BMI and behavioral factors on caries in Mexican urban/rural populations

Objectives: To evaluate the hypothesis of the impact of BMI and behavioral risk factors on dental caries experience in a cross-sectional study in two different Mexican populations, an urban and a rural.Methods: Adolescents (12 to 15-year-old) living in the urban city of Veracruz (UA) and in a rural town Tepancan (RA) in Mexico were enrolled. Caries status was recorded (ICDAS and DMFS) and Body Mass Index (BMI) was calculated (kg/m2) and classified (normal weight/overweight) according to WHO guidelines. Information about oral habits (toothbrushing/dental flossing/dental check-ups) and dietary intake patterns (consumption of sweets) were assessed via interview questionnaire. Data were analyzed (STATA) and a multinomial logistic regression model (MLRM) was run using ICDAS as a dependent variable.Results: A total of 464 subjects participated (UA=224; RA=240). Caries prevalence was higher in UA (42.4%) than in RA (33.7%) (p<0.01). UA had a higher prevalence of enamel lesions compared to RA (63.2% and 36.8%) and a lower prevalence of dentin lesions (22.5% and 77.5%, respectively) (p<0.01). BMI was similar for UA (21.98±3.35) and RA (21.86±2.44). In RA, caries experience (DMFS>0) was statistically higher in overweight subjects (p<0.05). In UA, better oral habits were reported, the association between regular dental-check ups and lower caries experience was significant (p<0.01) and the consumption of sweets was higher (p=0.04). Using the MLRM, the zone (UA or RA) was significantly related to caries severity (p<0.01).Conclusions: Caries was associated to behavioral and geographic situations. Higher caries prevalence was observed in UA despite adequate oral hygiene habits and frequent dental check-ups while higher caries severity was recorded in RA linked to the lack of them. BMI was not different between areas yet overweight was a statistically significant risk factor associated to caries experience in the rural population. Homogeneity in rural areas may reduce misinterpretation on data analysis.