Search

Your search keyword '"Bragg, Fiona"' showing total 291 results
Start Over Author "Bragg, Fiona"
291 results on '"Bragg, Fiona"'

1. Genetic drivers of heterogeneity in type 2 diabetes pathophysiology.

Author

Suzuki, Ken, Hatzikotoulas, Konstantinos, Southam, Lorraine, Taylor, Henry, Yin, Xianyong, Lorenz, Kim, Mandla, Ravi, Huerta-Chagoya, Alicia, Melloni, Giorgio, Kanoni, Stavroula, Rayner, Nigel, Bocher, Ozvan, Arruda, Ana, Sonehara, Kyuto, Namba, Shinichi, Lee, Simon, Preuss, Michael, Petty, Lauren, Schroeder, Philip, Vanderwerff, Brett, Kals, Mart, Bragg, Fiona, Lin, Kuang, Guo, Xiuqing, Zhang, Weihua, Yao, Jie, Kim, Young, Graff, Mariaelisa, Takeuchi, Fumihiko, Nano, Jana, Lamri, Amel, Nakatochi, Masahiro, Moon, Sanghoon, Scott, Robert, Cook, James, Lee, Jung-Jin, Pan, Ian, Taliun, Daniel, Parra, Esteban, Chai, Jin-Fang, Bielak, Lawrence, Tabara, Yasuharu, Hai, Yang, Thorleifsson, Gudmar, Grarup, Niels, Sofer, Tamar, Wuttke, Matthias, Sarnowski, Chloé, Gieger, Christian, Nousome, Darryl, Trompet, Stella, Kwak, Soo-Heon, Long, Jirong, Sun, Meng, Tong, Lin, Chen, Wei-Min, Nongmaithem, Suraj, Noordam, Raymond, Lim, Victor, Tam, Claudia, Joo, Yoonjung, Chen, Chien-Hsiun, Raffield, Laura, Prins, Bram, Nicolas, Aude, Yanek, Lisa, Chen, Guanjie, Brody, Jennifer, Kabagambe, Edmond, An, Ping, Xiang, Anny, Choi, Hyeok, Cade, Brian, Tan, Jingyi, Broadaway, K, Williamson, Alice, Kamali, Zoha, Cui, Jinrui, Thangam, Manonanthini, Adair, Linda, Adeyemo, Adebowale, Aguilar-Salinas, Carlos, Ahluwalia, Tarunveer, Anand, Sonia, Bertoni, Alain, Bork-Jensen, Jette, Brandslund, Ivan, Buchanan, Thomas, Burant, Charles, Butterworth, Adam, Canouil, Mickaël, Chan, Juliana, Chang, Li-Ching, Chee, Miao-Li, Chen, Ji, Chen, Shyh-Huei, Chen, Yuan-Tsong, Chen, Zhengming, Chuang, Lee-Ming, and Cushman, Mary

Subjects
Humans, Adipocytes, Chromatin, Coronary Artery Disease, Diabetes Mellitus, Type 2, Diabetic Nephropathies, Disease Progression, Endothelial Cells, Enteroendocrine Cells, Epigenomics, Genetic Predisposition to Disease, Genome-Wide Association Study, Islets of Langerhans, Multifactorial Inheritance, Peripheral Arterial Disease, Single-Cell Analysis
Abstract

Type 2 diabetes (T2D) is a heterogeneous disease that develops through diverse pathophysiological processes1,2 and molecular mechanisms that are often specific to cell type3,4. Here, to characterize the genetic contribution to these processes across ancestry groups, we aggregate genome-wide association study data from 2,535,601 individuals (39.7% not of European ancestry), including 428,452 cases of T2D. We identify 1,289 independent association signals at genome-wide significance (P

Published
2024

2. Proteo-genomic analyses in relatively lean Chinese adults identify proteins and pathways that affect general and central adiposity levels

Author

Iona, Andri, Yao, Pang, Pozarickij, Alfred, Kartsonaki, Christiana, Said, Saredo, Wright, Neil, Lin, Kuang, Millwood, Iona, Fry, Hannah, Mazidi, Mohsen, Wang, Baihan, Chen, Yiping, Du, Huaidong, Yang, Ling, Avery, Daniel, Schmidt, Dan, Sun, Dianjianyi, Pei, Pei, Lv, Jun, Yu, Canqing, Hill, Michael, Chen, Junshi, Bragg, Fiona, Bennett, Derrick, Walters, Robin, Li, Liming, Clarke, Robert, and Chen, Zhengming

Published
2024
Full Text
View/download PDF

4. Alcohol and mortality in Mexico: prospective study of 150 000 adults

Author

Trichia, Eirini, Alegre-Díaz, Jesus, Aguilar-Ramirez, Diego, Ramirez-Reyes, Raúl, Garcilazo-Ávila, Adrián, González-Carballo, Carlos, Bragg, Fiona, Friedrichs, Louisa Gnatiuc, Herrington, William G, Holland, Lisa, Torres, Jason, Wade, Rachel, Collins, Rory, Peto, Richard, Berumen, Jaime, Tapia-Conyer, Roberto, Kuri-Morales, Pablo, and Emberson, Jonathan R

Published
2024
Full Text
View/download PDF

7. Multi-ancestry genetic study of type 2 diabetes highlights the power of diverse populations for discovery and translation

Author

Mahajan, Anubha, Spracklen, Cassandra N, Zhang, Weihua, Ng, Maggie CY, Petty, Lauren E, Kitajima, Hidetoshi, Yu, Grace Z, Rüeger, Sina, Speidel, Leo, Kim, Young Jin, Horikoshi, Momoko, Mercader, Josep M, Taliun, Daniel, Moon, Sanghoon, Kwak, Soo-Heon, Robertson, Neil R, Rayner, Nigel W, Loh, Marie, Kim, Bong-Jo, Chiou, Joshua, Miguel-Escalada, Irene, della Briotta Parolo, Pietro, Lin, Kuang, Bragg, Fiona, Preuss, Michael H, Takeuchi, Fumihiko, Nano, Jana, Guo, Xiuqing, Lamri, Amel, Nakatochi, Masahiro, Scott, Robert A, Lee, Jung-Jin, Huerta-Chagoya, Alicia, Graff, Mariaelisa, Chai, Jin-Fang, Parra, Esteban J, Yao, Jie, Bielak, Lawrence F, Tabara, Yasuharu, Hai, Yang, Steinthorsdottir, Valgerdur, Cook, James P, Kals, Mart, Grarup, Niels, Schmidt, Ellen M, Pan, Ian, Sofer, Tamar, Wuttke, Matthias, Sarnowski, Chloe, Gieger, Christian, Nousome, Darryl, Trompet, Stella, Long, Jirong, Sun, Meng, Tong, Lin, Chen, Wei-Min, Ahmad, Meraj, Noordam, Raymond, Lim, Victor JY, Tam, Claudia HT, Joo, Yoonjung Yoonie, Chen, Chien-Hsiun, Raffield, Laura M, Lecoeur, Cécile, Prins, Bram Peter, Nicolas, Aude, Yanek, Lisa R, Chen, Guanjie, Jensen, Richard A, Tajuddin, Salman, Kabagambe, Edmond K, An, Ping, Xiang, Anny H, Choi, Hyeok Sun, Cade, Brian E, Tan, Jingyi, Flanagan, Jack, Abaitua, Fernando, Adair, Linda S, Adeyemo, Adebowale, Aguilar-Salinas, Carlos A, Akiyama, Masato, Anand, Sonia S, Bertoni, Alain, Bian, Zheng, Bork-Jensen, Jette, Brandslund, Ivan, Brody, Jennifer A, Brummett, Chad M, Buchanan, Thomas A, Canouil, Mickaël, Chan, Juliana CN, Chang, Li-Ching, Chee, Miao-Li, Chen, Ji, Chen, Shyh-Huei, Chen, Yuan-Tsong, Chen, Zhengming, Chuang, Lee-Ming, and Cushman, Mary

Subjects
Genetics, Diabetes, Human Genome, Metabolic and endocrine, Diabetes Mellitus, Type 2, Ethnicity, Genetic Predisposition to Disease, Genome-Wide Association Study, Humans, Polymorphism, Single Nucleotide, Risk Factors, FinnGen, eMERGE Consortium, Biological Sciences, Medical and Health Sciences, Developmental Biology
Abstract

We assembled an ancestrally diverse collection of genome-wide association studies (GWAS) of type 2 diabetes (T2D) in 180,834 affected individuals and 1,159,055 controls (48.9% non-European descent) through the Diabetes Meta-Analysis of Trans-Ethnic association studies (DIAMANTE) Consortium. Multi-ancestry GWAS meta-analysis identified 237 loci attaining stringent genome-wide significance (P 50% posterior probability. This improved fine-mapping enabled systematic assessment of candidate causal genes and molecular mechanisms through which T2D associations are mediated, laying the foundations for functional investigations. Multi-ancestry genetic risk scores enhanced transferability of T2D prediction across diverse populations. Our study provides a step toward more effective clinical translation of T2D GWAS to improve global health for all, irrespective of genetic background.

