Accuracy of anthropometric indicators of obesity to identify high blood pressure in adolescents—systematic review

Introduction

Anthropometric indicators such as body mass (BM), height, waist circumference (WC) and hip circumference (HC) have been described as useful tools to detect factors associated with cardiovascular risk (Cassiano et al., 2019), such as insulin resistance, metabolic syndrome, and dyslipidemia (De Faria et al., 2009; Mastroeni et al., 2019; Beck, Da Silva Lopes & Pitanga, 2011). Additionally, the predictive power of anthropometric indicators for screening for high blood pressure (HBP) in children and adolescents (De Quadros et al., 2019; Rimárová et al., 2018) has been described, and it has been reported that anthropometric indicators of obesity such as body mass index (BMI), waist-to-height ratio (WHtR) and WC have acceptable discriminatory power to identify HBP in adolescents (Araújo, Ramos & Barros, 2019; Liew et al., 2019; De Araújo Pinto et al., 2017).

Although the measurement of blood pressure levels is an important component of health assessment routines in the pediatric population, difficulties related to the measurement (e.g., need to use specific instrument or choose the appropriate cuff for the child/adolescent’s arm) or classification of measured information (e.g., insertion of pressure values in panels of growth curves) are configured as barriers for the measurement of blood pressure levels in environments with structure different from that observed in clinical centers, such as schools or sports clubs (Barroso et al., 2021). Thus, the use of anthropometric indicators can be a useful tool in the screening for HBP in children and adolescents.

In this sense, the aim of this review was to investigate the accuracy of anthropometric indicators of obesity for screening for HBP in adolescents.

Material & Methods

The method used in this systematic review was consistent with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) (Liberati et al., 2009) statement (Supplemental 1). The review was registered in the “International Prospective Register of Systematic Reviews” (PROSPERO) under number CRD42020151554, and is available in full at: http://www.crd.york.ac.uk/prospero/.

Results

Searches conducted in the investigated databases identified 15,615 studies, which after exclusion of duplicates, reading titles and abstracts, totaled 932 studies, whose information was analyzed in full. Of this total, 10 studies were part of the evidence summarization conducted in the present review (Fig. 1).

The evidence included in this review came from cross-sectional studies, four of them being carried out in the American continent (Beck, Da Silva Lopes & Pitanga, 2011; De Moraes & Da Veiga, 2014; Vogt Cureau & Reichert, 2013; Taylor & Hergenroeder, 2011), four in Asia (Abbaszadeh et al., 2017; Al-Bachir & Bakir, 2017; Febriana, Nurani & Julia, 2015; Kajale et al., 2014), one study in the African continent (Kruger et al., 2013) and one in Europe (Mazicioglu et al., 2010). All studies investigated the probable relationship of BMI in predicting high blood pressure levels. In addition to BMI, other anthropometric indicators of obesity such as WC (Beck, Da Silva Lopes & Pitanga, 2011; De Moraes & Da Veiga, 2014; Vogt Cureau & Reichert, 2013; Taylor & Hergenroeder, 2011; Abbaszadeh et al., 2017; Kajale et al., 2014; Mazicioglu et al., 2010), HC (Vogt Cureau & Reichert, 2013; Abbaszadeh et al., 2017), relaxed arm perimeter (RAP) (Mazicioglu et al., 2010), waist-to-hip ratio (WHR) (Abbaszadeh et al., 2017), WHtR (Beck, Da Silva Lopes & Pitanga, 2011; De Moraes & Da Veiga, 2014; Vogt Cureau & Reichert, 2013; Taylor & Hergenroeder, 2011; Abbaszadeh et al., 2017; Kajale et al., 2014; Mazicioglu et al., 2010), body adiposity index (BAI) (Vogt Cureau & Reichert, 2013) and conicity index (c index) (Beck, Da Silva Lopes & Pitanga, 2011) were investigated. Additionally, triceps and subscapularis skinfolds and the sum of skinfolds were used to calculate body fat percentage (%F) and the possible predictive capacity for HBP (Kajale et al., 2014; Mazicioglu et al., 2010) (Table 1).

