Glycemia Reduction in Type 2 Diabetes - Glycemic Outcomes.

BACKGROUND: The comparative effectiveness of glucose-lowering medications for use with metformin to maintain target glycated hemoglobin levels in persons with type 2 diabetes is uncertain. METHODS: In this trial involving participants with type 2 diabetes of less than 10 years' duration who were receiving metformin and had glycated hemoglobin levels of 6.8 to 8.5%, we compared the effectiveness of four commonly used glucose-lowering medications. We randomly assigned participants to receive insulin glargine U-100 (hereafter, glargine), the sulfonylurea glimepiride, the glucagon-like peptide-1 receptor agonist liraglutide, or sitagliptin, a dipeptidyl peptidase 4 inhibitor. The primary metabolic outcome was a glycated hemoglobin level, measured quarterly, of 7.0% or higher that was subsequently confirmed, and the secondary metabolic outcome was a confirmed glycated hemoglobin level greater than 7.5%. RESULTS: A total of 5047 participants (19.8% Black and 18.6% Hispanic or Latinx) who had received metformin for type 2 diabetes were followed for a mean of 5.0 years. The cumulative incidence of a glycated hemoglobin level of 7.0% or higher (the primary metabolic outcome) differed significantly among the four groups (P<0.001 for a global test of differences across groups); the rates with glargine (26.5 per 100 participant-years) and liraglutide (26.1) were similar and lower than those with glimepiride (30.4) and sitagliptin (38.1). The differences among the groups with respect to a glycated hemoglobin level greater than 7.5% (the secondary outcome) paralleled those of the primary outcome. There were no material differences with respect to the primary outcome across prespecified subgroups defined according to sex, age, or race or ethnic group; however, among participants with higher baseline glycated hemoglobin levels there appeared to be an even greater benefit with glargine, liraglutide, and glimepiride than with sitagliptin. Severe hypoglycemia was rare but significantly more frequent with glimepiride (in 2.2% of the participants) than with glargine (1.3%), liraglutide (1.0%), or sitagliptin (0.7%). Participants who received liraglutide reported more frequent gastrointestinal side effects and lost more weight than those in the other treatment groups. CONCLUSIONS: All four medications, when added to metformin, decreased glycated hemoglobin levels. However, glargine and liraglutide were significantly, albeit modestly, more effective in achieving and maintaining target glycated hemoglobin levels. (Funded by the National Institute of Diabetes and Digestive and Kidney Diseases and others; GRADE ClinicalTrials.gov number, NCT01794143.).

Modeling longitudinal change in biomarkers using data from a complex survey sampling design: An application to the Hispanic Community Health Study/Study of Latinos.

In observational cohort studies, there is frequently interest in modeling longitudinal change in a biomarker (ie, physiological measure indicative of metabolic dysregulation or disease; eg, blood pressure) in the absence of treatment (ie, medication), and its association with modifiable risk factors expected to affect health (eg, body mass index). However, individuals may start treatment during the study period, and consequently biomarker values observed while on treatment may be different than those that would have been observed in the absence of treatment. If treated individuals are excluded from analysis, then effect estimates may be biased if treated individuals differ systematically from untreated individuals. We addressed this concern in the setting of the Hispanic Community Health Study/Study of Latinos (HCHS/SOL), an observational cohort study that employed a complex survey sampling design to enable inference to a finite target population. We considered biomarker values measured while on treatment to be missing data, and applied missing data methodology (inverse probability weighting (IPW) and doubly robust estimation) to this problem. The proposed methods leverage information collected between study visits on when individuals started treatment, by adapting IPW and doubly robust approaches to model the treatment mechanism using survival analysis methods. This methodology also incorporates sampling weights and uses a bootstrap approach to estimate standard errors accounting for the complex survey sampling design. We investigated variance estimation for these methods, conducted simulation studies to assess statistical performance in finite samples, and applied the methodology to model temporal change in blood pressure in HCHS/SOL.

Comparisons of statistical methods for handling attrition in a follow-up visit with complex survey sampling.

