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186 results on '"Coleman, Ruth L."'

2. 11β-HSD1 inhibition in men mitigates prednisolone-induced adverse effects in a proof-of-concept randomised double-blind placebo-controlled trial

Author

Othonos, Nantia, Pofi, Riccardo, Arvaniti, Anastasia, White, Sarah, Bonaventura, Ilaria, Nikolaou, Nikolaos, Moolla, Ahmad, Marjot, Thomas, Stimson, Roland H., van Beek, André P., van Faassen, Martijn, Isidori, Andrea M., Bateman, Elizabeth, Sadler, Ross, Karpe, Fredrik, Stewart, Paul M., Webster, Craig, Duffy, Joanne, Eastell, Richard, Gossiel, Fatma, Cornfield, Thomas, Hodson, Leanne, Jane Escott, K., Whittaker, Andrew, Kirik, Ufuk, Coleman, Ruth L., Scott, Charles A. B., Milton, Joanne E., Agbaje, Olorunsola, Holman, Rury R., and Tomlinson, Jeremy W.

Published
2023
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4. Estimated glomerular filtration rate slope and risk of primary and secondary major adverse cardiovascular events and heart failure hospitalization in people with type 2 diabetes: An analysis of the EXSCEL trial.

Author

Oulhaj, Abderrahim, Aziz, Faisal, Suliman, Abubaker, Eller, Kathrin, Bentoumi, Rachid, Buse, John B., Al Mahmeed, Wael, von Lewinski, Dirk, Coleman, Ruth L., Holman, Rury R., and Sourij, Harald

Subjects
MAJOR adverse cardiovascular events, TYPE 2 diabetes, SYSTOLIC blood pressure, GLOMERULAR filtration rate, PEOPLE with diabetes
Abstract

Aim: The decline in estimated glomerular filtration rate (eGFR), a significant predictor of cardiovascular disease (CVD), occurs heterogeneously in people with diabetes because of various risk factors. We investigated the role of eGFR decline in predicting CVD events in people with type 2 diabetes in both primary and secondary CVD prevention settings. Materials and Methods: Bayesian joint modelling of repeated measures of eGFR and time to CVD event was applied to the Exenatide Study of Cardiovascular Event Lowering (EXSCEL) trial to examine the association between the eGFR slope and the incidence of major adverse CV event/hospitalization for heart failure (MACE/hHF) (non‐fatal myocardial infarction, non‐fatal stroke, CV death, or hospitalization for heart failure). The analysis was adjusted for age, sex, smoking, systolic blood pressure, baseline eGFR, antihypertensive and lipid‐lowering medication, diabetes duration, atrial fibrillation, high‐density cholesterol, total cholesterol, HbA1c and treatment allocation (once‐weekly exenatide or placebo). Results: Data from 11 101 trial participants with (n = 7942) and without (n = 3159) previous history of CVD were analysed. The mean ± SD eGFR slope per year in participants without and with previous CVD was −0.68 ± 1.67 and −1.03 ± 2.13 mL/min/1.73 m2, respectively. The 5‐year MACE/hHF incidences were 7.5% (95% CI 6.2, 8.8) and 20% (95% CI 19, 22), respectively. The 1‐SD decrease in the eGFR slope was associated with increased MACE/hHF risks of 48% (HR 1.48, 95% CI 1.12, 1.98, p = 0.007) and 33% (HR 1.33, 95% CI 1.18,1.51, p < 0.001) in participants without and with previous CVD, respectively. Conclusions: eGFR trajectories over time significantly predict incident MACE/hHF events in people with type 2 diabetes with and without existing CVD, with a higher hazard ratio for MACE/hHF in the latter group. [ABSTRACT FROM AUTHOR]

Published
2024
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9. Orthostatic Hypotension, Hypertension Treatment, and Cardiovascular Disease

Author

Juraschek, Stephen P., primary, Hu, Jiun-Ruey, additional, Cluett, Jennifer L., additional, Ishak, Anthony M., additional, Mita, Carol, additional, Lipsitz, Lewis A., additional, Appel, Lawrence J., additional, Beckett, Nigel S., additional, Coleman, Ruth L., additional, Cushman, William C., additional, Davis, Barry R., additional, Grandits, Greg, additional, Holman, Rury R., additional, Miller, Edgar R., additional, Peters, Ruth, additional, Staessen, Jan A., additional, Taylor, Addison A., additional, Thijs, Lutgarde, additional, Wright, Jackson T., additional, and Mukamal, Kenneth J., additional