Published
2022

9. Educational and social inequalities and cause-specific mortality in Mexico City: a prospective study

Author

Addey, Thomas, Alegre-Díaz, Jesus, Bragg, Fiona, Trichia, Eirini, Wade, Rachel, Santacruz-Benitez, Rogelio, Ramirez-Reyes, Raúl, Garcilazo-Ávila, Adrián, Gonzáles-Carballo, Carlos, Bello-Chavolla, Omar Yaxmehen, Antonio-Villa, Neftali Eduardo, Aguilar-Ramirez, Diego, Friedrichs, Louisa Gnatiuc, Lewington, Sarah, Peto, Richard, Collins, Rory, Berumen, Jaime, Emberson, Jonathan R, Kuri-Morales, Pablo, and Tapia-Conyer, Roberto

Published
2023
Full Text
View/download PDF

10. Identification of type 2 diabetes loci in 433,540 East Asian individuals

Author

Spracklen, Cassandra N, Horikoshi, Momoko, Kim, Young Jin, Lin, Kuang, Bragg, Fiona, Moon, Sanghoon, Suzuki, Ken, Tam, Claudia HT, Tabara, Yasuharu, Kwak, Soo-Heon, Takeuchi, Fumihiko, Long, Jirong, Lim, Victor JY, Chai, Jin-Fang, Chen, Chien-Hsiun, Nakatochi, Masahiro, Yao, Jie, Choi, Hyeok Sun, Iyengar, Apoorva K, Perrin, Hannah J, Brotman, Sarah M, van de Bunt, Martijn, Gloyn, Anna L, Below, Jennifer E, Boehnke, Michael, Bowden, Donald W, Chambers, John C, Mahajan, Anubha, McCarthy, Mark I, Ng, Maggie CY, Petty, Lauren E, Zhang, Weihua, Morris, Andrew P, Adair, Linda S, Akiyama, Masato, Bian, Zheng, Chan, Juliana CN, Chang, Li-Ching, Chee, Miao-Li, Chen, Yii-Der Ida, Chen, Yuan-Tsong, Chen, Zhengming, Chuang, Lee-Ming, Du, Shufa, Gordon-Larsen, Penny, Gross, Myron, Guo, Xiuqing, Guo, Yu, Han, Sohee, Howard, Annie-Green, Huang, Wei, Hung, Yi-Jen, Hwang, Mi Yeong, Hwu, Chii-Min, Ichihara, Sahoko, Isono, Masato, Jang, Hye-Mi, Jiang, Guozhi, Jonas, Jost B, Kamatani, Yoichiro, Katsuya, Tomohiro, Kawaguchi, Takahisa, Khor, Chiea-Chuen, Kohara, Katsuhiko, Lee, Myung-Shik, Lee, Nanette R, Li, Liming, Liu, Jianjun, Luk, Andrea O, Lv, Jun, Okada, Yukinori, Pereira, Mark A, Sabanayagam, Charumathi, Shi, Jinxiu, Shin, Dong Mun, So, Wing Yee, Takahashi, Atsushi, Tomlinson, Brian, Tsai, Fuu-Jen, van Dam, Rob M, Xiang, Yong-Bing, Yamamoto, Ken, Yamauchi, Toshimasa, Yoon, Kyungheon, Yu, Canqing, Yuan, Jian-Min, Zhang, Liang, Zheng, Wei, Igase, Michiya, Cho, Yoon Shin, Rotter, Jerome I, Wang, Ya-Xing, Sheu, Wayne HH, Yokota, Mitsuhiro, Wu, Jer-Yuarn, Cheng, Ching-Yu, Wong, Tien-Yin, Shu, Xiao-Ou, Kato, Norihiro, and Park, Kyong-Soo

Subjects
Human Genome, Diabetes, Biotechnology, Genetics, Aetiology, 2.1 Biological and endogenous factors, Metabolic and endocrine, Aldehyde Dehydrogenase, Mitochondrial, Alleles, Ankyrins, Asian People, Body Mass Index, Case-Control Studies, Diabetes Mellitus, Type 2, Europe, Eye Proteins, Asia, Eastern, Female, Genetic Predisposition to Disease, Genome-Wide Association Study, Homeodomain Proteins, Humans, Male, Nerve Tissue Proteins, RNA, Messenger, Transcription Factors, Transcription, Genetic, General Science & Technology
Abstract

Meta-analyses of genome-wide association studies (GWAS) have identified more than 240 loci that are associated with type 2 diabetes (T2D)1,2; however, most of these loci have been identified in analyses of individuals with European ancestry. Here, to examine T2D risk in East Asian individuals, we carried out a meta-analysis of GWAS data from 77,418 individuals with T2D and 356,122 healthy control individuals. In the main analysis, we identified 301 distinct association signals at 183 loci, and across T2D association models with and without consideration of body mass index and sex, we identified 61 loci that are newly implicated in predisposition to T2D. Common variants associated with T2D in both East Asian and European populations exhibited strongly correlated effect sizes. Previously undescribed associations include signals in or near GDAP1, PTF1A, SIX3, ALDH2, a microRNA cluster, and genes that affect the differentiation of muscle and adipose cells3. At another locus, expression quantitative trait loci at two overlapping T2D signals affect two genes-NKX6-3 and ANK1-in different tissues4-6. Association studies in diverse populations identify additional loci and elucidate disease-associated genes, biology, and pathways.

Published
2020

15. Conventional and genetic associations of BMI with major vascular and non-vascular disease incidence and mortality in a relatively lean Chinese population: U-shaped relationship revisited.

Author

Iona, Andri, Bragg, Fiona, Fairhurst-Hunter, Zammy, Millwood, Iona Y, Wright, Neil, Lin, Kuang, Yang, Ling, Du, Huaidong, Chen, Yiping, Pei, Pei, Cheng, Liang, Schmidt, Dan, Avery, Daniel, Yu, Canqing, Lv, Jun, Clarke, Robert, Walters, Robin, Li, Liming, Parish, Sarah, and Chen, Zhengming

Subjects
*BODY mass index, *CEREBRAL hemorrhage, *ISCHEMIC stroke, *CHINESE people, *STROKE
Abstract

Background Higher body mass index (BMI) is associated with higher incidence of cardiovascular and some non-cardiovascular diseases (CVDs/non-CVDs). However, uncertainty remains about its associations with mortality, particularly at lower BMI levels. Methods The prospective China Kadoorie Biobank recruited >512 000 adults aged 30–79 years in 2004–08 and genotyped a random subset of 76 000 participants. In conventional and Mendelian randomization (MR) analyses, Cox regression yielded adjusted hazard ratios (HRs) associating measured and genetically predicted BMI levels with incident risks of major vascular events (MVEs; conventional/MR 68 431/23 621), ischaemic heart disease (IHD; 50 698/12 177), ischaemic stroke (IS; 42 427/11 897) and intracerebral haemorrhage (ICH; 7644/4712), and with mortality risks of CVD (15 427/6781), non-CVD (26 915/4355) and all causes (42 342/6784), recorded during ∼12 years of follow-up. Results Overall, the mean BMI was 23.8 (standard deviation: 3.2) kg/m2 and 13% had BMIs of <20 kg/m2. Measured and genetically predicted BMI showed positive log-linear associations with MVE, IHD and IS, but a shallower positive association with ICH in conventional analyses. Adjusted HRs per 5 kg/m2 higher genetically predicted BMI were 1.50 (95% CI 1.41–1.58), 1.49 (1.38–1.61), 1.42 (1.31–1.54) and 1.64 (1.58–1.69) for MVE, IHD, IS and ICH, respectively. These were stronger than associations in conventional analyses [1.21 (1.20–1.23), 1.28 (1.26–1.29), 1.31 (1.29–1.33) and 1.14 (1.10–1.18), respectively]. At BMIs of ≥20 kg/m2, there were stronger positive log-linear associations of BMI with CVD, non-CVD and all-cause mortality in MR than in conventional analyses. Conclusions Among relatively lean Chinese adults, higher genetically predicted BMI was associated with higher risks of incident CVDs. Excess mortality risks at lower BMI in conventional analyses are likely not causal and may reflect residual reverse causality. [ABSTRACT FROM AUTHOR]

