Table Who Criteria

Risk Factor

Defining Level

Metabolic syndrome is diagnosed when the individual has: diabetes, IFG, IGT, or HOMAa insulin resistance AND AT LEAST TWO of the following: Waist-to-Hip Ratio

Women Triglycerides HDL

Women Urinary albumin excretion rate Blood pressure

> 140/90 mm Hg a IFG — impaired fasting glucose; IGT - impaired glucose tolerance; HOMA [resting determination of insulin sensitivity = (fasting glucose x 0.055551) x (fasting insulin/22.1)].

part of the same metabolic substrate for insulin resistance and diabetes mellitus, this is less clear for hypertension. More specifically, it is not apparent whether hypertension is another relatively common cardiovascular risk factor that tends to occur more frequently than not with other relatively common risk factors (associated with the prediabetic state) or whether, in fact, it is part of a metabolic substrate that is characteristic of individuals on the path to developing diabetes. When all five criteria are considered, we and others have noticed that there are striking differences in the prevalence of qualifying criteria among individuals of difference ethnicities. For example, in African Americans, hypertension is more likely to be a qualifying criterion than it is in Caucasians. Conversely, in Caucasians, lipid abnormalities predominate (studies of targeted risk reduction interventions through defined exercise [STRRIDE] data; Table 4.3).1213

Waist-circumference measures, although attempting to account for differences in women and men, clearly do not account for other differences in body habitus that might influence the normalization of this measure. For example, should a waist circumference of 92 cm be equally applicable in a woman that is 152 cm (60 in) tall as it is in a woman that is 183 cm (72 in) tall? Further, gender differences can be striking in the contribution of waist circumference to metabolic abnormalities. We have observed that women have less visceral fat (unpublished data), lower triglycerides, and much lower serum concentrations of small dense atherogenic low-density LDL cholesterol than do men, even given similar waist circumferences (STRRIDE data; Table 4.3).12 Similar observations can be made for African Americans when compared with Caucasians, i.e., at nearly identical waist circumferences, African Americans have lower visceral fat (unpublished observations) and lower triglycerides compared to Caucasians. Thus, more precision may be achievable in the diagnosis of metabolic syndrome if the criteria were differentially weighted by height, gender, and ethnicity. Finally, as we will argue, there are likely other measures, relatively easily obtained clinically, that contribute to the clinical picture of insulin resistance and may be mechanistically involved in its etiology. These include cardiorespiratory fitness, (e.g., time to exhaustion on a maximal treadmill exercise test) and concentrations of specific lipoprotein subspecies, such as small dense LDL cholesterol.

III. CROSS-SECTIONAL STUDIES OF THE IMPORTANCE OF PHYSICAL FITNESS AND EXERCISE TO THE DIAGNOSIS AND ETIOLOGY OF METABOLIC SYNDROME

Cross-sectional studies have consistently found that higher levels of cardiorespiratory fitness or physical activity are associated with decreased risk of morbidity and mortality from diabetes,1415 cardiovascular disease,1617 hypertension,1819 cancer,16 and metabolic syndrome.20,21 In 1999, data from the Aerobics Center Longitudinal Study (ACLS) of the Cooper Clinic in Dallas, Texas, were analyzed for the relationship between cardiorespiratory fitness and metabolic syndrome.20 This study was published before the ATP III definition of metabolic syndrome was available and, as a result, it used a slightly different definition of metabolic syndrome. In this study,

TABLE 4.3

Lipoprotein Subclass Distributions by Group and Gender/Race Statistical Comparisons

TABLE 4.3

Lipoprotein Subclass Distributions by Group and Gender/Race Statistical Comparisons

Black Women

White Women

Black Men

White Men

Gender

Race

n

= 40

n

= 108

n = 29

n =1 08

Difference

Difference

Cholesterol (mg/dL)

195

±30.8

207

±31.9

187 ± 29.9

185 ± 30.5

<.0001

F > ]

VI

0.2466

HDL-C (mg/dL)

53.0

± 12.0

52.8

± 12.4

42.4 ± 11.6

37.8 ± 11.9

<.0001

F >

M

0.1855

HDL size (nm)