Published
2023
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10. Effects of acarbose on cardiovascular and diabetes outcomes in patients with coronary heart disease and impaired glucose tolerance (ACE): a randomised, double-blind, placebo-controlled trial

Author

Keenan, Joanne, Milton, Joanne, Doran, Zoe, Bray, Chris, Rouleau, Jean L, Collier, Jane, Pocock, Stuart, Standl, Eberhard, Swedberg, Karl, Weng, Jianping, Zhao, Dong, Petrie, Mark C, Connolly, Eugene, Jhund, Pardeep, MacDonald, Michael, Myles, Rachel C, Bai, Rong, Li, Jing, Liu, Zhaoping, Liu, Zhenyu, Peng, Dantao, Tong, Qiguang, Wang, Chunxue, Yan, Xiaowei, Zhang, Yuqing, Zhou, Jingmin, Sattar, Naveed, Fisher, Miles, Petrie, John R, Bethel, M Angelyn, Xu, Wen, Hearn, Sarah, Kappai, Anurag, Su, Shu-Yi, Liyanage, Winitha, Paul, Sanjoy, Pozzi, Emanuela, Ring, Arne, Athwal, Rajbir, Batra, Priyanka, Ferch, Andrea, Groves, Natasha, Kennedy, Irene, Nawalaniec, Olga, Patel, Yash, Roberts, Rachel, Rush, Victoria, Starrett, Jayne, Tang, Jennifer, Bi, Jing, Jiang, Zhe, Wei, Hua, Wei, Xiaoshuai, Zhang, Xuan, Yin, Jun, Sun, Yu, Hu, Rong, Liu, Yang, Long, Jianjing, Long, Yuefeng, Qiao, Guofang, Qiao, Haoyi, Sun, Xiaochun, Zhang, Yucheng, Zhou, Jing, Wang, Bangning, Chen, Bin, Deng, Lili, Han, Xiaoning, Hu, Taohong, Hua, Qi, Huo, Yanming, Li, Hongmei, Li, Hongwei, Liu, Lihua, Lu, Juming, Ma, Changsheng, Peng, Jianjun, Pi, Lin, Wang, Bin, Wei, Guanglin, Yang, Ming, Zhang, Shuyang, Zhang, Likun, Zhao, Xia, Zhou, Yujie, Shi, Libin, Wang, Mingsheng, Wu, Lirong, Han, Lei, Liao, Ronghong, Ran, Boli, She, Qiang, Tan, Jiancong, Xia, Mei, Yang, Chengming, Chen, Lianglong, Xiong, Shangquan, Yu, Ling, Pu, Xiaodong, Wang, Yan, Xie, Qiang, Chen, Cibin, Chen, Jiyan, Dong, Yugang, Wu, Zhaohui, Yuan, Yong, Zhou, Wanxing, Zhou, Shuxian, Chen, Xiaochao, Wu, Chun, Zhang, Aidong, Li, Zicheng, Lai, Shayi, Yang, Jin, Wei, Jinru, Kuang, Riyu, Zhao, Zilin, Zhong, Guoqiang, Cao, Xufen, Hao, Yuming, Liu, Gang, Wang, Dongmei, Fang, Hui, Kong, Lingjun, Li, Haitao, Wang, Changqing, Wang, Li'na, Li, Xueqi, Dong, Pingshuan, Zhang, Shouyan, Liu, Xincan, Zhao, Yulan, Liu, Hengliang, Gu, Ye, Liao, Yuhua, Su, Xi, Wang, Daowen, Wang, Hairong, Yang, Bo, Guo, Ying, Ouyang, Ding'an, Yang, Tianlun, Zhang, Yumin, Han, Yajun, Lin, Xuefeng, Zhao, Ruiping, Man, Ronghai, Bian, Rongwen, Biao, Xu, Hasimu, Buaijiaer, Jin, Hui, Liu, Ping, Yu, Jiangyi, Zhang, Hang, Xu, Chongli, Guo, Yan, Lv, Ke, Tao, Yijia, Xu, Xin, Yang, Zhenyu, Li, Dongye, Qi, Chunmei, Zhang, Guohui, Gu, Xiang, Hong, Lang, Hu, Ling, Li, Juxiang, Yang, Ping, Liu, Bin, Wang, Gang, Lin, Hailong, Liu, Jun, Zhang, Shuying, Han, Ping, Jin, Yuanzhe, Li, Ling, Li, Zhanquan, Luan, Hong, Song, Mei, Xue, Li, Hua, Yu, Liu, Dongwu, Yuan, Zuyi, Ye, Jixian, Gao, Feng, Feng, Jinhua, Wang, A'li, Ye, Shengming, Li, Xiaoyan, Su, Guohai, Zhang, Shufang, Hou, Zishan, Jiang, Wenbin, Zhou, Changyong, Wang, Yanping, Qi, Wenbo, Bao, Xiaomei, Feng, Bo, Gong, Hui, Gu, Shuiming, Gu, Mingjun, Guo, Xingui, He, Ben, Huang, Ying, Jiang, Jinfa, Jiang, Yifeng, Jin, Huigen, Li, Yuehua, Liu, Qiliang, Lu, Guoping, Miao, Peizhi, Qin, Yongwen, Wang, Yuanming, Wu, Shiyao, Xu, Yawei, Ma, Jin, Chen, Xiaoping, Liu, Xiumin, Tang, Jianing, Wang, Jingping, Tao, Jianhong, Zhang, Jun, Zhang, Tingjie, Li, Decai, Du, Xinping, Jiang, Tiemin, Lin, Jingna, Lu, Chengzhi, Ma, Hongjun, Gao, Bo, Guo, Xukun, Li, Tong, Zheng, Shaoxiong, Li, Zhongcheng, Zhao, Shuwu, Qiu, Qiang, Li, Kaili, Liu, Junming, Tang, Baopeng, Yuan, Zhanjun, Zhou, Jianhua, Bai, Wenwei, Guo, Tao, Zhang, Ge, Zhang, Hong, Hao, Yinglu, Fu, Guosheng, Tang, Lijiang, Chen, Jialun, Holman, Rury R, Coleman, Ruth L, Chan, Juliana C N, Chiasson, Jean-Louis, Feng, Huimei, Ge, Junbo, Gerstein, Hertzel C, Gray, Richard, Huo, Yong, Lang, Zhihui, McMurray, John J, Rydén, Lars, Schröder, Stefan, Sun, Yihong, Theodorakis, Michael J, Tendera, Michal, Tucker, Lynne, Tuomilehto, Jaakko, Wei, Yidong, Yang, Wenying, Wang, Duolao, Hu, Dayi, and Pan, Changyu

