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Article

Obesity, Insulin Resistance, Caries, and Periodontitis: Syndemic Framework

by
Lorena Lúcia Costa Ladeira
1,
Gustavo Giacomelli Nascimento
2,3,4,
Fábio Renato Manzolli Leite
2,3,
Silas Alves-Costa
1,
Janaína Maiana Abreu Barbosa
5,
Claudia Maria Coelho Alves
1,5,
Erika Barbara Abreu Fonseca Thomaz
1,5,
Rosangela Fernandes Lucena Batista
5 and
Cecilia Claudia Costa Ribeiro
1,5,*
1
Postgraduate Program of Dentistry, Federal University of Maranhão, São Luís 65085-580, MA, Brazil
2
National Dental Research Institute Singapore, National Dental Centre Singapore, Singapore 168938, Singapore
3
Oral Health Academic Clinical Programme (ORH ACP), Duke-NUS Medical School, Singapore 169857, Singapore
4
Section for Periodontology, Department of Dentistry and Oral Health, Aarhus University, 8000 Aarhus, Denmark
5
Postgraduate Program of Public Health, Federal University of Maranhão, São Luís 65020-070, MA, Brazil
*
Author to whom correspondence should be addressed.
Nutrients 2023, 15(16), 3512; https://doi.org/10.3390/nu15163512
Submission received: 29 May 2023 / Revised: 3 August 2023 / Accepted: 5 August 2023 / Published: 9 August 2023
(This article belongs to the Section Nutrition and Obesity)
Browse Figure
Versions Notes

Abstract

:
(1) Background: To investigate the grouping of obesity and insulin resistance with caries and periodontitis from a syndemic perspective through pathways of socioeconomic inequalities, smoking, alcohol, and high sugar consumption in adolescence. (2) Methods: The population-based RPS Cohort study, São Luís, Brazil, in ages 18–19 years (n = 2515) was used. The outcomes were the grouping of pbesity and Insulin Resistance Phenotype (latent variable formed by Triglycerides/HDL ratio, TyG index, and VLDL) and the Chronic Oral Disease Burden (latent variable comprising caries, bleeding on probing, probing depth ≥ 4 mm, clinical attachment level ≥ 3 mm, and visible plaque index ≥ 15%). Socioeconomic Inequalities influencing the Behavioral Risk Factors (latent variable formed by added sugar, smoking, and alcohol) were analyzed using structural equation modeling. (3) Results: Socioeconomic Inequalities were associated with the Chronic Oral Disease Burden [Standardized Coefficient (SC) = 0.222, p < 0.001]. Behavioral Risk Factors were associated with increased Chronic Oral Disease Burden (SC = 0.103; p = 0.013). Obesity was associated with the Insulin Resistance Phenotype (SC = 0.072; p < 0.001) and the Chronic Oral Disease Burden (SC = 0.066; p = 0.005). The Insulin Resistance Phenotype and the Chronic Oral Disease Burden were associated (SC = 0.053; p = 0.032). (4) Conclusion: The grouping of obesity and early events of diabetes with caries and periodontitis call for a syndemic approach in adolescence.

