Faculty Bibliography

BACKGROUND: The prevalence of albuminuria in the general population is high, but awareness of it is low. Therefore, we sought to develop and validate a self-assessment tool that allows individuals to estimate their probability of having albuminuria. STUDY DESIGN: Cross-sectional study. SETTING & PARTICIPANTS: The population-based Reasons for Geographic and Racial Differences in Stroke (REGARDS) Study for model development and the National Health and Nutrition Examination Survey (NHANES) 1999-2004 for model validation. US adults 45 years or older in the REGARDS Study (n = 19,697) and NHANES 1999-2004 (n = 7,168). PREDICTOR: Candidate items for the self-assessment tool were collected using a combination of interviewer- and self-administered questionnaires. OUTCOME: Albuminuria was defined as a urinary albumin to urinary creatinine ratio >/=30 mg/g in spot samples. RESULTS: 8 items were included in the self-assessment tool (age, race, sex, current smoking, self-rated health, and self-reported history of diabetes, hypertension, and stroke). These items provided a C statistic of 0.709 (95% CI, 0.699-0.720) and good model fit (Hosmer-Lemeshow chi(2)P = 0.49). In the external validation data set, the C statistic for discriminating individuals with and without albuminuria using the self-assessment tool was 0.714. Using a threshold of >/=10% probability of albuminuria from the self-assessment tool, 36% of US adults 45 years or older in NHANES 1999-2004 would test positive and be recommended for screening. Sensitivity, specificity, and positive and negative predictive values for albuminuria associated with a probability >/=10% were 66%, 68%, 23%, and 93%, respectively. LIMITATIONS: Repeated urine samples were not available to assess the persistency of albuminuria. CONCLUSIONS: 8 self-report items provide good discrimination for the probability of having albuminuria. This tool may encourage individuals with a high probability to request albuminuria screening.

BACKGROUND: Observational studies suggest that there are differences in adherence to antihypertensive medications in different classes. Our objective was to quantify the association between antihypertensive drug class and adherence in clinical settings. METHODS AND RESULTS: Studies were identified through a systematic search of English-language articles published from the inception of computerized databases until February 1, 2009. Studies were included if they measured adherence to antihypertensives using medication refill data and contained sufficient data to calculate a measure of relative risk of adherence and its variance. An inverse-variance-weighted random-effects model was used to pool results. Hazard ratios (HRs) and odds ratios were pooled separately, and HRs were selected as the primary outcome. Seventeen studies met inclusion criteria. The pooled mean adherence by drug class ranged from 28% for beta-blockers to 65% for angiotensin II receptor blockers. There was better adherence to angiotensin II receptor blockers compared with angiotensin-converting enzyme inhibitors (HR, 1.33; 95% confidence interval, 1.13 to 1.57), calcium channel blockers (HR, 1.57; 95% confidence interval, 1.38 to 1.79), diuretics (HR, 1.95; 95% confidence interval, 1.73 to 2.20), and beta-blockers (HR, 2.09; 95% confidence interval, 1.14 to 3.85). Conversely, there was lower adherence to diuretics compared with the other drug classes. The same pattern was present when studies that used odds ratios were pooled. After publication bias was accounted for, there were no longer significant differences in adherence between angiotensin II receptor blockers and angiotensin-converting enzyme inhibitors or between diuretics and beta-blockers. CONCLUSION: In clinical settings, there are important differences in adherence to antihypertensives in separate classes, with lowest adherence to diuretics and beta-blockers and highest adherence to angiotensin II receptor blockers and angiotensin-converting enzyme inhibitors. However, adherence was suboptimal regardless of drug class.

Increased left ventricular (LV) mass and changes in LV geometry may precede hypertension onset. The authors examined the associations of LV mass and geometry, assessed by cardiac magnetic resonance imaging, with hypertension incidence in 2,567 normotensive participants enrolled in 2000-2002 in the Multi-Ethnic Study of Atherosclerosis, an ethnically diverse, population-based, US study. Over a median follow-up of 4.8 years, 745 (29%) participants developed hypertension. In a fully adjusted model including baseline blood pressure, the relative risks of incident hypertension from the lowest to highest LV mass quartile were 1.00 (referent), 1.13 (95% confidence interval (CI): 0.89, 1.43), 1.28 (95% CI: 1.00, 1.63), and 1.78 (95% CI: 1.38, 2.30) (P < 0.001 for linear trend). Higher levels of LV concentric geometry, defined by higher LV mass to end-diastolic volume quartiles, were associated with higher risk of incident hypertension in a fully adjusted model (P = 0.044 for linear trend). In a final model containing both quartiles of LV mass and LV mass/volume along with all covariates including baseline blood pressure, higher LV mass quartiles were associated with incident hypertension (P < 0.001 for linear trend), whereas higher LV mass/volume quartiles were not (P = 0.643 for linear trend). In this multiethnic cohort, alterations in LV mass preceded hypertension onset among normotensive individuals.

