Pub Date : 2025-02-03DOI: 10.1093/clinchem/hvae209
Jason Y Park
{"title":"Reflecting on 70 Years of Clinical Chemistry.","authors":"Jason Y Park","doi":"10.1093/clinchem/hvae209","DOIUrl":"10.1093/clinchem/hvae209","url":null,"abstract":"","PeriodicalId":10690,"journal":{"name":"Clinical chemistry","volume":" ","pages":"227-229"},"PeriodicalIF":7.1,"publicationDate":"2025-02-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143022388","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 : 2025-02-03DOI: 10.1093/clinchem/hvae167
Anna E Merrill, Steven R Lentz
{"title":"New American Society of Hematology Thrombophilia Guidelines Could Provoke Surge in Laboratory Testing.","authors":"Anna E Merrill, Steven R Lentz","doi":"10.1093/clinchem/hvae167","DOIUrl":"https://doi.org/10.1093/clinchem/hvae167","url":null,"abstract":"","PeriodicalId":10690,"journal":{"name":"Clinical chemistry","volume":"71 2","pages":"337-338"},"PeriodicalIF":7.1,"publicationDate":"2025-02-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143122370","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 : 2025-02-03DOI: 10.1093/clinchem/hvae164
Kang Yang, Yue Liu, Ji Zhang, Qian Yu, Feng Xu, Jiyuan Liu, Yuting Li, Xiaojie Zhang, Zhiqiang Wang, Ning Wang, Yuezhen Li, Yan Shi, Wan-Jin Chen
Background: Tandem repeats (TRs) are abundant in the human genome and associated with repeat expansion disorders. Our study aimed to develop a tandem repeat panel utilizing targeted long-read sequencing to evaluate known TRs associated with these disorders and assess its clinical utility.
Methods: We developed a targeted long-read sequencing panel for 70 TR loci, termed dynamic mutation third-generation sequencing (dmTGS), using the PacBio Sequel II platform. We tested 108 samples with suspected repeat expansion disorders and compared the results with conventional molecular methods.
Results: For 108 samples, dmTGS achieved an average of 8000 high-fidelity reads per sample, with a mean read length of 4.7 kb and read quality of 99.9%. dmTGS outperformed repeat-primed-PCR and fluorescence amplicon length analysis-PCR in distinguishing expanded from normal alleles and accurately quantifying repeat counts. The method demonstrated high concordance with confirmatory methods (rlinear = 0.991, P < 0.01), and detected mosaicism with sensitivities of 1% for FMR1 CGG premutation and 5% for full mutations. dmTGS successfully identified interruptive motifs in genes that conventional methods had missed. For variable number TRs in the PLIN4 gene, dmTGS identified precise repeat counts and sequence motifs. Screening 57 patients with suspected genetic muscular diseases, dmTGS confirmed repeat expansions in genes such as GIPC1, NOTCH2NLC, NUTM2B-AS1/LOC642361, and DMPK. Additionally, dmTGS detected CCG interruptions in CTG repeats in 8 myotonic dystrophy type 1 patients with detailed characterization.
Conclusions: dmTGS accurately detects repeat sizes and interruption motifs associated with repeat expansion disorders and demonstrates superior performance compared to conventional molecular methods.
