references.bib

@article{Pattaro:2015fu,
author = {Pattaro, Cristian and G{\"o}gele, Martin and Mascalzoni, Deborah and Melotti, Roberto and Schwienbacher, Christine and De Grandi, Alessandro and Foco, Luisa and D'Elia, Yuri and Linder, Barbara and Fuchsberger, Christian and Minelli, Cosetta and Egger, Clemens and Kofink, Lisa S and Zanigni, Stefano and Sch{\"a}fer, Torsten and Facheris, Maurizio F and Sm{\'a}rason, Sigur{\dh}ur V and Rossini, Alessandra and Hicks, Andrew A and Weiss, Helmuth and Pramstaller, Peter P},
title = {{The Cooperative Health Research in South Tyrol (CHRIS) study: rationale, objectives, and preliminary results.}},
journal = {Journal of translational medicine},
year = {2015},
volume = {13},
number = {1},
pages = {348},
publisher = {BioMed Central Ltd},
affiliation = {Center for Biomedicine, European Academy of Bolzano/Bozen (EURAC) (Affiliated to the University of L{\"u}beck, L{\"u}beck, Germany), Via Galvani 31, 39100, Bolzano/Bozen, Italy. cristian.pattaro@eurac.edu.},
doi = {10.1186/s12967-015-0704-9},
pmid = {26541195},
pmcid = {PMC4635524},
language = {English},
read = {Yes},
rating = {0},
date-added = {2015-11-24T11:40:49GMT},
date-modified = {2018-09-27T06:22:44GMT},
abstract = {The Cooperative Health Research In South Tyrol (CHRIS) study is a population-based study with a longitudinal lookout to investigate the genetic and molecular basis of age-related common chronic conditions and their interaction with life style and environment in the general population. All adults of the middle and upper Vinschgau/Val Venosta are invited, while 10,000 participants are anticipated by mid-2017. Family participation is encouraged for complete pedigree reconstruction and disease inheritance mapping. After a pilot study on the compliance with a paperless assessment mode, computer-assisted interviews have been implemented to screen for conditions of the cardiovascular, endocrine, metabolic, genitourinary, nervous, behavioral, and cognitive system. Fat intake, cardiac health, and tremor are assessed instrumentally. Nutrient intake, physical activity, and life-course smoking are measured semi-quantitatively. Participants are phenotyped for 73 blood and urine parameters and 60 aliquots per participant are biobanked (cryo-preserved urine, DNA, and whole and fractionated blood). Through liquid-chromatography mass-spectrometry analysis, metabolite profiling of the mitochondrial function is assessed. Samples are genotyped on 1 million variants with the Illumina HumanOmniExpressExome array and the first data release including 4570 fully phenotyped and genotyped samples is now available for analysis. Participants' follow-up is foreseen 6~years after the first visit. The target population is characterized by long-term social stability and homogeneous environment which should both favor the identification of enriched genetic variants. The CHRIS cohort is a valuable resource to assess the contribution of genomics, metabolomics, and environmental factors to human health and disease. It is awaited that this will result in the identification of novel molecular targets for disease prevention and treatment.},
url = {http://www.translational-medicine.com/content/13/1/348},
}

@article{canchola_correct_2017,
	title = {Correct use of percent coefficient of variation (\%{CV}) formula for log-transformed data},
	volume = {Volume 6},
	issn = {2374-6920},
	url = {https://medcraveonline.com/MOJPB/MOJPB-06-00200.pdf},
	doi = {10.15406/mojpb.2017.06.00200},
	abstract = {The coefficient of variation (CV) is a unitless measure typically used to evaluate the variability of a population relative to its standard deviation and is normally presented as a percentage [1]. When considering the percent coefficient of variation (\%CV) for log-transformed data, we have discovered the incorrect application of the standard \%CV form in obtaining the \%CV for log-transformed data. Upon review of various journals, we have noted the formula for the \%CV for log-transformed data was not being applied correctly. This communication provides a framework from which the correct mathematical formula for the \%CV can be applied to log-transformed data.},
	language = {English},
	number = {Issue 4},
	urldate = {2022-04-08},
	journal = {MOJ Proteomics \& Bioinformatics},
	author = {Canchola, Jesse A and Tang, Shaowu and Hemyari, Pari and Paxinos, Ellen and Marins, Ed},
	month = nov,
	year = {2017},
	note = {Publisher: MedCrave Publishing}
}

