Staff Publications

Staff Publications

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    'Staff publications' is the digital repository of Wageningen University & Research

    'Staff publications' contains references to publications authored by Wageningen University staff from 1976 onward.

    Publications authored by the staff of the Research Institutes are available from 1995 onwards.

    Full text documents are added when available. The database is updated daily and currently holds about 240,000 items, of which 72,000 in open access.

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    Repositioning of the global epicentre of non-optimal cholesterol
    Taddei, Cristina ; Zhou, Bin ; Bixby, Honor ; Carrillo-Larco, Rodrigo M. ; Danaei, Goodarz ; Jackson, Rod T. ; Farzadfar, Farshad ; Sophiea, Marisa K. ; Cesare, Mariachiara Di; Iurilli, Maria Laura Caminia ; Martinez, Andrea Rodriguez ; Asghari, Golaleh ; Dhana, Klodian ; Gulayin, Pablo ; Kakarmath, Sujay ; Santero, Marilina ; Voortman, Trudy ; Riley, Leanne M. ; Cowan, Melanie J. ; Savin, Stefan ; Bennett, James E. ; Stevens, Gretchen A. ; Paciorek, Christopher J. ; Aekplakorn, Wichai ; Cifkova, Renata ; Giampaoli, Simona ; Kengne, Andre Pascal ; Khang, Young Ho ; Kuulasmaa, Kari ; Laxmaiah, Avula ; Margozzini, Paula ; Mathur, Prashant ; Nordestgaard, Børge G. ; Zhao, Dong ; Aadahl, Mette ; Abarca-Gómez, Leandra ; Rahim, Hanan Abdul ; Abu-Rmeileh, Niveen M. ; Acosta-Cazares, Benjamin ; Adams, Robert J. ; Ferrieres, Jean ; Geleijnse, Johanna M. ; He, Yuna ; Jacobs, Jeremy M. ; Kromhout, Daan ; Ma, Guansheng ; Dam, Rob M. van; Wang, Qian ; Wang, Ya Xing ; Wang, Ying Wei - \ 2020
    Nature 582 (2020)7810. - ISSN 0028-0836 - p. 73 - 77.

    High blood cholesterol is typically considered a feature of wealthy western countries1,2. However, dietary and behavioural determinants of blood cholesterol are changing rapidly throughout the world3 and countries are using lipid-lowering medications at varying rates. These changes can have distinct effects on the levels of high-density lipoprotein (HDL) cholesterol and non-HDL cholesterol, which have different effects on human health4,5. However, the trends of HDL and non-HDL cholesterol levels over time have not been previously reported in a global analysis. Here we pooled 1,127 population-based studies that measured blood lipids in 102.6 million individuals aged 18 years and older to estimate trends from 1980 to 2018 in mean total, non-HDL and HDL cholesterol levels for 200 countries. Globally, there was little change in total or non-HDL cholesterol from 1980 to 2018. This was a net effect of increases in low- and middle-income countries, especially in east and southeast Asia, and decreases in high-income western countries, especially those in northwestern Europe, and in central and eastern Europe. As a result, countries with the highest level of non-HDL cholesterol—which is a marker of cardiovascular risk—changed from those in western Europe such as Belgium, Finland, Greenland, Iceland, Norway, Sweden, Switzerland and Malta in 1980 to those in Asia and the Pacific, such as Tokelau, Malaysia, The Philippines and Thailand. In 2017, high non-HDL cholesterol was responsible for an estimated 3.9 million (95% credible interval 3.7 million–4.2 million) worldwide deaths, half of which occurred in east, southeast and south Asia. The global repositioning of lipid-related risk, with non-optimal cholesterol shifting from a distinct feature of high-income countries in northwestern Europe, north America and Australasia to one that affects countries in east and southeast Asia and Oceania should motivate the use of population-based policies and personal interventions to improve nutrition and enhance access to treatment throughout the world.

