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2. Breng de bodem in het stedelijk gebied tot leven! : Leer meer over het bodemleven in het stedelijk gebied

Author

Mathu, L.F.A., Korthals, G.W., van Bochove, K., Boone, P., Mathu, L.F.A., Korthals, G.W., van Bochove, K., and Boone, P.

Abstract

Het stedelijk gebied moet groener, biodiverser en weerbaarder tegen het veranderende klimaat worden. Kan bodemleven hieraan bijdragen? Wat weten we al van het bodemleven dat hier zit? Hoe kunnen we het bodemleven hier stimuleren? In dit artikel gaan we in op deze vragen.

Published
2022

3. Evaluating a novel approach to stimulate open science collaborations:A case series of 'study-a-thon' events within the OHDSI and European IMI communities

Author

Hughes, N., Rijnbeek, P. R., Van Bochove, K., Duarte-Salles, T., Steinbeisser, C., Vizcaya, Prieto-Alhambra, D., Ryan, P., Hughes, N., Rijnbeek, P. R., Van Bochove, K., Duarte-Salles, T., Steinbeisser, C., Vizcaya, Prieto-Alhambra, D., and Ryan, P.

Abstract

Objective: We introduce and review the concept of a study-a-thon as a catalyst for open science in medicine, utilizing harmonized real world, observation health data, tools, skills, and methods to conduct network studies, generating insights for those wishing to use study-a-thons for future research. Materials and Methods: A series of historical study-a-thons since 2017 to present were reviewed for thematic insights as to the opportunity to accelerate the research method to conduct studies across therapeutic areas. Review of publications and experience of the authors generated insights to illustrate the conduct of study-a-thons, key learning, and direction for those wishing to conduct future such study-a-thons. Results: A review of six study-a-thons have provided insights into their scientific impact, and 13 areas of insights for those wishing to conduct future study-a-thons. Defining aspects of the study-a-thon method for rapid, collaborative research through network studies reinforce the need to clear scientific rationale, tools, skills, and methods being collaboratively to conduct a focused study. Well-characterized preparatory, execution and postevent phases, coalescing skills, experience, data, clinical input (ensuring representative clinical context to the research query), and well-defined, logical steps in conducting research via the study-a-thon method are critical. Conclusions: A study-a-thon is a focused multiday research event generating reliable evidence on a specific medical topic across different countries and health systems. In a study-a-thon, a multidisciplinary team collaborate to create an accelerated contribution to scientific evidence and clinical practice. It critically accelerates the research process, without inhibiting the quality of the research output and evidence generation, through a reproducible process.

Published
2022

4. Introducing PIONEER: a project to harness big data in prostate cancer research

Author

Omar M. I., Roobol M. J., Ribal M. J., Abbott T., Agapow P. -M., Araujo S., Asiimwe A., Auffray C., Balaur I., Beyer K., Bernini C., Bjartell A., Briganti A., Butler-Ransohoff J. -E., Campi R., Cavelaars M., De Meulder B., Devecseri Z., Voss M. D., Dimitropoulos K., Evans-Axelsson S., Franks B., Fullwood L., Horgan D., Smith E. J., Kiran A., Kivinummi K., Lambrecht M., Lancet D., Lindgren P., MacLennan S., Nogueira M. M., Moen F., Moinat M., Papineni K., Reich C., Reiche K., Rogiers S., Sartini C., van Bochove K., van Diggelen F., Van Hemelrijck M., Van Poppel H., Zong J., N'Dow J., Andersson E., Arala H., Auvinen A., Bangma C., Burke D., Cardone A., Casariego J., Cuperus G., Dabestani S., Esperto F., Fossati N., Fridhammar A., Gandaglia G., Tandefelt D. G., Horn F., Huber J., Hugosson J., Huisman H., Josefsson A., Kilkku O., Kreuz M., Lardas M., Lawson J., Lefresne F., Lejeune S., Longden-Chapman E., McVie G., Moris L., Mottet N., Murtola T., Nicholls C., Pang K. H., Pascoe K., Picozzi M., Plass K., Pohjanjousi P., Reaney M., Remmers S., Robinson P., Schalken J., Schravendeel M., Seisen T., Servan A., Shiranov K., Snijder R., Steinbeisser C., Taibi N., Talala K., Tilki D., Van den Broeck T., Vassilev Z., Voima O., Vradi E., Waldeck R., Weistra W., Willemse P. -P., Wirth M., Wolfinger R., Kermani N. Z., Omar, M. I., Roobol, M. J., Ribal, M. J., Abbott, T., Agapow, P. -M., Araujo, S., Asiimwe, A., Auffray, C., Balaur, I., Beyer, K., Bernini, C., Bjartell, A., Briganti, A., Butler-Ransohoff, J. -E., Campi, R., Cavelaars, M., De Meulder, B., Devecseri, Z., Voss, M. D., Dimitropoulos, K., Evans-Axelsson, S., Franks, B., Fullwood, L., Horgan, D., Smith, E. J., Kiran, A., Kivinummi, K., Lambrecht, M., Lancet, D., Lindgren, P., Maclennan, S., Nogueira, M. M., Moen, F., Moinat, M., Papineni, K., Reich, C., Reiche, K., Rogiers, S., Sartini, C., van Bochove, K., van Diggelen, F., Van Hemelrijck, M., Van Poppel, H., Zong, J., N'Dow, J., Andersson, E., Arala, H., Auvinen, A., Bangma, C., Burke, D., Cardone, A., Casariego, J., Cuperus, G., Dabestani, S., Esperto, F., Fossati, N., Fridhammar, A., Gandaglia, G., Tandefelt, D. G., Horn, F., Huber, J., Hugosson, J., Huisman, H., Josefsson, A., Kilkku, O., Kreuz, M., Lardas, M., Lawson, J., Lefresne, F., Lejeune, S., Longden-Chapman, E., Mcvie, G., Moris, L., Mottet, N., Murtola, T., Nicholls, C., Pang, K. H., Pascoe, K., Picozzi, M., Plass, K., Pohjanjousi, P., Reaney, M., Remmers, S., Robinson, P., Schalken, J., Schravendeel, M., Seisen, T., Servan, A., Shiranov, K., Snijder, R., Steinbeisser, C., Taibi, N., Talala, K., Tilki, D., Van den Broeck, T., Vassilev, Z., Voima, O., Vradi, E., Waldeck, R., Weistra, W., Willemse, P. -P., Wirth, M., Wolfinger, R., Kermani, N. Z., Publica, and Urology

Subjects
0301 basic medicine, Prioritization, Knowledge management, Urology, media_common.quotation_subject, education, Big data, Disease, 03 medical and health sciences, Prostate cancer, 0302 clinical medicine, SDG 3 - Good Health and Well-being, Multidisciplinary approach, Urological cancers Radboud Institute for Molecular Life Sciences [Radboudumc 15], Medicine, Quality (business), media_common, business.industry, Patient-centered outcomes, medicine.disease, 3. Good health, Patient management, 030104 developmental biology, Urological cancers Radboud Institute for Health Sciences [Radboudumc 15], 030220 oncology & carcinogenesis, business
Abstract

Prostate Cancer Diagnosis and Treatment Enhancement Through the Power of Big Data in Europe (PIONEER) is a European network of excellence for big data in prostate cancer, consisting of 32 private and public stakeholders from 9 countries across Europe. Launched by the Innovative Medicines Initiative 2 and part of the Big Data for Better Outcomes Programme (BD4BO), the overarching goal of PIONEER is to provide high-quality evidence on prostate cancer management by unlocking the potential of big data. The project has identified critical evidence gaps in prostate cancer care, via a detailed prioritization exercise including all key stakeholders. By standardizing and integrating existing high-quality and multidisciplinary data sources from patients with prostate cancer across different stages of the disease, the resulting big data will be assembled into a single innovative data platform for research. Based on a unique set of methodologies, PIONEER aims to advance the field of prostate cancer care with a particular focus on improving prostate-cancer-related outcomes, health system efficiency by streamlining patient management, and the quality of health and social care delivered to all men with prostate cancer and their families worldwide.Prostate Cancer Diagnosis and Treatment Enhancement Through the Power of Big Data in Europe (PIONEER) is a European network of excellence for big data in prostate cancer, consisting of 32 private and public stakeholders from 9 countries across Europe. In this Perspectives article, the authors introduce the PIONEER project and describe its aims and plans for ultimately improving prostate cancer care through the use of big data.