@article{todorovObjectOrientedFrameworkRobust2009,
  title = {An {{Object}}-{{Oriented Framework}} for {{Robust Multivariate Analysis}}},
  author = {Todorov, Valentin and Filzmoser, Peter},
  year = {2009},
  month = oct,
  volume = {32},
  pages = {1--47},
  issn = {1548-7660},
  doi = {10.18637/jss.v032.i03},
  copyright = {Copyright (c) 2009 Valentin Todorov, Peter Filzmoser},
  file = {/Users/jo/Zotero/storage/RLRE278Y/Todorov_Filzmoser_2009_An Object-Oriented Framework for Robust Multivariate Analysis.pdf;/Users/jo/Zotero/storage/ATSU7B5L/v032i03.html},
  journal = {Journal of Statistical Software},
  keywords = {multivariate analysis,software},
  language = {en},
  number = {1}
}

@article{langfelderFastFunctionsRobust2012,
  title = {Fast {{R Functions}} for {{Robust Correlations}} and {{Hierarchical Clustering}}},
  author = {Langfelder, Peter and Horvath, Steve},
  year = {2012},
  month = mar,
  volume = {46},
  pages = {1--17},
  issn = {1548-7660},
  doi = {10.18637/jss.v046.i11},
  copyright = {Copyright (c) 2010 Peter Langfelder, Steve Horvath},
  file = {/Users/jo/Zotero/storage/Q6VM2S9Y/Langfelder_Horvath_2012_Fast R Functions for Robust Correlations and Hierarchical Clustering.pdf;/Users/jo/Zotero/storage/8EJ3P2VH/v046i11.html},
  journal = {Journal of Statistical Software},
  keywords = {Clustering and machine learning,software},
  language = {en},
  number = {1}
}

@article{langfelderWGCNAPackageWeighted2008,
  title = {{{WGCNA}}: An {{R}} Package for Weighted Correlation Network Analysis.},
  author = {Langfelder, Peter and Horvath, Steve},
  year = {2008},
  volume = {9},
  pages = {559},
  doi = {10.1186/1471-2105-9-559},
  abstract = {BACKGROUND:Correlation networks are increasingly being used in bioinformatics applications. For example, weighted gene co-expression network analysis is a systems biology method for describing the correlation patterns among genes across microarray samples. Weighted correlation network analysis (WGCNA) can be used for finding clusters (modules) of highly correlated genes, for summarizing such clusters using the module eigengene or an intramodular hub gene, for relating modules to one another and to external sample traits (using eigengene network methodology), and for calculating module membership measures. Correlation networks facilitate network based gene screening methods that can be used to identify candidate biomarkers or therapeutic targets. These methods have been successfully applied in various biological contexts, e.g. cancer, mouse genetics, yeast genetics, and analysis of brain imaging data. While parts of the correlation network methodology have been described in separate publications, there is a need to provide a user-friendly, comprehensive, and consistent software implementation and an accompanying tutorial.RESULTS:The WGCNA R software package is a comprehensive collection of R functions for performing various aspects of weighted correlation network analysis. The package includes functions for network construction, module detection, gene selection, calculations of topological properties, data simulation, visualization, and interfacing with external software. Along with the R package we also present R software tutorials. While the methods development was motivated by gene expression data, the underlying data mining approach can be applied to a variety of different settings.CONCLUSION:The WGCNA package provides R functions for weighted correlation network analysis, e.g. co-expression network analysis of gene expression data. The R package along with its source code and additional material are freely available at http://www.genetics.ucla.edu/labs/horvath/CoexpressionNetwork/Rpackages/WGCNA.},
  file = {/Users/jo/Zotero/storage/IXMSPZ6P/Langfelder_Horvath_2008_WGCNA.pdf},
  journal = {BMC Bioinformatics},
  keywords = {Methodology,Network analysis},
  language = {English},
  number = {1},
  pmcid = {PMC2631488}
}

