Childhood obesity is considered a serious worldwide public health problem, because it increases the risk of chronic diseases such as Diabetes Mellitus, cardiovascular diseases and cancer and mental disorders [1-3]. In pediatric population over 5 years of age, obesity is defined as a body mass index above the 95th percentile or two standard deviations above the mean. Primary care is one of the main measures for prevention, diagnosis and management of this pathology. The clinical approach to childhood obesity is often insufficient to integrate the diagnosis, so the use of reliable, objective and efficient measurement tools is essential to corroborate the initial suspicion. For this reason, biochemical and echocardiographic criteria integrated into the diagnostic algorithm.

Somatometric parameters have been established for the approach to obesity, with the Body Mass Index (BMI) being the most widely used measurement [4]. Other useful measures are waist and hip circumference, Waist-Hip Ratio (WHR) and Waist-to-Height Ratio (WHtR) [5-7]. Systolic and diastolic blood pressure add to these parameters. Important biochemical factors for diagnosis of childhood obesity are fasting blood glucose, blood lipid profile (cholesterol, triglycerides, high, low and very low-density lipoproteins), and liver transaminases (glutamic oxaloacetic acid and glutamic pyruvic acid). Finally, transthoracic echocardiography also provides parameters that help confirm the diagnosis, such as measurement of diastolic Interventricular Septum (IVSd), pulmonary artery systolic pressure, Relative Wall Thickness (RPT), left ventricular mass, Left Ventricular Ejection Fraction (LVEF), pulsed Doppler velocities of the E wave and A wave of both ventricles (mitral E/A ratio, tricuspid E/A ratio), biventricular E´ wave tissue velocities (left and right lateral E´ ratio), and the ratio of these velocities to each other (left and right lateral E/E´ ratio) [8]. Although somatometric, biochemical, and echocardiographic criteria for childhood obesity are established, few studies exist on the cutoff point for these quantitative risk variables and their predictive value for this pathology. Therefore, the main objective of this study is to identify the most sensitive and practical somatometric, biochemical, and echocardiographic variables for childhood obesity diagnosis in the population aged 6 to 17 years at any level of health care.

A retrospective, analytical, cross-sectional, case-control clinical study was designed. All patients who attended the pediatric cardiology outpatient clinic of a third level care hospital were recruited over a one-year observation period and registered in an institutional electronic database. Inclusion criteria were patients between 6 and 17 years of age, both genders, without cardiovascular disease, with or without obesity, and absence of other comorbidities. We excluded patients not seen at our institution and those with incomplete medical records. Recruited patients underwent various somatometric, biochemical, and echocardiographic measurements. Somatometric measurements included weight, height, waist, hip, BMI, and systemic blood pressure. All measurements were taken by trained healthcare professionals following standardized protocols of the World Health Organization (WHO) and the American Heart Association (AHA). A SECA^® bascule was used to determine weight and height. Waist was measured with a flexible tape measure, taking into account the horizontal line from the narrowest area between the lowest rib and the iliac crest. Hip measurements was taken along the horizontal line at the level of the maximum posterior protuberance of the buttocks in a standing position with the upper limbs at the sides. BMI was calculated using the formula: weight in kilograms / (height in centimeters)². BMI categories were defined based on standards established by the International Obesity Task Force (IOTF) and the World Health Organization (WHO). The IOTF cutoffs for childhood overweight and obesity are an extrapolation of the adult BMI cutoffs for overweight (25–29.9 kg/m²) and obesity (30 kg/m²). The WHO system defines obesity as a BMI exceeding two standard deviations from the mean. Systemic blood pressure measured using a Welch Allyn^® aneroid sphygmomanometer. Biochemical variables obtained from blood samples taken from patients fasting for at least 8 hours, using the standardized techniques of our institutional laboratory (WHO/SIGN). Echocardiographic parameters obtained by a single certified pediatric echo cardiographer using a Phillips Epic 7 ultrasound system in accordance with the American Society of Echocardiography (ASE) guidelines.

Data obtained from the institutional electronic system compiled into an Excel spreadsheet, from which two group: a control group (without obesity) and a problem group (with obesity) based on a BMI greater than the 95th percentile for age. Numerical variables expressed as mean ± standard deviation with minimum and maximum variability ranges. Categorical variables are expressed as frequency (n) and percentage (%) in relation to the at-risk population. Comparative analysis of both groups was performed using parametric tests (Student t or chi-square) or non-parametric tests (Mann-Whitney U or Fisher's exact test), as appropriate. A p value less than 0.05 was consider statistically significant. The ROC curve was used to determine the cut-off point for quantitative data, and the variables were subsequently dichotomized to analyze the contingency table and obtain the odds ratio (OR) with a 95% confidence interval. Finally, for the most representative risk factors, a Fagan nomogram was perform to calculate the probability of the patient having or not having childhood obesity after having performed the diagnostic tests. The institutional ethics, research, and biosafety committees approve the study. Data were hand appropriately, taking into account data confidentiality.