Published
2020

5. Author Correction: Introducing PIONEER: a project to harness big data in prostate cancer research (Nature Reviews Urology, (2020), 17, 6, (351-362), 10.1038/s41585-020-0324-x)

Author

Omar M. I., Roobol M. J., Ribal M. J., Abbott T., Agapow P. -M., Araujo S., Asiimwe A., Auffray C., Balaur I., Beyer K., Bernini C., Bjartell A., Briganti A., Butler-Ransohoff J. -E., Campi R., Cavelaars M., De Meulder B., Devecseri Z., Voss M. D., Dimitropoulos K., Evans-Axelsson S., Franks B., Fullwood L., Horgan D., Smith E. J., Kiran A., Kivinummi K., Lambrecht M., Lancet D., Lindgren P., MacLennan S., Nogueira M. M., Moen F., Moinat M., Papineni K., Reich C., Reiche K., Rogiers S., Sartini C., van Bochove K., van Diggelen F., Van Hemelrijck M., Van Poppel H., Zong J., N'Dow J., Andersson E., Arala H., Auvinen A., Bangma C., Burke D., Cardone A., Casariego J., Cuperus G., Dabestani S., Esperto F., Fossati N., Fridhammar A., Gandaglia G., Tandefelt D. G., Horn F., Huber J., Hugosson J., Huisman H., Josefsson A., Kilkku O., Kreuz M., Lardas M., Lawson J., Lefresne F., Lejeune S., Longden-Chapman E., McVie G., Moris L., Mottet N., Murtola T., Nicholls C., Pang K. H., Pascoe K., Picozzi M., Plass K., Pohjanjousi P., Reaney M., Remmers S., Robinson P., Schalken J., Schravendeel M., Seisen T., Servan A., Shiranov K., Snijder R., Steinbeisser C., Taibi N., Talala K., Tilki D., Van den Broeck T., Vassilev Z., Voima O., Vradi E., Waldeck R., Weistra W., Willemse P. -P., Wirth M., Wolfinger R., Kermani N. Z., Omar, M. I., Roobol, M. J., Ribal, M. J., Abbott, T., Agapow, P. -M., Araujo, S., Asiimwe, A., Auffray, C., Balaur, I., Beyer, K., Bernini, C., Bjartell, A., Briganti, A., Butler-Ransohoff, J. -E., Campi, R., Cavelaars, M., De Meulder, B., Devecseri, Z., Voss, M. D., Dimitropoulos, K., Evans-Axelsson, S., Franks, B., Fullwood, L., Horgan, D., Smith, E. J., Kiran, A., Kivinummi, K., Lambrecht, M., Lancet, D., Lindgren, P., Maclennan, S., Nogueira, M. M., Moen, F., Moinat, M., Papineni, K., Reich, C., Reiche, K., Rogiers, S., Sartini, C., van Bochove, K., van Diggelen, F., Van Hemelrijck, M., Van Poppel, H., Zong, J., N'Dow, J., Andersson, E., Arala, H., Auvinen, A., Bangma, C., Burke, D., Cardone, A., Casariego, J., Cuperus, G., Dabestani, S., Esperto, F., Fossati, N., Fridhammar, A., Gandaglia, G., Tandefelt, D. G., Horn, F., Huber, J., Hugosson, J., Huisman, H., Josefsson, A., Kilkku, O., Kreuz, M., Lardas, M., Lawson, J., Lefresne, F., Lejeune, S., Longden-Chapman, E., Mcvie, G., Moris, L., Mottet, N., Murtola, T., Nicholls, C., Pang, K. H., Pascoe, K., Picozzi, M., Plass, K., Pohjanjousi, P., Reaney, M., Remmers, S., Robinson, P., Schalken, J., Schravendeel, M., Seisen, T., Servan, A., Shiranov, K., Snijder, R., Steinbeisser, C., Taibi, N., Talala, K., Tilki, D., Van den Broeck, T., Vassilev, Z., Voima, O., Vradi, E., Waldeck, R., Weistra, W., Willemse, P. -P., Wirth, M., Wolfinger, R., and Kermani, N. Z.

Abstract

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

Published
2020

6. Renin-angiotensin system blockers and susceptibility to COVID-19: a multinational open science cohort study.

Author

Morales, DR, Conover, MM, You, SC, Pratt, N, Kostka, K, Duarte-Salles, T, Fernández-Bertolín, S, Aragón, M, DuVall, SL, Lynch, K, Falconer, T, van Bochove, K, Sung, C, Matheny, ME, Lambert, CG, Nyberg, F, Alshammari, TM, Williams, AE, Park, RW, Weaver, J, Sena, AG, Schuemie, MJ, Rijnbeek, PR, Williams, RD, Lane, JCE, Prats-Uribe, A, Zhang, L, Areia, C, Krumholz, HM, Prieto-Alhambra, D, Ryan, PB, Hripcsak, G, Suchard, MA, Morales, DR, Conover, MM, You, SC, Pratt, N, Kostka, K, Duarte-Salles, T, Fernández-Bertolín, S, Aragón, M, DuVall, SL, Lynch, K, Falconer, T, van Bochove, K, Sung, C, Matheny, ME, Lambert, CG, Nyberg, F, Alshammari, TM, Williams, AE, Park, RW, Weaver, J, Sena, AG, Schuemie, MJ, Rijnbeek, PR, Williams, RD, Lane, JCE, Prats-Uribe, A, Zhang, L, Areia, C, Krumholz, HM, Prieto-Alhambra, D, Ryan, PB, Hripcsak, G, and Suchard, MA

Abstract

INTRODUCTION: Angiotensin converting enzyme inhibitors (ACEs) and angiotensin receptor blockers (ARBs) could influence infection risk of coronavirus disease (COVID-19). Observational studies to date lack pre-specification, transparency, rigorous ascertainment adjustment and international generalizability, with contradictory results. METHODS: Using electronic health records from Spain (SIDIAP) and the United States (Columbia University Irving Medical Center and Department of Veterans Affairs), we conducted a systematic cohort study with prevalent ACE, ARB, calcium channel blocker (CCB) and thiazide diuretic (THZ) use to determine relative risk of COVID-19 diagnosis and related hospitalization outcomes. The study addressed confounding through large-scale propensity score adjustment and negative control experiments. RESULTS: Following over 1.1 million antihypertensive users identified between November 2019 and January 2020, we observed no significant difference in relative COVID-19 diagnosis risk comparing ACE/ARB vs CCB/THZ monotherapy (hazard ratio: 0.98; 95% CI 0.84 - 1.14), nor any difference for mono/combination use (1.01; 0.90 - 1.15). ACE alone and ARB alone similarly showed no relative risk difference when compared to CCB/THZ monotherapy or mono/combination use. Directly comparing ACE vs. ARB demonstrated a moderately lower risk with ACE, non-significant for monotherapy (0.85; 0.69 - 1.05) and marginally significant for mono/combination users (0.88; 0.79 - 0.99). We observed, however, no significant difference between drug- classes for COVID-19 hospitalization or pneumonia risk across all comparisons. CONCLUSION: There is no clinically significant increased risk of COVID-19 diagnosis or hospitalization with ACE or ARB use. Users should not discontinue or change their treatment to avoid COVID-19.