@article{verri_hernandes_age_2022,
	title = {Age, {Sex}, {Body} {Mass} {Index}, {Diet} and {Menopause} {Related} {Metabolites} in a {Large} {Homogeneous} {Alpine} {Cohort}},
	volume = {12},
	copyright = {http://creativecommons.org/licenses/by/3.0/},
	issn = {2218-1989},
	url = {https://www.mdpi.com/2218-1989/12/3/205},
	doi = {10.3390/metabo12030205},
	abstract = {Metabolomics in human serum samples provide a snapshot of the current metabolic state of an individuum. Metabolite concentrations are influenced by both genetic and environmental factors. Concentrations of certain metabolites can further depend on age, sex, menopause, and diet of study participants. A better understanding of these relationships is pivotal for the planning of metabolomics studies involving human subjects and interpretation of their results. We generated one of the largest single-site targeted metabolomics data sets consisting of 175 quantified metabolites in 6872 study participants. We identified metabolites significantly associated with age, sex, body mass index, diet, and menopausal status. While most of our results agree with previous large-scale studies, we also found novel associations including serotonin as a sex and BMI-related metabolite and sarcosine and C2 carnitine showing significantly higher concentrations in post-menopausal women. Finally, we observed strong associations between higher consumption of food items and certain metabolites, mostly phosphatidylcholines and lysophosphatidylcholines. Most, and the strongest, relationships were found for habitual meat intake while no significant relationships were found for most fruits, vegetables, and grain products. Summarizing, our results reconfirm findings from previous population-based studies on an independent cohort. Together, these findings will ultimately enable the consolidation of sets of metabolites which are related to age, sex, BMI, and menopause as well as to participants’ diet.},
	language = {en},
	number = {3},
	urldate = {2022-02-28},
	journal = {Metabolites},
	author = {Verri Hernandes, Vinicius and Dordevic, Nikola and Hantikainen, Essi Marjatta and Sigurdsson, Baldur Bragi and Smárason, Sigurður Vidir and Garcia-Larsen, Vanessa and Gögele, Martin and Caprioli, Giulia and Bozzolan, Ilaria and Pramstaller, Peter P. and Rainer, Johannes},
	month = mar,
	year = {2022},
	note = {Number: 3 Publisher: Multidisciplinary Digital Publishing Institute},
	keywords = {metabolomics, aging, body mass index, diet, gender differences, menopause},
	pages = {205}
}

@article{ramsey_variation_2016,
	title = {Variation in serum biomarkers with sex and female hormonal status: implications for clinical tests},
	volume = {6},
	copyright = {2016 The Author(s)},
	issn = {2045-2322},
	shorttitle = {Variation in serum biomarkers with sex and female hormonal status},
	url = {https://www.nature.com/articles/srep26947},
	doi = {10.1038/srep26947},
	abstract = {Few serum biomarker tests are implemented in clinical practice and recent reports raise concerns about poor reproducibility of biomarker studies. Here, we investigated the potential role of sex and female hormonal status in this widespread irreproducibility. We examined 171 serum proteins and small molecules measured in 1,676 participants from the Netherlands Study of Depression and Anxiety. Concentrations of 96 molecules varied with sex and 66 molecules varied between oral contraceptive pill users, postmenopausal females and females in the follicular and luteal phases of the menstrual cycle (FDR-adjusted p-value {\textless}0.05). Simulations of biomarker studies yielded up to 40\% false discoveries when patient and control groups were not matched for sex and up to 41\% false discoveries when premenopausal females were not matched for oral contraceptive pill use. High accuracy (over 90\%) classification tools were developed to label samples with sex and female hormonal status where this information was not collected.},
	language = {en},
	number = {1},
	urldate = {2022-09-22},
	journal = {Scientific Reports},
	author = {Ramsey, Jordan M. and Cooper, Jason D. and Penninx, Brenda W. J. H. and Bahn, Sabine},
	month = may,
	year = {2016},
	note = {Number: 1
Publisher: Nature Publishing Group},
	keywords = {Metabolomics, Proteomics, Biomarkers, Sex Factors, Biotechnology, Sex-hormones, Contraceptives},
	pages = {26947}
}

