Prevalence of and risk factors for diabetic retinopathy in Koreans with type II diabetes: baseline characteristics of Seoul Metropolitan City-Diabetes Prevention Program (SMC-DPP) participants

2011 ◽  
Vol 96 (2) ◽  
pp. 151-155 ◽  
Author(s):  
Cheol-Young Park ◽  
Se Eun Park ◽  
Ji Cheol Bae ◽  
Won Jun Kim ◽  
Sung Woo Park ◽  
...  
Keyword(s):  
Risk Factors ◽  
Diabetic Retinopathy ◽  
Prevention Program ◽  
Type Ii Diabetes ◽  
Diabetes Prevention ◽  
Diabetes Prevention Program ◽  
Type Ii ◽  
Metropolitan City ◽  
Baseline Characteristics

scholarly journals The Impact of a Yearlong Diabetes Prevention Program-Based Lifestyle Intervention on Cardiovascular Health Metrics

2021 ◽  
Vol 12 ◽  
pp. 215013272110298
Author(s):  
Susan M. Devaraj ◽  
Bonny Rockette-Wagner ◽  
Rachel G. Miller ◽  
Vincent C. Arena ◽  
Jenna M. Napoleone ◽  
...  
Keyword(s):  
Risk Factors ◽  
Blood Pressure ◽  
Lifestyle Intervention ◽  
Prevention Program ◽  
Diabetes Prevention ◽  
Cardiovascular Health ◽  
Diabetes Prevention Program ◽  
Behavioral Risk ◽  
Community Based ◽  
The Impact

Introduction The American Heart Association created “Life’s Simple Seven” metrics to estimate progress toward improving US cardiovascular health in a standardized manner. Given the widespread use of federally funded Diabetes Prevention Program (DPP)-based lifestyle interventions such as the Group Lifestyle Balance (DPP-GLB), evaluation of change in health metrics within such a program is of national interest. This study examined change in cardiovascular health metric scores during the course of a yearlong DPP-GLB intervention. Methods Data were combined from 2 similar randomized trials offering a community based DPP-GLB lifestyle intervention to overweight/obese individuals with prediabetes and/or metabolic syndrome. Pre/post lifestyle intervention participation changes in 5 of the 7 cardiovascular health metrics were examined at 6 and 12 months (BMI, blood pressure, total cholesterol, fasting plasma glucose, physical activity). Smoking was rare and diet was not measured. Results Among 305 participants with complete data (81.8% of 373 eligible adults), significant improvements were demonstrated in all 5 risk factors measured continuously at 6 and 12 months. There were significant positive shifts in the “ideal” and “total” metric scores at both time points. Also noted were beneficial shifts in the proportion of participants across categories for BMI, activity, and blood pressure. Conclusion AHA-metrics could have clinical utility in estimating an individual’s cardiovascular health status and in capturing improvement in cardiometabolic/behavioral risk factors resulting from participation in a community-based translation of the DPP lifestyle intervention.

Impact of a Digital Diabetes Prevention Program on Risk Factors for Chronic Disease in a Workforce Cohort

2020 ◽  
Vol 62 (12) ◽  
pp. 1040-1045
Author(s):  
Charles E. Birse ◽  
Dov Shiffman ◽  
Anita Satish ◽  
Maren S. Fragala ◽  
Andre R. Arellano ◽  
...  
Keyword(s):  
Risk Factors ◽  
Chronic Disease ◽  
Prevention Program ◽  
Diabetes Prevention ◽  
Diabetes Prevention Program

Risk factors for type ii diabetes and diabetic retinopathy in a mexican-american population: proyecto ver

2002 ◽  
Vol 134 (3) ◽  
pp. 390-398 ◽  
Author(s):  
Sheila K West ◽  
Beatriz Munoz ◽  
Ronald Klein ◽  
Aimee T Broman ◽  
Rosario Sanchez ◽  
...  
Keyword(s):  
Risk Factors ◽  
Diabetic Retinopathy ◽  
Type Ii Diabetes ◽  
Mexican American ◽  
Type Ii ◽  
American Population ◽  
Mexican American Population

scholarly journals Predictive utilities of lipid traits, lipoprotein subfractions and other risk factors for incident diabetes: a machine learning approach in the Diabetes Prevention Program

