Testing a very low-carbohydrate adaption of the Diabetes Prevention Program among adults with prediabetes: study protocol for the Lifestyle Education about prediabetes (LEAP) trial.

BACKGROUND: The Center for Disease Control and Prevention's National Diabetes Prevention Program (NDPP) aims to help individuals with prediabetes avoid progression to type 2 diabetes mellitus (T2DM) through weight loss. Specifically, the NDPP teaches individuals to follow a low-fat, calorie-restricted diet and to engage in regular physical activity to achieve ≥ 5% body weight loss. Most NDPP participants, however, do not achieve this weight loss goal, and glycemic control remains largely unchanged. One promising opportunity to augment the NDPP's weight loss and glycemic effectiveness may be to teach participants to follow a very low-carbohydrate diet (VLCD), which can directly reduce post-prandial glycemia and facilitate weight loss by reducing circulating insulin and enabling lipolysis. To date, there have been no high-quality, randomized controlled trials to test whether a VLCD can prevent progression to T2DM among individuals with prediabetes. The aim of this study is to test the effectiveness of a VLCD version the NDPP (VLC-NDPP) versus the standard NDPP. We hypothesize the VLC-NDPP will demonstrate greater improvements in weight loss and glycemic control. METHODS: We propose to conduct a 12-month, 1:1, randomized controlled trial that will assign 300 adults with overweight or obesity and prediabetes to either the NDPP or VLC-NDPP. The primary outcome will be glycemic control as measured by change in hemoglobin A1c (HbA1c) from baseline to 12 months. Secondary outcomes will include percent body weight change and changes in glycemic variability, inflammatory markers, lipids, and interim HbA1c. We will evaluate progression to T2DM and initiation of anti-hyperglycemic agents. We will conduct qualitative interviews among a purposive sample of participants to explore barriers to and facilitators of dietary adherence. The principal quantitative analysis will be intent-to-treat using hierarchical linear mixed effects models to assess differences over time. DISCUSSION: The NDPP is the dominant public health strategy for T2DM prevention. Changing the program's dietary advice to include a carbohydrate-restricted eating pattern as an alternative option may enhance the program's effectiveness. If the VLC-NDPP shows promise, this trial would be a precursor to a multi-site trial with incident T2DM as the primary outcome. TRIAL REGISTRATION: NCT05235425. Registered February 11, 2022.

Prediction of transient ischemic attack and minor stroke in people with type 2 diabetes mellitus.

AIMS: People with type 2 diabetes (T2DM) have an increased risk of transient ischemic attack and minor stroke (TIA) which are frequently followed by an ischemic stroke. We aimed to develop a predictive model for incident TIA in people with T2DM. METHODS: We pooled data from two longitudinal cohort studies, Atherosclerosis Risk in Communities (ARIC) and the Cardiovascular Health Study (CHS), using a two-stage approach. First, we used a random effects model to interpolate risk factors of individuals between follow-up exams. Second, we used forward selection to develop a proportional hazards model for time to incident TIA. We internally validated our model using 10-fold cross-validation. RESULTS: Among 3575 participants with T2DM, mean (SD) age was 60 (10) years and body mass index was 30 (6) kg/m2. Sixty-nine incident TIAs occurred during 38,364 person-years of follow-up. The multivariable model included age at diagnosis of diabetes (hazard ratio 1.13 (95% confidence interval: 1.05,1.21) per year), systolic blood pressure (1.25 (1.04,1.49) per 10 mmHg), a quadratic function of diastolic blood pressure, and history of congestive heart failure (2.08 (1.26, 3.42)). The median cross-validated Harrell's C-index was 0.80. CONCLUSION: Blood pressure and heart failure are risk factors for the earliest stages of cerebrovascular disease.

Attrition Bias in an Observational Study of Very Low-Energy Diet: A Cohort Study.

OBJECTIVE: Obesity treatment is plagued by attrition. Estimates of attrition bias are needed. Thus, in this study, percent change from baseline BMI at 1, 2, and 3 years following enrollment in a 2-year weight management program using a very low-energy diet was calculated. Program data were supplemented with information from medical records. METHODS: Attrition was classified as occurring early (<6 months), late (6-21 months), at program completion (22-28 months), and after program completion (>28 months). Stepwise multivariable regression examined attrition and other covariates. RESULTS: A total of 881 subjects had ≥3 years of follow-up. BMI decreased by a mean (SD) of 11.8 (9.2), 8.6 (9.3), and 5.2 (10.0) kg/m2 at 1, 2, and 3 years after enrollment, respectively. At year 1, every 10-kg/m2 increase in baseline BMI was associated with a 2% (95% CI: 1%-3%) decrease in BMI. Individuals with early attrition decreased their mean BMI by 13% (11%-15%) less than program completers and by 9% (7%-11%) at 2 years. At 3 years, there was no significant difference in BMI between individuals with early attrition and program completers. However, BMI decreased 5% (3%- 8%) more in individuals who extended participation compared with program completers. CONCLUSIONS: Reported outcomes of weight management programs must account for program attrition.

