Perinatal Factors and its Association with Cardiometabolic Profile in Schoolchildren

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Therefore, this study aimed to verify the associations between perinatal variables and cardiometabolic profile in school-aged children and adolescents. METHODS Retrospective study that used cross-sectional data from a database of a larger study named “Schoolchildren’s health”. The study was carried out using a sample comprising children and adolescents, ranging from 6 to 17 years old from both genders. All participants were enrolled in private and public schools in the city of Santa Cruz do Sul, Brazil. A self-administered questionnaire was applied to children and parents, then anthropometric measurements of body mass index (BMI), body fat percentage (BF%) and waist circumference (WC) were obtained followed by a cardiorespiratory fitness (CRF) test. ​The association of perinatal variables with BMI, WC, BF%, and CRF was tested by multiple linear regression, using the “Enter” input method, being the unstandardized coefficient (B) and 95% confidence intervals of the associations were estimated. Data were analyzed with Statistical Package for the Social Sciences software, version 23.0 (IBM, Armonk, NY, USA). Statistical significance was established as p < 0.05. RESULTS A total of 1.431 children and adolescents were evaluated, with 803 (56.1%) of them being females and white subjects (83%) with mean age of 11.48 ± 2.75 years. Associations between perinatal factors and anthropometric measurements were found birth weight (BW) and BMI (B:1.13; 95%CI:0.74;1.51), BW and WC (B:2.20; 95%CI:1.37;3.04), BW and BF% (B:1.84; 95%CI:0.83;2.84). Gestational age also had associations with BMI (B:1.00; 95%CI:0.25;1.74); WC (B:1.88; 95%CI:0.25;3.50) and CRF (B:-168.91; 95%CI:-299.53;-38.29). Complications during pregnancy and BMI (B:0.48; 95%CI:0.02;0.93) and cesarean birth and BMI (B:0.43; 95%CI:0.01;0.86). CONCLUSIONS Associations exist between perinatal factors and future cardiometabolic profile. It is imperative to establish and reinforce efforts geared towards enhancing the health literacy of both adolescent boys and girls, along with pregnant women. Child health Metabolic profile Heart disease risk factors BACKGROUND Developmental origins of health and disease hypothesis propose a connection between the periconceptual, fetal, and early infant stages of life and the sustained development of metabolic disorders [ 1 , 2 ]. The risk of developing a disease at any point in one's life is shaped by the interplay between genetics and the cumulative impact of lifestyle and environmental exposures across the lifespan [ 3 ]. Cardiorespiratory fitness (CRF) evaluation serves as a significant indicator of children's health, thus promoting the evaluation of CRF is advisable during childhood and adolescence since it is associated with future cardiometabolic risk (CMR) [ 4 – 7 ]. Furthermore, it was shown that other measures like adiposity, including body mass index (BMI), waist circumference (WC), and body fat percentage (BF%), exert a more significant influence on CMR. Hence, those anthropometric measurements are important noninvasive and quantitative assessments of the human body and in pediatrics, they serve to assess a child's overall health, nutritional status, and their growth and developmental trajectory. These measurements serve as the primary benchmarks that clinicians use to gauge a child's health and well-being [ 8 ]. Anthropometric measurements are also a tool to predict CMR [ 9 ]. Perinatal factors such as maternal health history and socioeconomic status, which are amenable to modification, play role in determining offspring health [ 10 ]. Furthermore, the absence of complications during pregnancy and breastfeeding period imparts notable advantages to the child's health status [ 11 , 12 ]. Gestational age, a parameter encompassing a baby's size at birth, has been acknowledged as having lasting implications for health, including alterations in childhood growth and the risk of becoming overweight [ 13 ]. Generally those findings regarding associations between perinatal variables and future metabolic risk are usually found during adulthood. Therefore, this study aimed to verify the associations between perinatal variables and cardiometabolic profile in school-aged children and adolescents. METHODS Study design, sample, and ethics Cross-sectional study that used retrospective data from a database of a larger study named “Schoolchildren’s Health Study – phase V”. The study was carried out using a sample comprising children and adolescents, represented by both genders. These participants were drawn from both private and public schools located in the municipality of Santa Cruz do Sul, southern Brazil. The inclusion criteria encompassed children within the age range of 6 to 17 years old, enrolled at a school, those with biological mother information and either biological or legal fathers with all their pertinent data available in the database. Students with inconsistent data were excluded from the sample. This study received approval from the Committee of Ethics in Research with Human Subjects of the University of Santa Cruz do Sul with the approval number 839178. Every parent or legal guardian was comprehensively informed about the study procedures and expressed their consent by signing an informed consent form, thus authorizing their child's participation in the study. Data collection A self-administered questionnaire which included inquiries related to sociodemographic factors as age, skin color: white and non-white categories which was composed by black, pardo, indigenous and yellow/Asian according to the Brazilian census [ 14 ]. Sex and socioeconomic level according to Brazil's economic classification criteria where classes A and B are high class, C is middle class and D and E are lower class was answered by the students [ 15 ]. Perinatal factors and personal data were collected through a self-reporting questionnaire answered by the mother. The questions were composed by mother’s age at pregnancy, type of delivery: vaginal birth, cesarean or forceps delivery, gestational age classified as: term baby born between 38 to < 41 weeks, post-term < 42 weeks, pre-term < 37 weeks and extremely premature < 28 weeks [ 16 ]. The child's birth weight was analyzed as a continuous variable and the questions “Did you have prenatal care?” and “Did you have complications during pregnancy?” were analyzed dichotomously (yes/no). Measurements To calculate the body mass index (BMI), Weight and height measurements were taken using an anthropometric scale with an attached stadiometer (Filizola®). WC was measured using a 1 mm inelastic tape, focusing on the area between the ribs and the iliac crest, where the trunk is narrowest. To assess BF%, tricipital and subscapular skinfolds were measured using a Lange® caliper (MultiMed, Skinfold Caliper, USA). BF% was calculated using the Heyward and Stolarczyk equation [ 17 ]. Cardiorespiratory fitness (CRF) was assessed using the Six-Minute Run/Walk Test, which was conducted on a track field, following the established protocols by PROESP-BR [ 18 ]. The variables BMI, BF%, WC, CRF were analyzed as a continuous scale. Statistical analysis We described the data using mean and standard deviation for numerical variables, and absolute and relative frequencies for categorical variables. ​The association of perinatal variables with BMI, WC, BF%, and CRF was tested by multiple linear regression, using the “Enter” input method, being the unstandardized coefficient (B) and 95% confidence intervals of the associations were estimated. The perinatal factors included in the model were the mother’s age at gestation, prenatal care, complications during the gestation period, gestational age, exclusively breastfeeding, and presence of cardiac and respiratory disease until 6 months of age. ​ Data were analyzed with Statistical Package for the Social Sciences software, version 23.0 (IBM, Armonk, NY, USA). Statistical significance was established as p < 0.05. RESULTS Table 1 displays participants’ descriptive characteristics. A total of 1.431 children and adolescents were evaluated, with 803 (56.1%) of them being females and white subjects (83%) with mean age of 11.48 ± 2.75 years. The table provides information on the sample's socioeconomic status in which more than half of the sample was classified as middle income, classes B1-B2/C1-C2 (56.7%). The mean birth weight, anthropometric measurements and cardiorespiratory fitness were also presented. Table 1 Samples’ characteristics (n = 1.431). n (%) Mean (SD) Sex Male 628 (43.90) Female 803 (56.10) Skin color White 1.188 (83.00) Non-white 243 (17.00) Age (years) 11.48 ± 2.75 Socioeconomic level A 196 (13.70) B1-B2/C1-C2 839 (58.60) D-E 396 (27.70) Birth weight (kg) 3.28 ± 0.57 BMI (kg/m²) 20.32 ± 4.12 WC (cm) 66.03 ± 9.41 BF% 25.53 ± 10.40 CRF (m) 879.75 ± 186.85 Note: n: absolute frequency; %: relative frequency; SD: standard deviation; BMI: body mass index; WC: waist circumference; BF%: body fat percentage; CRF: cardiorespiratory fitness. Regarding perinatal characteristics, we observed a mean age of 26.91 years old for mothers during pregnancy, with the majority having undergone prenatal care 1.696 (79.5%) and did not have complications during gestation 1.516 (71.1%) (Table 1 ). Most of the babies were born full term 1.812 (85%). In relation to delivery type, vaginal births accounted for 1.072 (51.7%), while cesarean births were 1.001 (47%), with nearly equal numbers (Table 2 ). Table 2 Perinatal characteristics (n = 1.431). n (%) Mean (SD) Mother’s age at gestation (years) 26.91 ± 6.73 Prenatal care Yes 1.399 (97.80) No 32 (2.20) Gestation’s complications Yes 412 (28.80) No 1.019 (71.20) Gestational age Extreme prematurity 6 (0.40) Preterm 132 (9.20) Term 1.274 (89.00) Post-term 19 (1.30) Type of birth Vaginal birth 674 (47.10) Forceps 38 (2.70) Cesarean birth 719 (50.20) Note: n: absolute frequency; %: relative frequency; SD: standard deviation. Table 3 shows the results regarding associations between perinatal factors, anthropometric measurements, and cardiorespiratory fitness. We highlight the significant findings related to BW and BMI (B:1.13; 95%CI:0.74;1.51), gestational age and BMI in which babies born preterm had higher BMI than those born term and post-term (B:1.00; 95%CI: 0.25; 1.74). Moreover, we found a direct and positive association involving cesarean birth and BMI (B:0.43; 95%CI:0.01;0.86) which means that babies born through this method had higher BMI in the future compared with those who were born by vaginal birth or forceps delivery in which we did not find this