Published
2020

7. Consistent Biopsy Quality and Gleason Grading Within the Global Active Surveillance Global Action Plan 3 Initiative: A Prerequisite for Future Studies.

Author

Rannikko A., Moore C.M., Gnanapragasam V., Van Hemelrijck M., Dasgupta P., Bangma C., Villers A., Valdagni R., Perry A., Hugosson J., Rubio-Briones J., Bjartell A., Hefermehl L., Shiong L.L., Frydenberg M., Kakehi Y., Chung B.H., Obbink H., van der Linden W., Hulsen T., de Jonge C., Kattan M., Xinge J., Muir K., Lophatananon A., Fahey M., Steyerberg E., Zhang L., Guo W., Benfante N., Cowan J., Patil D., Tolosa E., Kim T.-K., Mamedov A., Lapointe V., Crump T., Kimberly-Duffell J., Santaolalla A., Olivier J., Rancati T., Ahlgren H., Mascaros J., Lofgren A., Lehmann K., Lin C.H., Hirama H., Lee K.S., Jenster G., Auvinen A., Haider M., van Bochove K., Carter B., Gledhill S., Buzza M., van der Kwast T.H., Helleman J., Nieboer D., Bruinsma S.M., Roobol M.J., Trock B., Ehdaie B., Carroll P., Filson C., Kim J., Logothetis C., Morgan T., Klotz L., Pickles T., Hyndman E., Rannikko A., Moore C.M., Gnanapragasam V., Van Hemelrijck M., Dasgupta P., Bangma C., Villers A., Valdagni R., Perry A., Hugosson J., Rubio-Briones J., Bjartell A., Hefermehl L., Shiong L.L., Frydenberg M., Kakehi Y., Chung B.H., Obbink H., van der Linden W., Hulsen T., de Jonge C., Kattan M., Xinge J., Muir K., Lophatananon A., Fahey M., Steyerberg E., Zhang L., Guo W., Benfante N., Cowan J., Patil D., Tolosa E., Kim T.-K., Mamedov A., Lapointe V., Crump T., Kimberly-Duffell J., Santaolalla A., Olivier J., Rancati T., Ahlgren H., Mascaros J., Lofgren A., Lehmann K., Lin C.H., Hirama H., Lee K.S., Jenster G., Auvinen A., Haider M., van Bochove K., Carter B., Gledhill S., Buzza M., van der Kwast T.H., Helleman J., Nieboer D., Bruinsma S.M., Roobol M.J., Trock B., Ehdaie B., Carroll P., Filson C., Kim J., Logothetis C., Morgan T., Klotz L., Pickles T., and Hyndman E.

Abstract

Within the Movember Foundation's Global Action Plan Prostate Cancer Active Surveillance (GAP3) initiative, 25 centers across the globe collaborate to standardize active surveillance (AS) protocols for men with low-risk prostate cancer (PCa). A centralized PCa AS database, comprising data of more than 15 000 patients worldwide, was created. Comparability of the histopathology between the different cohorts was assessed by a centralized pathology review of 445 biopsies from 15 GAP3 centers. Grade group 1 (Gleason score 6) in 85% and grade group >=2 (Gleason score >=7) in 15% showed 89% concordance at review with moderate agreement (kappa = 0.56). Average biopsy core length was similar among the analyzed cohorts. Recently established highly adverse pathologies, including cribriform and/or intraductal carcinoma, were observed in 3.6% of the reviewed biopsies. In conclusion, the centralized pathology review of 445 biopsies revealed comparable histopathology among the 15 GAP3 centers with a low frequency of high-risk features. This enables further data analyses-without correction-toward uniform global AS guidelines for men with low-risk PCa. Patient Summary: Movember Foundation's Global Action Plan Prostate Cancer Active Surveillance (GAP3) initiative combines data from 15 000 men with low-risk prostate cancer (PCa) across the globe to standardize active surveillance protocols. Histopathology review confirmed that the histopathology was consistent with low-risk PCa in most men and comparable between different centers. A centralized pathological review showed consistent biopsy quality and Gleason grading within the global active surveillance Global Action Plan 3 initiative, a prerequisite for future studies toward uniform global guidelines for active surveillance of men with low-risk prostate cancer.Copyright © 2018

Published
2019

8. Reasons for Discontinuing Active Surveillance: Assessment of 21 Centres in 12 Countries in the Movember GAP3 Consortium.

Author

Perry A., Gledhill S., Morgan T., Klotz L., Pickles T., Hyndman E., Moore C.M., Dasgupta P., Villers A., Valdagni R., Carter B., Hugosson J., Rubio-Briones J., Bjartell A., Hefermehl L., Lui Shiong L., Kakehi Y., Ha Chung B., van der Kwast T., Obbink H., Hulsen T., de Jonge C., Xinge J., Muir K., Lophatananon A., Steyerberg E., Zhang L., Santa Olalla A., Beckmann K., Denton B., Hayen A., Boutros P., Guo W., Benfante N., Cowan J., Patil D., Tolosa E., Kim T.-K., Mamedov A., La Pointe V., Crump T., Kimberly-Duffell J., Santaolalla A., Olivier J., Rancati T., Ahlgren H., Mascaros J., Lofgren A., Lehmann K., Han Lin C., Hirama H., Suk Lee K., Jenster G., Auvinen A., Haider M., van Bochove K., Buzza M., Bangma C., Bruinsma S., Fahey M., Van Hemelrijck M., Ji X., Kattan M.W., Helleman J., Roobol M.J., Nieboer D., Bangma C.H., van der Linden W., Frydenberg M., Rannikko A., Lee L.S., Gnanapragasam V.J., Trock B., Ehdaie B., Carroll P., Filson C., Kim J., Logothetis C., Perry A., Gledhill S., Morgan T., Klotz L., Pickles T., Hyndman E., Moore C.M., Dasgupta P., Villers A., Valdagni R., Carter B., Hugosson J., Rubio-Briones J., Bjartell A., Hefermehl L., Lui Shiong L., Kakehi Y., Ha Chung B., van der Kwast T., Obbink H., Hulsen T., de Jonge C., Xinge J., Muir K., Lophatananon A., Steyerberg E., Zhang L., Santa Olalla A., Beckmann K., Denton B., Hayen A., Boutros P., Guo W., Benfante N., Cowan J., Patil D., Tolosa E., Kim T.-K., Mamedov A., La Pointe V., Crump T., Kimberly-Duffell J., Santaolalla A., Olivier J., Rancati T., Ahlgren H., Mascaros J., Lofgren A., Lehmann K., Han Lin C., Hirama H., Suk Lee K., Jenster G., Auvinen A., Haider M., van Bochove K., Buzza M., Bangma C., Bruinsma S., Fahey M., Van Hemelrijck M., Ji X., Kattan M.W., Helleman J., Roobol M.J., Nieboer D., Bangma C.H., van der Linden W., Frydenberg M., Rannikko A., Lee L.S., Gnanapragasam V.J., Trock B., Ehdaie B., Carroll P., Filson C., Kim J., and Logothetis C.