2021 ◽  
Vol 9 (1) ◽  
pp. e001953
Author(s):  
Tibor V Varga ◽  
Jinxi Liu ◽  
Ronald B Goldberg ◽  
Guannan Chen ◽  
Samuel Dagogo-Jack ◽  
...  
Keyword(s):  
Machine Learning ◽  
Risk Factors ◽  
Logistic Regression ◽  
Prevention Program ◽  
Diabetes Prevention ◽  
Learning Algorithms ◽  
Diabetes Prevention Program ◽  
Diabetes Risk ◽  
Machine Learning Algorithms ◽  
Lipid Biomarkers

IntroductionAlthough various lipid and non-lipid analytes measured by nuclear magnetic resonance (NMR) spectroscopy have been associated with type 2 diabetes, a structured comparison of the ability of NMR-derived biomarkers and standard lipids to predict individual diabetes risk has not been undertaken in larger studies nor among individuals at high risk of diabetes.Research design and methodsCumulative discriminative utilities of various groups of biomarkers including NMR lipoproteins, related non-lipid biomarkers, standard lipids, and demographic and glycemic traits were compared for short-term (3.2 years) and long-term (15 years) diabetes development in the Diabetes Prevention Program, a multiethnic, placebo-controlled, randomized controlled trial of individuals with pre-diabetes in the USA (N=2590). Logistic regression, Cox proportional hazards model and six different hyperparameter-tuned machine learning algorithms were compared. The Matthews Correlation Coefficient (MCC) was used as the primary measure of discriminative utility.ResultsModels with baseline NMR analytes and their changes did not improve the discriminative utility of simpler models including standard lipids or demographic and glycemic traits. Across all algorithms, models with baseline 2-hour glucose performed the best (max MCC=0.36). Sophisticated machine learning algorithms performed similarly to logistic regression in this study.ConclusionsNMR lipoproteins and related non-lipid biomarkers were associated but did not augment discrimination of diabetes risk beyond traditional diabetes risk factors except for 2-hour glucose. Machine learning algorithms provided no meaningful improvement for discrimination compared with logistic regression, which suggests a lack of influential latent interactions among the analytes assessed in this study.Trial registration numberDiabetes Prevention Program: NCT00004992; Diabetes Prevention Program Outcomes Study: NCT00038727.

Consulting consumers to develop marketing and recruitment strategies for a diabetes prevention program

2009 ◽  
Vol 15 (2) ◽  
pp. 173 ◽  
Author(s):  
Dianne Berryman ◽  
Marie Gill ◽  
Jonathan Pietsch ◽  
Hannah Halloran
Keyword(s):  
Risk Factors ◽  
Type 2 Diabetes ◽  
Focus Groups ◽  
Prevention Program ◽  
Diabetes Prevention ◽  
Diabetes Prevention Program ◽  
Recruitment Strategies ◽  
Knowledge And Beliefs ◽  
Marketing Messages

Recruitment of participants to health education programs is a challenge often encountered in community health care settings. This paper outlines the process used to identify what strategies, approaches and messages raise community awareness of risk factors for type 2 diabetes and elicit action on the part of individuals to address risk factors. Consumer focus groups were conducted to explore people’s concerns, knowledge and beliefs around prevention of diabetes and with an aim to identify marketing messages and strategies for engaging participants in a diabetes prevention program. Findings from the focus groups were used to develop marketing messages that were then tested in further consumer consultations. They identified commonalities and differences between cultural groups. The key common point in relation to the marketing messages was the need to emphasise the consequences of type 2 diabetes and the individual relevance of risk factors. The importance of receiving information from trusted health professionals and the need to personalise messages of risk and encourage individual action was also highlighted in the research and incorporated into marketing and recruitment strategies.

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