9.17

± 0.34

9.05

± 0.36

8.90 ± 0.33

8.67 ± 0.34

<.0001

F >

M

0.0004

B > W

Small HDL (mg/dL Choi)

17.3

±4.91

17.1

± 5.09

19.3 ±4.78

19.8 ± 4.87

<.0001

M >

F

0.8559

Large HDL (mg/dL Choi)

35.6

± 13.0

35.7

±13.5

23.1 ± 12.6

18.0 ± 13.0

<.0001

F >

M

0.2034

LDL-C (mg/dL)

126

± 24.4

131

± 25.3

124 ± 23.8

121 ± 24.3

0.0074

F >

M

0.7239

LDL size (nm)

21.4

+ 0.75

21.2

+ 0.78

21.0 + 0.73

20.5 + 0.75

<.0001

F >

M

0.0022

B > W

LDL particle (nmol/L)

1250

±314

1351

± 326

1345 ±305

1414 ±312

0.0634

M >

F

0.0557

W> B

Small LDL (mg/dL Choi) $

8.50

±30.1

14.3

±31.2

16.2 ± 29.3

34.7 ± 29.9

<0001

M >

F

0.0112

W> B

Medium LDL (mg/dL Choi) $

30.1

±33.5

35.0

±34.8

50.3 ± 32.6

41.9 ± 33.3

0.0034

M >

F

0.5138

Large LDL (mg/dL Choi) $

85.9

±43.4

78.1

±45.0

56.1 ± 42.2

40.1 ± 43.0

<0001

F >

M

0.0212

B > W

IDL (mg/dL Choi) $

1.08

± 5.55

3.70

± 5.75

1.59 ±5.40

3.91 ± 5.51

0.9585

< .0001

W> B

Trilyceride (mg/dL) £

85.7

±77.9

143

± 80.8

112 ± 75.8

176 ± 77.4

0.0002

M >

F

< .0001

W> B

VLDL size (nm)

43.6

± 10.7

49.8

± 11.1

47.4 ± 10.4

53.9 ± 10.6

0.0019

M >

F

< .0001

W > B

Small VLDL (mg/dL Tg)

17.4

± 11.1

16.9

± 11.5

20.0 ± 10.8

18.3 ± 11.0

0.2107

0.5084

Medium VLDL (mg/dL Tg)

26.0

± 28.0

42.3

± 29.0

35.9 ± 27.2

50.5 ± 27.8

0.0121

M >

F

0.0001

W> B

Large VLDL (mg/dL Tg) $

5.98

± 62.5

46.0

± 64.8

22.5 ± 60.7

71.2 ± 62.0

0.0006

M >

F

< .0001

Note: Data expressed as adjusted mean ± SD; Data determined by analysis of covariance adjusting for differences in age and BMI; No interactions between gender and race were observed; £ ANCOVA performed using ranked data.

the variables associated with insulin resistance identified by Kaplan22 as part of the "deadly quartet" included systolic BP (> 140 mmHg), hypertriglyceridemia (> 150 mg/dl), hyperglycemia (fasting glucose > 110 mg/dl), and central adiposity (waist circumference > 100 cm for both men and women). They did not include HDL cholesterol in this study due to its strong correlation with TG. A total of 15,534 men and 3898 women were included in the study. Cardiorespiratory fitness was assessed by time to exhaustion on a treadmill-exercise test, and fitness categories were based on age and gender-normative data. Finally, the association between fitness and clustering of metabolic abnormalities was assessed using proportional odds logit models. For the men, the age-adjusted, cumulative-odds ratio for abnormal markers of metabolic syndrome was 10.1 (C.I. 9.1-11.2, P < 0.0001) when comparing the least-fit with the most-fit men, and was 3.0 (95 percent C.I. 2.7-3.4; P < 0.0001) when comparing the least fit with the moderately fit. For women, the odds ratio was 4.9 (C.I. 3.8-6.3; P < 0.0001) when comparing the least-fit to the most-fit women, and was 2.7 (C.I. 2.7-3.4; P < 0.0001) when comparing the least fit to the moderately fit. These data provide very strong evidence that a highly significant relation exists between cardiorespiratory fitness and clustering of factors of the metabolic syndrome.

