-
- Eric T Leung, Michael J Raboin, Jessica McKelvey, Adam Graham, Anne Lewis, Kamm Prongay, Aaron M Cohen, and Amanda Vinson.
- a Division of Bioinformatics and Computational Biology, Department of Medical Informatics and Clinical Epidemiology, Oregon Health and Science University , Portland , OR , USA.
- Amyloid. 2019 Sep 1; 26 (3): 139147139-147.
AbstractObjective: Amyloid A (AA) amyloidosis is found in humans and non-human primates, but quantifying disease risk prior to clinical symptoms is challenging. We applied machine learning to identify the best predictors of amyloidosis in rhesus macaques from available clinical and pathology records. To explore potential biomarkers, we also assessed whether changes in circulating serum amyloid A (SAA) or lipoprotein profiles accompany the disease. Methods: We conducted a retrospective study using 86 cases and 163 controls matched for age and sex. We performed data reduction on 62 clinical, pathological and demographic variables, and applied multivariate modelling and model selection with cross-validation. To test the performance of our final model, we applied it to a replication cohort of 2,775 macaques. Results: The strongest predictors of disease were colitis, gastrointestinal adenocarcinoma, endometriosis, arthritis, trauma, diarrhoea and number of pregnancies. Sensitivity and specificity of the risk model were predicted to be 82%, and were assessed at 79 and 72%, respectively. Total, low density lipoprotein and high density lipoprotein cholesterol levels were significantly lower, and SAA levels and triglyceride-to-HDL ratios were significantly higher in cases versus controls. Conclusion: Machine learning is a powerful approach to identifying macaques at risk of AA amyloidosis, which is accompanied by increased circulating SAA and altered lipoprotein profiles.
Notes
Knowledge, pearl, summary or comment to share?You can also include formatting, links, images and footnotes in your notes
- Simple formatting can be added to notes, such as
*italics*
,_underline_
or**bold**
. - Superscript can be denoted by
<sup>text</sup>
and subscript<sub>text</sub>
. - Numbered or bulleted lists can be created using either numbered lines
1. 2. 3.
, hyphens-
or asterisks*
. - Links can be included with:
[my link to pubmed](http://pubmed.com)
- Images can be included with:
![alt text](https://bestmedicaljournal.com/study_graph.jpg "Image Title Text")
- For footnotes use
[^1](This is a footnote.)
inline. - Or use an inline reference
[^1]
to refer to a longer footnote elseweher in the document[^1]: This is a long footnote.
.