Impact of an online multicomponent very-low-carbohydrate program in women with polycystic ovary syndrome: a pilot study.

OBJECTIVE: To study the impact of a very-low-carbohydrate (VLC) diet for 16 weeks in overweight or obese women with polycystic ovary syndrome (PCOS). DESIGN: Single-arm prospective pilot study. SETTING: We recruited participants using medical records from an academic medical center. PATIENTS: Twenty-nine overweight or obese women (body mass index, 25-50 kg/m2) with PCOS. INTERVENTIONS: We taught participants to follow a VLC diet and provided information about a variety of behavioral skills including mindfulness and positive affect using an online 16-week intervention. MAIN OUTCOME MEASURES: Changes in body weight, glycated hemoglobin, and PCOS-related quality of life. RESULTS: The intervention led to positive health outcomes including decreases in percent weight (mean difference = -7.67, SD = 6.10) and glycated hemoglobin level (mean difference = -0.21%, SD = 0.27), an increase in sex hormone binding globulin level (mean difference = 9.24 nmol/L, SD = 16.34), and increases in PCOS-related quality of life measures, including menstrual predictability (mean difference = 2.10, SD = 2.76) and body hair (mean difference = 1.14, SD = 1.04). The low-density lipoprotein cholesterol level increased (mean difference = 0.23 mmol/L, SD = 0.49). CONCLUSIONS: The results suggest that a VLC dietary intervention has potential to promote both weight loss and glycemic control in overweight and obese adults with PCOS, two key components in the prevention of type 2 diabetes. TRIAL REGISTRATION NUMBER: NCT03987854.

Chronic disease modeling and simulation software.

Computers allow describing the progress of a disease using computerized models. These models allow aggregating expert and clinical information to allow researchers and decision makers to forecast disease progression. To make this forecast reliable, good models and therefore good modeling tools are required. This paper will describe a new computer tool designed for chronic disease modeling. The modeling capabilities of this tool were used to model the Michigan model for diabetes. The modeling approach and its advantages such as simplicity, availability, and transparency are discussed.

Indirect estimation of a discrete-state discrete-time model using secondary data analysis of regression data.

Multi-state models of chronic disease are becoming increasingly important in medical research to describe the progression of complicated diseases. However, studies seldom observe health outcomes over long time periods. Therefore, current clinical research focuses on the secondary data analysis of the published literature to estimate a single transition probability within the entire model. Unfortunately, there are many difficulties when using secondary data, especially since the states and transitions of published studies may not be consistent with the proposed multi-state model. Early approaches to reconciling published studies with the theoretical framework of a multi-state model have been limited to data available as cumulative counts of progression. This paper presents an approach that allows the use of published regression data in a multi-state model when the published study may have ignored intermediary states in the multi-state model. Colloquially, we call this approach the Lemonade Method since when study data give you lemons, make lemonade. The approach uses maximum likelihood estimation. An example is provided for the progression of heart disease in people with diabetes.

Weight Mobility and Obesity in a Representative Sample of the US Adult Population.

BACKGROUND: Despite the attention given to the prevalence of obesity, surprisingly little is known about the incidence or reduction of obesity. We report the 1-year incidence and remission of obesity in a representative sample of the US population. METHODS: Individuals from the Medical Expenditure Panel Survey (MEPS) panel 17 were classified into standard obesity categories at enrollment and one year later. Incidence rates were calculated by age. RESULTS: Although the overall prevalence of obesity remained nearly constant, remission rates from obesity (stratified by age) ranged from 11 to 27% while incidence rates ranged from 6 to 16%. For almost all age levels, the proportion of individuals leaving an obese or overweight state was greater than or equal to the proportion who progressed to a more severe level of overweight or obesity. Overall, 36% of adults lost at least 2.5 kg/m2 in the one-year period; only 8% gained 2.5 kg/m2 or more. Individuals less than 25 years of age had higher rates of leaving overweight (23% versus <16%) and obesity (27% versus 24%) classifications than people of other ages. CONCLUSIONS: Prevalence rates of obesity are well documented in the United States, but incidence is understudied. Public health efforts that target young people with overweight or obesity may yield the greatest benefit.