association. The presence of complications during pregnancy was associated with current children’s BMI (B:.0.48; 95%CI:0.02;0.93) which showed us that the ones who were born with any complication had higher BMI. We also observed relations between BW and BF% (B:1.84; 95%CI: 0.83;2.84) and pregnancy complications and BF% (B:1.19; 95%CI:-0.00;2.38) showing that the BW and having complications during gestation are positively associated with BF% during school-age. BW and WC had a positive association, thus showing a growth in the WC measure in these children future (B:2.20; 95%CI:1.37;3.04). Gestational age and WC were also related, the association was found in pre-term babies (B:1.88; 95%CI:0.25;3.50). Furthermore, we emphasize the noteworthy discoveries pertaining to the positive relationship between having complications during gestational period and CRF (B:-31.76; 95%CI:-50.94;12.58) and that babies born extremely premature had lower CRF at school-age showed by an inverse association (B:-168.91; 95%CI:-299.53;-38.29). Table 3: Associations of perinatal factors and anthropometrics measurements and cardiorespiratory fitness. BMI B (95% CI) p BF% B (95% CI) p WC B (95% CI) p CRF B (95% CI) p Mother’s age at gestation -0.15 (-0.45;0.15) 0.33 0.00 (-0.07;0.08) 0.88 0.00 (0.03;-0.06) 0.94 0.76 (-0.51;2.04) 0.23 Did make prenatal care Yes REF REF REF REF No -0.18 (-1.52;1.14) 0.78 0.47 (-3.04;3.98) 0.79 -1.59 (-4.49;1.31) 0.28 -31.74 (-87.99;24.50) 0.26 Did have complications during gestation No REF REF REF REF Yes 0.48 (0.02;0.93) 0.03* 1.19 (-0.00;2.38) 0.05* 0.65 (-0.34;1.64) 0.19 -31.76 (-50.94;-12.58) <0.001* Gestational age Term REF REF REF REF Post-term -0.84 (-2.58;0.88) 0.33 -2.10 (-6.66;2.45) 0.36 -1.56 (-5.33;2.21) 0.41 31.11 (-41.89;104.11) 0.40 Preterm 21.00 (0.25;1.74) <0.001* 1.03 (-0.92;3.00) 0.20 1.88 (0.25;3.50) 0.02* 27.49 (-3.94;58.92) 0.08 Extreme prematurity -1.28 (-4.39;1.81) 0.41 -2.69 (-10.84;5.46) 0.51 -2.82 (-9.57;3.93) 0.41 -168.91 (-299.53; -38.29) 0.01* Type of birth Vaginal birth REF REF REF REF Forceps delivery -0.85 (-2.10; 0.39) 0.17 -2.15 (-5.44;1.12) 0.19 -1.54 (-4.26;1.17) 0.26 23.63 (-28.99;76.25) 0.37 Cesarean birth 0.43 (0.01;0.86) 0.04* 1.06 (-0.05;2.17) 0.06 1.06 (0.13;1.98) 0.24 -3.89 (-21.78;13.99) 0.67 Birth weight 1.13 (0.74;1.51) <0.001* 1.84 (0.83;2.84) <0.001* 2.20 (1.37;3.04) <0.001* -12.90 (-29.03; 3.22) 0.11 B: Unstandardized coefficient. 95% CI: Confidence interval. Model adjusted for schoolchildren’s age, sex, skin color, socioeconomic level, birth weight, mother’s age, prenatal care, complications during gestation, gestational age and type of birth. BMI: Body mass index (kg/m 2 ); BF%: Body fat percentage; WC: Waist circumference (cm); CRF: Cardiorespiratory fitness (m). DISCUSSION The aim of our study was to verify the associations between perinatal variables and cardiometabolic profile in school-aged children and adolescents. We found out some interesting results regarding birth weight (BW) and anthropometric measurements, those associations showed a positive relation between BW and BMI, BF% and WC during school-age. Cesarean also showed positive association with BMI in our sample, meaning that children born through this surgical method of birth delivery could have higher BMI than those born via vaginal birth or forceps delivery. Additionally, we discovered significant findings related to a pregnancy with no complications, gestational age and school-children’s cardiorespiratory fitness (CRF) where babies born from a healthy pregnancy had better CRF during childhood and adolescence and babies born extremely premature had a negative impact on its current CRF. Those variables assessed in our research are some of early life health predictors in children and adolescents [ 19 ]. The higher likelihood of developing obesity in adulthood among children with greater BW, indicates that the prenatal period represents a pivotal phase for shaping future body adiposity [ 20 ]. In our sample we found out that BW was positively associated with an increase in BMI, BF% and WC measurements that could represent risk for overweight and obesity in this population, which we could hypothesized that possibly indicate a higher cardiometabolic risk (CMR). The links between an elevated risk of cardiovascular and metabolic disorders transcend a spectrum of birth weights and postnatal growth patterns [ 21 ]. Gestational age, for example, can also play a role in CMR. Studies have demonstrated that preterm labor heightens the likelihood of hypertension, elevated insulin levels, and even early onset heart failure in offspring [ 22 ]. These individuals are more prone to having augmented left ventricular mass, irregular ventricular function, systemic arterial stiffness, and higher mean blood pressure [ 22 ]. These factors may predispose them to an elevated risk of cardiovascular disease (CVD) later in life [ 23 ]. In our study, preterm babies showed association with two indicators of increased cardiovascular risk: BMI and CRF. Those born extremely premature had worst CRF during school-age we can, therefore, hypothesized that those children had poor cardiovascular and/or respiratory system development, consequently, elevating those kids’ risk of having a CVD in the future since CRF is a determinant of morbidity and mortality [ 24 – 26 ]. It is also noteworthy that elevated BMI could also contribute to a poorer CRF [ 27 ]. Complications during pregnancy can have long-lasting effects on the health of the child, several risk factors like the obstetric complications as preterm labor and their long-term maternal effects serve as indicators for an elevated risk of acute cardiovascular complications during delivery and pose a long-term risk for CVD for both the mother and the offspring [ 28 , 29 ]. These complications can impact various metabolic traits in the offspring, its fetal growth and development [ 30 ]. In our research, the offspring of mothers without complications during pregnancy exhibited higher levels of CRF, suggesting better physical development and overall cardiorespiratory health. It is evidenced that that there is a significant contrast in the physiological stress experienced by the fetus during vaginal delivery compared to cesarean delivery (CD). The stress encountered in natural delivery plays a crucial role in triggering the release of stress-induced hormones in the fetus, subsequently influencing physiological changes, particularly in terms of immunological and metabolic development that could be a protective factor against CMR [ 22 ]. Despite the fact that there are some research associating CD and propensity to different kinds of infections, neurological and respiratory morbidities, the evidence regarding this type of delivery and CMR in school-aged children and adolescents is still little. There is a suggested elevated risk for young individuals born via CD where signs of overweight have been noted from infancy through young adulthood as demonstrated by Horta et al. same fact as seen is our study in which those kids born via this surgical method had higher body mass index during school-age [ 31 ]. It is well known that certain chronic diseases have their roots in early life, and these conditions can be influenced by exposure to various risk factors during the early stages of development and lifespan, these factors have a role in increasing health risks in adulthood [ 32 ]. By appraising perinatal biomarkers and obstetrical history linked to the presence of traditional cardiometabolic risk indicators an early assessment risk is facilitated. The relation between perinatal conditions and the early emergence of cardiometabolic risk biomarkers may be contingent on epigenetic programming [ 33 ]. Therefore, it is relevant to investigate those perinatal variables in order to identify factors that can be changed during children’s development window through early prevention and by changing lifestyle habits. Despite the valuable findings from this study, it is necessary to recognize its limitations. The self-reported questionnaire used to collect previous data could lead to bias, as the mothers and the children could have some trouble to recall information from the past. Furthermore, there are other factors that could have effects on cardiometabolic profile and increase cardiometabolic risk in this population that were not evaluated in this study, i.e lifestyle. The small sample size in the extremely born and post-term babies’ categories could be limitation. Nonetheless, it is important to recognize that this study, conducted in a sample from southern Brazil, stands out as a significant contribution to a low to middle-income country, such as Brazil. This research adopted an innovative approach by using a school-based sample that is representative of the population of its municipality. It is worth noting that, to the best of our knowledge, there have been no studies that have shown similar results in this specific population. This study not only fills a gap in the existing literature but also significantly advances our understanding of the impact that perinatal variables can have on outcomes during childhood and adolescence, providing valuable insights for future research and interventions. CONCLUSION In conclusion, our study has investigated the relationship between perinatal variables and its association with schoolchildren’s cardiometabolic profile, shedding light on the lasting impact of early life factors on future physical composition. The findings presented herein: associations between perinatal variables as birth weight, gestational age, complications during pregnancy and type of birth with adiposity measurements as body mass index, body fat percentage and waist circumference and cardiorespiratory fitness provide compelling evidence of a significant association between them during childhood and adolescence. These findings carry implications for both clinical practice and public health interventions. Recognizing the enduring influence of perinatal history and schoolchildren’s anthropometry can inform strategies for early identification of individuals at risk for certain health outcomes. Moreover, our study underscores the importance of a life-course perspective in understanding the complexities of human growth and development. Abbreviations CRF Cardiorespiratory fitness CMR Cardiometabolic risk BMI Body mass index WC Waist circumference BF% Body fat percentage CMDs Cardiometabolic diseases Declarations Ethics approval and consent to participate This study was approved by the Ethics Committee in Research with Human Subjects of the University of Santa Cruz do Sul with the approval number 839178. The study adhered to the principles