Published
2017
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12. Comparison of medical resources and costs among patients with coronary heart disease and impaired glucose tolerance in the Acarbose Cardiovascular Evaluation trial.

Author

Morrow, Liam Mc, Becker, Frauke, Coleman, Ruth L., Gerstein, Hertzel C., Rydén, Lars, Schöder, Stefan, Gray, Alastair M., Leal, Jose, and Holman, Rury R.

Subjects
CORONARY disease, CARDIAC patients, ACARBOSE, MEDICAL care costs, QUALITY of life
Abstract

Background: The Acarbose Cardiovascular Evaluation (ACE) trial (ISRCTN91899513) evaluated the alpha‐glucosidase inhibitor acarbose, compared with placebo, in 6522 patients with coronary heart disease and impaired glucose tolerance in China and showed a reduced incidence of diabetes. We assessed the within‐trial medical resource use and costs, and quality‐adjusted life years (QALYs). Methods: Resource use data were collected prospectively within the ACE trial. Hospitalizations, medications, and outpatient visits were valued using Chinese unit costs. Medication use was measured in drug days, with cardiovascular and diabetes drugs summed across the trial by participant. Health‐related quality of life was captured using the EuroQol‐5 Dimension‐3 Level questionnaire. Regression analyses were used to compare resource use, costs, and QALYs, accounting for regional variation. Costs and QALYs were discounted at 3% yearly. Results: Hospitalizations were 6% higher in the acarbose arm during the trial (rate ratio 1.06, p =.009), but there were no significant differences in total inpatient days (rate ratio 1.04, p =.30). Total costs per participant, including study drug, were significantly higher for acarbose (¥ [Yuan] 56 480, £6213), compared with placebo (¥48 079, £5289; mean ratio 1.18, p < 0.001). QALYs reported by participants in the acarbose arm (3.96 QALYs) were marginally higher than in the placebo arm (3.95 QALYs), but the difference was not statistically significant (0.01 QALYs; p =.58). Conclusions: Acarbose, compared with placebo, participants cost more due to study drug costs and reported no statistically significant difference in QALYs. These higher within‐trial costs could potentially be offset in future by savings from the acarbose‐related lower incidence of diabetes. [ABSTRACT FROM AUTHOR]

Published
2024
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15. Abstract P220: Effects Of Orthostatic Hypotension On Intensive Blood Pressure Treatment With Respect To All-cause Mortality: An Individual-level Meta-analysis

