Search

Objective: To explore demographic, socioeconomic, diabetes-related, and behavioral correlates of dietary intake of dairy, fruit, vegetables, sweetened soda, fiber, calcium, and saturated fat in youth with diabetes., Methods: Cross-sectional study of youth 10-22 years old with type 1 (T1DM, n = 2,176) and type 2 diabetes (T2DM, n = 365). Association of dietary intake, demographics, socioeconomic status, behavioral, and diabetes-related measures was explored with quantile regression., Results: T1DM males had lower consumption of vegetables, fruit, and fiber, and higher consumption of soda and saturated fat than females (P < .01). African Americans had lower dairy and higher soda intake than non-Hispanic T1DM whites (P < .01). Soda consumption was higher in older T2DM youth than in younger participants (P < .01). Lifestyle and physical activity patterns were also significantly associated with dietary intake., Conclusions and Implications: Identified demographic and behavioral correlates may help dietitians to focus on groups of youth with diabetes who have lower adherence to a healthful diet. Diet counseling groups may be tailored according to these major determinants., (Copyright © 2011 Society for Nutrition Education. Published by Elsevier Inc. All rights reserved.)

Background: Whether a disparity in diabetes-related death across education levels is widening, narrowing, or stable is not known. This analysis examined trends in diabetes-related death by education levels over a 17-year period., Methods: The first part of the analysis calculated diabetes-related mortality rates for adults aged 40-64 years and adults aged 65-79 years using U.S. Vital Statistics data from 1989 to 2005 to provide the number of deaths per year in the U.S. (the numerator) and also U.S. Census data to provide the population size (the denominator). The second part of the analysis examined trends by education level in possible mechanisms that link education and diabetes-related mortality using the U.S. National Health and Nutrition Examination Surveys (NHANES) program in 1988-1994 and 1999-2004., Results: Disparity in diabetes-related mortality across education levels widened from the late 1980s to 2005 overall and in the subgroups of men, women, blacks, whites, and Hispanics. Analysis of NHANES data indicated that progress in diabetes care and management (as indicated by HbA1c levels less than 8%) has helped people of all education levels but has been of greater benefit to those with higher education., Conclusions: The reduction of disparities in diabetes-related mortality requires improved policies and interventions that redress the slower pace of improvement in diabetes care and self-management among people with lower education.

In the Diabetes Prevention Program, treatment of subjects with impaired glucose tolerance with metformin >3.2 years reduced the risk of developing type 2 diabetes by 30% compared with placebo. This study describes the mechanisms of this effect. In proportional hazards regression models with 2,155 subjects, changes in weight, the insulinogenic index (IGR), fasting insulin, and proinsulin were predictive of diabetes, though to different degrees within each group. The mean change in weight, fasting insulin, and proinsulin, but not IGR, differed between groups during the study. The 1.7-kg weight loss with metformin versus a 0.3-kg gain with placebo alone explained 64% of the beneficial metformin effect on diabetes risk. Adjustment for weight, fasting insulin, proinsulin, and other metabolic factors combined explained 81% of the beneficial metformin effect, but it remained nominally significant (P = 0.034). After the addition of changes in fasting glucose, 99% of the group effect was explained and is no longer significant. Treatment of high-risk subjects with metformin results in modest weight loss and favorable changes in insulin sensitivity and proinsulin, which contribute to a reduction in the risk of diabetes apart from the associated reductions in fasting glucose.

Background: Diabetes is an important predictor of mortality patients with ACS. However, little is known about the association between diabetes and health status after ACS. The objective of this study was to examine the association between diabetes and patients' health status outcomes one year after an acute coronary syndrome (ACS)., Methods: This was a prospective cohort study of patients hospitalized with ACS. Patients were evaluated at baseline and one year with the Seattle Angina Questionnaire (SAQ). Socio-demographic and clinical characteristics were ascertained during index ACS hospitalization. One year SAQ Angina Frequency, Physical Limitation, and Health-Related Quality of Life (HRQoL) scales were the primary outcomes of the study., Results: Of 1199 patients, 326 (37%) had diabetes. Patients with diabetes were more likely to present with unstable angina (52% vs. 40%; p < 0.001), less likely to present with STEMI (20% vs. 31%; p < 0.001), and less likely to undergo coronary angiography (68% vs. 82%; p < 0.001). In multivariable analyses, the presence of diabetes was associated with significantly more angina (OR 1.36; 95% CI 1.01-1.38), cardiac-related physical limitation (OR 1.94; 95% CI 1.57-3.24) and HRQoL deficits (OR 1.43; 95% CI 1.01-2.04) at one year., Conclusion: Diabetes is associated with more angina, worse physical limitation, and worse HRQoL one year after an ACS. Future studies should assess whether health status outcomes of patients with diabetes could be improved through more aggressive ACS treatment or post-discharge surveillance and angina management.