Abstract

Background: Careful assessment of the reasons for discontinuation of active surveillance (AS) is required for men with prostate cancer (PCa). Objective(s): Using Movember's Global Action Plan Prostate Cancer Active Surveillance initiative (GAP3) database, we report on reasons for AS discontinuation. Design, setting, and participants: We compared data from 10 296 men on AS from 21 centres across 12 countries. Outcome measurements and statistical analysis: Cumulative incidence methods were used to estimate the cumulative incidence rates of AS discontinuation. Results and limitations: During 5-yr follow-up, 27.5% (95% confidence interval [CI]: 26.4-28.6%) men showed signs of disease progression, 12.8% (95% CI: 12.0-13.6%) converted to active treatment without evidence of progression, 1.7% (95% CI: 1.5-2.0%) continued to watchful waiting, and 1.7% (95% CI: 1.4-2.1%) died from other causes. Of the 7049 men who remained on AS, 2339 had follow-up for >5 yr, 4561 had follow-up for <5 yr, and 149 were lost to follow-up. Cumulative incidence of progression was 27.5% (95% CI: 26.4-28.6%) at 5 yr and 38.2% (95% CI: 36.7-39.9%) at 10 yr. A limitation is that not all centres were included due to limited information on the reason for discontinuation and limited follow-up. Conclusion(s): Our descriptive analyses of current AS practices worldwide showed that 43.6% of men drop out of AS during 5-yr follow-up, mainly due to signs of disease progression. Improvements in selection tools for AS are thus needed to correctly allocate men with PCa to AS, which will also reduce discontinuation due to conversion to active treatment without evidence of disease progression. Patient Summary: Our assessment of a worldwide database of men with prostate cancer (PCa) on active surveillance (AS) shows that 43.6% drop out of AS within 5 yr, mainly due to signs of disease progression. Better tools are needed to select and monitor men with PCa as part of AS. After about 5 yr, about 56% of men were still

Published
2019

9. Predicting Biopsy Outcomes During Active Surveillance for Prostate Cancer: External Validation of the Canary Prostate Active Surveillance Study Risk Calculators in Five Large Active Surveillance Cohorts.

Author

Santaolalla A., Gnanapragasam V., Van Hemelrijck M., Dasgupta P., Auvinen A., Haider M., van Bochove K., Carter B., Gledhill S., Buzza M., Bruinsma S., Helleman J., Drost F.-J.H., Nieboer D., Morgan T.M., Carroll P.R., Roobol M.J., Trock B., Ehdaie B., Carroll P., Filson C., Kim J., Logothetis C., Morgan T., Klotz L., Pickles T., Hyndman E., Bangma C., Roobol M., Villers A., Rannikko A., Valdagni R., Perry A., Hugosson J., Rubio-Briones J., Bjartell A., Hefermehl L., Shiong L.L., Frydenberg M., Kakehi Y., Chung B.H., van der Kwast T., van der Linden W., Hulsen T., de Jonge C., Kattan M., Xinge J., Muir K., Lophatananon A., Fahey M., Steyerberg E., Zhang L., Beckmann K., Denton B., Hayen A., Boutros P., Guo W., Benfante N., Cowan J., Patil D., Tolosa E., Kim T.-K., Mamedov A., Lapointe V., Crump T., Kimberly-Duffell J., Olivier J., Rancati T., Ahlgren H., Mascaros J., Lofgren A., Lin C.H., Hirama H., Lee K.S., Moore C.M., Jenster G., Santaolalla A., Gnanapragasam V., Van Hemelrijck M., Dasgupta P., Auvinen A., Haider M., van Bochove K., Carter B., Gledhill S., Buzza M., Bruinsma S., Helleman J., Drost F.-J.H., Nieboer D., Morgan T.M., Carroll P.R., Roobol M.J., Trock B., Ehdaie B., Carroll P., Filson C., Kim J., Logothetis C., Morgan T., Klotz L., Pickles T., Hyndman E., Bangma C., Roobol M., Villers A., Rannikko A., Valdagni R., Perry A., Hugosson J., Rubio-Briones J., Bjartell A., Hefermehl L., Shiong L.L., Frydenberg M., Kakehi Y., Chung B.H., van der Kwast T., van der Linden W., Hulsen T., de Jonge C., Kattan M., Xinge J., Muir K., Lophatananon A., Fahey M., Steyerberg E., Zhang L., Beckmann K., Denton B., Hayen A., Boutros P., Guo W., Benfante N., Cowan J., Patil D., Tolosa E., Kim T.-K., Mamedov A., Lapointe V., Crump T., Kimberly-Duffell J., Olivier J., Rancati T., Ahlgren H., Mascaros J., Lofgren A., Lin C.H., Hirama H., Lee K.S., Moore C.M., and Jenster G.

Abstract

Two active surveillance risk calculators to predict disease reclassification on prostate biopsy are externally validated by the Movember Foundation's Global Action Plan (GAP3) consortium. They proved to be clinically useful and could reduce unnecessary biopsies, but need recalibration to local settings. Background: Men with prostate cancer (PCa) on active surveillance (AS) are followed through regular prostate biopsies, a burdensome and often unnecessary intervention, not without risks. Identifying men with at a low risk of disease reclassification may help reduce the number of biopsies. Objective(s): To assess the external validity of two Canary Prostate Active Surveillance Study Risk Calculators (PASS-RCs), which estimate the probability of reclassification (Gleason grade >=7 with or without >34% of biopsy cores positive for PCa) on a surveillance biopsy, using a mix of months since last biopsy, age, body mass index, prostate-specific antigen, prostate volume, number of prior negative biopsies, and percentage (or ratio) of positive cores on last biopsy. Design, setting, and participants: We used data up to November 2017 from the Movember Foundation's Global Action Plan (GAP3) consortium, a global collaboration between AS studies. Outcome measurements and statistical analysis: External validity of the PASS-RCs for estimating reclassification on biopsy was assessed by calibration, discrimination, and decision curve analyses. Results and limitations: Five validation cohorts (Prostate Cancer Research International: Active Surveillance, Johns Hopkins, Toronto, Memorial Sloan Kettering Cancer Center, and University of California San Francisco), comprising 5105 men on AS, were eligible for analysis. The individual cohorts comprised 429-2416 men, with a median follow-up between 36 and 84 mo, in both community and academic practices mainly from western countries. Abilities of the PASS-RCs to discriminate between men with and without reclassification on biopsy were reasonab

Published
2019

10. Predicting Biopsy Outcomes During Active Surveillance for Prostate Cancer: External Validation of the Canary Prostate Active Surveillance Study Risk Calculators in Five Large Active Surveillance Cohorts

