Investigation of Underlying Association between Anthropometric and Cardiorespiratory Fitness Markers among Overweight and Obese Adolescents in Canada

By inergency On Mar 28, 2024

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This study provided a valuable opportunity to revisit and document various understudied anthropometric markers in the Canadian adolescent population. One such marker is WC, which has only recently received attention, with the first publication of normative values in Canada occurring in 2004. Notably, the data used to establish these norms originated from a survey conducted much earlier, in 1981 [55]. Consequently, considering the observed changes in obesity rates over the past few decades, the 2004 normative values likely underestimate current WC in Canadian adolescents. This is further supported by a 2010 study, which compared data from 1981 with 2007–2009, documenting increases in WC of 4.2 cm and 6.7 cm for boys and girls, respectively [18]. Conversely, the present study reveals an even greater increase, with values reaching 5.8 cm in boys and 7.4 cm in girls, suggesting a sustained secular trend. Remarkably, the observed trend in WC appears to exhibit a distinct trajectory compared to that of BMI.

4.2. Inclusion of CRF Markers

The inclusion of physiological markers represents a significant advancement in cardiometabolic risk assessment. The addition of markers such as VO₂peak or the number of 1 min stages completed (FMAP) in the 20 m shuttle run test provides independent insights into adolescents’ health, leading to a more comprehensive characterization of their risk profile. Moreover, integrating this physiological dimension aligns directly with the American Heart Association’s recommendation, as outlined in their scientific statement, which promotes cardiorespiratory fitness (CRF) as a “vital sign” that should be routinely monitored in clinical practice [36]. In this model, low CRF assumes a central role in the estimation of cardiometabolic risk. In our study, CRF could either be determined using VO₂peak or the number of stages completed. The choice between the two markers is left to the user, and this was chosen for pragmatic reasons. While diverse methods exist to measure VO₂peak, many are resource-intensive. Treadmills, bicycle ergometers, and expired gas analyzers can be costly, time-consuming, and require specialized expertise. Fortunately, simpler and more cost-effective field tests still yield valid results. Therefore, our model accepts VO₂peak values obtained through diverse methodologies (not necessarily the 20 m shuttle run test), promoting both versatility and accessibility.

Unlike anthropometric markers readily calculated from BM, BH, and WC and easily included in the proposed model, the number of 1 min stages completed, serving as an indicator of an individual’s FMAP, can only be obtained through the 20 m shuttle run test. Consequently, the obligatory inclusion of both CRF markers would restrict the possibility of obtaining a composite score, leading to the option of including only one of these two markers. In fact, the current study demonstrates the feasibility of developing an accurate and reliable model using a limited number of markers to predict the cardiometabolic risk among adolescents. While we advocate for the use of both CRF markers (Equation (1)), employing solely the VO₂peak value offers a viable alternative for overall risk assessment with a marginal impact of only ~−6%.

4.3. Individual and Composite Scores as Cardiometabolic Risk Markers

The current study provides a more complete assessment of cardiometabolic risk as opposed to studies using individual markers. However, establishing an overall risk classification that encompasses all markers simultaneously presents a challenge. The composite score offers the benefit of summarizing the risk when analyzing all markers concurrently. It is important to acknowledge that both the individual risk zones and the final composite score have inherent limitations due to their arbitrary nature. However, the delineation of the various risk zones largely mirrors the rationale used by the WHO to determine the BMI risk zones. Consequently, each risk zone was established using corresponding BMI values. For instance, to determine WC risk, we first identified the raw values corresponding to the 85th percentile for BMI. Subsequently, the raw score was compared with the corresponding Z-score. Thus, for anthropometric variables, it has been determined that the risk zone corresponding to the 85th percentile for BMI aligns with the 80th percentile for WC, WHtR, and BSA. Moreover, previous studies have already recognized the value aligned with the 80th percentile as the most appropriate for adolescents concerning WC [10], which is consistent with our findings. This approach, adopted in previous studies [4,29], acknowledges the unique characteristics of the target population instead of applying a universal cutoff point. In this context, we have forsaken the “conventional” cutoffs for WC, WHtR, and BSA in favor of thresholds more tailored to the Canadian population. Nonetheless, our model can be adapted for other populations based on the same rationale. This more stringent 80th percentile cutoff enhances the sensitivity of the screening tool by minimizing the likelihood of overlooking adolescents who may be at risk of health issues.

Our regression analysis revealed collinearity issues among the anthropometric markers, particularly between WC and WHtR. We prioritized retaining WC due to its wider use and more extensive research base. Following the same rationale, BSA was excluded in favor of BMI. However, excluding WHtR and BSA from the model does not diminish their clinical value. From an individual analysis, the BSA and WHtR indices can bolster the clinician’s evaluation by confirming the trends observed through BMI or WC measurements. Consequently, these two parameters can supplement the clinician’s assessment in addition to BMI or WC values. For instance, when both BMI and BSA categorize an adolescent into the same risk group, it indicates that the risk linked with general obesity is supported by two indicators rather than solely one. In essence, the incorporation of BSA and WHtR significantly enhances the clinical evaluation, facilitating a more precise diagnosis and personalized patient management, while providing a more complete vision of their cardiometabolic status and the associated risk of obesity-related complications.

