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1.
NCD Risk Factors Collaboration (Müller-Nurasyid, M. ; Döring, A. ; Meisinger, C. ; Peters, A. ; Stöckl, D.): National trends in total cholesterol obscure heterogeneous changes in HDL and non-HDL cholesterol and total-to-HDL cholesterol ratio: A pooled analysis of 458 population-based studies in Asian and Western countries. Int. J. Epidemiol. 49, 173-192 (2020)
2.
Sebert, S.* et al.: Cohort Profile: The DynaHEALTH consortium - a European consortium for a life-course bio-psychosocial model of healthy ageing of glucose homeostasis. Int. J. Epidemiol. 48, 1051-1051K (2019)
3.
Felix, J.F.* et al.: Cohort profile: Pregnancy and childhood epigenetics (PACE) consortium. Int. J. Epidemiol. 47, 22-23u (2018)
4.
Iqbal, K.* et al.: Comparison of metabolite networks from four German population-based studies. Int. J. Epidemiol. 47, 2070-2081 (2018)
5.
Kachuri, L.* et al.: Mendelian Randomization and mediation analysis of leukocyte telomere length and risk of lung and head and neck cancers. Int. J. Epidemiol. 48, 751-766 (2018)
6.
Zhou, B.* et al.: Contributions of mean and shape of blood pressure distribution to worldwide trends and variations in raised blood pressure: A pooled analysis of 1018 population-based measurement studies with 88.6 million participants. Int. J. Epidemiol. 47, 872–883i (2018)
7.
Aschard, H.* et al.: Evidence for large-scale gene-by-smoking interaction effects on pulmonary function. Int. J. Epidemiol. 46, 894-904 (2017)
8.
Svanes, C.* et al.: Father's environment before conception and asthma risk in his children: A multi-generation analysis of the respiratory health In Northern Europe study. Int. J. Epidemiol. 46, 235-245 (2017)
9.
Dietrich, S.* et al.: Random survival forest in practice: A method for modelling complex metabolomics data in time to event analysis. Int. J. Epidemiol. 45, 1406-1420 (2016)
10.
Lowe, A.J.* et al.: Cohort profile: Melbourne Atopy Cohort study (MACS). Int. J. Epidemiol. 46, 25-26 (2016)
11.
Vogt, S. et al.: Characterization of the metabolic profile associated with serum 25-hydroxyvitamin D: A cross-sectional analysis in population-based data. Int. J. Epidemiol. 45, 1469-1481 (2016)
12.
Ward-Caviness, C.K. et al.: Short-term NO2 exposure is associated with long-chain fatty acids in prospective cohorts from Augsburg, Germany: Results from an analysis of 138 metabolites and three exposures. Int. J. Epidemiol. 45, 1528-1538 (2016)
13.
Hägg, S.* et al.: Adiposity as a cause of cardiovascular disease: A Mendelian randomization study. Int. J. Epidemiol. 44, 578-586 (2015)
14.
Thrift, A.P.* et al.: Mendelian randomization study of height and risk of colorectal cancer. Int. J. Epidemiol. 44, 662-672 (2015)
15.
Gaye, A.* et al.: DataSHIELD: Taking the analysis to the data, not the data to the analysis. Int. J. Epidemiol. 43, 1929-1944 (2014)
16.
Menni, C.* et al.: Metabolomic markers reveal novel pathways of ageing and early development in human populations. Int. J. Epidemiol. 42, 1111-1119 (2013)
17.
Emerging Risk Factors Collaboration (Döring, A. ; Meisinger, C.): Adult height and the risk of cause-specific death and vascular morbidity in 1 million people: Individual participant meta-analysis. Int. J. Epidemiol. 41, 1419-1433 (2012)
18.
Orozco, G.* ; Ioannidis, J.P.* ; Morris, A.* ; Zeggini, E.* & DIAGRAM Consortium (Gieger, C. ; Grallert, H. ; Huth, C. ; Illig, T. ; Klopp, N. ; Meitinger, T. ; Petersen, A.-K. ; Thorand, B. ; Wichmann, H.-E.): Sex-specific differences in effect size estimates at established complex trait loci. Int. J. Epidemiol. 41, 1376-1382 (2012)
19.
Fortier, I.* et al.: Is rigorous retrospective harmonization possible? Application of the DataSHaPER approach across 53 large studies. Int. J. Epidemiol. 40, 1314-1328 (2011)
20.
Fortier, I.* et al.: Quality, quantity and harmony: The DataSHaPER approach to integrating data across bioclinical studies. Int. J. Epidemiol. 39, 1383-1393 (2010)