The Relationship of VO 2 Peak and the Blood Lactate Transition Threshold with Metabolic Syndrome and Its Component Disorders

Abstract

Background:

Improvements in cardiorespiratory fitness attenuate the risk for metabolic syndrome (MetS). However, the determinants of cardiorespiratory fitness measurements such as oxygen consumption (VO₂) peak and anaerobic threshold (AT) have not been investigated in persons with MetS. Therefore, the main aim of this study was to compare VO₂ peak and AT between subjects with and without MetS and to investigate determinants of cardiorespiratory fitness and its effects on the odds for MetS and its individual components.

Methods:

Thirty-one males with MetS and 24 healthy male participants each performed a VO₂ peak and a blood lactate transition threshold test. Waist circumference, body mass index (BMI), blood pressure, fasting plasma triglyceride, total cholesterol, high-density lipoprotein cholesterol, glucose, and insulin levels were measured. Separate multivariable linear regression models were developed in which VO₂ peak, AT, and the components of MetS were used as the dependent variables, while a multivariable logistic regression model was used for MetS.

Results:

The VO₂ peak [median (interquartile range)] was lower in subjects with MetS compared with controls [27.9 (23.0–31.0) vs. 35.0 (32.0–45.0) mL·min⁻¹·kg⁻¹; P < 0.0001]. Multivariable regression analysis demonstrated that there was a bidirectional association between MetS and VO₂ peak that was mediated by waist circumference and blood pressure. The VO₂ peak was a strong negative determinant of waist circumference (β = −0.36, P < 0.0001), but not of BMI (β = −0.13, P = 0.21).

Conclusions:

A higher VO₂ peak is associated with a lower odds ratio for MetS, which is related to greater cardiorespiratory fitness in a cyclical relationship that is mediated by blood pressure and waist circumference. A higher VO₂ peak is specifically associated with lower waist circumference, and vice versa, possibly by effects on visceral fat.

Introduction

Researchers have shown that an inverse relationship exists between cardiorespiratory fitness and the incidence of metabolic syndrome (MetS).^1

–6 Furthermore, Katzmarzyk et al.⁷ showed that cardiorespiratory fitness has a strong protective effect against all-cause and cardiovascular disease mortality in healthy men and in men with MetS, and a recent longitudinal study showed an inverse relationship between change in cardiorespiratory fitness and change in cardiometabolic disease risk.⁸ In addition, a large cross-sectional study observed a negative relationship of cardiorespiratory fitness with each of the components of MetS.⁹ Such findings have been confirmed in both longitudinal and cross-sectional studies, which have demonstrated that increased cardiorespiratory fitness attenuates each of the component diseases of MetS.¹⁰ However, there has been a lack of investigation of the determinants of measures of cardiorespiratory fitness, such as oxygen consumption (VO₂) peak and anaerobic threshold (AT), in persons with MetS. Also, no studies have examined the effect of MetS and its components on cardiorespiratory fitness and whether a cyclical relationship exists between these variables.

The principal aims of this study were therefore to use multivariable regression models to identify the main determinants of VO₂ peak and AT in a population of subjects with and without MetS and to determine the relationship of VO₂ peak and AT with MetS and its individual components. The hypothesis of this study was that improved cardiorespiratory fitness would attenuate the odds ratio for MetS via associations with the individual components of the syndrome, and that a bidirectional relationship exists between cardiorespiratory fitness and MetS and its component disorders.

Methods

The Human Ethics Committee of the University of the Witwatersrand, Johannesburg, South Africa, approved the study protocol (MO61104).

Participants and definition of MetS

Subjects were selected by convenience sampling from two physician practices in Johannesburg. Inclusion criteria included being male, overweight or obese with no health issues that would preclude them from performing intensive exercise. Any individuals taking lipid lowering agents, antihypertensive drugs or treatments for dysglycemia, or had undertaken regular exercise within 1 month before the start of the study were excluded from this study. Thirty-one males diagnosed with MetS and 24 healthy male subjects each performed a VO₂ peak and a blood lactate transition threshold (BLTT) test. All healthy participants were age matched with the patient group and had one or no component of the MetS. Patients were diagnosed with MetS by clinicians using the guidelines of Alberti et al.¹¹ The presence of any three of the five criteria denoted below constituted a diagnosis of MetS: waist circumference ≥94 cm; triglycerides ≥1.7 mmol·L⁻¹; high-density lipoprotein (HDL) cholesterol <1.0 mmol·L⁻¹; systolic blood pressure ≥130 mmHg and/or diastolic blood pressure ≥85 mmHg; and elevated fasting glucose (≥5.6 mmol·L⁻¹).