1. Introduction

Syndemia is the interaction of two or more diseases, co-occurring or sequential, triggered by economic, social, and environmental contextual risks, multiplying the overall burden of diseases in a scenario of social injustice [1,2]. Social vulnerability favors aggregating behavioral risk factors to form a syndemic interplay, like non-communicable disease (NCD) grouping [1].
Syndemic models allow for the investigation of pathways through which inequality contributes to behavioral risk factors resulting in disease grouping. When sifting through the literature, no studies using the syndemic approach to analyze a set of NCDs in adolescence, including oral diseases, were found. Caries and periodontitis are among the most prevalent NCDs worldwide [3]. These oral diseases are economically and socially determined, being more expressed in low- and middle-income countries or deprived populations in high-income settings [4]. Caries and periodontitis share common risk factors with each other and with other NCDs, such as an unhealthy diet high in added sugars [5], smoking, and alcohol consumption [4,5].
Caries and periodontitis are mediated by oral biofilm [6]. The excessive intake of fermentable carbohydrates, especially sugars, has been implicated in oral biofilm dysbiosis, resulting in both caries and periodontitis [6]. In addition, an unhealthy diet rich in sugars may contribute systemically to periodontal inflammation resulting from advanced glycation end-products (AGEs), oxidative stress, and inflammation [5,7,8]. Low-grade systemic inflammation has been pointed out as a universal mechanism behind the NCDs [9] and related to caries in childhood and adolescence [5,10]. High sugar intake has been associated with obesity [11], similar to caries and periodontitis, even among young persons [12,13].
Caries and gingivitis co-occur in early childhood [14] and seem to predict periodontitis in adult life [15]. Periodontitis, in turn, precedes type 2 diabetes for decades in adults [16]. However, insulin resistance may occur during periodontitis onset in adolescents, suggesting that periodontal diseases and diabetes occur concurrently throughout life [17]. In addition, untreated caries and tooth loss may predict all-cause mortality, especially mortalities due to NCDs, such as cardiovascular and cancer [18].
We have shown the grouping of caries and periodontal indicators, forming the phenomenon of Chronic Oral Disease Burden in young Brazilians [5] and in Americans from adolescence to elderhood [19]. Moreover, we have proposed an Insulin Resistance Phenotype to represent the early events of the diabetes continuum associated with earlier cardiovascular risk events in adolescents [20]. Thus, we hypothesize that a syndemic framework involving socioeconomic inequalities and behavioral risk exposures would result in grouping obesity and the Insulin Resistance Phenotype with caries and periodontitis among adolescents. Therefore, we modeled syndemic pathways from Socioeconomic Inequalities and Behavioral Risk Factors (high sugar consumption, smoking, and alcohol) toward the grouping of obesity and the Insulin Resistance Phenotype with a Chronic Oral Disease Burden at the end of the second decade of life.

2. Materials and Methods

2.1. Study Design

A population-based study was nested within the Consortium of Brazilian birth cohorts from Ribeirão Preto, Pelotas, and São Luís (RPS Birth cohorts) [21]. This cohort has been studying precursors of noncommunicable diseases at baseline (birth period), and at the end of the first (1st follow-up) and second decades of life (2nd follow-up).
The birth cohort included 94.1% (n = 2541) of all births in São Luís from March 1997 to February 1998 (baseline). From January to November 2016, 687 participants from the initial cohort, aged 18–19 years, were located. At that time, to increase sample power and prevent future losses, the cohort also had an open design (retrospective cohort) that included adolescents born in São Luís in 1997. The retrospective cohort was drawn using the Brazilian Living Birth Information System database (SINASC), generating a random sample (n = 1133). Additionally, individuals identified in schools and universities as long as they were registered in the SINASC (n = 695) were also included. The final study sample for the present study comprised 2515 adolescents from the original perspective and retrospective cohorts to ensure sample representativeness.
This study was approved by the Ethics and Research Committee of the Federal University of Maranhão University Hospital (IRB #1,302,489). All participants signed informed consent. We reported this study following the STROBE guidelines.

