Abstract
Objective
The metabolic syndrome is associated with increased cardiovascular risk and its prevalence increases with age. Various definitions of the metabolic syndrome exist, but whether some definitions are more predictive of future cardiovascular events in the elderly is unclear.
Materials/Methods
We compared the risk of incident cardiovascular events in elderly individuals ≥ 65 years from the Cardiovascular Health Study with and without the metabolic syndrome as defined by the EGIR, NCEP/AHA, AACE, IDF and modified WHO criteria (n=3390). Participants were without baseline diabetes or cardiovascular disease.
Results
Except for EGIR, all definitions of the metabolic syndrome were significantly associated with increased risk of incident cardiovascular (coronary or cerebrovascular) events. Adjusted hazard ratios (HRs) for risk of incident cardiovascular events as defined by the modified WHO, NCEP/AHA, AACE and IDF criteria ranged from 1.153 (p=0.045) for NCEP/AHA to 1.314 (p<0.001) for IDF, with 95% CI ranging from 1.003–1.503. Adjusted HR for EGIR was 1.087 (95% CI 0.908–1.301, p=0.362). Similarly, all definitions of the metabolic syndrome were significantly associated with incident coronary events except for the EGIR definition. Only the modified WHO definition was associated with increased risk for cerebrovascular events (adjusted HR 1.301, 95% CI 1.038–1.631, p=0.022).
Conclusion
While all metabolic syndrome definitions except EGIR were associated with total cardiovascular events and coronary events, only the modified WHO definition was also associated with risk of cerebrovascular events.
Keywords: Insulin resistance, obesity, coronary events, cerebrovascular events, geriatrics
Introduction
The metabolic syndrome is a cluster of cardiovascular risk factors [1] which has been associated with increased risks of cardiovascular events [2–4]. Different definitions of the metabolic syndrome exist, including definitions from the World Health Organization (WHO) [5], the National Cholesterol Education Program Third Adult Treatment Panel (NCEP-ATPIII) [6], the European Group for the Study of Insulin Resistance (EGIR) [7], the American Association of Clinical Endocrinologists (AACE) [8] and the International Diabetes Federation (IDF) [9]. The NCEP-ATPIII definition has subsequently been updated by the American Heart Association/National Heart, Lung, and Blood Institute (AHA/NHLBI) [1] and most recently in a joint interim statement that serves to harmonize the definition of metabolic syndrome [10]. The core components for defining metabolic syndrome among these definitions are similar. However, cutoff values, as well as relative importance of these core components differ among the definitions. For example, the IDF definition focuses on the importance of abdominal obesity, requiring its presence as defined by ethnic-specific waist circumferences [9].
The metabolic syndrome may be especially important in the elderly. Risk of cardiovascular events increases with age, as does the prevalence of the metabolic syndrome [11]. In a U.S. population from the National Health and Nutrition Examination Survey III, the prevalence of the metabolic syndrome increased from 7% in the 20–29 year age group to 44% for those aged 60–69 years [12]. Most studies evaluating the relationship between the metabolic syndrome and risk of cardiovascular events have been conducted in middle-age individuals [3,13,14]. Although recent studies indicate positive associations between the metabolic syndrome and risk of cardiovascular events in the elderly [11,15,16], these relationships are weaker than those reported in middle-age individuals, probably because age itself is an independent predictor of cardiovascular risk. In addition, limited and conflicting data exist as to which definition of the metabolic syndrome should be used in the elderly, who are already at higher risk of cardiovascular events due to their age [17,18].
The purpose of this study is to evaluate the relationship between the metabolic syndrome, as defined by the different available criteria, and incident cardiovascular events among an exclusively elderly U.S. population (≥ 65 years of age).
Methods
Data source
We used data from the Cardiovascular Health Study (CHS), which is a population-based prospective cohort study conducted by NHLBI to evaluate risk factors associated with cardiovascular disease and stroke in adults aged 65 years and older. The design and purpose of the CHS have been reported previously [19]. CHS participants were randomly chosen from a list of Medicare eligible individuals in four U.S. locations: Allegheny County, Pennsylvania; Forsyth County, North Carolina; Sacramento County, California; and Washington County, Maryland [20]. Subjects in the initial cohort were recruited between 1989 and 1990 and consisted of 5,201 men and women. Subjects were eligible for the study if they were: (i) 65 years or older; (ii) non-institutionalized individuals; (iii) expected to remain in the area for at least 3 years; and (iv) able to give informed consent. Between 1992 and 1993, 687 African American subjects were added to the original cohort. During each participant’s interview and examination at baseline, demographic data, current medications, blood pressure, history of medical conditions, lifestyle habits, fasting blood chemistry, echocardiography, electrocardiography and carotid ultrasonography were obtained. Anthropometric measurements were also obtained for each patient that included weight, height, waist and hip circumferences. Standing height was measured as distance from the sole of the feet to the top of the head with the participant standing erect and looking straight ahead. Weight was measured with the participant wearing underwear and examination suit, but no shoes, minus the weight of the suit. Waist circumference was measured at the level of the umbilicus, and hip circumference was measured at the level of maximul protrusion of the gluteal muscles, with the participant standing. Self-reported physical activity over the previous 2 weeks was assessed using the modified Health Interview Survey [21]. Physical activity intensity was classified as (1) no exercise, (2) light activity (walking at a casual pace at <2mph), (3) moderate intensity (physical activity at < 6 metabolic equivalent tasks [METs]), and (4) high intensity (physical activity at > 6 METs). Alcohol use and smoking history was assessed by standardized CHS questionnaires. University of Vermont’s Central Blood Analysis Laboratory analyzed each participant’s blood chemistry, which was drawn in the morning after an overnight fast. Further details on laboratory and blood sampling procedures, examinations, and quality assurance protocols have been published previously [19,20,22,23]. Subjects were followed with annual clinic visits and interim 6-month phone calls for a total of 11 years, followed by telephone follow-ups only from year 11 to 15. For this analysis, we used only the first 11 years of validated event data, as data for cardiovascular events after year 11 were obtained from telephone self report without validation from medical records. The CHS was approved by the University of Washington’s Data Coordinating Center and the investigational review boards at all locations. Analysis of CHS data for the purpose of comparing definitions of metabolic syndrome in its prediction of incident cardiovascular events was approved by the Virginia Commonwealth University Institutional Review Board.