CRF markers represent a distinct facet of cardiometabolic risk that needs to be considered. While a consensus on risk values for adolescent health remains elusive, some researchers have proposed cutoff value below 42 mL·kg⁻¹·min⁻¹ for boys and 35 mL·kg⁻¹·min⁻¹ for girls, albeit with certain variations [36,38,43]. While these values account for sex differences, no studies have specifically addressed age-related cutoff values. Interestingly, during the 1980s, a single cutoff point was established for children and adolescents aged 6 to 17, particularly among boys, due to the remarkable consistency of relative VO₂peak, around 50 mL·kg⁻¹·min⁻¹, throughout this period of physical growth [40]. However, current evidence clearly demonstrates that relative VO₂peak values tend to decline with age in both boys and girls [41,42,43]. This growing trend, spanning several decades, is indicative of the shift observed in today’s adolescents who are forsaking physical activities in favor of adopting more sedentary behaviors, such as engaging in activities like playing video games, for instance. Thus, this age-related decline in VO₂peak must be considered when estimating health risk. For instance, if the cutoff point is set at 42 mL·kg⁻¹·min⁻¹ for 12-year-old boys, considering their subsequent decline in VO₂peak, these same boys are likely to fall below this threshold at the age of 13. Thus, in this particular case, the cutoff point of 42 mL·kg⁻¹·min⁻¹ may provide a false sense of security for an individual of this age.

Given the observed decline, a higher cutoff threshold would be more appropriate for 12-year-olds compared to 13-year-olds, considering their inherently higher median VO₂peak. Based on this model, we note that more than 50% of adolescents in our sample are at high CRF risk. It is worth highlighting that this risk reaches its peak at the age of 17, with a value of 57.3% for boys and 65.8% for girls, corroborating recent published data [43]. These findings are undoubtedly concerning and warrant public health attention. It is crucial to take immediate measures to motivate adolescents to embrace a more active lifestyle, particularly older individuals who are already at a heightened risk of imminent cardiometabolic problems. One potential solution involves reevaluating the allocation of physical education (PE) within school curriculums. The significant reduction in PE minutes observed in Québec over the past three decades coincides with the decline in VO₂peak and FMAP values, suggesting a potential link that merits further investigation [43].

Regarding the number of 1 min stages completed (FMAP), as far as we know, only one prior study has investigated its association with cardiometabolic risk [38]. In general, the reported values align with the results of the present study, showing similar magnitudes when considering values per year of chronological age. Even though the data originates from different populations, which could account for certain variations, it is worth noting that both studies were conducted during the same timeframe (2016 vs. 2017 for the present study), thereby minimizing the potential impact of the secular trend.

Therefore, the inclusion of a marker directly focusing on adolescents’ functional capacity, like FMAP, undoubtedly constitutes a significant contribution to their cardiometabolic risk assessment. While the data primarily originates from the province of Québec, which accounts for slightly less than 25% of the Canadian population, the proposed model can serve as a valuable reference for other regions within Canada and potentially worldwide. In the absence of regional data, the values from this study can serve as a provisional risk assessment tool until region-specific values becomes available. FMAP represents a cardiometabolic risk marker that is equally, if not more, significant compared to VO₂peak. It holds the advantage of being less influenced by anthropometric characteristics in comparison to relative VO₂peak. Consequently, FMAP’s interpretation is simpler, both for adolescents and for healthcare professionals. Particularly during growth spurts, when considering available data, FMAP should take precedence over VO₂peak as the primary marker for assessing cardiometabolic risk. Nonetheless, for cardiometabolic composite risk assessment, regression Equation (2) demonstrates the feasibility of using VO₂peak alone when FMAP data is unavailable. Despite the absence of FMAP data, VO₂peak alone can be a sufficient metric for assessing composite cardiometabolic risk (regression Equation (2)) without compromising on validation and accuracy, which remain excellent.

4.5. Limitations and Strengths

This study has several limitations. First, the cross-sectional design restricts the ability to draw causal inferences. Second, the estimation of VO₂peak values instead of direct measurement introduces inherent uncertainties. Third, the analysis employs anthropometric markers derived from field measurements. These measurements may not consistently achieve the optimal level of precision required for drawing robust inferences, compared to those obtained from direct measures. Fourth, while the sample represents Canadian adolescents living in Québec, generalizing the findings to other regions requires caution. Furthermore, the associations between cardiometabolic risks and the measured markers are primarily based on existing literature, limiting the study’s ability to establish causality. However, this study also boasts notable strengths. The substantial participant size (N = 1864) provides a valid representation of Québec adolescents. Additionally, the stratification of several criteria, including age groups, sex, ethnicity, and socio-economic status, enhances the representativeness of the evaluated population. Beyond its user-friendly characteristics, the proposed model provides a level of validity and precision that ensures reliable data interpretation. Finally, despite the weaknesses mentioned above, all employed markers in this study are well established as valid and reliable measures, displaying acknowledged associations with cardiometabolic risks.

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