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Phosphorylated IGFBP-1 as a non-invasive predictor of liver fat in NAFLD.

Petäjä EM, Zhou Y, Havana M, Hakkarainen A, Lundbom N, Ihalainen J, Yki-Järvinen H - Sci Rep (2016)

Bottom Line: The model was significantly better than a model without fS-pIGFBP-1 or S-ALT or S-AST alone.Random Forest modeling identified fS-p-IGFBP-1 as one of the top five predictors of liver fat (adjusted R(2) = 0.39).Therefore, measurement of fS-pIGFBP-1 may help in non-invasive prediction of liver fat content.

View Article: PubMed Central - PubMed

Affiliation: Minerva Foundation Institute for Medical Research, Helsinki, Finland.

ABSTRACT
Insulin-like growth factor binding protein 1 (IGFBP-1) is a potentially interesting marker for liver fat in NAFLD as it is exclusively produced by the liver, and insulin is its main regulator. We determined whether measurement of fasting serum phosphorylated IGFBP-1 (fS-pIGFBP-1) helps to predict liver fat compared to routinely available clinical parameters and PNPLA3 genotype at rs738409. Liver fat content (proton magnetic resonance spectroscopy) was measured in 378 subjects (62% women, age 43 [30-54] years, BMI 32.7 [28.1-39.7] kg/m(2), 46% with NAFLD). Subjects were randomized to discovery and validation groups, which were matched for clinical and biochemical parameters and PNPLA3 genotype. Multiple linear regression and Random Forest modeling were used to identify predictors of liver fat. The final model, % Liver Fat Equation', included age, fS-pIGFBP-1, S-ALT, waist-to-hip ratio, fP-Glucose and fS-Insulin (adjusted R(2) = 0.44 in the discovery group, 0.49 in the validation group, 0.47 in all subjects). The model was significantly better than a model without fS-pIGFBP-1 or S-ALT or S-AST alone. Random Forest modeling identified fS-p-IGFBP-1 as one of the top five predictors of liver fat (adjusted R(2) = 0.39). Therefore, measurement of fS-pIGFBP-1 may help in non-invasive prediction of liver fat content.

No MeSH data available.


Related in: MedlinePlus

Random Forest model for prediction of liver fat content (%). Predictors were ranked by the importance score based on the percent increase in mean square error (%IncMSE), which measures the importance of a given variable in predicting liver fat content.
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f2: Random Forest model for prediction of liver fat content (%). Predictors were ranked by the importance score based on the percent increase in mean square error (%IncMSE), which measures the importance of a given variable in predicting liver fat content.

Mentions: The best predictors in univariate analysis within each group were also subjected to Random Forest modeling for prediction of liver fat (Fig. 2). This approach identified S-ALT, waist-to-hip ratio, fS-insulin, fS-triglycerides and fS-pIGFBP-1 as the top five variables explaining variation in liver fat content. The adjusted R2 was 0.39 in all subjects.


Phosphorylated IGFBP-1 as a non-invasive predictor of liver fat in NAFLD.

Petäjä EM, Zhou Y, Havana M, Hakkarainen A, Lundbom N, Ihalainen J, Yki-Järvinen H - Sci Rep (2016)

Random Forest model for prediction of liver fat content (%). Predictors were ranked by the importance score based on the percent increase in mean square error (%IncMSE), which measures the importance of a given variable in predicting liver fat content.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4835723&req=5

f2: Random Forest model for prediction of liver fat content (%). Predictors were ranked by the importance score based on the percent increase in mean square error (%IncMSE), which measures the importance of a given variable in predicting liver fat content.
Mentions: The best predictors in univariate analysis within each group were also subjected to Random Forest modeling for prediction of liver fat (Fig. 2). This approach identified S-ALT, waist-to-hip ratio, fS-insulin, fS-triglycerides and fS-pIGFBP-1 as the top five variables explaining variation in liver fat content. The adjusted R2 was 0.39 in all subjects.

Bottom Line: The model was significantly better than a model without fS-pIGFBP-1 or S-ALT or S-AST alone.Random Forest modeling identified fS-p-IGFBP-1 as one of the top five predictors of liver fat (adjusted R(2) = 0.39).Therefore, measurement of fS-pIGFBP-1 may help in non-invasive prediction of liver fat content.

View Article: PubMed Central - PubMed

Affiliation: Minerva Foundation Institute for Medical Research, Helsinki, Finland.

ABSTRACT
Insulin-like growth factor binding protein 1 (IGFBP-1) is a potentially interesting marker for liver fat in NAFLD as it is exclusively produced by the liver, and insulin is its main regulator. We determined whether measurement of fasting serum phosphorylated IGFBP-1 (fS-pIGFBP-1) helps to predict liver fat compared to routinely available clinical parameters and PNPLA3 genotype at rs738409. Liver fat content (proton magnetic resonance spectroscopy) was measured in 378 subjects (62% women, age 43 [30-54] years, BMI 32.7 [28.1-39.7] kg/m(2), 46% with NAFLD). Subjects were randomized to discovery and validation groups, which were matched for clinical and biochemical parameters and PNPLA3 genotype. Multiple linear regression and Random Forest modeling were used to identify predictors of liver fat. The final model, % Liver Fat Equation', included age, fS-pIGFBP-1, S-ALT, waist-to-hip ratio, fP-Glucose and fS-Insulin (adjusted R(2) = 0.44 in the discovery group, 0.49 in the validation group, 0.47 in all subjects). The model was significantly better than a model without fS-pIGFBP-1 or S-ALT or S-AST alone. Random Forest modeling identified fS-p-IGFBP-1 as one of the top five predictors of liver fat (adjusted R(2) = 0.39). Therefore, measurement of fS-pIGFBP-1 may help in non-invasive prediction of liver fat content.

No MeSH data available.


Related in: MedlinePlus