Related Experiment Video
Updated: Dec 24, 2025

Assessment of Child Anthropometry in a Large Epidemiologic Study
Published on: February 2, 2017
Red blood cell variables and correlations with body mass components in boys aged 10-17 years
Jasmina Pluncevic Gligoroska1, Serjoza Gontarev2, Vesela Maleska1
1Institute of Physiology and Anthropology, Faculty of Medicine, University Ss Cyril and Methodius, Skopje, Republic of Macedonia.
Abstract:
The objective of this study was to analyze the hematologic parameters and their correlation with body composition components in healthy boys at pubertal age. One hundred and ninety physically active male subjects, aged 10 to 17 years, mean age 13.87 ±4.5 years, were included in the study. Capillary blood was drawn from all subjects and the following hematologic parameters were measured: RBC, Hct, Hb, MCV, MCH, MCHC. The following body components derived from Matiegka anthropometric method were assessed: muscle mass (MM), bone mass (BM), and body fat mass (BF). The mean values (±SD) of hematologic parameters were: RBC= 4.87±0.41 x 10 < sup > 12 < /sup > /L, Hb=14.24±1.24 g/dL, Hct=43.83±3.8%. Anthropometric characteristics were as follows: body mass index (BMI) = 20.26± 3.27 kg/m < sup > 2 < /sup > , relative muscle mass (MM%) = 53.18± 3.19%, bone mass (BM%) = 18.83± 2.4% and body fat percentage (BF%) = 15.19± 2.64%. Correlation analysis between hematologic parameters and body composition showed a moderate to strong correlation between RBC, Hb and Hct and all body components. The strongest correlations were found between Hb and Hct, and muscle mass (r= 0.60; r= 0.61) and lean body mass (r= 0.59). The body fat mass showed also a positive association with RBC (r=0.47); Hb (r=0.47) and Hct (r=0.48). Our findings showed that the relationship between anthropometric measures and RBC variables in healthy physically active boys were positively correlated, but the level of association was higher with skeletal muscle mass.
Related Concept Videos
Coefficient of Correlation
If you suspect a linear relationship between x and y, then r can measure how strong the linear relationship is.
What the VALUE of r tells us:
The value of r is always between –1 and +1: –1 ≤ r ≤ 1.
The size of the correlation r indicates the...
Correlations
Factors Affecting Erythropoiesis
Several factors influence the erythrocyte production rate, with tissue oxygen level being among the most critical. Intense exercise or high altitudes can cause tissue hypoxia, which triggers the kidneys to release more erythropoietin (EPO) into the bloodstream.
EPO then...
Calculating and Interpreting the Linear Correlation Coefficient
Multiple Regression
Farmers can use multiple regression to determine the crop yield based on more than one factor, such as water availability, fertilizer, soil properties, etc. Here, the crop yield is the response or dependent variable as it depends on the other independent variables. The analysis requires the construction of a scatter plot...
Overview of Hematopoiesis
Developmental Phases of Hematopoiesis
Initially, HSCs are formed in the embryonic yolk sac, a critical site for early blood cell production. These stem cells subsequently migrate to other...