Measurements

All participants underwent a 75g, 2-hr oral glucose tolerance test (OGTT) for the detection of undiagnosed diabetes. Fasting and 2-hr glucose levels were measured using a glucose oxidase method; serum triglyceride, total cholesterol, and HDL cholesterol levels were assayed using enzymatic assay methods, and fasting insulin levels were measured using the Chemiluminescent Microparticle Immunoassay. All measurements were performed on the Architect i2000 Abbott AutoAnalyzer (Roche Diagnostics, Mannheim, Germany). The homeostasis model assessment of insulin resistance (HOMA-IR)¹² was calculated to assess the degree of insulin sensitivity.

Height was measured to the nearest millimeter using a Seca Stadiometer (Hamburg, Germany). Body mass was measured to the nearest gram using a Charder electronic scale (Taichung, Taiwan). Waist circumference was taken as the greatest reading around the girth, midway between the lateral lower ribs and the iliac crests. Blood pressure was measured using a Honsun sphygmomanometer (Shanghai, China), with the subject sitting and taking the average of two readings.

The BLTT test

A BLTT test was performed using a previously published methodology.¹³ In brief, an initial treadmill speed of 2.5 km·hr⁻¹ at a 5% gradient with speed increments every 4 min was used. The treadmill speed was modified depending on the response of a participant to ensure the collection of at least six blood samples for lactate analysis. After the participant signaled impending exhaustion, they were encouraged to continue to complete the 30-sec period already begun. The treadmill speed at the time of exhaustion as well as the time period spent on the treadmill was recorded. Electrocardiographic (ECG) responses and oxygen consumption (VO₂) were measured at the same time (Cortex Biophysik Metalyzer 3b CPX system, Leipzig, Germany). No ECG abnormalities were observed; the VO₂ level at exhaustion was recorded as the VO₂ peak. This protocol was designed taking into consideration the assumed low level of physical conditioning of persons with MetS.

Lactate concentration was measured from whole-blood samples taken from the fingertip using the Lactate Scout automated analyzer (Lactate Scout; SensLab, Leipzig, Germany). Blood samples (collected from finger capillary vessels using a lancet) were taken at the end of each exercise period within 30–40 sec of the participant standing still on the treadmill. The fingertip was first wiped with water to remove the sweat and then it was dried before the blood sample was collected. The participant continued exercising within 1 min of standing.

Determination of AT

The BLTT was defined in this study, for purposes of terminology, as the AT. The AT in this study was defined as the workload (treadmill velocity given as km·hr⁻¹) marked by a rapid rise in blood lactate denoting the upper limit of equilibrium between lactate production and clearance.¹⁴ The treadmill velocity at AT for the BLTT tests was determined using the ADAPT method.¹⁵

The ADAPT method determines AT from the individual shape of the velocity–blood lactate curve (not from a fixed blood lactate concentration) and is based on the Dmax _mod method.¹⁵ A modification of the ADAPT method was used for determining AT in this study.¹³

Statistical analysis

Data distribution was analyzed using the Shapiro–Wilk's W test. Normally distributed data were expressed as mean ± standard deviation, while nonparametric data were expressed as median (interquartile range). The former data were compared between subjects with and without MetS using the Student's t test, while the latter variables were compared using the Mann–Whitney U test.

