The association of telomere length with body mass index and immunological factors differs according to physical activity practice among children and adolescents

Abstract

Background

This study aims to verify the relationship between screen and sleep time, body mass index (BMI) and immunological factors with telomere length according to leisure-time physical activity (PA) in children and adolescents.

Methods

A cross-sectional study involving a sample of 476 schoolchildren of both sexes, aged seven to 17 years, from a community in southern Brazil. Behavioral variables (PA, sleep time, and screen time) were self-reported using a questionnaire. PA was classified as inactive and any PA (doing some physical activity). The associations of screen time, sleep time, BMI, and immunologic factors with telomere length were tested using multiple linear regression models, with the sample divided according to the schoolchildren’s leisure-time physical activity practices.

Results

An inverse association between BMI and telomere length (β: -0.239; 95% CI: -0.468; -0.010) and a direct association of leukocytes (β: 0.151; 95% CI: 0.029; 0.278) and neutrophils (β: 0.131; 95% CI: 0.008; 0.254) with telomeres were found in the inactive students. No association was found between screen time and sleep time and telomeres. No association was found among students who engaged in any PA.

Conclusion

The associations between telomeres, BMI, and immunologic factors were found only in inactive students. These results suggest that the association between BMI and immunological factors and telomere length may be influenced by physical activity.

Introduction

Telomeres are located at the ends of linear chromosomes and are responsible for maintaining genomic integrity [1]. During each cell division cycle, telomeres progressively shorten, culminating in a state of cellular senescence [2]. Therefore, telomeres are recognized as intrinsic biomarkers of the cellular aging process [3] and are susceptible to oxidative stress and systemic inflammation caused by external factors, which can accelerate the reduction of telomere length (TL) [4]. In response to oxidative stress, immune system cells can undergo changes that affect their function [5].

Immunological factors and obesity also appear to influence telomere length [6, 7]. A positive association between white blood cell count and TL has been suggested [8]. In obese individuals, adipose tissue is infiltrated by different macrophages, and this recruitment is associated with systemic inflammation and insulin resistance [9].

Studies suggest that lifestyle factors play a critical role in telomere shortening [10, 11]. Sedentary behavior has been observed to increase levels of pro-inflammatory cytokines and adipokines, contributing to an increase in chronic low-grade inflammation [12]. Poor sleep quality and sedentary behavior have been identified as significant contributors to telomere shortening [13]. Sleep duration and quality, in turn, have been shown to be associated with several health conditions in children and adolescents, including the presence of obesity, cardiometabolic risk factors, and mental health problems [14].

The relationship between physical activity (PA) and TL remains inconclusive, as highlighted by a meta-analysis that included different methods to assess PA [15]. It is worth noting that both insufficient and excessive PA have been shown to have an impact on inducing oxidative stress at the cellular level [5, 16]. In addition, studies show a U-shaped relationship [17] with telomere shortening. However, a large part of the world’s population does not reach the recommended levels of PA [18]. At a population level, recommendations indicate a practice of aerobic activities of moderate or vigorous intensity to maintain health [19], with the domain of leisure-time PA having a more pronounced association with health indicators [20].

In general, moderate exercise has been shown to have a significant effect on the rate of telomere length shortening compared to physical inactivity [15, 21]. The relationship between physical activity and telomere length is partly explained by the improvement of the inflammatory condition, through a reduction in the levels of pro-inflammatory proteins [22]. Furthermore, there is evidence that physical activity improves antioxidant activity, helping to balance oxidation in the long term [23].

Evidence in the literature suggests that regular PA practice seems to play a protective role in different contexts, such as less screen time, better sleep duration [24], better obesity indicators [25], and immune profile [6]. It is worth noting that few studies have investigated this relationship in children and adolescents [13, 26, 27]. Therefore, it is crucial to understand how PA influences the relationship between the development of chronic diseases such as obesity, immunological factors and behavioral factors with TL in children and adolescents. The importance of studying this age group is reinforced by evidence that this is the period when lifestyle habits are formed [28]. In this context, the aim of this study is to examine the relationship between screen time, sleep time, BMI and immunological factors with TL according to leisure time PA in children and adolescents.

Method

Sample/population

This is a cross-sectional study conducted using data from a more comprehensive survey entitled “School Health - Phase III”. The schools included in the survey were randomly selected, with stratification by conglomeration, taking into account the density of students in each region of the municipality (center, north, south, east and west), in both urban and rural areas. The study had a sample of 2502 children and adolescents between the ages of seven and 17, students from public and private schools in a municipality in southern Brazil. This study was developed with data from a subsample of 476 students, selected based on the TL samples available for analysis.