Inclusion and exclusion criteria
For the analyses in the current report, all participants with a baseline interview and examination, and follow-up or censoring information were included. Subjects were censored if they completed the follow-up observation but did not develop any cardiovascular events, or if they were lost to follow-up before the observation period was complete. Individuals with the following characteristics were excluded: (i) presence of diabetes at baseline (as defined by a fasting glucose ≥ 126 mg/dL, or a 2-hr glucose of ≥ 200 mg/dL after a 75-g oral glucose challenge, or use of anti-diabetic agents); (ii) history of coronary heart disease, as defined by a previous occurrence of myocardial infarction (MI), coronary revascularizations, angina, or silent MI (as documented by electrocardiogram findings) reported at baseline; (iii) heart failure; and (iv) history of stroke or transient ischemic attack (TIA). Individuals with prevalent cardiovascular disease and diabetes were excluded because they were already at risk for cardiovascular events regardless of the presence of the metabolic syndrome.
Definitions of the Metabolic Syndrome
We defined the presence of the metabolic syndrome among non-diabetic individuals in the CHS cohort using the following 5 definitions (see Table 1): (i) modified WHO [5], (ii) EGIR [7], (iii) AACE [8], (iv) IDF [9], and (v) NCEP as modified by AHA/NHLBI [1]. For the modified WHO criteria, we did not include microalbuminuria, which was in the original WHO definition, because microalbuminuria was not assessed at baseline in CHS (see Table 1). The NCEP criteria as modified by AHA/NHLBI identified the same individuals as would the most recent harmonization criteria jointly proposed by IDF, NHLBI, AHA, World Heart Federation, International Atherosclerosis Society, and International Association for the Study of Obesity [10]. This is due to the fact that the central obesity cut-offs for North America are the same for both the NCEP/AHA and the new harmonization criteria.
Table 1.
Definitions of the Metabolic Syndrome
| NCEP-ATPIII [1] | Modified WHO [5] | EGIR [7] | AACE [8] | IDF [9] | |
|---|---|---|---|---|---|
| Required | … | Required: Insulin resistance* in top 25%; glucose ≥110 mg/dL; 2 hr glucose ≥ 140 mg/dL | Required: Insulin resistance* (or fasting insulin) in top 25% | “High risk for insulin resistance” or BMI >25 kg/m2 or waist ≥102 cm (men) or ≥88 cm (women)† | Ethnic based waist: European: ≥ 94 cm (men) or ≥ 80 cm (women); Asian: ≥ 90 cm (men) or ≥80 cm (women) |
|
| |||||
| No. of abnormalities | ≥3 of: | And ≥2 of: | And ≥2 of: | And ≥2 of: | And ≥2 of: |
|
| |||||
| Glucose | ≥100 mg/dL | ≥110 mg/dL | ≥110 mg/dL; 2 hr glucose ≥ 140 mg/dL | ≥100 mg/dL | |
|
| |||||
| HDL cholesterol | <40 mg/dL (men) <50 mg/dL (women) or on medications |
<35 mg/dL (men) <40 mg/dL (women) |
<40 mg/dL | <40 mg/dL (men) <50 mg/dL (women) |
<40 mg/dL (men) <50 mg/dL (women) or on medications |
|
|
|
||||
| and/or | and/or | ||||
| Triglycerides | ≥150 mg/dL or on medications | ≥150 mg/dL | ≥180 mg/dL | ≥150 mg/dL | ≥150 mg/dL or on medications |
|
| |||||
| Obesity | Waist ≥ 102 cm (men) ≥88 cm (women) | Waist/hip ratio >0.9 (men); >0.85 (women); or BMI ≥30 kg/m2 | Waist ≥94 cm (men) ≥80 cm (women) | ||
|
| |||||
| Hypertension | ≥130/85 mm Hg or on medications | ≥140/90 mm Hg | ≥140/90 mm Hg | ≥130/85 mm Hg | ≥135/85 mmg Hg or on medications |
|
| |||||
| Other: | Microalbuminuria§: albumin excretion > 20 mcg/min | ||||
Insulin resistance was calculated by the homeostasis model assessment of insulin resistance (HOMA-IR). The upper quartile were individuals with HOMA-IR values > 3.87.
In this study, BMI and waist circumference cut-points were used to define individuals at “high risk for insulin resistance”.
Microalbuminuria was not used in the definition of the metabolic syndrome in this study as it was not assessed in CHS.
Insulin resistance was measured using the homeostasis model assessment (HOMA-IR). The upper quartile of values derived from HOMA [(fasting glucose [mmol/dL] × fasting insulin [μIU/ml])/22.5] [7,24] among non-diabetic individuals in the CHS cohort was used as a cut-off for the definition of insulin resistance.
