Plasma protein profiling reveals protein clusters related to BMI and insulin levels in middle-aged overweight subjects.

BACKGROUND: Biomarkers that allow detection of the onset of disease are of high interest since early detection would allow intervening with lifestyle and nutritional changes before the disease is manifested and pharmacological therapy is required. Our study aimed to improve the phenotypic characterization of overweight but apparently healthy subjects and to identify new candidate profiles for early biomarkers of obesity-related diseases such as cardiovascular disease and type 2 diabetes. METHODOLOGY/PRINCIPAL FINDINGS: In a population of 56 healthy, middle-aged overweight subjects Body Mass Index (BMI), fasting concentration of 124 plasma proteins and insulin were determined. The plasma proteins are implicated in chronic diseases, inflammation, endothelial function and metabolic signaling. Random Forest was applied to select proteins associated with BMI and plasma insulin. Subsequently, the selected proteins were analyzed by clustering methods to identify protein clusters associated with BMI and plasma insulin. Similar analyses were performed for a second population of 20 healthy, overweight older subjects to verify associations found in population I. In both populations similar clusters of proteins associated with BMI or insulin were identified. Leptin and a number of pro-inflammatory proteins, previously identified as possible biomarkers for obesity-related disease, e.g. Complement 3, C Reactive Protein, Serum Amyloid P, Vascular Endothelial Growth Factor clustered together and were positively associated with BMI and insulin. IL-3 and IL-13 clustered together with Apolipoprotein A1 and were inversely associated with BMI and might be potential new biomarkers. CONCLUSION/ SIGNIFICANCE: We identified clusters of plasma proteins associated with BMI and insulin in healthy populations. These clusters included previously reported biomarkers for obesity-related disease and potential new biomarkers such as IL-3 and IL-13. These plasma protein clusters could have potential applications for improved phenotypic characterization of volunteers in nutritional intervention studies or as biomarkers in the early detection of obesity-linked disease development and progression.

Sex-specific effects of CNTF, IL6 and UCP2 polymorphisms on weight gain.

The human proteins ciliary neurotrophic factor (CNTF) and interleukin-6 (IL6) and their receptors share structural homology with leptin and its receptor. In addition, uncoupling protein-2 (UCP2) has been shown to participate the regulation of leptin on food intake. All three proteins are active in the hypothalamus. Experiments have shown that CNTF and IL6, like leptin, can influence body weight in humans and animals, while the effect of UCP2 is not consistent. In a Dutch general population (n=545) we investigated associations of CNTF (null G/A, rs1800169), IL6 (174 G/C, rs1800795) and UCP2 (A55V, rs660339 and del/ins) polymorphisms with weight gain using interaction graphs and logistic regression analysis. The average follow-up period was 6.9 years. Individuals who gained weight (n=264) were compared with individuals who remained stable in weight (n=281). In women the CNTF polymorphism (odds ratio (OR)=2.15, 95%CI: 1.27-3.64, p=0.004) and in men the IL6 polymorphism by itself (OR=2.26, 95%CI: 1.08-4.75, p=0.03) or in combination with the CNTF polymorphism, were associated with weight gain. Furthermore, CNTF and IL6 polymorphisms in interaction with UCP2 polymorphisms had similar strong effects on weight gain in women and men, respectively. All observed effects were statistically shown to be independent of serum leptin level. These results are incorporated in a biological model for weight regulation with upstream effects of CNTF and IL6, and downstream effects of UCP2. The results of this study suggest a novel mechanism for weight regulation that is active in both women and men, but strongly influenced by sex.

The association of 83 plasma proteins with CHD mortality, BMI, HDL-, and total-cholesterol in men: applying multivariate statistics to identify proteins with prognostic value and biological relevance.

In this study, we applied the multivariate statistical tool Partial Least Squares (PLS) to analyze the relative importance of 83 plasma proteins in relation to coronary heart disease (CHD) mortality and the intermediate end points body mass index, HDL-cholesterol and total cholesterol. From a Dutch monitoring project for cardiovascular disease risk factors, men who died of CHD between initial participation (1987-1991) and end of follow-up (January 1, 2000) (N = 44) and matched controls (N = 44) were selected. Baseline plasma concentrations of proteins were measured by a multiplex immunoassay. With the use of PLS, we identified 15 proteins with prognostic value for CHD mortality and sets of proteins associated with the intermediate end points. Subsequently, sets of proteins and intermediate end points were analyzed together by Principal Components Analysis, indicating that proteins involved in inflammation explained most of the variance, followed by proteins involved in metabolism and proteins associated with total-C. This study is one of the first in which the association of a large number of plasma proteins with CHD mortality and intermediate end points is investigated by applying multivariate statistics, providing insight in the relationships among proteins, intermediate end points and CHD mortality, and a set of proteins with prognostic value.

Developing a discrimination rule between breast cancer patients and controls using proteomics mass spectrometric data: a three-step approach.

