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Class prediction models of thrombocytosis using genetic biomarkers.

ABSTRACT: Criteria for distinguishing among etiologies of thrombocytosis are limited in their capacity to delineate clonal (essential thrombocythemia [ET]) from nonclonal (reactive thrombocytosis [RT]) etiologies. We studied platelet transcript profiles of 126 subjects (48 controls, 38 RT, 40 ET [24 contained the JAK2V(617)F mutation]) to identify transcript subsets that segregated phenotypes. Cross-platform consistency was validated using quantitative real-time polymerase chain reaction (RT-PCR). Class prediction algorithms were developed to assign phenotypic class between the thrombocytosis cohorts, and by JAK2 genotype. Sex differences were rare in normal and ET cohorts (< 1% of genes) but were male-skewed for approximately 3% of RT genes. An 11-biomarker gene subset using the microarray data discriminated among the 3 cohorts with 86.3% accuracy, with 93.6% accuracy in 2-way class prediction (ET vs RT). Subsequent quantitative RT-PCR analysis established that these biomarkers were 87.1% accurate in prospective classification of a new cohort. A 4-biomarker gene subset predicted JAK2 wild-type ET in more than 85% patient samples using either microarray or RT-PCR profiling, with lower predictive capacity in JAK2V(617)F mutant ET patients. These results establish that distinct genetic biomarker subsets can predict thrombocytosis class using routine phlebotomy.

SUBMITTER: Gnatenko DV

PROVIDER: S-EPMC2803693 | biostudies-literature | 2010 Jan

REPOSITORIES: biostudies-literature

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Class prediction models of thrombocytosis using genetic biomarkers.

Gnatenko Dmitri V DV Zhu Wei W Xu Xiao X Samuel Edward T ET Monaghan Melissa M Zarrabi Mohammad H MH Kim Christi C Dhundale Anil A Bahou Wadie F WF

Blood 20090922 1

Criteria for distinguishing among etiologies of thrombocytosis are limited in their capacity to delineate clonal (essential thrombocythemia [ET]) from nonclonal (reactive thrombocytosis [RT]) etiologies. We studied platelet transcript profiles of 126 subjects (48 controls, 38 RT, 40 ET [24 contained the JAK2V(617)F mutation]) to identify transcript subsets that segregated phenotypes. Cross-platform consistency was validated using quantitative real-time polymerase chain reaction (RT-PCR). Class p ...[more]

PMID: 19773543

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Project description:ImportanceThe clinical value of current multifactorial algorithms for individualized assessment of dementia risk remains unclear.ObjectiveTo evaluate the clinical value associated with 4 widely used dementia risk scores in estimating 10-year dementia risk.Design, setting, and participantsThis prospective population-based UK Biobank cohort study assessed 4 dementia risk scores at baseline (2006-2010) and ascertained incident dementia during the following 10 years. Replication with a 20-year follow-up was based on the British Whitehall II study. For both analyses, participants who had no dementia at baseline, had complete data on at least 1 dementia risk score, and were linked to electronic health records from hospitalizations or mortality were included. Data analysis was conducted from July 5, 2022, to April 20, 2023.ExposuresFour existing dementia risk scores: the Cardiovascular Risk Factors, Aging and Dementia (CAIDE)-Clinical score, the CAIDE-APOE-supplemented score, the Brief Dementia Screening Indicator (BDSI), and the Australian National University Alzheimer Disease Risk Index (ANU-ADRI).Main outcomes and measuresDementia was ascertained from linked electronic health records. To evaluate how well each score predicted the 10-year risk of dementia, concordance (C) statistics, detection rate, false-positive rate, and the ratio of true to false positives were calculated for each risk score and for a model including age alone.ResultsOf 465 929 UK Biobank participants without dementia at baseline (mean [SD] age, 56.5 [8.1] years; range, 38-73 years; 252 778 [54.3%] female participants), 3421 were diagnosed with dementia at follow-up (7.5 per 10 000 person-years). If the threshold for a positive test result was calibrated to achieve a 5% false-positive rate, all 4 risk scores detected 9% to 16% of incident dementia and therefore missed 84% to 91% (failure rate). The corresponding failure rate was 84% for a model that included age only. For a positive test result calibrated to detect at least half of future incident dementia, the ratio of true to false positives ranged between 1 to 66 (for CAIDE-APOE-supplemented) and 1 to 116 (for ANU-ADRI). For age alone, the ratio was 1 to 43. The C statistic was 0.66 (95% CI, 0.65-0.67) for the CAIDE clinical version, 0.73 (95% CI, 0.72-0.73) for the CAIDE-APOE-supplemented, 0.68 (95% CI, 0.67-0.69) for BDSI, 0.59 (95% CI, 0.58-0.60) for ANU-ADRI, and 0.79 (95% CI, 0.79-0.80) for age alone. Similar C statistics were seen for 20-year dementia risk in the Whitehall II study cohort, which included 4865 participants (mean [SD] age, 54.9 [5.9] years; 1342 [27.6%] female participants). In a subgroup analysis of same-aged participants aged 65 (±1) years, discriminatory capacity of risk scores was low (C statistics between 0.52 and 0.60).Conclusions and relevanceIn these cohort studies, individualized assessments of dementia risk using existing risk prediction scores had high error rates. These findings suggest that the scores were of limited value in targeting people for dementia prevention. Further research is needed to develop more accurate algorithms for estimation of dementia risk.

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Class prediction models of thrombocytosis using genetic biomarkers.

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Class prediction models of thrombocytosis using genetic biomarkers.

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