in the Helsinki Declaration. Participants were provided with both verbal and written information regarding their voluntary involvement and the option to withdraw at any time. Data confidentiality was guaranteed, and participants granted written informed consent for their involvement. Furthermore, participants were reassured that their information would remain confidential. Consent for publication Not applicable. Availability of data and materials The corresponding author can provide the data utilized and/or analyzed in the present study upon reasonable request. Competing interests The authors declare that they have no competing interests. Funding This study was financed in part by the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior - Brasil (CAPES) - Finance Code 001. Authors' contributions KMK contributed with the conceptualization, methodology, analysis, data interpretation, writing and revision of the manuscript. LT was involved with methodology, analysis, data interpretation and revision of the manuscript. KAP revised and made substantial contributions to the manuscript. DNP contributed with supervision, conceptualization and revision. CR was responsible for funding acquisition, supervision, conceptualization and revision of the manuscript. All authors read and approved the final manuscript. Acknowledgements We thank all the support of the University of Santa Cruz do Sul—UNISC and Higher Education Personnel Improvement Coordination—Brazil (CAPES), as well as the collaboration of the schools who participated in this study and to our research group Health Research Laboratory - Laboratório de Pesquisa em Saúde (LAPES) and thank Michigan State University for the contributions and collaboration in our project “Schoolchildren’s health”. References Lacagnina S. The Developmental Origins of Health and Disease (DOHaD). Am J Lifestyle Med. 2019;14(1):47-50. http://dx.doi.org/10.1177/1559827619879694. Lecoutre S, Maqdasy S, Breton C. Maternal obesity as a risk factor for developing diabetes in offspring: An epigenetic point of view. World J Diabetes. 2021;12:366. http://dx.doi.org/10.4239/wjd.v12.i4.366. Cechinel LR, Batabyal RA, Freishtat RJ, Zohn IE. Parental obesity-induced changes in developmental programming. 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BMC Med. 2023; 21:23. https://doi.org/10.1186/s12916-022-02711-8. Horta BL, Gigante DP, Lima RC, Barros FC, Victora CG. Birth by caesarean section and prevalence of risk factors for non-communicable diseases in young adults: a birth cohort study. PLoS ONE. 2013; 8(9): e74301. Pehkonen J, Viinikainen J, Kari JT, Böckerman P, Lehtimäki T, Viikari J et al. Birth weight, adult weight, and cardiovascular biomarkers: Evidence from the Cardiovascular Young Finns Study. Prev Med. 2022;154:106894. https://doi.org/10.1016/j.ypmed.2021.106894. Oliveira WR, Rigo CP, Ferreira ARO, Ribeiro MVG, Perres MNC, Palma-Rigo K. Precocious evaluation of cardiovascular risk and its correlation with perinatal condition. An Acad Bras Ciênc. 2023;95(1):e20201702. https://doi.org/10.1590/0001-3765202320201702. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4438298","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":307879155,"identity":"16ae0cfe-e9fd-43b5-8ee8-ef11bf92b71e","order_by":0,"name":"Kamila Mohammad Kamal Mansour","email":"","orcid":"","institution":"University of Santa Cruz do Sul","correspondingAuthor":false,"prefix":"","firstName":"Kamila","middleName":"Mohammad Kamal","lastName":"Mansour","suffix":""},{"id":307879156,"identity":"ee887475-5c11-4b08-9ec5-82ad33499a13","order_by":1,"name":"Luciana Tornquist","email":"","orcid":"","institution":"University of Santa Cruz do Sul","correspondingAuthor":false,"prefix":"","firstName":"Luciana","middleName":"","lastName":"Tornquist","suffix":""},{"id":307879158,"identity":"c5cf67d5-4c66-4278-9910-5310d2a69bba","order_by":2,"name":"Karin Allor Pfeiffer","email":"","orcid":"","institution":"Michigan State University","correspondingAuthor":false,"prefix":"","firstName":"Karin","middleName":"Allor","lastName":"Pfeiffer","suffix":""},{"id":307879159,"identity":"c4dd2fc2-0258-4ea0-8778-9ac86093a63e","order_by":3,"name":"Dulciane Nunes Paiva","email":"","orcid":"","institution":"University of Santa Cruz do Sul","correspondingAuthor":false,"prefix":"","firstName":"Dulciane","middleName":"Nunes","lastName":"Paiva","suffix":""},{"id":307879162,"identity":"894ad524-0c08-483d-8e60-6e33d9ffc80a","order_by":4,"name":"Cézane Priscila Reuter","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA4klEQVRIiWNgGAWjYNCDDwwMPECKjUjlQHWMM0jWwswDZ+IB/O3tDz8XVNyTM7jffOyzbdsdGfn2A2yPK/BokThzIFl6xpliY4NjbMmzc9ue8RicSWA3PINHi4FEwgFp3raExA3HeIyZc9sO8xhIMLBJNuDTIv+w+Tfvv4T6Dcf4PzNbArXIzyCkRYKZTZq3ISHB4BgPMzMjUAvDDQJaJM6ksVnzHEswnHkszZix5xzQYWcS2w3xaeFvP/74Nk9Ngjzf4cOPGX6UHbaXbz987CE+LdgAI6kaRsEoGAWjYBSgAwAQNkZY1KWgggAAAABJRU5ErkJggg==","orcid":"","institution":"University of Santa Cruz do Sul","correspondingAuthor":true,"prefix":"","firstName":"Cézane","middleName":"Priscila","lastName":"Reuter","suffix":""}],"badges":[],"createdAt":"2024-05-17 18:34:00","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4438298/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4438298/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":69283777,"identity":"16252f2e-c2e1-41e9-8a6d-8d3b761ff7a5","added_by":"auto","created_at":"2024-11-18 19:17:15","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":641780,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4438298/v1/03000adb-1d9d-4a43-b941-85fbbdab129a.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"\u003cp\u003ePerinatal Factors and its Association with Cardiometabolic Profile in Schoolchildren\u003c/p\u003e","fulltext":[{"header":"BACKGROUND","content":"\u003cp\u003eDevelopmental origins of health and disease hypothesis propose a connection between the periconceptual, fetal, and early infant stages of life and the sustained development of metabolic disorders [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. The risk of developing a disease at any point in one's life is shaped by the interplay between genetics and the cumulative impact of lifestyle and environmental exposures across the lifespan [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eCardiorespiratory fitness (CRF) evaluation serves as a significant indicator of children's health, thus promoting the evaluation of CRF is advisable during childhood and adolescence since it is associated with future cardiometabolic risk (CMR) [\u003cspan additionalcitationids=\"CR5 CR6\" citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Furthermore, it was shown that other measures like adiposity, including body mass index (BMI), waist circumference (WC), and body fat percentage (BF%), exert a more significant influence on CMR. Hence, those anthropometric measurements are important noninvasive and quantitative assessments of the human body and in pediatrics, they serve to assess a child's overall health, nutritional status, and their growth and developmental trajectory. These measurements serve as the primary benchmarks that clinicians use to gauge a child's health and well-being [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Anthropometric measurements are also a tool to predict CMR [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e].\u003c/p\u003e \u003cp\u003ePerinatal factors such as maternal health history and socioeconomic status, which are amenable to modification, play role in determining offspring health [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Furthermore, the absence of complications during pregnancy and breastfeeding period imparts notable advantages to the child's health status [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Gestational age, a parameter encompassing a baby's size at birth, has been acknowledged as having lasting implications for health, including alterations in childhood growth and the risk of becoming overweight [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Generally those findings regarding associations between perinatal variables and future metabolic risk are usually found during adulthood. Therefore, this study aimed to verify the associations between perinatal variables and cardiometabolic profile in school-aged children and adolescents.\u003c/p\u003e"},{"header":"METHODS","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy design, sample, and ethics\u003c/h2\u003e \u003cp\u003eCross-sectional study that used retrospective data from a database of a larger study named \u0026ldquo;Schoolchildren\u0026rsquo;s Health Study \u0026ndash; phase V\u0026rdquo;. The study was carried out using a sample comprising children and adolescents, represented by both genders. These participants were drawn from both private and public schools located in the municipality of Santa Cruz do Sul, southern Brazil. The inclusion criteria encompassed children within the age range of 6 to 17 years old, enrolled at a school, those with biological mother information and either biological or legal fathers with all their pertinent data available in the database. Students with inconsistent data were excluded from the sample.\u003c/p\u003e \u003cp\u003e This study received approval from the Committee of Ethics in Research with Human Subjects of the University of Santa Cruz do Sul with the approval number 839178. Every parent or legal guardian was comprehensively informed about the study procedures and expressed their consent by signing an informed consent form, thus authorizing their child's participation in the study.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eData collection\u003c/h2\u003e \u003cp\u003eA self-administered questionnaire which included inquiries related to sociodemographic factors as age, skin color: white and non-white categories which was composed by black, pardo, indigenous and yellow/Asian according to the Brazilian census [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Sex and socioeconomic level according to Brazil's economic classification criteria where classes A and B are high class, C is middle class and D and E are lower class was answered by the students [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Perinatal factors and personal data were collected through a self-reporting questionnaire answered by the mother. The questions were composed by mother\u0026rsquo;s age at pregnancy, type of delivery: vaginal birth, cesarean or forceps delivery, gestational age classified as: term baby born between 38 to \u0026lt;\u0026thinsp;41 weeks, post-term\u0026thinsp;\u0026lt;\u0026thinsp;42 weeks, pre-term\u0026thinsp;\u0026lt;\u0026thinsp;37 weeks and extremely premature\u0026thinsp;\u0026lt;\u0026thinsp;28 weeks [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. The child's birth weight was analyzed as a continuous variable and the questions \u0026ldquo;Did you have prenatal care?