Univariate regression analyses were used to determine the principal correlates of VO₂ peak, AT, body mass index (BMI), and MetS and its individual components. It should be noted that the variables included in these univariate analyses were chosen based on evidence from the literature showing that these relationships were physiologically feasible. Variables that gave correlations in the univariate analyses of P < 0.20¹⁶ were then used as independent variables in separate multivariable regression models in which VO₂ peak, AT, BMI, and MetS and its components were the dependent variables. Continuous dependent variables were analyzed using linear multivariable regression, while MetS was analyzed using logistic multivariable regression modeling. Backward, stepwise regression analysis was then performed until only variables with P < 0.05 remained in the model. Multicollinearity was assessed using the variance inflation factor (VIF), and any variables with a VIF >5.0 were removed from the model. Dependent continuous variables that had a skewed distribution (BMI, waist, fasting glucose, VO₂ peak, and AT) were log transformed to normality before being used in the univariate or multivariate regression analyses. All multivariable regression models included both subject groups, that is, those with and those without MetS.

The sample size was calculated based on the ability to identify a significant difference in the mean VO₂ peak level between cases and controls using data from a previous study conducted in a similar population.¹³ This study found a significant difference in VO₂ peak between subjects with and without MetS using a sample size of 15 per group, and this was taken as the minimum sample size for the current study. However, the main aim of this study was to analyze the relationship of VO₂ peak with MetS and its component diseases using multivariable regression analyses. Therefore, in a post hoc analysis with the known total sample size of 55, we calculated that with a power of 90%, a P value of 0.05, and a maximum of 4 independent variables, the lowest effect size that could be detected using multivariable regression analysis would be 0.30.

Results

Characteristics of study groups

Table 1 describes the study participants' characteristics. Waist circumference, BMI, triglycerides, systolic and diastolic blood pressure, fasting insulin, and HOMA were higher in those with MetS, while HDL, AT, and VO₂ peak were lower. The maximum treadmill speed attained before exhaustion was also lower in the subjects with MetS, as was the length of time spent on the treadmill, however, the latter difference did not reach statistical significance.

Table 1.

Physical and Biochemical Characteristics of Study Participants

Variables	MetS (n = 31)	Controls (n = 24)	P
Age (years)	43.8 ± 7.91	42.7 ± 6.93	0.635
BMI (kg·m⁻²)	33.1 (30.2–33.6)	30.3 (26.5–33.5)	0.015
Waist circumference (cm)	115 (110–119)	105 (94.5–111)	<0.0005
Triglycerides (mmol·L⁻¹)	1.75 ± 0.54	1.32 ± 0.49	0.004
Total cholesterol (mmol·L⁻¹)	5.58 ± 1.37	5.50 ± 1.11	0.771
HDL (mmol·L⁻¹)	0.94 ± 0.22	1.11 ± 0.17	0.003
Systolic BP (mmHg)	137 ± 12.2	121 ± 10.4	<0.0005
Diastolic BP (mmHg)	87.7 ± 6.60	81.7 ± 6.88	0.003
Fasting glucose (mmol·L⁻¹)	5.30 (4.70–5.70)	5.10 (4.80–5.25)	0.359
Two-hour glucose (mmol·L⁻¹)	5.60 (3.70–7.10)	5.35 (4.50–6.30)	0.970
Fasting insulin (μU·mL⁻¹)	12.2 (9.00–16.4)	8.50 (8.10–10.3)	0.014
HOMA index	2.81 (2.24–3.91)	2.09 (1.73–2.38)	0.022
VO₂ peak (mL·min⁻¹·kg⁻¹)	27.9 (23.0–31.0)	35.0 (32.0–45.0)	<0.0005
AT (km·hr⁻¹)	5.21 (4.65–5.91)	5.90 (5.49–8.23)	0.016
Speed attained on treadmill (km·hr⁻¹)	6.50 (5.50–7.00)	7.50 (6.50–11.0)	0.0008
Time on treadmill (mins)	28.0 (24.0–36.0)	32.0 (25.0–40.0)	0.329

Data are given as mean ± standard deviation or median (interquartile range); P values are taken from the Student's t test for parametric data and from the Mann–Whitney U test for nonparametric data.

AT, anaerobic threshold; BMI, body mass index; BP, blood pressure; HDL, high-density lipoprotein; HOMA, homeostasis model assessment; MetS, metabolic syndrome; VO₂, oxygen consumption.