Objective: Diabetes Prevention Program (DPP) participants randomized to the intensive lifestyle intervention (ILS) had significantly reduced risk of diabetes compared with placebo participants. We explored the contribution of changes in weight, diet, and physical activity on the risk of developing diabetes among ILS participants., Research Design and Methods: For this study, we analyzed one arm of a randomized trial using Cox proportional hazards regression over 3.2 years of follow-up., Results: A total of 1,079 participants were aged 25-84 years (mean 50.6 years, BMI 33.9 kg/m(2)). Weight loss was the dominant predictor of reduced diabetes incidence (hazard ratio per 5-kg weight loss 0.42 [95% CI 0.35-0.51]; P < 0.0001). For every kilogram of weight loss, there was a 16% reduction in risk, adjusted for changes in diet and activity. Lower percent of calories from fat and increased physical activity predicted weight loss. Increased physical activity was important to help sustain weight loss. Among 495 participants not meeting the weight loss goal at year 1, those who achieved the physical activity goal had 44% lower diabetes incidence., Conclusions: Interventions to reduce diabetes risk should primarily target weight reduction.

Objective: The Diabetes Prevention Program (DPP) showed that intensive lifestyle intervention reduced the risk of diabetes by 58%. This paper examines demographic, psychosocial, and behavioral factors related to achieving weight loss and physical activity goals in the DPP lifestyle participants., Research Methods and Procedures: Lifestyle participants (n = 1079; mean age = 50.6, BMI = 33.9, 68% female, and 46% from minority groups) had goals of 7% weight loss and 150 min/wk of physical activity. Goal achievement was assessed at the end of the 16-session core curriculum (approximately week 24) and the final intervention visit (mean = 3.2 years) as a function of demographic, psychosocial, and behavioral variables., Results: Forty-nine percent met the weight loss goal and 74% met the activity goal initially, while 37% and 67%, respectively, met these goals long-term. Men and those with lower initial BMI were more likely to meet activity but not weight loss goals. Hispanic, Asian, and Native Americans were more likely to meet the long-term activity goals, and whites were more likely to meet the initial weight loss goal. In multivariate analyses, meeting the long-term weight loss goal and both activity goals increased with age, while psychosocial and depression measures were unrelated to goal achievement. Dietary self-monitoring was positively related to meeting both weight loss and activity goals, and meeting the activity goal was positively related to meeting the weight loss goal. Participants who met initial goals were 1.5 to 3.0 times more likely to meet these goals long-term., Discussion: Success at meeting the weight loss and activity goals increased with age. Initial success predicted long-term success. Self-monitoring and meeting activity goals were related to achieving and sustaining weight loss.