Published
2024
Full Text
View/download PDF

16. Prediabetes as a risk factor for all-cause and cause-specific mortality: a prospective analysis of 115,919 adults without diabetes in Mexico City

Author

Fermin-Martinez, Carlos A., primary, Bello-Chavolla, Omar Yaxmehen, additional, Paz-Cabrera, Cesar Daniel, additional, Ramirez-Garcia, Daniel, additional, Perezalonso-Espinosa, Jeronimo, additional, Fernandez-Chirino, Luisa, additional, Vargas-Vazquez, Arsenio, additional, Diaz-Sanchez, Juan Pablo, additional, Mendez-Labra, Padme Nailea, additional, Nuñez-Luna, Alejandra, additional, Basile-Alvarez, Martin, additional, Sanchez-Castro, Paulina, additional, Bragg, Fiona, additional, Gnatiuc Friedrichs, Louisa, additional, Aguilar-Ramirez, Diego, additional, Emberson, Jonathan R., additional, Berumen-Campos, Jaime, additional, Kuri-Morales, Pablo, additional, Tapia-Conyer, Roberto, additional, Alegre-Diaz, Jesus, additional, Seiglie, Jacqueline, additional, and Antonio-Villa, Neftai Eduardo, additional

Published
2024
Full Text
View/download PDF

17. Proteomic analyses in diverse populations improved risk prediction and identified new drug targets for type 2 diabetes

Author

Yao, Pang, primary, Iona, Andri, primary, Pozarickij, Alfred, primary, Said, Saredo, primary, Wright, Neil, primary, Lin, Kuang, primary, Millwood, Iona, primary, Fry, Hannah, primary, Kartsonaki, Christiana, primary, Mazidi, Mohsen, primary, Chen, Yiping, primary, Bragg, Fiona, primary, Liu, Bowen, primary, Yang, Ling, primary, Liu, Junxi, primary, Avery, Daniel, primary, Schmidt, Dan, primary, Sun, Dianjianyi, primary, Pei, Pei, primary, Lv, Jun, primary, Yu, Canqing, primary, Hill, Michael, primary, Bennett, Derrick, primary, Walters, Robin, primary, Li, Liming, primary, Clarke, Robert, primary, Du, Huaidong, primary, and Chen, Zhengming, primary

Published
2024
Full Text
View/download PDF

19. Cohort Profile: Indian Study of Healthy Ageing (ISHA-Barshi).

Author

Mhatre, Sharayu Sitaram, Bragg, Fiona, Panse, Nandkumar, Judge, Parminder Kaur, Manjrekar, Ankita, Burrett, Julie Ann, Patil, Suchita, Smith, George Davey, Kotkar, Lekha, Relton, Caroline L, Doibale, Pravin, Gadhave, Bipin, Chaturvedi, Pankaj, Sherliker, Paul, Jha, Prabhat, Lewington, Sarah, and Dikshit, Rajesh

Subjects
*INDOOR air pollution, *PHYSICAL sciences, *HEART disease related mortality, *GLOBAL burden of disease, *DISEASE risk factors, *MASTICATION, *AUTOPSY, *OBESITY in women
Abstract

The Indian Study of Healthy Ageing (ISHA) is a large-scale blood-based prospective study in India that aims to examine the burden, causes, and consequences of chronic diseases in the adult population. The study has recruited approximately 220,000 participants and is focused on testing hypotheses related to lifestyle, diet, obesity, and genetic determinants of cancer and other chronic diseases. The population studied is relatively lean, with low rates of tobacco smoking and alcohol drinking but high rates of tobacco chewing. The study will provide valuable insights into the risk factors for vascular and non-vascular mortality in the Indian population and contribute to understanding the impact of lifestyle transitions on chronic diseases. Data-sharing regulations are in place, and future collaborations are encouraged. The study collected baseline data from over 200,000 participants in India, including information on demographics, socioeconomic status, health behaviors, and physical measurements. The findings show that the participants in the study were, on average, younger and more highly educated than those under active follow-up. The study also found that the prevalence of underweight was similar between men and women, but overweight and obesity were slightly more common among women. The study provides valuable data on the health and aging of the Indian population. This table provides data on various health indicators for men and women in different age groups. The data includes the number of participants, age, education level, tobacco and alcohol use, body mass index, waist circumference, body fat percentage, blood pressure, lung function, grip strength, prevalence [Extracted from the article]

Published
2024
Full Text
View/download PDF

20. Proteomic Analyses in Diverse Populations Improved Risk Prediction and Identified New Drug Targets for Type 2 Diabetes.

Author

Yao, Pang, Iona, Andri, Pozarickij, Alfred, Said, Saredo, Wright, Neil, Lin, Kuang, Millwood, Iona, Fry, Hannah, Kartsonaki, Christiana, Mazidi, Mohsen, Chen, Yiping, Bragg, Fiona, Liu, Bowen, Yang, Ling, Liu, Junxi, Avery, Daniel, Schmidt, Dan, Sun, Dianjianyi, Pei, Pei, and Lv, Jun

Subjects
TYPE 2 diabetes, DRUG target, PROTEOMICS, LOCUS (Genetics), GENOME-wide association studies, FALSE memory syndrome
Abstract

OBJECTIVE: Integrated analyses of plasma proteomics and genetic data in prospective studies can help assess the causal relevance of proteins, improve risk prediction, and discover novel protein drug targets for type 2 diabetes (T2D). RESEARCH DESIGN AND METHODS: We measured plasma levels of 2,923 proteins using Olink Explore among ∼2,000 randomly selected participants from China Kadoorie Biobank (CKB) without prior diabetes at baseline. Cox regression assessed associations of individual protein with incident T2D (n = 92 cases). Proteomic-based risk models were developed with discrimination, calibration, reclassification assessed using area under the curve (AUC), calibration plots, and net reclassification index (NRI), respectively. Two-sample Mendelian randomization (MR) analyses using cis -protein quantitative trait loci identified in a genome-wide association study of CKB and UK Biobank for specific proteins were conducted to assess their causal relevance for T2D, along with colocalization analyses to examine shared causal variants between proteins and T2D. RESULTS: Overall, 33 proteins were significantly associated (false discovery rate <0.05) with risk of incident T2D, including IGFBP1, GHR, and amylase. The addition of these 33 proteins to a conventional risk prediction model improved AUC from 0.77 (0.73–0.82) to 0.88 (0.85–0.91) and NRI by 38%, with predicted risks well calibrated with observed risks. MR analyses provided support for the causal relevance for T2D of ENTR1, LPL, and PON3, with replication of ENTR1 and LPL in Europeans using different genetic instruments. Moreover, colocalization analyses showed strong evidence (pH4 > 0.6) of shared genetic variants of LPL and PON3 with T2D. CONCLUSIONS: Proteomic analyses in Chinese adults identified novel associations of multiple proteins with T2D with strong genetic evidence supporting their causal relevance and potential as novel drug targets for prevention and treatment of T2D. [ABSTRACT FROM AUTHOR]

Published
2024
Full Text
View/download PDF

21. Association of daytime napping in relation to risk of diabetes: evidence from a prospective study in Zhejiang, China

Author

Hao Wang, Lingli Chen, Dun Shen, Yuan Cao, Xiaoyi Zhang, Kaixu Xie, Chunmei Wang, Shuiqing Zhu, Yu Guo, Bragg Fiona, Min Yu, Zhengming Chen, and Liming Li

Subjects
Daytime napping, Diabetes, Prospective study, China, Nutrition. Foods and food supply, TX341-641, Nutritional diseases. Deficiency diseases, RC620-627
Abstract

Abstract Background Diabetes was a major risk factor for numerous chronic diseases. However, the associations between daytime napping and diabetes in the existing literature is still inconsistent. Methods The analysis included 53,916 participants aged 30–79 years of the China Kadoorie Biobank prospective study from Tongxiang. Incident diabetes were identified through linkage with incident diabetes surveillance systems, health insurance system, and death registries. Cox regressions were used to estimate the associations of daytime napping with diabetes. Results 5.11% of participants reported habitual daytime napping. During 488,233 person-years (median 9.4 years) of follow-up, 3333 incident diabetes, including 1249 males and 2084 females, were documented. After adjusting for socio-demographic status, behavioral lifestyle, BMI, waist circumference and snoring, as comparison with those without daytime napping, the hazard ratios for risk of diabetes were 1.39 (95% CI 1.21–1.59). The corresponding figures for males and females were 1.45 (95% CI 1.20–1.74) and 1.30 (95% CI 1.05–1.59), respectively. The corresponding figures for postmenopausal and premenopausal females were 1.41 (95% CI 1.08–1.80) and 1.13 (95% CI 0.78–1.59), respectively. Conclusions Habitual daytime napping is positively associated with risk of diabetes in adults, except premenopausal females.

