Body Mass Index and Caries: Machine Learning and Statistical Analytics of the Dental, Oral, Medical Epidemiological (DOME) Nationwide Big Data Study
The objectives of the research were to analyze the association between Body Mass Index (BMI) and dental caries using novel approaches of both statistical and machine learning (ML) models while adjusting for cardiovascular risk factors and metabolic syndrome (MetS) components, consequences, and relat...
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-12-01
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| Series: | Metabolites |
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| Online Access: | https://www.mdpi.com/2218-1989/13/1/37 |
| _version_ | 1797438809177063424 |
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| author | Ofir Ben-Assuli Ori Bar Gaya Geva Shlomit Siri Dorit Tzur Galit Almoznino |
| author_facet | Ofir Ben-Assuli Ori Bar Gaya Geva Shlomit Siri Dorit Tzur Galit Almoznino |
| author_sort | Ofir Ben-Assuli |
| collection | DOAJ |
| description | The objectives of the research were to analyze the association between Body Mass Index (BMI) and dental caries using novel approaches of both statistical and machine learning (ML) models while adjusting for cardiovascular risk factors and metabolic syndrome (MetS) components, consequences, and related conditions. This research is a data-driven analysis of the Dental, Oral, Medical Epidemiological (DOME) big data repository, that integrates comprehensive socio-demographic, medical, and dental databases of a nationwide sample of dental attendees to military dental clinics for 1 year aged 18–50 years. Obesity categories were defined according to the World Health Organization (WHO): under-weight: BMI < 18.5 kg/m<sup>2</sup>, normal weight: BMI 18.5 to 24.9 kg/m<sup>2</sup>, overweight: BMI 25 to 29.9 kg/m<sup>2</sup>, and obesity: BMI ≥ 30 kg/m<sup>2</sup>. General linear models were used with the mean number of decayed teeth as the dependent variable across BMI categories, adjusted for (1) socio-demographics, (2) health-related habits, and (3) each of the diseases comprising the MetS definition MetS and long-term sequelae as well as associated illnesses, such as hypertension, diabetes, hyperlipidemia, cardiovascular disease, obstructive sleep apnea (OSA) and non-alcoholic fatty liver disease (NAFLD). After the statistical analysis, we run the XGBoost machine learning algorithm on the same set of clinical features to explore the features’ importance according to the dichotomous target variable of decayed teeth as well as the obesity category. The study included 66,790 subjects with a mean age of 22.8 ± 7.1. The mean BMI score was 24.2 ± 4.3 kg/m<sup>2</sup>. The distribution of BMI categories: underweight (3113 subjects, 4.7%), normal weight (38,924 subjects, 59.2%), overweight (16,966, 25.8%), and obesity (6736, 10.2%). Compared to normal weight (2.02 ± 2.79), the number of decayed teeth was statistically significantly higher in subjects with obesity [2.40 ± 3.00; OR = 1.46 (1.35–1.57)], underweight [2.36 ± 3.04; OR = 1.40 (1.26–1.56)] and overweight [2.08 ± 2.76, OR = 1.05 (1.01–1.11)]. Following adjustment, the associations persisted for obesity [OR = 1.56 (1.39–1.76)] and underweight [OR = 1.29 (1.16–1.45)], but not for overweight [OR = 1.11 (1.05–1.17)]. Features important according to the XGBoost model were socioeconomic status, teeth brushing, birth country, and sweetened beverage consumption, which are well-known risk factors of caries. Among those variables was also our main theory independent variable: BMI categories. We also performed clinical features importance based on XGBoost with obesity set as the target variable and received an AUC of 0.702, and accuracy of 0.896, which are considered excellent discrimination, and the major features that are increasing the risk of obesity there were: hypertension, NAFLD, SES, smoking, teeth brushing, age as well as our main theory dependent variable: caries as a dichotomized variable (Yes/no). The study demonstrates a positive association between underweight and obesity BMI categories and caries, independent of the socio-demographic, health-related practices, and other systemic conditions related to MetS that were studied. Better allocation of resources is recommended, focusing on populations underweight and obese in need of dental care. |
| first_indexed | 2024-03-09T11:43:40Z |
| format | Article |
| id | doaj.art-8ca71df5db6a4632bf9c7c203165a440 |
| institution | Directory Open Access Journal |
| issn | 2218-1989 |
| language | English |
| last_indexed | 2024-03-09T11:43:40Z |
| publishDate | 2022-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Metabolites |