Determinants of VO₂ peak and AT

Univariate regression analysis demonstrated that both VO₂ peak and AT correlated significantly (P < 0.05) and inversely with MetS, BMI, waist circumference, and systolic blood pressure, while VO₂ peak correlated directly with AT (Table 2). These variables, as well as other variables from Table 2 that correlated with VO₂ peak and AT at P < 0.20, were included as independent variables in two separate backward, stepwise regression models in which VO₂ peak and AT were the dependent variables. In the first regression model, waist circumference and systolic blood pressure both correlated inversely with VO₂ peak, while AT correlated directly (Table 3). In the second regression model, only VO₂ peak correlated directly with AT (Table 3).

Table 2.

Univariate Regression Analyses of VO₂ Peak and Anaerobic Threshold with Study Variables

Variables	VO₂ peak	AT
MetS^a	−0.56 (<0.0001)	−0.34 (0.01)
Age	−0.21 (0.15)	−0.12 (0.39)
BMI	−0.69 (<0.0001)	−0.58 (<0.0001)
Waist circumference	−0.76 (<0.0001)	−0.66 (<0.0001)
Diastolic BP	−0.36 (0.01)	−0.25 (0.07)
Systolic BP	−0.46 (0.001)	−0.28 (0.04)
Fasting glucose level	−0.26 (0.07)	−0.16 (0.23)
Two-hour glucose level	−0.24 (0.12)	−0.17 (0.24)
HOMA index	−0.21 (0.21)	−0.12 (0.44)
VO₂ peak	—	0.78 (<0.0001)
AT	0.78 (<0.0001)	—

Data given as standardized b-coefficient (P value).

Presence of metabolic syndrome was coded as 1, absence was coded as 0.

Table 3.

Results of Backward, Stepwise Multiple Regression Analyses for Isolating Principal Determinants of VO₂ Peak and Anaerobic Threshold

Model No.	Dependent variable	Independent variables with β-coefficient ^a (P value)		R² for full model (P value)
1	VO₂ peak	Waist	−0.391 (0.0003)	0.708 (<0.0001)
		Systolic BP	−0.239 (0.006)
		AT	0.447 (<0.0001)
2	AT	VO₂ peak	0.755 (<0.0001)	0.561 (<0.0001)

Standardized b-coefficient; model 1 and model 2 included the following additional variables that were removed in a stepwise manner due to a lack of significance (P > 0.05)—Model 1: MetS, age, BMI, diastolic BP. Model 2: MetS, BMI, waist, and diastolic and systolic BP.

Relationship of MetS with VO₂ peak and AT

The univariate regression analyses in Table 2 show that MetS correlates significantly and inversely with both VO₂ peak and AT, while the multivariate models in Table 3 show that MetS does not remain in either model. This suggests that the relationship of MetS with VO₂ peak and AT is mediated by one of the MetS components or by another variable. To investigate this, forward regression models were produced in which MetS was the sole independent variable and VO₂ peak or AT was the dependent variable. Variables were then added to each model in a stepwise manner until the β-coefficient for MetS was rendered nonsignificant (P > 0.05). The variables chosen to be added to the model were those that were significant in the final regression models shown in Table 3. These forward regression analyses are shown in Table 4. The forward regression models for VO₂ peak demonstrate that waist and systolic blood pressure individually weakened the β-coefficient for MetS, but not to a level where the β-coefficient became nonsignificant. Only when these variables were added together (model 4) did the β-coefficient drop to a nonsignificant value. The forward regression model for AT demonstrated that VO₂ peak alone was able to attenuate the β-coefficient for MetS to a nonsignificant level (Table 4).

Table 4.

Forward Regression Models Demonstrating Variables That Attenuate the Effect of Metabolic Syndrome on VO₂ Peak and Anaerobic Threshold

Categorical variable	Model No.	Independent variables	β-coefficient ^a (P value)
VO₂ peak	1	MetS	−0.561 (<0.0001)
	2	MetS	−0.284 (0.009)
	2	Waist	−0.605 (<0.0001)
	3	MetS	−0.439 (0.003)
	3	Systolic BP	−0.226 (0.117)
	4	MetS	−0.200 (0.092)
		Waist	−0.590 (<0.0001)
		Systolic BP	−0.166 (0.136)
AT	1	MetS	−0.346 (0.009)
	2	MetS	0.120 (0.297)
	2	VO₂ peak	0.821 (<0.0001)

Standardized b-coefficient.