Published
2021
Full Text
View/download PDF

22. Mortality and recurrent vascular events after first incident stroke: a 9-year community-based study of 0·5 million Chinese adults

Author

Chen, Yiping, Wright, Neil, Guo, Yu, Turnbull, Iain, Kartsonaki, Christiana, Yang, Ling, Bian, Zheng, Pei, Pei, Pan, Dongxia, Zhang, Yidan, Qin, Haiqiang, Wang, Yilong, Lv, Jun, Liu, Ming, Hao, Zilong, Wang, Yongjun, Yu, Canqing, Peto, Richard, Collins, Rory, Li, Liming, Clarke, Robert, Chen, Zhengming, Chen, Junshi, Walters, Robin, Avery, Daniel, Bennett, Derrick, Boxall, Ruth, Bragg, Fiona, Chang, Yumei, Du, Huaidong, Gilbert, Simon, Hacker, Alex, Holmes, Michael, Kerosi, Rene, Lancaster, Garry, Lin, Kuang, McDonnell, John, Millwood, Iona, Nie, Qunhua, Ryder, Paul, Sansome, Sam, Schmidt, Dan, Sohoni, Rajani, Wang, Jenny, Wang, Lin, Yang, Xiaoming, Han, Xiao, Hou, Can, Jing, Biao, Liu, Chao, Pang, Zengchang, Gao, Ruqin, Li, Shanpeng, Wang, Shaojie, Liu, Yongmei, Du, Ranran, Zang, Yajing, Cheng, Liang, Tian, Xiaocao, Zhang, Hua, Zhai, Yaoming, Ning, Feng, Sun, Xiaohui, Li, Feifei, Lv, Silu, Wang, Junzheng, Hou, Wei, Zeng, Mingyuan, Jiang, Ge, Zhou, Xue, Yang, Liqiu, He, Hui, Yu, Bo, Li, Yanjie, Xu, Qinai, Kang, Quan, Guo, Ziyan, Wang, Dan, Hu, Ximin, Wang, Hongmei, Chen, Jinyan, Fu, Yan, Fu, Zhenwang, Wang, Xiaohuan, Weng, Min, Guo, Zhendong, Wu, Shukuan, Li, Yilei, Li, Huimei, Fu, Zhifang, Wu, Ming, Zhou, Yonglin, Zhou, Jinyi, Tao, Ran, Yang, Jie, Su, Jian, Liu, Fang, Zhang, Jun, Hu, Yihe, Lu, Yan, Ma, Liangcai, Tang, Aiyu, Zhang, Shuo, Jin, Jianrong, Liu, Jingchao, Tang, Zhenzhu, Chen, Naying, Huang, Ying, Li, Mingqiang, Meng, Jinhuai, Pan, Rong, Jiang, Qilian, Lan, Jian, Liu, Yun, Wei, Liuping, Chen, Ningyu, Wang, Ping, Meng, Fanwen, Qin, Yulu, Wang, Sisi, Wu, Xianping, Zhang, Ningmei, Chen, Xiaofang, Zhou, Weiwei, Luo, Guojin, Li, Jianguo, Zhong, Xunfu, Liu, Jiaqiu, Sun, Qiang, Ge, Pengfei, Ren, Xiaolan, Dong, Caixia, Zhang, Hui, Mao, Enke, Wang, Xiaoping, Wang, Tao, Zhang, Xi, Zhang, Ding, Zhou, Gang, Feng, Shixian, Chang, Liang, Fan, Lei, Gao, Yulian, He, Tianyou, Sun, Huarong, He, Pan, Hu, Chen, Zhang, Xukui, Wu, Huifang, Yu, Min, Hu, Ruying, Wang, Hao, Qian, Yijian, Wang, Chunmei, Xie, Kaixu, Chen, Lingli, Fu, Zhongxi, Xu, Qiaohua, Xu, Xin, Zhang, Hao, Long, Huajun, Li, Xianzhi, Zhang, Libo, and Qiu, Zhe

Published
2020
Full Text
View/download PDF

23. Blood glucose and diabetes mellitus as risk factors for cardiovascular disease in the China Kadoorie Biobank of 0.5 million people

Author

Bragg, Fiona J., Chen, Zhengming, and Bennett, Derrick

Subjects
616.4
Abstract

Background: In China, the incidence of diabetes is rising, but little is known about its impact on cardiovascular diseases (CVD). Moreover, substantial uncertainty remains, both in China and elsewhere, about the relevance to CVD of blood glucose levels below the glycaemic threshold for diabetes. This thesis assessed these associations in Chinese adults. Methods: In 2004-08, the China Kadoorie Biobank recruited 0.5 million adults aged 30-79 years from 10 diverse regions, recording 25 488 deaths by 1st January 2014. Logistic and Cox regression yielded adjusted odds (ORs) and hazard ratios (HRs), respectively, relating self-reported diabetes and, among people without self-reported diabetes, random plasma glucose (RPG) levels to prevalent and incident CVD. Results: At baseline, 5.9% of participants had self-reported (3.2%) or screen-detected (2.8%) diabetes. Self-reported diabetes was associated with approximately 2-fold increased odds of prevalent ischaemic heart disease (IHD) (n=14 771, OR 2.16, 95% CI 2.05-2.28) and stroke/transient ischaemic attack (n=8430, 1.88, 1.75-2.02). There were also significant excess risks of CVD mortality (n=6916, HR 2.07, 95% CI 1.90-2.26) and incident IHD (n=3465, 2.44, 2.18-2.73), ischaemic stroke (n=19 778, 1.68, 1.60-1.77) and intra-cerebral haemorrhage (ICH) (n=4423, 1.24, 1.07-1.44). These risks increased with longer diabetes duration and number of other presenting risk factors. Screen-detected diabetes was also associated with significant, though more modest, excess incident CVD risks. Among participants without self-reported diabetes, there was a positive log-linear association of RPG with prevalent and incident CVDs. In prospective analyses the association continued down to baseline RPG of ~4.0 mmol/L. After adjusting for regression dilution bias, each 1 mmol/L higher usual RPG was associated with ~10% increased risks of incident ischaemic CVDs and CVD mortality, and ~5% increased risks of incident ICH. In ~89 000 genotyped individuals, a RPG-related genetic risk score was associated significantly with RPG. It showed no significant association with ischaemic CVD risks, but power to detect such an association was low. Conclusions: Among adult Chinese, diabetes is associated with significantly increased risks of major CVDs. In individuals without diabetes, there is a positive log-linear association between RPG and CVDs, with no evidence of a threshold.

Published
2015

25. Estimating lifetime risk of diabetes in the Chinese population

Author

Bragg, Fiona and Chen, Zhengming

Subjects
Medical policy -- Management, Life expectancy -- Demographic aspects -- Health aspects, Type 2 diabetes -- Risk factors -- Diagnosis -- Care and treatment, Health risk assessment -- Methods, Company business management, Biological sciences
Abstract

Author(s): Fiona Bragg 1,2, Zhengming Chen 1,2,* The worldwide epidemic of diabetes continues to grow [1]. In China, the rise in prevalence has been notably rapid; about 12% of the [...]

Published
2022
Full Text
View/download PDF

27. Adiposity and risk of ischaemic and haemorrhagic stroke in 0·5 million Chinese men and women: a prospective cohort study