\u0026rdquo; and \u0026ldquo;Did you have complications during pregnancy?\u0026rdquo; were analyzed dichotomously (yes/no).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eMeasurements\u003c/h2\u003e \u003cp\u003eTo calculate the body mass index (BMI), Weight and height measurements were taken using an anthropometric scale with an attached stadiometer (Filizola\u0026reg;). WC was measured using a 1 mm inelastic tape, focusing on the area between the ribs and the iliac crest, where the trunk is narrowest. To assess BF%, tricipital and subscapular skinfolds were measured using a Lange\u0026reg; caliper (MultiMed, Skinfold Caliper, USA). BF% was calculated using the Heyward and Stolarczyk equation [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. Cardiorespiratory fitness (CRF) was assessed using the Six-Minute Run/Walk Test, which was conducted on a track field, following the established protocols by PROESP-BR [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. The variables BMI, BF%, WC, CRF were analyzed as a continuous scale.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eWe described the data using mean and standard deviation for numerical variables, and absolute and relative frequencies for categorical variables. ​The association of perinatal variables with BMI, WC, BF%, and CRF was tested by multiple linear regression, using the \u0026ldquo;Enter\u0026rdquo; input method, being the unstandardized coefficient (B) and 95% confidence intervals of the associations were estimated. The perinatal factors included in the model were the mother\u0026rsquo;s age at gestation, prenatal care, complications during the gestation period, gestational age, exclusively breastfeeding, and presence of cardiac and respiratory disease until 6 months of age. ​ Data were analyzed with Statistical Package for the Social Sciences software, version 23.0 (IBM, Armonk, NY, USA). Statistical significance was established as p\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/p\u003e \u003c/div\u003e"},{"header":"RESULTS","content":"\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e displays participants\u0026rsquo; descriptive characteristics. A total of 1.431 children and adolescents were evaluated, with 803 (56.1%) of them being females and white subjects (83%) with mean age of 11.48\u0026thinsp;\u0026plusmn;\u0026thinsp;2.75 years. The table provides information on the sample's socioeconomic status in which more than half of the sample was classified as middle income, classes B1-B2/C1-C2 (56.7%). The mean birth weight, anthropometric measurements and cardiorespiratory fitness were also presented.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSamples\u0026rsquo; characteristics (n\u0026thinsp;=\u0026thinsp;1.431).\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003en (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMean (SD)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSex\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e628 (43.90)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e803 (56.10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSkin color\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWhite\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.188 (83.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNon-white\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e243 (17.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAge (years)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e11.48\u0026thinsp;\u0026plusmn;\u0026thinsp;2.75\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSocioeconomic level\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e196 (13.70)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eB1-B2/C1-C2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e839 (58.60)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eD-E\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e396 (27.70)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eBirth weight (kg)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e3.28\u0026thinsp;\u0026plusmn;\u0026thinsp;0.57\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eBMI (kg/m\u0026sup2;)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e20.32\u0026thinsp;\u0026plusmn;\u0026thinsp;4.12\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eWC (cm)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e66.03\u0026thinsp;\u0026plusmn;\u0026thinsp;9.41\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eBF%\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e25.53\u0026thinsp;\u0026plusmn;\u0026thinsp;10.40\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCRF (m)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e879.75\u0026thinsp;\u0026plusmn;\u0026thinsp;186.85\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"3\"\u003eNote: n: absolute frequency; %: relative frequency; SD: standard deviation; BMI: body mass index; WC: waist circumference; BF%: body fat percentage; CRF: cardiorespiratory fitness.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eRegarding perinatal characteristics, we observed a mean age of 26.91 years old for mothers during pregnancy, with the majority having undergone prenatal care 1.696 (79.5%) and did not have complications during gestation 1.516 (71.1%) (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Most of the babies were born full term 1.812 (85%). In relation to delivery type, vaginal births accounted for 1.072 (51.7%), while cesarean births were 1.001 (47%), with nearly equal numbers (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003ePerinatal characteristics (n\u0026thinsp;=\u0026thinsp;1.431).\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003en (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMean (SD)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMother\u0026rsquo;s age at gestation (years)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e26.91\u0026thinsp;\u0026plusmn;\u0026thinsp;6.73\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePrenatal care\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.399 (97.80)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e32 (2.20)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eGestation\u0026rsquo;s complications\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e412 (28.80)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.019 (71.20)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eGestational age\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eExtreme prematurity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e6 (0.40)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePreterm\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e132 (9.20)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTerm\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.274 (89.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePost-term\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e19 (1.30)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eType of birth\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVaginal birth\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e674 (47.10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eForceps\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e38 (2.70)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCesarean birth\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e719 (50.20)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"3\"\u003eNote: n: absolute frequency; %: relative frequency; SD: standard deviation.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e3\u003c/span\u003e shows the results regarding associations between perinatal factors, anthropometric measurements, and cardiorespiratory fitness. We highlight the significant findings related to BW and BMI (B:1.13; 95%CI:0.74;1.51), gestational age and BMI in which babies born preterm had higher BMI than those born term and post-term (B:1.00; 95%CI: 0.25; 1.74). Moreover, we found a direct and positive association involving cesarean birth and BMI (B:0.43; 95%CI:0.01;0.86) which means that babies born through this method had higher BMI in the future compared with those who were born by vaginal birth or forceps delivery in which we did not find this association. The presence of complications during pregnancy was associated with current children\u0026rsquo;s BMI (B:.0.48; 95%CI:0.02;0.93) which showed us that the ones who were born with any complication had higher BMI. We also observed relations between BW and BF% (B:1.84; 95%CI: 0.83;2.84) and pregnancy complications and BF% (B:1.19; 95%CI:-0.00;2.38) showing that the BW and having complications during gestation are positively associated with BF% during school-age. BW and WC had a positive association, thus showing a growth in the WC measure in these children future (B:2.20; 95%CI:1.37;3.04). Gestational age and WC were also related, the association was found in pre-term babies (B:1.88; 95%CI:0.25;3.50). Furthermore, we emphasize the noteworthy discoveries pertaining to the positive relationship between having complications during gestational period and CRF (B:-31.76; 95%CI:-50.94;12.58) and that babies born extremely premature had lower CRF at school-age showed by an inverse association (B:-168.91; 95%CI:-299.53;-38.29).\u003c/p\u003e\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" align=\"left\" width=\"890\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" colspan=\"9\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eTable 3:\u003c/strong\u003e Associations of perinatal factors and anthropometrics measurements and cardiorespiratory fitness.