The present study was conducted meeting Resolution 466/2012 of the National Health Council of Brazil and approved by the University of Santa Cruz do Sul ethics committee (Opinion No. 714.216). Students’ participation in the study was approved by an informed consent form signed by their parents or legal guardians. Data were collected between March 2014 and December 2015.

Outcomes and exposures

Behavioral and anthropometric variables

All variables were assessed by trained researchers. The behavioral variables (PA, sleep time, and screen time) were assessed using a self-report questionnaire adapted from Barros and Nahas (2003) [29]. PA was assessed by the following question: “Do you usually engage in sports/physical activity?” (yes or no). It should be noted that the practice of PA was limited to the leisure domain, so that physical activities performed during physical education classes, commuting, and other domains were not considered. Based on this information, the students were divided into two groups: inactive, a group that didn’t do any leisure-time PA, and any PA, a group that reported doing some PA, regardless of how long it lasted.

Sleep duration was assessed by the following questions: ‘’What time do you go to bed during the week and on weekends?‘’ and ‘’What time do you wake up during the week and on weekends?‘’ We then calculated the weighted average sleep duration per day, which was derived from the total hours of sleep per week [30]. Screen time was assessed with the following question: ‘’How much time, in minutes, do you spend per day watching television, using a computer, or playing a video game?‘’ The total time spent on these three devices was summed and then classified as reduced screen time (less than 2 h per day) or increased screen time (2 h or more per day) [31].

Weight and height were measured using an anthropometric scale with attached stadiometer (Filizola^®) to calculate BMI using the weight/height2 formula defined by the World Health Organization [32].

Immunologic variables

Immunologic and genetic indicators were obtained by collecting 10 mL of blood from the brachial vein, of which approximately 5 mL was transferred to a Vacutainer tube containing EDTA anticoagulant. For immunologic analyses, leukocytes, neutrophils, and lymphocytes were assessed using an automated method (XS800i, Sysmex) with whole blood samples containing EDTA anticoagulant. The systemic immune inflammation index (SII) was derived from the neutrophil, platelet, and lymphocyte counts and calculated using the formula: SII = (neutrophils x platelets)/lymphocytes. This index serves as a direct and simplified marker to assess the inflammatory and immune status of the body [33].

Telomere length assessment

For the genetic evaluation, whole blood samples were collected and used to analyze telomere length. DNA was extracted using the salting out technique. The methodology used to analyze telomeres follows the protocol described by Cawthon in 2002 [34]. TL was assessed by qPCR using the StepOnePlus instrument (Applied Biosystems, Foster City, CA, USA). Primers, genomic DNA and Sybr Green PCR were added to each well of a 96-well plate for a total final volume of 10 µL. The normalized telomere to S ratio (T/S) was calculated using the formula: [2 CT (telomeres) / 2 CT (single copy gene)] = 2 -ΔCT. For standardization between plates, a standard curve was generated using a DNA reference sample with concentrations of 64 ng, 32 ng, 16 ng, 8 ng, 4 ng, 2 ng, 1 ng, 0.5 ng, and 0.25 ng. This curve was included as a standard to control for interplate variability, with an R² value > 0.98 being acceptable. To ensure quality and consistency, each sample was analyzed in triplicate and results were scored based on the agreement between the values obtained.

Adjustment variables

Sociodemographic variables (age, sex, and school area - urban/rural) were self-reported by the participants. Self-reported skin color was classified as white or non-white (including black, brown, yellow, and indigenous). The socioeconomic factor was assessed using the criteria of the Brazilian Association of Research Companies (ABEP), classifying economic classes as high (A1, A2, B1 and B2), middle (C1 and C2) and low (D and E). Sexual maturation was assessed using Tanner’s parameters, based on breast development in girls, testicular development in boys and the amount of pubic hair in both to determine the stages of development [35].

To estimate maximum oxygen consumption (VO2peak), an indirect 6-minute run and walk test was carried out, following the protocols of the Brazil Sports Project [36]. The calculation used to obtain VO2peak was as follows: VO2peak = 41.946 + 0.022 (distance in meters) − 0.875 (BMI) + 2.107 (sex), with a value of 0 for women and 1 for men [37].