Determinants of MetS and its component diseases

The results of univariate linear regression analyses for each of the cardiometabolic components of the MetS (fasting glucose, HDL, and triglyceride and systolic and diastolic blood pressure) against appropriate study variables are shown in Supplementary Table S1. Those variables that correlated with the MetS component at P < 0.20 were then included in a multivariable backward, stepwise linear regression model, and these results are shown in Table 5. A similar process was conducted for MetS using multivariable logistic regression analysis, and the results of the univariate and multivariate analyses are shown in Supplementary Table S2 and Table 5, respectively.

Table 5.

Results of Backward, Stepwise Multiple Regression Analyses for Isolating Principal Determinants of Metabolic Syndrome, Its Components, and Body Mass Index

Model No.^a	Dependent variable	Independent variables with β-coefficient ^b (P value)		R² for full model (P value)
1	Glucose	Age	0.429 (0.001)	0.394 (<0.0001)
1	Glucose	HOMA	0.415 (0.002)	0.394 (<0.0001)
2	HDL cholesterol	Age	0.293 (0.030)	0.069 (0.030)
3	Systolic BP	HOMA	0.377 (0.010)	0.296 (0.0008)
3	Systolic BP	VO₂ peak	−0.425 (0.004)	0.296 (0.0008)
4	Diastolic BP	VO₂ peak	−0.340 (0.017)	0.097 (0.017)
5	Waist circumference	Age	0.148 (0.029)	0.807 (<0.0001)
		BMI	0.580 (<0.0001)
		VO₂ peak	−0.364 (<0.0001)
6	BMI	VO₂ peak	−0.682 (<0.0001)	0.45 (<0.0001)
		Independent variable with odds ratio (95% CIs; P value)
7	MetS	VO₂ peak	0.843 (0.755–0.941; 0.002)

Models 1–6 are multivariable linear, while model 5 is a multivariable logistic regression model; models 1 and 2–7 included the following additional variables that were removed in a stepwise manner due to a lack of significance (P > 0.05)—Model 1: BMI, waist, VO₂ peak. Model 2: included no additional variables. Model 3: age, BMI, waist, 2-hr glucose, AT. Model 4: as for model 3 plus HOMA. Model 5: AT only. Model 6: age, AT. Model 7: BMI, HOMA, AT.

Standardized b-coefficient.

CIs, confidence intervals.

In the first linear regression model, age and HOMA were found to correlate directly with fasting glucose (Table 5). The second regression model demonstrated that only age correlated directly with HDL cholesterol. In the third and fourth models, VO₂ peak correlated with both systolic and diastolic blood pressure, respectively, while HOMA correlated directly with systolic blood pressure only. The fifth regression model showed that age and BMI both correlated directly with waist, while VO₂ peak correlated inversely. The sixth model, for BMI, demonstrated that the only correlate was VO₂ peak, which had a strong inverse association with BMI. The correlation of BMI with VO₂ peak may be a result of the strong relationship between BMI and waist (see model 5, Table 5). Therefore, waist circumference was included as an independent variable in the BMI model (model 6 in Table 5) and this attenuated the relationship of VO₂ peak with BMI to nonsignificance (β = −0.13, P = 0.21), but with waist being highly significant (β = 0.77, P < 0.0001). This does suggest that the relationship of VO₂ peak with BMI is due to confounding from waist circumference. No significant correlations were identified for triglyceride levels. In a logistic regression model, VO₂ peak was the only variable that correlated with MetS, with an increase in VO₂ peak related to reduced odds for MetS.

Discussion

The present study showed that VO₂ peak and AT were both lower in participants with MetS. Multiple regression analysis demonstrated that the lower VO₂ peak of participants with MetS is largely due to their higher waist circumference, but higher systolic blood pressure also plays a role. The lower AT in those with MetS was explained by their lower VO₂ peak. In addition, VO₂ peak was found to be the principal determinant of MetS in this population via the intermediary effects of blood pressure and waist circumference.