2.2. Data Collection

We collected socioeconomic information, including monthly family income, categorized as ≥5, 3 to <5, 1 to <3, or 1 Brazilian national minimum wage in 2016 (USD 252.1); adolescent’s education, categorized as college (incomplete or complete), high school, and middle school; household head’s education, similarly classified; and socioeconomic class using Brazilian Economic Classification from A to E classes, in which Class A is the wealthiest and Class E, the poorest. The adolescent’s sex was recorded as male (1) or female (2).
Smoking was a dichotomous categorical variable defined as current cigarette smoking. Problems related to alcohol use were measured using the Alcohol Use Disorder Identification (AUDIT) [22] and classified as low (score 0 to 4) or high risk (score of 5 or more).
Dietary information was obtained from Food Frequency Questionnaires (FFQ), composed of 106 foods and beverages, including frequency, portion size, and quantity, related to the last 12 months [23]. A quality–quantity FFQ estimated the portion sizes (small, medium, or large) using a photographic record to reduce diet measurement bias. The questionnaire was administered by adequately trained nutritionists using REDCap, a web application for online research and databases.
Added sugars refer to sugars and syrups incorporated into foods during preparation or processing or added to the table [24]. These sugars are the primary origin of discretionary calories in the human diet and have been implicated with obesity and NCDs, such as diabetes, cardiovascular diseases, caries, and periodontitis [5,14]. The daily added sugars intake (mL or g) was calculated by multiplying the frequency of consumption and the daily recorded portion size from added sugar present in beverages such as soft drinks, fruit-flavored juice, chocolate drinks, energy drinks, and a wide range of food groups, such as dairy products, bread, cookies, breakfast cereals, desserts, chocolate, mayonnaise, salty snacks, and cold cuts. Finally, the added-sugar consumption was estimated as the percentage of calories from sugar of daily total energy intake and the daily sugar intake in grams. The daily sugar limit for adolescents was categorized according to the American Heart Association’s guidelines up to <25 g [24] (ideal), and high exposure as 25 g to 49.9 g, 50 g to 74.9 g, and >75 g per day.
The adolescent’s height (in meters) was elicited using a stadiometer (Altura Exata®, Belo Horizonte, MG, Brazil) and the weight (kg) using a dual-energy X-ray absorptiometry (DEXA). The Body Mass Index (BMI) was calculated (kg/m2) and used as a categorical variable [25]: <25 kg/m2; 25 to <30 kg/m2; and ≥30 kg/m2.
A blood sample (40 mL) was collected from the cubital vein before a snack was served to the adolescents who were fasting for at least 2 h to analyze the serum level of triglycerides (mg/dL), high-density lipoprotein (HDL) (mg/dL), very low-density lipoprotein (VLDL) (mg/dL), and blood glucose (mg/dL). These markers were measured using the Sysmex XE-2100® (Sysmex Corporation, Kobe, Japan) hematology analyzer [20].
Six dentists examined the caries and periodontal indicators. The training process included 30 h of theoretical and practical aspects. It was performed on 13 adolescents and repeated within 24 h. Oral examinations were conducted under artificial light in a dental unit located within the research facilities. The following clinical parameters were gathered: the number of decayed teeth (DMF-T index) and teeth with visible plaque (VPI) were evaluated by examining four surfaces of all teeth, except for the third molars [26]. Bleeding on probing (BoP) (presence or absence of bleeding after periodontal probing), periodontal probing depth (PPD) (distance from the gingival margin to the most apical extent of probe penetration), and clinical attachment level (CAL) (distance from the cement–enamel junction to the most apical extent of probe penetration) were examined at six sites per tooth, excluding third molars. The inter-examiner Kappa index was 0.82 for the DMFT index, and the Interclass Correlation Coefficient was 0.88 for PPD, 0.84 for BoP, 0.93 VPI, and 0.97 for CAL.

2.3. Latent Variables

Latent variables are unobserved variables that reflect complex phenomena of multiple dimensions estimated by the shared variance among their effect indicators (observed variables) [27]. A latent variable estimation is the magnitude of the intercorrelations of their indicators, resulting in an effective estimate free of measurement errors and with greater power to detect differences [27,28]. The latent variables of this study were as follows: Socioeconomic Inequalities, Behavioral Risk Factors, Insulin Resistance Phenotype, and Chronic Oral Disease Burden.
Were deduced from the shared variance of the indicators: (a) monthly household income, (b) adolescent’s education, (c) household head’s education, and (d) socioeconomic class.
Behavioral Risk Factors were constructed from the shared variance of the indicators: (a) smoking, (b) alcohol abuse, and (c) added-sugar consumption.
Insulin Resistance Phenotype was composed of the shared variance of the indicators: (a) Triglycerides /HDL ratio, (b) VLDL concentration, and (c) TyG index. The TyG index was calculated by multiplying blood glucose by triglycerides, as in the formula Naperian logarithm (Ln [Triglyceride (mg/dL) × fasting glucose (mg/dL)/2]. All these indicators are markers of insulin resistance in young people [20].
Chronic Oral Disease Burden was estimated from the shared variance of the indicators: (a) the number of teeth with carious lesions, (b) VPI, (c) the number of teeth with BoP, (d) the number of teeth with PPD ≥4 mm, and (e) the number of teeth with CAL ≥3 mm [4,19,29].