Author

Juraschek, Stephen P, primary, Hu, Jiun-Ruey, additional, Cluett, Jennifer L, additional, Ishak, Anthony M, additional, Mita, Carol, additional, Lipsitz, Lewis A, additional, Appel, Lawrence J, additional, Beckett, Nigel S, additional, Coleman, Ruth L, additional, Cushman, William C, additional, Davis, Barry R, additional, Grandits, Greg, additional, Holman, Rury R, additional, Miller, Edgar R, additional, Peters, Ruth, additional, Staessen, Jan A, additional, Taylor, Addison A, additional, Wright, Jackson T, additional, and Mukamal, Kenneth J, additional

Published
2022
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16. Association Between More Intensive vs Less Intensive Blood Pressure Lowering and Risk of Mortality in Chronic Kidney Disease Stages 3 to 5: A Systematic Review and Meta-analysis

Author

Malhotra, Rakesh, Nguyen, Hoang Anh, Benavente, Oscar, Mete, Mihriye, Howard, Barbara V., Mant, Jonathan, Odden, Michelle C., Peralta, Carmen A., Cheung, Alfred K., Nadkarni, Girish N., Coleman, Ruth L., Holman, Rury R., Zanchetti, Alberto, Peters, Ruth, Beckett, Nigel, Staessen, Jan A., and Ix, Joachim H.

Published
2017
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17. Additional file 1 of Risk prediction models for incident type 2 diabetes in Chinese people with intermediate hyperglycemia: a systematic literature review and external validation study

Author

Xu, Shishi, Coleman, Ruth L., Wan, Qin, Gu, Yeqing, Meng, Ge, Song, Kun, Shi, Zumin, Xie, Qian, Tuomilehto, Jaakko, Holman, Rury R., Niu, Kaijun, and Tong, Nanwei

Abstract

Additional file 1. Supplementary Text. Figure S1. The literature review process of searching Asian-derived type 2 diabetes risk prediction models. Figure S2. Flowchart of the study population selection process of the ACE, Luzhou, and TCLSIH cohorts. Table S1. PRISMA checklist for reporting systematic review. Table S2. Checklist for critical appraisal and data extraction for systemic review of prediction modelling studies (CHARMS). Table S3. The performance of the included BASIC and EXTENDED models reported in their original studies. Table S4. Predictors included in the BASIC models (N = 21). Table S5. Predictors included in the EXTENDED models (N = 46). Table S6. Baseline characteristics of the participants with intermediate hyperglycemia at baseline of the ACE, Luzhou, and TCLSIH cohorts. Table S7. The validation results of the included BASIC models in intermediate hyperglycemia participants of the ACE, Luzhou, and TCLSIH cohorts. Table S8. The validation results of the included EXTENDED models in intermediate hyperglycemia participants of the ACE, Luzhou, and TCLSIH cohorts. Table S9. The validation results of the included BASIC models in non-diabetic participants of the Luzhou and TCLSIH cohorts. Table S10. The validation results of the included EXTENDED models in non-diabetic participants of the Luzhou and TCLSIH cohorts. Table S11. The validation results of the included BASIC models in intermediate hyperglycemia participants of the ACE, Luzhou, and TCLSIH cohorts when using complete cases for analysis. Table S12. The validation results of the included EXTENDED models in intermediate hyperglycemia participants of the ACE, Luzhou, and TCLSIH cohorts when using complete cases for analysis.

Published
2022
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22. Effects of Intensive Blood Pressure Treatment on Orthostatic Hypotension

Author

Juraschek, Stephen P., primary, Hu, Jiun-Ruey, additional, Cluett, Jennifer L., additional, Ishak, Anthony, additional, Mita, Carol, additional, Lipsitz, Lewis A., additional, Appel, Lawrence J., additional, Beckett, Nigel S., additional, Coleman, Ruth L., additional, Cushman, William C., additional, Davis, Barry R., additional, Grandits, Greg, additional, Holman, Rury R., additional, Miller, Edgar R., additional, Peters, Ruth, additional, Staessen, Jan A., additional, Taylor, Addison A., additional, Thijs, Lutgarde, additional, Wright, Jackson T., additional, and Mukamal, Kenneth J., additional

Published
2021
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24. Abstract MP36: Effects Of Intensive Blood Pressure Treatment On Orthostatic Hypotension: An Individual-level Meta-analysis