{"title":"dmTGS: Precise Targeted Enrichment Long-Read Sequencing Panel for Tandem Repeat Detection.","authors":"Kang Yang, Yue Liu, Ji Zhang, Qian Yu, Feng Xu, Jiyuan Liu, Yuting Li, Xiaojie Zhang, Zhiqiang Wang, Ning Wang, Yuezhen Li, Yan Shi, Wan-Jin Chen","doi":"10.1093/clinchem/hvae164","DOIUrl":"10.1093/clinchem/hvae164","url":null,"abstract":"<p><strong>Background: </strong>Tandem repeats (TRs) are abundant in the human genome and associated with repeat expansion disorders. Our study aimed to develop a tandem repeat panel utilizing targeted long-read sequencing to evaluate known TRs associated with these disorders and assess its clinical utility.</p><p><strong>Methods: </strong>We developed a targeted long-read sequencing panel for 70 TR loci, termed dynamic mutation third-generation sequencing (dmTGS), using the PacBio Sequel II platform. We tested 108 samples with suspected repeat expansion disorders and compared the results with conventional molecular methods.</p><p><strong>Results: </strong>For 108 samples, dmTGS achieved an average of 8000 high-fidelity reads per sample, with a mean read length of 4.7 kb and read quality of 99.9%. dmTGS outperformed repeat-primed-PCR and fluorescence amplicon length analysis-PCR in distinguishing expanded from normal alleles and accurately quantifying repeat counts. The method demonstrated high concordance with confirmatory methods (rlinear = 0.991, P < 0.01), and detected mosaicism with sensitivities of 1% for FMR1 CGG premutation and 5% for full mutations. dmTGS successfully identified interruptive motifs in genes that conventional methods had missed. For variable number TRs in the PLIN4 gene, dmTGS identified precise repeat counts and sequence motifs. Screening 57 patients with suspected genetic muscular diseases, dmTGS confirmed repeat expansions in genes such as GIPC1, NOTCH2NLC, NUTM2B-AS1/LOC642361, and DMPK. Additionally, dmTGS detected CCG interruptions in CTG repeats in 8 myotonic dystrophy type 1 patients with detailed characterization.</p><p><strong>Conclusions: </strong>dmTGS accurately detects repeat sizes and interruption motifs associated with repeat expansion disorders and demonstrates superior performance compared to conventional molecular methods.</p>","PeriodicalId":10690,"journal":{"name":"Clinical chemistry","volume":" ","pages":"319-331"},"PeriodicalIF":7.1,"publicationDate":"2025-02-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142567853","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 : 2025-02-03DOI: 10.1093/clinchem/hvae136
Paul E Young, Heba Badr, Anthony O Okorodudu
{"title":"Persistent Unexplained Polyclonal IgA Gammopathy in a Patient with Multiple Myeloma.","authors":"Paul E Young, Heba Badr, Anthony O Okorodudu","doi":"10.1093/clinchem/hvae136","DOIUrl":"https://doi.org/10.1093/clinchem/hvae136","url":null,"abstract":"","PeriodicalId":10690,"journal":{"name":"Clinical chemistry","volume":"71 2","pages":"335-336"},"PeriodicalIF":7.1,"publicationDate":"2025-02-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143122371","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 : 2025-01-30DOI: 10.1093/clinchem/hvae222
Benjamin N Wadström, Anders B Wulff, Kasper M Pedersen, Børge G Nordestgaard
Background: Small remnants may penetrate the arterial intima more efficiently compared to large triglyceride-rich lipoproteins (TGRL). We tested the hypothesis that the importance of remnant cholesterol for the risk of atherosclerotic cardiovascular disease (ASCVD) may depend on the size of the remnants and TGRL carrying cholesterol.
Methods: The cholesterol content of small remnants and large TGRL were measured in 25 572 individuals from the Copenhagen General Population Study (2003-2015) and in 222 721 individuals from the UK Biobank (2006-2010) using nuclear magnetic resonance spectroscopy. In the Copenhagen cohort during up to 15 years of follow-up and in the UK Biobank cohort during up to 16 years of follow-up, the numbers of individuals diagnosed with ASCVD (=myocardial infarction, ischemic stroke, and peripheral artery disease) in national health registries were 3869 and 11 424, respectively.
Results: Compared to individuals with low cholesterol content in both small remnants and large TGRL (cutpoints were median cholesterol content), multivariable-adjusted hazard ratios for risk of ASCVD were 1.21 (95% confidence interval: 1.07-1.37) for individuals with high cholesterol content in small remnants only and 0.94 (0.83-1.07) for individuals with high cholesterol content in large TGRL only; the multivariable-adjusted hazard ratio for risk of ASCVD per 10 percentile-units higher cholesterol content in small remnants vs that in large TGRL was 1.04 (1.01-1.07). In the UK Biobank cohort, corresponding hazard ratios were 1.11 (1.03-1.20), 1.01 (0.93-1.09), and 1.05 (1.04-1.07), respectively.