2.4. Theoretical Model

We constructed a theoretical model to investigate a syndemic framework involving socioeconomic inequalities, behavioral risk factors for NCDs, and the grouping of obesity and early signs of diabetes risk with the co-occurrence of caries and periodontitis in adolescents. Socioeconomic Inequalities were the ancestral variable influencing the Behavioral Risk Factors resulting in the NCDs outcomes, namely: obesity, Insulin Resistance Phenotype with Chronic Oral Disease Burden. The model was adjusted for sex (Figure 1).

2.5. Statistical Analysis

Structural equation modeling (SEM) is an epidemiological tool allowing for the construction of latent variables and the interpretation of the results of multiple regressions simultaneously, assisting in evaluating variables involved in complex phenomena and minimizing biases arising from measurement errors [27].
The latent variables of Socioeconomic Inequalities, Behavioral Risk Factors, Insulin Resistance Phenotype, and Chronic Oral Disease Burden were constructed based on exploratory factor analysis and confirmatory factor analysis [27].
As a sensitivity analysis, due to the high correlation between smoking and alcohol consumption, we also analyzed a model with the interaction between these two variables by multiplying their indicators.
The weighted least squares estimator with mean and variance fit (WLSMV) and Theta parameterization were performed to control for residual variance. The evaluation of the overall fit quality was assessed using the indicators: (a) Root Mean Square Error of Approximation (RMSEA) with the upper bound of the 90% confidence interval below 0.08 and (b) Comparative Fit Index (CFI) and Tucker–Lewis Index (TLI) > 0.90 [27]. We assumed the standardized coefficient (SC) as significant if p < 0.05. Missing data were imputed by Maximum Likelihood Estimation (MLE), assuming that these data were missing randomly [20]. We performed the analyses using the Mplus® 8.0 software.

3. Results

Among the 2515 adolescents enrolled in the study, 3.56% (n = 89) were smokers, 19.43% (n = 488) had a higher risk for alcohol abuse, 6% (n = 151) had BMI ≥30 kg/m2, and 81.35% (n = 2033) consumed >25 g of added sugar per day. Regarding insulin resistance, 13.76% (n = 346) had TG/HDL-C ratio ≥3 mg/dL, 16.98% (n = 427) had VLDL-c above 30 mg/dL, and the mean TyG index was 8.22 mg/dL. The mean number of decayed teeth was 1.17 (SD ± 1.2). The mean number of teeth with PPD ≥4 mm was 1.17 (±2.47), with CAL ≥3 mm was 13.98 (±7.20), and with BoP was 11.65 (±6.67) (Table 1).
The fit indices indicated a good fit for the proposed model: RMSEA (0.067), 90% CI (0.064–0.070), CFI (0.938), and TLI (0.920) (Table 2). All effect indicators for the latent variables Socioeconomic Inequalities, Insulin Resistance Phenotype, and Chronic Oral Disease Burden showed convergent factor loadings (SC > 0.3; p < 0.001) (Table 3).
Socioeconomic Inequalities were associated with a higher Chronic Oral Disease Burden in adolescents (SC = 0.222; p < 0.001). Meanwhile, Socioeconomic Inequalities were inversely associated with obesity (SC = −0.099; p = 0.010) (Table 4).
There was a strong correlation between the indicators of the latent variable Behavioral Risk Factors (p < 0.001) (Table 3), which was associated with Chronic Oral Disease Burden (SC = 0.102; p = 0.013).
Proximally, revealing a syndemic framework, we observed the grouping of NCDs. Obesity was associated with the Insulin Resistance Phenotype (SC = 0.098; p < 0.001) and the Chronic Oral Disease Burden (SC = 0.052; p = 0.033). The Insulin Resistance Phenotype and the Chronic Oral Disease Burden were also associated (SC = 0.089; p = 0.007) (Table 4).
Girls had lower levels of Behavioral Risk Factors (SC = −0.216; p < 0.001) and Chronic Oral Disease Burden (SC = −0.141; p < 0.001).
Supplementary Table S2 shows a significant correlation between the insulin resistance indicators (Triglycerides /HDL, TyG index, and VLDL) with each other, with obesity, and with the periodontal disease indicators. Caries and periodontal disease indicators were associated with each other.