Author

Juraschek, Stephen P, primary, Hu, Jiun-Ruey, additional, Cluett, Jennifer, additional, Ishak, Anthony, additional, Mita, Carol, additional, Lipsitz, Lewis, additional, Appel, Lawrence J, additional, Beckett, Nigel, additional, Coleman, Ruth L, additional, CUSHMAN, WILLIAM, additional, Davis, Barry R, additional, Grandits, Greg, additional, Holman, Rury R, additional, Miller, Edgar R, additional, Peters, Ruth, additional, Staessen, Jan A, additional, Taylor, Addison, additional, Thijs, Lutgarde, additional, Wright, Jackson T, additional, and Mukamal, Kenneth J, additional

Published
2020
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36. Health selection into neighborhoods among patients enrolled in a clinical trial

Author

Massachusetts Institute of Technology. Department of Urban Studies and Planning, Arcaya, Mariana Clair, Alvarez, Mariajose, Coleman, Ruth L., Razak, Fahad, Holman, Rury R., Massachusetts Institute of Technology. Department of Urban Studies and Planning, Arcaya, Mariana Clair, Alvarez, Mariajose, Coleman, Ruth L., Razak, Fahad, and Holman, Rury R.

Abstract

Health selection into neighborhoods may contribute to geographic health disparities. We demonstrate the potential for clinical trial data to help clarify the causal role of health on locational attainment. We used data from the 20-year United Kingdom Prospective Diabetes Study (UKPDS) to explore whether random assignment to intensive blood-glucose control therapy, which improved long-term health outcomes after median 10 years follow-up, subsequently affected what neighborhoods patients lived in. We extracted postcode-level deprivation indices for the 2710 surviving participants of UKPDS living in England at study end in 1996/1997. We observed small neighborhood advantages in the intensive versus conventional therapy group, although these differences were not statistically significant. This analysis failed to show conclusive evidence of health selection into neighborhoods, but data suggest the hypothesis may be worthy of exploration in other clinical trials or in a meta-analysis. Keywords: Neighborhoods, Self-selection, Health, Equity, Socioeconomic status

Published
2019

37. Historical HbA1c Values May Explain the Type 2 Diabetes Legacy Effect: UKPDS 88.

Author

Lind, Marcus, Imberg, Henrik, Coleman, Ruth L., Nerman, Olle, and Holman, Rury R.

Abstract

Objective: Type 2 diabetes all-cause mortality (ACM) and myocardial infarction (MI) glycemic legacy effects have not been explained. We examined their relationships with prior individual HbA1c values and explored the potential impact of instituting earlier, compared with delayed, glucose-lowering therapy.Research Design and Methods: Twenty-year ACM and MI hazard functions were estimated from diagnosis of type 2 diabetes in 3,802 UK Prospective Diabetes Study participants. Impact of HbA1c values over time was analyzed by weighting them according to their influence on downstream ACM and MI risks.Results: Hazard ratios for a one percentage unit higher HbA1c for ACM were 1.08 (95% CI 1.07-1.09), 1.18 (1.15-1.21), and 1.36 (1.30-1.42) at 5, 10, and 20 years, respectively, and for MI was 1.13 (1.11-1.15) at 5 years, increasing to 1.31 (1.25-1.36) at 20 years. Imposing a one percentage unit lower HbA1c from diagnosis generated an 18.8% (95% CI 21.1-16.0) ACM risk reduction 10-15 years later, whereas delaying this reduction until 10 years after diagnosis showed a sevenfold lower 2.7% (3.1-2.3) risk reduction. Corresponding MI risk reductions were 19.7% (22.4-16.5) when lowering HbA1c at diagnosis, and threefold lower 6.5% (7.4-5.3%) when imposed 10 years later.Conclusions: The glycemic legacy effects seen in type 2 diabetes are explained largely by historical HbA1c values having a greater impact than recent values on clinical outcomes. Early detection of diabetes and intensive glucose control from the time of diagnosis is essential to maximize reduction of the long-term risk of glycemic complications. [ABSTRACT FROM AUTHOR]

Published
2021
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44. Impact of Acarbose on Incident Diabetes and Regression to Normoglycemia in People With Coronary Heart Disease and Impaired Glucose Tolerance: Insights From the ACE Trial.