The MetS group had a significantly lower VO₂ peak and a lower final treadmill speed than the non-MetS group (Table 1), which both demonstrate the lower functional capacity of the former group. These findings agree with the Kuoppio study,⁴ which found that directly determined VO₂ max was lower in persons with MetS. Also, Laaksonen et al.¹⁷ showed that men with a VO₂ max of <29.1 mL·min⁻¹·kg⁻¹ were three to four times more likely to have MetS than those with VO₂ max of ≥35.5 mL·min⁻¹·kg⁻¹. These two values are in agreement with the VO₂ peak values of the subject groups in this study (Table 1). Furthermore, researchers have also found an inverse relationship between cardiorespiratory fitness and the incidence of MetS.^1,2,5,9 Within the present study, the higher VO₂ peak in the subjects without MetS is not due to their training more than the MetS group, because none of the study participants had undertaken regular exercise for a minimum of 1 month before the start of this investigation.

The regression analysis using VO₂ peak as the dependent variable (Table 3) and the forward regression analysis in which the influence of MetS on VO₂ peak is attenuated by waist circumference, and systolic blood pressure (Table 4) demonstrates that the lower VO₂ peak of participants with MetS is predominantly associated with their higher waist circumference, with systolic blood pressure having a more minor input. This finding highlights the importance of reducing waist circumference in individuals with MetS to improve energy generation and health. It is interesting to note that both longitudinal¹⁸ and cross-sectional studies¹⁹ have shown that, of all anthropometric variables, only waist circumference was inversely correlated with fitness level. It is not known why waist circumference specifically influences VO₂ peak. One possible explanation is that waist circumference is a proxy indicator of visceral fat mass²⁰ and this body fat depot is a strong negative regulator of whole-body insulin sensitivity²¹ and may therefore limit glucose metabolism in skeletal muscle. In addition, visceral adiposity is associated with higher cardiovascular risk²² and is known to correlate inversely with VO₂ peak.²³ It is also important to note that an inverse relationship has been observed between cardiorespiratory fitness and liver fat content,²⁴ and it is well recognized that visceral fat mass is directly associated with steatohepatitis.²⁵ It is therefore possible that the inverse association of waist circumference with VO₂ peak is partially mediated by steatohepatitis, with the latter factor possibly influencing VO₂ peak by its effects on the vasculature.²⁶

The backward and forward regression analyses demonstrating that the lower AT values in participants with MetS are largely associated with their lower VO₂ peak levels (Tables 3 and 4) support the notion of a lower level of aerobic fitness in patients with MetS, as has been demonstrated in other studies.^9,16,27 Also, the higher velocity attained at AT by the healthy participants, when compared with the participants with MetS, confirms the observation that persons with MetS exhibit a lower level of physical conditioning than their healthier counterparts.⁴

In the multivariable regression models for VO₂ peak and AT (Tables 2 and 3), the decision to include AT as an independent variable in the former, and VO₂ peak in the latter model was based on a study by Aunola et al.,²⁸ in which a factor model was constructed for aerobic work capacity. The model showed that maximal aerobic power (VO₂ max) correlated strongly with AT and vice versa (r = 0.92). This model agrees with our findings of a bidirectional relationship between AT and VO₂ peak.

Cardiorespiratory fitness (VO₂ peak) was shown to be a negative determinant of MetS, waist circumference, and systolic and diastolic blood pressure (Table 5). These results demonstrate that improved cardiorespiratory fitness attenuates MetS risk largely through its effects on blood pressure and waist circumference. The inverse relationship of VO₂ peak with blood pressure forms the basis of Fick's law, which states that VO₂ peak is the product of cardiac output and the arteriovenous oxygen difference.²⁹ Our findings are further supported by studies showing that blood pressure correlates negatively with VO₂ peak³⁰ and that aerobic exercise improves blood pressure.³¹ The inverse relationship of VO₂ peak with waist circumference was independent of the relationship of BMI with waist (Table 5), and in a separate regression model the inverse association of VO₂ peak with BMI was shown to be due to confounding from waist circumference. These data suggest that cardiorespiratory fitness has a greater influence on waist circumference than BMI. This finding is supported by data from studies showing that exercise and other weight loss interventions have a greater effect on visceral than subcutaneous percentage fat loss.³²