4. Discussion

We highlighted a syndemic framework linking obesity and the Insulin Resistance Phenotype with the Chronic Oral Disease Burden at the end of the second decade of life. Socioeconomic Inequalities were associated with a higher Chronic Oral Disease Burden in adolescents. Behavioral Risk Factors were associated with Chronic Oral Disease Burden.
Although NCDs’ co-occurrence has been previously shown, studies frequently analyzed the association of two conditions only, such as caries and periodontitis [4]; obesity and caries [13]; obesity and periodontitis [12]; obesity and insulin resistance [30]; and insulin resistance and periodontitis [10]. Our findings are pioneers in identifying the co-existence of multiple conditions by the end of the second decade of life, namely, insulin resistance, obesity, caries, and periodontitis. These findings stimulate a reflection on the approaches toward oral disease prevention and treatment, based mainly on intervention models targeting the oral biofilm [31]. Moreover, it supports more effective recommendations for tackling NCDs in youth, targeting socioeconomic, commercial determinants, and behavioral risk factors. This would impact not only the oral disease burden but also reduce rates of obesity, diabetes, and other NCDs in the future.
In epidemiological studies, we have used the Chronic Oral Disease Burden as a latent variable to analyze the correlation between caries and periodontitis indicators [4,19,29]. We draw attention to the fact that the Chronic Oral Disease Burden is not a diagnostic tool to be used in the clinical setting; instead, it is an epidemiological approach for understanding why the indicators of caries and periodontitis group through life and investigating their common risk factors [4,19,29]. In addition, this latent variable allows us to analyze the periodontal indicators in a continuous manner, dispensing cut-off points to determine case definition, which persists in disagreement and remains challenging in younger populations [32,33].
Intermediately, higher exposure to Behavioral Risk Factors increased the Chronic Oral Disease Burden. Strategies encompassing economic, social, structural, and commercial determinants for these behavioral risks may be more effective in reducing the burden of NCDs, including oral ones [34]. We cite, as examples of public policy measures, the successful implementation of anti-smoking laws in countries like Brazil [35], the regulation of access to alcoholic beverages, and market regulations that include the taxation, labeling, and regulation of sugar contents in products [36]. As alarming, we identified a set of Behavioral Risk Factors adopted by adolescents—sugar consumption, smoking, and alcohol abuse—that converged toward the latent variable Behavioral Risk Factors. While these variables measured different conditions, their convergence might be understood. For instance, sugar activates the central nervous system’s hedonic reward mechanism, inducing a dependence similar to addictive drugs [37], explaining its correlation with alcohol and smoking.
Ancestrally, as the primary determinant of health in our syndemic model, Socioeconomic Inequalities increased the Chronic Oral Disease Burden, reflecting aspects related to low education, deprived access to health services, insufficient oral hygiene practices and self-care, and food insecurity [38]. The social determinants of health are a universal phenomenon identified in low-, middle- and high-income countries, where socioeconomic disparities determine the poorest oral health indicators [39]. Oral diseases mainly affect disadvantaged and socially marginalized populations, showing that people experience health inequalities according to their position on the social scale [39].
Unexpectedly, in this study, higher Socioeconomic Inequalities were inversely associated with obesity. Brazil is currently experiencing a nutritional transition, shifting from nutritional deficit to obesity, especially among the poorest. In this context, in São Luís, the state capital with the lowest Human Development Index in Brazil, only 4.1% of the adolescents were obese, whereas, in more affluent regions of the country, the prevalence of obesity ranges from 6.6% to 11.1% [40]. Thus, it becomes evident that patterns of socioeconomic inequalities associate differently with obesity across the different Brazilian regions.
The sensitivity analysis revealed that high sugar consumption and the interaction between smoking and alcohol consumption were associated with the Chronic Oral Disease Burden (Table S1). The role of sugars in the etiology of caries is well-known, where the metabolism of sugars by dental biofilm results in dysbiosis, pH drop, and, consequently, tooth demineralization [6]. Concerning periodontitis, sugar may act locally, resulting in biofilm accumulation and dysbiosis [6,8], and systemically, involving oxidative stress and low-grade systemic inflammation [8,41]. Our findings shed light on the high number of Brazilian adolescents consuming sugar at a rate of above 25 g/day, considered the highest cutoff point for future cardiovascular diseases, according to the American Heart Association [24]. Smoking consumption is recognized as a cause of periodontitis [42]. As our population was composed of adolescents, it is relevant to consider that the harmful effects of smoking and alcohol are dose-dependent and cumulative [43].
In the sensitivity analysis, we observed that Socioeconomic Inequalities increased added-sugar consumption by adolescents. Inequalities lead to an unsafe environment, favoring unhealthy risk behaviors and resulting in exposure to processed foods rich in sugar, besides smoking and alcohol [36]. Low-income populations are more exposed to unhealthy diets, sugar-rich foods, and beverages that are cheaper and more accessible to their purchasing power [44].
Limitations of our study include its cross-sectional design, which prevents the drawing of causal relationships between the presumed exposures and outcomes. However, without the pretension to assume temporality, we highlighted a syndemic framework that requires common strategies to tackle multiple NCDs simultaneously. Fasting for 2 h (minimum) instead of extended periods (8 to 12 h) could be pointed out as a study limitation when evaluating insulin resistance. However, fasting has little effect on lipid profile measurements, resulting in international guidelines stating that blood analysis can be performed without fasting [45,46,47]. Furthermore, the Insulin Resistance Phenotype was analyzed as a continuous variable dispensing cutoff value, which represented the shared variance values among triglycerides/HDL, TyG, and VLDL, reducing measurement errors for any isolated indicator [20].
As strengths of our study, we showed the occurrence of a grouping of NCDs in adolescence: obesity, diabetes precursors, caries, and periodontitis. Moreover, the SEM analytical approach allowed for the studying of multiple outcomes, including complex conditions analyzed as latent variables, such as the Insulin Resistance Phenotype and the Chronic Oral Disease Burden, reducing the measurement error of these phenomena.
Adolescence is one of the most sensitive periods of human development, representing a “window of opportunity” for health interventions since several behaviors that begin at this life stage may affect future health [48]. We identified the co-occurrence of obesity and the early events of diabetes with caries and periodontitis at the end of the second decade of life. Our findings alert the need for a syndemic approach to adolescent health, directing efforts toward social, economic, and commercial determinants and behavioral risk factors to address NCDs, including oral diseases.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/nu15163512/s1, Supplementary Table S1. Sensitivity analysis including the standardized coefficient, standard error, and p value for the total effects of the association between Socioeconomic Inequalities, Insulin Resistance Phenotype, Chronic Oral Diseases Burden, added sugar, and smoking and alcohol interaction in adolescents (São Luís, Brazil, 2016). Supplementary Table S2. Correlation matrix between the indicators of Insulin Resistance Phenotype, obesity, and Chronic Oral Disease Burden (São Luís, Brazil, 2016).