Design-based analysis, which accounts for the design features of the study, is commonly used to conduct data analysis in studies with complex survey sampling, such as the Hispanic Community Health Study/Study of Latinos (HCHS/SOL). In this type of longitudinal study, attrition has often been a problem. Although there have been various statistical approaches proposed to handle attrition, such as inverse probability weighting (IPW), non-response cell weighting (NRCW), multiple imputation (MI), and full information maximum likelihood (FIML) approach, there has not been a systematic assessment of these methods to compare their performance in design-based analyses. In this article, we perform extensive simulation studies and compare the performance of different missing data methods in linear and generalized linear population models, and under different missing data mechanism. We find that the design-based analysis is able to produce valid estimation and statistical inference when the missing data are handled appropriately using IPW, NRCW, MI, or FIML approach under missing-completely-at-random or missing-at-random missing mechanism and when the missingness model is correctly specified or over-specified. We also illustrate the use of these methods using data from HCHS/SOL.

A doubly robust method to handle missing multilevel outcome data with application to the China Health and Nutrition Survey.

Missing data are common in longitudinal cohort studies and can lead to bias, particularly in studies with informative missingness. Many common methods for handling informatively missing data in survey samples require correctly specifying a model for missingness. Although doubly robust methods exist to provide unbiased regression coefficients in the presence of missing outcome data, these methods do not account for correlation due to clustering inherent in longitudinal or cluster-sampled studies. In this work, we developed a doubly robust method to estimate the regression of an outcome on a predictor in the presence of missing multilevel data on the outcome, which results in consistent estimation of regression coefficients assuming correct specification of either (1) the probability of missingness or (2) the outcome model. This method involves specification of separate hierarchical models for missingness and for the outcome, conditional on observed auxiliary variables and cluster-specific random effects, to account for correlation among observations. We showed this proposed estimator is doubly robust and derived its asymptotic distribution, conducted simulation studies to compare the method to an existing doubly robust method developed for independent data, and applied the method to data from the China Health and Nutrition Survey, an ongoing multilevel longitudinal cohort study.

Demographic and sociocultural risk factors for adulthood weight gain in Hispanic/Latinos: results from the Hispanic Community Health Study / Study of Latinos (HCHS/SOL).

BACKGROUND: United States (US) Hispanic/Latinos experience a disproportionate burden of obesity, which may in part be related to demographic or sociocultural factors, including acculturation to an US diet or inactive lifestyle. Therefore, we sought to describe the association between adulthood weight histories and demographic and sociocultural factors in a large diverse community-based cohort of US Hispanic/Latinos. METHODS: We estimated the effect of several factors on weight gain across adulthood, using multivariable linear mixed models to leverage 38,759 self-reported current body weights and weight histories recalled for 21, 45 and 65 years of age, from 15,203 adults at least 21 years of age at the baseline visit of the Hispanic Community Health Study/Study of Latinos (2008-2011). RESULTS: The average rate of weight gain was nearly 10 kg per decade in early adulthood, but slowed to < 5 kg a decade among individuals 60+ years of age. Birth cohort, gender, nativity or age at immigration, Hispanic/Latino background, and study site each significantly modified the form of the predicted adulthood weight trajectory. Among immigrants, weight gain during the 5 years post-migration was on average 0.88 kg (95% CI: 0.04, 1.72) greater than the weight gain during the 5 years prior. The rate of weight gain appeared to slow after 15 years post-migration. CONCLUSIONS: Using self-reported and weight history data in a diverse sample of US Hispanic/Latinos, we revealed that both demographic and sociocultural factors were associated with the patterning of adulthood weight gain in this sample. Given the steep rate of weight gain in this population and the fact that many Hispanic/Latinos living in the US immigrated as adults, efforts to promote weight maintenance across the life course, including after immigration, should be a top priority for promoting Hispanic/Latino health and addressing US health disparities more broadly.

Long-term consequences of early sexual initiation on young adult health: A causal inference approach.

