The DOI matches its Crossref record; 36 of the 36 references it cites were found in a registry or an index.
Cohort Profile: The Korean Genome and Epidemiology Study (KoGES) Consortium
Open accessAbstract
Worldwide globalization and Westernization in social and economic aspects have led to drastic changes in South Korea during the past several decades. These changes include individual health behaviours, which were reflected as increased prevalence of non-communicable chronic diseases (NCDs), such as type 2 diabetes mellitus (T2DM), hypertension, obesity and cardiovascular disease (CVD).1 These NCDs are known to be caused by both environmental risk factors and predisposing genetic factors. Population decline is another issue in South Korea; the recorded fertility rate was ≤ 1.3 births per woman, and ≥ 10% of the population were elderly individuals aged ≥ 65 years according to the Population and Housing Census results of 2005-2010.2,3 We have also been observing an increased influx and efflux of the population due to globalization. In particular, there has been a rising tendency in the marriage-based inflow of South Asian women during the last decade. To attempt to solve public health issues resulting from these population trends and prepare for personalized and preventive health care in the future, the Korean government (National Research Institute of Health (NIH), Centers for Disease Control and Prevention and the Ministry of Health and Welfare, Korea) initiated a large prospective cohort study with government funding, named the Korean genome and epidemiology study (KoGES). The study is a consortium project consisting of six prospective cohort studies that would be categorized into population-based and gene-environment model studies (Figure 1). The aim of the KoGES was to establish a genome epidemiological study platform for the research community with a health database and biobank, to investigate the genetic and environmental aetiology of common complex diseases in Koreans (i.e. T2DM, hypertension, obesity, metabolic syndrome, osteoporosis, CVD,and cancer) and causes of death with long-term follow-up. The ultimate goal of the KoGES was to develop comprehensive and applicable health care guidelines for common complex diseases in Koreans, reduce the burden of chronic diseases and improve the quality of life. Flow diagram of baseline recruitment and follow-up for the Korean genome and epidemiology study (KoGES). *The follow-up survey was conducted in 6 out of 11 counties (baseline number from the six counties = 21 715). **The follow-up recruitment included only immigrant mothers and children. ***The follow-up survey was continued only in Japan due to difficulties in the follow-up of Chinese participants (see the ‘Who is in the cohort consortium and how often have they been followed up?’ section). The population-based cohorts in the KoGES, including the KoGES_Ansan and Ansung study, the KoGES_health examinee (HEXA) study and the KoGES_cardiovascular disease association study (CAVAS), consist of community-dwellers and participants recruited from the national health examinee registry, men and women, aged ≥ 40 years at baseline (Figure 1). The KoGES gene-environment model studies include the KoGES_twin and family study, the KoGES_immigrant study and KoGES_emigrant study (Japan and China). For baseline recruitment, eligible participants were asked to volunteer through on-site invitation, mailed letters, telephone calls, media campaign or community leader-mediated conferences. The responders were invited to visit the survey sites, including ≥ 50 national and international medical schools, hospitals and health institutions (Figure 2), for an interview, a questionnaire administered by trained staff and physical examination. The followed-up participants were invited to complete the surveys by mail and telephone calls periodically. The inclusion criteria for baseline and follow-up recruitment for each cohort are described in Supplementary material (available as Supplementary data at IJE online). Geographical location of the survey sites in the Korean genome and epidemiology study. In Korea (A); Japan (B); China (C). The selected baseline characteristics of participants and disease prevalence are summarized by cohort in Tables 1, 2 and 3, with additional data in Supplementary Table 2 (available as Supplementary data at IJE online). The age-standardized prevalence rates for T2DM, hypertension and obesity among the population-based cohort studies are compared in Table 2. When compared with the age-standardized prevalence rates (≥ 30 years) reported in the Korea National Health and Nutrition Examination Survey (KNHANES III, 2005: hypertension, 28.0%; diabetes, 9.1%; obesity, 34.8%),1 the prevalence of hypertension appeared to be higher in the KoGES population-based studies. A total of 7224 incident cancer cases and 4351 all-cause deaths were identified in the population-based studies between 2001 and 2013 (Table 3). The distribution patterns of leading primary cancer sites by sex were observed to be similar in the constituent studies, which were also comparable with the national cancer statistics, Korea Central Cancer Reg