Author

Chen, Junshi, Chen, Zhengming, Clarke, Robert, Collins, Rory, Guo, Yu, Li, Liming, Lv, Jun, Peto, Richard, Walters, Robin, Avery, Daniel, Bennett, Derrick, Boxall, Ruth, Bragg, Fiona, Chang, Yumei, Chen, Yiping, Du, Huaidong, Gilbert, Simon, Hacker, Alex, Holmes, Michael, Iona, Andri, Kartsonaki, Christiana, Kerosi, Rene, Kurmi, Om, Lewington, Sarah, Lancaster, Garry, Lin, Kuang, McDonnell, John, Millwood, Iona, Nie, Qunhua, Radhakrishnan, Jayakrishnan, Ryder, Paul, Sansome, Sam, Schmidt, Dan, Sherliker, Paul, Sohoni, Rajani, Stevens, Becky, Turnbull, Iain, Wang, Jenny, Wang, Lin, Wright, Neil, Yang, Ling, Yang, Xiaoming, Bian, Zheng, Chen, Ge, Han, Xiao, Hou, Can, Pei, Pei, Qu, Shuzhen, Tan, Yunlong, Yu, Canqing, Pang, Zengchang, Gao, Ruqin, Wang, Shaojie, Liu, Yongmei, Du, Ranran, Zang, Yajing, Cheng, Liang, Tian, Xiaocao, Zhang, Hua, Lv, Silu, Wang, Junzheng, Hou, Wei, Yin, Jiyuan, Jiang, Ge, Zhou, Xue, Yang, Liqiu, He, Hui, Yu, Bo, Li, Yanjie, Mu, Huaiyi, Xu, Qinai, Dou, Meiling, Ren, Jiaojiao, Wang, Shanqing, Hu, Ximin, Wang, Hongmei, Chen, Jinyan, Fu, Yan, Fu, Zhenwang, Wang, Xiaohuan, Weng, Min, Zheng, Xiangyang, Li, Yilei, Li, Huimei, Wang, Yanjun, Wu, Ming, Zhou, Jinyi, Tao, Ran, Yang, Jie, Ni, Chuanming, Zhang, Jun, Hu, Yihe, Lu, Yan, Ma, Liangcai, Tang, Aiyu, Zhang, Shuo, Jin, Jianrong, Liu, Jingchao, Tang, Zhenzhu, Chen, Naying, Huang, Ying, Li, Mingqiang, Meng, Jinhuai, Pan, Rong, Jiang, Qilian, Zhang, Weiyuan, Liu, Yun, Wei, Liuping, Zhou, Liyuan, Chen, Ningyu, Guan, Hairong, Wu, Xianping, Zhang, Ningmei, Chen, Xiaofang, Tang, Xuefeng, Luo, Guojin, Li, Jianguo, Zhong, Xunfu, Liu, Jiaqiu, Sun, Qiang, Ge, Pengfei, Ren, Xiaolan, Dong, Caixia, Zhang, Hui, Mao, Enke, Wang, Xiaoping, Wang, Tao, Zhang, Xi, Zhang, Ding, Zhou, Gang, Feng, Shixian, Chang, Liang, Fan, Lei, Gao, Yulian, He, Tianyou, Sun, Huarong, He, Pan, Hu, Chen, Lv, Qiannan, Zhang, Xukui, Yu, Min, Hu, Ruying, Wang, Hao, Qian, Yijian, Wang, Chunmei, Xie, Kaixue, Chen, Lingli, Zhang, Yidan, Pan, Dongxia, Huang, Yuelong, Chen, Biyun, Yin, Li, Jin, Donghui, Liu, Huilin, Fu, Zhongxi, Xu, Qiaohua, Xu, Xin, Zhang, Hao, Xiong, Youping, Long, Huajun, Li, Xianzhi, Zhang, Libo, Qiu, Zhe, Parish, Sarah, Holmes, Michael V, Lacey, Ben, Liu, Fang, Zhang, Zengzhi, and Walters, Robin G

Published
2018
Full Text
View/download PDF

28. Age-specific association between blood pressure and vascular and non-vascular chronic diseases in 0·5 million adults in China: a prospective cohort study

Author

Chen, Junshi, Chen, Zhengming, Clarke, Robert, Collins, Rory, Guo, Yu, Li, Liming, Lv, Jun, Peto, Richard, Walters, Robin, Avery, Daniel, Bennett, Derrick, Boxall, Ruth, Bragg, Fiona, Chang, Yumei, Chen, Yiping, Du, Huaidong, Gilbert, Simon, Hacker, Alex, Holmes, Michael, Iona, Andri, Kartsonaki, Christiana, Kerosi, Rene, Kurmi, Om, Lewington, Sarah, Lancaster, Garry, Lin, Kuang, McDonnell, John, Millwood, Iona, Nie, Qunhua, Radhakrishnan, Jayakrishnan, Ryder, Paul, Sansome, Sam, Schmidt, Dan, Sherliker, Paul, Sohoni, Rajani, Stevens, Becky, Turnbull, Iain, Wang, Jenny, Wang, Lin, Wright, Neil, Yang, Ling, Yang, Xiaoming, Bian, Zheng, Chen, Ge, Han, Xiao, Hou, Can, Pei, Pei, Qu, Shuzhen, Tan, Yunlong, Yu, Canqing, Pang, Zengchang, Gao, Ruqin, Wang, Shaojie, Liu, Yongmei, Du, Ranran, Zang, Yajing, Cheng, Liang, Tian, Xiaocao, Zhang, Hua, Lv, Silu, Wang, Junzheng, Hou, Wei, Yin, Jiyuan, Jiang, Ge, Zhou, Xue, Yang, Liqiu, He, Hui, Yu, Bo, Li, Yanjie, Mu, Huaiyi, Xu, Qinai, Dou, Meiling, Ren, Jiaojiao, Wang, Shanqing, Hu, Ximin, Wang, Hongmei, Chen, Jinyan, Fu, Yan, Fu, Zhenwang, Wang, Xiaohuan, Weng, Min, Zheng, Xiangyang, Li, Yilei, Li, Huimei, Wang, Yanjun, Wu, Ming, Zhou, Jinyi, Tao, Ran, Yang, Jie, Ni, Chuanming, Zhang, Jun, Hu, Yihe, Lu, Yan, Ma, Liangcai, Tang, Aiyu, Zhang, Shuo, Jin, Jianrong, Liu, Jingchao, Tang, Zhenzhu, Chen, Naying, Huang, Ying, Li, Mingqiang, Meng, Jinhuai, Pan, Rong, Jiang, Qilian, Zhang, Weiyuan, Liu, Yun, Wei, Liuping, Zhou, Liyuan, Chen, Ningyu, Guan, Hairong, Wu, Xianping, Zhang, Ningmei, Chen, Xiaofang, Tang, Xuefeng, Luo, Guojin, Li, Jianguo, Zhong, Xunfu, Liu, Jiaqiu, Sun, Qiang, Ge, Pengfei, Ren, Xiaolan, Dong, Caixia, Zhang, Hui, Mao, Enke, Wang, Xiaoping, Wang, Tao, Zhang, Xi, Zhang, Ding, Zhou, Gang, Feng, Shixian, Chang, Liang, Fan, Lei, Gao, Yulian, He, Tianyou, Sun, Huarong, He, Pan, Hu, Chen, Lv, Qiannan, Zhang, Xukui, Yu, Min, Hu, Ruying, Wang, Hao, Qian, Yijian, Wang, Chunmei, Xie, Kaixue, Chen, Lingli, Zhang, Yidan, Pan, Dongxia, Huang, Yuelong, Chen, Biyun, Yin, Li, Jin, Donghui, Liu, Huilin, Fu, Zhongxi, Xu, Qiaohua, Xu, Xin, Zhang, Hao, Xiong, Youping, Long, Huajun, Li, Xianzhi, Zhang, Libo, Qiu, Zhe, Lacey, Ben, Kong, Xiang Ling, and Walters, Robin G

Published
2018
Full Text
View/download PDF

29. Smoking and smoking cessation in relation to risk of diabetes in Chinese men and women: a 9-year prospective study of 0·5 million people