Statistical analysis

Initially, all variables were described using mean and standard deviation for numerical variables and absolute and relative frequencies for categorical variables. For categorical variables, the chi-squared test was used to compare frequencies between the Inactive and Any PA groups. An independent t-test was used to compare numerical variables, and Levene’s test was used to assess the equality of variances between groups.

The associations between screen time, sleep time, BMI, and immunological factors and TL were tested using multiple linear regression models, with the sample divided according to schoolchildren’s leisure-time PA practices. Four different models were analyzed because the immunological variables were collinear. The models included: (1) screen time, sleep duration and BMI; (2) leukocytes; (3) neutrophils and lymphocytes; (4) SII. Models were adjusted for age, sex, skin color, sexual maturity, socioeconomic status, and VO2peak. Models 2, 3 and 4 were also adjusted for the variables in model 1. All analyses were performed in SPSS version 23.0 (IBM Corp) and alpha < 0.05 was assumed.

Results

Table 1 presents the sample characteristics and the comparison of variables between the total PA, inactive and any PA groups. Of the 476 children and adolescents evaluated, 56.3% reported not performing any PA during leisure time. The total sample was predominantly female (57.6%) with a mean age of 12.9 ± 2.7 years and a mean TL of 1.08 ± 0.45. The PA groups did not differ in any of the variables assessed.

Table 1.

Sample characteristics and comparison of variables between the inactive and all PA groups

	All	Inactive	Any PA
	n (%)	n (%)	n (%)	p
	476 (100)	268 (56.3)	208 (43.7)
Sex				0.122a
Male	202 (42.4)	107 (39.9)	95 (45.7)
Female	274 (57.6)	161 (60.1)	103 (54.3)
Skin Color				0.309a
White	362 (76.1)	201 (75.0)	161 (77.4)
Non-white	114 (23.9)	67 (25.0)	47 (22.6)
Puberal Status				0.388a
Prepuberal	78 (16.4)	50 (18.7)	28 (13.5)
Initial development	101 (21.2)	55 (20.5)	46 (22.1)
Continuous maturation	249 (52.3)	134 (50.0)	115 (55.3)
Maturated	48 (10.1)	29 (10.8)	19 (9.1)
Socioeconomic status				0.509a
Low/ Middle	232 (48.7)	131 (48.9)	101 (48.6)
High	244 (51.3)	137 (51.1)	107 (51.4)
Sleep duration				0.144a
Adequate	242 (50.8)	130 (48.5)	112 (53.8)
Inadequate	234 (49.2)	138 (51.5)	96 (46.2)
Screen time				0.538a
Adequate	126 (26.5)	71 (26.5)	55 (26.4)
High	350 (73.5)	197 (73.5)	153 (73.6)
	mean ± sd	mean ± sd	mean ± sd
Telomere length (kb)	1.08 ± 0.45	1.06 ± 0.41	1.10 ± 0.51	0.306 b
Age (years)	12.9 ± 2.7	12.7 ± 2.8	13.1 ± 2.6	0.110b
BMI (kg/m²)	20.8 ± 4.5	20.6 ± 4.6	21.1 ± 4.3	0.241b
VO2pico (ml.kg-1.min-1)	44.1 ± 6.8	44.0 ± 7.1	44.3 ± 6.6	0.633b
Leukocytes (µL)	7183.6 ± 2083.1	7102.8 ± 1997.2	7287.8 ± 2189.4	0.337b
Lymphocytes (µL)	2385.9 ± 628.7	2349.3 ± 624.7	2433.1 ± 632.3	0.149b
Neutrophils (µL)	3983.5 ± 2081.1	3885.6 ± 1707.3	4109.6 ± 2479.9	0.245b
Platelets (µL)	261220.5 ± 61010.0	261432.8 ± 60169.1	260947.1 ± 62221.4	0.931b
SII	462.6 ± 303.6	456.5 ± 258.7	470.5 ± 353.7	0.617b

^aChi-square test; ^bIndependent T-test for homogeneous variances. PA: physical activity; BMI: body mass index; VO2 max: Maximal oxygen uptake; SII: systemic immune-inflammation index; sd: standard deviation

Table 2 explores the associations between screen time, sleep, BMI and immunological factors with telomere length, according to leisure-time PA practice. In the Inactive Group, there was a significant inverse association between BMI and TL (β: -0.239; 95% CI: -0.468; -0.010), indicating that among students who did not practice leisure-time PA, a higher BMI was associated with shorter telomeres. In addition, there was a significant direct relationship between leukocytes (β: 0.151; 95% CI: 0.029; 0.278) and neutrophils (β: 0.131; 95% CI: 0.008; 0.254) with telomere length, indicating that higher levels of these immune system cells were associated with longer telomeres in the inactive group. SII also showed a direct significant association with TL in this group (β: 0.139; 95% CI: 0.0156; 0.263). No association was observed in the Any PA group.