Author Contributions

L.L.C.L., E.B.A.F.T., C.M.C.A., R.F.L.B. and C.C.C.R. contributed to the conception, design, data acquisition, analysis, and interpretation and drafted and critically revised the manuscript; G.G.N. and J.M.A.B. contributed to the conception and data interpretation and critically revised the manuscript. F.R.M.L. and S.A.-C. drafted and critically revised the manuscript. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by the Department of Science and Technology (DECIT/Brazilian Ministry of Health): Process 17617/2017-29.

Institutional Review Board Statement

All procedures performed in studies involving human participants were in accordance with the ethical standards of the Institutional Review Board of the Research Ethics Committee of the University Hospital/ Federal University of Maranhão (process number 1,302,489) and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards.

Informed Consent Statement

Informed consent was obtained from all individual participants included in this study.

Data Availability Statement

The data that support the findings of this study are available on request from the corresponding author. The data are not publicly available due to privacy or ethical restrictions.

Acknowledgments

We would like to thank the funding and/or support of the Department of Science and Technology (DECIT/Brazilian Ministry of Health), the National Council for Scientific and Technological Development CNPq- Research Productivity Fellow—Level 2 315360/2021-6 and 308917/2021-9, the São Paulo Research Foundation (FAPESP), the Maranhão State Research Foundation for Scientific and Technological Development (FAPEMA), the Foundation of Support to Teaching, Research and Assistance of Clinics Hospital of Ribeirão Preto Medical School, and the University Hospital of the Federal University of Maranhão, for their support in the logistics of data collection. University of São Paulo (FAEPA), Coordination for the Improvement of Higher Education Personnel (CAPES): Finance Code 001 and Amazônia Legal 0810/2020/88881.510244/2020-01.

