BACKGROUND: Multivariable equations are recommended by primary prevention guidelines to assess absolute risk of cardiovascular disease (CVD). However, current equations have several limitations. Therefore, we developed and validated the American Heart Association Predicting Risk of CVD EVENTs (PREVENT) equations among US adults 30 to 79 years of age without known CVD. METHODS: The derivation sample included individual-level participant data from 25 data sets (N=3 281 919) between 1992 and 2017. The primary outcome was CVD (atherosclerotic CVD and heart failure). Predictors included traditional risk factors (smoking status, systolic blood pressure, cholesterol, antihypertensive or statin use, and diabetes) and estimated glomerular filtration rate. Models were sex-specific, race-free, developed on the age scale, and adjusted for competing risk of non-CVD death. Analyses were conducted in each data set and meta-analyzed. Discrimination was assessed using the Harrell C-statistic. Calibration was calculated as the slope of the observed versus predicted risk by decile. Additional equations to predict each CVD subtype (atherosclerotic CVD and heart failure) and include optional predictors (urine albumin-to-creatinine ratio and hemoglobin A1c), and social deprivation index were also developed. External validation was performed in 3 330 085 participants from 21 additional data sets. RESULTS: Among 6 612 004 adults included, mean±SD age was 53±12 years, and 56% were women. Over a mean±SD follow-up of 4.8±3.1 years, there were 211 515 incident total CVD events. The median C-statistics in external validation for CVD were 0.794 (interquartile interval, 0.763–0.809) in female and 0.757 (0.727–0.778) in male participants. The calibration slopes were 1.03 (interquartile interval, 0.81–1.16) and 0.94 (0.81–1.13) among female and male participants, respectively. Similar estimates for discrimination and calibration were observed for atherosclerotic CVD– and heart failure–specific models. The improvement in discrimination was small but statistically significant when urine albumin-to-creatinine ratio, hemoglobin A1c, and social deprivation index were added together to the base model to total CVD (ΔC-statistic [interquartile interval] 0.004 [0.004–0.005] and 0.005 [0.004–0.007] among female and male participants, respectively). Calibration improved significantly when the urine albumin-to-creatinine ratio was added to the base model among those with marked albuminuria (>300 mg/g; 1.05 [0.84–1.20] versus 1.39 [1.14–1.65]; P =0.01). CONCLUSIONS: PREVENT equations accurately and precisely predicted risk for incident CVD and CVD subtypes in a large, diverse, and contemporary sample of US adults by using routinely available clinical variables.

Journal of Medical Internet Research - International Scientific Journal for Medical Research, Information and Communication on the Internet

We investigated whether microalbuminuria was associated with the metabolic syndrome by comparing the strength of the association between microalbuminuria and the syndrome as a whole and its individual components. This investigation included 5659 women and men aged 20 to 80 years from the cross-sectional, nationally representative, Third National Health and Nutrition Examination Survey (NHANES III: 1988-1994). Metabolic syndrome was defined as any three of the following: increased waist circumference, increased triglycerides, decreased HDL cholesterol, increased blood pressure, or high fasting glucose. Microalbuminuria was defined as urinary albumin/creatinine ratio of 30 to 300 mg/g. Microalbuminuria was present in 7.8% of women and 5.0% of men. Log linear analysis revealed a significant association between the metabolic syndrome and microalbuminuria in both genders (women chi(2) = 44.1; men chi(2) = 59.6; P <.0001 for both). Microalbuminuria was more common in both women (odds ratio [OR] = 2.2; 95% confidence interval [CI] 1.44, 3.34) and men (OR = 4.1; 95% CI 2.45, 6.74) with metabolic syndrome compared to those without it; 34% of women and 42% of men with microalbuminuria also had metabolic syndrome. After adjusting for other components of the metabolic syndrome, hypertension demonstrated the strongest association with microalbuminuria in both women (OR = 3.34; 95% CI 2.45, 4.55) and men (OR = 2.51; 95% CI 1.63, 3.86). Microalbuminuria and metabolic syndrome are associated in a large, nationally representative cohort, possibly due to early renal effects of hypertension, and it may be useful to consider microalbuminuria as a component of the metabolic syndrome.

OBJECTIVE—To prospectively investigate predictors of the incident metabolic syndrome in nondiabetic adults. RESEARCH DESIGN AND METHODS—This analysis included 714 white, black, and Hispanic participants in the Insulin Resistance Atherosclerosis Study (IRAS) who were free of the metabolic syndrome at baseline; 139 of these developed the metabolic syndrome in the subsequent 5 years. We examined measures of glucose (fasting and 2 h), insulin (fasting and 2 h, acute insulin response, insulin sensitivity [S_i], and proinsulin), lipids (HDL and triglycerides), blood pressure (systolic and diastolic), waist circumference, and baseline physical activity (total energy expenditure) as predictors of the metabolic syndrome. Logistic regression models were adjusted for age, sex, study site, ethnicity, and impaired glucose tolerance. Signal detection analysis was used to identify the characteristics of the highest risk group. RESULTS—The best predictors of incident metabolic syndrome were waist circumference (odds ratio [OR] 1.7 [1.3–2.0] per 11 cm), HDL cholesterol (0.6 [0.4–0.7] per 15 mg/dl), and proinsulin (1.7 [1.4–2.0] per 3.3 pmol/l). Signal detection analysis identified waist circumference (>89 cm in women, >102 cm in men) as the optimal predictor. CONCLUSIONS—These findings suggest that obesity may precede the development of other metabolic syndrome components. Interventions that address obesity and reduce waist circumference may reduce the incidence of the metabolic syndrome in nondiabetic adults.

Our current understanding of Asian American mortality patterns has been distorted by the historical aggregation of diverse Asian subgroups on death certificates, masking important differences in the leading causes of death across subgroups. In this analysis, we aim to fill an important knowledge gap in Asian American health by reporting leading causes of mortality by disaggregated Asian American subgroups. We examined national mortality records for the six largest Asian subgroups (Asian Indian, Chinese, Filipino, Japanese, Korean, Vietnamese) and non-Hispanic Whites (NHWs) from 2003-2011, and ranked the leading causes of death. We calculated all-cause and cause-specific age-adjusted rates, temporal trends with annual percent changes, and rate ratios by race/ethnicity and sex. Rankings revealed that as an aggregated group, cancer was the leading cause of death for Asian Americans. When disaggregated, there was notable heterogeneity. Among women, cancer was the leading cause of death for every group except Asian Indians. In men, cancer was the leading cause of death among Chinese, Korean, and Vietnamese men, while heart disease was the leading cause of death among Asian Indians, Filipino and Japanese men. The proportion of death due to heart disease for Asian Indian males was nearly double that of cancer (31% vs. 18%). Temporal trends showed increased mortality of cancer and diabetes in Asian Indians and Vietnamese; increased stroke mortality in Asian Indians; increased suicide mortality in Koreans; and increased mortality from Alzheimer’s disease for all racial/ethnic groups from 2003-2011. All-cause rate ratios revealed that overall mortality is lower in Asian Americans compared to NHWs. Our findings show heterogeneity in the leading causes of death among Asian American subgroups. Additional research should focus on culturally competent and cost-effective approaches to prevent and treat specific diseases among these growing diverse populations.