Using Ensemble Methods to Improve the Performance of Prediction - Statistical Analysis of a Myocardial Infarction Data Set from the HUNT Study

The main focus of this thesis is to evaluate the use of ensemble methods to improve the performance of prediction, when applied on a myocardial infarction (MI) data set from the HUNT study. The data set comes from a prospective case-control study with a 10-years follow-up (fatal and non-fatal) where MI was used as the primary endpoints. The subjects of this study were 200 healthy individuals with age 60-79 from the HUNT2 study. The cases (n 1 = 100) experienced an MI within the 10-year observation period, whereas the controls (n 2 = 100) remained health during the follow-up. Several risk factors for experiencing a MI have been identified over the last years and are used in risk prediction models. The most popular prediction model is the Framingham score. However, about 15-20% of patients experiencing MI did not score high at any of the traditional risk factors. Recent studies have shown that microRNAs, which are small non-coding RNA molecules, have a large potential as diagnostic biomarkers for cardiovascular disease. It is thus interesting to investigate if microRNAs also have a potential as predictive biomarkers for predicting future instances of MI. Logistic regression and tree-based methods are commonly used to predict a binary out- come, when predictor variables are observed. In recent years we have seen increased popu- larity of ensemble methods. One such method is bagging (bootstrap aggregation). Bagging is performed by resampling a data set many times, customizing a prediction model for each resampled data set and then combining prediction models for these data sets into a new prediction. In this thesis we examine how bagging can be applied to classification trees and to logistic regression. We also investigate the closely related ensemble methods random forests and random GLMs, which include only a subset of predictors in each step of the model fitting. The predictive performance of 6 different statistical models (a pruned tree, logistic GLM, bagged tree, bagged GLM, random forest and random GLM) is evaluated through a simulation study, where we use the area under the curve score and the Brier score for assessing prediction accuracy. We then fit our 6 models to the HUNT data set in order to obtain conclusions about which predictors are relevant for predicting a future MI event. The conclusion is that ensemble methods increase the predictive performance, in particular when applied to clas- sification trees. The best predictive power was obtained by fitting a random GLM. Further, we have seen that microRNAs are highly relevant for predicting MI, and that the predictors BMI, serum triglycerides and serum glucose non fasting, which are not included in the Framingham risk score, are of high importance

Metabolic characterization of breast cancer for improved precision medicine

Brystkreft er kreftformen som rammer flest kvinner, 1 av 12 kvinner vil bli diagnostisert med brystkreft før fylte 75 år i Norge, og forekomsten fortsetter å øke. Det er en heterogen og kompleks sykdom, pasienter med lik diagnose kan respondere ulikt på samme behandling, og dermed ha forskjellig utfall. Det trengs mer kunnskap om sykdommen for å kunne utvikle mer persontilpasset behandling og bedre metoder for å overvåke behandlingsrespons. Prognosen for de fleste brystkreftpasienter er god, med best prognose ved tidlig oppdagelse. Derfor trengs det også mer kunnskap om de tidlige biologiske mekanismene bak dannelsen av brystkreft, slik at kvinner med høy risiko kan identifiseres tidlig og tilbys tettere oppfølgning, noe som potensielt kan bidra til redusert forekomst av avansert sykdom. Kreftceller har et endret energiomsetning (metabolisme) i forhold til vanlige, friske celler. Raskt voksende kreftceller omdanner næringsstoffer til biomasse samtidig som de må opprettholde en høy energiproduksjon. Denne prosessen kan observeres ved å måle konsentrasjonen av små molekyler, kalt metabolitter, som er aktive komponenter av cellenes energiomsetning. Den metodiske tilnærmingen som benyttes for å måle metabolittene kalles metabolomikk, og kan gjøres blant annet ved magnetisk resonans spektroskopi (MRS), hvor et bredt panel av metabolitter observeres samtidig. Denne metoden har for eksempel vist at metabolske profiler av vevsprøver fra brystkreftpasienter kan si noe om prognosen til pasientene. Hovedmålet i denne avhandlingen har vært å identifisere prognostiske og prediktive biomarkører for brystkreft gjennom en metabolsk tilnærming. For å kunne identifisere robuste biomarkører er det avgjørende å vite hvordan pre-analytiske prosesser kan påvirke metabolittene vi måler. I en biobank blir biologisk materiale oppbevart i fryst form, ofte over mange år. Hvor mange ganger disse prøvene er blitt tint og fryst igjen før analyse kan variere. Det er derfor viktig å vite effekten av slike sykluser på metabolittene, for å kunne tolke resultatene riktig. Artikkel II i denne avhandlingen er en metodeartikkel, hvor det er blitt undersøkt hvordan metabolitter målt i serum og urin, og lipoprotein partikler målt i serum, blir påvirket av gjentatte fryse- og tinesykluser. Denne studien viste at det ikke observeres særlige systematiske effekter av opptil 5 fryse og tine sykluser, noe som betyr at MRS er en god metode for analyser av biobank-prøver. I Artikkel I ble de metabolske effektene av behandling med neoadjuvant kjemoterapi i brystkreftpasienter undersøkt, både i vevsbiopsier og i serum. Tilgangen til to typer biologisk materiale i denne studien gjorde det mulig å undersøke korrelasjonsmønstre mellom metabolitter målt i vev og i serum, i tillegg til innad i hver type biologisk materiale. Svake korrelasjoner ble observert mellom konsentrasjonene av samme metabolitter målt både i vev og i serum. Studien viste også at de metabolske prof Dette skyldes mest sannsynlig at den metabolske profilen av serum gir et mer helhetlig bilde av pasientens tilstand fordi blodet sirkulerer gjennom alle vev og organer i kroppen, mens den metabolske profilen til en vevsprøve beskriver mer direkte hva som foregår i selve svulsten. I Artikkel III ble cirka 2400 serumprøver av friske kvinner fra HUNT2 studien analysert, hvorav halvparten senere utviklet brystkreft. I denne studien fant vi assosiasjoner mellom fremtidig brystkreft og en rekke variabler knyttet til ulike egenskaper av lipoproteiner. Variablene var assosiert med en signifikant økning i risiko, men var ikke sterke nok til å utvikle en robust modell for prediksjon av fremtidig brystkreft. Samlet sett viser avhandlingen at metabolomikk har stor nytteverdi innen brystkreftforskning og kan være et verktøy for utvikling av kliniske biomarkører for forbedret persontilpasset diagnostikk og behandling.ilene av vevsprøvene, men ikke profilene fra serum, kunne predikere overlevelse

