Precision Medicine Screening Using Whole Genome Sequencing and Advanced Imaging To Identify Disease Risk in Adults

Perkins, Bradley A.; Caskey, C. Thomas; Brar, Pamila; Dec, Eric; Karow, David S.; Kahn, Andrew; Hou, Claire; Shah, Naisha; Boeldt, Debbie; Coughlin, Erin; Hands, Gabby; Lavrenko, Victor; Yu, J. C.; Procko, Andrea; Appis, Julia; Dale, Anders M.; Guo, Lining; Jönsson, Thomas J.; Wittmann, Bryan M.; Bartha, István; Ramakrishnan, S.; Bernal, Axel; Brewer, James M.; Brewerton, Suzanne; Biggs, William H.; Turpaz, Yaron; Telenti, Amalio; Venter, J. Craig

doi:10.1101/133538

Cited by 10 publications

(14 citation statements)

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“…Therefore, the results of our systematic review <0.001 a Data in parentheses are number of studies. b One study 15 did not specify the whole-body MRI and was therefore not included in this subgroup analysis.…”

Section: Discussionmentioning

confidence: 99%

“…Reviewing titles and abstracts of the MEDLINE and Embase databases resulted in 19 studies that were potentially eligible for inclusion. 5,8,11,12,[14][15][16][17][18][19][20][21][22][23][24][25][26][27][28] After reviewing the full text, five studies were excluded because data were also used in another article from the same group, comprising a larger number of patients 8,18,25,27,28 ; one study was excluded because it only reported study rationale and design, 22 and one study was excluded because it was not clear whether the head and neck region was included in the FOV. 22 Eventually, 12 studies were included in this systematic review, published between 2005 and 2018.…”

Section: Literature Searchmentioning

confidence: 99%

“…22 Eventually, 12 studies were included in this systematic review, published between 2005 and 2018. 5,11,12,[14][15][16][17][19][20][21]23,26 Screening the reference lists of these articles did not result in other potentially relevant studies. The principal characteristics of the included studies are presented in Table 4.…”

Section: Literature Searchmentioning

confidence: 99%

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Whole‐body MRI for preventive health screening: A systematic review of the literature

Kwee

2019

Magnetic Resonance Imaging

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BackgroundThe yield of whole‐body MRI for preventive health screening is currently not completely clear.PurposeTo systematically review the prevalence of whole‐body MRI findings in asymptomatic subjects.Study TypeSystematic review and meta‐analysis.SubjectsMEDLINE and Embase were searched for original studies reporting whole‐body MRI findings in asymptomatic adults without known disease, syndrome, or genetic mutation. Twelve studies, comprising 5373 asymptomatic subjects, were included.Field Strength/Sequence1.5T or 3.0T, whole‐body MRI.AssessmentThe whole‐body MRI literature findings were extracted and reviewed by two radiologists in consensus for designation as either critical or indeterminate incidental finding.Statistical TestsData were pooled using a random effects model on the assumption that most subjects had ≤1 critical or indeterminate incidental finding. Heterogeneity was assessed by the I 2 statistic.ResultsPooled prevalences of critical and indeterminate incidental findings together and separately were 32.1% (95% confidence interval [CI]: 18.3%, 50.1%), 13.4% (95% CI: 9.0%, 19.5%), and 13.9% (95% CI: 5.4%, 31.3%), respectively. There was substantial between‐study heterogeneity (I 2 = 95.6–99.1). Pooled prevalence of critical and indeterminate incidental findings together was significantly higher in studies that included (cardio)vascular and/or colon MRI compared with studies that did not (49.7% [95% CI, 26.7%, 72.9%] vs. 23.0% [95% CI, 5.5%, 60.3%], P < 0.001). Pooled proportion of reported verified critical and indeterminate incidental findings was 12.6% (95% CI: 3.2%, 38.8%). Six studies reported false‐positive findings, yielding a pooled proportion of 16.0% (95% CI: 1.9%, 65.8%). None of the included studies reported long‐term (>5‐year) verification of negative findings. Only one study reported false‐negative findings, with a proportion of 2.0%.Data ConclusionPrevalence of critical and indeterminate incidental whole‐body MRI findings in asymptomatic subjects is overall substantial and with variability dependent to some degree on the protocol. Verification data are lacking. The proportion of false‐positive findings appears to be substantial. Level of Evidence: 4 Technical Efficacy: Stage 3J. Magn. Reson. Imaging 2019;50:1489–1503.

