Small for gestational age and early childhood caries: the BRISA cohort study

This study tests the hypothesis that children 12–30 months born small for gestational age (SGA) aged are more susceptible to severe early childhood caries (S-ECC). We used data on 865 children aged 12–30 months from a prospective cohort study conducted in a city in the northeast of Brazil. The study outcome was S-ECC, defined based on the proportion of decayed tooth surfaces (cavitated or not). The main exposure variable was SGA, defined according to the Kramer criterion and the INTERGROWTH-21st standard. Direct (SGA → S-ECC) and indirect effects were estimated using structural equation modeling, calculating standardized factor loadings (SFL) and P-values (alpha = 5%). The final models showed a good fit. SGA influenced S-ECC in the direct and indirect paths. In the group of SGA children with 12 or more erupted teeth defined according to the Kramer criterion, the direct effect was positive (SFL = 0.163; P = 0.019); while among all SGA children defined according to the INTERGROWTH-21st standard, the direct effect was negative (SFL =  − 0.711; P < 0.001). Age and number of erupted teeth may influence the occurrence of S-ECC in SGA children, as the number of teeth affects the time of exposure to disease risk factors.


Sample.
The study sample consisted of liveborn singleton children who participated in the baseline study, were followed up at T2 and T3, and underwent a dental examination (n = 865) and their mothers.
The sample of 865 children was estimated to have a power of 95.76% to detect associations between SGA and S-ECC, calculated based on the following parameters: alpha = 0.05 (bilateral); mean (± standard deviation) number of carious surfaces equal to 0.40 (± 1.54) in the exposed group and 1.69 (± 7.63) in the unexposed group; sample of 46 exposed and 819 unexposed individuals.
The study sample was selected using convenience sampling because it was not possible to obtain a representative random sample of pregnant women since there is not a reliable record of pregnant women and/or women receiving antenatal care in the state of Maranhão.Available data is neither updated nor validated.So, the women were selected during visits to public and private health centers for antenatal/follow-up care.Inclusion criteria included having performed an ultrasound scan before 20 weeks of gestation and not having completed more than 25 weeks of gestation at the time of baseline data collection.The baseline data were collected at the Clinical Research Center (CEPEC) between January 2010 and June 2011.
Data collection procedures.The data were collected using clinical examinations and three questionnaires: the mother's questionnaire at T1, including questions on socioeconomic and demographic characteristics; the newborn questionnaire at T2, containing questions on weight and gestational age at birth; and the child questionnaire at T3, to obtain data on age, diet, and consumption of sugar-sweetened foods.
A dental examination was performed at T3 to assess DDE, S-ECC, VPI (used as a proxy measure of oral hygiene), and the number of teeth.At the end of the examination, the dentist brushed the child's teeth to check for bleeding gums during brushing.Six examiners were trained to perform the examination (inter-and intra-rater Kappa ≥ 0.80).The data were recorded on a specific individual form for each child.The dental examinations were performed at the Maternal and Infant Health Unit (HUUMI) of the Federal University of Maranhão University Hospital (HUUFMA) following World Health Organization (WHO) recommendations: under artificial lighting, using mouth mirrors, periodontal probes, compressed air, distilled water, and a three-way syringe 18 .
Variables.The study outcome was S-ECC, characterized by the ratio between the number of decayed tooth surfaces and the total number of tooth surfaces.The presence of the disease was determined using the Nyvad codes and criteria: 1-Active caries without surface discontinuity (intact surface); 2-active caries (microcavity); 3-active caries (cavity); 5-inactive caries (microcavity); and 6-inactive caries (cavity) 19 .Nyvad is a validated classification system 19 , has been shown to have high reproducibility for lesion severity assessment when compared to the International System for the Detection and Evaluation of Caries (ICDAS) 20 , and has been used in international studies 21,22 .
The main independent variable was SGA 23 , determined based on Kramer et al. 24 and the INTERGROWTH-21st standard 25 .According to Kramer, SGA is defined by dividing birthweight (in g) by the weight on the 50th percentile line of the birthweight for gestational age curve based on the Canadian birthweight standards 24 .
According to the INTERGROWTH-21st standard, SGA babies have birthweight below the 10th percentile, meaning they are smaller than 90% of (most) other babies of the same gestational age.The INTERGROWTH-21st www.nature.com/scientificreports/standards were produced using data from a multicenter multiethnic population-based project conducted between 2009 and 2014 in eight developed and developing countries, including Brazil.The primary aim of the project was to study growth, health, nutrition, and neurodevelopment from 14 weeks of gestation to 2 years of age 25 .
The potential mediating variables tested by this study were DDE, number of erupted teeth, and visible plaque index (VPI).DDE was diagnosed using the Fédération Dentaire Internationale Defects of Enamel index (1982) 26 , which categorizes DDE as diffuse opacities, demarcated opacities, and hypoplasia.Tooth surfaces with any of the three types of DDE were counted.The analysis considered the total number of teeth present in the oral cavity.The tooth was considered present in the oral cavity when any part of the tooth was visible during the examination.The VPI was also assessed during the dental examination.
The confounding variables were socioeconomic status, oral hygiene, and eating habits.Socioeconomic status was a latent variable measured using the following variables: family income (in minimum wages for the baseline year 2010); occupation of the head of the family (from unskilled job to manager/business owner); economic classification based on the criteria proposed by the Brazilian Market Research Association (ABEP)-(A/B; C; or D/E) 27 ; and maternal education (≤ 4 to ≥ 12 years).These variables are important indicators of caries 28 .Oral hygiene and eating habits were assessed by asking the child's mother/caregiver the following questions: Do you brush your child's teeth every night before they go to bed? (yes/no); Does your child eat sugar-sweetened foods?(yes/no).
These variables were chosen based on the assumption that teeth with enamel defects are likely to have higher levels of cariogenic biofilm formation (measured by the VPI) when fermentable sugars are consumed, increasing the incidence of S-ECC.The consumption of sugar-sweetened foods was chosen as a measure of eating habits because sugar is the main cause of caries 29 .The VPI was used because this measure indicates severe chronic plaque buildup, which, in the presence of sugar, can lead to caries 30 .Nighttime brushing was chosen as a measure of oral hygiene habits because evidence suggests that this method is more effective for controlling cariogenic activity than daytime brushing 10 .

