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Article

The Association between Mid-Upper Arm Circumference and Blood Pressure in an Italian Population of School-Aged Children and Adolescents with Lipid Disorders

by
Francesco Martino
1,*,
Tarcisio Niglio
2,
Francesco Barillà
3,
Eliana Martino
1,
Vincenzo Paravati
1 and
Pier Paolo Bassareo
4,5,6
1
Department of Internal Medicine, Anaesthesiology, and Cardiovascular Science, La Sapienza University, 00161 Rome, Italy
2
Istituto Superiore di Sanità, 00161 Rome, Italy
3
Department of Systems Medicine, Tor Vergata University, 00133 Rome, Italy
4
School of Medicine, University College of Dublin, DO4 W6F6 Dublin, Ireland
5
Department of Cardiology, Mater Misericordiae University Hospital, D07 R2WY Dublin, Ireland
6
Children’s Health Ireland at Crumlin, D12 N512 Dublin, Ireland
*
Author to whom correspondence should be addressed.
J. Clin. Med. 2024, 13(3), 663; https://doi.org/10.3390/jcm13030663
Submission received: 2 December 2023 / Revised: 12 January 2024 / Accepted: 22 January 2024 / Published: 24 January 2024
(This article belongs to the Special Issue Hypertension in Childhood and Adolescence)
Versions Notes

Abstract

:
Background: Many anthropometric measurements have been investigated concerning their association with blood pressure (BP) in paediatric age groups. This study aims to find a relationship between mid-upper arm circumference (MUAC) and BP in a population of children and adolescents aged 1–18 years. Methods: 5853 subjects (2977 females and 2876 males) were studied. MUAC, body mass index (BMI), and BP were measured. The individuals in the study were subdivided and grouped by gender and type of school attended in Italy: 1–5 years (pre-school), 6–10 years (primary school), 11–13 years (secondary school), 14–18 years (high school). Results: In the age range of 6–13 years, all the subjects with MUAC > 50th percentile had systolic and diastolic BP significantly higher than children with MUAC below 50th percentile (p < 0.0001). In the age range 14–18 years, the relationship persisted only in females (p < 0.001 and p < 0.05 for diastolic and systolic BP, respectively). A linear relationship was found between MUAC and BMI. Conclusions: In Italian children of both genders aged 6–13, arm distribution of body fat is strongly associated with increased systolic and diastolic BP. As such, a simple anthropometric measurement like MUAC might represent a tool to identify young subjects who are at risk for HTN.

1. Introduction

Arterial hypertension (HTN) is considered one of the main and potentially reversible causes of cardiovascular diseases (CVD) and death worldwide [1,2]. In addition, HTN is a risk factor for stroke, chronic renal disease, and vascular dementia [3,4]. The estimated prevalence of HTN in children is 3–5%. It increases up to 10–11% after puberty, thus representing a significant burden [5,6]. A recent meta-analysis of 47 studies carried out over a period of 25 years (1990–2004) confirmed that the overall prevalence of HTN in children is 4% [5]. Other more recent studies conducted in Australia, Canada, and central Europe reported a disease prevalence of 3.1%, 4.5%, and 6%, respectively [7,8,9]. In Italy, the overall prevalence is 4.1% and increases with weight. In fact, it is 1.4% in children with normal weight but rises to 7.1% in those who are overweight and to 25% in those who are obese [10]. In babies, secondary HTN is the most diffuse form of sustained high blood pressure (BP). However, circumstances have changed over the last two to three decades, and the prevalence of primary HTN has shown an upward trend after the age of 6 [11].
The American Heart Association and American Academy of Paediatrics have highlighted the importance of primary prevention in reducing the risk of CVD in young people. Unfortunately, paediatric HTN is a significant risk factor for developing early atherosclerosis [12,13]. Therefore, starting primary prevention at an early age is crucial [14,15]. Given this context, regular BP checks are recommended from childhood, according to the suggestions provided by the American Academy of Paediatrics as well as the European Society of Cardiology [16,17]. The current guidelines recommend at least three BP checks/year after the age of 3. Multiple BP measurements are needed before classifying a child as hypertensive. After diagnosing HTN in a child, secondary high BP should be ruled out [16,17,18].
Overweight and obesity are independent risk factors in the development of HTN from infancy [19,20]. In fact, paediatric HTN is quite widespread among overweight and obese children [21,22]. This is particularly evident in ethnic minorities as testified by the Houston Screening Project [23]. Both paediatric HTN and obesity prevalences are growing, and there is a direct relationship between BP and body mass index (BMI). In some studies, the prevalence of HTN is about 30% in obese children [24]. In a large retrospective study involving 801,019 children and adolescents aged 3–17, a normal–high weight (that is, a BMI over the 60th percentile by age) was associated with an increased risk of developing HTN. Not only that is true, but the higher the BMI, the higher the risk [25]. In a Canadian study, after measuring BP at 12.9, 15.2, and 17 years, each unit increase in BMI was associated with an increase in systolic BP equal to 0.7 mmHg [26]. BMI, waist circumference (WC), waist-to-height ratio (WHtR), and waist-to-flank ratio are anthropometric measurements related to the onset of paediatric HTN [27]. Among them, BMI and WC are the best in predicting the occurrence of high BP [28,29]. However, other studies demonstrated that, since BMI is essentially linked with abdominal fat distribution, whereas BP increases the most with height, BMI may underestimate the risk of HTN in short stature children and overestimate the same risk in tall subjects [30,31,32,33,34]. Because of such an inconsistency, new anthropometric measurements have been suggested as predictors of HTN in childhood, such as a body shape index (ABSI), body roundness index (BRI), and waist-by-hip ratio. All of them have proven to be linked with the occurrence of HTN, metabolic syndrome, and cardio-metabolic risk factors (CMRF) [35,36,37]. In addition, the association between ABSI and height does not depend on BMI [38,39]. Other studies carried out in Asia showed that WHR is superior to BMI and WC in predicting the onset of HTN in adolescence [35]. Nevertheless, other studies have questioned this point [40]. Also, neck circumference (NC), wrist circumference (WrC), WHtR, and waist-by-hip ratio are able to identify CMRF in the paediatric as well as adult populations [41,42,43,44].
While the association between BMI and raised BP has been well known for a long time, lean body mass (LBM) might also be associated with abnormal BP in young adults who are not overweight or obese. In fact, LBM estimated by mid-upper arm muscle circumference (MAMC) seems to be correlated with cardiac mass and stroke volume, which in turn strongly influence BP [45,46]. MAMC is calculated from mid-upper arm circumference (MUAC) minus triceps skinfold thickness [47,48,49]. At present there are very few studies on MUAC in cardiology as well [47,50].
This study aims to check if MUAC is able to predict HTN in a population of Italian children and adolescents.

2. Materials and Methods

The present study was carried out by analysing a sample size of children and adolescents undergoing examinations at the Lipid Clinic Research Centre (Department of Paediatrics, Sapienza University of Rome, Rome, Italy) to confirm the presence of abnormal lipid values previously detected during an ordinary laboratory test. The data were gathered over the period of 1995–2011.
Out of over 50,000 subjects who were examined, in only 5853 of them were all the required demographic (age below 18 years), anatomical (height, weight, MUAC) and physiological (heart rate, systolic and diastolic BP) features required to be enrolled in the study available. A significant percentage of them had high cholesterol levels. Subjects with secondary HTN as well as those taking medications for BP control were excluded from the study.

2.1. Mid-Upper Arm Circumference and Anthropometric Measurements

MUAC was measured to the nearest 0.1 cm using a non-elastic flexible tape in the standing position with abdomen relaxed, arms at their sides, and feet together. It was measured at the mid-point between the tip of the shoulder and the tip of the elbow of the left arm [51].
Body weight was measured in light clothes and without shoes and was approximated to the nearest 0.1 kg on a mobile digital scale (Seca, Hamburg, Germany). Height was measured to the nearest 0.1 cm using a wall mounted stadiometer (Seca, Hamburg, Germany). BMI was calculated in each participant as weight (kilograms) over height (meters squared).
At least three consecutive measurements for all anthropometric variables were taken for each child, and their mean was used for analysis.