Although early sexual initiation has been linked to negative outcomes, it is unknown whether these effects are causal. In this study, we use propensity score methods to estimate the causal effect of early sexual initiation on young adult sexual risk behaviors and health outcomes using data from the National Longitudinal Study of Adolescent to Adult Health. We found that early sexual initiation predicted having 2 or more partners (for both males and females) and having a sexually transmitted infection in the past year (females only) but did not predict depressive symptoms in the past week (for either gender). These results underscore the importance of continued programmatic efforts to delay age of sexual initiation, particularly for females.

Patterns of adolescent sexual behavior predicting young adult sexually transmitted infections: a latent class analysis approach.

Adolescent sexual behavior is multidimensional, yet most studies of the topic use variable-oriented methods that reduce behaviors to a single dimension. In this study, we used a person-oriented approach to model adolescent sexual behavior comprehensively, using data from the National Longitudinal Study of Adolescent Health. We identified five latent classes of adolescent sexual behavior: Abstinent (39%), Oral Sex (10%), Low-Risk (25%), Multi-Partner Normative (12%), and Multi-Partner Early (13%). Membership in riskier classes of sexual behavior was predicted by substance use and depressive symptoms. Class membership was also associated with young adult STI outcomes although these associations differed by gender. Male adolescents' STI rates increased with membership in classes with more risky behaviors whereas females' rates were consistent among all sexually active classes. These findings demonstrate the advantages of examining adolescent sexuality in a way that emphasizes its complexity.

A framework for estimating causal effects in latent class analysis: is there a causal link between early sex and subsequent profiles of delinquency?

Prevention scientists use latent class analysis (LCA) with increasing frequency to characterize complex behavior patterns and profiles of risk. Often, the most important research questions in these studies involve establishing characteristics that predict membership in the latent classes, thus describing the composition of the subgroups and suggesting possible points of intervention. More recently, prevention scientists have begun to adopt modern methods for drawing causal inference from observational data because of the bias that can be introduced by confounders. This same issue of confounding exists in any analysis of observational data, including prediction of latent class membership. This study demonstrates a straightforward approach to causal inference in LCA that builds on propensity score methods. We demonstrate this approach by examining the causal effect of early sex on subsequent delinquency latent classes using data from 1,890 adolescents in 11th and 12th grade from wave I of the National Longitudinal Study of Adolescent Health. Prior to the statistical adjustment for potential confounders, early sex was significantly associated with delinquency latent class membership for both genders (p = 0.02). However, the propensity score adjusted analysis indicated no evidence for a causal effect of early sex on delinquency class membership (p = 0.76) for either gender. Sample R and SAS code is included in an Appendix in the ESM so that prevention scientists may adopt this approach to causal inference in LCA in their own work.

Association of Baseline Factors With Glycemic Outcomes in GRADE: A Comparative Effectiveness Randomized Clinical Trial.

OBJECTIVE: To describe the individual and joint associations of baseline factors with glycemia, and also with differential effectiveness of medications added to metformin. RESEARCH DESIGN AND METHODS: Glycemia Reduction Approaches in Diabetes: A Comparative Effectiveness Study (GRADE) participants (with type 2 diabetes diagnosed for <10 years, on metformin, and with HbA1c 6.8-8.5%; N = 5,047) were randomly assigned to a basal insulin (glargine), sulfonylurea (glimepiride), glucagon-like peptide 1 agonist (liraglutide), or dipeptidyl peptidase 4 inhibitor (sitagliptin). The glycemic outcome was HbA1c ≥7.0%, subsequently confirmed. Univariate and multivariate regression and classification and regression tree (CART) analyses were used to assess the association of baseline factors with the glycemic outcome at years 1 and 4. RESULTS: In univariate analyses at baseline, younger age (<58 years), Hispanic ethnicity, higher HbA1c, fasting glucose, and triglyceride levels, lower insulin secretion, and relatively greater insulin resistance were associated with the glycemic outcome at years 1 and/or 4. No factors were associated with differential effectiveness of the medications by year 4. In multivariate analyses, treatment group, younger age, and higher baseline HbA1c and fasting glucose were jointly associated with the glycemic outcome by year 4. The superiority of glargine and liraglutide at year 4 persisted after multiple baseline factors were controlled for. CART analyses indicated that failure to maintain HbA1c <7% by year 4 was more likely for younger participants and those with baseline HbA1c ≥7.4%. CONCLUSIONS: Several baseline factors were associated with the glycemic outcome but not with differential effectiveness of the four medications. Failure to maintain HbA1c <7% was largely driven by younger age and higher HbA1c at baseline. Factors that predict earlier glycemic deterioration could help in targeting patients for more aggressive management.