The effect of morning vs evening exercise training on glycaemic control and serum metabolites in overweight/obese men : A randomised trial

Aims/hypothesis We determined whether the time of day of exercise training (morning vs evening) would modulate the effects of consumption of a high-fat diet (HFD) on glycaemic control, whole-body health markers and serum metabolomics. Methods In this three-armed parallel-group randomised trial undertaken at a university in Melbourne, Australia, overweight/obese men consumed an HFD (65% of energy from fat) for 11 consecutive days. Participants were recruited via social media and community advertisements. Eligibility criteria for participation were male sex, age 30–45 years, BMI 27.0–35.0 kg/m2 and sedentary lifestyle. The main exclusion criteria were known CVD or type 2 diabetes, taking prescription medications, and shift-work. After 5 days, participants were allocated using a computer random generator to either exercise in the morning (06:30 hours), exercise in the evening (18:30 hours) or no exercise for the subsequent 5 days. Participants and researchers were not blinded to group assignment. Changes in serum metabolites, circulating lipids, cardiorespiratory fitness, BP, and glycaemic control (from continuous glucose monitoring) were compared between groups. Results Twenty-five participants were randomised (morning exercise n = 9; evening exercise n = 8; no exercise n = 8) and 24 participants completed the study and were included in analyses (n = 8 per group). Five days of HFD induced marked perturbations in serum metabolites related to lipid and amino acid metabolism. Exercise training had a smaller impact than the HFD on changes in circulating metabolites, and only exercise undertaken in the evening was able to partly reverse some of the HFD-induced changes in metabolomic profiles. Twenty-four-hour glucose concentrations were lower after 5 days of HFD compared with the participants’ habitual diet (5.3 ± 0.4 vs 5.6 ± 0.4 mmol/l, p = 0.001). There were no significant changes in 24 h glucose concentrations for either exercise group but lower nocturnal glucose levels were observed in participants who trained in the evening, compared with when they consumed the HFD alone (4.9 ± 0.4 vs 5.3 ± 0.3 mmol/l, p = 0.04). Compared with the no-exercise group, peak oxygen uptake improved after both morning (estimated effect 1.3 ml min−1 kg−1 [95% CI 0.5, 2.0], p = 0.003) and evening exercise (estimated effect 1.4 ml min−1 kg−1 [95% CI 0.6, 2.2], p = 0.001). Fasting blood glucose, insulin, cholesterol, triacylglycerol and LDL-cholesterol concentrations decreased only in participants allocated to evening exercise training. There were no unintended or adverse effects. Conclusions/interpretation A short-term HFD in overweight/obese men induced substantial alterations in lipid- and amino acid-related serum metabolites. Improvements in cardiorespiratory fitness were similar regardless of the time of day of exercise training. However, improvements in glycaemic control and partial reversal of HFD-induced changes in metabolic profiles were only observed when participants exercise trained in the evening. Trial registration anzctr.org.au registration no. ACTRN12617000304336. Funding This study was funded by the Novo Nordisk Foundation (NNF14OC0011493)