Conflicts of Interest

The authors declare no conflict of interest.

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Nutrients 15 03512 g001
Figure 1. The theoretical model proposed for the analysis of the association between Socioeconomic Inequalities, Behavioral Risk Factors, obesity, Insulin Resistance Phenotype, and Chronic Oral Disease Burden in adolescents.
Figure 1. The theoretical model proposed for the analysis of the association between Socioeconomic Inequalities, Behavioral Risk Factors, obesity, Insulin Resistance Phenotype, and Chronic Oral Disease Burden in adolescents.
Nutrients 15 03512 g001
Table 1. Sociodemographic, behavioral risk, oral disease, and metabolic risk indicators in adolescents (São Luís, Brazil, 2016).
Table 1. Sociodemographic, behavioral risk, oral disease, and metabolic risk indicators in adolescents (São Luís, Brazil, 2016).
VariablesN%MeanStandard
Deviation
Income, Brazilian minimum monthly wage
≥528511.33--
3 to <533813.44--
1 to <3107942.90--
<179731.69--
Missing160.64
Educational level of the adolescent
College (incomplete)67226.72--
High school175869.90--
Middle school833.30--
Missing20.08--
Educational level of the head of the family
College32512.92--
Incomplete college813.22--
High school126050.10--
Middle school56322.39--
Illiterate28611.37--
Family economic class (ABEP)
A944.22--
B56525.37--
C and D111850.20--
E45020.21
Sex
Male119747.59--
Female131852.41--
Adolescent age
18 years174269.26--
19 years77330.74--
Added sugar
<25 g46618.65--
25 g a 49.9 g70528.21--
50 g a 74.9 g52621.05--
>75 g80232.09--
Smoking
Yes893.56--
No241496.44--
Risk of alcohol abuse
High risk48819.43--
Low risk2.02380.57--
Height (cm)166.819.11--
Weight (kg)61.4813.13--
Body Mass Index (BMI)
Non-obese (BMI < 25 kg/m2)190575.75--
Overweight (BMI ≥ 25 to <30 kg/m2)45918.25--
Obese (BMI ≥ 30 kg/m2)1516.00--
Triglycerides--91.0149.28
HDL--49.3711.92
Blood glucose level--91.9815.78
Triglycerides /HDL--2.051.72
VLDL--18.189.73
TyG--8.220.46
Number of decayed teeth--1.582.14
Visible plaque index (%)
<15%95239.97--
≥15%143060.03--
Bleeding on probing--11.656.67
Periodontal probing depth ≥ 4--1.172.47
Clinical attachment level ≥ 3 mm--13.987.20
Table 2. Adjustment measures of the structural equation model to analyze the association between Socioeconomic Inequalities, Behavioral Risk Factors, obesity, Insulin Resistance Phenotype, and Chronic Oral Disease Burden in adolescents (São Luís, Brazil, 2016).
Table 2. Adjustment measures of the structural equation model to analyze the association between Socioeconomic Inequalities, Behavioral Risk Factors, obesity, Insulin Resistance Phenotype, and Chronic Oral Disease Burden in adolescents (São Luís, Brazil, 2016).
EstimatorsExpected IndicesModel Indices
X2 * 475.064
Degrees of freedom 120
p value X2 0.0000
RMSEA †<0.050.067
90% CI ‡<0.080.64–0.070
p §>0.050.890
CFI ||>0.900.938
TLI #>0.900.920
* Chi-squared test. † Root means square error of approximation. ‡ Confidence interval. § p value. || Comparative fit index. # Tucker–Lewis index.
Table 3. Factor loading, standard error, and p-value for the effect indicators of the latent variables: Socioeconomic Inequalities, Behavioral Risk Factors, Insulin Resistance Phenotype, and Chronic Oral Disease Burden (São Luís, Brazil, 2016).
Table 3. Factor loading, standard error, and p-value for the effect indicators of the latent variables: Socioeconomic Inequalities, Behavioral Risk Factors, Insulin Resistance Phenotype, and Chronic Oral Disease Burden (São Luís, Brazil, 2016).
Latent VariableStandardized CoefficientStandardized Errorp
Socioeconomic Inequalities
 Household income0.6210.026<0.001
 Educational level of the adolescent0.5290.023<0.001
 Educational level of the head of the family0.7120.024<0.001
 Economic class0.8540.027<0.001
Behavioral Risk Factors
 Added Sugar0.2210.038<0.001
 Smoking0.9950.098<0.001
 Alcohol abuse0.6280.066<0.001
Insulin Resistance Phenotype
 Triglycerides/HDL0.7700.052<0.001
 VLDL0.9020.061<0.001
 TyG Index0.9390.063<0.001
Chronic Oral Disease Burden
 Decayed component (DMFT Index)0.3350.018<0.001
 Visible Plaque Index (VPI Index)0.6050.015<0.001
 Bleeding on probing0.5160.018<0.001
 Periodontal probing depth ≥ 4 mm0.6740.013<0.001
 Clinical attachment level ≥ 3 mm0.6720.013<0.001
Table 4. Standardized coefficient, standard error, and p value for the total effects of the association between Socioeconomic Inequalities, Insulin Resistance Phenotype, Chronic Oral Diseases Burden, added sugar, and smoking and alcohol interaction in adolescents (São Luís, Brazil 2016).
Table 4. Standardized coefficient, standard error, and p value for the total effects of the association between Socioeconomic Inequalities, Insulin Resistance Phenotype, Chronic Oral Diseases Burden, added sugar, and smoking and alcohol interaction in adolescents (São Luís, Brazil 2016).
Explanatory VariablesOutcomeStandardized CoefficientStandardized Errorp
Socioeconomic InequalitiesChronic Oral Diseases Burden0.2220.026<0.001
Socioeconomic InequalitiesObesity−0.0990.0320.010
ObesityInsulin Resistance Phenotype0.0980.027<0.001
ObesityChronic Oral Diseases Burden0.0890.0330.007
Insulin Resistance PhenotypeChronic Oral Diseases Burden0.0520.0250.033
Behavioral Risk FactorsChronic Oral Diseases Burden0.1020.0410.013
SexChronic Oral Diseases Burden−0.1410.024<0.001
SexBehavioral Risk Factors−0.2160.036<0.001
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MDPI and ACS Style