Impact of Insulin Sensitivity and ß-Cell Function Over Time on Glycemic Outcomes in the Glycemia Reduction Approaches in Diabetes: A Comparative Effectiveness Study (GRADE): Differential Treatment Effects of Dual Therapy.

OBJECTIVE: To compare the effects of insulin sensitivity and ß-cell function over time on HbA1c and durability of glycemic control in response to dual therapy. RESEARCH DESIGN AND METHODS: GRADE participants were randomized to glimepiride (n = 1,254), liraglutide (n = 1,262), or sitagliptin (n = 1,268) added to baseline metformin and followed for mean ± SD 5.0 ± 1.3 years, with HbA1c assessed quarterly and oral glucose tolerance tests at baseline, 1, 3, and 5 years. We related time-varying insulin sensitivity (HOMA 2 of insulin sensitivity [HOMA2-%S]) and early (0-30 min) and total (0-120 min) C-peptide (CP) responses to changes in HbA1c and glycemic failure (primary outcome HbA1c ≥7% [53 mmol/mol] and secondary outcome HbA1c >7.5% [58 mmol/mol]) and examined differential treatment responses. RESULTS: Higher HOMA2-%S was associated with greater initial HbA1c lowering (3 months) but not subsequent HbA1c rise. Greater CP responses were associated with a greater initial treatment response and slower subsequent HbA1c rise. Higher HOMA2-%S and CP responses were each associated with lower risk of primary and secondary outcomes. These associations differed by treatment. In the sitagliptin group, HOMA2-%S and CP responses had greater impact on initial HbA1c reduction (test of heterogeneity, P = 0.009 HOMA2-%S, P = 0.018 early CP, P = 0.001 total CP) and risk of primary outcome (P = 0.005 HOMA2-%S, P = 0.11 early CP, P = 0.025 total CP) but lesser impact on HbA1c rise (P = 0.175 HOMA2-%S, P = 0.006 early CP, P < 0.001 total CP) in comparisons with the glimepiride and liraglutide groups. There were no differential treatment effects on secondary outcome. CONCLUSIONS: Insulin sensitivity and ß-cell function affected treatment outcomes irrespective of drug assignment, with greater impact in the sitagliptin group on initial (short-term) HbA1c response in comparison with the glimepiride and liraglutide groups.

The Use of Rescue Insulin in the Glycemia Reduction Approaches in Diabetes: A Comparative Effectiveness Study (GRADE).

OBJECTIVE: To describe rescue insulin use and associated factors in the Glycemia Reduction Approaches in Diabetes: A Comparative Effectiveness Study (GRADE). RESEARCH DESIGN AND METHODS: GRADE participants (type 2 diabetes duration <10 years, baseline A1C 6.8%-8.5% on metformin monotherapy, N = 5,047) were randomly assigned to insulin glargine U-100, glimepiride, liraglutide, or sitagliptin and followed quarterly for a mean of 5 years. Rescue insulin (glargine or aspart) was to be started within 6 weeks of A1C >7.5%, confirmed. Reasons for delaying rescue insulin were reported by staff-completed survey. RESULTS: Nearly one-half of GRADE participants (N = 2,387 [47.3%]) met the threshold for rescue insulin. Among participants assigned to glimepiride, liraglutide, or sitagliptin, rescue glargine was added by 69% (39% within 6 weeks). Rescue aspart was added by 44% of glargine-assigned participants (19% within 6 weeks) and by 30% of non-glargine-assigned participants (14% within 6 weeks). Higher A1C values were associated with adding rescue insulin. Intention to change health behaviors (diet/lifestyle, adherence to current treatment) and not wanting to take insulin were among the most common reasons reported for not adding rescue insulin within 6 weeks. CONCLUSIONS: Proportionately, rescue glargine, when required, was more often used than rescue aspart, and higher A1C values were associated with greater rescue insulin use. Wanting to use noninsulin strategies to improve glycemia was commonly reported, although multiple factors likely contributed to not using rescue insulin. These findings highlight the persistent challenge of intensifying type 2 diabetes treatment with insulin, even in a clinical trial.