Author

Chen, Junshi, Chen, Zhengming, Clarke, Robert, Collins, Rory, Guo, Yu, Li, Liming, Lv, Jun, Peto, Richard, Walters, Robin, Avery, Daniel, Bennett, Derrick, Boxall, Ruth, Bragg, Fiona, Chang, Yumei, Chen, Yiping, Du, Huaidong, Gilbert, Simon, Hacker, Alex, Holmes, Michael, Kartsonaki, Christiana, Kerosi, Rene, Lancaster, Garry, Lin, Kuang, McDonnell, John, Millwood, Iona, Nie, Qunhua, Radhakrishnan, Jayakrishnan, Ryder, Paul, Sansome, Sam, Schmidt, Dan, Sohoni, Rajani, Stevens, Becky, Turnbull, Iain, Wang, Jenny, Wang, Lin, Wright, Neil, Yang, Ling, Yang, Xiaoming, Bian, Zheng, Chen, Ge, Han, Xiao, Hou, Can, Pei, Pei, Qu, Shuzhen, Tan, Yunlong, Yu, Canqing, Pang, Zengchang, Gao, Ruqin, Wang, Shaojie, Liu, Yongmei, Du, Ranran, Zang, Yajing, Cheng, Liang, Tian, Xiaocao, Zhang, Hua, Lv, Silu, Wang, Junzheng, Hou, Wei, Yin, Jiyuan, Jiang, Ge, Zhou, Xue, Yang, Liqiu, He, Hui, Yu, Bo, Li, Yanjie, Mu, Huaiyi, Xu, Qinai, Dou, Meiling, Ren, Jiaojiao, Wang, Shanqing, Hu, Ximin, Wang, Hongmei, Chen, Jinyan, Fu, Yan, Fu, Zhenwang, Wang, Xiaohuan, Weng, Min, Zheng, Xiangyang, Li, Yilei, Li, Huimei, Wang, Yanjun, Wu, Ming, Zhou, Jinyi, Tao, Ran, Yang, Jie, Ni, Chuanming, Zhang, Jun, Hu, Yihe, Lu, Yan, Ma, Liangcai, Tang, Aiyu, Zhang, Shuo, Jin, Jianrong, Liu, Jingchao, Tang, Zhenzhu, Chen, Naying, Huang, Ying, Li, Mingqiang, Meng, Jinhuai, Pan, Rong, Jiang, Qilian, Zhang, Weiyuan, Liu, Yun, Wei, Liuping, Zhou, Liyuan, Chen, Ningyu, Guan, Hairong, Wu, Xianping, Zhang, Ningmei, Chen, Xiaofang, Tang, Xuefeng, Luo, Guojin, Li, Jianguo, Zhong, Xunfu, Liu, Jiaqiu, Sun, Qiang, Ge, Pengfei, Ren, Xiaolan, Dong, Caixia, Zhang, Hui, Mao, Enke, Wang, Xiaoping, Wang, Tao, Zhang, Xi, Zhang, Ding, Zhou, Gang, Feng, Shixian, Chang, Liang, Fan, Lei, Gao, Yulian, He, Tianyou, Sun, Huarong, He, Pan, Hu, Chen, Lv, Qiannan, Zhang, Xukui, Yu, Min, Hu, Ruying, Wang, Hao, Qian, Yijian, Wang, Chunmei, Xie, Kaixue, Chen, Lingli, Zhang, Yidan, Pan, Dongxia, Huang, Yuelong, Chen, Biyun, Yin, Li, Jin, Donghui, Liu, Huilin, Fu, Zhongxi, Xu, Qiaohua, Xu, Xin, Zhang, Hao, Xiong, Youping, Long, Huajun, Li, Xianzhi, Zhang, Libo, Qiu, Zhe, Liu, Xin, and Wang, Kang

Published
2018
Full Text
View/download PDF

31. The relationship between sleep duration and obesity risk among school students: a cross-sectional study in Zhejiang, China

Author

Hao Wang, Ruying Hu, Huaidong Du, Bragg Fiona, Jieming Zhong, and Min Yu

Subjects
Sleep duration, Overweight, Obesity, Adolescents, Factors, Nutrition. Foods and food supply, TX341-641, Nutritional diseases. Deficiency diseases, RC620-627
Abstract

Abstract Background Obesity has been identified as a major risk factor for a large number of chronic diseases. Understanding factors related to adolescent obesity is critical for prevention of chronic diseases. The associations between sleep duration and obesity among adolescents in the existing literature are controversial. Our study was designed to determine the prevalence of short sleep duration, and assess the association of sleep duration and obesity, among middle and high school students in Zhejiang, China. Methods 18,403 Students in 442 schools were recruited and surveyed using an anonymous, self-administered questionnaires. Weighted multivariable logistic regression models were used for data analyses. Results The mean (SD) age of the students was 15.9 (1.8) years. 49.7% of students were girls. The mean (SD) height and weight were 166.2 (8.5) cm and 54.6 (11.1) kg, respectively. The overall prevalence of obesity and overweight were 3.4% (95% CI: 3.0–3.8) and 7.8% (95% CI: 7.4–8.3), respectively. The overall prevalence of short sleep duration among students was 66.0% (95% CI: 63.8–68.1), higher among girls than boys (69.8% vs. 62.1%) (P

Published
2018
Full Text
View/download PDF

33. Body composition and risk factors for cardiovascular disease in global multi-ethnic populations

Author

Carter, Jennifer L., primary, Abdullah, Noraidatulakma, additional, Bragg, Fiona, additional, Murad, Nor Azian Abdul, additional, Taylor, Hannah, additional, Fong, Chin Siok, additional, Lacey, Benjamin, additional, Sherliker, Paul, additional, Karpe, Fredrik, additional, Mustafa, Norlaila, additional, Lewington, Sarah, additional, and Jamal, Rahman, additional

Published
2023
Full Text
View/download PDF

37. Diabetes and infectious disease mortality in Mexico City

Author

Bragg, Fiona, primary, Kuri-Morales, Pablo, additional, Berumen, Jaime, additional, Garcilazo-Ávila, Adrián, additional, Gonzáles-Carballo, Carlos, additional, Ramírez-Reyes, Raúl, additional, Santacruz-Benitez, Rogelio, additional, Aguilar-Ramirez, Diego, additional, Gnatiuc Friedrichs, Louisa, additional, Herrington, William G, additional, Hill, Michael, additional, Trichia, Eirini, additional, Wade, Rachel, additional, Collins, Rory, additional, Peto, Richard, additional, Emberson, Jonathan R, additional, Alegre-Diaz, Jesus, additional, and Tapia-Conyer, Roberto, additional

Published
2023
Full Text
View/download PDF

38. Multi-ancestry genome-wide study in >2.5 million individuals reveals heterogeneity in mechanistic pathways of type 2 diabetes and complications

