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

Early nasogastric tube feeding in optimising treatment for hyperemesis gravidarum: The MOTHER randomised controlled trial (Maternal and Offspring outcomes after Treatment of HyperEmesis by Refeeding)

Background: Hyperemesis gravidarum (HG), or intractable vomiting during pregnancy, is the single most frequent cause of hospital admission in early pregnancy. HG has a major impact on maternal quality of life and has repeatedly been associated with poor pregnancy outcome such as low birth weight. Currently, women with HG are admitted to hospital for intravenous fluid replacement, without receiving specific nutritional attention. Nasogastric tube feeding is sometimes used as last resort treatment. At present no randomised trials on dietary or rehydration interventions have been performed. Small observational studies indicate that enteral tube feeding may have the ability to effectively treat dehydration and malnutrition and alleviate nausea and vomiting symptoms. We aim to evaluate the effectiveness of early enteral tube feeding in addition to standard care on nausea and vomiting symptoms and pregnancy outcomes in HG patients. Methods/Design: The MOTHER trial is a multicentre open label randomised controlled trial ( www.studies-obsgyn.nl/mother ). Women ≥ 18 years hospitalised for HG between 5 + 0 and 19 + 6 weeks gestation are eligible for participation. After informed consent participants are randomly allocated to standard care with intravenous rehydration or early enteral tube feeding in addition to standard care. All women keep a weekly diary to record symptoms and dietary intake until 20 weeks gestation. The primary outcome will be neonatal birth weight. Secondary outcomes will be the 24-h Pregnancy Unique Quantification of Emesis and nausea score (PUQE-24), maternal weight gain, dietary intake, duration of hospital stay, number of readmissions, quality of life and side-effects. Also gestational age at birth, placental weight, umbilical cord plasma lipid concentration and neonatal morbidity will be evaluated. Analysis will be according to the intention to treat principle. Discussion: With this trial we aim to clarify whether early enteral tube feeding is more effective in treating HG than intravenous rehydration alone and improves pregnancy outcome. Trial registration: Trial registration number: NTR4197. Date of registration: October 2nd 2013

Plasma lipid profiles discriminate bacterial from viral infection in febrile children

Fever is the most common reason that children present to Emergency Departments. Clinical signs and symptoms suggestive of bacterial infection ar

Statistical applications in nutrigenomics : analyzing multiple genes and proteins in relation to complex diseases in humans

Background The recent advances in technology provide the possibility to obtain large genomic datasets that contain information on large numbers of variables, while the sample sizes are moderate to small. This has lead to statistical challenges in the analysis of multiple genes and proteins in relation to complex diseases. In this thesis approaches are investigated to analyze large genomic datasets, taking complex relationships between genes, proteins and complex diseases into account. These approaches are applied to real data to investigate whether biologically relevant information from the dataset could be obtained or whether models could be obtained that are useful for diagnostic or prognostic purposes. Results We developed a general framework for the analysis of genetic, transcriptomic and proteomic data to obtain insight in biological mechanisms. This framework consists of the following steps: detection of heterogeneity, dimensionality reduction to deal with the large numbers of variables, statistical interpretation and biological interpretation. We found that within this multi-step approach application of a combination of methods, including methods that take interactions into account, is useful within the dimensionality reduction step. In this way more information is captured compared to applying only one method. After selection of relevant variables in the dimensionality reduction step, applying visualization tools, e.g. the interaction entropy graph, together with traditional statistical methods showed to be helpful for statistical interpretation whether variables contribute by their main and/or interaction effect to the outcome of interest. In the last step, biological interpretation of the statistical results was facilitated by literature search, pathway analysis and database mining. Discussion The general framework discussed in this thesis provides the possibility to analyze large nutrigenomic datasets. Although the contribution of genomic research to public health is at the moment limited, new advances in genomic research, e.g. genome-wide association studies, statistical approaches as discussed in this thesis, are promising and genomic research might in the near future lead to applications that translate into improvement of public health. <br/

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

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 (

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. Copyright © 2008 the American Physiological Society. Molecular Sequence Numbers: GENBANK: AF053097, AF217591, BF546374, BF558849, BI303604, CO562777, J00746, L22655, M12981, M15402, NM_022177, NM_080885, TC480469, U39609, XM_213797, XM_214338, XM_219807, XM_219819, XM_220230, XM_223355, XM_226922, XM_234506, XM_235527, XM_341195, XM_341538, XM_341683, XM_341728, XM_342245, XM_342316, XM_343571, XM_344988; Chemicals / CAS: Dietary Sucros

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 to be restricted to functionally annotated genes. A structured approach towards 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 datasets 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 interaction 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 is annotated. Consequently, pathway analysis tools ignore half of the information present in the microarray dataset. 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 datasets. It includes all genes in the dataset, takes gene-gene interactions into account and provides an objective threshold for gene selection

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 6.9years. 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

Early enteral tube feeding in optimizing treatment of hyperemesis gravidarum: The Maternal and Offspring outcomes after Treatment of HyperEmesis by Refeeding (MOTHER) randomized controlled trial

Background: Hyperemesis gravidarum (HG) leads to dehydration, poor nutritional intake, and weight loss. HG has been associated with adverse pregnancy outcomes such as low birth weight. Information about the potential effectiveness of treatments for HG is limited. Objective: We hypothesized that in women with HG, early enteral tube feeding in addition to standard care improves birth weight. Design: We performed a multicenter, open-label randomized controlled trial [Maternal and Offspring outcomes after Treatment of HyperEmesis by Refeeding (MOTHER)] in 19 hospitals in the Netherlands. A total of 116 women hospitalized for HG between 5 and 20 wk of gestation were randomly allocated to enteral tube feeding for ≥7 d in addition to standard care with intravenous rehydration and antiemetic treatment or to standard care alone. Women were encouraged to continue tube feeding at home. On the basis of our power calculation, a sample size of 120 women was anticipated. Analyses were performed according to the intention-to-treat principle. Results: Between October 2014 and March 2016 we randomly allocated 59 women to enteral tube feeding and 57 women to standard care. The mean ± SD birth weight was 3160 ± 770 g in the enteral tube feeding group compared with 3200 ± 680 g in the standard care group (mean difference: -40 g, 95% CI: -230, 310 g). Secondary outcomes, including maternal weight gain, duration of hospital stay, readmission rate, nausea and vomiting symptoms, decrease in quality of life, psychological distress, prematurity, and small-for-gestationalage, also were comparable. Of the women allocated to enteral tube feeding, 28 (47%) were treated according to protocol. Enteral tube feeding was discontinued within 7 d of placement in the remaining women, primarily because of its adverse effects (34%). Conclusions: In women with HG, early enteral tube feeding does not improve birth weight or secondary outcomes. Many women discontinued tube feeding because of discomfort, suggesting that it is poorly tolerated as an early routine treatment of HG