Drawing causal inferences using propensity scores: a practical guide for community psychologists.

Confounding present in observational data impede community psychologists' ability to draw causal inferences. This paper describes propensity score methods as a conceptually straightforward approach to drawing causal inferences from observational data. A step-by-step demonstration of three propensity score methods-weighting, matching, and subclassification-is presented in the context of an empirical examination of the causal effect of preschool experiences (Head Start vs. parental care) on reading development in kindergarten. Although the unadjusted population estimate indicated that children with parental care had substantially higher reading scores than children who attended Head Start, all propensity score adjustments reduce the size of this overall causal effect by more than half. The causal effect was also defined and estimated among children who attended Head Start. Results provide no evidence for improved reading if those children had instead received parental care. We carefully define different causal effects and discuss their respective policy implications, summarize advantages and limitations of each propensity score method, and provide SAS and R syntax so that community psychologists may conduct causal inference in their own research.

Polyvictimization and Adolescent Health and Well-Being in Ethiopia: The Mediating Role of Resilience.

Interpersonal violence is a pervasive experience affecting one billion children and adolescents annually, resulting in adverse health and well-being outcomes. Evidence suggests that polyvictimization, the experience of multiple forms of violence, is associated with more harmful consequences for adolescents than experiencing individual types of violence, although data from low-and middle-income countries are limited. This study analyzed data on over 4100 adolescents from the Gender and Adolescence, Global Evidence Study in Ethiopia to examine the association between polyvictimization and adolescent mental and physical health and the mediating role of resilience using linear regression and path analysis. We hypothesized that adolescents experiencing polyvictimization would experience worse mental and physical health than those experiencing no types or individual types of victimization, and that resilience would mediate these relationships. Half of sampled girls and over half of boys experienced polyvictimization. Among both sexes, polyvictimization was associated with worse mental but not worse physical health. Resilience mediated the association between polyvictimization and mental health among girls only. Strengthening resilience among girls may be an effective avenue for mitigating polyvictimization's negative mental health effects, but additional research and programming for preventing and identifying polyvictimized adolescents and linking them to care is needed.

Higher burden of cardiometabolic and socioeconomic risk factors in women with type 2 diabetes: an analysis of the Glycemic Reduction Approaches in Diabetes (GRADE) baseline cohort.

INTRODUCTION: Type 2 diabetes mellitus (T2DM) is a powerful risk factor for cardiovascular disease (CVD), conferring a greater relative risk in women than men. We sought to examine sex differences in cardiometabolic risk factors and management in the contemporary cohort represented by the Glycemia Reduction Approaches in Diabetes: A Comparative Effectiveness Study (GRADE). RESEARCH DESIGN AND METHODS: GRADE enrolled 5047 participants (1837 women, 3210 men) with T2DM on metformin monotherapy at baseline. The current report is a cross-sectional analysis of baseline data collected July 2013 to August 2017. RESULTS: Compared with men, women had a higher mean body mass index (BMI), greater prevalence of severe obesity (BMI≥40 kg/m2), higher mean LDL cholesterol, greater prevalence of low HDL cholesterol, and were less likely to receive statin treatment and achieve target LDL, with a generally greater prevalence of these risk factors in younger women. Women with hypertension were equally likely to achieve blood pressure targets as men; however, women were less likely to receive ACE inhibitors or angiotensin receptor blockers. Women were more likely to be divorced, separated or widowed, and had fewer years of education and lower incomes. CONCLUSIONS: This contemporary cohort demonstrates that women with T2DM continue to have a greater burden of cardiometabolic and socioeconomic risk factors than men, particularly younger women. Attention to these persisting disparities is needed to reduce the burden of CVD in women. TRIAL REGISTRATION NUMBER: ClinicalTrials.gov (NCT01794143).