Author

Suzuki, Ken, Hatzikotoulas, Konstantinos, Southam, Lorraine, Taylor, Henry J., Yin, Xianyong, Lorenz, Kim M., Mandla, Ravi, Huerta-Chagoya, Alicia, Rayner, Nigel W., Bocher, Ozvan, Ana Luiza de, S. V. Arruda, Sonehara, Kyuto, Namba, Shinichi, Lee, Simon S. K., Preuss, Michael H., Petty, Lauren E., Schroeder, Philip, Vanderwerff, Brett, Kals, Mart, Bragg, Fiona, Lin, Kuang, Guo, Xiuqing, Zhang, Weihua, Yao, Jie, Kim, Young Jin, Graff, Mariaelisa, Takeuchi, Fumihiko, Nano, Jana, Lamri, Amel, Nakatochi, Masahiro, Moon, Sanghoon, Scott, Robert A., Cook, James P., Lee, Jung-Jin, Pan, Ian, Taliun, Daniel, Parra, Esteban J., Chai, Jin-Fang, Bielak, Lawrence F., Tabara, Yasuharu, Hai, Yang, Thorleifsson, Gudmar, Grarup, Niels, Sofer, Tamar, Wuttke, Matthias, Sarnowski, Chloé, Gieger, Christian, Nousome, Darryl, Trompet, Stella, Kwak, Soo-Heon, Long, Jirong, Sun, Meng, Tong, Lin, Chen, Wei-Min, Nongmaithem, Suraj S., Noordam, Raymond, Lim, Victor J. Y., Tam, Claudia H. T., Joo, Yoonjung Yoonie, Chen, Chien-Hsiun, Raffield, Laura M., Prins, Bram Peter, Nicolas, Aude, Yanek, Lisa R., Chen, Guanjie, Brody, Jennifer A., Kabagambe, Edmond, An, Ping, Xiang, Anny H., Choi, Hyeok Sun, Cade, Brian E., Tan, Jingyi, Alaine Broadaway, K., Williamson, Alice, Kamali, Zoha, Cui, Jinrui, Adair, Linda S., Adeyemo, Adebowale, Aguilar-Salinas, Carlos A., Ahluwalia, Tarunveer S., Anand, Sonia S., Bertoni, Alain, Bork-Jensen, Jette, Brandslund, Ivan, Buchanan, Thomas A., Burant, Charles F., Butterworth, Adam S., Canouil, Mickaël, Chan, Juliana C. N., Chang, Li-Ching, Chee, Miao-Li, Chen, Ji, Chen, Shyh-Huei, Chen, Yuan-Tsong, Chen, Zhengming, Chuang, Lee-Ming, Cushman, Mary, Danesh, John, Das, Swapan K., Janaka de Silva, H., Dedoussis, George, Dimitrov, Latchezar, Doumatey, Ayo P., Du, Shufa, Duan, Qing, Eckardt, Kai-Uwe, Emery, Leslie S., Evans, Daniel S., Evans, Michele K., Fischer, Krista, Floyd, James S., Ford, Ian, Franco, Oscar H., Frayling, Timothy M., Freedman, Barry I., Genter, Pauline, Gerstein, Hertzel C., Giedraitis, Vilmantas, González-Villalpando, Clicerio, González-Villalpando, Maria Elena, Gordon-Larsen, Penny, Gross, Myron, Guare, Lindsay A., Hackinger, Sophie, Han, Sohee, Hattersley, Andrew T., Herder, Christian, Horikoshi, Momoko, Howard, Annie-Green, Hsueh, Willa, Huang, Mengna, Huang, Wei, Hung, Yi-Jen, Hwang, Mi Yeong, Hwu, Chii-Min, Ichihara, Sahoko, Ikram, Mohammad Arfan, Ingelsson, Martin, Islam, Md. Tariqul, Isono, Masato, Jang, Hye-Mi, Jasmine, Farzana, Jiang, Guozhi, Jonas, Jost B., Jørgensen, Torben, Kandeel, Fouad R., Kasturiratne, Anuradhani, Katsuya, Tomohiro, Kaur, Varinderpal, Kawaguchi, Takahisa, Keaton, Jacob M., Kho, Abel N., Khor, Chiea-Chuen, Kibriya, Muhammad G., Kim, Duk-Hwan, Kronenberg, Florian, Kuusisto, Johanna, Läll, Kristi, Lange, Leslie A., Lee, Kyung Min, Lee, Myung-Shik, Lee, Nanette R., Leong, Aaron, Li, Liming, Li, Yun, Li-Gao, Ruifang, Lithgart, Symen, Lindgren, Cecilia M., Linneberg, Allan, Liu, Ching-Ti, Liu, Jianjun, Locke, Adam E., Louie, Tin, Luan, Jian’an, Luk, Andrea O., Luo, Xi, Lv, Jun, Lynch, Julie A., Lyssenko, Valeriya, Maeda, Shiro, Mamakou, Vasiliki, Mansuri, Sohail Rafik, Matsuda, Koichi, Meitinger, Thomas, Metspalu, Andres, Mo, Huan, Morris, Andrew D., Nadler, Jerry L., Nalls, Michael A., Nayak, Uma, Ntalla, Ioanna, Okada, Yukinori, Orozco, Lorena, Patel, Sanjay R., Patil, Snehal, Pei, Pei, Pereira, Mark A, Peters, Annette, Pirie, Fraser J., Polikowsky, Hannah G., Porneala, Bianca, Prasad, Gauri, Rasmussen-Torvik, Laura J., Reiner, Alexander P., Roden, Michael, Rohde, Rebecca, Roll, Katheryn, Sabanayagam, Charumathi, Sandow, Kevin, Sankareswaran, Alagu, Sattar, Naveed, Schönherr, Sebastian, Shahriar, Mohammad, Shen, Botong, Shi, Jinxiu, Shin, Dong Mun, Shojima, Nobuhiro, Smith, Jennifer A., So, Wing Yee, Stančáková, Alena, Steinthorsdottir, Valgerdur, Stilp, Adrienne M., Strauch, Konstantin, Taylor, Kent D., Thorand, Barbara, Thorsteinsdottir, Unnur, Tomlinson, Brian, Tran, Tam C., Tsai, Fuu-Jen, Tuomilehto, Jaakko, Tusie-Luna, Teresa, Udler, Miriam S., Valladares-Salgado, Adan, van Dam, Rob M., van Klinken, Jan B., Varma, Rohit, Wacher-Rodarte, Niels, Wheeler, Eleanor, Wickremasinghe, Ananda R., van Dijk, Ko Willems, Witte, Daniel R., Yajnik, Chittaranjan S., Yamamoto, Ken, Yamamoto, Kenichi, Yoon, Kyungheon, Yu, Canqing, Yuan, Jian-Min, Yusuf, Salim, Zawistowski, Matthew, Zhang, Liang, Zheng, Wei, Project, Biobank Japan, BioBank, Penn Medicine, Center, Regeneron Genetics, Consortium, eMERGE, Raffel, Leslie J, Igase, Michiya, Ipp, Eli, Redline, Susan, Cho, Yoon Shin, Lind, Lars, Province, Michael A., Fornage, Myriam, Hanis, Craig L., Ingelsson, Erik, Zonderman, Alan B., Psaty, Bruce M., Wang, Ya-Xing, Rotimi, Charles N., Becker, Diane M., Matsuda, Fumihiko, Liu, Yongmei, Yokota, Mitsuhiro, Kardia, Sharon L. R., Peyser, Patricia A., Pankow, James S., Engert, James C., Bonnefond, Amélie, Froguel, Philippe, Wilson, James G., Sheu, Wayne H. H., Wu, Jer-Yuarn, Geoffrey Hayes, M., Ma, Ronald C. W., Wong, Tien-Yin, Mook-Kanamori, Dennis O., Tuomi, Tiinamaija, Chandak, Giriraj R., Collins, Francis S., Bharadwaj, Dwaipayan, Paré, Guillaume, Sale, Michèle M., Ahsan, Habibul, Motala, Ayesha A., Shu, Xiao-Ou, Park, Kyong-Soo, Jukema, J Wouter, Cruz, Miguel, Chen, Yii-Der Ida, Rich, Stephen S., McKean-Cowdin, Roberta, Grallert, Harald, Cheng, Ching-Yu, Ghanbari, Mohsen, Tai, E-Shyong, Dupuis, Josee, Kato, Norihiro, Laakso, Markku, Köttgen, Anna, Koh, Woon-Puay, Bowden, Donald W., Palmer, Colin N. A., Kooner, Jaspal S., Kooperberg, Charles, Liu, Simin, North, Kari E., Saleheen, Danish, Hansen, Torben, Pedersen, Oluf, Wareham, Nicholas J., Lee, Juyoung, Kim, Bong-Jo, Millwood, Iona Y., Walters, Robin G., Stefansson, Kari, Goodarzi, Mark O., Mohlke, Karen L., Langenberg, Claudia, Haiman, Christopher A., Loos, Ruth J. F., Florez, Jose C., Rader, Daniel J., Ritchie, Marylyn D., Zöllner, Sebastian, Mägi, Reedik, Denny, Joshua C., Yamauchi, Toshimasa, Kadowaki, Takashi, Chambers, John C., Ng, Maggie C. Y., Sim, Xueling, Below, Jennifer E., Tsao, Philip S., Chang, Kyong-Mi, McCarthy, Mark I., Meigs, James B., Mahajan, Anubha, Spracklen, Cassandra N., Mercader, Josep M., Boehnke, Michael, Rotter, Jerome I., Vujkovic, Marijana, Voight, Benjamin F., Morris, Andrew P., and Zeggini, Eleftheria

Subjects
Article
Abstract

Type 2 diabetes (T2D) is a heterogeneous disease that develops through diverse pathophysiological processes. To characterise the genetic contribution to these processes across ancestry groups, we aggregate genome-wide association study (GWAS) data from 2,535,601 individuals (39.7% non-European ancestry), including 428,452 T2D cases. We identify 1,289 independent association signals at genome-wide significance (P