Author

Drost, FJH, Nieboer, D, Morgan, TM, Carroll, PR, Roobol, MJ, Trock, B, Ehdaie, B, Carroll, P, Filson, C, Kim, J, Logothetis, C, Morgan, T, Klotz, L, Pickles, T, Hyndman, E, Moore, CM, Gnanapragasam, V, Van Hemelrijck, M, Dasgupta, P, Bangma, C, Roobol, M, Villers, A, Rannikko, A, Valdagni, R, Perry, A, Hugosson, J, Rubio-Briones, J, Bjartell, A, Hefermehl, L, Shiong, LL, Frydenberg, M, Kakehi, Y, Chung, BH, van der Kwast, T, van der Linden, W, Hulsen, T, de Jonge, C, Kattan, M, Xinge, J, Muir, K, Lophatananon, A, Fahey, M, Steyerberg, E, Zhang, L, Beckmann, K, Denton, B, Hayen, A, Boutros, P, Guo, W, Benfante, N, Cowan, J, Patil, D, Tolosa, E, Kim, TK, Mamedov, A, Lapointe, V, Crump, T, Kimberly-Duffell, J, Santaolalla, A, Olivier, J, Rancati, T, Ahlgren, H, Mascarós, J, Löfgren, A, Lin, CH, Hirama, H, Lee, KS, Jenster, G, Auvinen, A, Haider, M, van Bochove, K, Carter, B, Gledhill, S, Buzza, M, Bruinsma, S, Helleman, J, Drost, FJH, Nieboer, D, Morgan, TM, Carroll, PR, Roobol, MJ, Trock, B, Ehdaie, B, Carroll, P, Filson, C, Kim, J, Logothetis, C, Morgan, T, Klotz, L, Pickles, T, Hyndman, E, Moore, CM, Gnanapragasam, V, Van Hemelrijck, M, Dasgupta, P, Bangma, C, Roobol, M, Villers, A, Rannikko, A, Valdagni, R, Perry, A, Hugosson, J, Rubio-Briones, J, Bjartell, A, Hefermehl, L, Shiong, LL, Frydenberg, M, Kakehi, Y, Chung, BH, van der Kwast, T, van der Linden, W, Hulsen, T, de Jonge, C, Kattan, M, Xinge, J, Muir, K, Lophatananon, A, Fahey, M, Steyerberg, E, Zhang, L, Beckmann, K, Denton, B, Hayen, A, Boutros, P, Guo, W, Benfante, N, Cowan, J, Patil, D, Tolosa, E, Kim, TK, Mamedov, A, Lapointe, V, Crump, T, Kimberly-Duffell, J, Santaolalla, A, Olivier, J, Rancati, T, Ahlgren, H, Mascarós, J, Löfgren, A, Lin, CH, Hirama, H, Lee, KS, Jenster, G, Auvinen, A, Haider, M, van Bochove, K, Carter, B, Gledhill, S, Buzza, M, Bruinsma, S, and Helleman, J

Abstract

© 2019 European Association of Urology Two active surveillance risk calculators to predict disease reclassification on prostate biopsy are externally validated by the Movember Foundation's Global Action Plan (GAP3) consortium. They proved to be clinically useful and could reduce unnecessary biopsies, but need recalibration to local settings.

Published
2019

11. Systematically linking tranSMART, Galaxy and EGA for reusing human translational research data [version 1; referees: awaiting peer review]

Author

Zhang, C, Bijlard, J, Staiger, C., Scollen, S., van Enckevort, D., Hoogstrate, Youri, Senf, Alexander, Hiltemann, Saskia, Repo, Susanna, Pipping, W, Bierkens, M., Payralbe, S, Stringer, B, Heringa, J, Stubbs, Andrew, Bonino Da Silva Santos, LO, Belien, J.A.M., Weistra, W, Azevedo, R.V.D.M., van Bochove, K, Meijer, G., Boiten, Jan-Willem, Rambla, Jordi, Fijneman, R.J., Spalding, JD, Abeln, S, Bioinformatics, Integrative Bioinformatics, and AIMMS

Published
2017
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12. The Movember Foundation's GAP3 cohort

Author

Bruinsma, S.M. (Sophie), Zhang, L. (Liying), Roobol-Bouts, M.J. (Monique), Bangma, C.H. (Chris), Steyerberg, E.W. (Ewout), Nieboer, D. (Daan), Van Hemelrijck, M. (Mieke), Trock, B.J. (Bruce), Ehdaie, B. (Behfar), Carroll, P.R. (Peter), Filson, C. (Christopher), Kim, J. (Jeri), Morgan, T. (Todd), Klotz, L. (Laurence), Pickles, T. (Tom), Hyndman, E. (Eric), Moore, C.M. (Caroline), Gnanapragasam, V. (Vincent), Dasgupta, P. (Prokar), Villers, A. (Arnoud), Rannikko, A.S. (Antti), Valdagni, R. (Riccardo), Perry, A. (Antoinette), Hugosson, J. (Jonas), Rubio-Briones, J. (Jose), Bjartell, A. (Anders), Hefermehl, L. (Lukas), Lui Shiong, L. (Lee), Frydenberg, M. (Mark), Kakehi, Y. (Yoshiyuki), Ha Chung, B. (Byung), Kwast, Th.H. (Theo) van der, Obbink, H. (Henk), van der Linden, W. (Wim), Hulsen, T. (Tim), de Jonge, C. (Cees), Kattan, M.W. (Michael), Xinge, J. (Ji), Muir, K. (Kenneth), Lophatananon, A. (Artitaya), Fahey, M. (Michael), Guo, W. (Wei), Milan, T. (Tanya), Benfante, N. (Nicole), Cowan, J. (Janet), Patil, D. (Dattatraya), Sanford, R. (Rachel), Kim, T.-K. (Tae-Kyung), Mamedov, A. (Alexandre), LaPointe, V. (Vincent), Crump, T. (Trafford), Hamoudi, R. (Rifat), Kimberly-Duffell, J. (Jenna), Santaolalla, A. (Aida), Olivier, J. (Jonathan), Janetti, E.B. (Emanuele Bianchi), Rancati, T. (Tiziana), Ahlgren, H. (Helén), Mascarós, J. (Juanma), Löfgren, A. (Annica), Lehmann, K. (Kurt), Han Lin, C. (Catherine), Hirama, H. (Hiromi), Jenster, G.W. (Guido), Auvinen, A. (Anssi), Haider, M. (Masoom), van Bochove, K. (Kees), Carter, B. (Ballentine), Kirk-Burnnand, R. (Rachelle), Gledhill, S. (Sam), Buzza, M. (Mark), Bruinsma, S.M. (Sophie), Zhang, L. (Liying), Roobol-Bouts, M.J. (Monique), Bangma, C.H. (Chris), Steyerberg, E.W. (Ewout), Nieboer, D. (Daan), Van Hemelrijck, M. (Mieke), Trock, B.J. (Bruce), Ehdaie, B. (Behfar), Carroll, P.R. (Peter), Filson, C. (Christopher), Kim, J. (Jeri), Morgan, T. (Todd), Klotz, L. (Laurence), Pickles, T. (Tom), Hyndman, E. (Eric), Moore, C.M. (Caroline), Gnanapragasam, V. (Vincent), Dasgupta, P. (Prokar), Villers, A. (Arnoud), Rannikko, A.S. (Antti), Valdagni, R. (Riccardo), Perry, A. (Antoinette), Hugosson, J. (Jonas), Rubio-Briones, J. (Jose), Bjartell, A. (Anders), Hefermehl, L. (Lukas), Lui Shiong, L. (Lee), Frydenberg, M. (Mark), Kakehi, Y. (Yoshiyuki), Ha Chung, B. (Byung), Kwast, Th.H. (Theo) van der, Obbink, H. (Henk), van der Linden, W. (Wim), Hulsen, T. (Tim), de Jonge, C. (Cees), Kattan, M.W. (Michael), Xinge, J. (Ji), Muir, K. (Kenneth), Lophatananon, A. (Artitaya), Fahey, M. (Michael), Guo, W. (Wei), Milan, T. (Tanya), Benfante, N. (Nicole), Cowan, J. (Janet), Patil, D. (Dattatraya), Sanford, R. (Rachel), Kim, T.-K. (Tae-Kyung), Mamedov, A. (Alexandre), LaPointe, V. (Vincent), Crump, T. (Trafford), Hamoudi, R. (Rifat), Kimberly-Duffell, J. (Jenna), Santaolalla, A. (Aida), Olivier, J. (Jonathan), Janetti, E.B. (Emanuele Bianchi), Rancati, T. (Tiziana), Ahlgren, H. (Helén), Mascarós, J. (Juanma), Löfgren, A. (Annica), Lehmann, K. (Kurt), Han Lin, C. (Catherine), Hirama, H. (Hiromi), Jenster, G.W. (Guido), Auvinen, A. (Anssi), Haider, M. (Masoom), van Bochove, K. (Kees), Carter, B. (Ballentine), Kirk-Burnnand, R. (Rachelle), Gledhill, S. (Sam), and Buzza, M. (Mark)