Preconception Cardiometabolic Markers and Birth Outcomes Among Women in the Hispanic Community Health Study/Study of Latinos.

Background: Associations between preconception cardiometabolic markers and birth outcomes have been noted, but data are scarce for Hispanics/Latinos. We examined the association between preconception cardiometabolic markers, birthweight and preterm birth among U.S. Hispanic/Latina women. Materials and Methods: The Hispanic Community Health Study/Study of Latinos is a cohort study of U.S. adults 18-74 years of age, including 3,798 women of reproductive age (18-44 years) from four field centers representing Hispanic/Latino backgrounds of Cuban, Dominican, Mexican, Puerto Rican, Central American, and South American. A baseline clinic examination (2008-2011) and a second clinic examination (2014-2017), including ascertainment of birth outcomes, allowed for identification of 517 singleton live births between the exams. Preconception cardiometabolic markers included abdominal obesity (waist circumference ≥88 cm), body mass index >30 kg/m2, high blood pressure (systolic ≥120 mmHg and diastolic ≥80 mmHg), elevated triglycerides (≥150 mg/dL), low high-density lipoprotein cholesterol (<50 mg/dL), elevated fasting glucose (≥100 mg/dL), and insulin. Complex survey linear regression modeled the association between cardiometabolic markers and birthweight-for-gestational age z-score; complex survey logistic regression modeled the association with preterm birth. Analyses adjusted for Hispanic/Latina background, field center, years between baseline and birth, age, and nulliparity. Results: In adjusted linear regression models, elevated fasting glucose was associated with higher birthweight z-scores (ß = 0.56, 95% confidence interval [95% CI] 0.14 to 0.99), even after further adjustment for maternal percent body fat (ß = 0.53, 95% CI 0.10 to 0.95). In adjusted logistic regression models, high blood pressure (odds ratio [OR] = 2.57, 95% CI 1.13 to 5.88) and increased insulin (OR = 1.50, 95% CI 1.06 to 2.14, for a 10 mU/L increase) were associated with higher odds for preterm birth. Conclusions: Infant birthweight and preterm birth may be influenced by selected cardiometabolic risk factors before pregnancy among Hispanic/Latina women.

Preconception Diet Quality Is Associated with Birth Weight for Gestational Age Among Women in the Hispanic Community Health Study/Study of Latinos.

BACKGROUND: The nutritional status of women in the preconception period is of paramount importance due to its role in reproduction. OBJECTIVE: Our aim was to assess overall diet quality during the preconception period and its association with infant birth weight adjusted for gestational age (GA). DESIGN: This is an observational longitudinal cohort of Hispanic people living in the United States. PARTICIPANTS/SETTING: Data are from the Hispanic Community Health Study/Study of Latinos baseline (2008-2011) and second clinic examinations (2014-2017). Included are the first 497 singleton live-born infants among the 2,556 women (younger than 45 years) who attended the second visit. Field sites were located in Miami, FL; Bronx, NY; Chicago, IL; and San Diego, CA, and represent individuals with heritage from Cuba, Dominican Republic, Mexico, Puerto Rico, and Central and South America. MAIN EXPOSURE: Diet assessment included two 24-hour recalls from baseline. The 2010 Healthy Eating Index (HEI-2010) was used to measure diet quality, with higher scores indicating better quality. STATISTICAL ANALYSES PERFORMED: Complex survey linear regression estimated the association between HEI-2010 scores (continuous variable and categorized into tertiles) and birth-weight z score and birth weight for GA percentile. RESULTS: Mean (standard deviation) age of women was 25.8 (5.2) years and 36.4% were classified as underweight or normal weight, 30.0% were overweight, and 33.6% had obesity at baseline. Mean (standard deviation) HEI-2010 score was 56.5 (13.4), and by weight classifications was 54.4 (14.1) for underweight or normal weight and 57.7 (12.8) for overweight or obesity. Median (interquartile range) birth-weight z score was 0.5 (interquartile range [IQR], -0.2 to 1.3) overall and 0.2 (IQR, -0.5 to 1.0), 0.6 (IQR, -0.2 to 1.3), and 0.5 (IQR, -0.2 to 1.4) for the first, second, and third HEI-2010 tertile, respectively. Median birth weight for GA percentile was 68.2 (IQR, 40.2 to 89.7) overall, and 56.8 (IQR, 29.6 to 85.0), 71.5 (IQR, 42.8 to 90.0), and 70.1 (IQR, 42.9 to 91.2) by HEI-2010 tertile. In adjusted models, the highest tertile of the HEI-2010 score was associated with a higher birth-weight z score and birth weight for GA percentile, and the continuous HEI-2010 score was only associated with birth weight for GA percentile. Preconception body mass index (calculated as kg/m2) did not modify these associations. CONCLUSIONS: Overall diet quality, as measured by the HEI-2010, in the preconception period is associated with infant birth weight adjusted for GA among US Hispanic and Latina women.