It is important to note that when constructing the multivariable regression models for use in this study it was assumed that there was a bidirectional, causal relationship between cardiorespiratory fitness and MetS and its components. Evidence for cardiorespiratory fitness attenuating cardiometabolic disease risk includes studies demonstrating that increasing baseline levels of cardiorespiratory fitness reduce the risk of both incident MetS⁵ and hypertension^31,33 and that increases in cardiorespiratory fitness over time lead to reductions in cardiovascular disease (CVD) risk factors.⁸ Unfortunately, there are no prospective studies that have analyzed the effects of MetS or associated variables on changes in cardiorespiratory fitness. However, cross-sectional studies have shown that high blood pressure is associated with lower VO₂ peak in elite athletes³⁰ and that type 2 diabetic subjects have lower VO₂ peak levels when compared with nondiabetic subjects matched for BMI and sedentary time.³⁴ Furthermore, it is scientifically plausible that MetS may be related to reduced cardiorespiratory fitness via the effects of insulin resistance on glucose metabolism, high blood pressure on cardiac output, dyslipidemia on vascular function, and a proinflammatory environment on endothelial activation and insulin sensitivity.

Univariate analyses demonstrated strong positive associations of both fasting and postprandial glucose levels with HDL (Supplementary Table S1). This was an unexpected finding and is not supported by data from the literature, which shows that HDL levels are lower in subjects with dysglycemia, an effect attributed to insulin resistance and inflammatory cytokines.³⁵ This finding may be a statistical anomaly driven by a relatively small sample size or it may be the result of confounding from an unmeasured variable.

The main limitation of this study is its cross-sectional format, which does not allow us to investigate causal relationships. In addition, the relatively small sample size may affect the ability of the study to observe significant relationships between variables. Thus, a post hoc assessment of the minimum effect size that could be detected in the regression models at a P < 0.05 and with 90% power, with four independent variables, was 0.30. This is considered to be high³⁶ and may explain our inability to detect significant associations for triglyceride levels. Also, this study was conducted with male subjects only, who were selected using convenience sampling. Despite these limitations, we were able to identify important associations between study variables and to observe significant differences across the study groups that are supported by data from previous investigations.

In conclusion, the main finding of this study is that a bidirectional relationship exists between MetS and cardiorespiratory fitness with waist circumference and to a lesser extent systolic blood pressure, acting as the mediators between these two variables. These data suggest that improving the VO₂ peak may attenuate the odds for MetS predominantly through reductions in waist circumference and systolic blood pressure, which in turn may improve cardiorespiratory fitness. Future studies must therefore be directed at reducing body fat mass through dietary interventions and measuring the effects of changes in specific body fat depots, such as visceral adipose tissue, on VO₂ peak and cardiometabolic variables. It is interesting to note that a recent longitudinal study has shown that improvements in cardiorespiratory fitness lead to reduced cardiometabolic risk partially through the intermediary effect of waist circumference.⁸ Such studies highlight the importance of targeting specific body fat depots for interventions that may attenuate cardiometabolic disease risk profiles and enhance cardiorespiratory fitness.

Footnotes

Author Disclosure Statement

No conflicting financial interests exist.

Funding Information

Lancet laboratories sponsored the blood analyses.

Supplementary Material

Supplementary Table S1

Supplementary Table S2

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Supplementary Material

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The Relationship of VO 2 Peak and the Blood Lactate Transition Threshold with Metabolic Syndrome and Its Component Disorders

Abstract

Background:

Methods:

Results:

Conclusions:

Introduction

Methods

Participants and definition of MetS

Measurements

The BLTT test

Determination of AT

Statistical analysis

Results

Characteristics of study groups

Determinants of VO2 peak and AT

Relationship of MetS with VO2 peak and AT

Determinants of MetS and its component diseases

Discussion

Footnotes

Author Disclosure Statement

Funding Information

Supplementary Material

References

Supplementary Material

Determinants of VO₂ peak and AT

Relationship of MetS with VO₂ peak and AT