Author

Gerstein, Hertzel C., Coleman, Ruth L., Scott, Charles A.B., Xu, Shishi, Tuomilehto, Jaakko, Rydén, Lars, Holman, Rury R., and ACE Study Group

Subjects
*CORONARY disease, *BLOOD sugar, *ACARBOSE, *GLUCOSE tolerance tests, *CHINESE people, *HYPERGLYCEMIA, *BLOOD sugar analysis, *DIABETES prevention, *GLUCOSE intolerance, *DISEASE progression, *RESEARCH, *RESEARCH methodology, *DIABETES, *DISEASE incidence, *MEDICAL cooperation, *EVALUATION research, *COMPARATIVE studies, *GLYCOSIDASES, *BLIND experiment, *ANGIOTENSIN receptors, *PREDIABETIC state, *LONGITUDINAL method, *CHEMICAL inhibitors, *DISEASE complications
Abstract

Objective: We examined the impact of acarbose, an α-glucosidase inhibitor, on incident diabetes and regression to normoglycemia in 6,522 Acarbose Cardiovascular Evaluation (ACE) trial participants in China who had impaired glucose tolerance (IGT) and coronary heart disease (CHD).Research Design and Methods: Participants were randomly assigned to acarbose or placebo and followed with four monthly fasting plasma glucose (FPG) tests and annual oral glucose tolerance tests. Incident diabetes was defined as two successive diagnostic FPG levels ≥7 mmol/L or 2-h plasma glucose (PG) levels ≥11.1 mmol/L while taking study medication or a masked adjudicated confirmation of this diagnosis. Regression to normoglycemia was defined as FPG <6.1 mmol/L and 2-h PG <7.8 mmol/L. Intention-to-treat and on-treatment analyses were conducted using Poisson regression models, overall and for subgroups (age, sex, CHD type, HbA1c, FPG, 2-h PG, BMI, estimated glomerular filtration rate, for IGT alone, for IGT + impaired fasting glucose, and for use of thiazides, ACE inhibitors [ACEis]/angiotensin receptor blockers [ARBs], β-blockers, calcium channel blockers, or statins).Results: Incident diabetes was less frequent with acarbose compared with placebo (3.2 and 3.8 per 100 person-years, respectively; rate ratio 0.82 [95% CI 0.71, 0.94], P = 0.005), with no evidence of differential effects within the predefined subgroups after accounting for multiple testing. Regression to normoglycemia occurred more frequently in those randomized to acarbose compared with placebo (16.3 and 14.1 per 100 person-years, respectively; 1.16 [1.08, 1.25], P < 0.0001). This effect was greater in participants not taking an ACEi or ARB (1.36 [1.21, 1.53], Pinteraction = 0.0006). The likelihood of remaining in normoglycemic regression did not differ between the acarbose and placebo groups (P = 0.41).Conclusions: Acarbose reduced the incidence of diabetes and promoted regression to normoglycemia in Chinese people with IGT and CHD. [ABSTRACT FROM AUTHOR]

Published
2020
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46. Effects of acarbose on cardiovascular and diabetes outcomes in patients with coronary heart disease and impaired glucose tolerance (ACE): a randomised, double-blind, placebo-controlled trial