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.103603603603602%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.162162162162161%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eBMI\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eB (95% CI)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.22072072072072%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003ep\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.387387387387387%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eBF%\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eB (95% CI)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.22072072072072%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003ep\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.387387387387387%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eWC\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eB (95% CI)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.22072072072072%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003ep\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.63963963963964%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eCRF\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eB (95% CI)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.657657657657658%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003ep\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.103603603603602%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.162162162162161%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.22072072072072%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.387387387387387%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.22072072072072%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.387387387387387%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.22072072072072%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.63963963963964%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.657657657657658%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.103603603603602%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eMother\u0026rsquo;s age at gestation\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.162162162162161%\" valign=\"top\"\u003e\n \u003cp\u003e-0.15\u003c/p\u003e\n \u003cp\u003e(-0.45;0.15)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.22072072072072%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.387387387387387%\" valign=\"top\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003cp\u003e(-0.07;0.08)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.22072072072072%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.387387387387387%\" valign=\"top\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003cp\u003e(0.03;-0.06)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.22072072072072%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.63963963963964%\" valign=\"top\"\u003e\n \u003cp\u003e0.76\u003c/p\u003e\n \u003cp\u003e(-0.51;2.04)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.657657657657658%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.23\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.103603603603602%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eDid make prenatal care\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.162162162162161%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.22072072072072%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.387387387387387%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.22072072072072%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.387387387387387%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.22072072072072%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.63963963963964%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.657657657657658%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.103603603603602%\" valign=\"top\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.162162162162161%\" valign=\"top\"\u003e\n \u003cp\u003eREF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.22072072072072%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.387387387387387%\" valign=\"top\"\u003e\n \u003cp\u003eREF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.22072072072072%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.387387387387387%\" valign=\"top\"\u003e\n \u003cp\u003eREF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.22072072072072%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.63963963963964%\" valign=\"top\"\u003e\n \u003cp\u003eREF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.657657657657658%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.103603603603602%\" valign=\"top\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.162162162162161%\" valign=\"top\"\u003e\n \u003cp\u003e-0.18\u003c/p\u003e\n \u003cp\u003e(-1.52;1.14)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.22072072072072%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.387387387387387%\" valign=\"top\"\u003e\n \u003cp\u003e0.47\u003c/p\u003e\n \u003cp\u003e(-3.04;3.98)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.22072072072072%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.387387387387387%\" valign=\"top\"\u003e\n \u003cp\u003e-1.59\u003c/p\u003e\n \u003cp\u003e(-4.49;1.31)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.22072072072072%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.63963963963964%\" valign=\"top\"\u003e\n \u003cp\u003e-31.74\u003c/p\u003e\n \u003cp\u003e(-87.99;24.50)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.657657657657658%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.26\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.103603603603602%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eDid have complications during gestation\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.162162162162161%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.22072072072072%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.387387387387387%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.22072072072072%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.387387387387387%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.22072072072072%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.63963963963964%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.657657657657658%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.103603603603602%\" valign=\"top\"\u003e\n 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\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.103603603603602%\" valign=\"top\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.162162162162161%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.48\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(0.02;0.93)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.22072072072072%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e0.03*\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.387387387387387%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.19\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(-0.00;2.38)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.22072072072072%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e0.05*\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.387387387387387%\" valign=\"top\"\u003e\n \u003cp\u003e0.65\u003c/p\u003e\n \u003cp\u003e(-0.34;1.64)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.22072072072072%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.63963963963964%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e-31.76\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(-50.94;-12.58)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.657657657657658%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001*\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.103603603603602%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eGestational age\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.162162162162161%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.22072072072072%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.387387387387387%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.22072072072072%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.387387387387387%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.22072072072072%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.63963963963964%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.657657657657658%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.103603603603602%\" valign=\"top\"\u003e\n \u003cp\u003eTerm\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.162162162162161%\" valign=\"top\"\u003e\n \u003cp\u003eREF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.22072072072072%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.387387387387387%\" valign=\"top\"\u003e\n \u003cp\u003eREF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.22072072072072%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.387387387387387%\" valign=\"top\"\u003e\n \u003cp\u003eREF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.22072072072072%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.63963963963964%\" valign=\"top\"\u003e\n \u003cp\u003eREF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.657657657657658%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.103603603603602%\" valign=\"top\"\u003e\n \u003cp\u003ePost-term\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.162162162162161%\" valign=\"top\"\u003e\n \u003cp\u003e-0.84\u003c/p\u003e\n \u003cp\u003e(-2.58;0.88)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.22072072072072%\" valign=\"top\"\u003e\n \u003cp\u003e0.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.387387387387387%\" valign=\"top\"\u003e\n \u003cp\u003e-2.10\u003c/p\u003e\n \u003cp\u003e(-6.66;2.45)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.22072072072072%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.387387387387387%\" valign=\"top\"\u003e\n \u003cp\u003e-1.56\u003c/p\u003e\n \u003cp\u003e(-5.33;2.21)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.22072072072072%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.63963963963964%\" valign=\"top\"\u003e\n \u003cp\u003e31.11\u003c/p\u003e\n \u003cp\u003e(-41.89;104.11)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.657657657657658%\" valign=\"top\"\u003e\n \u003cp\u003e0.40\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.103603603603602%\" valign=\"top\"\u003e\n \u003cp\u003ePreterm\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.162162162162161%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e21.00\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(0.25;1.74)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.22072072072072%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001*\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.387387387387387%\" valign=\"top\"\u003e\n \u003cp\u003e1.03\u003c/p\u003e\n \u003cp\u003e(-0.92;3.