Abstract

Objectives: The Movember Foundation launched the Global Action Plan Prostate Cancer Active Surveillance (GAP3) initiative to create a global consensus on the selection and monitoring of men with low-risk prostate cancer (PCa) on active surveillance (AS). The aim of this study is to present data on inclusion and follow-up for AS in this unique global AS database. Patients and Methods: Between 2014 and 2016, the database was created by combining patient data from 25 established AS cohorts worldwide (USA, Canada, Australasia, UK and Europe). Data on a total of 15 101 patients were included. Descriptive statistics were used to report patients' clinical and demographic characteristics at the time of PCa diagnosis, clinical follow-up, discontinuation of AS and subsequent treatment. Cumulative incidence curves were used to report discontinuation rates over time. Results: At diagnosis, the median (interquartile range [IQR]) patient age was 65 (60–70) years and the median prostate-specific antigen level was 5.4 (4.0–7.3) ng/mL. Most patients had clinical stage T1 disease (71.8%), a biopsy Gleason score of 6 (88.8%) and one tumour-positive biopsy core (60.3%). Patients on AS had a median follow-up time of 2.2 (1.0–5.0) years. After 5, 10 and 15 years of follow-up, respectively, 58%, 39% and 23% of patients were still on AS. The current version of GAP3 has limited data on magnetic resonance imaging (MRI), quality of life and genomic testing. Conclusions: GAP3 is the largest worldwide collaboration integrating patient data from men with PCa on AS. The results will allow individual patients and clinicians to have greater confidence in the personalized decision to either delay or proceed with active treatment. Longer follow-up and the evaluation of MRI, new genomic markers and patient-related outcomes will result in even more valuable data and eventually in better patient outcomes.

Published
2018
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13. Expert consensus document: Semantics in active surveillance for men with localized prostate cancer-results of a modified Delphi consensus procedure.

Author

van der Linden W., Bruinsma S.M., Roobol M.J., Carroll P.R., Klotz L., Pickles T., Moore C.M., Gnanapragasam V.J., Villers A., Rannikko A., Valdagni R., Frydenberg M., Kakehi Y., Filson C.P., Bangma C.H., Trock B., Ehdaie B., Kim J., Morgan T., Hyndman E., Van Hemelrijck M., Dasgupta P., Perry A., Hugosson J., Rubio-Briones J., Bjartell A., Hefermehl L., Lui Shiong L., Ha Chung B., Suk Lee K., van der Kwast T., Obbink H., Hulsen T., de Jonge C., Kattan M., Xinge J., Muir K., Lophatananon A., Fahey M., Steyerberg E., Nieboer D., Zhang L., Guo W., Milan T., Benfante N., Cowan J., Patil D., Sanford R., Kim T.K., Mamedov A., LaPointe V., Crump T., Hamoudi R., Kimberly-Duffell J., Santaolalla A., Olivier J., Janetti E.B., Rancati T., Ahlgren H., Mascaros J., Lofgren A., Lehmann K., Han Lin C., Hirama H., Jenster G., Auvinen A., Haider M., van Bochove K., Carter B., Kirk-Burnnand R., Gledhill S., Buzza M., van der Linden W., Bruinsma S.M., Roobol M.J., Carroll P.R., Klotz L., Pickles T., Moore C.M., Gnanapragasam V.J., Villers A., Rannikko A., Valdagni R., Frydenberg M., Kakehi Y., Filson C.P., Bangma C.H., Trock B., Ehdaie B., Kim J., Morgan T., Hyndman E., Van Hemelrijck M., Dasgupta P., Perry A., Hugosson J., Rubio-Briones J., Bjartell A., Hefermehl L., Lui Shiong L., Ha Chung B., Suk Lee K., van der Kwast T., Obbink H., Hulsen T., de Jonge C., Kattan M., Xinge J., Muir K., Lophatananon A., Fahey M., Steyerberg E., Nieboer D., Zhang L., Guo W., Milan T., Benfante N., Cowan J., Patil D., Sanford R., Kim T.K., Mamedov A., LaPointe V., Crump T., Hamoudi R., Kimberly-Duffell J., Santaolalla A., Olivier J., Janetti E.B., Rancati T., Ahlgren H., Mascaros J., Lofgren A., Lehmann K., Han Lin C., Hirama H., Jenster G., Auvinen A., Haider M., van Bochove K., Carter B., Kirk-Burnnand R., Gledhill S., and Buzza M.

Abstract

Active surveillance (AS) is broadly described as a management option for men with low-risk prostate cancer, but semantic heterogeneity exists in both the literature and in guidelines. To address this issue, a panel of leading prostate cancer specialists in the field of AS participated in a consensus-forming project using a modified Delphi method to reach international consensus on definitions of terms related to this management option. An iterative three-round sequence of online questionnaires designed to address 61 individual items was completed by each panel member. Consensus was considered to be reached if >=70% of the experts agreed on a definition. To facilitate a common understanding among all experts involved and resolve potential ambiguities, a face-to-face consensus meeting was held between Delphi survey rounds two and three. Convenience sampling was used to construct the panel of experts. In total, 12 experts from Australia, France, Finland, Italy, the Netherlands, Japan, the UK, Canada and the USA participated. By the end of the Delphi process, formal consensus was achieved for 100% (n = 61) of the terms and a glossary was then developed. Agreement between international experts has been reached on relevant terms and subsequent definitions regarding AS for patients with localized prostate cancer. This standard terminology could support multidisciplinary communication, reduce the extent of variations in clinical practice and optimize clinical decision making.Copyright © 2017 Macmillan Publishers Limited, part of Springer Nature. All rights reserved.

Published
2017

14. Systematically linking tranSMART, Galaxy and EGA for reusing human translational research data

Author

Zhang, C. (Chao), Bijlard, J. (Jochem), Staiger, C. (Christine), Scollen, S. (Serena), Enckevort, D. (David) van, Hoogstrate, Y. (Youri), Senf, A. (Alexander), Hiltemann, S. (Saskia), Repo, S. (Susanna), Pipping, W. (Wibo), Bierkens, M. (Mariska), Payralbe, S. (Stefan), Stringer, B. (Bas), Heringa, J. (Jaap), Stubbs, A.P. (Andrew), Bonino Da Silva Santos, L.O. (Luiz Olavo), Beliën, J.A.M. (Jeroen), Weistra, W. (Ward), Azevedo, R. (Rita), van Bochove, K. (Kees), Meijer, G.A., Boiten, J.-W. (Jan-Willem), Rambla, J. (Jordi), Fijneman, R.J.A. (Remond J. A.), Spalding, J.D. (J. Dylan), Abeln, S. (Sanne), Zhang, C. (Chao), Bijlard, J. (Jochem), Staiger, C. (Christine), Scollen, S. (Serena), Enckevort, D. (David) van, Hoogstrate, Y. (Youri), Senf, A. (Alexander), Hiltemann, S. (Saskia), Repo, S. (Susanna), Pipping, W. (Wibo), Bierkens, M. (Mariska), Payralbe, S. (Stefan), Stringer, B. (Bas), Heringa, J. (Jaap), Stubbs, A.P. (Andrew), Bonino Da Silva Santos, L.O. (Luiz Olavo), Beliën, J.A.M. (Jeroen), Weistra, W. (Ward), Azevedo, R. (Rita), van Bochove, K. (Kees), Meijer, G.A., Boiten, J.-W. (Jan-Willem), Rambla, J. (Jordi), Fijneman, R.J.A. (Remond J. A.), Spalding, J.D. (J. Dylan), and Abeln, S. (Sanne)

