To explain the variation of OGTT dynamics by biological mechanisms: a novel approach based on principal components analysis in women with history of GDM

Abstract

Early reexamination of carbohydrate metabolism via an oral glucose tolerance test (OGTT) is recommended after pregnancy with gestational diabetes (GDM). In this report, we aimed to assess the dominant patterns of dynamic OGTT measurements and subsequently explain them by meanings of the underlying pathophysiological processes. Principal components analysis (PCA), a statistical procedure that aims to reduce the dimensionality of multiple interrelated measures to a set of linearly uncorrelated variables (the principal components) was performed on OGTT data of glucose, insulin and C-peptide in addition to age and body mass index (BMI) of 151 women (n = 110 females after GDM and n = 41 controls) at 3-6 mo after delivery. These components were explained by frequently sampled intravenous glucose tolerance test (FSIGT) parameters. Moreover, their relation with the later development of overt diabetes was studied. Three principal components (PC) were identified, which explained 71.5% of the variation of the original 17 variables. PC1 (explained 47.1%) was closely related to postprandial OGTT levels and FSIGT-derived insulin sensitivity (r = 0.68), indicating that it mirrors insulin sensitivity in the skeletal muscle. PC2 (explained 17.3%) and PC3 (explained 7.1%) were shown to be associated with β-cell failure and fasting (i.e., hepatic) insulin resistance, respectively. All three components were related with diabetes progression (occurred in n = 25 females after GDM) and showed significant changes in long-term trajectories. A high amount of the postpartum OGTT data is explained by principal components, representing pathophysiological mechanisms on the pathway of impaired carbohydrate metabolism. Our results improve our understanding of the underlying biological processes to provide an accurate postgestational risk stratification.

Novel Indices of Glucose Homeostasis Derived from Principal Component Analysis: Application for Metabolic Assessment in Pregnancy

“…This study is aimed at assessing the performance of PCAderived novel indices of glucose metabolism during pregnancy, which were previously developed in postpartum women with history of GDM, in terms of the extent of their correlation with traditional fasting and dynamic parameters of insulin resistance and secretion obtained from OGTT data. Thereby, three PCS were calculated by using the eigenvectors as described in our previous publication [4]. The results showed that all three indices PCS1 to 3 behaved similarly as compared to our previous study in postpartum women [4]: PCS1 showed close associations with various indices of insulin sensitivity.…”

“…Thereby, three PCS were calculated by using the eigenvectors as described in our previous publication [4]. The results showed that all three indices PCS1 to 3 behaved similarly as compared to our previous study in postpartum women [4]: PCS1 showed close associations with various indices of insulin sensitivity. Moreover, it was inversely related to parameters of insulin secretion suggesting that it also embodies the adaptation to impaired insulin action, which physiologically occurs in normal glucose-tolerant individuals.…”

“…Scores. PCS were calculated as the product of two input matrices: A × V = S. A is a matrix of centred and standardized data of the actual measurements of glucose, insulin (after log transformation), and C-peptide (after square root transformation) as well as age and BMI (after log transformation) and V is a matrix containing the loadings of each variable on the first three principal components, i.e., the matrix containing the eigenvectors derived from our previous publication [4]. To obtain meaningful indices, the derived PCS were subsequently rescaled by ðS + 100Þ/17, where S is the output matrix on the original scale.…”

“…For this purpose, several indices have been developed to describe the amount of insulin action and secretion, aimed at providing more detailed information on glucose disposal [3]. Recently, we introduced principal component analysis (an unsupervised machine learning algorithm) as a novel approach to explain the dominant patterns of dynamic OGTT measurements [4]. Thereby, three principal components were identified which explained 71.5% of the total variance of glucose, insulin, and C-peptide concentrations measured during a 2 h OGTT at three to six months after delivery.…”

Application of Penalized Regression Techniques in Modelling Insulin Sensitivity by Correlated Metabolic Parameters

“…Hence, the motivation of our simulation scenarios was rather to give some examples and our simulation scenarios are motivated (and restricted) by the key questions raised by our real data example due to correlated covariables. Moreover, it has to be mentioned, that there might be other methods, which might be used to deal with these problems like orthogonal transformation by (sparse) principal components analysis, which we have recently proposed for the analysis of OGTT data in another report [ 32 ].…”