Conclusion: The importance of remnant cholesterol for the risk of ASCVD may depend on the size of the TGRL and remnants carrying cholesterol.
{"title":"Small Remnants versus Large Triglyceride-Rich Lipoproteins in Risk of Atherosclerotic Cardiovascular Disease.","authors":"Benjamin N Wadström, Anders B Wulff, Kasper M Pedersen, Børge G Nordestgaard","doi":"10.1093/clinchem/hvae222","DOIUrl":"https://doi.org/10.1093/clinchem/hvae222","url":null,"abstract":"<p><strong>Background: </strong>Small remnants may penetrate the arterial intima more efficiently compared to large triglyceride-rich lipoproteins (TGRL). We tested the hypothesis that the importance of remnant cholesterol for the risk of atherosclerotic cardiovascular disease (ASCVD) may depend on the size of the remnants and TGRL carrying cholesterol.</p><p><strong>Methods: </strong>The cholesterol content of small remnants and large TGRL were measured in 25 572 individuals from the Copenhagen General Population Study (2003-2015) and in 222 721 individuals from the UK Biobank (2006-2010) using nuclear magnetic resonance spectroscopy. In the Copenhagen cohort during up to 15 years of follow-up and in the UK Biobank cohort during up to 16 years of follow-up, the numbers of individuals diagnosed with ASCVD (=myocardial infarction, ischemic stroke, and peripheral artery disease) in national health registries were 3869 and 11 424, respectively.</p><p><strong>Results: </strong>Compared to individuals with low cholesterol content in both small remnants and large TGRL (cutpoints were median cholesterol content), multivariable-adjusted hazard ratios for risk of ASCVD were 1.21 (95% confidence interval: 1.07-1.37) for individuals with high cholesterol content in small remnants only and 0.94 (0.83-1.07) for individuals with high cholesterol content in large TGRL only; the multivariable-adjusted hazard ratio for risk of ASCVD per 10 percentile-units higher cholesterol content in small remnants vs that in large TGRL was 1.04 (1.01-1.07). In the UK Biobank cohort, corresponding hazard ratios were 1.11 (1.03-1.20), 1.01 (0.93-1.09), and 1.05 (1.04-1.07), respectively.</p><p><strong>Conclusion: </strong>The importance of remnant cholesterol for the risk of ASCVD may depend on the size of the TGRL and remnants carrying cholesterol.</p>","PeriodicalId":10690,"journal":{"name":"Clinical chemistry","volume":" ","pages":""},"PeriodicalIF":7.1,"publicationDate":"2025-01-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143064132","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 : 2025-01-29DOI: 10.1093/clinchem/hvaf002
Vahid Azimi,Ann M Gronowski
{"title":"The Role of Laboratory Medicine in Improving Maternal Health Outcomes and Reducing Disparities.","authors":"Vahid Azimi,Ann M Gronowski","doi":"10.1093/clinchem/hvaf002","DOIUrl":"https://doi.org/10.1093/clinchem/hvaf002","url":null,"abstract":"","PeriodicalId":10690,"journal":{"name":"Clinical chemistry","volume":"147 1","pages":""},"PeriodicalIF":9.3,"publicationDate":"2025-01-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143056972","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 : 2025-01-21DOI: 10.1093/clinchem/hvae225
Helene Péré,David Veyer,Valerie Taly
{"title":"How Can Digital PCR Support the Rapid Development of New Detection Tests in Future Pandemics?","authors":"Helene Péré,David Veyer,Valerie Taly","doi":"10.1093/clinchem/hvae225","DOIUrl":"https://doi.org/10.1093/clinchem/hvae225","url":null,"abstract":"","PeriodicalId":10690,"journal":{"name":"Clinical chemistry","volume":"27 1","pages":""},"PeriodicalIF":9.3,"publicationDate":"2025-01-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142991731","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}