BACKGROUND: Polypills, which include multiple medications for reducing cardiovascular disease (CVD) risk in a single pill, have been proposed for population-wide use. The number of US adults eligible for polypills and potential benefits are unknown. METHODS: The National Health and Nutrition Examination Survey 2003-2004 and 2007-2008 were analyzed to estimate treatment rates for medications proposed for inclusion in polypills (aspirin, statin, an angiotensin-converting enzyme [ACE] inhibitor, and a thiazide-type diuretic for those without and a beta-blocker for those with a history of myocardial infarction) among US adults. The number of coronary heart disease (CHD) and stroke events potentially prevented through polypill use was projected by published meta-analyses and 3 large population-based cohort studies. Two polypill eligibility criteria were analyzed: (1) US adults >/=55 years and (2) US adults with a history of CVD. RESULTS: There are 67.6 million US adults >/=55 years and 15.4 million US adults with a history of CVD and, thus, eligible for polypills using the 2 outlined criteria. In 2007 to 2008, 37.3% of US adults >/=55 years and 57.0% of those with a history of CVD were taking statins. Use of other polypill medications was also low. Polypill use by US adults aged >/=55 years is projected to potentially prevent 3.2 million CHD events and 1.7 million strokes over 10 years. Among those with a history of CVD, the potential to prevent of 0.9 million CHD events and 0.5 million strokes is projected. CONCLUSIONS: Polypills have the potential to lower CVD incidence substantially among US adults.

BACKGROUND: Among individuals without diabetes, elevated hemoglobin A1c (HbA1c) has been associated with increased morbidity and mortality, but the literature is sparse regarding the prognostic importance of low HbA1c. METHODS AND RESULTS: National Health and Nutrition Examination Survey III (NHANES III) participants, 20 years and older, were followed up to 12 years (median follow-up, 8.8 years) for all-cause mortality. Cox proportional hazards regression was used to calculate hazard ratios (HR) and 95% confidence intervals (CI) for the association between HbA1c levels and all-cause mortality for 14 099 participants without diabetes. There were 1825 deaths during the follow-up period. Participants with a low HbA1c (<4.0%) had the highest levels of mean red blood cell volume, ferritin, and liver enzymes and the lowest levels of mean total cholesterol and diastolic blood pressure compared with their counterparts with HbA1c levels between 4.0% and 6.4%. An HbA1c <4.0% versus 5.0% to 5.4% was associated with an increased risk of all-cause mortality (HR, 3.73; 95% CI, 1.45 to 9.63) after adjustment for age, race-ethnicity, and sex. This association was attenuated but remained statistically significant after further multivariable adjustment for lifestyle, cardiovascular factors, metabolic factors, red blood cell indices, iron storage indices, and liver function indices (HR, 2.90; 95% CI, 1.25 to 6.76). CONCLUSIONS: In this nationally representative cohort, low HbA1c was associated with increased all-cause mortality among US adults without diabetes. Additional research is needed to confirm these results and identify potential mechanisms that may be underlying this association.

OBJECTIVE: New clinical practice recommendations include A1C as an alternative to fasting glucose as a diagnostic test for identifying pre-diabetes. The impact of these new recommendations on the diagnosis of pre-diabetes is unknown. RESEARCH DESIGN AND METHODS: Data from the National Health and Nutrition Examination Survey 1999-2006 (n = 7,029) were analyzed to determine the percentage and number of U.S. adults without diabetes classified as having pre-diabetes by the elevated A1C (5.7-6.4%) and by the impaired fasting glucose (IFG) (fasting glucose 100-125 mg/dl) criterion separately. Test characteristics (sensitivity, specificity, and positive and negative predictive values) using IFG as the reference standard were calculated. RESULTS: The prevalence of pre-diabetes among U.S. adults was 12.6% by the A1C criterion and 28.2% by the fasting glucose criterion. Only 7.7% of U.S. adults, reflecting 61 and 27% of those with pre-diabetes by A1C and fasting glucose, respectively, had pre-diabetes according to both definitions. A1C used alone would reclassify 37.6 million Americans with IFG to not having pre-diabetes and 8.9 million without IFG to having pre-diabetes (46.5 million reclassified). Using IFG as the reference standard, pre-diabetes by the A1C criterion has 27% sensitivity, 93% specificity, 61% positive predictive value, and 77% negative predictive value. CONCLUSIONS: Using A1C as the pre-diabetes criterion would reclassify the pre-diabetes diagnosis of nearly 50 million Americans. It is imperative that clinicians and health systems understand the differences and similarities in using A1C or IFG in diagnosis of pre-diabetes.

BACKGROUND: Nonadherence to statins limits the benefits of this common drug class. Individual studies assessing predictors of nonadherence have produced inconsistent results. OBJECTIVE: To identify reliable predictors of nonadherence to statins through systematic review and meta-analysis. METHODS: Multiple databases, including MEDLINE, EMBASE, and PsycINFO, were searched (from inception through February 2009) to identify studies that evaluated predictors of nonadherence to statins. Studies were selected using a priori defined criteria, and each study was reviewed by 2 authors who abstracted data on study characteristics and outcomes. Relative risks were then pooled, using an inverse-variance weighted random-effects model. RESULTS: Twenty-two cohort studies met inclusion criteria. Age had a U-shaped association with adherence; the oldest (>/=70 years) and youngest (<50 years) subjects had lower adherence than the middle-aged (50-69 years) subjects. Women and patients with lower incomes were more likely to be nonadherent than were men (odds of nonadherence 1.07; 95% CI 1.04 to 1.11) and those with higher incomes (odds of nonadherence 1.18; 95% CI 1.10 to 1.28), respectively. A history of cardiovascular disease predicted better adherence to statins (odds of nonadherence 0.68; 95% CI 0.66 to 0.78). Similarly, a diagnosis of hypertension or diabetes was associated with better adherence. Although there were too few studies for quantitative pooling, increased testing of lipid levels and lower out-of-pocket costs appeared to be associated with better adherence. There was substantial (I(2) range 68.7-96.3%) heterogeneity between studies across factors. CONCLUSIONS: Several sociodemographic, medical, and health-care utilization characteristics are associated with statin nonadherence. These factors may be useful guides for targeting statin adherence interventions.