Published
2023

41. Risks of Stroke and Heart Disease Following Hysterectomy and Oophorectomy in Chinese Premenopausal Women

Author

Poorthuis, Michiel H.F., primary, Yao, Pang, additional, Chen, Yiping, additional, Guo, Yu, additional, Shi, Liya, additional, Li, Liming, additional, Chen, Zhengming, additional, Clarke, Robert, additional, Yang, Ling, additional, Chen, Junshi, additional, Collins, Rory, additional, Lv, Jun, additional, Peto, Richard, additional, Walters, Robin, additional, Avery, Daniel, additional, Bennett, Derrick, additional, Boxall, Ruth, additional, Bragg, Fiona, additional, Chang, Yumei, additional, Du, Huaidong, additional, Gilbert, Simon, additional, Hacker, Alex, additional, Holmes, Michael, additional, Kartsonaki, Christiana, additional, Kerosi, Rene, additional, Lin, Kuang, additional, Millwood, Iona, additional, Nie, Qunhua, additional, Ryder, Paul, additional, Sansome, Sam, additional, Schmidt, Dan, additional, Sohoni, Rajani, additional, Turnbull, Iain, additional, Wang, Lin, additional, Wright, Neil, additional, Yang, Xiaoming, additional, Han, Xiao, additional, Hou, Can, additional, Jing, Biao, additional, Liu, Chao, additional, Pei, Pei, additional, Tan, Yunlong, additional, Yu, Canqing, additional, Pang, Zengchang, additional, Gao, Ruqin, additional, Li, Shanpeng, additional, Wang, Shaojie, additional, Liu, Yongmei, additional, Du, Ranran, additional, Zang, Yajing, additional, Cheng, Liang, additional, Tian, Xiaocao, additional, Zhang, Hua, additional, Zhai, Yaoming, additional, Ning, Feng, additional, Sun, Xiaohui, additional, Li, Feifei, additional, Lv, Silu, additional, Wang, Junzheng, additional, Hou, Wei, additional, Zeng, Mingyuan, additional, Jiang, Ge, additional, Zhou, Xue, additional, Yang, Liqiu, additional, He, Hui, additional, Yu, Bo, additional, Li, Yanjie, additional, Xu, Qinai, additional, Kang, Quan, additional, Guo, Ziyan, additional, Wang, Dan, additional, Hu, Ximin, additional, Wang, Hongmei, additional, Chen, Jinyan, additional, Fu, Yan, additional, Fu, Zhenwang, additional, Wang, Xiaohuan, additional, Weng, Min, additional, Guo, Zhendong, additional, Wu, Shukuan, additional, Li, Yilei, additional, Li, Huimei, additional, Fu, Zhifang, additional, Wu, Ming, additional, Zhou, Yonglin, additional, Zhou, Jinyi, additional, Tao, Ran, additional, Yang, Jie, additional, Su, Jian, additional, Liu, Fang, additional, Zhang, Jun, additional, Hu, Yihe, additional, Lu, Yan, additional, Ma, Liangcai, additional, Tang, Aiyu, additional, Zhang, Shuo, additional, Jin, Jianrong, additional, Liu, Jingchao, additional, Tang, Zhenzhu, additional, Chen, Naying, additional, Huang, Ying, additional, Li, Mingqiang, additional, Meng, Jinhuai, additional, Pan, Rong, additional, Jiang, Qilian, additional, Lan, Jian, additional, Liu, Yun, additional, Wei, Liuping, additional, Zhou, Liyuan, additional, Chen, Ningyu, additional, Wang, Ping, additional, Meng, Fanwen, additional, Qin, Yulu, additional, Wang, Sisi, additional, Wu, Xianping, additional, Zhang, Ningmei, additional, Chen, Xiaofang, additional, Zhou, Weiwei, additional, Luo, Guojin, additional, Li, Jianguo, additional, Zhong, Xunfu, additional, Liu, Jiaqiu, additional, Sun, Qiang, additional, Ge, Pengfei, additional, Ren, Xiaolan, additional, Dong, Caixia, additional, Zhang, Hui, additional, Mao, Enke, additional, Wang, Xiaoping, additional, Wang, Tao, additional, Zhang, Xi, additional, Zhang, Ding, additional, Zhou, Gang, additional, Feng, Shixian, additional, Chang, Liang, additional, Fan, Lei, additional, Gao, Yulian, additional, He, Tianyou, additional, Sun, Huarong, additional, He, Pan, additional, Hu, Chen, additional, Zhang, Xukui, additional, Wu, Huifang, additional, Yu, Min, additional, Hu, Ruying, additional, Wang, Hao, additional, Qian, Yijian, additional, Wang, Chunmei, additional, Xie, Kaixu, additional, Chen, Lingli, additional, Zhang, Yidan, additional, Pan, Dongxia, additional, Gu, Qijun, additional, Huang, Yuelong, additional, Chen, Biyun, additional, Yin, Li, additional, Liu, Huilin, additional, Fu, Zhongxi, additional, Xu, Qiaohua, additional, Xu, Xin, additional, Zhang, Hao, additional, Long, Huajun, additional, Li, Xianzhi, additional, Zhang, Libo, additional, and Qiu, Zhe, additional

Published
2022
Full Text
View/download PDF

43. Evaluation of type 2 diabetes genetic risk variants in Chinese adults: findings from 93,000 individuals from the China Kadoorie Biobank

Author

Gan, Wei, Walters, Robin G., Holmes, Michael V., Bragg, Fiona, Millwood, Iona Y., Banasik, Karina, Chen, Yiping, Du, Huaidong, Iona, Andri, Mahajan, Anubha, Yang, Ling, Bian, Zheng, Guo, Yu, Clarke, Robert J., Li, Liming, McCarthy, Mark I., Chen, Zhengming, and on behalf of the China Kadoorie Biobank Collaborative Group

Published
2016
Full Text
View/download PDF

44. Body mass index and COVID-19 mortality: prospective study of 120 000 Mexican adults

Author

Alegre-Díaz, Jesus, primary, Friedrichs, Louisa G, additional, Ramirez-Reyes, Raul, additional, Wade, Rachel, additional, Bragg, Fiona, additional, Herrington, William G, additional, Clarke, Robert, additional, Peto, Richard, additional, Collins, Rory, additional, Kuri-Morales, Pablo, additional, Emberson, Jonathan R, additional, and Tapia-Conyer, Roberto, additional

Published
2022
Full Text
View/download PDF

48. Adiposity, metabolomic biomarkers, and risk of nonalcoholic fatty liver disease: a case-cohort study

Author

Pang, Yuanjie, primary, Kartsonaki, Christiana, additional, Lv, Jun, additional, Millwood, Iona Y, additional, Fairhurst-Hunter, Zammy, additional, Turnbull, Iain, additional, Bragg, Fiona, additional, Hill, Michael R, additional, Yu, Canqing, additional, Guo, Yu, additional, Chen, Yiping, additional, Yang, Ling, additional, Clarke, Robert, additional, Walters, Robin G, additional, Wu, Ming, additional, Chen, Junshi, additional, Li, Liming, additional, Chen, Zhengming, additional, and Holmes, Michael V, additional

Published
2022
Full Text
View/download PDF

49. Additional file 1 of Association between frequency of dairy product consumption and hypertension: a cross-sectional study in Zhejiang Province, China

Author

Wang, Hao, Chen, Lingli, Cao, Yuan, Xie, Kaixu, Wang, Chunmei, Pei, Pei, Guo, Yu, Bragg, Fiona, Yu, Min, Chen, Zhengming, and Li, Liming

Abstract

Additional file 1. Table S1. Unadjusted and adjusted β coefficients for SBP and DBP associated with frequency of dairy product consumption among adults without self-reported physician-diagnosed hypertension in Zhejiang. Table S2. Unadjusted and adjusted prevalence ratios for hypertension associated with frequency of dairy product consumption among adults in Zhejiang. Table S3. Adjusted odds ratios for hypertension associated with consuming dairy products weekly vs. never according to participant characteristics.

Published
2022
Full Text
View/download PDF

50. Additional file 1 of Predictive value of circulating NMR metabolic biomarkers for type 2 diabetes risk in the UK Biobank study

Author

Bragg, Fiona, Trichia, Eirini, Aguilar-Ramirez, Diego, Bešević, Jelena, Lewington, Sarah, and Emberson, Jonathan

Abstract

Additional file 1: Predictive value of circulating NMR metabolic biomarkers for type 2 diabetes risk in the UK Biobank study. Table S1. Distribution of metabolic biomarkers and their associations with incident type 2 diabetes among 50,519 participants in the association analyses population. Table S2. Diagnosis and medication codes for assessment of type 2 diabetes status in primary and secondary healthcare and death registry records and UK Biobank verbal interview. Table S3. Baseline characteristics of participants with and without NMR-metabolomics profiling. Table S4. Baseline characteristics of 50,519 participants in the association analyses population by incident type 2 diabetes status. Table S5. Associations of the first 11 metabolic biomarker principal components with risk of incident type 2 diabetes among 50,519 participants in the association analyses population. Table S6. Regression models for risk of incident type 2 diabetes among 65,684 participants in the risk prediction population. Table S7. Performance of risk prediction models for incident type 2 diabetes among 13,695 participants taking lipid-lowering medication at recruitment. Table S8. Performance of risk prediction models including waist-to-hip ratio for incident type 2 diabetes among 65,684 participants in the risk prediction population. Table S9. Performance of risk prediction models for incident type 2 diabetes incorporating co-variates, where relevant, as continuous variables among 65,684 participants in the risk prediction population. Fig. S1. Participant exclusions to derive risk prediction and association analyses populations. Fig. S2. Cross-correlations of metabolic biomarkers. Fig. S3. Associations of metabolic biomarkers with risk of incident type 2 diabetes among 50,519 participants in the association analyses population. Fig. S4. Associations of metabolic biomarkers with risk of incident type 2 diabetes by sex among 50,519 participants in the association analyses population. Fig. S5. Associations of metabolic biomarkers with risk of incident type 2 diabetes by age among 50,519 participants in the association analyses population. Fig. S6. Associations of metabolic biomarkers with risk of incident type 2 diabetes excluding the first three years of follow−up in the association analyses population. Fig. S7. Importance of the first 20 metabolic biomarker principal components. Fig. S8. Characterisation of the first 11 metabolic biomarker principal components among 65,684 participants in the risk prediction population. Fig. S9. Comparison of biomarkers measured by NMR and routine clinical chemistry assays among 65,684 participants in the risk prediction population.

Published
2022
Full Text
View/download PDF

Catalog

Books, media, physical & digital resources
See catalog results