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1,275 results on '"Chuang, Lee-Ming"'

154. National surveillance for type 2 diabetes mellitus in Taiwanese children

Author

Wei, Jung-Nan, Sung, Fung-Chang, Lin, Chau-Ching, Lin, Ruey-Shiung, Chiang, Chuan-Chi, and Chuang, Lee-Ming

Subjects
Obesity in children -- Health aspects, Diabetes in children -- Statistics, Diabetes in children -- Risk factors, Type 2 diabetes -- Statistics, Type 2 diabetes -- Risk factors
Abstract

Obesity is the most common cause of diabetes in Taiwanese children, according to a study of 2,926,700 children. In this large group of children, blood and urine tests revealed that 9 boys and 15 girls per 100,000 children had diabetes. Half of the children had type 2 diabetes, according to a smaller study of 253 diabetic children. Obese children were 18 times more likely to develop type 2 diabetes than normal-weight children.

Published
2003

156. Breast-Feeding and Childhood-Onset Type 1 Diabetes: A pooled analysis of individual participant data from 43 observational studies

Author

Cardwell, Chris R., Stene, Lars C., Ludvigsson, Johnny, Rosenbauer, Joachim, Cinek, Ondrej, Svensson, Jannet, Perez-Bravo, Francisco, Memon, Anjum, Gimeno, Suely G., Wadsworth, Emma J.K., Strotmeyer, Elsa S., Goldacre, Michael J., Radon, Katja, Chuang, Lee-Ming, Parslow, Roger C., Chetwynd, Amanda, Karavanaki, Kyriaki, Brigis, Girts, Pozzilli, Paolo, UrbonaitĖ, Brone, Schober, Edith, Devoti, Gabriele, Sipetic, Sandra, Joner, Geir, Ionescu-Tirgoviste, Constantin, de Beaufort, Carine E., Harrild, Kirsten, Benson, Victoria, Savilahti, Erkki, Ponsonby, Anne-Louise, Salem, Mona, Rabiei, Samira, and Patterson, Chris C.

Published
2012
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167. Birth order and childhood type 1 diabetes risk: a pooled analysis of 31 observational studies

Author

Cardwell, Chris R, Stene, Lars C, Joner, Geir, Bulsara, Max K, Cinek, Ondrej, Rosenbauer, Joachim, Ludvigsson, Johnny, Svensson, Jannet, Goldacre, Michael J, Waldhoer, Thomas, Jarosz-Chobot, Przemysława, Gimeno, Suely GA, Chuang, Lee-Ming, Roberts, Christine L, Parslow, Roger C, Wadsworth, Emma JK, Chetwynd, Amanda, Brigis, Girts, Urbonaitė, Brone, Šipetić, Sandra, Schober, Edith, Devoti, Gabriele, Ionescu-Tirgoviste, Constantin, de Beaufort, Carine E, Stoyanov, Denka, Buschard, Karsten, Radon, Katja, Glatthaar, Christopher, and Patterson, Chris C

Published
2011
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174. The Effects of Bariatric Surgery on Renal, Neurological, and Ophthalmic Complications in Patients with Type 2 Diabetes: the Taiwan Diabesity Study

Author

Chang, Yi-Cheng, primary, Chao, Seh-Huang, additional, Chen, Ching-Chu, additional, Ser, Kong-Han, additional, Chong, Keong, additional, Lu, Chieh-Hsiang, additional, Hsieh, Meng-Lun, additional, Huang, Yu-Yao, additional, Lee, Yi-Chih, additional, Hsu, Chih-Cheng, additional, Chuang, Lee-Ming, additional, and Lee, Wei-Jei, additional

Published
2020
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179. Overweight and obesity are associated with clustering of metabolic risk factors in early pregnancy and the risk of GDM

Author

Yen, I-Weng, primary, Lee, Chien-Nan, additional, Lin, Ming-Wei, additional, Fan, Kang-Chih, additional, Wei, Jung-Nan, additional, Chen, Kuan-Yu, additional, Chen, Szu-Chi, additional, Tai, Yi-Yun, additional, Kuo, Chun-Heng, additional, Lin, Chia-Hung, additional, Hsu, Chih-Yao, additional, Chuang, Lee-Ming, additional, Lin, Shin-Yu, additional, and Li, Hung-Yuan, additional