Table 2.

Association of screen time and sleep, BMI and immunological factors with TL according to PA (n = 476)

	Inactives		Any PA
	β (IC 95%)	p	β (IC 95%)	p
MODEL 1
Sleep duration		0.053		0.936
Adequate	0		0
Inadequate	0.121 (-0.001; 0.243)		-0.006 (-0.148; 0.136)
Screen time		0.282		0.821
Adequate	0		0
High	0.067 (-0.055; 0.188)		0.017 (-0.129; 0.162)
BMI	-0.239 (-0.468; -0.010)	0.041	-0.073 (-0.342; 0.195)	0.591
MODEL 2
Leukocytes	0.151 (0.029; 0.278)	0.015	-0.064 (-0.208; 0.081)	0.384
MODEL 3
Lymphocytes	0.030 (-0.100; 0.161)	0.645	0.073 (-0.078; 0.223)	0.341
Neutrophils	0.131 (0.008; 0.254)	0.039	-0.101 (-0.246; 0.043)	0.168
MODEL 4
SII	0.139 (0.0156; 0.263)	0.028	-0.065 (-0.207; 0.077)	0.370

0: Reference category; BMI: body mass index; Index: systemic immune-inflammation index; 95%CI: 95% confidence interval. Model 1 adjusted for: age, sex, skin color, sexual maturation, socioeconomic status, VO2peak. Model 2, 3 and 4D adjusted for: age, sex, skin color, sexual maturation, socioeconomic status, VO2peak, screen time, physical activity, sleep duration and BMI

Discussion

The main findings of this study indicate that physical inactivity influences the relationship between BMI and immunological factors and TL, showing that among schoolchildren who do not practice leisure-time PA, the presence of a high BMI is associated with shorter telomeres. Our results are in line with the findings of observational/cross-sectional research conducted in the adult population [38, 39]. This observation underscores the importance of regular physical activity not only for body composition, but also for cellular health, as indicated by telomere length.

Studies exploring the relationship between BMI and TL have been extensively investigated to understand the influence of lifestyle factors associated with telomere shortening in the adult population [40]. Research in children and adolescents has shown that improvements in physical activity levels, particularly during the lifestyle intervention phase, play a significant role in preserving telomere length in individuals with high BMI [26, 27].

Our study identified a direct association between leukocyte, neutrophil and SII levels and telomere length in this group, indicating that higher concentrations of these immune system cells are associated with longer telomeres in the inactive. At first glance, this finding seems to contradict the literature, which has shown that a greater presence of immune cells in our bodies is directly associated with higher levels of inflammation [41]. Inflammation triggers oxidative stress, and regular physical activity has been associated with lower levels of oxidative stress and inflammation, thus playing a preventive role in chronic diseases [16, 38].

Studies show that oxidative stress and inflammation have the ability to accelerate telomere attrition [42, 43], making them potential mediators in the link between telomere activity and length. However, the complexity of the immune system with its various cells and molecules suggests that there may be different responses and specific functions for different types of immune cells [44]. It is important to note that lymphocytes, for example, have shown a positive association with telomere length [45]. This association can be explained by the fact that lymphocytes express the enzyme telomerase, albeit in small quantities, which plays a fundamental role in telomere reconstruction [46]. Therefore, the presence and activity of this enzyme may positively influence telomere length, contrary to the general expectation associated with inflammation and telomere shortening. These findings suggest that the role of the immune system in modulating telomere length is complex and bidirectional, highlighting the importance of understanding the nuances of specific cellular interactions [42].

The lack of evidence in the child and adolescent population on the relationship between immunological factors and TL, and the role of physical activity in this relationship, limits understanding of the associations observed in our study. It is possible that the interaction of physical activity with time in other behaviors, such as screen time and sleep, could be a pathway to other associations. For example, a previous study showed that replacing screen time with time spent in physical activity or sleep may be beneficial for inflammation levels [47].

To date, research on the interactions between lifestyle elements such as sedentary behavior, physical activity, and screen time in child and adolescent populations is limited and inconsistent. In the literature, we found studies that identified associations, while others did not observe significant relationships between physical activity and telomere length [32, 33].