2.2. Blood Pressure and Other Measurements

BP was measured using a mercury sphygmomanometer, with an appropriate cuff for each child’s upper arm size; three cuffs with different bladder sizes (8 × 13, 9 × 23, and 12 × 22 cm) were used [52].
Systolic BP was defined by the onset of the first Korotkoff sound and diastolic BP was indicated by the fifth Korotkoff sound (disappearance of Korotkoff sound). BP measurement was approximated to the nearest 2 mmHg. BP was measured in the clinic by the same physician on the same occasion as the anthropometric measurements, while children were sitting and with the cubital fossa supported at heart level, after at least 5 min of rest. Three measurements were taken over a given period of 5 min and their average was used for calculation [53].

2.3. Ethics

Each enrolled child’s legal guardian signed a written informed consent document in accordance with EU Regulation n.679/2016.
This is an observational and retrospective scientific work. It was performed by using the clinical records of children and adolescents examined in Rome, Italy, at Policlinico Umberto I over a period of about fifteen years. Italian law (see D.Lgs. n.211/2003 on Gazzetta Ufficiale of Repubblica Italiana n.184 9 August 2003—supplement to “serie generale”) does not require the authorization of the Ethics Committee in cases of epidemiological and observational studies. The authors used the available clinical records and did not perform BP and anthropometric measurements themselves.

2.4. Statistics

All data were collected into a Microsoft Access 2017 database and were analysed by Epi-Info 7 programs (CDC and NIH, 2021 Italian version 7.2.5.0). Statistical analysis estimated descriptive statistics, frequencies, and significance of group differences. Statistical significance between and within groups was calculated based on continuous variables. An analysis of variance (ANOVA) was performed to test the equality of means between groups for continuous variables, including Bonferroni, Kruskal–Wallis, and Newman–Keuls pairwise mean comparison tests. A Chi-square Yates’ correction test was used for non-continuous variables in StatCalc and Analysis programs. The odds ratio was calculated as well. A p level less than 0.05 was considered significant.

3. Results

This study was performed on a sample of 5853 children (2977 females and 2876 males). The percentage of dyslipidemic patients was 61.6% (1914 females and 1682 males). Anthropometric measurements (height, weight, body mass index (BMI), MUAC, and heart rate) and their relationships are shown in Table 1, Table 2, Table 3 and Table 4 and stratified by gender and age.
Table 5 reports mean diastolic BP and Table 3 shows mean systolic BP stratified by gender, age, and MUAC. We used the MUAC 50th percentile to divide all children (stratified by gender and age) into two further groups: those with MUAC below 50th percentile and those with MUAC over 50th percentile.
In regards to MUAC, the 50th percentile was used as a cut-off simply because in our sample size, all the subjects with MUAC > 50th percentile had a BMI that was significantly higher than children with MUAC below 50th percentile (see Table 4 as well). These differences were statistically significant.
Table 5 and Table 6 show mean diastolic and systolic BP grouped by gender, sex, and type of school attended in Italy: 1–5 years (pre-school), 6–10 years (primary school), 11–13 years (secondary school), 14–18 years (high school). All children aged 6–13 showed statistically significant differences in diastolic and systolic BP after stratification by MUAC 50th percentile. In the 14–18 years age range, the relationship persisted only in females.
The results illustrated in Table 7 and Table 8 show an odds ratio between 1.9 and 2.8 thus demonstrating, in our sample, an association between BP and MUAC. At the same time, the 95% confidence interval does not include the value 1, which means that the results are statistically significant.

4. Discussion

Cardiovascular diseases (CVD) are the leading cause of early death in the US and the rest of the world [54]. In Italy, they are responsible for 34.81% of all deaths [55]. CVDs are multifactorial. Some of the risk factors cannot be changed (age, gender, and genetics), while others are modifiable (such as diet, alcohol consumption, and smoking habits). In particular, the latter may contribute to cause both HTN and overweight/obesity [56,57,58]. HTN in children and adolescents is related to the development of CVD in adulthood. For example, Daniels et al. demonstrated that 8% of hypertensive children and adolescents have already developed an increase in their cardiac mass, which is a known risk factor for developing CVD [59]. HTN, even in paediatric age, is often asymptomatic; therefore, the early identification of the subjects who have high BP plays a pivotal role in terms of primary prevention [60,61,62]. This is with a goal of preventing the onset of CVD later on. In fact, CVDs are the consequence of a chronic degenerative inflammatory process which starts in the perinatal period and infancy [15,63,64,65]. Therefore, BP screening in paediatric age groups is crucial in attempting to reduce the global CVD risk burden [14].
MUAC is often used in assessing nutritional status [66]. It is also an important marker of obesity in children [67,68,69]. Several studies have found that MUAC is linked with BP in paediatric age groups. For example, in the Brompton study, BP was linked with MUAC at all ages studied [70]. Larger MUAC at 1 year of age predicted higher systolic BP at 6 years [71].
The results of this study show that both systolic and diastolic BP are significantly influenced by MUAC in the age range 6–13 years. This is consistent with the results outlined by Ma et al. In fact, MUAC was a simple, cheap, and accurate tool for identifying children of Chinese Han ethnicity aged 7–12 years with HTN. MUAC was equivalent in accuracy to BMI and WC as a screening test for HTN [72]. This is confirmed in Italian children of both genders as well. In children aged 1–5 years, the relationship is lost, whereas in those aged 14–18, it is weaker and persists just in females. This is probably due to the fact that in MUAC, tissue composition is different between the two genders. MUAC is a combination of fat and muscle mass. The proportion of muscle mass is greater in males and increases with time, and this is why the relationship between MUAC and BP is lost in adolescent males. Conversely, it persists, although attenuated, in females. The novelty of this study is that the association between MUAC and BP is confirmed even in a population of different ethnicity and with hypercholesterolaemia.
MUAC in turn has a direct relationship with BMI. As such, MUAC has been suggested as an alternative tool in screening overweight and obesity in paediatric age groups [73,74,75].
The association between juvenile overweight/obesity and HTN is well known [76,77]. It is multifactorial and involves fluid and sodium retention, insulin resistance, the sympathic nervous system activation, the renin–angiotensin–aldosterone axis stimulation, and other mechanisms like adipokines release and endothelial dysfunction [78]. Regarding the first mechanism (e.g., fluid and sodium retention), human and mammalian kidneys are embedded in adipose tissue. This in turn can enter the renal hilum and medulla, thus reducing blood flow, causing tubular compression, and increasing sodium and fluid absorption [79]. Concerning insulin resistance, it leads to fluid and sodium retention and vasoconstriction, thus increasing BP [80,81]. Weight loss is associated with a decrease in BP and an increase in insulin sensitivity [82]. Children with borderline BP had higher weight and insulin blood levels compared with their normotensive peers [83]. Again, insulin resistance can increase BP through the renin–angiotensin–aldosterone system activation as well as acting through the central nervous system [84]. An imbalance in the link between the sympathetic nervous system, heart, arterial vessels, and renin–angiotensin–aldosterone system leads to HTN [85]. Fasting supresses the sympathetic nervous system activation, whereas an excessive carbohydrates and lipids intake activates it [86]. The renin–angiotensin–aldosterone system is involved in regulating the tone and sodium absorption of efferent glomerular arterioles on the basis of salt intake and BP levels. A system imbalance can alter natriuresis and BP control [78]. Increased plasmatic renin activity leads to an increase in aldosterone activity and BP which is higher in obese adolescents than in their counterparts with normal weight. Furthermore, weight loss in obese adolescents is related to a significant decrease in aldosterone levels and BP levels [87]. Another mechanism is inflammation with the release of adipokines which causes marked vasoconstriction and increased BP [88,89,90]. Again, an excess in body fat causes endothelial dysfunction and reduces nitric oxide release, thus favouring the development of HTN [91,92,93].
The authors performed quite a similar study in 2013. However, the 2013 paper was conducted on a different and smaller sample size (n = 370 vs. n = 5835). A more advanced statistical analysis was performed in the present study. The 2013 results showed that MUAC was significantly higher in hypertensives than normotensives. Systolic BP was correlated with MUAC which in turn was strongly related with BMI [56].
The present study has its strengths and limitations. The strengths include the large sample size and complex statistics applied to confirm the association between MUAC and systolic and diastolic BP. As for the limitations, the statistical analysis used showed no cause–effect relationship between MUAC and BP. Neither an ANOVA nor an odds ratio allow for the determination of causation or direct influence but rather show difference between groups and likelihood/odds of an outcome, respectively. There is a positive association between MUAC and BMI as well as a non-consequential correlation between MUAC and BP. This can be used as a simple method for diagnosis even by somebody who is not a doctor. In other words, high BP can be suspected simply by measuring MUAC with a dressmaker’s tape. Of course, the definite diagnosis of HTN remains a clinician’s responsibility. Furthermore, since a significant percentage of the subjects in the study had high cholesterol levels, the results of this study may not be extended to other populations.