Statistical analysis.
The association between SGA, DDE, and other covariates and S-ECC was assessed using a theoretical model (Fig. 1) tested using SEM.The statistical analyses were performed using Stata/SE 12.0 and Mplus 7.3 software.Absolute and relative frequencies were presented for the three study intervals to identify follow-up losses.Differences were estimated using chi-squared or Fisher's exact test.
The latent variable was modeled using variable indicators.A good latent variable should have adequate convergent validity, showing that the indicator correlates with other indicators used to measure the same construct.Standardized factor loadings (SFL) greater than 0.60 indicate adequate convergent validity.A latent variable must also have adequate discriminant validity, when correlations between indicators are not excessively high (> 0.95), showing that each indicator measures different aspects of the construct.Negative loadings indicate an inverse association and positive loadings indicate a direct association 31 .
We adopted the SFL thresholds proposed by Kline 31 .Coefficients with values close to 0.10 indicate a small effect; around 0.30, a medium effect; and greater than 0.50, a strong effect.The tested models were evaluated using fit indices.The following values were considered acceptable: RMSEA (root mean square error of www.nature.com/scientificreports/approximation) < 0.05; CFI (comparative fit index) and TLI (Tucker-Lewis index) > 0.95; and WRMR (weighted root mean square residual) < 0.95.Chi-squared, degrees of freedom, and P-values were calculated, but were not adopted as parameters to determine model fit due to the large sample size, which could influence the results of these tests.The models were evaluated using mean-and variance-adjusted weighted least squares (WLSMV), indicated for the analysis of categorical data 31 .Theta parameterization was used to control for differences in residual variance.The automatic command "MODINDICES" was used to suggest modifications to the initial model.When the proposed modifications are considered theoretically plausible and the modification index is greater than 10,000, a new model can be developed and analyzed.
Ethical considerations.This study protocol was approved by the HUUFMA Research Ethics Committee (Reference Code 4771/2008-30).All mothers signed an informed consent form explaining the nature of the study.Thus, all methods were performed following relevant guidelines and regulations.

Results
The incidence of SGA based on the Kramer criterion and INTERGROWTH-21st standard was 18.18% (n = 251) and 2.97% (n = 41), respectively.The incidence of gum bleeding during brushing was 6.71% (n = 58), and 12.26% (n = 106) of the children were diagnosed with some type of EDD.The incidence of S-ECC was 5.20% (n = 45), with a mean of 0.37% (± 2.39%) of decayed tooth surfaces.The average number of teeth per child at the time of the examination was 9.75 (± 3.75) (Table 1).The ratios for the variables in Table 1 remained similar between T1, T2, and T3, indicating that sample losses to follow-up were non-differential.
The Supplementary Material presents the prevalence ratios for S-ECC by age (months), showing that the prevalence of caries increases with the increasing number of teeth.
The latent variable socioeconomic status showed a high SFL (around 0.50) across all variables.SGA affected S-ECC in both the direct and indirect paths.In the group of SGA children with 12 or more teeth defined according to the Kramer criterion, the direct effect was positive (SFL = 0.163; P = 0.019), while in the group defined according to the INTERGROWTH-21st standard, the direct effect was negative (SFL = − 0.711; P < 0.001) (Table 3).
SGA defined according to the Kramer criterion had a direct effect on the number of teeth in all SGA children (SFL = − 0.176; P < 0.001) and in SGA children with 12 or more teeth (SFL = 0.175; P = 0.034) (Table 3).
The number of teeth influenced the VPI in all SGA children based on both the Kramer criterion (SFL = 0.142; P < 0.001) and INTERGROWTH-21st standard (SFL = 0.122; P < 0.001).SGA had a specific negative indirect effect on S-ECC mediated by the number of teeth and VPI (SFL = − 0.017; P = 0.047).SGA did not have an effect on S-ECC in the other indirect paths tested (Table 3).