Eight studies used the 90th or 95th SBP and/or DBP percentiles to classify individuals with HBP, according to sex and age (Beck, Da Silva Lopes & Pitanga, 2011; De Moraes & Da Veiga, 2014; Vogt Cureau & Reichert, 2013; Taylor & Hergenroeder, 2011; Febriana, Nurani & Julia, 2015; Kajale et al., 2014; Kruger et al., 2013; Mazicioglu et al., 2010). One study followed the guidelines (Roccella, 1996) for classifying pressure levels determined according to age, sex and adjusted height (Abbaszadeh et al., 2017). Thus, in the aforementioned study (Abbaszadeh et al., 2017), if the mean of three measurements of SBP or DBP was greater than the 90th percentile and less than the 95th percentile, adolescents were classified as prehypertensive, if the mean of SBP or DBP was greater than the 95th percentile, adolescents were classified as hypertensive, and if the mean of the three measurements of SBP or DBP was less than the 90th percentile, adolescents were classified as normal blood pressure. In addition, adolescents with SBP > 120 mmHg and DBP > 80 mmHg, but with a percentile < 95, were considered prehypertensive (Abbaszadeh et al., 2017). In another study (Al-Bachir & Bakir, 2017), values of SBP > 135 mmHg and DBP > 89 mmHg were adopted to classify adolescents with HBP. Furthermore, eight studies indicated that anthropometric indicators of obesity had an acceptable predictive capacity to identify HBP in adolescents (Beck, Da Silva Lopes & Pitanga, 2011; Vogt Cureau & Reichert, 2013; Taylor & Hergenroeder, 2011; Abbaszadeh et al., 2017; Al-Bachir & Bakir, 2017; Febriana, Nurani & Julia, 2015; Kajale et al., 2014; Mazicioglu et al., 2010). However, only in one study (Beck, Da Silva Lopes & Pitanga, 2011) the cutoff points of the anthropometric indicators showed higher AUC values: BMI [AUC: 0.95 (95% CI [0.87–1.00])], WC [AUC: 0.96 (95% CI [0.92–1.00])] and WHtR [AUC: 0.93 (95% CI: 0.85-100)] among females; and WC [AUC: 0.80 (95% CI [0.72–0.89])] among males (Beck, Da Silva Lopes & Pitanga, 2011) (Table 2).

Seven studies proposed cutoff points for WC (Beck, Da Silva Lopes & Pitanga, 2011; De Moraes & Da Veiga, 2014; Vogt Cureau & Reichert, 2013; Taylor & Hergenroeder, 2011; Abbaszadeh et al., 2017; Kajale et al., 2014; Mazicioglu et al., 2010) and seven studies proposed cutoff points for BMI (Beck, Da Silva Lopes & Pitanga, 2011; Vogt Cureau & Reichert, 2013; Abbaszadeh et al., 2017; Al-Bachir & Bakir, 2017; Febriana, Nurani & Julia, 2015; Kajale et al., 2014; Mazicioglu et al., 2010). Additionally, cutoff points were developed for WHtR (Beck, Da Silva Lopes & Pitanga, 2011; Vogt Cureau & Reichert, 2013; Abbaszadeh et al., 2017; Febriana, Nurani & Julia, 2015; Kajale et al., 2014; Kruger et al., 2013; Mazicioglu et al., 2010), WHR (Abbaszadeh et al., 2017), BAI (Vogt Cureau & Reichert, 2013), triceps skinfold (TSF) (Kajale et al., 2014; Mazicioglu et al., 2010), wrist perimeter (WP) (Kajale et al., 2014), c index (Beck, Da Silva Lopes & Pitanga, 2011), body mass (BM), height, waist-to-arm-span ratio (WASR), mid-upper-arm circumference (MUAC), arm fat area (AFA), arm span (AS) and %F (Mazicioglu et al., 2010) (Table 2).