5. Conclusions

In conclusion, in Italian children of both genders aged 6–13 and with lipid disorders, peripheral distribution of body fat is associated with increased systolic and diastolic BP. As such, a simple anthropometric measurement like MUAC might represent a tool to identify young subjects who are at risk of HTN [94].

Author Contributions

Conceptualization, F.M. and P.P.B.; methodology, T.N.; writing—original draft preparation, F.M., T.N. and P.P.B.; writing—review and editing, F.B., E.M. and V.P. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Written informed consent has been obtained from the patients and/or their parents to publish this paper.

Data Availability Statement

The analysed data are available on request to T.N.

Conflicts of Interest

The authors declare no conflicts of interest.

References

  1. Mills, K.T.; Stefanescu, A.; He, J. The global epidemiology of hypertension. Nat. Rev. Nephrol. 2020, 16, 223–237. [Google Scholar] [CrossRef]
  2. Stergiou, G.S.; Palatini, P.; Parati, G.; O’Brien, E.; Januszewicz, A.; Lurbe, E.; Persu, A.; Mancia, G.; Kreutz, R.; European Society of Hypertension Council and the European Society of Hypertension Working Group on Blood Pressure Monitoring and Cardiovascular Variability. 2021 European Society of Hypertension practice guidelines for office and out-of-office blood pressure measurement. J. Hyperten. 2021, 39, 1293–1302. [Google Scholar] [CrossRef]
  3. Zhou, B.; Perel, P.; Mensah, G.A.; Ezzati, M. Global epidemiology, health burden and effective interventions for elevated blood pressure and hypertension. Nat. Rev. Cardiol. 2021, 18, 785–802. [Google Scholar] [CrossRef]
  4. Jia, L.; Du, Y.; Chu, L.; Zhang, Z.; Li, F.; Lyu, D.; Li, Y.; Li, Y.; Zhu, M.; Jiao, H.; et al. Prevalence, risk factors, and management of dementia and mild cognitive impairment in adults aged 60 years or older in China: A cross-sectional study. Lancet Public Health 2020, 5, e661. [Google Scholar] [CrossRef]
  5. Song, P.; Zhang, Y.; Yu, J.; Zha, M.; Zhu, Y.; Rahimi, K.; Rudan, I. Global prevalence of hypertension in children. A systematic review and Meta-analysis. JAMA Pediatr. 2019, 173, 1154–1163. [Google Scholar] [CrossRef]
  6. Sharma, A.K.; Metzger, D.L.; Rodd, C.J. Prevalence and severity of high blood pressure among children based on the 2017 American Academy of Pediatrics Guidelines. JAMA Pediatr. 2018, 172, 557–565. [Google Scholar] [CrossRef]
  7. Larkins, N.G.; Teixeira-Pinto, A.; Kim, S.; Burgner, D.P.; Craig, J.C. The population-based prevalence of hypertension and correlates of blood pressure among Australian children. Pediatr. Nephrol. 2019, 34, 1107–1115. [Google Scholar] [CrossRef]
  8. Robinson, S.K.; Rodd, C.J.; Metzger, D.L.; Sharma, A.K. Prevalence of high blood pressure among Canadian Children: 2017 American Academy of Pediatrics guidelines with the Canadian Health Measures Survey. Paediatr. Child Health 2020, 26, e158–e165. [Google Scholar] [CrossRef]
  9. Martin, L.; Oepen, J.; Reinehr, T.; Wabitsch, M.; Claussnitzer, G.; Waldeck, E.; Ingrisch, S.; Stachow, R.; Oelert, M.; Wiegand, S.; et al. Ethnicity and cardiovascular risk factors: Evaluation of 40,921 normal-weight, overweight or obese children and adolescents living in Central Europe. Int. J. Obes. 2015, 39, 45–51. [Google Scholar] [CrossRef]
  10. Genovesi, S.; Antolini, L.; Giussani, M.; Pieruzzi, F.; Galbiati, S.; Valsecchi, M.G.; Brambilla, P.; Stella, A. Usefulness of waist circumference for the identification of childhood hypertension. J. Hypertens. 2008, 26, 1563–1570. [Google Scholar] [CrossRef]
  11. Gupta-Malhotra, M.; Banker, A.; Shete, S.; Hashmi, S.S.; Tyson, J.E.; Barratt, M.S.; Hecht, J.T.; Milewicz, D.M.; Boerwinkle, E. Essential hypertension vs. secondary hypertension among children. Am. J. Hypertens. 2015, 28, 73–80. [Google Scholar] [CrossRef] [PubMed]
  12. Weintraub, W.S.; Daniels, S.R.; Burke, L.E.; Franklin, B.A.; Goff, D.C., Jr.; Hayman, L.L.; Lloyd-Jones, D.; Pandey, D.K.; Sanchez, E.J.; Schram, A.P.; et al. Value of primordial and primary prevention for cardiovascular disease: A policy statement from the American Heart Association. Circulation 2011, 124, 967–990. [Google Scholar] [CrossRef] [PubMed]
  13. Expert Panel on Integrated Guidelines for Cardiovascular Health and Risk Reduction in Children and Adolescents; National Heart, Lung, and Blood Institute. Expert panel on integrated guidelines for cardiovascular health and risk reduction in children and adolescents: Summary report. Pediatrics 2011, 128 (Suppl. S5), S213–S256. [Google Scholar] [CrossRef] [PubMed]
  14. Raitakari, O.; Pahkala, K.; Magnussen, C.G. Prevention of atherosclerosis from childhood. Nat. Rev. Cardiol. 2022, 19, 543–554. [Google Scholar] [CrossRef] [PubMed]
  15. Martino, F.; Bassareo, P.P.; Martino, E.; Romeo, F.; Calcaterra, G.; Perrone Filardi, P.; Indolfi, C.; Nodari, S.; Montemurro, V.; Guccione, P.; et al. Cardiovascular prevention in childhood: A consensus document of the Italian Society of Cardiology Working Group on Congenital Heart Disease and Cardiovascular Prevention in Paediatric Age. J. Cardiovasc. Med. 2023, 24, 492–505. [Google Scholar] [CrossRef] [PubMed]
  16. Du, T.; Fernandez, C.; Barshop, R.; Chen, W.; Urbina, E.M.; Bazzano, L.A. 2017 Pediatric Hypertension Guidelines Improve Prediction of Adult Cardiovascular Outcomes. Hypertension 2019, 73, 1217–1223. [Google Scholar] [CrossRef] [PubMed]
  17. de Simone, G.; Mancusi, C.; Hanssen, H.; Genovesi, S.; Lurbe, E.; Parati, G.; Sendzikaite, S.; Valerio, G.; Di Bonito, P.; Di Salvo, G.; et al. Hypertension in children and adolescents. Eur. Heart J. 2022, 43, 3290–3301. [Google Scholar] [CrossRef] [PubMed]
  18. Bassareo, P.P.; Calcaterra, G.; Sabatino, J.; Oreto, L.; Ciliberti, P.; Perrone, M.; Martino, F.; D’Alto, M.; Chessa, M.; DI Salvo, G.; et al. Primary and secondary paediatric hypertension. J. Cardiovasc. Med. 2023, 24 (Suppl. S1), e77–e85. [Google Scholar] [CrossRef]