Author

Holman, Rury R, primary, Coleman, Ruth L, additional, Chan, Juliana C N, additional, Chiasson, Jean-Louis, additional, Feng, Huimei, additional, Ge, Junbo, additional, Gerstein, Hertzel C, additional, Gray, Richard, additional, Huo, Yong, additional, Lang, Zhihui, additional, McMurray, John J, additional, Rydén, Lars, additional, Schröder, Stefan, additional, Sun, Yihong, additional, Theodorakis, Michael J, additional, Tendera, Michal, additional, Tucker, Lynne, additional, Tuomilehto, Jaakko, additional, Wei, Yidong, additional, Yang, Wenying, additional, Wang, Duolao, additional, Hu, Dayi, additional, Pan, Changyu, additional, Keenan, Joanne, additional, Milton, Joanne, additional, Doran, Zoe, additional, Bray, Chris, additional, Rouleau, Jean L, additional, Collier, Jane, additional, Pocock, Stuart, additional, Standl, Eberhard, additional, Swedberg, Karl, additional, Weng, Jianping, additional, Zhao, Dong, additional, Petrie, Mark C, additional, Connolly, Eugene, additional, Jhund, Pardeep, additional, MacDonald, Michael, additional, Myles, Rachel C, additional, Bai, Rong, additional, Li, Jing, additional, Liu, Zhaoping, additional, Liu, Zhenyu, additional, Peng, Dantao, additional, Tong, Qiguang, additional, Wang, Chunxue, additional, Yan, Xiaowei, additional, Zhang, Yuqing, additional, Zhou, Jingmin, additional, Sattar, Naveed, additional, Fisher, Miles, additional, Petrie, John R, additional, Bethel, M Angelyn, additional, Xu, Wen, additional, Hearn, Sarah, additional, Kappai, Anurag, additional, Su, Shu-Yi, additional, Liyanage, Winitha, additional, Paul, Sanjoy, additional, Pozzi, Emanuela, additional, Ring, Arne, additional, Athwal, Rajbir, additional, Batra, Priyanka, additional, Ferch, Andrea, additional, Groves, Natasha, additional, Kennedy, Irene, additional, Nawalaniec, Olga, additional, Patel, Yash, additional, Roberts, Rachel, additional, Rush, Victoria, additional, Starrett, Jayne, additional, Tang, Jennifer, additional, Bi, Jing, additional, Jiang, Zhe, additional, Wei, Hua, additional, Wei, Xiaoshuai, additional, Zhang, Xuan, additional, Yin, Jun, additional, Sun, Yu, additional, Hu, Rong, additional, Liu, Yang, additional, Long, Jianjing, additional, Long, Yuefeng, additional, Qiao, Guofang, additional, Qiao, Haoyi, additional, Sun, Xiaochun, additional, Zhang, Yucheng, additional, Zhou, Jing, additional, Wang, Bangning, additional, Chen, Bin, additional, Deng, Lili, additional, Han, Xiaoning, additional, Hu, Taohong, additional, Hua, Qi, additional, Huo, Yanming, additional, Li, Hongmei, additional, Li, Hongwei, additional, Liu, Lihua, additional, Lu, Juming, additional, Ma, Changsheng, additional, Peng, Jianjun, additional, Pi, Lin, additional, Wang, Bin, additional, Wei, Guanglin, additional, Yang, Ming, additional, Zhang, Shuyang, additional, Zhang, Likun, additional, Zhao, Xia, additional, Zhou, Yujie, additional, Shi, Libin, additional, Wang, Mingsheng, additional, Wu, Lirong, additional, Han, Lei, additional, Liao, Ronghong, additional, Ran, Boli, additional, She, Qiang, additional, Tan, Jiancong, additional, Xia, Mei, additional, Yang, Chengming, additional, Chen, Lianglong, additional, Xiong, Shangquan, additional, Yu, Ling, additional, Pu, Xiaodong, additional, Wang, Yan, additional, Xie, Qiang, additional, Chen, Cibin, additional, Chen, Jiyan, additional, Dong, Yugang, additional, Wu, Zhaohui, additional, Yuan, Yong, additional, Zhou, Wanxing, additional, Zhou, Shuxian, additional, Chen, Xiaochao, additional, Wu, Chun, additional, Zhang, Aidong, additional, Li, Zicheng, additional, Lai, Shayi, additional, Yang, Jin, additional, Wei, Jinru, additional, Kuang, Riyu, additional, Zhao, Zilin, additional, Zhong, Guoqiang, additional, Cao, Xufen, additional, Hao, Yuming, additional, Liu, Gang, additional, Wang, Dongmei, additional, Fang, Hui, additional, Kong, Lingjun, additional, Li, Haitao, additional, Wang, Changqing, additional, Wang, Li'na, additional, Li, Xueqi, additional, Dong, Pingshuan, additional, Zhang, Shouyan, additional, Liu, Xincan, additional, Zhao, Yulan, additional, Liu, Hengliang, additional, Gu, Ye, additional, Liao, Yuhua, additional, Su, Xi, additional, Wang, Daowen, additional, Wang, Hairong, additional, Yang, Bo, additional, Guo, Ying, additional, Ouyang, Ding'an, additional, Yang, Tianlun, additional, Zhang, Yumin, additional, Han, Yajun, additional, Lin, Xuefeng, additional, Zhao, Ruiping, additional, Man, Ronghai, additional, Bian, Rongwen, additional, Biao, Xu, additional, Hasimu, Buaijiaer, additional, Jin, Hui, additional, Liu, Ping, additional, Yu, Jiangyi, additional, Zhang, Hang, additional, Xu, Chongli, additional, Guo, Yan, additional, Lv, Ke, additional, Tao, Yijia, additional, Xu, Xin, additional, Yang, Zhenyu, additional, Li, Dongye, additional, Qi, Chunmei, additional, Zhang, Guohui, additional, Gu, Xiang, additional, Hong, Lang, additional, Hu, Ling, additional, Li, Juxiang, additional, Yang, Ping, additional, Liu, Bin, additional, Wang, Gang, additional, Lin, Hailong, additional, Liu, Jun, additional, Zhang, Shuying, additional, Han, Ping, additional, Jin, Yuanzhe, additional, Li, Ling, additional, Li, Zhanquan, additional, Luan, Hong, additional, Song, Mei, additional, Xue, Li, additional, Hua, Yu, additional, Liu, Dongwu, additional, Yuan, Zuyi, additional, Ye, Jixian, additional, Gao, Feng, additional, Feng, Jinhua, additional, Wang, A'li, additional, Ye, Shengming, additional, Li, Xiaoyan, additional, Su, Guohai, additional, Zhang, Shufang, additional, Hou, Zishan, additional, Jiang, Wenbin, additional, Zhou, Changyong, additional, Wang, Yanping, additional, Qi, Wenbo, additional, Bao, Xiaomei, additional, Feng, Bo, additional, Gong, Hui, additional, Gu, Shuiming, additional, Gu, Mingjun, additional, Guo, Xingui, additional, He, Ben, additional, Huang, Ying, additional, Jiang, Jinfa, additional, Jiang, Yifeng, additional, Jin, Huigen, additional, Li, Yuehua, additional, Liu, Qiliang, additional, Lu, Guoping, additional, Miao, Peizhi, additional, Qin, Yongwen, additional, Wang, Yuanming, additional, Wu, Shiyao, additional, Xu, Yawei, additional, Ma, Jin, additional, Chen, Xiaoping, additional, Liu, Xiumin, additional, Tang, Jianing, additional, Wang, Jingping, additional, Tao, Jianhong, additional, Zhang, Jun, additional, Zhang, Tingjie, additional, Li, Decai, additional, Du, Xinping, additional, Jiang, Tiemin, additional, Lin, Jingna, additional, Lu, Chengzhi, additional, Ma, Hongjun, additional, Gao, Bo, additional, Guo, Xukun, additional, Li, Tong, additional, Zheng, Shaoxiong, additional, Li, Zhongcheng, additional, Zhao, Shuwu, additional, Qiu, Qiang, additional, Li, Kaili, additional, Liu, Junming, additional, Tang, Baopeng, additional, Yuan, Zhanjun, additional, Zhou, Jianhua, additional, Bai, Wenwei, additional, Guo, Tao, additional, Zhang, Ge, additional, Zhang, Hong, additional, Hao, Yinglu, additional, Fu, Guosheng, additional, Tang, Lijiang, additional, and Chen, Jialun, additional