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
World Population Prospects The 2006 Revision
Zlotnik, H. (2007) · United Nations eBooks · Cited by 7,898
An integrated map of genetic variation from 1,092 human genomes
The 1000 Genomes Project Consortium (2012) · Nature · doi:10.1038/nature11632 · Cited by 7,711
Mendelian randomization: genetic anchors for causal inference in epidemiological studies
Davey Smith, G., & Hemani, G. (2014) · Human Molecular Genetics · doi:10.1093/hmg/ddu328 · Cited by 4,677
Cancer Statistics in Korea: Incidence, Mortality, Survival, and Prevalence in 2012
Jung, K.-W., Won, Y.-J., Kong, H.-J., Oh, C.-M., Cho, H., Lee, D. H., & Lee, K. H. (2015) · Cancer Research and Treatment · doi:10.4143/crt.2015.060 · Cited by 1,160
Cho, Y. S., Go, M. J., Kim, Y. J., Heo, J. Y., Oh, J. H., Ban, H.-J., Yoon, D., Lee, M. H., Kim, D.-J., Park, M., et al. (2009) · Nature Genetics · doi:10.1038/ng.357 · Cited by 1,015
Self-citationValidation and reproducibility of food frequency questionnaire for Korean genome epidemiologic study
Ahn, Y., Kwon, E., Shim, J. E., Park, M. K., Joo, Y., Kimm, K., Park, C., & Kim, D. H. (2007) · European Journal of Clinical Nutrition · doi:10.1038/sj.ejcn.1602657 · Cited by 784
Mapping Human Genetic Diversity in Asia
The HUGO Pan-Asian SNP Consortium, Abdulla, M. A., Ahmed, I., Assawamakin, A., Bhak, J., Brahmachari, S. K., Calacal, G. C., Chaurasia, A., Chen, C.-H., Chen, J., et al. (2009) · Science · doi:10.1126/science.1177074 · Cited by 708
DIAGRAM Consortium, Cho, Y. S., MuTHER Consortium, Chen, C.-H., Hu, C., Long, J., Hee Ong, R. T., Sim, X., Takeuchi, F., Wu, Y., et al. (2012) · Nature Genetics · doi:10.1038/ng.1019 · Cited by 611
Self-citationKato, N., Takeuchi, F., Tabara, Y., Kelly, T. N., Go, M. J., Sim, X., Tay, W. T., Chen, C.-H., Zhang, Y., Yamamoto, K., et al. (2011) · Nature Genetics · doi:10.1038/ng.834 · Cited by 566
Self-citationBurgess, S., Daniel, R. M., Butterworth, A. S., Thompson, S. G., & the EPIC-InterAct Consortium (2015) · International Journal of Epidemiology · doi:10.1093/ije/dyu176 · Cited by 543
Cited by
Son, D.-H., Lee, H. S., Lee, Y.-J., Lee, J.-H., & Han, J.-H. (2022) · Nutrition, Metabolism and Cardiovascular Diseases · doi:10.1016/j.numecd.2021.11.017 · Cited by 394
Development of type 2 diabetes mellitus in people with intermediate hyperglycaemia
Richter, B., Hemmingsen, B., Metzendorf, M.-I., & Takwoingi, Y. (2018) · Cochrane Database of Systematic Reviews · doi:10.1002/14651858.cd012661.pub2 · Cited by 283
Genome-wide study on 72,298 individuals in Korean biobank data for 76 traits
Nam, K., Kim, J., & Lee, S. (2022) · Cell Genomics · doi:10.1016/j.xgen.2022.100189 · Cited by 77
Jhee, J. H., Joo, Y. S., Kee, Y. K., Jung, S.-Y., Park, S., Yoon, C.-Y., Han, S. H., Yoo, T.-H., Kang, S.-W., & Park, J. T. (2019) · Clinical Journal of the American Society of Nephrology · doi:10.2215/cjn.09540818 · Cited by 60
Song, Y., Choi, J.-E., Kwon, Y.-J., Chang, H.-J., Kim, J. O., Park, D.-H., Park, J.-M., Kim, S.-J., Lee, J. W., & Hong, K.-W. (2021) · Journal of Translational Medicine · doi:10.1186/s12967-021-02751-3 · Cited by 41
Metadata
- Authors
- Kim, Y., Han, B.-G., & the KoGES group
- Venue
- International Journal of Epidemiology
- Publisher
- Oxford University Press (OUP)
- Published
- 2016-04-16
- Volume
- 46
- Issue
- 2
- Pages
- e20-e20
- ISSN
- 0300-5771 (ISSN-L)
- 1464-3685
- OpenAlex
- W2341084900
- Cited by
- 1,017
- References
- 36
- FWCI
- 37.18
- Type
- journal article
- Language
- en
- Open access
- hybrid
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Cohort Profile: The Korean Genome and Epidemiology Study (KoGES) Consortium. https://app.cochat.ai/papers/10.1093/ije/dyv316
- The paper
- Kim, Y., Han, B.-G., & the KoGES group. (2017). Cohort Profile: The Korean Genome and Epidemiology Study (KoGES) Consortium. International Journal of Epidemiology. https://doi.org/10.1093/ije/dyv316
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.