The Effect of Attrition on Reported Diabetes Remission Rates Following Roux-en-Y Gastric Bypass: a Sensitivity Analysis.

OBJECTIVE: Attrition, or loss to follow-up, is a common problem in studies of type 2 diabetes remission following roux-en-Y gastric bypass (RYGB) and is often correlated with weight loss. Thus, reported rates of remission may be inflated by attrition bias. We investigate the effect of attrition bias on reported diabetes remission rates following RYGB. METHODS: Using sensitivity analyses, we identified sets of attrition and remission rates that produced simulated outcomes within 95% confidence intervals of the reported outcomes from five studies of diabetes remission following RYGB. RESULTS: Potential attrition bias varied greatly, yielding possible remission rates of diabetes ranging from 20 to 40% at 1 year. For studies with the attrition greater than ~ 20%, estimates that ignored attrition overestimated diabetes remission rates. Kaplan-Meier estimates were less affected by attrition. Potential for bias was most evident in the study with the largest sample size. CONCLUSION: Researchers, clinicians, and policymakers can measure potential attrition bias in clinical studies. In the case of remission of diabetes following RYGB, the potential bias in reported remission rates is generally less than 10%, varies considerably among studies, and is primarily driven by attrition rate and study size. Studies with very large sample sizes may provide a narrow confidence interval around a biased estimate.

Temporary Reductions in Insulin Requirements Are Associated with Hypoglycemia in Type 2 Diabetes.

Background: In patients with type 2 diabetes, insulin therapy necessitates regular and frequent dosage titration to overcome variations in insulin requirements. The goal of this study was to evaluate changes in insulin requirements, using data from a technology-based insulin-titration service. Methods: To keep glycemia stable, the service adjusts and records insulin dosage at least weekly. Therefore, insulin dosage closely tracks insulin requirement. Events of considerable and persistent decrease in insulin requirements were identified by reductions in total daily dose (TDD) of insulin ≥25%. Periods ended when a persistent increase in TDD of insulin has started. The average frequency of hypoglycemia was expressed as any glucose reading <54 mg/dL (both inside or outside periods of decrease in insulin dosage) divided by the total number of months for each patient. Results: Patients (n = 246) were followed for 2.8 ± 0.9 years. Reductions of TDD of insulin were experienced by 70.3% of the patients, occurred 0.8 ± 0.5 times per year, lasted 10.0 ± 7.7 weeks, and insulin requirements declined by 39.9% ± 12.6%. The frequency of hypoglycemia (<54 mg/dL) was low, at 0.5 ± 0.6 per month, and the difference in frequencies in biphasic/premixed and basal-bolus insulin regimens was not statistically significant. Hypoglycemia was 6.5 times more prevalent during reductions in TDD of insulin. Conclusions: Sizeable changes in insulin requirements occur over time, which demand persistent and frequent titration to preserve treatment safety.

A computer simulation model of diabetes progression, quality of life, and cost.

OBJECTIVE: To develop and validate a comprehensive computer simulation model to assess the impact of screening, prevention, and treatment strategies on type 2 diabetes and its complications, comorbidities, quality of life, and cost. RESEARCH DESIGN AND METHODS: The incidence of type 2 diabetes and its complications and comorbidities were derived from population-based epidemiologic studies and randomized, controlled clinical trials. Health utility scores were derived for patients with type 2 diabetes using the Quality of Well Being-Self-Administered. Direct medical costs were derived for managed care patients with type 2 diabetes using paid insurance claims. Monte Carlo techniques were used to implement a semi-Markov model. Performance of the model was assessed using baseline and 4- and 10-year follow-up data from the older-onset diabetic population studied in the Wisconsin Epidemiologic Study of Diabetic Retinopathy (WESDR). RESULTS: Applying the model to the baseline WESDR population with type 2 diabetes, we predicted mortality to be 51% at 10 years. The prevalences of stroke and myocardial infarction were predicted to be 18 and 19% at 10 years. The prevalences of nonproliferative diabetic retinopathy, proliferative retinopathy, and macular edema were predicted to be 45, 16, and 18%, respectively; the prevalences of microalbuminuria, proteinuria, and end-stage renal disease were predicted to be 19, 39, and 3%, respectively; and the prevalences of clinical neuropathy and amputation were predicted to be 52 and 5%, respectively, at 10 years. Over 10 years, average undiscounted total direct medical costs were estimated to be USD $53,000 per person. Among survivors, the average utility score was estimated to be 0.56 at 10 years. CONCLUSIONS: Our computer simulation model accurately predicted survival and the cardiovascular, microvascular, and neuropathic complications observed in the WESDR cohort with type 2 diabetes over 10 years. The model can be used to predict the progression of diabetes and its complications, comorbidities, quality of life, and cost and to assess the relative effectiveness, cost-effectiveness, and cost-utility of alternative strategies for the prevention and treatment of type 2 diabetes.