86 patients were recruited in the study and divided into two groups: Group A (obese, n = 35) and Group B (non-obese, n = 51) whose characteristics are shown in Table 1. It can be observed that there is a statistically significant difference between obese patients compared to non-obese patients in the following variables: weight, BMI, BMI percentile, waist, hip, waist/hip ratio, systolic and diastolic blood pressure, triglycerides, cholesterol, HDL, LDL,

VLDL, SGOT, SGPT, Pulmonary artery systolic pressure (PASP), Interventricular septum thickness in diastole (IVSd), LV ejection fraction (LVEF), LV longitudinal deformation (STRAIN), LV mass, ratio E/E'mitral, TAPSE and posterior wall of the LV in diastole (PPVId).

Table 1: Risk Factors for Childhood Obesity.

In Figure 1 you can see the ROC curves to calculate the maximum sensitivity and specificity value for all the risk factors previously identified. The following cut-off points can be observed in the somatometric, biochemical and echocardiographic variables: Weight 40.1 kg (AUC = 0.81), BMI 19.1 kg/m2 (AUC = 0.96), waist 59 cm (AUC =0.94), Hip 67 cm (AUC=0.86), Waist Hip Index 0.86 (AUC=0.90), systolic blood pressure 95 mmHg (AUC=0.82), diastolic blood pressure 60mmHg (AUC=0.78), Triglycerides 60 mg/dl (AUC=0.84), Cholesterol 114 mg/dl (AUC = 0.75), HDL 55 mg/dl (AUC = 0.86), VLDL 17.5 mg/dl (AUC = 0.65), LDL 79.6 mg/dl (AUC = 0.686), TGO 18.5 U/L (AUC = 0.62), TGP 14.5 U/L (AUC = 0.75), PSAP 29.5mmHg (AUC = 0.65), SIVD 8.2 mm (AUC = 0.85), STRAIN-20.1% (AUC = 0.70), LVEF 69.5% (AUC = 0.33), LV mass 88.8g (AUC = 0.78), E/E M.

HDL High density lipid; LDL Low density lipid; VLDL, Very low density lipid; AST, aspartate aminotransferase; ALT, alanine aminotransferase; sPAP, systolic pulmonary arterial pressure; IVSd, ventricular septum in diastole; EF, eyection fraction; MV E/E Ratio, mitral valve E velocity of passive mitral filling E'peak MV annular velocity in early diastole; LVPWd, LV posterior wall in diastole; TAPSE, tricuspid annular plane systolic excursion.

Figure 2 shows the Fagan nomograms for the three most important risk factors. The hip has a prior probability of 41% with a positive likelihood ratio of 51 and a negative likelihood ratio of 0. Triglycerides have a prior probability of 57% with a positive likelihood ratio of 12 and a negative likelihood ratio of 0.35. Finally, the interventricular septal thickness in diastole has a prior probability of 84% with a positive likelihood ratio of 0 and a negative likelihood ratio of 0.51.

Figure 1: ROC Curves of Risk Factors for Childhood Obesity.

Table 2: Shows That The Risk Factors That Most Influence Childhood Obesity Are Hips Greater Than 70 Cm (OR 2390.3, 95% CI -94.6 To 60386.1), Triglycerides Greater Than 60 Mg/Dl (OR 36.1, 95% CI -7.7 To 169.2) And Interventricular Septum Thickness In Diastole (Ivsd) Greater Than 8 Mm (OR 27.4, 95% CI -1.6 To 477.6).