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.22072072072072%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.387387387387387%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.88\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(0.25;3.50)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.22072072072072%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e0.02*\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.63963963963964%\" valign=\"top\"\u003e\n \u003cp\u003e27.49\u003c/p\u003e\n \u003cp\u003e(-3.94;58.92)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.657657657657658%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.08\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.103603603603602%\" valign=\"top\"\u003e\n \u003cp\u003eExtreme prematurity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.162162162162161%\" valign=\"top\"\u003e\n \u003cp\u003e-1.28\u003c/p\u003e\n \u003cp\u003e(-4.39;1.81)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.22072072072072%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.387387387387387%\" valign=\"top\"\u003e\n \u003cp\u003e-2.69\u003c/p\u003e\n \u003cp\u003e(-10.84;5.46)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.22072072072072%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.387387387387387%\" valign=\"top\"\u003e\n \u003cp\u003e-2.82\u003c/p\u003e\n \u003cp\u003e(-9.57;3.93)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.22072072072072%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.63963963963964%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e-168.91\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(-299.53; -38.29)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.657657657657658%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e0.01*\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.103603603603602%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eType of birth\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.162162162162161%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.22072072072072%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.387387387387387%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.22072072072072%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.387387387387387%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.22072072072072%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.63963963963964%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.657657657657658%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.103603603603602%\" valign=\"top\"\u003e\n \u003cp\u003eVaginal birth\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.162162162162161%\" valign=\"top\"\u003e\n \u003cp\u003eREF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.22072072072072%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.387387387387387%\" valign=\"top\"\u003e\n \u003cp\u003eREF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.22072072072072%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.387387387387387%\" valign=\"top\"\u003e\n \u003cp\u003eREF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.22072072072072%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.63963963963964%\" valign=\"top\"\u003e\n \u003cp\u003eREF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.657657657657658%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.103603603603602%\" valign=\"top\"\u003e\n \u003cp\u003eForceps delivery\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.162162162162161%\"\u003e\n \u003cp\u003e-0.85\u003c/p\u003e\n \u003cp\u003e(-2.10; 0.39)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.22072072072072%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.387387387387387%\" valign=\"top\"\u003e\n \u003cp\u003e-2.15\u003c/p\u003e\n \u003cp\u003e(-5.44;1.12)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.22072072072072%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.387387387387387%\" valign=\"top\"\u003e\n \u003cp\u003e-1.54\u003c/p\u003e\n \u003cp\u003e(-4.26;1.17)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.22072072072072%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.63963963963964%\" valign=\"top\"\u003e\n \u003cp\u003e23.63\u003c/p\u003e\n \u003cp\u003e(-28.99;76.25)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.657657657657658%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.37\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.103603603603602%\" valign=\"top\"\u003e\n \u003cp\u003eCesarean birth\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.162162162162161%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.43\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(0.01;0.86)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.22072072072072%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e0.04*\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.387387387387387%\" valign=\"top\"\u003e\n \u003cp\u003e1.06\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(-0.05;2.17)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.22072072072072%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.387387387387387%\" valign=\"top\"\u003e\n \u003cp\u003e1.06\u003c/p\u003e\n \u003cp\u003e(0.13;1.98)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.22072072072072%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.63963963963964%\" valign=\"top\"\u003e\n \u003cp\u003e-3.89\u003c/p\u003e\n \u003cp\u003e(-21.78;13.99)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.657657657657658%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.67\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.103603603603602%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eBirth weight\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.162162162162161%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.13\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(0.74;1.51)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.22072072072072%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001*\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.387387387387387%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.84\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(0.83;2.84)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.22072072072072%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001*\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.387387387387387%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e2.20\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(1.37;3.04)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.22072072072072%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001*\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.63963963963964%\" valign=\"top\"\u003e\n \u003cp\u003e-12.90\u003c/p\u003e\n \u003cp\u003e(-29.03; 3.22)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.657657657657658%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.11\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eB: Unstandardized coefficient. 95% CI: Confidence interval. Model adjusted for schoolchildren\u0026rsquo;s age, sex, skin color, socioeconomic level, birth weight, mother\u0026rsquo;s age, prenatal care, complications during gestation, gestational age and type of birth. BMI: Body mass index (kg/m\u003csup\u003e2\u003c/sup\u003e); BF%: Body fat percentage; WC: Waist circumference (cm); CRF: Cardiorespiratory fitness (m).\u003c/p\u003e"},{"header":"DISCUSSION","content":"\u003cp\u003eThe aim of our study was to verify the associations between perinatal variables and cardiometabolic profile in school-aged children and adolescents. We found out some interesting results regarding birth weight (BW) and anthropometric measurements, those associations showed a positive relation between BW and BMI, BF% and WC during school-age. Cesarean also showed positive association with BMI in our sample, meaning that children born through this surgical method of birth delivery could have higher BMI than those born via vaginal birth or forceps delivery. Additionally, we discovered significant findings related to a pregnancy with no complications, gestational age and school-children\u0026rsquo;s cardiorespiratory fitness (CRF) where babies born from a healthy pregnancy had better CRF during childhood and adolescence and babies born extremely premature had a negative impact on its current CRF. Those variables assessed in our research are some of early life health predictors in children and adolescents [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe higher likelihood of developing obesity in adulthood among children with greater BW, indicates that the prenatal period represents a pivotal phase for shaping future body adiposity [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. In our sample we found out that BW was positively associated with an increase in BMI, BF% and WC measurements that could represent risk for overweight and obesity in this population, which we could hypothesized that possibly indicate a higher cardiometabolic risk (CMR).\u003c/p\u003e \u003cp\u003eThe links between an elevated risk of cardiovascular and metabolic disorders transcend a spectrum of birth weights and postnatal growth patterns [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. Gestational age, for example, can also play a role in CMR. Studies have demonstrated that preterm labor heightens the likelihood of hypertension, elevated insulin levels, and even early onset heart failure in offspring [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. These individuals are more prone to having augmented left ventricular mass, irregular ventricular function, systemic arterial stiffness, and higher mean blood pressure [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. These factors may predispose them to an elevated risk of cardiovascular disease (CVD) later in life [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. In our study, preterm babies showed association with two indicators of increased cardiovascular risk: BMI and CRF. Those born extremely premature had worst CRF during school-age we can, therefore, hypothesized that those children had poor cardiovascular and/or respiratory system development, consequently, elevating those kids\u0026rsquo; risk of having a CVD in the future since CRF is a determinant of morbidity and mortality [\u003cspan additionalcitationids=\"CR25\" citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. It is also noteworthy that elevated BMI could also contribute to a poorer CRF [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eComplications during pregnancy can have long-lasting effects on the health of the child, several risk factors like the obstetric complications as preterm labor and their long-term maternal effects serve as indicators for an elevated risk of acute cardiovascular complications during delivery and pose a long-term risk for CVD for both the mother and the offspring [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. These complications can impact various metabolic traits in the offspring, its fetal growth and development [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. In our research, the offspring of mothers without complications during pregnancy exhibited higher levels of CRF, suggesting better physical development and overall cardiorespiratory health.