  19. Genovesi, S.; Giussani, M.; Pieruzzi, F.; Vigorita, F.; Arcovio, C.; Cavuto, S.; Stella, A. Results of blood pressure screening in a population of school-aged children in the province of Milan: Role of overweight. J. Hypertens. 2005, 23, 493–497. [Google Scholar] [CrossRef]
  20. Skinner, A.C.; Perrin, E.M.; Moss, L.A.; Skelton, J.A. Cardiometabolic risks and severity of obesity in children and young adults. N. Engl. J. Med. 2015, 373, 1307–1317. [Google Scholar] [CrossRef]
  21. Falkner, B. Recent clinical and translational advances in paediatric hypertension. Hypertension 2015, 65, 926–931. [Google Scholar] [CrossRef] [PubMed]
  22. Litwin, M.; Kulaga, Z. Obesity, metabolic syndrome, and primary hypertension. Pediatr. Nephrol. 2021, 36, 825–837. [Google Scholar] [CrossRef] [PubMed]
  23. Sorof, J.M.; Lai, D.; Turner, J.; Poffenbarger, T.; Portman, R.J. Overweight, ethnicity, and the prevalence of hypertension in sTOT-Col-aged children. Pediatrics 2004, 113 Pt 1, 475–482. [Google Scholar] [CrossRef] [PubMed]
  24. Sorof, J.; Daniels, S. Obesity hypertension in children: A problem of epidemic proportions. Hypertension 2002, 40, 441–447. [Google Scholar] [CrossRef] [PubMed]
  25. Koebnick, C.; Sidell, M.A.; Li, X.; Woolford, S.J.; Kuizon, B.D.; Kunani, P. Association of High Normal Body Weight in Youths With Risk of Hypertension. JAMA Netw. Open 2023, 6, e231987. [Google Scholar] [CrossRef] [PubMed]
  26. Maximova, K.; O’Loughlin, J.; Paradis, G.; Hanley, J.A.; Lynch, J. Changes in anthropometric characteristics and blood pressure during adolescence. Epidemiology 2010, 21, 324–331. [Google Scholar] [CrossRef] [PubMed]
  27. Wang, M.; Kelishadi, R.; Khadilkar, A.; Mi Hong, Y.; Nawarycz, T.; Krzywińska-Wiewiorowska, M.; Aounallah-Skhiri, H.; Esmaeil Motlagh, M.; Soon Kim, H.; Khadilkar, V.; et al. Body mass index percentiles and elevated blood pressure among children and adolescents. J. Hum. Hypertens. 2020, 34, 319–325. [Google Scholar] [CrossRef]
  28. Deng, W.W.; Wang, J.; Liu, M.M.; Wang, D.; Zhao, Y.; Liu, Y.Q.; Wang, H.; Dong, G.H. Body mass index compared with abdominal obesity indicators in relation to prehypertension and hypertension in adults: The CHPSNE study. Am. J. Hypertens. 2013, 26, 58–67. [Google Scholar] [CrossRef]
  29. Beck, C.C.; Lopes Ada, S.; Pitanga, F.J. Anthropometric indictors as predictors of high blood pressure in adolescents. Arq. Bras. Cardiol. 2011, 96, 126–133. [Google Scholar] [CrossRef]
  30. Li, W.C.; Chen, I.C.; Chang, Y.C.; Loke, S.S.; Wang, S.H.; Hsiao, K.Y. Waist-to-height ratio, waist circumference, and body mass index as indices of cardiometabolic risk among 36,642 Taiwanese adults. Eur. J. Nutr. 2013, 52, 57–65. [Google Scholar] [CrossRef]
  31. Carpenter, C.L.; Yan, E.; Chen, S.; Hong, K.; Arechiga, A.; Kim, W.S.; Deng, M.; Li, Z.; Heber, D. Body fat and body-mass index among a multiethnic sample of college-age men and women. J. Obes. 2013, 2013, 790654. [Google Scholar] [CrossRef] [PubMed]
  32. Nightingale, C.M.; Rudnicka, A.R.; Owen, C.G.; Cook, D.G.; Whincup, P.H. Patterns of body size and adiposity among UK children of South Asian, black African-Caribbean and white European origin: Child Heart And health Study in England (CHASE Study). Int. J. Epidemiol. 2011, 40, 33–44. [Google Scholar] [CrossRef]
  33. Schneider, H.J.; Klotsche, J.; Silber, S.; Stalla, G.K.; Wittchen, H.U. Measuring abdominal obesity: Effects of height on distribution of cardiometabolic risk factors risk using waist circumference and waist-to-height ratio. Diabetes Care 2011, 34, e7. [Google Scholar] [CrossRef] [PubMed]
  34. Song, L.; Shen, L.; Li, H.; Liu, B.; Zheng, X.; Liang, Y.; Yuan, J.; Wang, Y. Height and prevalence of hypertension in a middle-aged and older Chinese population. Sci. Rep. 2016, 6, 39480. [Google Scholar] [CrossRef]
  35. Bauer, K.W.; Marcus, M.D.; El Ghormli, L.; Ogden, C.L.; Foster, G.D. Cardio-metabolic risk screening among adolescents: Understanding the utility of body mass index, waist circumference and waist to height ratio. Pediatr. Obes. 2015, 10, 329–337. [Google Scholar] [CrossRef] [PubMed]
  36. Krakauer, N.Y.; Krakauer, J.C. A new body shape index predicts mortality hazard independently of body mass index. PLoS ONE 2012, 7, e39504. [Google Scholar] [CrossRef]
  37. Kasaeian, A.; Hemati, Z.; Heshmat, R.; Baygi, F.; Heshmati, J.; Mahdavi-Gorabi, A.; Abdar, M.E.; Motlagh, M.E.; Shafiee, G.; Qorban, M.; et al. Erratum: Author Correction: Association of a body shape index and hip index with cardiometabolic risk factors in children and adolescents: The CASPIAN-V study. J. Diabetes Metab. Disord. 2021, 20, 2135. [Google Scholar] [CrossRef]
  38. Bertoli, S.; Leone, A.; Krakauer, N.Y.; Bedogni, G.; Vanzulli, A.; Redaelli, V.I. Association of Body Shape Index (ABSI) with cardiometabolic risk factors: A cross-sectional study of 6081 Caucasian adults. PLoS ONE 2017, 12, e0185013. [Google Scholar] [CrossRef]
  39. Leone, A.; Vizzuso, S.; Brambilla, P.; Mameli, C.; Ravella, S.; De Amicis, R.; Battezzati, A.; Zuccotti, G.; Bertoli, S.; Verduci, E. Evaluation of different adiposity indices and association with metabolic syndrome risk in obese children: Is there a winner? Int. J. Mol. Sci. 2020, 21, 4083. [Google Scholar] [CrossRef]
  40. Jung, C.; Fischer, N.; Fritzenwanger, M.; Figulla, H.R. Anthropometric indices as predictors of the metabolic syndrome and its components in adolescents. Pediatr. Int. 2010, 52, 402–409. [Google Scholar] [CrossRef]
  41. Ataie-Jafari, A.; Heshmat, R.; Kelishadi, R.; Ardalan, G.; Mahmoudarabi, M.; Rezapoor, A.; Motlagh, M.E. Generalized or abdominal obesity: Which one better identifies cardiometabolic risk factors among children and adolescents? The CASPIAN III study. J. Trop. Pediatr. 2014, 60, 377–385. [Google Scholar] [CrossRef] [PubMed]
  42. Kelishadi, R.; Heidari-Beni, M.; Qorbani, M.; Motamed-Gorji, N.; Motlagh, M.E.; Ziaodini, H. Association between neck and wrist circumferences and cardiometabolic risk in children and adolescents: The CASPIAN-V study. Nutrition 2017, 43, 32–38. [Google Scholar] [CrossRef] [PubMed]