Published
2017
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50. Long‐term risk of cardiovascular disease in individuals with latent autoimmune diabetes in adults (UKPDS 85).

Author

Maddaloni, Ernesto, Coleman, Ruth L., Pozzilli, Paolo, and Holman, Rury R.

Subjects
*AUTOANTIBODIES, *CARDIOVASCULAR diseases, *SYSTOLIC blood pressure, *TYPE 2 diabetes, *BODY mass index, *DIABETES
Abstract

Aims: Latent autoimmune diabetes in adults (LADA) is diagnosed in up to 12% of adults with clinically diagnosed type 2 diabetes (T2D). LADA tends to have healthier cardiovascular (CV) risk profiles than T2D, but it remains uncertain whether the risk of CV events differs between the two. We examined the risk of CV events in patients enrolled in the United Kingdom Prospective Diabetes Study (UKPDS) according to LADA status. Materials and methods: Diabetes autoantibodies (AAb) were measured in 5062 UKPDS participants. The incidence of major adverse CV events (MACE), defined as CV death, non‐fatal myocardial infarction or non‐fatal stroke, was compared in those with LADA (≥1 AAb test positive) and those without LADA (AAb negative). Results: There were 567 participants (11.2%) with LADA. Compared with participants with T2D, they were younger, with higher mean HbA1c and HDL‐cholesterol values, and with lower body mass index and total cholesterol and systolic blood pressure values (all P < 0.01). After a median (25th, 75th percentile) 17.3 (12.6–20.7) years of follow‐up, MACE occurred in 157 (17.4 per 1000 person‐years) participants with LADA and in 1544 (23.5 per 1000 person‐years) participants with T2D (HR, 0.73; 95% confidence interval [CI], 0.62–0.86; P < 0.001). However, after adjustment for confounders, this difference was no longer significant (HRadj, 0.90; 95% CI, 0.76–1.07; P = 0.22). Conclusions: In adults thought to have newly diagnosed T2D, the long‐term risk of MACE was lower in those with LADA. However, this did not differ after adjustment for traditional CV risk factors, suggesting that measurement of AAb in addition to traditional CV risk factors will not aid in stratification of CV risk in clinically diagnosed T2D. [ABSTRACT FROM AUTHOR]

Published
2019
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