@article{candia_assessment_2017,
	title = {Assessment of {Variability} in the {SOMAscan} {Assay}},
	volume = {7},
	copyright = {2017 The Author(s)},
	issn = {2045-2322},
	url = {https://www.nature.com/articles/s41598-017-14755-5},
	doi = {10.1038/s41598-017-14755-5},
	abstract = {SOMAscan is an aptamer-based proteomics assay capable of measuring 1,305 human protein analytes in serum, plasma, and other biological matrices with high sensitivity and specificity. In this work, we present a comprehensive meta-analysis of performance based on multiple serum and plasma runs using the current 1.3 k assay, as well as the previous 1.1 k version. We discuss normalization procedures and examine different strategies to minimize intra- and interplate nuisance effects. We implement a meta-analysis based on calibrator samples to characterize the coefficient of variation and signal-over-background intensity of each protein analyte. By incorporating coefficient of variation estimates into a theoretical model of statistical variability, we also provide a framework to enable rigorous statistical tests of significance in intervention studies and clinical trials, as well as quality control within and across laboratories. Furthermore, we investigate the stability of healthy subject baselines and determine the set of analytes that exhibit biologically stable baselines after technical variability is factored in. This work is accompanied by an interactive web-based tool, an initiative with the potential to become the cornerstone of a regularly updated, high quality repository with data sharing, reproducibility, and reusability as ultimate goals.},
	language = {en},
	number = {1},
	urldate = {2023-01-25},
	journal = {Scientific Reports},
	author = {Candia, Julián and Cheung, Foo and Kotliarov, Yuri and Fantoni, Giovanna and Sellers, Brian and Griesman, Trevor and Huang, Jinghe and Stuccio, Sarah and Zingone, Adriana and Ryan, Bríd M. and Tsang, John S. and Biancotto, Angélique},
	month = oct,
	year = {2017},
	note = {Number: 1
Publisher: Nature Publishing Group},
	keywords = {Computational biology and bioinformatics, Proteomics},
	pages = {14248},
	file = {Full Text PDF:/home/jo/Zotero/storage/ARC9LIC9/Candia et al. - 2017 - Assessment of Variability in the SOMAscan Assay.pdf:application/pdf},
}

@article{broadhurstGuidelinesConsiderationsUse2018,
	abstract = {Background:Quality assurance (QA) and quality control (QC) are two quality management processes that are integral to the success of metabolomics including their application for the acquisition of high quality data in any high-throughput analytical chemistry laboratory. QA defines all the planned and systematic activities implemented before samples are collected, to provide confidence that a subsequent analytical process will fulfil predetermined requirements for quality. QC can be defined as the operational techniques and activities used to measure and report these quality requirements after data acquisition.Aim of review:This tutorial review will guide the reader through the use of system suitability and QC samples, why these samples should be applied and how the quality of data can be reported.Key scientific concepts of review:System suitability samples are applied to assess the operation and lack of contamination of the analytical platform prior to sample analysis. Isotopically-labelled internal standards are applied to assess system stability for each sample analysed. Pooled QC samples are applied to condition the analytical platform, perform intra-study reproducibility measurements (QC) and to correct~mathematically for systematic errors. Standard reference materials and long-term reference QC samples are applied for inter-study and inter-laboratory assessment of data.},
	author = {Broadhurst, David and Goodacre, Royston and Reinke, Stacey N. and Kuligowski, Julia and Wilson, Ian D. and Lewis, Matthew R. and Dunn, Warwick B.},
	doi = {10.1007/s11306-018-1367-3},
	journal = {Metabolomics : Official journal of the Metabolomic Society},
	keywords = {Data analysis,Mass Spectrometry,Metabolomics,Methodology},
	language = {English},
	number = {6},
	pages = {72},
	pmcid = {PMC5960010},
	title = {Guidelines and Considerations for the Use of System Suitability and Quality Control Samples in Mass Spectrometry Assays Applied in Untargeted Clinical Metabolomic Studies.},
	volume = {14},
	year = {2018},
	Bdsk-Url-1 = {https://doi.org/10.1007/s11306-018-1367-3}
	}