Published
2019
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184. Maternal Age at Birth and Childhood Type 1 Diabetes: A Pooled Analysis of 30 Observational Studies

Author

Cardwell, Chris R., Stene, Lars C., Joner, Geir, Bulsara, Max K., Cinek, Ondrej, Rosenbauer, Joachim, Ludvigsson, Johnny, Jané, Mireia, Svensson, Jannet, Goldacre, Michael J., Waldhoer, Thomas, Jarosz-Chobot, Przemysława, Gimeno, Suely G.A., Chuang, Lee-Ming, Parslow, Roger C., Wadsworth, Emma J.K., Chetwynd, Amanda, Pozzilli, Paolo, Brigis, Girts, Urbonaitė, Brone, Šipetić, Sandra, Schober, Edith, Devoti, Gabriele, Ionescu-Tirgoviste, Constantin, de Beaufort, Carine E., Stoyanov, Denka, Buschard, Karsten, and Patterson, Chris C.

Published
2010

189. Different cutoffs of hypertension, risk of incident diabetes and progression of insulin resistance: A prospective cohort study.

Author

Lin, Chia-Hung, Wei, Jung-Nan, Fan, Kang-Chih, Fang, Chi-Tai, Wu, Wan-Chen, Yang, Chung-Yi, Lin, Mao-Shin, Shih, Shyang-Rong, Hua, Cyue-Huei, Hsein, Yenh-Chen, Lin, Jou-Wei, Chuang, Lee-Ming, and Li, Hung-Yuan

Subjects
INSULIN resistance, DIABETES, HYPERTENSION, HYPERTENSION risk factors, GLUCOSE tolerance tests, HYPERTENSION epidemiology, LONGITUDINAL method
Abstract

Background/purpose: Hypertension is a risk factor of incident diabetes. In 2017, the ACC/AHA updated the definition of hypertension to above 130/80 mmHg, while the 2018 ESC/ESH guideline and the JNC7 criteria remained the cutoff of 140/90 mmHg. This study was aimed to investigate how different cutoffs of hypertension affect the association of hypertension to incident diabetes and the progression of insulin resistance.Methods: A total of 1177 subjects without diabetes at baseline were followed for 4.5 years. Diabetes was diagnosed by the results of oral glucose tolerance tests and hemoglobin A1c, or if anti-diabetic agents were used.Results: Hypertension by both criteria was associated with incident diabetes. Change of HOMA2-IR every 5 years (ΔHOMA2-IR/5 yr) was higher in subjects with hypertension than those without (adjusted p = 0.044). Subjects with treated hypertension had the highest risk of diabetes (HR 2.98, p < 0.001) and ΔHOMA2-IR/5 yr, compared with subjects with normal blood pressure. However, the associations of hypertension, HR of incident diabetes and ΔHOMA2-IR/5 yr were attenuated by the 2017 ACC/AHA criteria, as compared with that by the JNC7 and 2018 ESC/ESH criteria.Conclusion: Hypertension by both criteria is associated with incident diabetes and accelerated progression of insulin resistance, and the associations are attenuated by the 2017 ACC/AHA criteria. [ABSTRACT FROM AUTHOR]

Published
2022
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190. Cluster Analysis of Cardiovascular Phenotypes in Patients With Type 2 Diabetes and Established Atherosclerotic Cardiovascular Disease: A Potential Approach to Precision Medicine.

Author

Sharma, Abhinav, Yinggan Zheng, Ezekowitz, Justin A., Westerhout, Cynthia M., Udell, Jacob A., Goodman, Shaun G., Armstrong, Paul W., Buse, John B., Green, Jennifer B., Josse, Robert G., Kaufman, Keith D., McGuire, Darren K., Ambrosio, Giuseppe, Lee-Ming Chuang, Lopes, Renato D., Peterson, Eric D., Holman, Rury R., Zheng, Yinggan, and Chuang, Lee-Ming

Subjects
TYPE 2 diabetes, CLUSTER analysis (Statistics), CARDIOVASCULAR diseases, INDIVIDUALIZED medicine, PROPORTIONAL hazards models
Abstract