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Section: Discussionmentioning

confidence: 99%

Section: Literature Searchmentioning

confidence: 99%

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Whole‐body MRI for preventive health screening: A systematic review of the literature

Kwee

2019

Magnetic Resonance Imaging

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“…Untargeted clinical metabolomic profiling of individual patient samples has found early application in the screening of inherited metabolic diseases, 26,37,64,65 but other uses, such as probing of undiagnosed cases for disease insights, as well as health and wellness screenings, have started to emerge. [66][67][68][69][70]…”

Section: Introductionmentioning

confidence: 99%

Metabolomics in the clinic: A review of the shared and unique features of untargeted metabolomics for clinical research and clinical testing

et al. 2018

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Metabolomics is the untargeted measurement of the metabolome, which is composed of the complement of small molecules detected in a biological sample. As such, metabolomic analysis produces a global biochemical phenotype. It is a technology that has been utilized in the research setting for over a decade. The metabolome is directly linked to and is influenced by genetics, epigenetics, environmental factors, and the microbiome—all of which affect health. Metabolomics can be applied to human clinical diagnostics and to other fields such as veterinary medicine, nutrition, exercise, physiology, agriculture/plant biochemistry, and toxicology. Applications of metabolomics in clinical testing are emerging, but several aspects of its use as a clinical test differ from applications focused on research or biomarker discovery and need to be considered for metabolomics clinical test data to have optimum impact, be meaningful, and be used responsibly. In this review, we deconstruct aspects and challenges of metabolomics for clinical testing by illustrating the significance of test design, accurate and precise data acquisition, quality control, data processing, n‐of‐1 comparison to a reference population, and biochemical pathway analysis. We describe how metabolomics technology is integral to defining individual biochemical phenotypes, elaborates on human health and disease, and fits within the precision medicine landscape. Finally, we conclude by outlining some future steps needed to bring metabolomics into the clinical space and to be recognized by the broader medical and regulatory fields.

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“…Molecular technologies, including metabolomics (metabolites), transcriptomics (RNA), proteomics (proteins), and epigenomics also have been employed to reveal the functional significance of genomic variations 15,16,17,18 . In particular, the integration of DNA sequencing with metabolomics has proven useful for discoveries of disease-associated genes and biomarkers 19,20,21 .…”

Section: Introductionmentioning

confidence: 99%

Precision Medicine Advancements Using Whole Genome Sequencing, Noninvasive Whole Body Imaging, and Functional Diagnostics

Hou

Martin

et al. 2018

Preprint

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We report the results of a three-year precision medicine study that enrolled 1190 presumed healthy participants at a single research clinic. To enable a better assessment of disease risk and improve diagnosis, a precision health platform that integrates non-invasive functional measurements and clinical tests combined with whole genome sequencing (WGS) was developed. The platform included WGS, comprehensive quantitative non-contrast whole body (WB) and brain magnetic resonance imaging/angiography (MRI/MRA), computed tomography (CT) coronary artery calcium scoring, electrocardiogram, echocardiogram, continuous cardiac monitoring, clinical laboratory tests, and metabolomics. In our cohort, 24.3% had medically significant genetic findings (MSF) which may contribute to increased risk of disease. A total of 206 unique medically significant variants in 111 genes were identified, and forty individuals (3.4%) had more than one MSF. Phenotypic testing revealed: 34.2% of our cohort had a metabolomics profile suggestive of insulin resistance, 29.2% had elevated liver fat identified by MRI, 16.4% had clinically important cardiac structure or cardiac function abnormalities on cardiac MRI or ECHO, 8.8% had a high cardiovascular risk on CT coronary artery calcium scoring (Agatston calcium score > 400, Relative Risk of 7.2), 8.0% had arrhythmia found on continuous rhythm monitoring, 6.5% had cardiac conduction disorders found on EKG, 2% had previously undetected tumors detected by WB MRI, and 2.5% had previously undetected aneurysms detected by non-contrast MRI/MRA. Using family histories, personal histories, and test results, clinical and phenotypic findings were correlated with genomic findings in 130 study participants (63.1%) with high to moderate penetrance variants, suggesting the precision health platform improves the diagnostic process in asymptomatic individuals who were at risk. Cardiovascular and endocrine diseases achieved considerable clinical associations between MSFs and clinical phenotypes (89% and 72%, respectively). These findings demonstrate the value of integrating WGS and noninvasive clinical assessments for a rapid and integrated point-of-care clinical diagnosis of age-related diseases that contribute to premature mortality.

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Precision Medicine Screening Using Whole Genome Sequencing and Advanced Imaging To Identify Disease Risk in Adults

Abstract: BACKGROUND

Cited by 10 publications

References 48 publications

Whole‐body MRI for preventive health screening: A systematic review of the literature

Whole‐body MRI for preventive health screening: A systematic review of the literature

Metabolomics in the clinic: A review of the shared and unique features of untargeted metabolomics for clinical research and clinical testing

Precision Medicine Advancements Using Whole Genome Sequencing, Noninvasive Whole Body Imaging, and Functional Diagnostics

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