ObjectiveTo determine whether metformin or lifestyle modification can lower rates of all-cause and cause-specific mortality in the Diabetes Prevention Program and Diabetes Prevention Program Outcomes Study.Research design and methodsFrom 1996 to 1999, 3,234 adults at high risk for type 2 diabetes were randomized to an intensive lifestyle intervention, masked metformin, or placebo. Placebo and lifestyle interventions stopped in 2001, and a modified lifestyle program was offered to everyone, but unmasked study metformin continued in those originally randomized. Causes of deaths through 31 December 2018 were adjudicated by blinded reviews. All-cause and cause-specific mortality hazard ratios (HRs) were estimated from Cox proportional hazards regression models and Fine-Gray models, respectively.ResultsOver a median of 21 years (interquartile range 20-21), 453 participants died. Cancer was the leading cause of death (n = 170), followed by cardiovascular disease (n = 131). Compared with placebo, metformin did not influence mortality from all causes (HR 0.99 [95% CI 0.79, 1.25]), cancer (HR 1.04 [95% CI 0.72, 1.52]), or cardiovascular disease (HR 1.08 [95% CI 0.70, 1.66]). Similarly, lifestyle modification did not impact all-cause (HR 1.02 [95% CI 0.81, 1.28]), cancer (HR 1.07 [95% CI 0.74, 1.55]), or cardiovascular disease (HR 1.18 [95% CI 0.77, 1.81]) mortality. Analyses adjusted for diabetes status and duration, BMI, cumulative glycemic exposure, and cardiovascular risks yielded results similar to those for all-cause mortality.ConclusionsCancer was the leading cause of mortality among adults at high risk for type 2 diabetes. Although metformin and lifestyle modification prevented diabetes, neither strategy reduced all-cause, cancer, or cardiovascular mortality rates.

Sex hormone binding globulin (SHBG) levels are reported to be inversely associated with diabetes risk. It is unknown whether diabetes prevention interventions increase SHBG and whether resultant changes in SHBG affect diabetes risk. The purpose of this analysis was to determine whether intensive lifestyle intervention (ILS) or metformin changed circulating SHBG and if resultant changes influenced diabetes risk in the Diabetes Prevention Program (DPP). This is a secondary analysis from the DPP (1996-2001), a randomized trial of ILS or metformin versus placebo on diabetes risk over a mean follow-up of 3.2 years. The DPP was conducted across 27 academic study centers in the USA. Men, premenopausal and postmenopausal women without hormone use in the DPP were evaluated. The DPP included overweight/obese persons with elevated fasting glucose and impaired glucose tolerance. Main outcomes measures were changes in SHBG levels at 1 year and risk of diabetes over 3 years. ILS resulted in significantly higher increases (postmenopausal women: p

Coronary artery disease (CAD) is more frequent among individuals with dysglycemia. Preventive interventions for diabetes can improve cardiometabolic risk factors (CRFs), but it is unclear whether the benefits on CRFs are similar for individuals at different genetic risk for CAD. We built a 201-variant polygenic risk score (PRS) for CAD and tested for interaction with diabetes prevention strategies on 1-year changes in CRFs in 2,658 Diabetes Prevention Program (DPP) participants. We also examined whether separate lifestyle behaviors interact with PRS and affect changes in CRFs in each intervention group. Participants in both the lifestyle and metformin interventions had greater improvement in the majority of recognized CRFs compared with placebo (P < 0.001) irrespective of CAD genetic risk (P interaction > 0.05). We detected nominal significant interactions between PRS and dietary quality and physical activity on 1-year change in BMI, fasting glucose, triglycerides, and HDL cholesterol in individuals randomized to metformin or placebo, but none of them achieved the multiple-testing correction for significance. This study confirms that diabetes preventive interventions improve CRFs regardless of CAD genetic risk and delivers hypothesis-generating data on the varying benefit of increasing physical activity and improving diet on intermediate cardiovascular risk factors depending on individual CAD genetic risk profile.

Many community-based translations of evidence-based interventions are designed as one-arm studies due to ethical and other considerations. Evaluating the impacts of such programs is challenging. Here, we examine the effectiveness of the lifestyle intervention implemented by the Special Diabetes Program for Indians Diabetes Prevention (SDPI-DP) demonstration project, a translational lifestyle intervention among American Indian and Alaska Native communities. Data from the landmark Diabetes Prevention Program placebo group was used as a historical control. We compared the use of propensity score (PS) and disease risk score (DRS) matching to adjust for potential confounder imbalance between groups. The unadjusted hazard ratio (HR) for diabetes risk was 0.35 for SDPI-DP lifestyle intervention vs. control. However, when relevant diabetes risk factors were considered, the adjusted HR estimates were attenuated toward 1, ranging from 0.56 (95% CI 0.44-0.71) to 0.69 (95% CI 0.56-0.96). The differences in estimated HRs using the PS and DRS approaches were relatively small but DRS matching resulted in more participants being matched and smaller standard errors of effect estimates. Carefully employed, publicly available randomized clinical trial data can be used as a historical control to evaluate the intervention effectiveness of one-arm community translational initiatives. It is critical to use a proper statistical method to balance the distributions of potential confounders between comparison groups in this kind of evaluations.