Reconciliation of Type 2 Diabetes Remission Rates in Studies of Roux-en-Y Gastric Bypass.

OBJECTIVE: Bariatric surgery may induce remission of type 2 diabetes in obese patients. However, estimates of remission rates reported in the literature range from 25 to 81%, contributing to the uncertainty patients and physicians both face as they assess treatment options. This analysis attempts to reconcile the seemingly disparate rates of diabetes remission reported in studies of Roux-en-Y gastric bypass (RYGB) surgery. It examines variation in the methodologies used to derive the estimates and proposes outcomes that should be reported by all studies. RESEARCH DESIGN AND METHODS: A literature review yielded 10 large (n > 100), recent (index surgery since 2000) studies of diabetes remission after RYGB. These studies differed in definitions of remission (partial vs. complete), lengths of follow-up (1 year vs. ≥3 years), reported outcomes (cumulative vs. prevalent remission), and risks of attrition bias. RESULTS: Reported rates of partial remission were 10-30 percentage points higher than rates of complete remission. Study duration explained 69% of the variability in cumulative remission rates, plateauing at 3 years. Adjustment for attrition increased the explained variability to 87%. Attrition-adjusted, 3-year cumulative, complete remission rates ranged from 63 to 65%; however, this does not account for relapse. Attrition-adjusted, 3-year prevalent complete remission rates that accounted for relapse were 23%. CONCLUSIONS: Variations in reported rates of diabetes remission after RYGB are primarily related to definitions and study duration. Future studies should report both cumulative and prevalent remission to aid decision making and more easily compare studies.

Improving diabetes processes of care in managed care.

OBJECTIVE: To evaluate the impact of systematic patient evaluation and patient and provider feedback on the processes and intermediate outcomes of diabetes care in Independent Practice Association model internal medicine practices. RESEARCH DESIGN AND METHODS: Nine practices providing care to managed care patients were randomly assigned as intervention or comparison sites. Intervention-site subjects had Annual Diabetes Assessment Program (ADAP) assessments (HbA(1c), blood pressure, lipids, smoking, retinal photos, urine microalbumin, and foot examination) at years 1 and 2. Comparison-site subjects had ADAP assessments at year 2. At Intervention sites, year 1 ADAP results were reviewed with subjects, mailed to providers, and incorporated into electronic medical records with guideline-generated suggestions for treatment and follow-up. Medical records were evaluated for both groups for the year before both the year 1 and year 2 ADAP assessments. Processes and intermediate outcomes were compared using linear and logistic mixed hierarchical models. RESULTS: Of 284 eligible subjects, 103 of 173 (60%) at the Intervention sites and 71 of 111 (64%) at the comparison sites participated; 83 of 103 (81%) of the intervention-site subjects returned for follow-up at year 2. Performance of the six recommended assessments improved in intervention-site subjects at year 2 compared with year 1 (5.8 vs. 4.3, P = 0.0001) and compared with comparison-site subjects at year 2 (4.2, P = 0.014). No significant changes were noted in intermediate outcomes. CONCLUSIONS: The ADAP significantly improved processes of care but not intermediate outcomes. Additional interventions are needed to improve intermediate outcomes.

A discrete-state discrete-time model using indirect observation.

This research was motivated by a desire to model the progression of a chronic disease through various disease stages when data are not available to directly estimate all the transition parameters in the model. This is a common occurrence when time and expense make it unfeasible to follow a single cohort to estimate all the transition parameters. One difficulty of developing a model of chronic disease progression from such data is that the available studies often do not include the transitions of interest. For example, in our model of diabetic nephropathy, many clinical studies did not differentiate between patients without nephropathy and those who had microalbuminuria (a pre-clinical stage of nephropathy). Another difficulty was a lack of data to directly estimate parameters of interest. We consider models which can accommodate such difficulties. In this paper we consider the problem of estimating parameters of a discrete-time Markov process when longitudinal data describing the entire process are not available. First, we present a likelihood approach to estimate parameters of a discrete-time Markov model. Next, we use simulation to investigate the finite-sample behaviour of our approach. Finally, we present two examples: a model of diabetic nephropathy and a model of cardiovascular disease in diabetes.