Discussion
Our findings reveal a direct association between SGA and S-ECC, as well as an indirect association mediated by the number of teeth and VPI.The hypothesis that SGA increases the risk of DDE-mediated S-ECC was not supported.
The theoretical basis for this relationship is that nutritional deficiency during pregnancy, which is predicted to be more prevalent in low-income populations 32 such as the study sample, increases the risk of EDD and, consequently, S-ECC.Studies have suggested a relationship between SGA and EDD mediated by vitamin D deficiency [33][34][35][36] .While we did not evaluate nutritional deficiency, considering the socioeconomic characteristics of the study sample, a relationship between SGA and EDD mediated by vitamin D deficiency may be assumed in the present study.
Nonetheless, SGA has multiple causes, including nutritional, genetic, and hormonal factors 37,38 .Certain factors may lead to intrauterine growth restriction in this population without having a causal effect on EDD, resulting in the absence of an association between SGA and caries.
Age may also be an important factor, as the older the child, the greater the influence of extrinsic variables, which may have a confounding effect on results.The fact that our study sample is very young (up to 30 months) reduces confounding and helps us understand the influence of antenatal factors such as SGA, allowing us to better observe their effects.However, the relationship between adverse birth events and caries, which is a disease that develops over time, may have been underestimated in the study population because, due to the young age of our sample, the disease may not have had time to manifest itself 1 .
The data also point to an indirect association mediated by the number of teeth and VPI: the smaller the children for gestational age, the smaller the number of teeth, the lower the prevalence of VPI, and the lower the incidence of caries at age at the time of examination.Some authors suggest that adverse perinatal factors such as SGA are possible causal factors for delayed tooth eruption 39 .Furthermore, colonization with Streptococcus mutans is determined by the eruption of deciduous teeth and increasing numbers of teeth increase the risk of infection by S. mutans, thus increasing the risk of caries 40 .
Thus, SGA may have influenced S. mutans colonization, as SGA children had fewer teeth at the time of the examination compared to children with adequate weight for gestational age.Castro et al. found an association between delayed tooth eruption and low prevalence of severe caries 41 , which may partially explain the discrepancies in the associations found between SGA and S-ECC.In this respect, the association was positive in SGA Neither night brushing nor consumption of sugar-sweetened foods showed a correlation with S-ECC.This may be due to the low variability of this data.Both variables are prone to social desirability bias when respondents tend to provide answers that are more socially acceptable than their true opinions or behavior, leading to data homogeneity and hampering the detection of possible associations.However, our findings do show an association between VPI and S-ECC.The former is an important proxy measure for the presence of mature biofilm and is less susceptible to measurement errors, thus supporting the above explanation 1 .
Our results point to the importance of taking a life-course approach to the treatment of oral disease, as distal variables can play an important role in the causality of the events investigated by this study 42 .Older children with teeth are exposed to risks for longer and therefore more susceptible to oral diseases such as caries 43,44 .
Although we identified a significant specific indirect path, the main results were derived from estimates of direct effects, which could have been achieved using conventional regression models.However, it is important to note that, conceptually, conventional regression methods are not appropriate for assessing temporal order, since they do not consider the temporal sequence between factors.SEM, on the other hand, addresses these considerations, yet requires a large amount of data preparation to obtain model-fit criteria before analysis to test multiple paths between variables and analyze different parameters.Depending on the number of variables and sample size, models may reach saturation.To minimize the limitations of SEM, we used a more parsimonious model considering the main variables related to the outcome and paying careful attention to data preparation.
Another study limitation is the use of the Nyvad criteria.Although validated and used in international studies, the visual-tactile method may not provide a accurate assessment of caries activity.To minimize possible errors, examiners received prior training, obtaining an acceptable level of inter-and intra-rater reliability.
Despite performing additional analyses including only children with at least 12 teeth, we were not able to completely remove the confounding effect of the number of erupted teeth as our sample included very young children (12-30 months), most of whom had few teeth.Further research involving children with a complete set of deciduous teeth may help gain a better understanding of the relationship between SGA and S-ECC; however, these studies may be more susceptible to other biases and confounders, such as memory bias, loss of teeth due to caries or other reasons, and longer exposure to other risk factors, hampering the measurement of disease incidence and producing estimates that point in the direction of non-association.
Another limitation is the use of the Kramer criterion, which is based on Canadian birthweight standards, for Brazilian children, potentially leading to an overestimation of SGA children.The Kramer criterion seeks to minimize methodological biases of other birthweight for gestational age curves 45 .To minimize this problem, we also analyzed the association between SGA and E-ECC using the INTERGROWTH-21st standard, which uses data from Brazilian children.It is also important to highlight that the lower incidence of SGA children using this standard observed by this study may be explained by the fact that this measure tends to underestimate SGA 46 .

Figure 1 .
Figure 1.Structural equation modeling of the hypothesis (SGA increases the risk of DDE-mediated S-ECC).

Table 2 .
Fit indices for the final model.São Luís/MA.Brazil, 2023.SGA small for gestational age, RMSEA root mean square error of approximation, CI confidence interval, CFI comparative fit index, TLI Tucker Lewis index, WRMR weighted root mean square residual.a Chi-squared test.