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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

Spearman correlation between liver fat content measured using proton magnetic resonance spectroscopy (1H-MRS) and liver fat content estimated with the ‘% Liver fat equation’, ρ = 0.62 (95% CI 0.55–0.68), P < 0.0001.
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f1: Spearman correlation between liver fat content measured using proton magnetic resonance spectroscopy (1H-MRS) and liver fat content estimated with the ‘% Liver fat equation’, ρ = 0.62 (95% CI 0.55–0.68), P < 0.0001.

Mentions: The variables that had the best predictive value in the discovery group in univariate analysis were entered in multivariate linear regression analysis to create an equation for prediction of liver fat. The significant variables and their possible interactions were examined. The final variables for multiple linear regression analysis were derived using backward stepwise regression method based on Akaike Information Criteria (AIC). These variables were age, fS-pIGFBP-1, an interaction term (age times fS-pIGFBP-1), fS-alanine aminotransferase (ALT), waist-to-hip ratio, fasting plasma (fP)-Glucose and fS-Insulin. The final multiple linear regression model (‘% Liver fat equation’) in the discovery group (adjusted R2 = 0.44, P < 0.0001) is shown in Table 3. For calculation, Supplementary Table 1 can be used. The adjusted R2 was 0.49 in the validation group and 0.47 in all subjects. The adjusted R2 was 0.44 in all subjects, if fS-Insulin was omitted from the model (P < 0.0001 vs. the best model) and 0.46 if fS-pIGFBP-1 was omitted (P < 0.05 vs. the best model). The ‘% Liver fat equation’ also predicted liver fat significantly better than liver enzymes alone: aspartate aminotransferase (AST) only (adjusted R2 = 0.15), ALT only (adjusted R2 = 0.25), or both (adjusted R2 = 0.25, P < 0.0001 for all comparisons). The correlation coefficient between predicted liver fat content using ‘% Liver fat equation’ and liver fat measured using 1H-MRS was ρ = 0.62, P < 0.0001 (Fig. 1). The area under the receiver operator characteristic (AUROC) to predict NAFLD by ‘%Liver fat equation’ was 0.84 (0.80–0.88) which was significantly greater than that predicted by the Fatty Liver Index18 (0.72 [0.67–0.77], p < 0.0001) or the Hepatic Steatosis Index19 (0.62 [0.57–0.68], p < 0.0001).


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)

Spearman correlation between liver fat content measured using proton magnetic resonance spectroscopy (1H-MRS) and liver fat content estimated with the ‘% Liver fat equation’, ρ = 0.62 (95% CI 0.55–0.68), P < 0.0001.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

f1: Spearman correlation between liver fat content measured using proton magnetic resonance spectroscopy (1H-MRS) and liver fat content estimated with the ‘% Liver fat equation’, ρ = 0.62 (95% CI 0.55–0.68), P < 0.0001.
Mentions: The variables that had the best predictive value in the discovery group in univariate analysis were entered in multivariate linear regression analysis to create an equation for prediction of liver fat. The significant variables and their possible interactions were examined. The final variables for multiple linear regression analysis were derived using backward stepwise regression method based on Akaike Information Criteria (AIC). These variables were age, fS-pIGFBP-1, an interaction term (age times fS-pIGFBP-1), fS-alanine aminotransferase (ALT), waist-to-hip ratio, fasting plasma (fP)-Glucose and fS-Insulin. The final multiple linear regression model (‘% Liver fat equation’) in the discovery group (adjusted R2 = 0.44, P < 0.0001) is shown in Table 3. For calculation, Supplementary Table 1 can be used. The adjusted R2 was 0.49 in the validation group and 0.47 in all subjects. The adjusted R2 was 0.44 in all subjects, if fS-Insulin was omitted from the model (P < 0.0001 vs. the best model) and 0.46 if fS-pIGFBP-1 was omitted (P < 0.05 vs. the best model). The ‘% Liver fat equation’ also predicted liver fat significantly better than liver enzymes alone: aspartate aminotransferase (AST) only (adjusted R2 = 0.15), ALT only (adjusted R2 = 0.25), or both (adjusted R2 = 0.25, P < 0.0001 for all comparisons). The correlation coefficient between predicted liver fat content using ‘% Liver fat equation’ and liver fat measured using 1H-MRS was ρ = 0.62, P < 0.0001 (Fig. 1). The area under the receiver operator characteristic (AUROC) to predict NAFLD by ‘%Liver fat equation’ was 0.84 (0.80–0.88) which was significantly greater than that predicted by the Fatty Liver Index18 (0.72 [0.67–0.77], p < 0.0001) or the Hepatic Steatosis Index19 (0.62 [0.57–0.68], p < 0.0001).

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