E. F. Pfeiffer
R. Lang, K. H. Voigt, L. Fehm and E. F. Pfeiffer
M. Hinz, Y. Abdel Rahman, N. Katsilambros, H. Schatz, K. E. Schröder and E. F. Pfeiffer
Manuela Hische, Olga Luis-Dominguez, Andreas F H Pfeiffer, Peter E Schwarz, Joachim Selbig and Joachim Spranger
The prevalence of unknown impaired fasting glucose (IFG), impaired glucose tolerance (IGT), or type 2 diabetes mellitus (T2DM) is high. Numerous studies demonstrated that IFG, IGT, or T2DM are associated with increased cardiovascular risk, therefore an improved identification strategy would be desirable. The objective of this study was to create a simple and reliable tool to identify individuals with impaired glucose metabolism (IGM).
Design and methods
A cohort of 1737 individuals (1055 controls, 682 with previously unknown IGM) was screened by 75 g oral glucose tolerance test (OGTT). Supervised machine learning was used to automatically generate decision trees to identify individuals with IGM. To evaluate the accuracy of identification, a tenfold cross-validation was performed. Resulting trees were subsequently re-evaluated in a second, independent cohort of 1998 individuals (1253 controls, 745 unknown IGM).
A clinical decision tree included age and systolic blood pressure (sensitivity 89.3%, specificity 37.4%, and positive predictive value (PPV) 48.0%), while a tree based on clinical and laboratory data included fasting glucose and systolic blood pressure (sensitivity 89.7%, specificity 54.6%, and PPV 56.2%). The inclusion of additional parameters did not improve test quality. The external validation approach confirmed the presented decision trees.
We proposed a simple tool to identify individuals with existing IGM. From a practical perspective, fasting blood glucose and blood pressure measurements should be regularly measured in all individuals presenting in outpatient clinics. An OGTT appears to be useful only if the subjects are older than 48 years or show abnormalities in fasting glucose or blood pressure.
Daniel J Cuthbertson, Martin O Weickert, Daniel Lythgoe, Victoria S Sprung, Rebecca Dobson, Fariba Shoajee-Moradie, Margot Umpleby, Andreas F H Pfeiffer, E Louise Thomas, Jimmy D Bell, Helen Jones and Graham J Kemp
Background and aims
Simple clinical algorithms including the fatty liver index (FLI) and lipid accumulation product (LAP) have been developed as surrogate markers for non-alcoholic fatty liver disease (NAFLD), constructed using (semi-quantitative) ultrasonography. This study aimed to validate FLI and LAP as measures of hepatic steatosis, as determined quantitatively by proton magnetic resonance spectroscopy (1H-MRS).
Data were collected from 168 patients with NAFLD and 168 controls who had undergone clinical, biochemical and anthropometric assessment. Values of FLI and LAP were determined and assessed both as predictors of the presence of hepatic steatosis (liver fat >5.5%) and of actual liver fat content, as measured by 1H-MRS. The discriminative ability of FLI and LAP was estimated using the area under the receiver operator characteristic curve (AUROC). As FLI can also be interpreted as a predictive probability of hepatic steatosis, we assessed how well calibrated it was in our cohort. Linear regression with prediction intervals was used to assess the ability of FLI and LAP to predict liver fat content. Further validation was provided in 54 patients with type 2 diabetes mellitus.
FLI, LAP and alanine transferase discriminated between patients with and without steatosis with an AUROC of 0.79 (IQR=0.74, 0.84), 0.78 (IQR=0.72, 0.83) and 0.83 (IQR=0.79, 0.88) respectively although could not quantitatively predict liver fat. Additionally, the algorithms accurately matched the observed percentages of patients with hepatic steatosis in our cohort.
FLI and LAP may be used to identify patients with hepatic steatosis clinically or for research purposes but could not predict liver fat content.