BACKGROUNDThe accurate and prompt diagnosis of infections is essential for improving patient outcomes and preventing bacterial drug resistance. Host gene expression profiling as an approach to infection diagnosis holds great potential in assisting early and accurate diagnosis of infection.METHODSTo improve the precision of infection diagnosis, we developed InfectDiagno, a rank-based ensemble machine learning algorithm for infection diagnosis via host gene expression patterns. Eleven data sets were used as training data sets for the method development, and the InfectDiagno algorithm was optimized by multi-cohort training samples. Nine data sets were used as independent validation data sets for the method. We further validated the diagnostic capacity of InfectDiagno in a prospective clinical cohort.RESULTSAfter selecting 100 feature genes based on their gene expression ranks for infection prediction, we trained a classifier using both a noninfected-vs-infected area under the receiver-operating characteristic curve (area under the curve [AUC] 0.95 [95% CI, 0.93-0.97]) and a bacterial-vs-viral AUC 0.95 (95% CI, 0.93-0.97). We then used the noninfected/infected classifier together with the bacterial/viral classifier to build a discriminating infection diagnosis model. The sensitivity was 0.931 and 0.872, and specificity 0.963 and 0.929, for bacterial and viral infections, respectively. We then applied InfectDiagno to a prospective clinical cohort (n = 517), and found it classified 95% of the samples correctly.CONCLUSIONSOur study shows that the InfectDiagno algorithm is a powerful and robust tool to accurately identify infection in a real-world patient population, which has the potential to profoundly improve clinical care in the field of infection diagnosis.
{"title":"Robust Diagnosis of Acute Bacterial and Viral Infections via Host Gene Expression Rank-Based Ensemble Machine Learning Algorithm: A Multi-Cohort Model Development and Validation Study.","authors":"Yifei Shen,Dongsheng Han,Wenxin Qu,Fei Yu,Dan Zhang,Yifan Xu,Enhui Shen,Qinjie Chu,Michael P Timko,Longjiang Fan,Shufa Zheng,Yu Chen","doi":"10.1093/clinchem/hvae220","DOIUrl":"https://doi.org/10.1093/clinchem/hvae220","url":null,"abstract":"BACKGROUNDThe accurate and prompt diagnosis of infections is essential for improving patient outcomes and preventing bacterial drug resistance. Host gene expression profiling as an approach to infection diagnosis holds great potential in assisting early and accurate diagnosis of infection.METHODSTo improve the precision of infection diagnosis, we developed InfectDiagno, a rank-based ensemble machine learning algorithm for infection diagnosis via host gene expression patterns. Eleven data sets were used as training data sets for the method development, and the InfectDiagno algorithm was optimized by multi-cohort training samples. Nine data sets were used as independent validation data sets for the method. We further validated the diagnostic capacity of InfectDiagno in a prospective clinical cohort.RESULTSAfter selecting 100 feature genes based on their gene expression ranks for infection prediction, we trained a classifier using both a noninfected-vs-infected area under the receiver-operating characteristic curve (area under the curve [AUC] 0.95 [95% CI, 0.93-0.97]) and a bacterial-vs-viral AUC 0.95 (95% CI, 0.93-0.97). We then used the noninfected/infected classifier together with the bacterial/viral classifier to build a discriminating infection diagnosis model. The sensitivity was 0.931 and 0.872, and specificity 0.963 and 0.929, for bacterial and viral infections, respectively. We then applied InfectDiagno to a prospective clinical cohort (n = 517), and found it classified 95% of the samples correctly.CONCLUSIONSOur study shows that the InfectDiagno algorithm is a powerful and robust tool to accurately identify infection in a real-world patient population, which has the potential to profoundly improve clinical care in the field of infection diagnosis.","PeriodicalId":10690,"journal":{"name":"Clinical chemistry","volume":"10 1","pages":""},"PeriodicalIF":9.3,"publicationDate":"2025-01-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142991730","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 : 2025-01-11DOI: 10.1093/clinchem/hvae210
Brendan V Graham, Stephen R Master, Amrom E Obstfeld, Robert B Wilson