Effect of Repeated Freeze‒Thaw Cycles on NMR-Measured Lipoproteins and Metabolites in Biofluids

Metabolic profiling of biofluids by nuclear magnetic resonance (NMR) spectroscopy serves as an important tool in disease characterization, and its accuracy largely depends on the quality of samples. We aimed to explore possible effects of repeated freeze–thaw cycles (FTCs) on concentrations of lipoprotein parameters in serum and metabolite concentrations in serum and urine samples. After one to five FTCs, serum and urine samples (n= 20) were analyzed by NMR spectroscopy, and 112 lipoprotein parameters, 20 serum metabolites, and 35 urine metabolites were quantified by a commercial analytical platform. Principal component analysis showed no systematic changes related to FTCs, and samples from the same donor were closely clustered, showing a higher between-subject variation than within-subject variation. The coefficients of variation were small (medians of 4.3%, 11.0%, and 4.9% for lipoprotein parameters and serum and urine metabolites, respectively). Minor, but significant accumulated freeze–thaw effects were observed for 32 lipoprotein parameters and one serum metabolite (acetic acid) when comparing FTC1 to further FTCs. Remaining lipoprotein and metabolite concentrations showed no significant change. In conclusion, five FTCs did not significantly alter the concentrations of urine metabolites and introduced only minor changes to serum lipoprotein parameters and metabolites evaluated by the NMR-based platform.acceptedVersio

Circulating microRNAs as predictive biomarkers of myocardial infarction: Evidence from the HUNT study

Abstract Background and aims Several risk prediction models for coronary heart disease (CHD) are available today, however, they only explain a modest proportion of the incidence. Circulating microRNAs (miRs) have recently been associated with processes in CHD development, and may therefore represent new potential risk markers. The aim of the study was to assess the incremental value of adding circulating miRs to the Framingham Risk Score (FRS). Methods This is a case-control study with a 10-year observation period, with fatal and non-fatal myocardial infarction (MI) as endpoint. At baseline, ten candidate miRs were quantified by real-time polymerase chain reaction in serum samples from 195 healthy participants (60–79 years old). During the follow-up, 96 participants experienced either a fatal (n = 36) or a non-fatal MI (n = 60), whereas the controls (n = 99) remained healthy. By using best subset logistic regression, we identified the miRs that together with the FRS for hard CHD best predicted future MI. The model evaluation was performed by 10-fold cross-validation reporting area under curve (AUC) from the receiver operating characteristic curve (ROC). Results The best miR-based logistic regression risk-prediction model for MI consisted of a combination of miR-21-5p, miR-26a-5p, mir-29c-3p, miR-144-3p and miR-151a-5p. By adding these 5 miRs to the FRS, AUC increased from 0.66 to 0.80. In comparison, adding other important CHD risk factors (waist-hip ratio, triglycerides, glucose, creatinine) to the FRS only increased AUC from 0.66 to 0.68. Conclusions Circulating levels of miRs can add value on top of traditional risk markers in predicting future MI in healthy individuals

Effect of Repeated Freeze‒Thaw Cycles on NMR-Measured Lipoproteins and Metabolites in Biofluids

Metabolic profiling of biofluids by nuclear magnetic resonance (NMR) spectroscopy serves as an important tool in disease characterization, and its accuracy largely depends on the quality of samples. We aimed to explore possible effects of repeated freeze–thaw cycles (FTCs) on concentrations of lipoprotein parameters in serum and metabolite concentrations in serum and urine samples. After one to five FTCs, serum and urine samples (n= 20) were analyzed by NMR spectroscopy, and 112 lipoprotein parameters, 20 serum metabolites, and 35 urine metabolites were quantified by a commercial analytical platform. Principal component analysis showed no systematic changes related to FTCs, and samples from the same donor were closely clustered, showing a higher between-subject variation than within-subject variation. The coefficients of variation were small (medians of 4.3%, 11.0%, and 4.9% for lipoprotein parameters and serum and urine metabolites, respectively). Minor, but significant accumulated freeze–thaw effects were observed for 32 lipoprotein parameters and one serum metabolite (acetic acid) when comparing FTC1 to further FTCs. Remaining lipoprotein and metabolite concentrations showed no significant change. In conclusion, five FTCs did not significantly alter the concentrations of urine metabolites and introduced only minor changes to serum lipoprotein parameters and metabolites evaluated by the NMR-based platform