To discriminate between breast cancer patients and controls, we used a three-step approach to obtain our decision rule. First, we ranked the mass/charge values using random forests, because it generates importance indices that take possible interactions into account. We observed that the top ranked variables consisted of highly correlated contiguous mass/charge values, which were grouped in the second step into new variables. Finally, these newly created variables were used as predictors to find a suitable discrimination rule. In this last step, we compared three different methods, namely Classification and Regression Tree (CART), logistic regression and penalized logistic regression. Logistic regression and penalized logistic regression performed equally well and both had a higher classification accuracy than CART. The model obtained with penalized logistic regression was chosen as we hypothesized that this model would provide a better classification accuracy in the validation set. The solution had a good performance on the training set with a classification accuracy of 86.3%, and a sensitivity and specificity of 86.8% and 85.7%, respectively.

A framework to identify physiological responses in microarray-based gene expression studies: selection and interpretation of biologically relevant genes.

In whole genome microarray studies major gene expression changes are easily identified, but it is a challenge to capture small, but biologically important, changes. Pathway-based programs can capture small effects but may have the disadvantage of being restricted to functionally annotated genes. A structured approach toward the identification of major and small changes for interpretation of biological effects is needed. We present a structured approach, a framework, that addresses different considerations in 1) the identification of informative genes in microarray data sets and 2) the interpretation of their biological relevance. The steps of this framework include gene ranking, gene selection, gene grouping, and biological interpretation. Random forests (RF), which takes gene-gene interactions into account, is examined to rank and select genes. For human, mouse, and rat whole genome arrays, less than half of the probes on the array are annotated. Consequently, pathway analysis tools ignore half of the information present in the microarray data set. The framework described takes all genes into account. RF is a useful tool to rank genes by taking interactions into account. Applying a permutation approach, we were able to define an objective threshold for gene selection. RF combined with self-organizing maps identified genes with coordinated but small gene expression responses that were not fully annotated but corresponded to the same biological process. The presented approach provides a flexible framework for biological interpretation of microarray data sets. It includes all genes in the data set, takes gene-gene interactions into account, and provides an objective threshold for gene selection.

Analysis of multiple SNPs in genetic association studies: comparison of three multi-locus methods to prioritize and select SNPs.

Nonparametric approaches have been developed that are able to analyze large numbers of single nucleotide polymorphisms (SNPs) in modest sample sizes. These approaches have different selection features and may not provide similar results when applied to the same dataset. Therefore, we compared the results of three approaches (set association, random forests and multifactor dimensionality reduction [MDR]) to select from a total of 93 candidate SNPs a subset of SNPs that are important in determining high-density lipoprotein (HDL)-cholesterol levels. The study population consisted of a random sample from a Dutch monitoring project for cardiovascular disease risk factors and was dichotomized into cases (low HDL-cholesterol, n = 533) and non-cases (high HDL-cholesterol, n = 545) based on gender-specific median values for HDL cholesterol. Clearly, all three approaches prioritized three SNPs as important (CETP Taq1B, CETP-629 C/A and LPL Ser447X). Two SNPs with weaker main effects were additionally prioritized by random forests (APOC3 3175 G/C and CCR2 Val62Ile), whereas MTHFR 677 C/T was selected in combination with CETP Taq1B as best model by MDR. Obtained p-values for the selected models were significant for the set association approach (p =.0019), random forests (p<.01) and MDR (p<.02). In conclusion, the application of a combination of multi-locus methods is a useful approach in genetic association studies to select a well-defined set of important SNPs for further statistical and epidemiological interpretation, providing increased confidence and more information compared with the application of only one method.

The challenge for genetic epidemiologists: how to analyze large numbers of SNPs in relation to complex diseases.

Genetic epidemiologists have taken the challenge to identify genetic polymorphisms involved in the development of diseases. Many have collected data on large numbers of genetic markers but are not familiar with available methods to assess their association with complex diseases. Statistical methods have been developed for analyzing the relation between large numbers of genetic and environmental predictors to disease or disease-related variables in genetic association studies. In this commentary we discuss logistic regression analysis, neural networks, including the parameter decreasing method (PDM) and genetic programming optimized neural networks (GPNN) and several non-parametric methods, which include the set association approach, combinatorial partitioning method (CPM), restricted partitioning method (RPM), multifactor dimensionality reduction (MDR) method and the random forests approach. The relative strengths and weaknesses of these methods are highlighted. Logistic regression and neural networks can handle only a limited number of predictor variables, depending on the number of observations in the dataset. Therefore, they are less useful than the non-parametric methods to approach association studies with large numbers of predictor variables. GPNN on the other hand may be a useful approach to select and model important predictors, but its performance to select the important effects in the presence of large numbers of predictors needs to be examined. Both the set association approach and random forests approach are able to handle a large number of predictors and are useful in reducing these predictors to a subset of predictors with an important contribution to disease. The combinatorial methods give more insight in combination patterns for sets of genetic and/or environmental predictor variables that may be related to the outcome variable. As the non-parametric methods have different strengths and weaknesses we conclude that to approach genetic association studies using the case-control design, the application of a combination of several methods, including the set association approach, MDR and the random forests approach, will likely be a useful strategy to find the important genes and interaction patterns involved in complex diseases.