Obesity as a regional public health problem is a multifactorial disease, which includes genetic factors among others. Its etiology also involves lifestyle as an important determinant, due to the intake of high-calorie density foods, sugary drinks, lack of physical activity and the change in routine activity after a global pandemic. The metabolic demand in obesity is increased due to the greater amount of adipose tissue and lean mass in addition to an increase in blood volume, which conditions a greater preload on the heart. This excess body fat is stored to a greater extent at the visceral and pericardial level, conditioning an increase in waist circumference, which is why this is one of the most used indicators for its clinical diagnosis [4]. Measuring abdominal obesity can be considered a first step to identify children and adolescents at cardiometabolic risk; recently, the Consensus Statement of the International Society of Atherosclerosis and the International Chair of Cardiometabolic Risk recommended that health professionals be trained in measuring waist circumference. Furthermore, its usefulness has been demonstrated as a risk factor for the development of cardiovascular diseases such as hypertension [2], coronary artery disease and diabetes mellitus, which currently have a higher morbidity and mortality rate worldwide [1-9]. In contrast, there is little evidence in the literature regarding the hip as a single predictor of childhood obesity, as demonstrated by our study. Among the advantages of using this parameter is its practicality at any level of health care, as it is inexpensive, reproducible by healthcare personnel, and accessible at all levels. Furthermore, it is a parameter that is not susceptible to modification by the position or prandial state of patients because its correct measurement involves bony anatomical references that are difficult to change. However, there is no evidence in the literature that this parameter alone is a predictor of childhood obesity. Therefore, it has been associated with other somatometric measures such as the waist-to-height ratio (WHR). This index predicts abdominal fat and takes body size into account. A general cutoff value of 0.50 for WHR has been proposed in adults and adolescents to indicate abdominal adiposity. In the pediatric population, a WHR greater than 0.50 was associated with cardiovascular abnormalities, and normal-weight children showed a less favorable metabolic profile if their WHR was greater than 0.50. Mehta et al. proposed the use of two pediatric cutoff points derived from BMI-for-age and sex growth charts, the WHR 0.5 and WHR 0.55 cutoff points as equivalent to the 85th and 95th BMI percentiles, respectively, to diagnose overweight and abdominal adiposity [7,9]. Another critical aspect of obesity is the accumulation of adipose tissue and dyslipidemia, which manifests clinically through altered lipid profiles, such as increased cholesterol, low density lipoproteins (LDL), triglycerides (TG), and decreased high-density lipoproteins (HDL). In obesity, the lipid profile is significantly altered, and recent studies have reported that sphingolipids and phosphatidylethanolamines (PE) are key factors in cardiometabolic complications. Despite these findings, there is still a gap in research that specifically addresses pediatric health and lipid profiles in children and adolescents with obesity [10]. In our study, triglyceride levels above 60 mg/dl represent the main risk and predictor of obesity in the pediatric stage in the lipid profile, together with cholesterol levels above 115 mg/dl and LDL levels above 80 mg/dl. Based on these findings, we recommend using triglyceride levels as a biochemical predictor of obesity in children. Pediatric echocardiographic evaluation is considered a valuable method for the early detection of subclinical alterations related to cardiovascular morbidity in overweight and obese children. Obesity has been associated with alterations in myocardial structure that determine alterations in cardiac mechanics such as left ventricular dilation, increased wall stress with compensatory hypertrophy that favors the development of heart failure. Among the main echocardiographic alterations in pediatric patients with obesity are hypertrophy of the interventricular septum and left ventricle, left or right ventricular dysfunction, decreased velocities in the analysis of diastolic function by tissue Doppler and alterations in global myocardial deformation (STRAIN) [8-11]. According to our results, the most significant parameters of echocardiographic evaluation to predict obesity risk were the thickness of the interventricular septum in diastole greater than 8 mm and the left ventricular mass calculated by M mode greater than 89 g/m2, with relevance in the decrease of the global longitudinal deformation of the left ventricle (STRAIN) greater than -20%, which translates into states of subclinical systolic myocardial dysfunction with preserved ejection fraction, but which guide us and give us alarm data to prevent cardiovascular complications in the future.

Figure 2: Fagan Nomogram of the Most Relevant Risk Factors for Childhood Obesity.

Currently, the Body Shape Index A (ABSI) and the Body Roundness Index (BRI) are being used in some specific populations in comparison with the BMI to predict the risk of mortality related to obesity, cardiovascular disease and diabetes using somatometric measures that involve not only height and weight, but also waist circumference, which currently represents one of the most important somatometric parameters to determine abdominal fat [12-14]. This, in addition to the biochemical evaluation (lipid profile analysis) and echocardiographic (measurement of interventricular septum thickness [14-24], ventricular mass and biventricular systolic-diastolic function) of our patients, represents a great advance in the clinical algorithm of obesity, marking the course of prevention, diagnosis, timely and adequate treatment in the pediatric population.