Weight-loss interventions generally improve lipid profiles and reduce cardiovascular disease risk, but effects are variable and may depend on genetic factors. We performed a genetic association analysis of data from 2,993 participants in the Diabetes Prevention Program to test the hypotheses that a genetic risk score (GRS) based on deleterious alleles at 32 lipid-associated single-nucleotide polymorphisms modifies the effects of lifestyle and/or metformin interventions on lipid levels and nuclear magnetic resonance (NMR) lipoprotein subfraction size and number. Twenty-three loci previously associated with fasting LDL-C, HDL-C, or triglycerides replicated (P = 0.04-1 × 10(-17)). Except for total HDL particles (r = -0.03, P = 0.26), all components of the lipid profile correlated with the GRS (partial |r| = 0.07-0.17, P = 5 × 10(-5)-1 10(-19)). The GRS was associated with higher baseline-adjusted 1-year LDL cholesterol levels (β = +0.87, SEE ± 0.22 mg/dl/allele, P = 8 × 10(-5), P(interaction) = 0.02) in the lifestyle intervention group, but not in the placebo (β = +0.20, SEE ± 0.22 mg/dl/allele, P = 0.35) or metformin (β = -0.03, SEE ± 0.22 mg/dl/allele, P = 0.90; P(interaction) = 0.64) groups. Similarly, a higher GRS predicted a greater number of baseline-adjusted small LDL particles at 1 year in the lifestyle intervention arm (β = +0.30, SEE ± 0.012 ln nmol/L/allele, P = 0.01, P(interaction) = 0.01) but not in the placebo (β = -0.002, SEE ± 0.008 ln nmol/L/allele, P = 0.74) or metformin (β = +0.013, SEE ± 0.008 nmol/L/allele, P = 0.12; P(interaction) = 0.24) groups. Our findings suggest that a high genetic burden confers an adverse lipid profile and predicts attenuated response in LDL-C levels and small LDL particle number to dietary and physical activity interventions aimed at weight loss.

Objective In their Strategic Impact Goal Statement, the American Heart Association focused on primordial prevention of cardiovascular risk factors by defining metrics for ideal cardiovascular health ( ICH). The prevalence of ICH among youth with type 1 diabetes is unknown. Youth with type 1 diabetes face an increased risk of cardiovascular disease ( CVD) as they age. The purpose of this report was to examine the prevalence of ICH in a population of youth with type 1 diabetes and to examine the association of ICH with measures of cardiovascular structure and function. Research design and methods This report is based on SEARCH CVD an ancillary study to the SEARCH for Diabetes in Youth. A total of 190 adolescents with type 1 diabetes had complete data on all of the ICH metrics at baseline and had measures of arterial stiffness [pulse wave velocity ( PWV), brachial distensibility ( BrachD), and augmentation index ( AIx)] and carotid intima-media thickness completed at a follow-up visit [on average 5 yr after baseline (interquartile range 4-5)]. Results No subjects met the ICH criteria for all 7 metrics. Meeting an increasing number of ICH metrics was significantly associated with lower arterial stiffness [lower PWV of the trunk (β = −0.02 ±0.01; p = 0.004) and AIx (β = −2.2 ±0.66; p = 0.001), and increased BrachD (β = 0.14 ±0.07; p = 0.04)]. Conclusions Increasing number of ICH metrics was significantly associated with decreased arterial stiffness, but prevalence of ICH in this population was low. Youth with type 1 diabetes could benefit from improvements in their cardiovascular health. [ABSTRACT FROM AUTHOR]