Body mass index (BMI) was obtained by dividing the subject’s weight in kilograms by the height squared in meters. The participants were classified into three groups based on their BMI: those with BMIs <25kg/m2 were defined as having normal weight, those whose BMIs were between 25 and 29.9 kg/m2 were considered overweight, and those whose BMIs ≥30 kg/m2 were considered obese [25,26].
Outcomes
The primary outcome of this study was the occurrence of any first cardiovascular event, defined as any incident MI, silent MI, TIA, stroke, angina, claudication, coronary artery bypass surgery (CABG), angioplasty, or death due to coronary disease during the 11 years of follow-up. These outcomes were adjudicated by the CHS Events Subcommittee. The algorithms for identifying claudication [27], MI [28], stroke [23] and deaths due to coronary disease [28] have been reported previously. Secondary outcomes for this report included incident coronary events and cerebrovascular events evaluated separately. For the secondary outcomes, coronary events included MI, CABG, angioplasty, angina, silent MI and deaths due to coronary disease. Cerebrovascular events included stroke or TIA.
Statistical Analysis
Descriptive statistics for participants with and without the metabolic syndrome as defined by WHO (modified), EGIR, NCEP/AHA, AACE and IDF definitions were performed. We evaluated the relationship between presence of the metabolic syndrome defined by these 5 criteria and incident cardiovascular events using Cox regression analyses. Other important risk factors for cardiovascular events were evaluated for inclusion as a covariate in the multivariate model if the univariate p-values were <0.2, or as a confounder if its inclusion in the multivariate model changed the hazard ratio (HR) estimate by more than 10%. Preliminary multivariate models were compared using −2 log likelihood tests before a final multivariate model were constructed. The final model included the following covariates: sex, age, race, smoking status, alcohol use, exercise intensity, income, family history of early cardiovascular events, LDL concentrations, and aspirin use. Although race was not found to be significant in the univariate analyses, it was included as a covariate in the final model, as race is an established confounder in other studies [29]. HRs and their associated 95% confidence intervals (CI) were calculated. P-values of <0.05 were considered statistically significant. All statistical analyses were performed using SAS 9.2 (SAS Institute Inc., Cary, N.C.)
Results
Demographics of the Cohort
Of the original 5888 subjects in the CHS cohort, a total of 3390 subjects were included for analyses. Reasons for exclusion included diabetes (n= 1327), history of coronary heart disease (n=1123), heart failure (n=262), stroke (n=244), and TIA (n=161), and lack of follow-up or censor information (n=89). Some individuals had more than one excluding conditions. Of the individuals included for analyses, the prevalence of the metabolic syndrome as defined by NCEP/AHA, modified WHO, EGIR, AACE, and IDF were 34.5%, 34.5%, 16.0%, 33.2% and 39.2%, respectively.
The demographic and metabolic characteristics of subjects fulfilling each definition of metabolic syndrome were similar (Table 2). For each definition, most of the participants with metabolic syndrome (approximately 80%) were between 65–74 years of age. They were most likely overweight (BMI between 25–29 kg/m2) or obese (BMI ≥ 30 kg/m2). In addition, rates and proportions of common cardiovascular and demographic factors, such as gender, ethnicity, lipid values, blood pressure, fasting glucose, family history of MI, and aspirin use, were similar among the different definitions. The majority of participants with the metabolic syndrome never smoked, exercised at a low intensity, and reported no alcohol intake. The family income for most participants was less than $25000.
Table 2.
Baseline characteristics among 3390 non-diabetic elderly individuals with the metabolic syndrome, as defined by the NCEP/AHA, modified WHO, EGIR, AACE, and IDF criteria
| NCEP/AHA | Modified WHO | EGIR | AACE | IDF | |
|---|---|---|---|---|---|
| No. subjects with MetS | 1170 | 1171 | 543 | 1126 | 1330 |
| Gender: males | 398 (34%) | 494 (42%) | 212 (39%) | 429 (38%) | 473 (36%) |
| Age (yr) | 73±5 | 73±5 | 72±5 | 73±5 | 73±5 |
| 65–74 yr | 944 (80%) | 923 (79%) | 448 (82%) | 907 (81%) | 1060 (80%) |
| 75–84 yr | 195 (17%) | 207 (18%) | 85 (16%) | 183 (16%) | 231 (17%) |
| ≥85 yr | 31 (3%) | 41 (3%) | 10 (2%) | 36 (3%) | 39 (3%) |
| Race | |||||
| Caucasian | 1027 (88%) | 1056 (90%) | 479 (88%) | 1040 (92%) | 1174 (88%) |
| Black | 136 (11%) | 107 (9%) | 61 (11%) | 79 (7%) | 147 (11%) |
| Other | 7 (1%) | 8 (1%) | 3 (1%) | 7 (1%) | 9 (1%) |
| BMI (kg/m2) | 29±4 | 28±4 | 29±4 | 29±3 | 28±4 |
| Normal <25 | 224 (19%) | 287 (25%) | 66 (13%) | 101 (9%) | 250 (19%) |
| Overweight 25–29 | 565 (48%) | 541 (46%) | 273 (50%) | 695 (62%) | 697 (52%) |
| Obese >30 | 380 (33%) | 341 (29%) | 204 (37%) | 330 (29%) | 382 (29%) |
| TG (mg/dl) | 172±79 | 159±78 | 172±80 | 164±76 | 160±70 |
| HDL (mg/dl) | 48±13 | 51±14 | 48±12 | 50±13 | 50±14 |
| LDL (mg/dl) | 137±36 | 135±35 | 133±34 | 138±35 | 137±36 |