@article{candia_assessment_2022,
	title = {Assessment of variability in the plasma 7k {SomaScan} proteomics assay},
	volume = {12},
	issn = {2045-2322},
	doi = {10.1038/s41598-022-22116-0},
	abstract = {SomaScan is a high-throughput, aptamer-based proteomics assay designed for the simultaneous measurement of thousands of proteins with a broad range of endogenous concentrations. In its most current version, the 7k SomaScan assay v4.1 is capable of measuring 7288 human proteins. In this work, we present an extensive technical assessment of this platform based on a study of 2050 samples across 22 plates. Included in the study design were inter-plate technical duplicates from 102 human subjects, which allowed us to characterize different normalization procedures, evaluate assay variability by multiple analytical approaches, present signal-over-background metrics, and discuss potential specificity issues. By providing detailed performance assessments on this wide range of technical aspects, we aim for this work to serve as a valuable resource for the growing community of SomaScan users.},
	language = {eng},
	number = {1},
	journal = {Scientific Reports},
	author = {Candia, Julián and Daya, Gulzar N. and Tanaka, Toshiko and Ferrucci, Luigi and Walker, Keenan A.},
	month = oct,
	year = {2022},
	pmid = {36229504},
	pmcid = {PMC9561184},
	keywords = {Proteomics, Humans, Biomarkers, SomaLogic},
	pages = {17147}
}

@article{enroth_systemic_2018,
	title = {Systemic and specific effects of antihypertensive and lipid-lowering medication on plasma protein biomarkers for cardiovascular diseases},
	volume = {8},
	copyright = {2018 The Author(s)},
	issn = {2045-2322},
	url = {https://www.nature.com/articles/s41598-018-23860-y},
	doi = {10.1038/s41598-018-23860-y},
	abstract = {A large fraction of the adult population is on lifelong medication for cardiovascular disorders, but the metabolic consequences are largely unknown. This study determines the effects of common anti-hypertensive and lipid lowering drugs on circulating plasma protein biomarkers. We studied 425 proteins in plasma together with anthropometric and lifestyle variables, and the genetic profile in a cross-sectional cohort. We found 8406 covariate-protein associations, and a two-stage GWAS identified 17253 SNPs to be associated with 109 proteins. By computationally removing variation due to lifestyle and genetic factors, we could determine that medication, per se, affected the abundance levels of 35.7\% of the plasma proteins. Medication either affected a single, a few, or a large number of protein, and were found to have a negative or positive influence on known disease pathways and biomarkers. Anti-hypertensive or lipid lowering drugs affected 33.1\% of the proteins. Angiotensin-converting enzyme inhibitors showed the strongest lowering effect by decreasing plasma levels of myostatin. Cell-culture experiments showed that angiotensin-converting enzyme inhibitors reducted myostatin RNA levels. Thus, understanding the effects of lifelong medication on the plasma proteome is important both for sharpening the diagnostic precision of protein biomarkers and in disease management.},
	language = {en},
	number = {1},
	urldate = {2023-05-08},
	journal = {Scientific Reports},
	author = {Enroth, Stefan and Maturi, Varun and Berggrund, Malin and Enroth, Sofia Bosdotter and Moustakas, Aristidis and Johansson, Åsa and Gyllensten, Ulf},
	month = apr,
	year = {2018},
	note = {Number: 1
Publisher: Nature Publishing Group},
	keywords = {Plasma proteomics, Biomarkers, Proteome informatics, Medication, lipid lowering medication, antihypertensive medication},
	pages = {5531},
	file = {41598_2018_23860_MOESM1_ESM.xlsx:/home/jo/Zotero/storage/FB8QWIGN/41598_2018_23860_MOESM1_ESM.xlsx:application/vnd.openxmlformats-officedocument.spreadsheetml.sheet;Full Text PDF:/home/jo/Zotero/storage/9MWUKCCC/Enroth et al. - 2018 - Systemic and specific effects of antihypertensive .pdf:application/pdf},
}