    Use of Repeated Blood Pressure and Cholesterol Measurements to Improve Cardiovascular Disease Risk Prediction : An Individual-Participant-Data Meta-Analysis
    Paige, Ellie ; Barrett, Jessica ; Pennells, Lisa ; Sweeting, Michael ; Willeit, Peter ; Angelantonio, Emanuele Di; Gudnason, Vilmundur ; Nordestgaard, Børge G. ; Psaty, Bruce M. ; Goldbourt, Uri ; Best, Lyle G. ; Assmann, Gerd ; Salonen, Jukka T. ; Nietert, Paul J. ; Verschuren, W.M.M. ; Brunner, Eric J. ; Kronmal, Richard A. ; Salomaa, Veikko ; Bakker, Stephan L.J. ; Dagenais, Gilles R. ; Sato, Shinichi ; Jansson, Jan Håkan ; Willeit, Johann ; Onat, Altan ; La Cámara, Agustin Gómez De; Roussel, Ronan ; Völzke, Henry ; Dankner, Rachel ; Tipping, Robert W. ; Meade, Tom W. ; Donfrancesco, Chiara ; Kuller, Lewis H. ; Peters, Annette ; Gallacher, John ; Kromhout, Daan ; Iso, Hiroyasu ; Knuiman, Matthew W. ; Casiglia, Edoardo ; Kavousi, Maryam ; Palmieri, Luigi ; Sundström, Johan ; Davis, Barry R. ; Njølstad, Inger ; Couper, David ; Danesh, John ; Thompson, Simon G. ; Wood, Angela M. - \ 2017
    American Journal of Epidemiology 186 (2017)8. - ISSN 0002-9262 - p. 899 - 907.
    Cardiovascular disease - Longitudinal measurements - Repeated measurements - Risk factors - Risk prediction
    The added value of incorporating information from repeated blood pressure and cholesterol measurements to predict cardiovascular disease (CVD) risk has not been rigorously assessed. We used data on 191,445 adults from the Emerging Risk Factors Collaboration (38 cohorts from 17 countries with data encompassing 1962-2014) with more than 1 million measurements of systolic blood pressure, total cholesterol, and high-density lipoprotein cholesterol. Over a median 12 years of follow-up, 21,170 CVD events occurred. Risk prediction models using cumulative mean values of repeated measurements and summary measures from longitudinal modeling of the repeated measurements were compared with models using measurements from a single time point. Risk discrimination (Cindex) and net reclassification were calculated, and changes in C-indices were meta-analyzed across studies. Compared with the single-time-point model, the cumulative means and longitudinal models increased the C-index by 0.0040 (95% confidence interval (CI): 0.0023, 0.0057) and 0.0023 (95% CI: 0.0005, 0.0042), respectively. Reclassification was also improved in both models; compared with the single-time-point model, overall net reclassification improvements were 0.0369 (95% CI: 0.0303, 0.0436) for the cumulative-means model and 0.0177 (95% CI: 0.0110, 0.0243) for the longitudinal model. In conclusion, incorporating repeated measurements of blood pressure and cholesterol into CVD risk prediction models slightly improves risk prediction.
    Association of Cardiometabolic Multimorbidity With Mortality
    Angelantonio, Emanuele Di; Kaptoge, Stephen ; Wormser, David ; Willeit, Peter ; Butterworth, Adam S. ; Bansal, Narinder ; O’Keeffe, Linda M. ; Gao, Pei ; Wood, Angela M. ; Burgess, Stephen ; Freitag, Daniel F. ; Pennells, Lisa ; Peters, Sanne A. ; Hart, Carole L. ; Håheim, Lise Lund ; Gillum, Richard F. ; Nordestgaard, Børge G. ; Psaty, Bruce M. ; Yeap, Bu B. ; Knuiman, Matthew W. ; Nietert, Paul J. ; Kauhanen, Jussi ; Salonen, Jukka T. ; Kuller, Lewis H. ; Simons, Leon A. ; Schouw, Yvonne T. van der; Barrett-Connor, Elizabeth ; Selmer, Randi ; Crespo, Carlos J. ; Rodriguez, Beatriz ; Verschuren, Monique W.M. ; Salomaa, Veikko ; Svärdsudd, Kurt ; Harst, Pim Van Der; Björkelund, Cecilia ; Wilhelmsen, Lars ; Wallace, Robert B. ; Brenner, Hermann ; Amouyel, Philippe ; Barr, Elizabeth L.M. ; Iso, Hiroyasu ; Onat, Altan ; Trevisan, Maurizio ; agostino, Ralph B. D'; Cooper, Cyrus ; Kavousi, Maryam ; Welin, Lennart ; Roussel, Ronan ; Hu, Frank B. ; Sato, Shinichi ; Davidson, Karina W. ; Howard, Barbara V. ; Leening, Maarten J.G. ; Rosengren, Annika ; Dörr, Marcus ; Deeg, Dorly J.H. ; Kiechl, Stefan ; Stehouwer, Coen D.A. ; Nissinen, Aulikki ; Giampaoli, Simona ; Donfrancesco, Chiara ; Kromhout, Daan ; Price, Jackie F. ; Peters, Annette ; Meade, Tom W. ; Casiglia, Edoardo ; Lawlor, Debbie A. ; Gallacher, John ; Nagel, Dorothea ; Franco, Oscar H. ; Assmann, Gerd ; Dagenais, Gilles R. ; Jukema, Wouter J. ; Sundström, Johan ; Woodward, Mark ; Brunner, Eric J. ; Khaw, Kay-Tee ; Wareham, Nicholas J. ; Whitsel, Eric A. ; Njølstad, Inger ; Hedblad, Bo ; Wassertheil-Smoller, Sylvia ; Engström, Gunnar ; Rosamond, Wayne D. ; Selvin, Elizabeth ; Sattar, Naveed ; Thompson, Simon G. ; Danesh, John - \ 2015
    JAMA: The Journal of the American Medical Association 314 (2015)1. - ISSN 0098-7484 - p. 52 - 60.
    Importance The prevalence of cardiometabolic multimorbidity is increasing.