In another study of the relationship between metabolic syndrome, physical activity and fitness by Carroll et al., 23 similar findings were reported. This study, only in men presenting for preventive assessment at a private hospital in the United Kingdom (n = 711), reported age-adjusted odds ratios for metabolic clustering of 0.28 (95 percent C.I., 0.14-0.57) for moderate fitness versus low fitness and 0.12 (95 percent C.I. 0.05-0.32) for high fitness versus low fitness (P < 0.0001). They also reported that even after exclusion of obesity in the metabolic syndrome definition, the relationship between cardiorespiratory fitness and metabolic syndrome was still significant. Similar relationships were observed for physical-activity measures obtained via recall questionnaire. The confidence level for a trend between physical activity and metabolic syndrome was less (P < 0.05) than it was for cardiorespiratory fitness and metabolic syndrome (P < 0.0001). This is likely due to the nature of the measure, as physical-activity questionnaires are inherently less accurate than the more highly reproducible time to exhaustion on an exercise test.

More recently, Irwin et al. 24 examined the relationships between fitness and metabolic syndrome in a smaller sample of women of three ethnicities (African American, n = 49; Native American, n = 46; and Caucasian, n = 51). In this study, the current ATP III definition for metabolic syndrome was used. Physical activity was determined prospectively from detailed subject records that included all physical activity performed during two consecutive four-day periods. Cardiorespiratory fitness was determined from maximal treadmill time during a graded exercise test. They reported significant inverse relations between metabolic syndrome and higher levels of moderate-intensity physical activity (P < 0.01), vigorous-intensity physical activity (P < 0.01), and maximal treadmill time (P < 0.0004) among an ethnically diverse population of women. This appears to be the first study of these relationships in minority women. Importantly, they found that while all associations were statistically significant, the strongest association was between metabolic syndrome and maximal treadmill time. They suggested that cardiorespiratory fitness was a more objective, albeit indirect, measure of physical activity, and as such is a more accurate exposure variable. It is important to note that physical activity records are generally a reflection of recent activity levels, whereas cardiorespiratory fitness likely reflects a much longer-term effect of regular habitual physical activity.

Highlighting the consistency and generalizability of the relationship between physical activity, cardiorespiratory fitness, and metabolic syndrome are similar data reported by Lakka et al. 25 in Finnish men, and by Panagiotakos et al.26 in Greek men and women. In the Lakka study, the relationship between physical activity and metabolic syndrome and between cardiorespiratory fitness and metabolic syndrome was significant, and the magnitude and level of significance was once again much greater for the relation between cardiorespiratory fitness and metabolic syndrome than for physical activity and metabolic syndrome. In fact, total leisure-time physical activity was found to be significantly related to metabolic syndrome when adjusted for age only, or for age and multiple other factors (P < 0.03). Neither low-intensity physical activity nor moderate and vigorous physical activity was significantly related with metabolic syndrome using either statistical model. However, cardiorespiratory fitness was inversely related to metabolic syndrome (P < 0.001) for both the age-adjusted model and for the model that adjusted for age, smoking, alcohol consumption, and socioeconomic status. They found that the least-fit men were almost seven times more likely to have the metabolic syndrome than the most-fit men even when major confounders were controlled.

Furthermore, Lakka found, even after controlling for body-mass index, that the least-fit men had a nearly fourfold likelihood of having the metabolic syndrome when compared with the most-fit men, suggesting a strong, independent relationship between cardiorespiratory fitness and prevalence of metabolic syndrome. The observation that cardiorespiratory fitness independent of body-mass index is a very strong predictor of metabolic risk, diabetes risk, and overall cardiovascular risk is supported by numerous studies from the Aerobics Center Longitudinal Study of Dallas.27-30

In aggregate, these findings imply that cardiorespiratory fitness should be included as a defining variable of the metabolic syndrome. In fact, one of the conclusions of Lakka et al.25 was that the measurement of peak oxygen consumption (VO2) (a measure highly correlated with time to exhaustion in an exercise test) in sedentary men with cardiovascular risk factors might provide an efficient means for targeting individuals who would benefit from interventions to prevent the metabolic syndrome and its consequences. Presumably, these relationships hold as strongly in women as in men, although the data is not as extensive in women.

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