1. Rubino F, Cummings DE, Eckel RH, Cohen R V, Wilding JPH, Brown WA, et Definition and diagnostic criteria of clinical obesity. Lancet Diabetes Endocrinol. 2025; 3: 221-262.
2. Jeong SI, Kim SH. Obesity and hypertension in children and adolescents. Clin Hypertens. 2024; 30.
3. Mangner N, Scheuermann K, Winzer E, Wagner I, Hoellriegel R, Sandri M, et Childhood obesity. JACC cardiovasc imaging. 2014; 12: 1198-205.
4. Powell-Wiley TM, Poirier P, Burke LE, Després JP, Gordon-Larsen P, Lavie CJ, et al. Obesity and cardiovascular disease: A scientific statement from the American heart association. Circulation. 2021; 143.
5. Xie L, Kim J, Almandoz JP, Clark J, Mathew MS, Cartwright BR, et al. Anthropometry for predicting cardiometabolic disease risk factors in adolescents. Obesity. 2024; 32: 1558-1567.
6. Sommer I, Teufer B, Szelag M, Nussbaumer-Streit B, Titscher V, Klerings I, et al. The performance of anthropometric tools to determine obesity: a systematic review and meta-analysis. Sci Rep. 2020; 10: 12699.
7. Liu N, Wang B, Zhang G, Shen M, Cheng P, Guo Z, et al. Waist-to-hip ratio better reflect beta-cell function and predicts diabetes risk in adult with overweight or obesity. Ann Med. 2025; 57.
8. Zermeño Ugalde P, Gallegos García V, Gaytán Hernández D, Castro Ramírez Relación del índice cintura-talla (ICT) con cintura e Índice de Cintura Cadera como predictor para obesidad y riesgo metabólico en adolescentes de secundaria. RESPYN Revista Salud Pública y Nutrición. 2020; 19: 19-27.
9. Niu W, Li C, Wang Z, Liang The correlation between insulin-like growth factor 1 and left ventricular mass index in obese children. BMC Endocr Disord. 2025; 25: 99.
10. Von der Born J, Baberowski S, Memaran N, Grams L, Homeyer D, Borchert-Mörlins B, et al. Impact of sex and obesity on echocardiographic parameters in children and Pediatr Cardiol. 2022; 43: 1502-1516.
11. Lopez L, Saurers DL, Barker PCA, Cohen MS, Colan SD, Dwyer J, et al. Guidelines for performing a comprehensive pediatric transthoracic echocardiogram: Recommendations from the american society of Echocardiography. J Am Soc Echocardiogr. 2024; 2: 119-170.
12. Schroder H, Juton C, Goran MI, Warnberg J, Oses M, Gonzalez-Gross M, et Twenty-year trend in the prevalence of increased cardiometabolic risk, measured by abdominal obesity, among Spanish children and adolescents across body mass index categories. BMC Med. 2024; 22: 1-8.
13. Gomwe H, Phiri L, Marange CS. Waist-based anthropometric measures and central adiposity-related comorbidities in children. S Afr Fam Pract. 2024; 66.
14. Tan LJ, Shin S. Mediating effects of waist circumference and BMI on the association between meal frequency and mortality. Public Health Nutr. 2025; 28: 1-9.
15. Kelly AS, Armstrong SC, Michalsky MP, Fox Obesity in Adolescents. JAMA. 2024; 332: 738.
16. Ashwell M, Gunn P, Gibson S. Waist?to?height ratio is a better screening tool than waist circumference and BMI for adult cardiometabolic risk factors: systematic review and meta? analysis. Obes Rev. 2012; 13: 275-286.
17. Huang Y, Sulek K, Stinson SE, Holm LA, Kim M, Trost K, et al. Lipid profiling identifies modifiable signatures of cardiometabolic risk in children and adolescents with obesity. Nat Med. 2025; 31: 294-305.
18. Hurtado-Sierra D, Colin-Ramirez E, Lena-Hernandez LE, Sanchez-Cornelio C, Trujeque-Ruiz L, Aguilar-Morales TI, et al. Subclinical cardiac abnormalities detected by echocardiography in overweight and obese Mexican schoolchildren. Arch Cardiol Mex. 2020; 89: 222-232.
19. Ghanem S, Mostafa M, Ayad Early echocardiography abnormalities in obese children and adolescent and reversibility of these abnormalities after significant weight reduction. J Saudi Heart Assoc. 2010; 22: 13-18.
20. Dehghan B, Sedighi M, Rostampour N, Hashemi E, Hovsepian S, Sabri MR, et Childhood Obesity Is Associated With Subclinical Left Ventricular Dysfunction Detected by Speckle Tracking Echocardiography. Clin Pediatr. 2024; 63: 936-941.
21. Batalli-Kepuska A, Bajraktari G, Zejnullahu M, Azemi M, Shala M, Batalli A, et al. Abnormal systolic and diastolic myocardial function in obese asymptomatic Int J Cardiol. 2013; 168: 2347-2351.
22. Bertoli S, Leone A, Krakauer NY, Bedogni G, Vanzulli A, Redaelli VI, et Association of Body Shape Index (ABSI) with cardio-metabolic risk factors: A cross-sectional study of 6081 Caucasian adults. PLoS One. 2017; 12: 1-15.
23. Stefanescu A, Revilla L, Lopez T, Sanchez SE, Williams MA, Gelaye B. Using A Body Shape Index (ABSI) and Body Roundness Index (BRI) to predict risk of metabolic syndrome in Peruvian adults. J Int Med Res. 2020; 48: 1-12.
24. Gazarova M, Galsneiderova M, Meciarova L. Obesity diagnosis and mortality risk based on a body shape index (ABSI) and other indices and anthropometric parameters in university students. Rocz Panstw Zakl Hig. 2019; 70: 267-275.