| SBP (mm Hg) | 140±20 | 141±20 | 138±19 | 140±20 | 141±19 |
| DBP (mm Hg) | 72±11 | 72±11.0 | 72±11 | 72±11 | 72±11 |
| Fasting glucose (mg/dl) | 104±9 | 104±9 | 104±9 | 103±10 | 104±9 |
| Family Hx of MI | 331 (31%) | 334 (31%) | 150 (30%) | 313 (30%) | 369 (30%) |
| Aspirin Usea | 344 (29%) | 349 (30%) | 162 (30%) | 333 (30%) | 392 (30%) |
| Smoking: | |||||
| Never | 576 (49%) | 559 (48%) | 255 (47%) | 553 (49%) | 637 (48%) |
| Former | 444 (38%) | 481 (41%) | 227 (42%) | 447 (40%) | 527 (40%) |
| Current | 148 (13%) | 129 (11%) | 61 (11%) | 124 (11%) | 164 (12%) |
| Exercise intensity | |||||
| No exercise | 103 (9%) | 93 (8%) | 44 (8%) | 90 (8%) | 114 (9%) |
| Low intensity | 585 (50%) | 579 (50%) | 277 (51%) | 552 (49%) | 651 (49%) |
| Moderate Intensity | 374 (32%) | 379 (32%) | 170 (31%) | 373 (33%) | 428 (32%) |
| High Intensity | 108 (9%) | 119 (10%) | 52 (10%) | 110 (10%) | 137 (10%) |
| Alcohol | |||||
| 0 drinks daily | 578 (50%) | 546 (47%) | 286 (53%) | 538 (48%) | 634 (48%) |
| <1 drink/day | 435 (38%) | 438 (38%) | 194 (36%) | 421 (38%) | 495 (38%) |
| <2 drink/day | 58 (5%) | 69 (6%) | 18 (3.5%) | 64 (6%) | 66 (5%) |
| <3 drink/day | 40 (3%) | 44 (4%) | 22 (4%) | 41 (4%) | 60 (4%) |
| >3 drink/day | 47 (4%) | 61 (5%) | 18 (3.5%) | 49 (4%) | 63 (5%) |
| Income | |||||
| < $5000 | 49 (4%) | 56 (5%) | 26 (5%) | 41 (4%) | 55 (4%) |
| $5000–7999 | 107 (10%) | 101 (9%) | 46 (9%) | 93 (9%) | 113 (9%) |
| $8000–11999 | 131 (12%) | 124 (11%) | 73 (14%) | 124 (12%) | 146 (12%) |
| $12000–15999 | 180 (16%) | 159 (14%) | 76 (15%) | 165 (15%) | 205 (16%) |
| $16000–24999 | 229 (21%) | 227 (20%) | 99 (19%) | 223 (21%) | 252 (20%) |
| $25000–34999 | 182 (16%) | 175 (15%) | 88 (17%) | 178 (16%) | 205 (16%) |
| $35000–49999 | 106 (10%) | 119 (11%) | 49 (9%) | 109 (10%) | 124 (10%) |
| >$50000 | 133 (12%) | 152 (14%) | 64 (12%) | 138 (13%) | 162 (13%) |
Data are mean±SD, or frequency (% participants)
MetS, metabolic syndrome; NCEP/AHA, National Cholesterol Education Program-Adult Treatment Panel III; WHO, the World Health Organization; EGIR, European Group for the study of Insulin Resistance; AACE-American Association of Clinical Endocrinologists; IDF, International Diabetes Federation; BMI, body mass index; TG, triglycerides; HDL, high density lipoproteins; LDL, low density lipoproteins; SBP, systolic blood pressure; DBP, diastolic blood pressure; Hx, history; MI, myocardial infarction.
Aspirin use was defined as present if use is greater than 2 days in previous 2 weeks
Cardiovascular Events
The number of first cardiovascular events (including both coronary and cerebrovascular events) among individuals with and without the metabolic syndrome according to each definition are shown in Table 3. In univariate analyses, the WHO (modified), NCEP/AHA, IDF, and AACE definitions of metabolic syndrome were all significantly associated with an increased risk of incident cardiovascular events. However, the metabolic syndrome as defined by the EGIR criteria was not associated with increased risk (p=0.427).
Table 3.
Number and risk of any cardiovascular events, risk of coronary events and cerebrovascular events among elderly individuals with and without the metabolic syndrome as defined by various definitions
| Any Cardiovascular Events | Coronary Events | Cerebrovascular Events | ||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Univariate | Multivariatea | Univariate | Multivariatea | Univariate | Multivariatea | |||||||
| HR (95% CI) | P | HR (95% CI) | P | HR (95% CI) | P | HR (95% CI) | P | HR (95% CI) | P | HR (95% CI) | P | |
| NCEP-AHA | 1.153 (1.017–1.308) | 0.026 | 1.153 (1.003–1.326) | 0.045 | 1.246 (1.061–1.463) | 0.007 | 1.273 (1.067–1.519) | 0.007 | 1.068 (0.866–1.319) | 0.538 | 1.004 (0.794–1.269) | 0.975 |
| Modified WHO | 1.244 (1.098–1.41) | <0.001 | 1.197 (1.044–1.373) | 0.010 | 1.245 (1.06–1.462) | 0.008 | 1.193 (1.001–1.421) | 0.049 | 1.398 (1.139–1.715) | 0.001 | 1.301 (1.038–1.631) | 0.022 |
| EGIR | 1.069 (0.907–1.259) | 0.4268 | 1.087 (0.908–1.301) | 0.362 | 1.058 (0.857–1.307) | 0.598 | 1.033 (0.819–1.302) | 0.785 | 1.194 (0.919–1.553) | 0.185 | 1.207 (0.902–1.615) | 0.205 |
| AACE | 1.243 (1.097–1.41) | <0.001 | 1.261 (1.098–1.449) | 0.001 | 1.285 (1.094–1.509) | 0.002 | 1.305 (1.094–1.557) | 0.003 | 1.275 (1.037–1.569) | 0.022 | 1.256 (0.998–1.581) | 0.053 |
| IDF | 1.300 (1.15–1.469) | <0.001 | 1.314 (1.149–1.503) | <0.001 | 1.355 (1.158–1.585) | <0.001 | 1.387 (1.168–1.648) | <0.001 | 1.196 (0.976–1.467) | 0.085 | 1.123 (0.896–1.406) | 0.315 |
Adjusted for sex, age, race, smoking status, alcohol use, exercise intensity, income, family history, LDL, and aspirin use
HR, Hazard ratio
We next performed multivariate analyses to evaluate if relationships between metabolic syndrome and cardiovascular events were independent of other major cardiovascular risk factors. Results of the multivariate analyses were similar to those of univariate models. The WHO (modified), NCEP/AHA, IDF, and AACE definitions of the metabolic syndrome were all significantly associated with increased risks of incident cardiovascular event in the multivariate models. However, metabolic syndrome as defined by the EGIR criteria was not associated with increased risk (p=0.785).