@article{ramsey_variation_2016,
	title = {Variation in serum biomarkers with sex and female hormonal status: implications for clinical tests},
	volume = {6},
	copyright = {2016 The Author(s)},
	issn = {2045-2322},
	shorttitle = {Variation in serum biomarkers with sex and female hormonal status},
	url = {https://www.nature.com/articles/srep26947},
	doi = {10.1038/srep26947},
	abstract = {Few serum biomarker tests are implemented in clinical practice and recent reports raise concerns about poor reproducibility of biomarker studies. Here, we investigated the potential role of sex and female hormonal status in this widespread irreproducibility. We examined 171 serum proteins and small molecules measured in 1,676 participants from the Netherlands Study of Depression and Anxiety. Concentrations of 96 molecules varied with sex and 66 molecules varied between oral contraceptive pill users, postmenopausal females and females in the follicular and luteal phases of the menstrual cycle (FDR-adjusted p-value {\textless}0.05). Simulations of biomarker studies yielded up to 40\% false discoveries when patient and control groups were not matched for sex and up to 41\% false discoveries when premenopausal females were not matched for oral contraceptive pill use. High accuracy (over 90\%) classification tools were developed to label samples with sex and female hormonal status where this information was not collected.},
	language = {en},
	number = {1},
	urldate = {2022-09-22},
	journal = {Scientific Reports},
	author = {Ramsey, Jordan M. and Cooper, Jason D. and Penninx, Brenda W. J. H. and Bahn, Sabine},
	month = may,
	year = {2016},
	note = {Number: 1
Publisher: Nature Publishing Group},
	keywords = {Metabolomics, Proteomics, Biomarkers, Sex Factors, Biotechnology, Sex-hormones, Contraceptives},
	pages = {26947},
	file = {Full Text PDF:/home/jo/Zotero/storage/6MAK75DY/Ramsey et al. - 2016 - Variation in serum biomarkers with sex and female .pdf:application/pdf;Snapshot:/home/jo/Zotero/storage/DQ6L6847/srep26947.html:text/html},
}