  43. Ataie-Jafari, A.; Namazi, N.; Djalalinia, S.; Chaghamirzayi, P.; Abdar, M.E.; Zadehe, S.S. Neck circumference and its association with cardiometabolic risk factors: A systematic review and meta-analysis. Diabetol. Metab. Syndr. 2018, 10, 72. [Google Scholar] [CrossRef] [PubMed]
  44. Namazi, N.; Djalalinia, S.; Mahdavi-Gorabi, A.; Asayesh, H.; Mansourian, M.; Noroozi, M.; Qorbani, M. Association of wrist circumference with cardio-metabolic risk factors: A systematic review and meta-analysis. Eat Weight Disord. 2020, 25, 151–161. [Google Scholar] [CrossRef] [PubMed]
  45. Vaziri, Y.; Bulduk, S.; Shadman, Z.; Bulduk, E.O.; Hedayati, M.; Koc, H.; Er, F.; Erdogan, C.S. Lean Body Mass as a Predictive Value of Hypertension in Young Adults, in Ankara, Turkey. Iran. J. Public Health 2015, 44, 1643–1654. [Google Scholar] [PubMed]
  46. Liu, Y.; Sun, G.; Li, Y. A prospective cohort study on the association of lean body mass estimated by mid-upper arm muscle circumference with hypertension risk in Chinese residents. J. Clin. Hypertens. 2022, 24, 329–338. [Google Scholar] [CrossRef]
  47. Landi, F.; Russo, A.; Liperoti, R.; Pahor, M.; Tosato, M.; Capoluongo, E.; Bernabei, R.; Onder, G. Midarm muscle circumference, physical performance and mortality: Results from the aging and longevity study in the Sirente geographic area (ilSIRENTE study). Clin. Nutr. 2010, 29, 441–447. [Google Scholar] [CrossRef]
  48. Tian, S.; Xu, Y. Association of sarcopenic obesity with the risk of all-cause mortality: A meta-analysis of prospective cohort studies. Geriatr. Gerontol. Int. 2016, 16, 155–166. [Google Scholar] [CrossRef]
  49. He, L.; Yang, N.; Wang, J.; Huang, J.; Li, W.; Xu, L.; Ping, F.; Li, Y.; Zhang, H. Mid-Arm Muscle and Subcutaneous Fat Associated with All-Cause Mortality Independent of BMI: A Prospective Cohort Study. Obesity 2021, 29, 1203–1214. [Google Scholar] [CrossRef]
  50. Miyazaki, S.; Hayashino, S.; Matsumoto, I.; Kurozumi, M.; Namba, T.; Takagi, Y.; Kunikata, J.; Minamino, T. Mid-arm muscle circumference as an indicator of exercise tolerance in chronic heart failure. Geriatr. Gerontol. Int. 2021, 21, 411–415. [Google Scholar] [CrossRef]
  51. Bassareo, P.P.; Marras, A.R.; Barbanti, C.; Mercuro, G. Comparison between waist and mid-upper arm circumferences in influencing systolic blood pressure in adolescence: The SHARP (Sardinian Hypertensive Adolescent Research Programme) study. J. Pediatr. Neonat. Individual. Med. 2013, 2, e020207. [Google Scholar]
  52. National High Blood Pressure Education Program. Working Group on High Blood Pressure in Children and Adolescents. The fourth report on the diagnosis, evaluation, and treatment of high blood pressure in children and adolescents. Pediatrics 2004, 114, 555–576. [Google Scholar] [CrossRef]
  53. Martino, F.; Puddu, P.E.; Pannarale, G.; Colantoni, C.; Martino, E.; Zanoni, C.; Barillà, F. Hypertension in children and adolescents attending a lipid clinic. Eur. J. Pediatr. 2013, 172, 1573–1579. [Google Scholar] [CrossRef] [PubMed]
  54. GBD 2017 Causes of Death Collaborators. Global, regional, and national age-sex-specific mortality for 282 causes of death in 195 countries and territories, 1980-2017: A systematic analysis for the Global Burden of Disease Study 2017. Lancet 2018, 392, 1736–1788. [Google Scholar] [CrossRef]
  55. Hartley, A.; Marshall, D.C.; Salciccioli, J.D.; Sikkel, M.B.; Maruthappu, M.; Shalhoub, J. Trends in mortality from ischemic heart disease and cerebrovascular disease in Europe: 1980 to 2009. Circulation 2016, 133, 1916–1926. [Google Scholar] [CrossRef]
  56. Hajar, R. Risk Factors for Coronary Artery Disease: Historical Perspectives. Heart Views 2017, 18, 109–114. [Google Scholar] [CrossRef]
  57. Mozaffarian, D.; Benjamin, E.J.; Go, A.S.; Arnett, D.K.; Blaha, M.J.; Cushman, M.; de Ferranti, S.; Després, J.P.; Fullerton, H.J.; Howard, V.J.; et al. Heart disease and stroke statistics—2015 update: A report from the American Heart Association. Circulation 2015, 131, e29–e322. [Google Scholar] [CrossRef]
  58. Institute of Medicine (US) Committee on a National Surveillance System for Cardiovascular and Select Chronic Diseases. A Nationwide Framework for Surveillance of Cardiovascular and Chronic Lung Diseases; National Academies Press: Washington, DC, USA, 2011. [Google Scholar]
  59. Daniels, S.R.; Pratt, C.A.; Hayman, L.L. Reduction of risk for cardiovascular disease in children and adolescents. Circulation 2011, 124, 1673–1686. [Google Scholar] [CrossRef]
  60. Kavey, R.E.W.; Daniels, S.R.; Lauer, R.M.; Atkins, D.L.; Hayman, L.L.; Taubert, K. American Heart Association guidelines for primary prevention of atherosclerotic cardiovascular disease beginning in childhood. Circulation 2003, 107, 1562–1566. [Google Scholar] [CrossRef]
  61. Chobanian, A.V.; Bakris, G.L.; Black, H.R.; Cushman, W.C.; Green, L.A.; Izzo, J.L., Jr.; Jones, D.W.; Materson, B.J.; Oparil, S.; Wright, J.T., Jr.; et al. The Seventh Report of the Joint National Committee on Prevention, Detection, Evaluation, and Treatment of High Blood Pressure: The JNC 7 report. JAMA 2003, 289, 2560–2572. [Google Scholar] [CrossRef]
  62. Lieberman, E. Hypertension in childhood and adolescence. In Clinical Hypertension, 8th ed.; Kaplan, N.M., Ed.; Williams & Wilkins: Baltimore, MA, USA, 2002; pp. 512–526. [Google Scholar]
  63. Martino, F.; Barilla’, F.; Martino, E.; Placanica, G.; Paravati, V.; Bassareo, P.P. Nailfold capillaroscopy reveals early peripheral microcirculation abnormalities in children affected by heterozygous familial hypercholesterolemia. Microvasc. Res. 2023, 148, 104545. [Google Scholar] [CrossRef]