Abstract

The availability of high-throughput molecular profiling techniques has provided more accurate and informative data for regular clinical studies. Nevertheless, complex computational workflows are required to interpret these data. Over the past years, the data volume has been growing explosively, requiring robust human data management to organise and integrate the data efficiently. For this reason, we set up an ELIXIR implementation study, together with the Translational research IT (TraIT) programme, to design a data ecosystem that is able t

Published
2017
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16. CODE-EHR best practice framework for the use of structured electronic healthcare records in clinical research.

Author

Kotecha D, Asselbergs FW, Achenbach S, Anker SD, Atar D, Baigent C, Banerjee A, Beger B, Brobert G, Casadei B, Ceccarelli C, Cowie MR, Crea F, Cronin M, Denaxas S, Derix A, Fitzsimons D, Fredriksson M, Gale CP, Gkoutos GV, Goettsch W, Hemingway H, Ingvar M, Jonas A, Kazmierski R, Løgstrup S, Thomas Lumbers R, Lüscher TF, McGreavy P, Piña IL, Roessig L, Steinbeisser C, Sundgren M, Tyl B, van Thiel G, van Bochove K, Vardas PE, Villanueva T, Vrana M, Weber W, Weidinger F, Windecker S, Wood A, and Grobbee DE

Subjects
Delivery of Health Care, Electronics, Humans, Pandemics prevention & control, COVID-19 epidemiology, Electronic Health Records
Abstract

Big data is central to new developments in global clinical science aiming to improve the lives of patients. Technological advances have led to the routine use of structured electronic healthcare records with the potential to address key gaps in clinical evidence. The covid-19 pandemic has demonstrated the potential of big data and related analytics, but also important pitfalls. Verification, validation, and data privacy, as well as the social mandate to undertake research are key challenges. The European Society of Cardiology and the BigData@Heart consortium have brought together a range of international stakeholders, including patient representatives, clinicians, scientists, regulators, journal editors and industry. We propose the CODE-EHR Minimum Standards Framework as a means to improve the design of studies, enhance transparency and develop a roadmap towards more robust and effective utilisation of healthcare data for research purposes., (This article has been co-published with permission in The BMJ, the Lancet Digital Health, and the European Heart Journal © the Authors 2022.)

Published
2022
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17. Renin-angiotensin system blockers and susceptibility to COVID-19: an international, open science, cohort analysis.

Author

Morales DR, Conover MM, You SC, Pratt N, Kostka K, Duarte-Salles T, Fernández-Bertolín S, Aragón M, DuVall SL, Lynch K, Falconer T, van Bochove K, Sung C, Matheny ME, Lambert CG, Nyberg F, Alshammari TM, Williams AE, Park RW, Weaver J, Sena AG, Schuemie MJ, Rijnbeek PR, Williams RD, Lane JCE, Prats-Uribe A, Zhang L, Areia C, Krumholz HM, Prieto-Alhambra D, Ryan PB, Hripcsak G, and Suchard MA

Abstract

Background: Angiotensin-converting enzyme inhibitors (ACEIs) and angiotensin receptor blockers (ARBs) have been postulated to affect susceptibility to COVID-19. Observational studies so far have lacked rigorous ascertainment adjustment and international generalisability. We aimed to determine whether use of ACEIs or ARBs is associated with an increased susceptibility to COVID-19 in patients with hypertension., Methods: In this international, open science, cohort analysis, we used electronic health records from Spain (Information Systems for Research in Primary Care [SIDIAP]) and the USA (Columbia University Irving Medical Center data warehouse [CUIMC] and Department of Veterans Affairs Observational Medical Outcomes Partnership [VA-OMOP]) to identify patients aged 18 years or older with at least one prescription for ACEIs and ARBs (target cohort) or calcium channel blockers (CCBs) and thiazide or thiazide-like diuretics (THZs; comparator cohort) between Nov 1, 2019, and Jan 31, 2020. Users were defined separately as receiving either monotherapy with these four drug classes, or monotherapy or combination therapy (combination use) with other antihypertensive medications. We assessed four outcomes: COVID-19 diagnosis; hospital admission with COVID-19; hospital admission with pneumonia; and hospital admission with pneumonia, acute respiratory distress syndrome, acute kidney injury, or sepsis. We built large-scale propensity score methods derived through a data-driven approach and negative control experiments across ten pairwise comparisons, with results meta-analysed to generate 1280 study effects. For each study effect, we did negative control outcome experiments using a possible 123 controls identified through a data-rich algorithm. This process used a set of predefined baseline patient characteristics to provide the most accurate prediction of treatment and balance among patient cohorts across characteristics. The study is registered with the EU Post-Authorisation Studies register, EUPAS35296., Findings: Among 1 355 349 antihypertensive users (363 785 ACEI or ARB monotherapy users, 248 915 CCB or THZ monotherapy users, 711 799 ACEI or ARB combination users, and 473 076 CCB or THZ combination users) included in analyses, no association was observed between COVID-19 diagnosis and exposure to ACEI or ARB monotherapy versus CCB or THZ monotherapy (calibrated hazard ratio [HR] 0·98, 95% CI 0·84-1·14) or combination use exposure (1·01, 0·90-1·15). ACEIs alone similarly showed no relative risk difference when compared with CCB or THZ monotherapy (HR 0·91, 95% CI 0·68-1·21; with heterogeneity of >40%) or combination use (0·95, 0·83-1·07). Directly comparing ACEIs with ARBs demonstrated a moderately lower risk with ACEIs, which was significant with combination use (HR 0·88, 95% CI 0·79-0·99) and non-significant for monotherapy (0·85, 0·69-1·05). We observed no significant difference between drug classes for risk of hospital admission with COVID-19, hospital admission with pneumonia, or hospital admission with pneumonia, acute respiratory distress syndrome, acute kidney injury, or sepsis across all comparisons., Interpretation: No clinically significant increased risk of COVID-19 diagnosis or hospital admission-related outcomes associated with ACEI or ARB use was observed, suggesting users should not discontinue or change their treatment to decrease their risk of COVID-19., Funding: Wellcome Trust, UK National Institute for Health Research, US National Institutes of Health, US Department of Veterans Affairs, Janssen Research & Development, IQVIA, South Korean Ministry of Health and Welfare Republic, Australian National Health and Medical Research Council, and European Health Data and Evidence Network., (Copyright © 2021 The Author(s). Published by Elsevier Ltd. This is an Open Access article under the CC BY 4.0 license. Published by Elsevier Ltd.. All rights reserved.)

Published
2021
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18. Organic matter reduces the amount of detectable environmental DNA in freshwater.