@article{kangasniemi_ethinylestradiol_2023,
	title = {Ethinylestradiol in combined hormonal contraceptive has a broader effect on serum proteome compared with estradiol valerate: a randomized controlled trial},
	volume = {38},
	issn = {1460-2350},
	shorttitle = {Ethinylestradiol in combined hormonal contraceptive has a broader effect on serum proteome compared with estradiol valerate},
	doi = {10.1093/humrep/deac250},
	abstract = {STUDY QUESTION: Does an estradiol-based combined oral contraceptive (COC) have a milder effect on the serum proteome than an ethinylestradiol (EE)-based COC or dienogest (DNG) only?
SUMMARY ANSWER: The changes in serum proteome were multifold after the use of a synthetic EE-based COC compared to natural estrogen COC or progestin-only preparation.
WHAT IS KNOWN ALREADY: EE-based COCs widely affect metabolism, inflammation, hepatic protein synthesis and blood coagulation. Studies comparing serum proteomes after the use of COCs containing EE and natural estrogens are lacking.
STUDY DESIGN, SIZE, DURATION: This was a spin-off from a randomized, controlled, two-center clinical trial. Women (n = 59) were randomized to use either EE + DNG, estradiol valerate (EV) + DNG or DNG only continuously for 9 weeks.
PARTICIPANTS/MATERIALS, SETTING, METHODS: Participants were healthy, young, white volunteer women. Serum samples were collected before and after 9 weeks of hormonal exposure. Samples from 44 women were available for analysis (EE + DNG n = 14, EV + DNG n = 16 and DNG only n = 14). Serum proteins were analyzed by quantitative, discovery-type label-free proteomics.
MAIN RESULTS AND THE ROLE OF CHANCE: Altogether, 446 proteins/protein families with two or more unique peptides were detected and quantified. The number of proteins/families that altered over the 9-week period within the study groups was 121 for EE + DNG and 5 for EV + DNG, while no changes were detected for DNG only. When alterations were compared between the groups, significant differences were detected for 63 proteins/protein families, of which 58 were between the EE + DNG and EV + DNG groups. The most affected functions during the use of EE + DNG were the complement system, acute phase response signaling, metabolism and the coagulation system. The results were validated by fetuin-B and cortisol-binding globulin ELISA and sex hormone-binding globulin immunoassay.
LARGE SCALE DATA: Data are available via ProteomeXchange with identifiers PXD033617 (low abundance fraction) and PXD033618 (high abundance fraction).
LIMITATIONS, REASONS FOR CAUTION: The power analysis of the trial was not based on the proteomic analysis of this spin-off study. In the future, targeted proteomic analysis with samples from another trial should be carried out in order to confirm the results.
WIDER IMPLICATIONS OF THE FINDINGS: The EE-based COC exerted a broader effect on the serum proteome than the EV-based COC or the DNG-only preparation. These results demonstrate that the effects of EE in COCs go far beyond the established endpoint markers of estrogen action, while the EV combination is closer to the progestin-only preparation. The study indicates that EV could provide a preferable option to EE in COCs in the future and signals a need for further studies comparing the clinical health outcomes of COCs containing EE and natural estrogens.
STUDY FUNDING/COMPETING INTEREST(S): Funding for this researcher-initiated study was obtained from the Helsinki University Hospital research funds, the Hospital District of Helsinki and Uusimaa, the Sigrid Juselius Foundation, the Academy of Finland, the Finnish Medical Association, the University of Oulu Graduate School, the Emil Aaltonen Foundation, the Swedish Cultural Foundation in Finland, the Novo Nordisk Foundation, Orion Research Foundation and the Northern Ostrobothnia Regional Fund. The funders had no role in study design, data collection and analysis, publishing decisions or manuscript preparation. T.P. has received honoraria for lectures, consultations and research grants from Exeltis, Gedeon Richter, MSD, Merck, Pfizer, Roche, Stragen and Mithra Pharmaceuticals. O.H. occasionally serves on advisory boards for Bayer AG and Gedeon Richter and has designed and lectured at educational events for these companies. The other authors have nothing to disclose. O.H. occasionally serves on advisory boards for Bayer AG and Gedeon Richter and has designed and lectured at educational events for these companies. The other authors have nothing to disclose.
TRIAL REGISTRATION NUMBER: ClinicalTrials.gov NCT02352090.
TRIAL REGISTRATION DATE: 27 January 2015.
DATE OF FIRST PATIENT’S ENROLMENT: 1 April 2015.},
	language = {eng},
	number = {1},
	journal = {Human Reproduction (Oxford, England)},
	author = {Kangasniemi, M. H. and Arffman, R. K. and Joenväärä, S. and Haverinen, A. and Luiro, K. and Tohmola, T. and Renkonen, R. and Heikinheimo, O. and Tapanainen, J. S. and Piltonen, T. T.},
	month = jan,
	year = {2023},
	pmid = {36416543},
	pmcid = {PMC9825269},
	keywords = {Proteomics, Humans, Female, Estradiol, Estrogens, metabolism, Proteome, complement, Contraceptives, Oral, Combined, Ethinyl Estradiol, acute phase signaling, coagulation, combined contraceptive, estradiol valerate, ethinylestradiol, Levonorgestrel, Progestins, proteome, randomized controlled trial},
	pages = {89--102}
}