Objective: Phenotypic heterogeneity among patients with type 2 diabetes mellitus (T2DM) and atherosclerotic cardiovascular disease (ASCVD) is ill defined. We used cluster analysis machine-learning algorithms to identify phenotypes among trial participants with T2DM and ASCVD.Research Design and Methods: We used data from the Trial Evaluating Cardiovascular Outcomes with Sitagliptin (TECOS) study (n = 14,671), a cardiovascular outcome safety trial comparing sitagliptin with placebo in patients with T2DM and ASCVD (median follow-up 3.0 years). Cluster analysis using 40 baseline variables was conducted, with associations between clusters and the primary composite outcome (cardiovascular death, nonfatal myocardial infarction, nonfatal stroke, or hospitalization for unstable angina) assessed by Cox proportional hazards models. We replicated the results using the Exenatide Study of Cardiovascular Event Lowering (EXSCEL) trial.Results: Four distinct phenotypes were identified: cluster I included Caucasian men with a high prevalence of coronary artery disease; cluster II included Asian patients with a low BMI; cluster III included women with noncoronary ASCVD disease; and cluster IV included patients with heart failure and kidney dysfunction. The primary outcome occurred, respectively, in 11.6%, 8.6%, 10.3%, and 16.8% of patients in clusters I to IV. The crude difference in cardiovascular risk for the highest versus lowest risk cluster (cluster IV vs. II) was statistically significant (hazard ratio 2.74 [95% CI 2.29-3.29]). Similar phenotypes and outcomes were identified in EXSCEL.Conclusions: In patients with T2DM and ASCVD, cluster analysis identified four clinically distinct groups. Further cardiovascular phenotyping is warranted to inform patient care and optimize clinical trial designs. [ABSTRACT FROM AUTHOR]

Published
2022
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194. Clinical measures of physical fitness predict insulin resistance in people at risk for diabetes

Author

Chen, Chiao-Nan, Chuang, Lee-Ming, and Wu, Ying-Tai

Subjects
Muscle strength -- Evaluation, Insulin resistance -- Risk factors -- Care and treatment, Type 2 diabetes -- Risk factors -- Care and treatment, Body mass index -- Evaluation, Physical fitness -- Evaluation, Health
Abstract

Background and Purpose. Physical inactivity has been well documented as a risk factor for type 2 diabetes. Previous studies measured the level of physical activity either with questionnaires or with direct measurements of maximum oxygen uptake. However, questionnaires are patient-report measures, and methods for obtaining direct maximum oxygen uptake measurements often are not available clinically. The purpose of this study was to investigate whether clinical measurement of health-related physical fitness with a simple test battery can predict insulin resistance, a precursor of type 2 diabetes, in people at risk for diabetes. Subjects and Methods. A total of 151 volunteers with at least one diabetes risk factor (overweight, hypertension, dyslipidemia, family history, impaired glucose tolerance, gestational diabetes, or delivering a baby weighing more than 4.0 kg) were recruited. Insulin resistance (as determined with the homeostasis model assessment of insulin resistance [HOMA-IR]), physical fitness (including body composition, as determined with the body mass index and waist circumference), muscle strength (handgrip strength [force-generating capacity]), muscle endurance (sit-up test), flexibility (sit-and-reach test), and cardiorespiratory endurance (step test) were measured, and a physical activity questionnaire was administered. Backward regression analysis was used to build the prediction models for insulin resistance from components of physical fitness and physical activity. Results. Body mass index, muscle strength, and cardiorespiratory fitness predicted HOMA-IR in men (adjusted [R.sup.2] = .264). In women, age, waist circumference, and cardiorespiratory fitness were the predictors of HOMA-IR (adjusted [R.sup.2] = .438). Discussion and Conclusion. Clinical measures of physical fitness can predict insulin resistance in people at risk for diabetes. The findings support the validity of clinical measures of physical fitness for predicting insulin resistance in people at risk for diabetes., Insulin Resistance and Physical Fitness The prevalence of type 2 diabetes is increasing worldwide in adults of all ages. (1,2) The disease and the associated complications, such as macrovascular disease [...]

Published
2008

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