Optimization of Metformin in the GRADE Cohort: Effect on Glycemia and Body Weight.

OBJECTIVE: We evaluated the effect of optimizing metformin dosing on glycemia and body weight in type 2 diabetes. RESEARCH DESIGN AND METHODS: This was a prespecified analysis of 6,823 participants in the Glycemia Reduction Approaches in Diabetes: A Comparative Effectiveness Study (GRADE) taking metformin as the sole glucose-lowering drug who completed a 4- to 14-week (mean ± SD 7.9 ± 2.4) run-in in which metformin was adjusted to 2,000 mg/day or a maximally tolerated lower dose. Participants had type 2 diabetes for <10 years and an HbA1c ≥6.8% (51 mmol/mol) while taking ≥500 mg of metformin/day. Participants also received diet and exercise counseling. The primary outcome was the change in HbA1c during run-in. RESULTS: Adjusted for duration of run-in, the mean ± SD change in HbA1c was -0.65 ± 0.02% (-7.1 ± 0.2 mmol/mol) when the dose was increased by ≥1,000 mg/day, -0.48 ± 0.02% (-5.2 ± 0.2 mmol/mol) when the dose was unchanged, and -0.23 ± 0.07% (-2.5 ± 0.8 mmol/mol) when the dose was decreased (n = 2,169, 3,548, and 192, respectively). Higher HbA1c at entry predicted greater reduction in HbA1c (P < 0.001) in univariate and multivariate analyses. Weight loss adjusted for duration of run-in averaged 0.91 ± 0.05 kg in participants who increased metformin by ≥1,000 mg/day (n = 1,894). CONCLUSIONS: Optimizing metformin to 2,000 mg/day or a maximally tolerated lower dose combined with emphasis on medication adherence and lifestyle can improve glycemia in type 2 diabetes and HbA1c values ≥6.8% (51 mmol/mol). These findings may help guide efforts to optimize metformin therapy among persons with type 2 diabetes and suboptimal glycemic control.

Hot Deck Multiple Imputation for Handling Missing Accelerometer Data.

Missing data due to non-wear are common in accelerometer studies measuring physical activity and sedentary behavior. Accelerometer output are high-dimensional time-series data that are episodic and often highly skewed, presenting unique challenges for handling missing data. Common methods for missing accelerometry either are ad-hoc, require restrictive parametric assumptions, or do not appropriately impute bouts. This study developed a flexible hot deck multiple imputation (MI; i.e., "replacing" missing data with observed values) procedure to handle missing accelerometry. For each missing segment of accelerometry, "donor pools" contained observed segments from either the same or different participants, and 10 imputed segments were randomly drawn from the donor pool according to selection weights, where the donor pool and selection weight depended on variables associated with non-wear and/or accelerometer-based measures. A simulation study of 2,550 women compared hot deck MI to two standard methods in the field: available case (AC) analysis (i.e., analyzing all observed accelerometry with no restriction on wear time or number of days) and complete case (CC) analysis (i.e., analyzing only participants that wore the accelerometer for ≥10 hours for 4-7 days). This was repeated using accelerometry from the entire 24-hour day and daytime (10am- 8pm) only, and data were missing at random. For the entire 24-hour day, MI produced less bias and better 95% confidence interval (CI) coverage than AC and CC. For the daytime only, MI produced less bias and better 95% CI coverage than AC; CC produced similar bias and 95% CI coverage, but longer 95% CIs than MI.