We explored the utility of different algorithms for diabetes case identification by using electronic health records. Inpatient and outpatient diagnosis codes, as well as data on laboratory results and dispensing of antidiabetic medications were extracted from electronic health records of Kaiser Permanente Southern California members who were less than 20 years of age in 2009. Diabetes cases were ascertained by using the SEARCH for Diabetes in Youth Study protocol and comprised the “gold standard.” Sensitivity, specificity, positive and negative predictive values, accuracy, and the area under the receiver operating characteristic curve (AUC) were compared in 1,000 bootstrapped samples. Based on data from 792,992 youth, of whom 1,568 had diabetes (77.2%, type 1 diabetes; 22.2%, type 2 diabetes; 0.6%, other), case identification accuracy was highest in 75% of bootstrapped samples for those who had 1 or more outpatient diabetes diagnoses or 1 or more insulin prescriptions (sensitivity, 95.9%; positive predictive value, 95.5%; AUC, 97.9%) and in 25% of samples for those who had 2 or more outpatient diabetes diagnoses and 1 or more antidiabetic medications (sensitivity, 92.4%; positive predictive value, 98.4%; AUC, 96.2%). Having 1 or more outpatient type 1 diabetes diagnoses (International Classification of Diseases, Ninth Revision, Clinical Modification, code 250.x1 or 250.x3) had the highest accuracy (94.4%) and AUC (94.1%) for type 1 diabetes; the absence of type 1 diabetes diagnosis had the highest accuracy (93.8%) and AUC (93.6%) for identifying type 2 diabetes. Information in the electronic health records from managed health care organizations provides an efficient and cost-effective source of data for childhood diabetes surveillance. [ABSTRACT FROM PUBLISHER]

Despite a worse cardiovascular disease (CVD) risk profile, Hispanics have lower CVD mortality than non-Hispanic Whites in studies based on death certificates. This study examined 310 deaths that occurred between 1984 and 1998 among 1,862 Hispanic and non-Hispanic White participants in the San Luis Valley Diabetes Study, using medical records to classify cause of death. Among persons without diabetes, the age-adjusted all-cause mortality rate was 6.1/1,000 person-years in non-Hispanic Whites and 7.4/1,000 person-years in Hispanics. Among persons with diabetes, it was 24.3/1,000 person-years in non-Hispanic Whites and 21.9/1,000 person-years in Hispanics. Among nondiabetics, the age-adjusted CVD mortality rate was 2.5/1,000 person-years in non-Hispanic Whites and 1.6/1,000 person-years in Hispanics. Among diabetics, it was 12.9/1,000 person-years in non-Hispanic Whites and 8.8/1,000 person-years in Hispanics. Among nondiabetics, the adjusted hazard ratio for CVD death in Hispanics compared with non-Hispanic Whites was 0.65 (95% confidence interval (CI): 0.34, 1.23). The hazard ratio for coronary heart disease death was 0.95 (95% CI: 0.35, 2.59). Among diabetics, the hazard ratio for CVD death, after adjustment for conventional and diabetes risk factors, was 0.44 (95% CI: 0.26, 0.74), and for coronary heart disease death it was 0.43 (95% CI: 0.21, 0.91). A statistically significant decreased risk of CVD death was observed only in male Hispanics with diabetes. Competing mortality or factors that interact with diabetes may explain these differences. [ABSTRACT FROM PUBLISHER]

Abstract: We evaluated geographic variation in type 1 and type 2 diabetes mellitus (T1DM, T2DM) in four regions of the United States. Data on 807 incident T1DM cases diabetes and 313 T2DM cases occurring in 2002–03 in South Carolina (SC) and Colorado (CO), 5 counties in Washington (WA), and an 8 county region around Cincinnati, Ohio (OH) among youth aged 10–19 years were obtained from the SEARCH for Diabetes in Youth Study. Geographic patterns were evaluated in a Bayesian framework. Incidence rates differed between the study regions, even within race/ethnic groups. Significant small-area variation within study region was observed for T1DM and T2DM. Evidence for joint spatial correlation between T1DM and T2DM was present at the county level for SC (r SC=0.31) and CO non-Hispanic Whites (r CO=0.40) and CO Hispanics (r CO=0.72). At the tract level, no evidence for meaningful joint spatial correlation was observed (r SC=−0.02; r CO=−0.02; r OH=0.03; and r WA=0.09). Our study provides evidence for the presence of both regional and small area, localized variation in type 1 and type 2 incidence among youth aged 10–19 years in the United States. [Copyright &y& Elsevier]