel selection, novel gene–disease associations, phenotype spectrum broadening, or gene lists variability. The additional findings led to changes in clinical management in most patients (94%). Conclusions Our findings support slice testing as an efficient and flexible platform that facilitates updates to gene lists to achieve high clinical sensitivity and utility.","PeriodicalId":10690,"journal":{"name":"Clinical chemistry","volume":"170 1","pages":""},"PeriodicalIF":9.3,"publicationDate":"2024-11-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142678610","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-11-15DOI: 10.1093/clinchem/hvae168
Nicholas C Spies, Leah Militello, Christopher W Farnsworth, Joe M El-Khoury, Thomas J S Durant, Mark A Zaydman
Background Intravenous (IV) fluid contamination within clinical specimens causes an operational burden on the laboratory when detected, and potential patient harm when undetected. Even mild contamination is often sufficient to meaningfully alter results across multiple analytes. A recently reported unsupervised learning approach was more sensitive than routine workflows, but still lacked sensitivity to mild but significant contamination. Here, we leverage ensemble learning to more sensitively detect contaminated results using an approach which is explainable and generalizable across institutions. Methods An ensemble-based machine learning pipeline of general and fluid-specific models was trained on real-world and simulated contamination and internally and externally validated. Benchmarks for performance assessment were derived from in silico simulations, in vitro experiments, and expert review. Fluid-specific regression models estimated contamination severity. SHapley Additive exPlanation (SHAP) values were calculated to explain specimen-level predictions, and algorithmic fairness was evaluated by comparing flag rates across demographic and clinical subgroups. Results The sensitivities, specificities, and Matthews correlation coefficients were 0.858, 0.993, and 0.747 for the internal validation set, and 1.00, 0.980, and 0.387 for the external set. SHAP values provided plausible explanations for dextrose- and ketoacidosis-related hyperglycemia. Flag rates from the pipeline were higher than the current workflow, with improved detection of contamination events expected to exceed allowable limits for measurement error and reference change values. Conclusions An accurate, generalizable, and explainable ensemble-based machine learning pipeline was developed and validated for sensitively detecting IV fluid contamination. Implementing this pipeline would help identify errors that are poorly detected by current clinical workflows and a previously described unsupervised machine learning-based method.
{"title":"Prospective and External Validation of an Ensemble Learning Approach to Sensitively Detect Intravenous Fluid Contamination in Basic Metabolic Panels","authors":"Nicholas C Spies, Leah Militello, Christopher W Farnsworth, Joe M El-Khoury, Thomas J S Durant, Mark A Zaydman","doi":"10.1093/clinchem/hvae168","DOIUrl":"https://doi.org/10.1093/clinchem/hvae168","url":null,"abstract":"Background Intravenous (IV) fluid contamination within clinical specimens causes an operational burden on the laboratory when detected, and potential patient harm when undetected. Even mild contamination is often sufficient to meaningfully alter results across multiple analytes. A recently reported unsupervised learning approach was more sensitive than routine workflows, but still lacked sensitivity to mild but significant contamination. Here, we leverage ensemble learning to more sensitively detect contaminated results using an approach which is explainable and generalizable across institutions. Methods An ensemble-based machine learning pipeline of general and fluid-specific models was trained on real-world and simulated contamination and internally and externally validated. Benchmarks for performance assessment were derived from in silico simulations, in vitro experiments, and expert review. Fluid-specific regression models estimated contamination severity. SHapley Additive exPlanation (SHAP) values were calculated to explain specimen-level predictions, and algorithmic fairness was evaluated by comparing flag rates across demographic and clinical subgroups. Results The sensitivities, specificities, and Matthews correlation coefficients were 0.858, 0.993, and 0.747 for the internal validation set, and 1.00, 0.980, and 0.387 for the external set. SHAP values provided plausible explanations for dextrose- and ketoacidosis-related hyperglycemia. Flag rates from the pipeline were higher than the current workflow, with improved detection of contamination events expected to exceed allowable limits for measurement error and reference change values. Conclusions An accurate, generalizable, and explainable ensemble-based machine learning pipeline was developed and validated for sensitively detecting IV fluid contamination. Implementing this pipeline would help identify errors that are poorly detected by current clinical workflows and a previously described unsupervised machine learning-based method.","PeriodicalId":10690,"journal":{"name":"Clinical chemistry","volume":"64 1","pages":""},"PeriodicalIF":9.3,"publicationDate":"2024-11-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142637629","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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