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10.1016/j.ekir.2018.02.001 http://scihub22266oqcxt.onion/10.1016/j.ekir.2018.02.001 C5976820!5976820!29854978     free     free     free Deprecated: Implicit conversion from float 229.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 229.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 229.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       Kidney+Int+Rep 2018 ; 3 (3): 691-700 Nephropedia Template TP {{tp|p=29854978|t=2018. Association Between Newborn Metabolic Profiles and Pediatric Kidney Disease |pdf=|usr=}}{{29854978}} gab.com Text Association Between Newborn Metabolic Profiles and Pediatric Kidney Disease JO: Kidney Int Rep http://www.kidney.de/pget.php?&tw=1&pmid=29854978 #FOAvip Introduction: Metabolomics offers considerable promise in early disease detection. We set out to test the hypothesis that routine newborn metabolic profiles at birth, obtained through screening for inborn errors of metabolism, would be associated with kidney disease and add incremental information to known clinical risk factors. Methods: We conducted a population-level cohort study in Ontario, Canada, using metabolic profiles from 1,288,905 newborns from 2006 to 2015. The primary outcome was chronic kidney disease (CKD) or dialysis. Individual metabolites and their ratio combinations were examined by logistic regression after adjustment for established risk factors for kidney disease and incremental risk prediction measured. Results: CKD occurred in 2086 (0.16%, median time 612 days) and dialysis in 641 (0.05%, median time 99 days) infants and children. Individual metabolites consisted of amino acids, acylcarnitines, markers of fatty acid oxidation, and others. Base models incorporating clinical risk factors only provided c-statistics of 0.61 for CKD and 0.70 for dialysis. The addition of identified metabolites to risk prediciton models resulted in significant incremental improvement in the performance of both models (CKD model: c-statistic 0.66 NRI 0.36 IDI 0.04, dialysis model: c-statistic 0.77 NRI 0.57 IDI 0.09). This was consistent after internal validation using bootstrapping and a sensitivity analysis excluding outcomes within the first 30 days. Conclusion: Routinely collected screening metabolites at birth are associated with CKD and the need for dialytic therapies in infants and children, and add incremental information to traditional clinical risk factors. Twit Text FOAVip Association Between Newborn Metabolic Profiles and Pediatric Kidney Disease ????Kidney Int Rep http://www.kidney.de/pget.php?&tw=1&pmid=29854978 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=29854978 #FOAvip English Wikipedia <ref>{{Cite pmid|29854978}}</ref> Association Between Newborn Metabolic Profiles and Pediatric Kidney Disease #MMPMID29854978 Sood MM; Murphy MS; Hawken S; Wong CA; Potter BK; Burns KD; Tsampalieros A; Atkinson KM; Chakraborty P; Wilson K Kidney Int Rep 2018[May]; 3 (3): 691-700 PMID29854978show ga Introduction: Metabolomics offers considerable promise in early disease detection. We set out to test the hypothesis that routine newborn metabolic profiles at birth, obtained through screening for inborn errors of metabolism, would be associated with kidney disease and add incremental information to known clinical risk factors. Methods: We conducted a population-level cohort study in Ontario, Canada, using metabolic profiles from 1,288,905 newborns from 2006 to 2015. The primary outcome was chronic kidney disease (CKD) or dialysis. Individual metabolites and their ratio combinations were examined by logistic regression after adjustment for established risk factors for kidney disease and incremental risk prediction measured. Results: CKD occurred in 2086 (0.16%, median time 612 days) and dialysis in 641 (0.05%, median time 99 days) infants and children. Individual metabolites consisted of amino acids, acylcarnitines, markers of fatty acid oxidation, and others. Base models incorporating clinical risk factors only provided c-statistics of 0.61 for CKD and 0.70 for dialysis. The addition of identified metabolites to risk prediciton models resulted in significant incremental improvement in the performance of both models (CKD model: c-statistic 0.66 NRI 0.36 IDI 0.04, dialysis model: c-statistic 0.77 NRI 0.57 IDI 0.09). This was consistent after internal validation using bootstrapping and a sensitivity analysis excluding outcomes within the first 30 days. Conclusion: Routinely collected screening metabolites at birth are associated with CKD and the need for dialytic therapies in infants and children, and add incremental information to traditional clinical risk factors. äAssociation Between Newborn Metabolic Profiles and Pediatric Kidney Di(epmc).pdf DeepDyve Pubget Overpricing