  64. Napoli, C.; D’Armiento, F.P.; Mancini, F.P.; Postiglione, A.; Witztum, J.L.; Palumbo, G.; Palinski, W. Fatty streak formation occurs in human fetal aortas and is greatly enhanced by maternal hypercholesterolemia. Intimal accumulation of low density lipoprotein and its oxidation precede monocyte recruitment into early atherosclerotic lesions. J. Clin. Investig. 1997, 100, 2680–2690. [Google Scholar] [CrossRef]
  65. McGill, H.C., Jr.; McMahan, C.A.; Herderick, E.E.; Malcom, G.T.; Tracy, R.E.; Strong, J.P. Origin of atherosclerosis in childhood and adolescence. Am. J. Clin. Nutr. 2000, 72, 1307s–1315s. [Google Scholar] [CrossRef] [PubMed]
  66. Turton, P. The use of mid upper arm circumference in the assessment of nutritional status: The Mursi. Midwife Health Visit Community Nurse 1985, 21, 81–86. [Google Scholar] [PubMed]
  67. de Almeida, C.A.; Del Ciampo, L.A.; Ricco, R.G.; Silva, S.M., Jr.; Naves, R.B.; Pina, J.F. Assessment of mid-upper arm circumference as a method for obesity screening in preschool children. J. Pediatr. (Rio. J.) 2003, 79, 455–460. [Google Scholar] [CrossRef] [PubMed]
  68. Mazıcıoğlu, M.M.; Hatipoğlu, N.; Oztürk, A.; Ciçek, B.; Ustünbaş, H.B.; Kurtoğlu, S. Waist circumference and mid-upper arm circumference in evaluation of obesity in children aged between 6 and 17 years. J. Clin. Res. Pediatr. Endocrinol. 2010, 2, 144–145. [Google Scholar] [CrossRef] [PubMed]
  69. Lu, Q.; Wang, R.; Lou, D.H.; Ma, C.M.; Liu, X.L.; Yin, F.Z. Mid-upper-arm circumference and arm-to-height ratio in evaluation of overweight and obesity in Han children. Pediatr. Neonatol. 2014, 55, 14–19. [Google Scholar] [CrossRef]
  70. de Swiet, M.; Fayers, P.; Shinebourne, E.A. Blood pressure in first 10 years of life: The Brompton study. BMJ 1992, 304, 23–26. [Google Scholar] [CrossRef]
  71. Joglekar, C.V.; Fall, C.H.; Deshpande, V.U.; Joshi, N.; Bhalerao, A.; Solat, V.; Deokar, T.M.; Chougule, S.D.; Leary, S.D.; Osmond, C.; et al. Newborn size, infant and childhood growth, and body composition and cardiovascular disease risk factors at the age of 6 years: The Pune Maternal Nutrition Study. Int. J. Obes. 2007, 31, 1534–1544. [Google Scholar] [CrossRef]
  72. Ma, C.M.; Li, Y.; Gao, G.Q.; Yin, F.Z.; Wang, R.; Liu, X.L.; Lu, Q. Mid-upper arm circumference as a screening measure for identifying children with hypertension. Blood Press. Monit. 2015, 20, 189–193. [Google Scholar] [CrossRef]
  73. Chaput, J.P.; Katzmarzyk, P.T.; Barnes, J.D.; Fogelholm, M.; Hu, G.; Kuriyan, R.; Kurpad, A.; Lambert, E.V.; Maher, C.; Maia, J.; et al. Mid-upper arm circumference as a screening tool for identifying children with obesity: A 12-country study. Pediatr. Obes. 2017, 12, 439–445. [Google Scholar] [CrossRef] [PubMed]
  74. Talma, H.; van Dommelen, P.; Schweizer, J.J.; Bakker, B.; Kist-van Holthe, J.E.; Chinapaw, J.M.M.; Hirasing, R.A. Is mid-upper arm circumference in Dutch children useful in identifying obesity? Arch. Dis. Child. 2019, 104, 159–165. [Google Scholar] [CrossRef]
  75. Sisay, B.G.; Haile, D.; Hassen, H.Y.; Gebreyesus, S.H. Performance of mid-upper arm circumference as a screening tool for identifying adolescents with overweight and obesity. PLoS ONE 2020, 15, e0235063. [Google Scholar] [CrossRef] [PubMed]
  76. Brady, T.M. Obesity-Related Hypertension in Children. Front. Pediatr. 2017, 5, 197. [Google Scholar] [CrossRef] [PubMed]
  77. Lopez-Jimenez, F.; Almahmeed, W.; Bays, H.; Cuevas, A.; Di Angelantonio, E.; le Roux, C.W.; Sattar, N.; Sun, M.C.; Wittert, G.; Pinto, F.J.; et al. Obesity and cardiovascular disease: Mechanistic insights and management strategies. A joint position paper by the World Heart Federation and World Obesity Federation. Eur. J. Prev. Cardiol. 2022, 29, 2218–2237. [Google Scholar] [CrossRef] [PubMed]
  78. Di Salvo, G.; Pacileo, G.; del Giudice, E.M.; Rea, A.; Natale, F.; Castaldi, B.; Gala, S.; Fratta, F.; Limongelli, G.; Calabrò, P.; et al. Obesità in età pediatrica ed ipertensione arteriosa [Obesity in children and hypertension]. G. Ital. Di Cardiol. 2008, 9, 394–401. [Google Scholar]
  79. Hall, J.E.; Brands, M.W.; Henegar, J.R.; Shek, E.W. Abnormal kidney function as a cause and a consequence of obesity hypertension. Clin. Exp. Pharmacol. Physiol. 1998, 25, 58–64. [Google Scholar] [CrossRef]
  80. Al-Beltagi, M.; Bediwy, A.S.; Saeed, N.K. Insulin-resistance in paediatric age: Its magnitude and implications. World J. Diabetes 2022, 13, 282–307. [Google Scholar] [CrossRef]
  81. Ormazabal, V.; Nair, S.; Elfeky, O.; Aguayo, C.; Salomon, C.; Zuñiga, F.A. Association between insulin resistance and the development of cardiovascular disease. Cardiovas Diabetol. 2018, 17, 122. [Google Scholar] [CrossRef]
  82. Dornfeld, L.P.; Maxwell, M.H.; Waks, A.U.; Schroth, P.; Tuck, M.L. Obesity and hypertension: Long-term effects of weight reduction on blood pressure. Int. J. Obes. 1985, 9, 381–389. [Google Scholar]
  83. Julius, S.; Majahalme, S.; Nesbitt, S.; Grant, E.; Kaciroti, N.; Ombao, H.; Vriz, O.; Valentini, M.C.; Amerena, J.; Gleiberman, L. A “gender blind” relationship of lean body mass and blood pressure in the Tecumseh study. Am. J. Hypertens. 2002, 15, 258–263. [Google Scholar] [CrossRef] [PubMed]
  84. Sharma, V.; McNeill, J.H. The etiology of hypertension in the metabolic syndrome part four: The systemic perspective—The role of the neuroendocrine and immune systems, and the challenge of integration. Curr. Vasc. Pharmacol. 2006, 4, 349–381. [Google Scholar] [PubMed]
  85. Weidmann, P. Pressor factors and cardiovascular pressor responsiveness in essential hypertension. Int. J. Obes. 1981, 5 (Suppl. S1), 51–67. [Google Scholar] [PubMed]
  86. Reaven, G.M.; Chang, H.; Hoffman, B.B.; Azhar, S. Resistance to insulin-stimulated glucose uptake in adipocytes isolated from spontaneously hypertensive rats. Diabetes 1989, 38, 1155–1560. [Google Scholar] [CrossRef]
  87. Rocchini, A.P.; Katch, V.; Kveselis, D.; Moorehead, C.; Martin, M.; Lampman, R.; Gregory, M. Insulin and renal sodium retention in obese adolescents. Hypertension 1989, 14, 367–374. [Google Scholar] [CrossRef]