Assessing treatment response and prognosis by serum and tissue metabolomics in breast cancer patients

Patients with locally advanced breast cancer have a worse prognosis compared to patients with localized tumors and require neoadjuvant treatment before surgery. The aim of this study was to characterize the systemic metabolic effect of neoadjuvant chemotherapy in patients with large primary breast cancers and to relate these changes to treatment response and long-term survival. This study included 132 patients with large primary breast tumors randomized to receive neoadjuvant chemotherapy with or without the addition of the antiangiogenic drug Bevacizumab. Tumor biopsies and serum were collected before and during treatment and, serum additionally 6 weeks after surgery. Samples were analyzed by nuclear magnetic resonance spectroscopy (NMR). Correlation analysis showed low correlations between metabolites measured in cancer tissue and serum. Multilevel partial least squares discriminant analysis (PLS-DA) showed clear changes in serum metabolite levels during treatment (p-values ≤ 0.001), including unfavorable changes in lipid levels. PLS-DA revealed metabolic differences between tissue samples from survivors and nonsurvivors collected 12 weeks into treatment with an accuracy of 72% (p-value = 0.005); however, this was not evident in serum samples. Our results demonstrate a potential clinical application for serum-metabolomics for patient monitoring during and after treatment, and indicate potential for tissue NMR spectroscopy for predicting patient survival

Circulating microRNAs as predictive biomarkers of myocardial infarction: Evidence from the HUNT study.

Background and aims Several risk prediction models for coronary heart disease (CHD) are available today, however, they only explain a modest proportion of the incidence. Circulating microRNAs (miRs) have recently been associated with processes in CHD development, and may therefore represent new potential risk markers. The aim of the study was to assess the incremental value of adding circulating miRs to the Framingham Risk Score (FRS). Methods This is a case-control study with a 10-year observation period, with fatal and non-fatal myocardial infarction (MI) as endpoint. At baseline, ten candidate miRs were quantified by real-time polymerase chain reaction in serum samples from 195 healthy participants (60–79 years old). During the follow-up, 96 participants experienced either a fatal (n = 36) or a non-fatal MI (n = 60), whereas the controls (n = 99) remained healthy. By using best subset logistic regression, we identified the miRs that together with the FRS for hard CHD best predicted future MI. The model evaluation was performed by 10-fold cross-validation reporting area under curve (AUC) from the receiver operating characteristic curve (ROC). Results The best miR-based logistic regression risk-prediction model for MI consisted of a combination of miR-21-5p, miR-26a-5p, mir-29c-3p, miR-144-3p and miR-151a-5p. By adding these 5 miRs to the FRS, AUC increased from 0.66 to 0.80. In comparison, adding other important CHD risk factors (waist-hip ratio, triglycerides, glucose, creatinine) to the FRS only increased AUC from 0.66 to 0.68. Conclusions Circulating levels of miRs can add value on top of traditional risk markers in predicting future MI in healthy individuals

Association of serum cortisol and cortisone levels and risk of recurrence after endocrine treatment in breast cancer

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Serum levels of inflammation-related markers and metabolites predict response to neoadjuvant chemotherapy with and without bevacizumab in breast cancers

Angiogenesis is necessary for tumor growth and has been targeted in breast cancer; however, it is unclear which patients will respond and benefit from antiangiogenic therapy. We report noninvasive monitoring of patient response to neoadjuvant chemotherapy given alone or in combination with anti‐vascular endothelial growth factor (bevacizumab) in a randomized clinical trial. At four time points during neoadjuvant chemotherapy ± bevacizumab of receptor tyrosine‐protein kinase erbB‐2‐negative breast cancers, we measured metabolites and inflammation‐related markers in patient's serum. We report significant changes in the levels of several molecules induced by bevacizumab, the most prominent being an increase in pentraxin 3 (PTX3) and von Willebrand factor (VWF). Serum levels of AXL, VWF and pulmonary and activation‐regulated cytokine (PARC/CCL18) reflected response to chemotherapy alone or in combination with bevacizumab. We further analyzed serum cytokines in relation to tumor characteristics such as gene expression, tumor metabolites and tumor infiltrating leukocytes. We found that VWF and growth‐differentiation factor 15 tumor mRNA levels correlated with their respective serum protein levels suggesting that these cytokines may be produced by tumors and outflow to the bloodstream while influencing the tumor microenvironment locally. Finally, we used binomial logistic regression which allowed to predict patient's response using only 10 noninvasive biomarkers. Our study highlights the potential of monitoring circulating levels of cytokines and metabolites during breast cancer therapy