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Quantifying cardiometabolic risk using modifiable non-self-reported risk factors.

ABSTRACT: BACKGROUND:Sensitive general cardiometabolic risk assessment tools of modifiable risk factors would be helpful and practical in a range of primary prevention interventions or for preventive health maintenance. PURPOSE:To develop and validate a cumulative general cardiometabolic risk score that focuses on non-self-reported modifiable risk factors such as glycosylated hemoglobin (HbA1c) and BMI so as to be sensitive to small changes across a span of major modifiable risk factors, which may not individually cross clinical cut-off points for risk categories. METHODS:We prospectively followed 2,359 cardiovascular disease (CVD)-free subjects from the Framingham offspring cohort over a 14-year follow-up. Baseline (fifth offspring examination cycle) included HbA1c and cholesterol measurements. Gender-specific Cox proportional hazards models were considered to evaluate the effects of non-self-reported modifiable risk factors (blood pressure, total cholesterol, high-density lipoprotein cholesterol, smoking, BMI, and HbA1c) on general CVD risk. We constructed 10-year general cardiometabolic risk score functions and evaluated its predictive performance in 2012-2013. RESULTS:HbA1c was significantly related to general CVD risk. The proposed cardiometabolic general CVD risk model showed good predictive performance as determined by cross-validated discrimination (male C-index=0.703, 95% CI=0.668, 0.734; female C-index=0.762, 95% CI=0.726, 0.801) and calibration (lack-of-fit chi-square=9.05 [p=0.338] and 12.54 [p=0.128] for men and women, respectively). CONCLUSIONS:This study presents a risk factor algorithm that provides a convenient and informative way to quantify cardiometabolic risk on the basis of modifiable risk factors that can motivate an individual's commitment to prevention and intervention.

SUBMITTER: Marino M

PROVIDER: S-EPMC4107093 | biostudies-literature | 2014 Aug

REPOSITORIES: biostudies-literature

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Quantifying cardiometabolic risk using modifiable non-self-reported risk factors.

Marino Miguel M Li Yi Y Pencina Michael J MJ D'Agostino Ralph B RB Berkman Lisa F LF Buxton Orfeu M OM

American journal of preventive medicine 20140617 2

<h4>Background</h4>Sensitive general cardiometabolic risk assessment tools of modifiable risk factors would be helpful and practical in a range of primary prevention interventions or for preventive health maintenance.<h4>Purpose</h4>To develop and validate a cumulative general cardiometabolic risk score that focuses on non-self-reported modifiable risk factors such as glycosylated hemoglobin (HbA1c) and BMI so as to be sensitive to small changes across a span of major modifiable risk factors, wh ...[more]

PMID: 24951039

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Project description:Self-reported short or long sleep duration has been associated with adverse cardiometabolic health outcomes in laboratory and epidemiologic studies, but interpretation of such data has been limited by methodologic issues.Adult respondents of the 2007-2008 US National Health and Nutrition Examination Survey (NHANES) were examined in a cross-sectional analysis (N=5649). Self-reported sleep duration was categorized as very short (<5 h), short (5-6 h), normal (7-8 h), or long (?9 h). Obesity, diabetes mellitus (DM), hypertension, and hyperlipidemia were objectively assessed by self-reported history. Statistical analyses included univariate comparisons across sleep duration categories for all variables. Binary logistic regression analyses and cardiometabolic factor as outcome, with sleep duration category as predictor, were assessed with and without covariates. Observed relationships were further assessed for dependence on race/ethnicity.In adjusted analyses, very short sleep was associated with self-reported hypertension (odds ratio [OR], 2.02, [95% confidence interval {CI},1.45-2.81]; P<0.0001), self-reported hyperlipidemia (OR, 1.96 [95% CI, 1.43-2.69]; P<0.0001), objective hyperlipidemia (OR, 1.41 [95% CI, 1.04-1.91]; P=0.03), self-reported DM (OR, 1.76 [95% CI, 1.13-2.74]; P=0.01), and objective obesity (OR, 1.53 [95% CI, 1.03-1.43]; P=0.005). Regarding short sleep (5-6 h), in adjusted analyses, elevated risk was seen for self-reported hypertension (OR, 1.22 [95% CI, 1.02-1.45]; P=0.03) self-reported obesity (OR, 1.21 [95% CI, 1.03-1.43]; P=0.02), and objective obesity (OR, 1.17 [95% CI, 1.00-1.38]; P<0.05). Regarding long sleep (?9 h), no elevated risk was found for any outcomes. Interactions with race/ethnicity were significant for all outcomes; race/ethnicity differences in patterns of risk varied by outcome studied. In particular, the relationship between very short sleep and obesity was strongest among blacks and the relationship between short sleep and hypertension is strongest among non-Hispanic whites, blacks, and non-Mexican Hispanics/Latinos.Short sleep duration is associated with self-reported and objectively determined adverse cardiometabolic outcomes, even after adjustment for many covariates. Also, these patterns of risk depend on race/ethnicity.

Project description:Studies on sugar intake and its link to cardiometabolic risk show inconsistent results, partly due to dietary misreporting. Cost-effective and easily measured nutritional biomarkers that can complement dietary data are warranted. Measurement of 24-h urinary sugars is a biomarker of sugar intake, but there are knowledge gaps regarding the use of overnight urine samples. We aim to compare (1) overnight urinary sucrose and fructose measured with liquid chromatography-tandem mass spectrometry, (2) self-reported sugar intake measured with web-based 4-day food records, (3) their composite measure, and (4) these different measures' (1-3) cross-sectional associations with cardiometabolic risk factors in 991 adults in the Malmö Offspring Study (18-69 years, 54% women). The correlations between the reported intakes of total sugar, added sugar and sucrose was higher for urinary sucrose than fructose, and the correlations for the sum or urinary sucrose and fructose (U-sugars) varied between r?0.2-0.3 (P < 0.01) in men and women. Differences in the direction of associations were observed for some cardiometabolic risk factors between U-sugars and reported added sugar intake, as well as between the sexes. In women, U-sugars, but not reported added sugar intake, were positively associated with systolic and diastolic blood pressure and fasting glucose. Both U-sugars and added sugar were positively associated with BMI and waist circumference in women, whereas among men, U-sugars were negatively associated with BMI and waist circumference, and no association was observed for added sugar. The composite measure of added sugars and U-sugars was positively associated with BMI, waist circumference and systolic blood pressure and negatively associated with HDL cholesterol in women (P < 0.05). Conclusively, we demonstrate statistically significant, but not very high, correlations between reported sugar intakes and U-sugars. Results indicate that overnight urinary sugars may be used as a complement to self-reported dietary data when investigating associations between sugar exposure and cardiometabolic risk. However, future studies are highly needed to validate the overnight urinary sugars as a biomarker because its use, instead of 24-h urine, facilitates data collection.

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Quantifying cardiometabolic risk using modifiable non-self-reported risk factors.

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Quantifying cardiometabolic risk using modifiable non-self-reported risk factors.

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