Four studies reported in detail the selection of subjects, with high methodological quality/low risk of bias (Beck, Da Silva Lopes & Pitanga, 2011; De Moraes & Da Veiga, 2014; Vogt Cureau & Reichert, 2013; Kajale et al., 2014). Seven studies showed low concern about the applicability of results (Beck, Da Silva Lopes & Pitanga, 2011; De Moraes & Da Veiga, 2014; Vogt Cureau & Reichert, 2013; Abbaszadeh et al., 2017; Al-Bachir & Bakir, 2017; Febriana, Nurani & Julia, 2015; Kajale et al., 2014) (Table 3). Regarding the index test, only two studies showed high methodological quality/low risk of bias with regard to the way in which anthropometric measurements were performed and the presentation of anthropometric indicators of obesity (Al-Bachir & Bakir, 2017; Kajale et al., 2014). Regarding the adopted reference standard, only one study showed high methodological quality/low risk of bias (Abbaszadeh et al., 2017) and two studies showed low concern about applicability of results (Abbaszadeh et al., 2017; Febriana, Nurani & Julia, 2015). In addition, the same studies performed BP measurements at different visits. With regard to domain four—flow and time, only two studies showed low risk of bias (Abbaszadeh et al., 2017; Febriana, Nurani & Julia, 2015) (Table 3).

Discussion

This systematic review study synthesized evidence from ten cross-sectional studies, which involved a total of 17,764 participants. The information summarized in this review indicated that anthropometric indicators (BMI, WC, WHtR, TSF, BAI, C Index, BM, WASR, AFA, MUAC, FP and AS) had low diagnostic accuracy for HBP screening in adolescents.

In seven studies (Beck, Da Silva Lopes & Pitanga, 2011; Vogt Cureau & Reichert, 2013; Abbaszadeh et al., 2017; Al-Bachir & Bakir, 2017; Febriana, Nurani & Julia, 2015; Kajale et al., 2014; Mazicioglu et al., 2010), the cutoff points for BMI to be used in screening for HBP in adolescents showed low predictive capacity (AUC < 0.70). In the study carried out in the South of Brazil with 660 adolescents of both sexes, the AUC values for BMI were 0.79 (95% CI [0.68–0.89]) for male adolescents (Beck, Da Silva Lopes & Pitanga, 2011). In another study carried out in the South of Brazil with 1,702 adolescents of both sexes, the AUC values identified for BMI were 0.71 (95% CI [0.67–0.74]) for female adolescents and 0.64 (95% CI [0.60–0.68]) for male adolescents (Vogt Cureau & Reichert, 2013). In one study carried out in Iran with 1,046 female adolescents, the AUC values obtained using BMI as the investigated anthropometric obesity indicator were 0.71 (95% CI [0.66–0.76]) for SBP, and 0.67 (95% CI [0.62–0.73]) for DBP (Abbaszadeh et al., 2017). A study carried out in Syria with 2,064 male adolescents showed AUC value for SBP of 0.52, while for DBP, the AUC value identified was 0.59 (Al-Bachir & Bakir, 2017). It is noteworthy that the 95% CI values for the described AUC were not available (Al-Bachir & Bakir, 2017). In another study (Mazicioglu et al., 2010), the AUC values identified for BMI as a possible tool to be used in the screening for HBP in female adolescents aged 11-14 years were 0.65 (95% CI [0.62–0.69]), while for female adolescents aged 15-17 years, the AUC values verified were 0.63 (95% CI [0.59–0.66]) (Mazicioglu et al., 2010). In addition, for male adolescents aged 11-14 years, the AUC values verified were 0.69 (95% CI [0.65–0.73]), while for male adolescents aged 15-17 years, the identified AUC values were 0.64 (95% CI [0.60–0.67]) (Mazicioglu et al., 2010). It is noteworthy that although two studies (Febriana, Nurani & Julia, 2015; Kajale et al., 2014) did not present AUC values for the proposed cutoff points, the authors concluded that the cutoff points had good diagnostic capacity for HBP. When stratified by sex, one study (Beck, Da Silva Lopes & Pitanga, 2011) found that BMI had moderate predictive capacity to identify HBP in female adolescents (AUC: 0.95; 95% CI [0.87–1.00]). In these studies, the proposed cutoff points ranged from 19.2 kg/m² to 24.0 kg/m² for females and from 21.8 kg/m² to 26.1 kg/m² for males (Beck, Da Silva Lopes & Pitanga, 2011; Kajale et al., 2014). One study presented the cutoff point as z score (Febriana, Nurani & Julia, 2015). This difference in cutoff points may be due to the different age groups of studies analyzed, which results in different body mass (BM) (in Kgs or z-scores) and height values.