    Objective To estimate reductions in life expectancy associated with cardiometabolic multimorbidity.

    Design, Setting, and Participants Age- and sex-adjusted mortality rates and hazard ratios (HRs) were calculated using individual participant data from the Emerging Risk Factors Collaboration (689 300 participants; 91 cohorts; years of baseline surveys: 1960-2007; latest mortality follow-up: April 2013; 128 843 deaths). The HRs from the Emerging Risk Factors Collaboration were compared with those from the UK Biobank (499 808 participants; years of baseline surveys: 2006-2010; latest mortality follow-up: November 2013; 7995 deaths). Cumulative survival was estimated by applying calculated age-specific HRs for mortality to contemporary US age-specific death rates.

    Exposures A history of 2 or more of the following: diabetes mellitus, stroke, myocardial infarction (MI).

    Main Outcomes and Measures All-cause mortality and estimated reductions in life expectancy.
    Results In participants in the Emerging Risk Factors Collaboration without a history of diabetes, stroke, or MI at baseline (reference group), the all-cause mortality rate adjusted to the age of 60 years was 6.8 per 1000 person-years. Mortality rates per 1000 person-years were 15.6 in participants with a history of diabetes, 16.1 in those with stroke, 16.8 in those with MI, 32.0 in those with both diabetes and MI, 32.5 in those with both diabetes and stroke, 32.8 in those with both stroke and MI, and 59.5 in those with diabetes, stroke, and MI. Compared with the reference group, the HRs for all-cause mortality were 1.9 (95% CI, 1.8-2.0) in participants with a history of diabetes, 2.1 (95% CI, 2.0-2.2) in those with stroke, 2.0 (95% CI, 1.9-2.2) in those with MI, 3.7 (95% CI, 3.3-4.1) in those with both diabetes and MI, 3.8 (95% CI, 3.5-4.2) in those with both diabetes and stroke, 3.5 (95% CI, 3.1-4.0) in those with both stroke and MI, and 6.9 (95% CI, 5.7-8.3) in those with diabetes, stroke, and MI. The HRs from the Emerging Risk Factors Collaboration were similar to those from the more recently recruited UK Biobank. The HRs were little changed after further adjustment for markers of established intermediate pathways (eg, levels of lipids and blood pressure) and lifestyle factors (eg, smoking, diet). At the age of 60 years, a history of any 2 of these conditions was associated with 12 years of reduced life expectancy and a history of all 3 of these conditions was associated with 15 years of reduced life expectancy.

    Conclusions and Relevance Mortality associated with a history of diabetes, stroke, or MI was similar for each condition. Because any combination of these conditions was associated with multiplicative mortality risk, life expectancy was substantially lower in people with multimorbidity.
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