  88. Ellulu, M.S.; Patimah, I.; Khaza’ai, H.; Rahmat, A.; Abed, Y. Obesity and inflammation: The linking mechanism and the complications. Arch. Med. Sci. 2017, 13, 851–863. [Google Scholar] [CrossRef]
  89. Ouchi, N.; Parker, J.L.; Lugus, J.J.; Walsh, K. Adipokines in inflammation and metabolic disease. Nat. Rev. Immunol. 2011, 11, 85–97. [Google Scholar] [CrossRef]
  90. Kwaifa, I.K.; Bahari, H.; Yong, Y.K.; Noor, S.M. Endothelial Dysfunction in Obesity-Induced Inflammation: Molecular Mechanisms and Clinical Implications. Biomolecules 2020, 10, 291. [Google Scholar] [CrossRef]
  91. Barton, M.; Baretella, O.; Meyer, M.R. Obesity and risk of vascular disease: Importance of endothelium-dependent vasoconstriction. Br. J. Pharmacol. 2012, 165, 591–602. [Google Scholar] [CrossRef]
  92. Pecoraro, L.; Zoller, T.; Atkinson, R.L.; Nisi, F.; Antoniazzi, F.; Cavarzere, P.; Piacentini, G.; Pietrobelli, A. Correction: Supportive treatment of vascular dysfunction in pediatric subjects with obesity: The OBELIX study. Nutr. Diabetes 2022, 12, 5. [Google Scholar] [CrossRef]
  93. Ait-Aissa, K.; Nguyen, Q.M.; Gabani, M.; Kassan, A.; Kumar, S.; Choi, S.K.; Gonzalez, A.A.; Khataei, T.; Sahyoun, A.M.; Chen, C.; et al. MicroRNAs and obesity-induced endothelial dysfunction: Key paradigms in molecular therapy. Cardiovasc. Diabetol. 2020, 19, 136. [Google Scholar] [CrossRef] [PubMed]
  94. Bassareo, P.P.; Mercuro, G. Pediatric hypertension: An update on a burning problem. World J. Cardiol. 2014, 6, 253–259. [Google Scholar] [CrossRef] [PubMed]
Table 1. Patients’ height and weight.
Table 1. Patients’ height and weight.
F + MFemale Male
Age (y)Pt
(n)		Pt
(n)		Height
Mean ± SD
(cm)		Weight
Mean ± SD
(kg)		Pt
(n)		Height
Mean ± SD
(cm)		Weight
Mean ± SD
(kg)
174------3------
25734------23------
31357582 ± 710 ± 26082 ± 611 ± 2
420110994 ± 213 ± 19295 ± 214 ± 1
5238108103 ± 316 ± 1130104 ± 317 ± 3
6259140116 ± 13 23 ± 11119112 ± 721 ± 4
7485267120 ± 824 ± 7218122 ± 725 ± 6
8611302126 ± 728 ± 7309127 ± 728 ± 6
9689346130 ± 8 30 ± 8343131 ± 831 ± 8
10774385136 ± 935 ± 11389137 ± 937 ± 12
11511248145 ± 840 ± 10263145 ± 742 ± 12
12605280151 ± 746 ± 10325149 ± 945 ± 12
13541279156 ± 1051 ± 13262155 ± 1150 ± 13
14385207158 ± 855 ± 13178162 ± 859 ± 12
1514778164 ± 1064 ± 1769168 ± 863 ± 15
1611673170 ± 1068 ± 1743176 ± 980 ± 26
175923177 ± 974 ± 1836176 ± 680 ± 27
183319183 ± 368 ± 214177 ± 567 ± 7
total58532977 2876
Acronyms: F = female; M = male; y = years; Pt = patients; n = number; cm = centimetres; kg = kilograms; SD = standard deviation; --- = not reported.
Table 2. Patients’ BMI and heart rate.
Table 2. Patients’ BMI and heart rate.
F + MFemale Male
Age (y)Pt
(n)		Pt
(n)		BMI
Mean ± SD
(Ratio)		Heart Rate
Mean ± SD
(rpm)		Pt
(n)		BMI
Mean ± SD
(Ratio)		Heart Rate
Mean ± SD
(bpm)
174------3------
25734------23------
31357516.0 ± 1103 ± 116016.2 ± 2106 ± 22
420110916.1 ± 399 ± 139215.8 ± 298 ± 11
523810816.3 ± 293 ± 1513016.6 ± 293 ± 12
625914016.9 ± 386 ± 1211916.8 ± 284 ± 8
748526717.1 ± 383 ± 1021817.4 ± 381 ± 9
861130217.7 ± 384 ± 1030917.8 ± 381 ± 10
968934617.9 ± 382 ± 1034318.2 ± 481 ± 10
1077438519.0 ± 480 ± 1038919.6 ± 579 ± 10
1151124818.8 ± 378 ± 1026320.5 ± 477 ± 9
1260528020.0 ± 378 ± 832520.5 ± 476 ± 9
1354127921.0 ± 475 ± 926220.6 ± 476 ± 8
1438520721.6 ± 476 ± 817822.4 ± 476 ± 9
151477823.3 ± 576 ± 76922.1 ± 469 ± 7
161167322.7 ± 373 ± 124324.2 ± 571 ± 10
17592322.2 ± 467 ± 113624.1 ± 669 ± 8
18331924.5 ± 661 ± 151422.0 ± 366 ± 9
total58532977 2876
Acronyms: F = female; M = male; Pt = patients; n = number; bpm = beats per minute; SD = standard deviation; --- = not reported.
Table 3. Anthropometric measurements.
Table 3. Anthropometric measurements.
F + MFemale Male
Age (y)Pt
(n)		Pt
(n)		BMI
Mean ± SD
(Ratio)		MUAC
Mean ± SD
(cm)		Pt
(n)		BMI
Mean ± SD
(Ratio)		MUAC
Mean ± SD
(cm)
174------3------
25734------23------
31357516.0 ± 116 ± 16016.2 ± 215 ± 3
420110916.1 ± 317 ± 19215.8 ± 215 ± 2
523810816.3 ± 218 ± 213016.6 ± 218 ± 2
625914016.9 ± 319 ± 211916.8 ± 218 ± 2
748526717.1 ± 318 ± 321817.4 ± 319 ± 3
861130217.7 ± 319 ± 230917.8 ± 320 ± 2
968934617.9 ± 320 ± 334318.2 ± 422 ± 3
1077438519.0 ± 421 ± 338919.6 ± 521 ± 3
1151124818.8 ± 321 ± 326320.5 ± 423 ± 5
1260528020.0 ± 323 ± 432520.5 ± 423 ± 3
1354127921.0 ± 424 ± 326220.6 ± 424 ± 3
1438520721.6 ± 424 ± 617822.4 ± 425 ± 3
151477823.3 ± 528 ± 56922.1 ± 425 ± 4
161167322.7 ± 325 ± 24324.2 ± 528 ± 4
17592322.2 ± 427 ± 33624.1 ± 629 ± 6
18331924.5 ± 627 ± 41422.0 ± 329 ± 2
total58532977 2876
Acronyms: F = female; M = male; Pt = patients; n = number; y = years; cm = centimetres; SD = standard deviation; --- = not reported.
Table 4. Relationship between MUAC and BMI.
Table 4. Relationship between MUAC and BMI.
Age [y]
Pt (n)		GenderMUAC 50th CentilePt
(n)		BMISDp
[1,5]
(110)		Fbelow28152<0.0001 (1)
over24183
Mbelow33151<0.001 (2)
over25172
F + Mbelow61151<0.0001 (3)
over49183
[6,9]
(1135)		Fbelow301152<0.0001 (4)
over229213
Mbelow327163<0.0001 (5)
over278224
F + Mbelow628153<0.0001 (6)
over507214
[10,12]
(757)		Fbelow207172<0.0001 (7)
over151233
Mbelow199172<0.0001 (8)
over200233
F + Mbelow406172<0.0001 (9)
over351233
[13,17]
(295)		Fbelow95203<0.0001 (10)
over59266
Mbelow65193< 0.0001 (11)
over76254
F + Mbelow160193<0.0001 (12)
over135255
Acronyms: F = female; M = male; y = years; Pt = patients; n = number; SD = standard deviation; --- = not reported. (1) p < 0.0001 (Kruskal–Wallis H = 22.4390 with df = 1), (2) p = 0.0003 (Kruskal–Wallis H = 13.3006 with df = 1), (3) p < 0.0001 (Kruskal–Wallis H = 36.4810 with df = 1), (4) p < 0.0001 (Kruskal–Wallis H = 292.8857 with df = 1), (5) p < 0.0001 (Kruskal–Wallis H = 352.0538 with df = 1), (6) p < 0.0001 (Kruskal–Wallis H = 644.6958 with df = 1), (7) p < 0.0001 (Kruskal–Wallis H = 184.9815 with df = 1), (8) p < 0.0001 (Kruskal–Wallis H = 228.8176 with df = 1), (9) p < 0.0001 (Kruskal–Wallis H = 418.7230 with df = 1), (10) p < 0.0001 (Kruskal–Wallis H = 66.3119 with df = 1), (11) p < 0.0001 (Kruskal–Wallis H = 22.4390 with df = 1), (12) p < 0.0001 (Kruskal–Wallis H = 139.8159 with df = 1). Mean systolic and diastolic BP were different when stratified by gender and age. All these differences were statistically significant with p < 0.001.
