COMMENTARIES

A Simple Demographic Model to Predict the Prevalence of Cardiometabolic Risk Factors

Richard E Scranton, M.D., MPH, Division of Aging, Brigham and Women's Hospital,
Harvard Medical School, 1620 Tremont Street,
Boston, MA 02120
Tel: 508 532-0832
E-mail: richardscrantonmd@rcn.com

Cardiometabolic risk (CMR) factors, including obesity, dyslipidemia, increased blood pressure, and insulin resistance, are health conditions known to increase the risk of cardiovascular disease [1,2]. The growing concern of errant weight coupled with prevalent metabolic derangements has raised awareness from all sectors of the healthcare society. How best to manage this unfortunate reality has also been of interest to public heath officials, ministries of health, and professional medical societies. Lifestyle modification is the commonly cited solution. Despite the bulging medical costs often attributed to this prevalent condition, many large employers fail to provide or cover lifestyle modification services [3]. Insurance companies are ever conscious of the greater healthcare costs attributable to plan participants who are overweight [4] which may lead to underwriters of individual healthcare plans to exclude or increase the premiums for obese individuals. Of even greater concern is the cost associated with those who are both overweight and have a cluster of metabolic risk factors [5], which in turn may drive insurers and perhaps employer-based healthcare plans to exclude or limit the benefits of such individuals [6,7]. Yet this course of action would only compound the problem. Avoidance of the fact that these metabolic derangements are increasing will only lead to escalating costs both in terms of healthcare utilization and lost work performance. Instead, acknowledging the presence of CMR factors along with timely and appropriate interventions is needed.

          “Metabolic Syndrome” was coined as the term to capture this cluster of metabolic derangements. The National Cholesterol Education Program Adult Treatment Panel III (NCEP-ATPIII) [8] defines this syndrome as having any three of the following five risk factors: central obesity (waist circumference > 102 cm for men and > 88 cm for women), elevated triglycerides ≥ 150 mg/dL (1.7 mmol/L), reduced high density lipoprotein of < 40 mg/dl (1.03 mmol/L) in males and < 50 mg/dl (1.29 mmol/L) in females, raised blood pressure: systolic BP ≥ 130 or diastolic BP ≥ 85 mm Hg, or elevated fasting glucose of ≥ 100 mg/dL (5.6 mmol/L). The international diabetes federation (IDF) [9] defines the syndrome as the presence of central obesity (waist circumference of ≥ 94cm for men and ≥ 80 cm women) plus any two of the following four factors: elevated triglyceride of ≥ 150 mg/dL (1.7 mmol/L), reduced high density lipoprotein of < 40 mg/dL (1.03 mmol/L) in males and < 50 mg/dL (1.29 mmol/L) in females, raised blood pressure: systolic BP ≥ 130 or diastolic BP ≥ 85 mm Hg, or elevated fasting glucose of ≥ 100 mg/dL (5.6 mmol/L). Individuals receiving treatment to lower triglycerides and glucose or raise HDL cholesterol would also be included in the definition. Both of these definitions have been used to estimate the prevalence of metabolic syndrome in various countries [9,10].

          Recently, the term metabolic syndrome has been the topic of debate raising doubt that metabolic syndrome represents a true “disease” [11-13]. This debate has lead to indecisiveness while the prevalence of this clustering of metabolic derangements goes unabated. In addition, how best to identify this high risk population is another issue since many of the measurements are clinically based and not readily available from an administrative level. Both definitions perform well when determining population-based prevalence estimates from observation cohorts that have the necessary clinical measures [10]. However the reliance upon clinical measures makes it difficult to estimate the prevalence of metabolic syndrome to healthcare systems and insurers.

          In order to address this limitation, Hollenbeak et al. [14] developed a model that predicted the prevalence of cardiometabolic risk (CMR) clusters using only simple demographic data. We sought to test whether Hollenbeak’s model could accurately predict the prevalence of CMR clusters in two large and diverse observational studies, the Atherosclerosis Risk in Communities Study (ARIC) and the Framingham Offspring Study (FOS) [15]. ARIC, a prospective study of four community sites in Maryland, Mississippi, Missouri, and North Carolina, was designed to study risk factors and the natural history of atherosclerosis and cardiovascular disease among an ethnically diverse cohort of nearly 16,000. Framingham Offspring Study began in 1971 with the recruitment of 5,124 men and women, consisting of offspring of participants in the original Framingham Heart Study. FOS participants have completed six regular physical examinations and surveys of lifestyle factors (including extensive laboratory tests and anthropometric examinations) at intervals of 4 to 6 years and have been followed for morbidity and mortality over that time period.

          In our study we used both the National Cholesterol Education Program and the International Diabetes Federation definition of metabolic syndrome. The predicted versus observed prevalence of metabolic syndrome based on NCEP criteria were similar in both cohorts (predicted 51.3% versus observed 52.1% for ARIC and predicted 48.2% versus observed 41.1% for FOS). Differences in the demographics including race between ARIC and FOS resulted in slightly different proportions in the CMR factor estimates. As to be expected, use of the IDF definition provided slightly greater prevalence estimates of metabolic syndrome in both cohorts (45.8% in FOS and 58.8% in ARIC). However, the predicted prevalence using the simple demographic model was similar (51.4% in FOS and 53.5% in ARIC). Age was a key factor that accounted for differences in the metabolic prevalence estimates between the ARIC and FOS. However, the observed and predicted estimates were closer with advancing age, consistent with the fact that the risk factors increase with age. In the group < 55 years of age, it is also important to note that the mean age in FOS was younger, which likely accounts for the lower observed prevalence. Further investigation evaluating how various characteristics account for differences in sets of multiple CMR factors across varying populations is needed.

          This simple demographic model (i.e. mean age, proportion of men, smokers, and race) adequately predicted the prevalence of CMR factors in both the FOS and ARIC observational cohorts using either NCEP or IDF metabolic syndrome definition. Because of concerns over morbidity related to CMR factors, estimating their prevalence is of paramount importance. The development of such a simple model would allow various groups to better identify their patient population at risk. By raising awareness, appropriate education and interventions could be developed to hopefully curb the onslaught of costs associated with unrecognized cardiometabolic risk.

References

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