\u003c/p\u003e \u003cp\u003eIt is evidenced that that there is a significant contrast in the physiological stress experienced by the fetus during vaginal delivery compared to cesarean delivery (CD). The stress encountered in natural delivery plays a crucial role in triggering the release of stress-induced hormones in the fetus, subsequently influencing physiological changes, particularly in terms of immunological and metabolic development that could be a protective factor against CMR [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. Despite the fact that there are some research associating CD and propensity to different kinds of infections, neurological and respiratory morbidities, the evidence regarding this type of delivery and CMR in school-aged children and adolescents is still little. There is a suggested elevated risk for young individuals born via CD where signs of overweight have been noted from infancy through young adulthood as demonstrated by Horta et al. same fact as seen is our study in which those kids born via this surgical method had higher body mass index during school-age [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIt is well known that certain chronic diseases have their roots in early life, and these conditions can be influenced by exposure to various risk factors during the early stages of development and lifespan, these factors have a role in increasing health risks in adulthood [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. By appraising perinatal biomarkers and obstetrical history linked to the presence of traditional cardiometabolic risk indicators an early assessment risk is facilitated. The relation between perinatal conditions and the early emergence of cardiometabolic risk biomarkers may be contingent on epigenetic programming [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. Therefore, it is relevant to investigate those perinatal variables in order to identify factors that can be changed during children\u0026rsquo;s development window through early prevention and by changing lifestyle habits.\u003c/p\u003e \u003cp\u003eDespite the valuable findings from this study, it is necessary to recognize its limitations. The self-reported questionnaire used to collect previous data could lead to bias, as the mothers and the children could have some trouble to recall information from the past. Furthermore, there are other factors that could have effects on cardiometabolic profile and increase cardiometabolic risk in this population that were not evaluated in this study, i.e lifestyle. The small sample size in the extremely born and post-term babies\u0026rsquo; categories could be limitation. Nonetheless, it is important to recognize that this study, conducted in a sample from southern Brazil, stands out as a significant contribution to a low to middle-income country, such as Brazil. This research adopted an innovative approach by using a school-based sample that is representative of the population of its municipality. It is worth noting that, to the best of our knowledge, there have been no studies that have shown similar results in this specific population. This study not only fills a gap in the existing literature but also significantly advances our understanding of the impact that perinatal variables can have on outcomes during childhood and adolescence, providing valuable insights for future research and interventions.\u003c/p\u003e"},{"header":"CONCLUSION","content":"\u003cp\u003eIn conclusion, our study has investigated the relationship between perinatal variables and its association with schoolchildren\u0026rsquo;s cardiometabolic profile, shedding light on the lasting impact of early life factors on future physical composition. The findings presented herein: associations between perinatal variables as birth weight, gestational age, complications during pregnancy and type of birth with adiposity measurements as body mass index, body fat percentage and waist circumference and cardiorespiratory fitness provide compelling evidence of a significant association between them during childhood and adolescence.\u003c/p\u003e \u003cp\u003eThese findings carry implications for both clinical practice and public health interventions. Recognizing the enduring influence of perinatal history and schoolchildren\u0026rsquo;s anthropometry can inform strategies for early identification of individuals at risk for certain health outcomes. Moreover, our study underscores the importance of a life-course perspective in understanding the complexities of human growth and development.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"566\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"50%\" valign=\"top\"\u003e\n \u003cp\u003eCRF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"50%\" valign=\"top\"\u003e\n \u003cp\u003eCardiorespiratory fitness\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"50%\" valign=\"top\"\u003e\n \u003cp\u003eCMR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"50%\" valign=\"top\"\u003e\n \u003cp\u003eCardiometabolic risk\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"50%\" valign=\"top\"\u003e\n \u003cp\u003eBMI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"50%\" valign=\"top\"\u003e\n \u003cp\u003eBody mass index\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"50%\" valign=\"top\"\u003e\n \u003cp\u003eWC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"50%\" valign=\"top\"\u003e\n \u003cp\u003eWaist circumference\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"50%\" valign=\"top\"\u003e\n \u003cp\u003eBF%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"50%\" valign=\"top\"\u003e\n \u003cp\u003eBody fat percentage\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"50%\" valign=\"top\"\u003e\n \u003cp\u003eCMDs\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"50%\" valign=\"top\"\u003e\n \u003cp\u003eCardiometabolic diseases\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cem\u003eEthics approval and consent to participate\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThis study was approved by\u0026nbsp;the Ethics Committee in Research with Human Subjects of the University of Santa Cruz do Sul with the approval number 839178. The study adhered to the principles in the Helsinki Declaration. Participants were provided with both verbal and written information regarding their voluntary involvement and the option to withdraw at any time. Data confidentiality was guaranteed, and participants granted written informed consent for their involvement. Furthermore, participants were reassured that their information would remain confidential.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eConsent for publication\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eAvailability of data and materials\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe corresponding author can provide the data utilized and/or analyzed in the present study upon reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eCompeting interests\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eFunding\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThis study was financed in part by the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior - Brasil (CAPES) - Finance Code 001.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eAuthors' contributions\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eKMK contributed with the\u0026nbsp;conceptualization, methodology, analysis, data interpretation, writing and revision of the manuscript. LT was involved with methodology, analysis, data interpretation and revision of the manuscript. KAP revised and made substantial contributions to the manuscript. DNP contributed with supervision, conceptualization and revision. CR was responsible for funding acquisition, supervision, conceptualization and revision of the manuscript.\u0026nbsp;All authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eAcknowledgements\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eWe thank all the support of the University of Santa Cruz do Sul—UNISC and Higher Education Personnel Improvement Coordination—Brazil (CAPES), as well as the collaboration of the schools who participated in this study and to our research group Health Research Laboratory - Laboratório de Pesquisa em Saúde (LAPES) and thank Michigan State University for the contributions and collaboration in our project “Schoolchildren’s health”.\u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eLacagnina S. The Developmental Origins of Health and Disease (DOHaD). Am J Lifestyle Med. 2019;14(1):47-50. http://dx.doi.org/10.1177/1559827619879694.\u003c/li\u003e\n\u003cli\u003eLecoutre S, Maqdasy S, Breton C. Maternal obesity as a risk factor for developing diabetes in offspring: An epigenetic point of view. World J Diabetes. 2021;12:366. http://dx.doi.org/10.4239/wjd.v12.i4.366.\u003c/li\u003e\n\u003cli\u003eCechinel LR, Batabyal RA, Freishtat RJ, Zohn IE. Parental obesity-induced changes in developmental programming. Front Cell Dev Biol. 2022;7;10:918080. http://dx.doi.org/10.3389/fcell.2022.918080.\u003c/li\u003e\n\u003cli\u003eMorikawa SY, Fujihara K, Hatta M, Osawa T, Ishizawa M, FuruKawa K, et al. Relationships among cardiorespiratory fitness, muscular fitness, and cardiometabolic risk factors in Japanese adolescents: Niigata screening for and preventing the development of non-communicable disease study Agano (NICE EVIDENCE Study-Agano) 2. Pediatr Diabetes. 2018;19(4):593- 602. http://dx.doi.org/10.1111/pedi.12623.\u003c/li\u003e\n\u003cli\u003eCristi-Montero C, Courel-Ib\u0026aacute;\u0026ntilde;ez J, Ortega FB, Castro-Pi\u0026ntilde;ero J, Santaliestra-Pasias A, Polito A, Vanhelst J, Marcos A, Moreno LM, Ruiz JR; HELENA study group. Mediation role of cardiorespiratory fitness on the association between fatness and cardiometabolic risk in European adolescents: The HELENA study. J Sport Health Sci. 2021;10(3):360-367. http://dx.doi.org/10.1016/j.jshs.2019.08.003. \u003c/li\u003e\n\u003cli\u003eBagatini NC, Feil Pinho CD, Leites GT, da Cunha Voser R, Gaya AR, Santos Cunha GD. Effects of cardiorespiratory fitness and body mass index on cardiometabolic risk factors in schoolchildren. BMC Pediatr. 2023;23(1):454. http://dx.doi.org/10.1186/s12887-023-04266-w.\u003c/li\u003e\n\u003cli\u003eJohansson L, Putri RR, Danielsson P, Hagstr\u0026ouml;mer M, Marcus C. Associations between cardiorespiratory fitness and cardiometabolic risk factors in children and adolescents with obesity. Sci Rep. 2023;13(1):7289. http://dx.doi.org/10.1038/s41598-023-34374-7. \u003c/li\u003e\n\u003cli\u003eFryar CD, Carroll MD, Gu Q, Afful J, Ogden CL. Anthropometric reference data for children and adults: United States, 2015-2018. 