Author

van Bochove K, Bakker FT, Beentjes KK, Hemerik L, Vos RA, and Gravendeel B

Abstract

Environmental DNA (eDNA) is used for monitoring the occurrence of freshwater organisms. Various studies show a relation between the amount of eDNA detected and target organism abundance, thus providing a potential proxy for reconstructing population densities. However, environmental factors such as water temperature and microbial activity are known to affect the amount of eDNA present as well. In this study, we use controlled aquarium experiments using Gammarus pulex L. (Amphipoda) to investigate the relationship between the amount of detectable eDNA through time, pH, and levels of organic material. We found eDNA to degrade faster when organic material was added to the aquarium water, but that pH had no significant effect. We infer that eDNA contained inside cells and mitochondria is extra resilient against degradation, though this may not reflect actual presence of target species. These results indicate that, although estimation of population density might be possible using eDNA, measured eDNA concentration could, in the future, be corrected for local environmental conditions in order to ensure accurate comparisons., Competing Interests: None declared., (© 2020 The Authors. Ecology and Evolution published by John Wiley & Sons Ltd.)

Published
2020
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19. Dietary medium chain fatty acid supplementation leads to reduced VLDL lipolysis and uptake rates in comparison to linoleic acid supplementation.

Author

van Schalkwijk DB, Pasman WJ, Hendriks HF, Verheij ER, Rubingh CM, van Bochove K, Vaes WH, Adiels M, Freidig AP, and de Graaf AA

Subjects
Adult, Dietary Fats administration & dosage, Dietary Supplements analysis, Double-Blind Method, Fasting, Fatty Acids administration & dosage, Fatty Acids metabolism, Humans, Linoleic Acid administration & dosage, Lipoproteins, LDL metabolism, Male, Middle Aged, Dietary Fats metabolism, Linoleic Acid metabolism, Lipolysis, Lipoproteins, VLDL metabolism
Abstract

Dietary medium chain fatty acids (MCFA) and linoleic acid follow different metabolic routes, and linoleic acid activates PPAR receptors. Both these mechanisms may modify lipoprotein and fatty acid metabolism after dietary intervention. Our objective was to investigate how dietary MCFA and linoleic acid supplementation and body fat distribution affect the fasting lipoprotein subclass profile, lipoprotein kinetics, and postprandial fatty acid kinetics. In a randomized double blind cross-over trial, 12 male subjects (age 51±7 years; BMI 28.5±0.8 kg/m2), were divided into 2 groups according to waist-hip ratio. They were supplemented with 60 grams/day MCFA (mainly C8:0, C10:0) or linoleic acid for three weeks, with a wash-out period of six weeks in between. Lipoprotein subclasses were measured using HPLC. Lipoprotein and fatty acid metabolism were studied using a combination of several stable isotope tracers. Lipoprotein and tracer data were analyzed using computational modeling. Lipoprotein subclass concentrations in the VLDL and LDL range were significantly higher after MCFA than after linoleic acid intervention. In addition, LDL subclass concentrations were higher in lower body obese individuals. Differences in VLDL metabolism were found to occur in lipoprotein lipolysis and uptake, not production; MCFAs were elongated intensively, in contrast to linoleic acid. Dietary MCFA supplementation led to a less favorable lipoprotein profile than linoleic acid supplementation. These differences were not due to elevated VLDL production, but rather to lower lipolysis and uptake rates.

Published
2014
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20. Clustering by plasma lipoprotein profile reveals two distinct subgroups with positive lipid response to fenofibrate therapy.

Author

van Bochove K, van Schalkwijk DB, Parnell LD, Lai CQ, Ordovás JM, de Graaf AA, van Ommen B, and Arnett DK

Subjects
Cluster Analysis, Dyslipidemias blood, Female, Humans, Lipoproteins classification, Male, Dyslipidemias drug therapy, Fenofibrate therapeutic use, Hypolipidemic Agents therapeutic use, Lipoproteins blood
Abstract

Fibrates lower triglycerides and raise HDL cholesterol in dyslipidemic patients, but show heterogeneous treatment response. We used k-means clustering to identify three representative NMR lipoprotein profiles for 775 subjects from the GOLDN population, and study the response to fenofibrate in corresponding subgroups. The subjects in each subgroup showed differences in conventional lipid characteristics and in presence/absence of cardiovascular risk factors at baseline; there were subgroups with a low, medium and high degree of dyslipidemia. Modeling analysis suggests that the difference between the subgroups with low and medium dyslipidemia is influenced mainly by hepatic uptake dysfunction, while the difference between subgroups with medium and high dyslipidemia is influenced mainly by extrahepatic lipolysis disfunction. The medium and high dyslipidemia subgroups showed a positive, yet distinct lipid response to fenofibrate treatment. When comparing our subgroups to known subgrouping methods, we identified an additional 33% of the population with favorable lipid response to fenofibrate compared to a standard baseline triglyceride cutoff method. Compared to a standard HDL cholesterol cutoff method, the addition was 18%. In conclusion, by using constructing subgroups based on representative lipoprotein profiles, we have identified two subgroups of subjects with positive lipid response to fenofibrate therapy and with different underlying disturbances in lipoprotein metabolism. The total subgroup with positive lipid response to fenofibrate is larger than subgroups identified with baseline triglyceride and HDL cholesterol cutoffs.

Published
2012
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21. Answering biological questions: querying a systems biology database for nutrigenomics.

Author

Evelo CT, van Bochove K, and Saito JT

Abstract

The requirement of systems biology for connecting different levels of biological research leads directly to a need for integrating vast amounts of diverse information in general and of omics data in particular. The nutritional phenotype database addresses this challenge for nutrigenomics. A particularly urgent objective in coping with the data avalanche is making biologically meaningful information accessible to the researcher. This contribution describes how we intend to meet this objective with the nutritional phenotype database. We outline relevant parts of the system architecture, describe the kinds of data managed by it, and show how the system can support retrieval of biologically meaningful information by means of ontologies, full-text queries, and structured queries. Our contribution points out critical points, describes several technical hurdles. It demonstrates how pathway analysis can improve queries and comparisons for nutrition studies. Finally, three directions for future research are given.

Published
2011
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22. Improved cholesterol phenotype analysis by a model relating lipoprotein life cycle processes to particle size.

Author

van Schalkwijk DB, de Graaf AA, van Ommen B, van Bochove K, Rensen PC, Havekes LM, van de Pas NC, Hoefsloot HC, van der Greef J, and Freidig AP

Subjects
Cholesterol genetics, Humans, Lipoproteins blood, Lipoproteins chemistry, Particle Size, Phenotype, Triglycerides blood, Triglycerides metabolism, Cholesterol blood, Cholesterol chemistry, Lipoproteins metabolism, Models, Biological
Abstract

Increased plasma cholesterol is a known risk factor for cardiovascular disease. Lipoprotein particles transport both cholesterol and triglycerides through the blood. It is thought that the size distribution of these particles codetermines cardiovascular disease risk. New types of measurements can determine the concentration of many lipoprotein size-classes but exactly how each small class relates to disease risk is difficult to clear up. Because relating physiological process status to disease risk seems promising, we propose investigating how lipoprotein production, lipolysis, and uptake processes depend on particle size. To do this, we introduced a novel model framework (Particle Profiler) and evaluated its feasibility. The framework was tested using existing stable isotope flux data. The model framework implementation we present here reproduced the flux data and derived lipoprotein size pattern changes that corresponded to measured changes. It also sensitively indicated changes in lipoprotein metabolism between patient groups that are biologically plausible. Finally, the model was able to reproduce the cholesterol and triglyceride phenotype of known genetic diseases like familial hypercholesterolemia and familial hyperchylomicronemia. In the future, Particle Profiler can be applied for analyzing detailed lipoprotein size profile data and deriving rates of various lipolysis and uptake processes if an independent production estimate is given.

Published
2009
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