How Well Do U.S. Hispanics Adhere to the Dietary Guidelines for Americans? Results from the Hispanic Community Health Study/Study of Latinos.

Purpose: To examine alignment between 2010 Dietary Guidelines for Americans (DGA) and dietary choices of individuals in the Hispanic Community Health Study/Study of Latinos (HCHS/SOL) between 2008 and 2011. Methods: Data are from 15,633 adults 18-74 years from the population-based cohort in HCHS/SOL. The Healthy Eating Index (HEI) 2010 was used to measure diet quality. Means and standard errors (SEs) for the HEI total and each of the 12 component scores were calculated overall and by Hispanic/Latino heritage, sex, age group, and measures of acculturation. Linear regression was used to examine correlates of the HEI 2010 total score. All analyses accounted for complex survey design. Results: The overall HEI mean of 63.8 (SE: 0.4) varied across groups from a high (healthier diet) of 71 (SE: 0.9) among Mexicans to a low of 56 (SE: 0.1) among Puerto Ricans. The proportion with a maximum score for the HEI components varied across heritage groups; >25% of adults adhered to recommendations for total proteins, and seafood and plant proteins, whole fruits, and greens and beans, with the exception of Cubans and Puerto Ricans, who had lower adherence scores for the latter two. The components with the lowest adherence were sodium (<2%) and fatty acids (overall 7.4%) among all heritage groups. Characteristics associated with better adherence included sociodemographic variables, Spanish language preference, weight status, medical conditions, and lifestyle behaviors. Conclusions: Individuals with Mexican, Dominican, and Central American heritage had better overall dietary quality compared to other groups. However, all can improve their eating habits to align more with the DGAs by reducing sodium consumption and improving fatty acid ratios.

Association of the DASH dietary pattern with insulin resistance and diabetes in US Hispanic/Latino adults: results from the Hispanic Community Health Study/Study of Latinos (HCHS/SOL).

OBJECTIVE: To examine the association between diet quality and both diabetes status and insulin resistance in Hispanic/Latino adults, and the extent to which differences in diet quality contribute to differences in outcomes across Hispanic/Latino heritage. RESEARCH DESIGN AND METHODS: Cross-sectional study. Data are from 15 942 individuals enrolled in the Hispanic Community Health Study/Study of Latinos. Diet was ascertained using two 24-hour dietary recalls, and diet quality was measured using the Dietary Approaches to Stop Hypertension (DASH) score (range: 0-80, lowest to highest). Diabetes status was defined based on self-reported diagnosis, use of antihyperglycemic medications, or unrecognized diabetes (determined by baseline laboratory measures). Insulin resistance was determined using homeostatic model assessment of ß-cell function and insulin resistance (HOMA-IR). The association between DASH and diabetes status was examined using multinomial logistic regression. The association between DASH and HOMA-IR was assessed using linear regression, and we tested whether the association was modified by Hispanic/Latino heritage or diabetes status. RESULTS: DASH score was highest in those with self-reported diabetes (controlled) and no medications (44.8%). A higher DASH score was associated with a lower HOMA-IR, and the association was the same regardless of diabetes status (p>0.8 for the interaction). CONCLUSIONS: The association between DASH and diabetes status was strongest for those with controlled self-reported diabetes and who were not taking antihyperglycemic medications. A higher DASH score was associated with less insulin resistance among Hispanics/Latinos. Differences in DASH scores by Hispanic/Latino heritage did not explain the differences in prevalence of diabetes and insulin resistance reported in the diverse Hispanic/Latino population. CLINICAL TRIAL NUMBER: NCT02060344.