2021;3(46) Vital and health statistics. Series 3, Analytical and epidemiological studies; no. 46. https://stacks.cdc.gov/view/cdc/100478. Accessed 15 Jan 2024.\u003c/li\u003e\n\u003cli\u003eLiu J, Tse LA, Liu Z, Rangarajan S, Hu B, Yin L et al. PURE (Prospective Urban Rural Epidemiology) study in China. Predictive Values of Anthropometric Measurements for Cardiometabolic Risk Factors and Cardiovascular Diseases Among 44 048 Chinese. J Am Heart Assoc. 2019;20;8(16):e010870. http://dx.doi.org/10.1161/JAHA.118.010870.\u003c/li\u003e\n\u003cli\u003eWarrington NM, Beaumont RN, Horikoshi M, et al. Maternal and fetal genetic effects on birth weight and their relevance to cardio-metabolic risk factors. Nat Genet. 2019;51(5):804\u0026ndash;814. http://dx.doi.org/10.1038/s41588-019-0403-1.\u003c/li\u003e\n\u003cli\u003eNormia J, Laitinen K, Isolauri E, Poussa T, Jaakkola J, Ojala T. Impact of intrauterine and post-natal nutritional determinants on blood pressure at 4 years of age. J Hum Nutr Diet. 2013;26:544-552. http://dx.doi.org/10.1111/jhn.12115.\u003c/li\u003e\n\u003cli\u003eGodfrey KM, Reynolds RM, Prescott SL, Nyirenda M, Jaddoe VW, Eriksson JG, Broekman BF. Influence of maternal obesity on the long-term health of offspring. Lancet Diabetes Endocrinol. 2017;5(1):53-64. http://dx.doi.org/10.1016/S2213-8587(16)30107-3. \u003c/li\u003e\n\u003cli\u003eNordman H, J\u0026auml;\u0026auml;skel\u0026auml;inen J, Voutilainen R. Birth size as a determinant of cardiometabolic risk factors in children. Horm Res Paediatr. 2020;93(3):144-153. http://dx.doi.org/10.1159/000509932.\u003c/li\u003e\n\u003cli\u003eInstituto Brasileiro de Geografia e Estat\u0026iacute;stica. Panorama. Censo 2022. Available at: https://censo2022.ibge.gov.br/panorama/. Accessed 10 Jan 2024.\u003c/li\u003e\n\u003cli\u003eAssocia\u0026ccedil;\u0026atilde;o Brasileira de Empresas de Pesquisa. Crit\u0026eacute;rio de Classifica\u0026ccedil;\u0026atilde;o Econ\u0026ocirc;mica Brasil 2016. S\u0026atilde;o Paulo. http://www.abep.org/criterio-brasil. Accessed 30 May 2023.\u003c/li\u003e\n\u003cli\u003eRubens CE, Sadovsky Y, Muglia L, Gravett MG, Lackritz E, Gravett C. Prevention of preterm birth: harnessing science to address the global epidemic. Sci Transl Med. 2014;6(262):262sr5. https://doi.org/10.1126/scitranslmed.3009871.\u003c/li\u003e\n\u003cli\u003eHEYWARD, V. H.; STOLARCZYK, L. M. Avalia\u0026ccedil;\u0026atilde;o da composi\u0026ccedil;\u0026atilde;o corporal aplicada. S\u0026atilde;o Paulo: Manole, 2000.\u003c/li\u003e\n\u003cli\u003eGaya AC. Projeto Esporte Brasil: PROESP-BR. Manual de aplica\u0026ccedil;\u0026atilde;o de medidas e testes, normas e crit\u0026eacute;rios de avalia\u0026ccedil;\u0026atilde;o. Porto Alegre, 2009.\u003c/li\u003e\n\u003cli\u003eHeerman WJ, Sommer EC, Slaughter JC, Samuels LR, Martin NC, Barkin SL. Predicting Early Emergence of Childhood Obesity in Underserved Preschoolers. J Pediatr. 2019;213:115-120. http://dx.doi.org/10.1016/j.jpeds.2019.06.031. \u003c/li\u003e\n\u003cli\u003eDrozdz D, Alvarez-Pitti J, W\u0026oacute;jcik M, Borghi C, Gabbianelli R, Mazur A, Herceg-Čavrak V, Lopez-Valcarcel BG, Brzeziński M, Lurbe E, W\u0026uuml;hl E. Obesity and Cardiometabolic Risk Factors: From Childhood to Adulthood. Nutrients. 2021;13(11):4176. http://dx.doi.org/: 10.3390/nu13114176.\u003c/li\u003e\n\u003cli\u003eLurbe E. Ingelfinger, J. Developmental and Early Life Origins of Cardiometabolic Risk Factors: Novel Findings and Implications. Hypertension. 2021;77:308\u0026ndash;318. http://dx.doi.org/10.1161/hypertensionaha.120.14592.\u003c/li\u003e\n\u003cli\u003eGalin S, Wainstock T, Sheiner E, Landau D, Walfisch A. Elective cesarean delivery and long-term cardiovascular morbidity in the offspring - a population-based cohort analysis. J Matern Fetal Neonatal Med. 2022;35(14):2708-2715. https://doi.org/10.1080/14767058.2020.1797668.\u003c/li\u003e\n\u003cli\u003eLu D, Yu Y, Ludvigsson JF, Oberg AS, S\u0026oslash;rensen HT, L\u0026aacute;szl\u0026oacute; KD, Li J, Cnattingius S. Birth Weight, Gestational Age, and Risk of Cardiovascular Disease in Early Adulthood: Influence of Familial Factors. Am J Epidemiol. 2023;192(6):866-877. https://doi.org/10.1093/aje/kwac223.\u003c/li\u003e\n\u003cli\u003eLucchini M, Pini N, Fifer WP, Burtchen N, Signorini MG. Characterization of cardiorespiratory phase synchronization and directionality in late premature and full term infants. Physiol Meas. 2018;39(6):064001. https://doi.org/10.1088/1361-6579/aac553. \u003c/li\u003e\n\u003cli\u003eHasenstab KA, Nawaz S, Lang IM, Shaker R, Jadcherla SR. Pharyngoesophageal and cardiorespiratory interactions: potential implications for premature infants at risk of clinically significant cardiorespiratory events. Am J Physiol Gastrointest Liver Physiol. 2019;316(2):G304-G312. https://doi.org/10.1152/ajpgi.00303.2018. \u003c/li\u003e\n\u003cli\u003eWeston KS, Wisl\u0026oslash;ff U, Coombes JS. High-intensity interval training in patients with lifestyle-induced cardiometabolic disease: a systematic review and meta-analysis. Br J Sports Med. 2014;48(16):1227-34. https://doi.org/10.1136/bjsports-2013-092576.\u003c/li\u003e\n\u003cli\u003eHardeep Singh, Vandana Esht, Mohammad A. Shaphe, Nikita Rathore, Aksh Chahal, Faizan Z. Kashoo. Relationship between body mass index and cardiorespiratory fitness to interpret health risks among sedentary university students from Northern India: A correlation study, Clinical Epidemiology and Global Health,Volume 20,2023,101254.\u003c/li\u003e\n\u003cli\u003eNeiger R. Long-Term Effects of Pregnancy Complications on Maternal Health: A Review. J Clin Med. 2017;6(8):76. https://doi.org/10.3390/jcm6080076.\u003c/li\u003e\n\u003cli\u003eQuesada O, Scantlebury DC, Briller JE, Michos ED, Aggarwal NR. Markers of Cardiovascular Risk Associated with Pregnancy. Curr Cardiol Rep. 2023;25(2):77-87. https://doi.org/10.1007/s11886-022-01830-1. \u003c/li\u003e\n\u003cli\u003eElhakeem A, Ronkainen J, Mansell T, et al. Effect of common pregnancy and perinatal complications on offspring metabolic traits across the life course: a multi-cohort study. BMC Med. 2023; 21:23. https://doi.org/10.1186/s12916-022-02711-8.\u003c/li\u003e\n\u003cli\u003eHorta BL, Gigante DP, Lima RC, Barros FC, Victora CG. Birth by caesarean section and prevalence of risk factors for non-communicable diseases in young adults: a birth cohort study. PLoS ONE. 2013; 8(9): e74301.\u003c/li\u003e\n\u003cli\u003ePehkonen J, Viinikainen J, Kari JT, B\u0026ouml;ckerman P, Lehtim\u0026auml;ki T, Viikari J et al. Birth weight, adult weight, and cardiovascular biomarkers: Evidence from the Cardiovascular Young Finns Study. Prev Med. 2022;154:106894. https://doi.org/10.1016/j.ypmed.2021.106894.\u003c/li\u003e\n\u003cli\u003eOliveira WR, Rigo CP, Ferreira ARO, Ribeiro MVG, Perres MNC, Palma-Rigo K. Precocious evaluation of cardiovascular risk and its correlation with perinatal condition. An Acad Bras Ci\u0026ecirc;nc. 2023;95(1):e20201702. https://doi.org/10.1590/0001-3765202320201702.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Child health, Metabolic profile, Heart disease risk factors","lastPublishedDoi":"10.21203/rs.3.rs-4438298/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4438298/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBACKGROUND\u003c/h2\u003e \u003cp\u003eDevelopmental origins of health and disease suggests a link between the periconceptual, fetal, and early infant phases of life and the persistent development of metabolic disorders. Therefore, this study aimed to verify the associations between perinatal variables and cardiometabolic profile in school-aged children and adolescents.\u003c/p\u003e\u003ch2\u003eMETHODS\u003c/h2\u003e \u003cp\u003eRetrospective study that used cross-sectional data from a database of a larger study named \u0026ldquo;Schoolchildren\u0026rsquo;s health\u0026rdquo;. The study was carried out using a sample comprising children and adolescents, ranging from 6 to 17 years old from both genders. All participants were enrolled in private and public schools in the city of Santa Cruz do Sul, Brazil. A self-administered questionnaire was applied to children and parents, then anthropometric measurements of body mass index (BMI), body fat percentage (BF%) and waist circumference (WC) were obtained followed by a cardiorespiratory fitness (CRF) test. ​The association of perinatal variables with BMI, WC, BF%, and CRF was tested by multiple linear regression, using the \u0026ldquo;Enter\u0026rdquo; input method, being the unstandardized coefficient (B) and 95% confidence intervals of the associations were estimated. Data were analyzed with Statistical Package for the Social Sciences software, version 23.0 (IBM, Armonk, NY, USA). Statistical significance was established as p\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/p\u003e\u003ch2\u003eRESULTS\u003c/h2\u003e \u003cp\u003eA total of 1.431 children and adolescents were evaluated, with 803 (56.1%) of them being females and white subjects (83%) with mean age of 11.48\u0026thinsp;\u0026plusmn;\u0026thinsp;2.75 years. Associations between perinatal factors and anthropometric measurements were found birth weight (BW) and BMI (B:1.13; 95%CI:0.74;1.51), BW and WC (B:2.20; 95%CI:1.37;3.04), BW and BF% (B:1.84; 95%CI:0.83;2.84). Gestational age also had associations with BMI (B:1.00; 95%CI:0.25;1.74); WC (B:1.88; 95%CI:0.25;3.50) and CRF (B:-168.91; 95%CI:-299.53;-38.29). Complications during pregnancy and BMI (B:0.48; 95%CI:0.02;0.93) and cesarean birth and BMI (B:0.43; 95%CI:0.01;0.86).\u003c/p\u003e\u003ch2\u003eCONCLUSIONS\u003c/h2\u003e \u003cp\u003eAssociations exist between perinatal factors and future cardiometabolic profile. It is imperative to establish and reinforce efforts geared towards enhancing the health literacy of both adolescent boys and girls, along with pregnant women.\u003c/p\u003e","manuscriptTitle":"Perinatal Factors and its Association with Cardiometabolic Profile in Schoolchildren","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-05-31 10:12:14","doi":"10.21203/rs.3.rs-4438298/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"026ea05a-0ebb-4a80-b6a7-2467bce49455","owner":[],"postedDate":"May 31st, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-08-07T10:30:24+00:00","versionOfRecord":[],"versionCreatedAt":"2024-05-31 10:12:14","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4438298","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4438298","identity":"rs-4438298","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}