Background Multianalyte machine learning (ML) models can potentially identify previously undetectable wrong blood in tube (WBIT) errors, improving upon current single-analyte delta check methodology. However, WBIT detection model performance has not been assessed in a real-world, low-prevalence context. To estimate real-world positive predictive values, we propose a methodology to assess WBIT detection models by evaluating the impact of missing data and by using a “low prevalence” validation data set. Methods We trained a range of model specifications using various predictors in a pediatric setting. We assessed the top-performing model on a modified, “low prevalence” validation data set across a range of probability thresholds. Model performance was also compared to a pre-positive patient identification (pre-PPID) dataset. Results An Extreme Gradient Boosting (XGBoost) model with minimal preprocessing performed the best for both complete blood count with differential white cell count (CBC with Diff) tests (accuracy 0.9715) and complete blood count without differential white cell count (CBC without Diff) tests (accuracy 0.9647). Assessment on a downsampled, “low prevalence” validation data set resulted in estimated positive predictive values ranging from 0.01 to 0.67 (CBC with Diff) and 0.01 to 0.75 (CBC without Diff), depending on the probability threshold chosen. A comparison of prospective performance to PPID data demonstrated a large decrease in estimated WBIT errors. Conclusions We find that ML models can accurately predict WBITs in a primarily pediatric setting. Evaluating model performance across a range of probability thresholds minimizes the number of false positives while still providing added safety benefits. The decrease in estimated WBITS post-PPID implementation shows the potential safety benefits of a WBIT model for hospitals not using PPID when collecting laboratory specimens.
{"title":"A Multianalyte Machine Learning Model to Detect Wrong Blood in Complete Blood Count Tube Errors in a Pediatric Setting","authors":"Brendan V Graham, Stephen R Master, Amrom E Obstfeld, Robert B Wilson","doi":"10.1093/clinchem/hvae210","DOIUrl":"https://doi.org/10.1093/clinchem/hvae210","url":null,"abstract":"Background Multianalyte machine learning (ML) models can potentially identify previously undetectable wrong blood in tube (WBIT) errors, improving upon current single-analyte delta check methodology. However, WBIT detection model performance has not been assessed in a real-world, low-prevalence context. To estimate real-world positive predictive values, we propose a methodology to assess WBIT detection models by evaluating the impact of missing data and by using a “low prevalence” validation data set. Methods We trained a range of model specifications using various predictors in a pediatric setting. We assessed the top-performing model on a modified, “low prevalence” validation data set across a range of probability thresholds. Model performance was also compared to a pre-positive patient identification (pre-PPID) dataset. Results An Extreme Gradient Boosting (XGBoost) model with minimal preprocessing performed the best for both complete blood count with differential white cell count (CBC with Diff) tests (accuracy 0.9715) and complete blood count without differential white cell count (CBC without Diff) tests (accuracy 0.9647). Assessment on a downsampled, “low prevalence” validation data set resulted in estimated positive predictive values ranging from 0.01 to 0.67 (CBC with Diff) and 0.01 to 0.75 (CBC without Diff), depending on the probability threshold chosen. A comparison of prospective performance to PPID data demonstrated a large decrease in estimated WBIT errors. Conclusions We find that ML models can accurately predict WBITs in a primarily pediatric setting. Evaluating model performance across a range of probability thresholds minimizes the number of false positives while still providing added safety benefits. The decrease in estimated WBITS post-PPID implementation shows the potential safety benefits of a WBIT model for hospitals not using PPID when collecting laboratory specimens.","PeriodicalId":10690,"journal":{"name":"Clinical chemistry","volume":"26 1","pages":""},"PeriodicalIF":9.3,"publicationDate":"2025-01-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142962793","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}