Most studies (5/7 studies) whose objective was to propose cutoff points for WC in order to identify HBP reported low predictive capacity of this anthropometric obesity indicator (95% CI of AUC > 0.50 to < 0.70). However, one study (Beck, Da Silva Lopes & Pitanga, 2011) showed AUC values > 0.70, indicating high diagnostic test accuracy (Akobeng, 2007; Fischer, Bachman & Jaeschke, 2003) for female adolescents (AUC 0.96; 95% CI [0.92–1.00]) and moderate diagnostic test accuracy (Akobeng, 2007; Fischer, Bachman & Jaeschke, 2003) for male adolescents (AUC 0.80; 95% CI [0.72–0.89]). Similarly, another study (Mazicioglu et al., 2010) also identified that the thresholds verified for the investigated obesity indicator (WC) had moderate predictive capacity (Akobeng, 2007; Fischer, Bachman & Jaeschke, 2003) (AUC > 0.70) for male adolescents aged 11–14 years (AUC 0.74; 95% CI [0.70–0.76]). Regarding cutoff points proposed by these studies, WC differences of up to 16.8 cm among studies for females and up to 15.0 cm for males were described. It is possible that WC measurement protocols adopted by studies such as the midpoint between the upper edge of the iliac crest and the last rib (Beck, Da Silva Lopes & Pitanga, 2011) or the highest point of the iliac crest (Taylor & Hergenroeder, 2011) or for not showing how WC was measured (Mazicioglu et al., 2010) may have contributed to differences between proposed cutoff points. Another aspect that needs to be reported is the fact that the three studies (Beck, Da Silva Lopes & Pitanga, 2011; Taylor & Hergenroeder, 2011; Mazicioglu et al., 2010) that reported acceptable predictive capacity of WC had low methodological quality/high risk of bias.

In seven studies (Beck, Da Silva Lopes & Pitanga, 2011; Vogt Cureau & Reichert, 2013; Abbaszadeh et al., 2017; Febriana, Nurani & Julia, 2015; Kajale et al., 2014; Kruger et al., 2013; Mazicioglu et al., 2010) the cutoff points for WHtR showed low predictive capacity to identify HBP in adolescents. However, in a study conducted with the participation of 660 schoolchildren aged 14–19 years (Beck, Da Silva Lopes & Pitanga, 2011), the AUC value for WHtR to determine HBP in female adolescents was 0.93 (95% CI [0.85–1.00]), demonstrating moderate predictive capacity (Akobeng, 2007; Fischer, Bachman & Jaeschke, 2003). It is necessary to highlight that only one study (Abbaszadeh et al., 2017) used a large number of diagnostic strategies (AUC, sensitivity, specificity, PPV and NPV and the optimal point) to describe cutoff points. However, this study was carried out only with female adolescents. Regarding WHtR values to be used to identify adolescents with HBP, variations from 0.41 to 0.55 for females and from 0.42 to 0.53 for males were found (Beck, Da Silva Lopes & Pitanga, 2011; Vogt Cureau & Reichert, 2013; Abbaszadeh et al., 2017; Febriana, Nurani & Julia, 2015; Kajale et al., 2014; Kruger et al., 2013; Mazicioglu et al., 2010).