Table 5. Relationship between MUAC and diastolic BP.
Table 5. Relationship between MUAC and diastolic BP.
Age [y]
Pt (n)		GenderMUAC Patients
(n)		Mean
DBP		Std. Dev.p
[1,5]
(110)		Fbelow28619n.s.
over246211
Mbelow33606n.s.
over25626
F + Mbelow61617n.s.
over49628
[6,9]
(1135)		Fbelow301609<0.0001 (1)
over2296410
Mbelow327609<0.0001 (2)
over278649
F + Mbelow628609<0.0001 (3)
over5076410
[10,12]
(757)		Fbelow2076010<0.001 (4)
over1516510
Mbelow199618<0.0001 (5)
over2006512
F + Mbelow406619<0.0001 (6)
over3516511
[13,17]
(295)		Fbelow95629<0.001 (7)
over596810
Mbelow65669n.s.
over766510
F + Mbelow160639trend (8)
over1356610
Acronyms: F = female; M = male; y = years; Pt = patients; n = number; SD = standard deviation; DBP = diastolic blood pressure; n.s. = not statistically significant. (1) p < 0.0001 (Kruskal–Wallis H = 23.8709 with df = 1), (2) p < 0.0001 (Kruskal–Wallis H = 17.4307 with df = 1), (3) p < 0.0001 (Kruskal–Wallis H = 40.9695 with df = 1), (4) p = 0.0003 (Kruskal–Wallis H = 12.8884 with df = 1), (5) p = 0.0003 (Kruskal–Wallis H = 13.0276 with df = 1), (6) p < 0.0001 (Kruskal–Wallis H = 26.6238 with df = 1), (7) p = 0.0009 (Kruskal–Wallis H = 10.9229 with df = 1), (8) p = 0.0535 (Kruskal–Wallis H = 3.7272 with df = 1).
Table 6. Relationship between MUAC and systolic BP.
Table 6. Relationship between MUAC and systolic BP.
Age [y]
Pt (n)		GenderMUAC Patients
(n)		Mean
SBP		Std. Dev.p
[1,5]
(110)		Fbelow289912n.s.
over2410012
Mbelow33979n.s.
over259910
F + Mbelow619810n.s.
over499910
[6,9]
(1135)		Fbelow3019610<0.0001 (9)
over22910310
Mbelow327969<0.0001 (10)
over27810410
F + Mbelow628969<0.0001 (11)
over50710310
[10,12]
(757)		Fbelow2079910<0.0001 (12)
over15110910
Mbelow1991029<0.0001 (13)
over20011111
F + Mbelow4061019<0.0001 (14)
over35111011
[13,17]
(295)		Fbelow9510610<0.05 (15)
over5911213
Mbelow6511211n.s.
over7611313
F + Mbelow16010811<0.05 (16)
over13511313
Acronyms: F = female; M = male; y = years; Pt = patients; n = number; SD = standard deviation; SBP = systolic blood pressure; n.s. = not statistically significant. (9) p < 0.0001 (Kruskal–Wallis H = 68.3079 with df = 1), (10) p < 0.0001 (Kruskal–Wallis H = 71.1723 with df = 1), (11) p < 0.0001 (Kruskal–Wallis H = 139.0866 with df = 1), (12) p < 0.0001 (Kruskal–Wallis H = 63.1113 with df = 1), (13) p < 0.0001 (Kruskal–Wallis H = 55.9281 with df = 1), (14) p < 0.0001 (Kruskal–Wallis H = 122.8109 with df = 1), (15) p = 0.0225 (Kruskal–Wallis H = 5.2053 with df = 1), (16) p = 0.0101 (Kruskal–Wallis H = 6.6194 with df = 1).
Table 7. The odds ratio of the children’s frequencies when making a comparison between diastolic BP (50th percentile) and MUAC (50th percentile).
Table 7. The odds ratio of the children’s frequencies when making a comparison between diastolic BP (50th percentile) and MUAC (50th percentile).
MUAC 50th
DBPBelowOverTotal
Below9045871491
Row%60.63%39.37%100.00%
Col%72.03%56.33%64.91%
Over351455806
Row%43.55%56.45%100.00%
Col%27.97%43.67%35.09%
Total125510422297
Row%54.64%45.36%100.00%
Col%100.00%100.00%100.00%
Point95% Confidence Interval
EstimateLowerUpper
PARAMETERS: Odds-based
Odds Ratio (cross product)1.99631.67802.3751 (T)
Odds Ratio (MLE)1.99571.67782.3753 (M)
1.67152.3843 (F)
PARAMETERS: Risk-based
Risk Ratio (RR)1.39231.27421.5213 (T)
Risk Difference (RD%)17.082112.855121.3090 (T)
STATISTICAL TESTSChi-square1-tailed p2-tailed p
Chi-square—uncorrected61.5946 0.0000000000
Chi-square—Mantel–Haenszel61.5678 0.0000000000
Chi-square—corrected (Yates)60.9073 0.0000000000
Mid-p exact 0.0000000000
Fisher exact 0.00000000000.0000000000
Table 8. The odds ratio of children’s frequencies when making a comparison between systolic BP (50th percentile) and MUAC (50th percentile of sample).
Table 8. The odds ratio of children’s frequencies when making a comparison between systolic BP (50th percentile) and MUAC (50th percentile of sample).
MUAC 50th
SBPBelowOverTotal
Below9505451495
Row%63.55%36.45%100.00%
Col%75.70%52.30%65.08%
Over305497802
Row%38.03%61.97%100.00%
Col%24.30%47.70%34.92%
Total125510422297
Row%54.64%45.36%100.00%
Col%100.00%100.00%100.00%
Point95% Confidence Interval
EstimateLowerUpper
PARAMETERS: Odds-based
Odds Ratio (cross product)2.84042.37913.3913 (T)
Odds Ratio (MLE)2.83912.37893.3918 (M)
2.36983.4052 (F)
PARAMETERS: Risk-based
Risk Ratio (RR)1.67091.51751.8399 (T)
Risk Difference (RD%)25.515221.363029.6675 (T)
STATISTICAL TESTSChi-square1-tailed p2-tailed p
Chi-square—uncorrected137.1084 0.0000000000
Chi-square—Mantel–Haenszel137.0487 0.0000000000
Chi-square—corrected (Yates)136.0808 0.0000000000
Mid-p exact 0.0000000000
Fisher exact 0.00000000000.0000000000
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MDPI and ACS Style

Martino, F.; Niglio, T.; Barillà, F.; Martino, E.; Paravati, V.; Bassareo, P.P. The Association between Mid-Upper Arm Circumference and Blood Pressure in an Italian Population of School-Aged Children and Adolescents with Lipid Disorders. J. Clin. Med. 2024, 13, 663. https://doi.org/10.3390/jcm13030663

AMA Style

Martino F, Niglio T, Barillà F, Martino E, Paravati V, Bassareo PP. The Association between Mid-Upper Arm Circumference and Blood Pressure in an Italian Population of School-Aged Children and Adolescents with Lipid Disorders. Journal of Clinical Medicine. 2024; 13(3):663. https://doi.org/10.3390/jcm13030663

Chicago/Turabian Style

Martino, Francesco, Tarcisio Niglio, Francesco Barillà, Eliana Martino, Vincenzo Paravati, and Pier Paolo Bassareo. 2024. "The Association between Mid-Upper Arm Circumference and Blood Pressure in an Italian Population of School-Aged Children and Adolescents with Lipid Disorders" Journal of Clinical Medicine 13, no. 3: 663. https://doi.org/10.3390/jcm13030663

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