Ladeira, L.L.C.; Nascimento, G.G.; Leite, F.R.M.; Alves-Costa, S.; Barbosa, J.M.A.; Alves, C.M.C.; Thomaz, E.B.A.F.; Batista, R.F.L.; Ribeiro, C.C.C. Obesity, Insulin Resistance, Caries, and Periodontitis: Syndemic Framework. Nutrients 2023, 15, 3512. https://doi.org/10.3390/nu15163512

AMA Style

Ladeira LLC, Nascimento GG, Leite FRM, Alves-Costa S, Barbosa JMA, Alves CMC, Thomaz EBAF, Batista RFL, Ribeiro CCC. Obesity, Insulin Resistance, Caries, and Periodontitis: Syndemic Framework. Nutrients. 2023; 15(16):3512. https://doi.org/10.3390/nu15163512

Chicago/Turabian Style

Ladeira, Lorena Lúcia Costa, Gustavo Giacomelli Nascimento, Fábio Renato Manzolli Leite, Silas Alves-Costa, Janaína Maiana Abreu Barbosa, Claudia Maria Coelho Alves, Erika Barbara Abreu Fonseca Thomaz, Rosangela Fernandes Lucena Batista, and Cecilia Claudia Costa Ribeiro. 2023. "Obesity, Insulin Resistance, Caries, and Periodontitis: Syndemic Framework" Nutrients 15, no. 16: 3512. https://doi.org/10.3390/nu15163512

APA Style

Ladeira, L. L. C., Nascimento, G. G., Leite, F. R. M., Alves-Costa, S., Barbosa, J. M. A., Alves, C. M. C., Thomaz, E. B. A. F., Batista, R. F. L., & Ribeiro, C. C. C. (2023). Obesity, Insulin Resistance, Caries, and Periodontitis: Syndemic Framework. Nutrients, 15(16), 3512. https://doi.org/10.3390/nu15163512

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