Coronary and Cerebrovascular Events
Univariate and multivariate HRs of coronary and cerebrovascular events were separately analyzed as secondary endpoints (Table 3). As in total cardiovascular events, all definitions of the metabolic syndrome, except for EGIR, were significantly associated with increased risk of incident coronary events in univariate and multivariate models. In contrast to coronary events, only the AACE and modified WHO criteria were significantly associated with cerebrovascular events in univariate analyses, with HRs of 1.275 (95% CI 1.037–1.569, p=0.022) and 1.398 (95% CI 1.139–1.715, p=0.001), respectively. After adjustments for other major cardiovascular risk factors, only the modified WHO criteria remained significant (HR=1.301, 95% CI 1.038–1.631, p=0.022).
Discussion
The metabolic syndrome is highly prevalent in elderly individuals [11,12] and has been associated with future cardiovascular events [2–4]. Currently, various definitions of the metabolic syndrome exist. Whether all of these definitions equally identify individuals at risk has been seldom studied, especially among North American elderly individuals, in whom the risk of cardiovascular events is highest. We sought to compare the various definitions of the metabolic syndrome and their association with risks of future cardiovascular events, including coronary and cerebrovascular events.
In this report using data from the CHS study, the AACE, NCEP/AHA and modified WHO definitions identified similar number of elderly individuals with the metabolic syndrome. Using the EGIR definition, less participants were identified as having the metabolic syndrome, which is consistent with prior studies suggesting that the requirement of the presence of insulin resistance in this definition leads to a conservative estimate for the prevalence of the metabolic syndrome [17,30,31]. The highest prevalence of metabolic syndrome resulted from the IDF definition. Other studies have found similar results, proposing that the difference in prevalence may come from the IDF definition’s requirement of the presence of abdominal obesity rather than insulin resistance [31,32].
We found that most definitions of the metabolic syndrome (NECP/AHA, modified WHO, AACE, and IDF) except EGIR were associated with increased risk of total cardiovascular and coronary events in U.S. elderly individuals. This finding is consistent with existing literature. In a study based on a non-diabetic Finnish elderly population, all the above definitions of the metabolic syndrome were predictive of cardiovascular mortality [18]. Our results are also similar to those of a study involving a population of British women aged 60–79 years, in whom the IDF, WHO, and NCEP-ATPIII definitions of the metabolic syndrome were modestly associated with coronary heart disease (age-adjusted HR of 1.32 [95% CI 1.03–1.70, p=0.03], 1.45 [95% CI 1.00–2.10, p=0.04], and 1.38 [95% CI 1.00–1.93, p=0.05], respectively) [33].
The EGIR definition was not associated with any increased cardiovascular risk, including coronary and cerebrovascular risk, in both the univariate and adjusted analyses. Previous conflicting findings exist for the ability of the EGIR definition to predict cardiovascular risk. For example, the Hoorn Study of Dutch men and women aged 50–75 years (mean of 60 years) compared the cardiovascular risk among NCEP-ATPIII, WHO, EGIR, and AACE metabolic syndrome definitions and concluded that the metabolic syndrome, regardless of the definition, was associated with a higher risk of nonfatal and fatal cardiovascular disease [30]. In this current report, with a North American elderly population with mean age of 72 years, the EGIR definition was not significantly associated with increased cardiovascular risk. Our findings corroborate results from a similar investigation involving elderly non-diabetic Finns [18]. Although EGIR in that study was initially found to be associated with increased cardiovascular disease mortality, the association disappeared after exclusion of subjects who already had cardiovascular disease at baseline, as in our study [18].
Additional studies may be needed to confirm our finding of the inability of the EGIR definition to predict cardiovascular risk. It is possible that with increased age, risk factors other than insulin resistance or fasting insulin, which are required components in the EGIR definition, may play a more important role in cardiovascular risk among the elderly. While the WHO definition also requires the presence of insulin resistance, presence of abnormal fasting or 2-hr glucose (Table 1) also satisfies this requirement. Impaired fasting glucose alone has been reported to carry a higher risk of cardiovascular events than the combined components of metabolic syndrome [34], and this may explain why the modified WHO, but not the EGIR definition, is significantly associated with increased cardiovascular risk in this study. It is also possible that fasting insulin concentrations, a component of HOMA-IR used to assess insulin resistance in this study, may not be important in predicting cardiovascular risk in the elderly. Indeed, we performed sensitivity analyses using upper 25th percentile of fasting insulin levels in lieu of insulin resistance for the EGIR definition (data not shown). Results of the sensitivity analysis did not alter the conclusion of the lack of relationship between the EGIR definition and any incident cardiovascular, coronary or cerebrovascular events.