According to studies included in the present review, the anthropometric indicators TSF, BAI, C index, BM, WASR, AFA, MUAC, FP and AS showed low predictive capacity to identify HBP in adolescents (AUC > 0.50 to < 0.70). However, only one study  (Mazicioglu et al., 2010) showed AUC values greater than 0.70 for MUAC with AUC = 0.75 (95% CI [0.71–0.78]) for male adolescents aged 11–14 years, indicating that MUAC had moderate predictive capacity to identify HBP in adolescents (Akobeng, 2007; Fischer, Bachman & Jaeschke, 2003). It is speculated that AUC values < 0.70 may be related to the fact that although these anthropometric indicators are indeed used to discriminate obesity (whole body, localized or abdominal body fat), they may have limited ability to distinguish adolescents positive or not for HBP (Fischer, Bachman & Jaeschke, 2003). However, this assumption is restricted to results derived from studies in which information regarding this diagnostic measure was described (Beck, Da Silva Lopes & Pitanga, 2011; Vogt Cureau & Reichert, 2013; Abbaszadeh et al., 2017), since some of the studies added to the present review did not contain such information (Kajale et al., 2014; Mazicioglu et al., 2010).

This review identified high risk of bias with regard to subject selection and measurement procedures for anthropometric indicators of obesity in seven studies (Beck, Da Silva Lopes & Pitanga, 2011; De Moraes & Da Veiga, 2014; Vogt Cureau & Reichert, 2013; Taylor & Hergenroeder, 2011; Abbaszadeh et al., 2017; Kruger et al., 2013; Mazicioglu et al., 2010), and high risk of bias in the measurement of blood pressure levels in five studies (Beck, Da Silva Lopes & Pitanga, 2011; De Moraes & Da Veiga, 2014; Vogt Cureau & Reichert, 2013; Kruger et al., 2013; Mazicioglu et al., 2010). Furthermore, one study did not make it clear how procedures to measure blood pressure levels were performed (Taylor & Hergenroeder, 2011). Another aspect that deserves attention is that although eight studies (Beck, Da Silva Lopes & Pitanga, 2011; Vogt Cureau & Reichert, 2013; Taylor & Hergenroeder, 2011; Abbaszadeh et al., 2017; Al-Bachir & Bakir, 2017; Febriana, Nurani & Julia, 2015; Kajale et al., 2014; Kruger et al., 2013; Mazicioglu et al., 2010) have concluded that the anthropometric indicators of obesity had good predictive capacity for HBP, only in one study (Beck, Da Silva Lopes & Pitanga, 2011) the AUC values were acceptable for use in a clinical context (Akobeng, 2007; Fischer, Bachman & Jaeschke, 2003). Thus, in addition to high sensitivity and specificity values, the stipulated cut-off points must have high LR+ and low LR-values, in addition to allowing a definitive diagnostic condition for the investigated health outcome, regardless of the estimated prevalence. In this context, the use of diagnostic measures such as PPV and NPV are suggested.

Although the use of anthropometric indicators as diagnostic tools to be used for screening for HBP in adolescents has recognized clinical relevance, ethnicity can play a determining role in growth patterns, and consequently, in the precision and accuracy of anthropometric indicators used. Thus, not considering ethnic variation in the assessment of anthropometric obesity indexes to be used for screening for HBP in adolescents can lead to the identification of inaccurate results. However, even though the summarized evidence has been heterogeneous with respect to the investigated populations (adolescents from different regions of the globe—different ethnicities), the small number of studies impaired proposing results and possible suggestion of thresholds to be adopted for the diagnosis of HBP according to ethnicity.

This review presents strengths and limitations that must be considered. The large number of databases investigated (five) in order to identify evidence related to the topic of interest is considered a strong point of this review. Additionally, the analysis of studies in three different languages is another strong point of this review. Despite the careful search carried out by the researchers, it is possible that studies related to the theme covered in this review have not been identified, which is considered a limitation. Thus, the search in a greater number of databases and in the gray literature (for example, course conclusion works and specialization monographs) could contribute to the fact that possible information regarding the theme is not left out of the study. Moreover, the systematic search in other languages could also contribute to bringing new evidence on the investigated topic.

Conclusion

Based on the summarized information, caution is suggested in the use of anthropometric indicators of obesity in the HBP screening in adolescents. Although there were studies that have suggested the use of these indicators, these suggestions were not based on AUC measures with high predictive capacity. Thus, further studies are needed that report a high predictive capacity of anthropometric indicators of obesity for the HBP screening.

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