Telomere length appears to be linked to a healthy life and has an inverse relationship with the risk of diseases associated with aging in several populations [48]. Several mechanisms may explain the relationship between PA and TL, such as changes in telomerase activity, oxidative stress, inflammation and decreased content of satellite cells in skeletal muscle [48]. Reducing oxidative stress and inflammation can induce protective effects on telomeric DNA by reducing DNA chain breaks [49].

The literature still does not present a consensus regarding which exercise intensity has the greatest influence on telomere dynamics. But evidence suggests that the most beneficial level of exercise intensity in this context is moderate intensity exercise. Furthermore, there is an important gap in relation to other parameters related to the assessment of PA, such as the assessment of levels, intensity, duration and frequency, which limit the understanding of this relationship [50]. Future studies should seek to include detailed information regarding participants’ PA, as well as detail the characteristics of the sample studied, including level of physical fitness. This would help to understand more deeply the relationship between PA and TL, allowing us to know which types, intensity and duration of activities would be most beneficial in protecting TL.

Our findings suggest a complex interaction between behavioral factors, BMI, and immune response that may directly influence telomere integrity in children and adolescents who do not engage in leisure-time PA. This deeper understanding of the associations between these variables contributes significantly to the discussion of the impact of lifestyle on telomere length and may provide an important basis for interventions aimed at this population.

Although the results presented enrich academic understanding and provide a solid basis for future research, it is prudent to point out some of the limitations inherent in this study. First, the use of a cross-sectional design makes it impossible to establish causal relationships between the variables studied. In addition, despite the inclusion of relevant confounding variables in the analysis, we cannot exclude the possibility that other unmeasured factors may influence the associations identified, such as eating habits. It should be noted that the data on lifestyle factors were obtained by self-report using an adapted questionnaire. Especially in relation to the PA variable, which plays an important role in our study. We cannot rule out the possibility of memory and social desirability bias inherent in self-reported measures. Furthermore, our measure of PA did not consider the type, duration, or intensity of PA.

However, our results highlight that any PA, even without considering these parameters, can be beneficial to health and TL preservation. This evidence can provide relevant insights to promote programs and policies to promote a healthy lifestyle, reinforcing the importance of encouraging the practice of PA in children and adolescents. This study is a pioneer in exploring the relationship between telomeres, BMI and immunological factors in schoolchildren and adolescents. The results show that the likely influence of BMI and immunological factors on telomere length can be significantly influenced by regular physical activity. This finding not only reinforces the importance of physical activity for cellular health in this age group, but also points to nuances in the complex relationships between physical status, immune response and telomere integrity.

Conclusion

The associations between telomeres, BMI, and immunologic factors were observed only in schoolchildren who did not engage in PA. These results suggest that the association between BMI and immunological factors and telomere length may be influenced by physical activity. This relationship underscores the importance of regular exercise in maintaining telomere integrity and suggests a beneficial effect of PA in this specific context.

Abbreviations

ABEP

Brazilian Research Companies

BMI

Body mass index

DNA

Deoxyribonucleic acid

PA

Physical Activity

SII

Systemic Immune Inflammation Index

SPSS

Statistical Package for Social Sciences

TL

Telomere length

VO2peak

Maximum oxygen consumption

Author contributions

NQF: Conceptualization; Data Curation; Formal Analysis; Investigation; Methodology; Writing – Original Draft Preparation. LT: Data Curation; Formal Analysis; Investigation; Methodology; Writing – Original Draft Preparation. APS: Investigation; Methodology; Writing – Original Draft Preparation. PFT: Investigation; Methodology. HFD: Investigation. ARMV: Investigation; Methodology; Supervision. CPR: Funding Acquisition; Methodology; Project Administration; Supervision.

Funding

Higher Education Personnel Improvement Coordination—Brazil (CAPES - Finance Code 001.). Brazilian Agencies Foundation for Research Support of Rio Grande do Sul (FAPERGS).

Data availability

The database in the present study is not publicly available since its information could compromise the subject’s privacy and consent regarding the research. However, upon request, the corresponding author can provide the data.

Declarations

Ethics approval and consent to participate

The present study was conducted meeting Resolution 466/2012 of the National Health Council of Brazil and approved by the University of Santa Cruz do Sul ethics committee (Opinion No. 714.216). Students’ participation in the study was approved by an informed consent form signed by their parents or legal guardians.

Consent for publication

Not applicable.

Competing interests

The authors declare no competing interests.