Only the metabolic syndrome as defined by the modified WHO criteria also predicted increased risk of cerebrovascular events in our study. Several prospective studies have evaluated the value of different metabolic syndrome definitions to predict stroke, but they varied with respect to the characteristics of the study population. In an elderly non-diabetic Finnish population, the WHO, EGIR, NCEP-ATPIII and IDF definitions, but not the AACE criteria, significantly predicted stroke (HR ranged from 1.49 to 1.80) [35]. However, persons with previous MIs were included in the aforementioned analysis. In a non-diabetic middle-age (mean age of 58 years) Swedish population without history of coronary or cerebrovascular events, the NCEP, but not EGIR or IDF definitions, was associated with increased risk of stroke, after adjusting for age and sex [17]. Hence, the contribution of metabolic syndrome as defined by various definitions to the risk of stroke may depend on the demographics of the population studied.
The strengths of this study included a prospective follow-up design, length of follow-up, validated methods for defining cardiovascular events, and the fact that participants were closely followed. Importantly, this study evaluated a distinctly elderly population from the U.S. This added to the translational potential of this work by addressing a practical knowledge gap. Currently, most studies evaluating the risk of cardiovascular events conferred by the metabolic syndrome involve middle-age individuals. Risk estimates reported in these studies cannot be readily used in the elderly, as age itself is also an independent predictor of cardiovascular risk, and may dilute the risk associated with the metabolic syndrome in this age group. By studying an elderly U.S. population, we were able to provide risk estimates of different metabolic syndrome definitions in this distinct population, whose risk for cardiovascular events is already high independent of the presence of the metabolic syndrome.
There are several limitations in this present study. One limitation is that the CHS participants only included Medicare eligible subjects that are non-institutionalized. The exclusion of older adults living in nursing homes may underestimate the prevalence of the metabolic syndrome and subjects at risk for incident cardiovascular events. Furthermore, this also decreased the number of individuals > 85 years that could be included in the analyses. Another limitation is that a modified WHO definition was used. We were not able to include microalbuminuria in the WHO criteria, as microalbuminuria assessments were not performed in CHS. In addition, the euglycemic clamp to measure insulin resistance was used in the original WHO criteria; in our study, we used HOMA-IR. Nonetheless, HOMA-IR has been found to closely resemble the glucose clamp technique in assessing insulin sensitivity and was reliable when used in large-scale studies with fasting blood samples to measure insulin sensitivity [36].
In summary, the results of the present study show that all metabolic syndrome definitions with the exception of EGIR are associated with increased risks of cardiovascular events in elderly subjects. Only the modified WHO definition significantly predicts all three endpoints: total cardiovascular events, coronary, and cerebrovascular events. Further studies will be required to confirm whether the modified WHO definition is more clinically useful than other definitions in classifying the presence of the metabolic syndrome in the elderly.
Acknowledgments
Funding: This work was supported in part by National Institutes of Health Grant K23HD049454 (to K.I.C), Virginia Commonwealth University School of Pharmacy Summer Research Fellowship (to C.M.V.), and Fulbright Scholarship (to H.H.Z.). The Cardiovascular Health Study (CHS) was conducted and supported by the NHLBI in collaboration with the CHS Study Investigators. This Manuscript was prepared using a limited access dataset obtained from the NHLBI and does not necessarily reflect the opinions or views of the CHS or the NHLBI.
List of abbreviations
- WHO
World Health Organization
- NCEP-ATPIII
National Cholesterol Education Program Third Adult Treatment Panel
- AHA
American Heart Association
- NHLBI
National Heart, Lung, and Blood Institute
- EGIR
European Group for the Study of Insulin Resistance
- AACE
American Association of Clinical Endocrinologists
- IDF
International Diabetes Federation
- CHS
Cardiovascular Health Study
- METs
Metabolic equivalent tasks
- MI
Myocardial infarction
- TIA
Transient ischemic attack
- HOMA-IR
Homeostasis model assessment of insulin resistance
- CABG
Coronary artery bypass surgery
- HR
Hazard ratio
- CI
Confidence interval
Footnotes
Conflicts of Interest:
All authors have nothing to declare
Author Contributions: CMV collected data and wrote drafts of the manuscript. HHZ performed statistical analysis. JRL performed data interpretation and participated in manuscript writing. KIC designed this study, supervised data management, performed statistical analyses and data interpretation, wrote and finalized manuscript.
Institutional Approval:
This study was approved by the Virginia Commonwealth University Institutional Review Board
Disclosures: None
Publisher's Disclaimer: This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final citable form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.
References
- 1.Grundy SM, Cleeman JI, Daniels SR, et al. Diagnosis and management of the metabolic syndrome: an American Heart Association/National Heart, Lung, and Blood Institute Scientific Statement. Circulation. 2005;112(17):2735–2752. doi: 10.1161/CIRCULATIONAHA.105.169404. [DOI] [PubMed] [Google Scholar]
- 2.Bonora E, Kiechl S, Willeit J, et al. Carotid atherosclerosis and coronary heart disease in the metabolic syndrome: prospective data from the Bruneck study. Diabetes Care. 2003;26(4):1251–1257. doi: 10.2337/diacare.26.4.1251. [DOI] [PubMed] [Google Scholar]
- 3.Lakka HM, Laaksonen DE, Lakka TA, et al. The metabolic syndrome and total and cardiovascular disease mortality in middle-aged men. JAMA. 2002;288(21):2709–2716. doi: 10.1001/jama.288.21.2709. [DOI] [PubMed] [Google Scholar]
- 4.Malik S, Wong ND, Franklin SS, et al. Impact of the metabolic syndrome on mortality from coronary heart disease, cardiovascular disease, and all causes in United States adults. Circulation. 2004;110(10):1245–1250. doi: 10.1161/01.CIR.0000140677.20606.0E. [DOI] [PubMed] [Google Scholar]
- 5.Alberti KG, Zimmet PZ. Definition, diagnosis and classification of diabetes mellitus and its complications. Part 1: diagnosis and classification of diabetes mellitus provisional report of a WHO consultation. Diabet Med. 1998;15(7):539–553. doi: 10.1002/(SICI)1096-9136(199807)15:7<539::AID-DIA668>3.0.CO;2-S. [DOI] [PubMed] [Google Scholar]
- 6.Third Report of the National Cholesterol Education Program (NCEP) Expert Panel on Detection, Evaluation, and Treatment of High Blood Cholesterol in Adults (Adult Treatment Panel III) final report. Circulation. 2002;106(25):3143–3421. [PubMed] [Google Scholar]
- 7.Balkau B, Charles MA. Comment on the provisional report from the WHO consultation. European Group for the Study of Insulin Resistance (EGIR) Diabet Med. 1999;16(5):442–443. doi: 10.1046/j.1464-5491.1999.00059.x. [DOI] [PubMed] [Google Scholar]
- 8.Einhorn D, Reaven GM, Cobin RH, et al. American College of Endocrinology position statement on the insulin resistance syndrome. Endocr Pract. 2003;9(3):237–252. [PubMed] [Google Scholar]
- 9.Alberti KG, Zimmet P, Shaw J. Metabolic syndrome--a new world-wide definition. A Consensus Statement from the International Diabetes Federation. Diabet Med. 2006;23(5):469–480. doi: 10.1111/j.1464-5491.2006.01858.x. [DOI] [PubMed] [Google Scholar]
- 10.Alberti KG, Eckel RH, Grundy SM, et al. Harmonizing the metabolic syndrome: a joint interim statement of the International Diabetes Federation Task Force on Epidemiology and Prevention; National Heart, Lung, and Blood Institute; American Heart Association; World Heart Federation; International Atherosclerosis Society; and International Association for the Study of Obesity. Circulation. 2009;120(16):1640–1645. doi: 10.1161/CIRCULATIONAHA.109.192644. [DOI] [PubMed] [Google Scholar]
- 11.Alexander CM, Landsman PB, Teutsch SM, Haffner SM. NCEP-defined metabolic syndrome, diabetes, and prevalence of coronary heart disease among NHANES III participants age 50 years and older. Diabetes. 2003;52(5):1210–1214. doi: 10.2337/diabetes.52.5.1210. [DOI] [PubMed] [Google Scholar]
- 12.Ford ES, Giles WH, Dietz WH. Prevalence of the metabolic syndrome among US adults: findings from the third National Health and Nutrition Examination Survey. JAMA. 2002;287(3):356–359. doi: 10.1001/jama.287.3.356. [DOI] [PubMed] [Google Scholar]
- 13.Chei CL, Yamagishi K, Tanigawa T, et al. Metabolic Syndrome and the Risk of Ischemic Heart Disease and Stroke among Middle-Aged Japanese. Hypertens Res. 2008;31(10):1887–1894. doi: 10.1291/hypres.31.1887. [DOI] [PubMed] [Google Scholar]
- 14.Pannier B, Thomas F, Bean K, Jego B, Benetos A, Guize L. The metabolic syndrome: similar deleterious impact on all-cause mortality in hypertensive and normotensive subjects. J Hypertens. 2008;26(6):1223–1228. doi: 10.1097/HJH.0b013e3282fd9936. [DOI] [PubMed] [Google Scholar]
- 15.Butler J, Rodondi N, Zhu Y, et al. Metabolic syndrome and the risk of cardiovascular disease in older adults. J Am Coll Cardiol. 2006;47(8):1595–1602. doi: 10.1016/j.jacc.2005.12.046. [DOI] [PubMed] [Google Scholar]
- 16.McNeill AM, Katz R, Girman CJ, et al. Metabolic syndrome and cardiovascular disease in older people: The cardiovascular health study. J Am Geriatr Soc. 2006;54(9):1317–1324. doi: 10.1111/j.1532-5415.2006.00862.x. [DOI] [PubMed] [Google Scholar]
- 17.Nilsson PM, Engstrom G, Hedblad B. The metabolic syndrome and incidence of cardiovascular disease in non-diabetic subjects--a population-based study comparing three different definitions. Diabet Med. 2007;24(5):464–472. doi: 10.1111/j.1464-5491.2007.02142.x. [DOI] [PubMed] [Google Scholar]
- 18.Wang J, Ruotsalainen S, Moilanen L, Lepisto P, Laakso M, Kuusisto J. The metabolic syndrome predicts cardiovascular mortality: a 13-year follow-up study in elderly non-diabetic Finns. Eur Heart J. 2007;28(7):857–864. doi: 10.1093/eurheartj/ehl524. [DOI] [PubMed] [Google Scholar]
- 19.Fried LP, Borhani NO, Enright P, et al. The Cardiovascular Health Study: design and rationale. Ann Epidemiol. 1991;1(3):263–276. doi: 10.1016/1047-2797(91)90005-w. [DOI] [PubMed] [Google Scholar]
- 20.Tell GS, Fried LP, Hermanson B, Manolio TA, Newman AB, Borhani NO. Recruitment of adults 65 years and older as participants in the Cardiovascular Health Study. Ann Epidemiol. 1993;3(4):358–366. doi: 10.1016/1047-2797(93)90062-9. [DOI] [PubMed] [Google Scholar]
- 21.Siscovick DS, Fried L, Mittelmark M, Rutan G, Bild D, O’Leary DH. Exercise intensity and subclinical cardiovascular disease in the elderly. The Cardiovascular Health Study. Am J Epidemiol. 1997;145(11):977–986. doi: 10.1093/oxfordjournals.aje.a009066. [DOI] [PubMed] [Google Scholar]
- 22.Cushman M, Cornell ES, Howard PR, Bovill EG, Tracy RP. Laboratory methods and quality assurance in the Cardiovascular Health Study. Clin Chem. 1995;41(2):264–270. [PubMed] [Google Scholar]
- 23.Price TR, Psaty B, O’Leary D, Burke G, Gardin J. Assessment of cerebrovascular disease in the Cardiovascular Health Study. Ann Epidemiol. 1993;3(5):504–507. doi: 10.1016/1047-2797(93)90105-d. [DOI] [PubMed] [Google Scholar]
- 24.Matthews DR, Hosker JP, Rudenski AS, Naylor BA, Treacher DF, Turner RC. Homeostasis model assessment: insulin resistance and beta-cell function from fasting plasma glucose and insulin concentrations in man. Diabetologia. 1985;28(7):412–419. doi: 10.1007/BF00280883. [DOI] [PubMed] [Google Scholar]
- 25.National Heart Lung and Blood Institute. Clinical guidelines on the identification, evaluation and treatment of overweight and obesity in adults. [accessed 02/15/11];NIH Publication No 98-4083. 1998 ( http://www.nhlbi.nih.gov/guidelines/obesity/ob_gdlns.htm.
- 26.Villareal DT, Apovian CM, Kushner RF, Klein S. Obesity in older adults: technical review and position statement of the American Society for Nutrition and NAASO, The Obesity Society. Obes Res. 2005;13(11):1849–1863. doi: 10.1038/oby.2005.228. [DOI] [PubMed] [Google Scholar]
- 27.Newman AB, Naydeck BL, Sutton-Tyrrell K, Polak JF, Kuller LH. The role of comorbidity in the assessment of intermittent claudication in older adults. J Clin Epidemiol. 2001;54(3):294–300. doi: 10.1016/s0895-4356(00)00308-5. [DOI] [PubMed] [Google Scholar]
- 28.Ives DG, Fitzpatrick AL, Bild DE, et al. Surveillance and ascertainment of cardiovascular events. The Cardiovascular Health Study. Ann Epidemiol. 1995;5(4):278–285. doi: 10.1016/1047-2797(94)00093-9. [DOI] [PubMed] [Google Scholar]
- 29.Smith SC, Jr, Clark LT, Cooper RS, et al. Discovering the full spectrum of cardiovascular disease: Minority Health Summit 2003: report of the Obesity, Metabolic Syndrome, and Hypertension Writing Group. Circulation. 2005;111(10):e134–e139. doi: 10.1161/01.CIR.0000157743.54710.04. [DOI] [PubMed] [Google Scholar]
- 30.Dekker JM, Girman C, Rhodes T, et al. Metabolic syndrome and 10-year cardiovascular disease risk in the Hoorn Study. Circulation. 2005;112(5):666–673. doi: 10.1161/CIRCULATIONAHA.104.516948. [DOI] [PubMed] [Google Scholar]
- 31.Strazzullo P, Barbato A, Siani A, et al. Diagnostic criteria for metabolic syndrome: a comparative analysis in an unselected sample of adult male population. Metabolism. 2008;57(3):355–361. doi: 10.1016/j.metabol.2007.10.010. [DOI] [PubMed] [Google Scholar]
- 32.Choi KM, Kim SM, Kim YE, Choi DS, Baik SH, Lee J. Prevalence and cardiovascular disease risk of the metabolic syndrome using National Cholesterol Education Program and International Diabetes Federation definitions in the Korean population. Metabolism. 2007;56(4):552–558. doi: 10.1016/j.metabol.2006.12.003. [DOI] [PubMed] [Google Scholar]
- 33.Lawlor DA, Ebrahim S, Davey SG. The metabolic syndrome and coronary heart disease in older women: findings from the British Women’s Heart and Health Study. Diabet Med. 2004;21(8):906–913. doi: 10.1111/j.1464-5491.2004.01245.x. [DOI] [PubMed] [Google Scholar]
- 34.Mozaffarian D, Kamineni A, Prineas RJ, Siscovick DS. Metabolic syndrome and mortality in older adults: the Cardiovascular Health Study. Arch Intern Med. 2008;168(9):969–978. doi: 10.1001/archinte.168.9.969. [DOI] [PubMed] [Google Scholar]
- 35.Wang J, Ruotsalainen S, Moilanen L, Lepisto P, Laakso M, Kuusisto J. The metabolic syndrome predicts incident stroke: a 14-year follow-up study in elderly people in Finland. Stroke. 2008;39(4):1078–1083. doi: 10.1161/STROKEAHA.107.499830. [DOI] [PubMed] [Google Scholar]
- 36.Bonora E, Targher G, Alberiche M, et al. Homeostasis model assessment closely mirrors the glucose clamp technique in the assessment of insulin sensitivity: studies in subjects with various degrees of glucose tolerance and insulin sensitivity. Diabetes Care. 2000;23(1):57–63. doi: 10.2337/diacare.23.1.57. [DOI] [PubMed] [Google Scholar]