| spelling | doaj.art-8ca71df5db6a4632bf9c7c203165a4402023-11-30T23:27:57ZengMDPI AGMetabolites2218-19892022-12-011313710.3390/metabo13010037Body Mass Index and Caries: Machine Learning and Statistical Analytics of the Dental, Oral, Medical Epidemiological (DOME) Nationwide Big Data StudyOfir Ben-Assuli0Ori Bar1Gaya Geva2Shlomit Siri3Dorit Tzur4Galit Almoznino5Faculty of Business Administration, Ono Academic College, Kiryat Ono 55000, IsraelFaculty of Dental Medicine, Hebrew University of Jerusalem, Jerusalem 91120, IsraelFaculty of Business Administration, Ono Academic College, Kiryat Ono 55000, IsraelFaculty of Business Administration, Ono Academic College, Kiryat Ono 55000, IsraelMedical Information Department, General Surgeon Headquarter, Medical Corps, Israel Defense Forces, Tel-Hashomer, Ramat Gan 02149, IsraelFaculty of Dental Medicine, Hebrew University of Jerusalem, Big Biomedical Data Research Laboratory, Dean’s Office, Hadassah Medical Center, Jerusalem, 91120, IsraelThe objectives of the research were to analyze the association between Body Mass Index (BMI) and dental caries using novel approaches of both statistical and machine learning (ML) models while adjusting for cardiovascular risk factors and metabolic syndrome (MetS) components, consequences, and related conditions. This research is a data-driven analysis of the Dental, Oral, Medical Epidemiological (DOME) big data repository, that integrates comprehensive socio-demographic, medical, and dental databases of a nationwide sample of dental attendees to military dental clinics for 1 year aged 18–50 years. Obesity categories were defined according to the World Health Organization (WHO): under-weight: BMI < 18.5 kg/m<sup>2</sup>, normal weight: BMI 18.5 to 24.9 kg/m<sup>2</sup>, overweight: BMI 25 to 29.9 kg/m<sup>2</sup>, and obesity: BMI ≥ 30 kg/m<sup>2</sup>. General linear models were used with the mean number of decayed teeth as the dependent variable across BMI categories, adjusted for (1) socio-demographics, (2) health-related habits, and (3) each of the diseases comprising the MetS definition MetS and long-term sequelae as well as associated illnesses, such as hypertension, diabetes, hyperlipidemia, cardiovascular disease, obstructive sleep apnea (OSA) and non-alcoholic fatty liver disease (NAFLD). After the statistical analysis, we run the XGBoost machine learning algorithm on the same set of clinical features to explore the features’ importance according to the dichotomous target variable of decayed teeth as well as the obesity category. The study included 66,790 subjects with a mean age of 22.8 ± 7.1. The mean BMI score was 24.2 ± 4.3 kg/m<sup>2</sup>. The distribution of BMI categories: underweight (3113 subjects, 4.7%), normal weight (38,924 subjects, 59.2%), overweight (16,966, 25.8%), and obesity (6736, 10.2%). Compared to normal weight (2.02 ± 2.79), the number of decayed teeth was statistically significantly higher in subjects with obesity [2.40 ± 3.00; OR = 1.46 (1.35–1.57)], underweight [2.36 ± 3.04; OR = 1.40 (1.26–1.56)] and overweight [2.08 ± 2.76, OR = 1.05 (1.01–1.11)]. Following adjustment, the associations persisted for obesity [OR = 1.56 (1.39–1.76)] and underweight [OR = 1.29 (1.16–1.45)], but not for overweight [OR = 1.11 (1.05–1.17)]. Features important according to the XGBoost model were socioeconomic status, teeth brushing, birth country, and sweetened beverage consumption, which are well-known risk factors of caries. Among those variables was also our main theory independent variable: BMI categories. We also performed clinical features importance based on XGBoost with obesity set as the target variable and received an AUC of 0.702, and accuracy of 0.896, which are considered excellent discrimination, and the major features that are increasing the risk of obesity there were: hypertension, NAFLD, SES, smoking, teeth brushing, age as well as our main theory dependent variable: caries as a dichotomized variable (Yes/no). The study demonstrates a positive association between underweight and obesity BMI categories and caries, independent of the socio-demographic, health-related practices, and other systemic conditions related to MetS that were studied. Better allocation of resources is recommended, focusing on populations underweight and obese in need of dental care.https://www.mdpi.com/2218-1989/13/1/37dental cariesdecayed teethbody mass index (BMI)obesityMetabolic syndromedata-driven analytics |
| spellingShingle | Ofir Ben-Assuli Ori Bar Gaya Geva Shlomit Siri Dorit Tzur Galit Almoznino Body Mass Index and Caries: Machine Learning and Statistical Analytics of the Dental, Oral, Medical Epidemiological (DOME) Nationwide Big Data Study Metabolites dental caries decayed teeth body mass index (BMI) obesity Metabolic syndrome data-driven analytics |
| title | Body Mass Index and Caries: Machine Learning and Statistical Analytics of the Dental, Oral, Medical Epidemiological (DOME) Nationwide Big Data Study |
| title_full | Body Mass Index and Caries: Machine Learning and Statistical Analytics of the Dental, Oral, Medical Epidemiological (DOME) Nationwide Big Data Study |
| title_fullStr | Body Mass Index and Caries: Machine Learning and Statistical Analytics of the Dental, Oral, Medical Epidemiological (DOME) Nationwide Big Data Study |
| title_full_unstemmed | Body Mass Index and Caries: Machine Learning and Statistical Analytics of the Dental, Oral, Medical Epidemiological (DOME) Nationwide Big Data Study |
| title_short | Body Mass Index and Caries: Machine Learning and Statistical Analytics of the Dental, Oral, Medical Epidemiological (DOME) Nationwide Big Data Study |
| title_sort | body mass index and caries machine learning and statistical analytics of the dental oral medical epidemiological dome nationwide big data study |
| topic | dental caries decayed teeth body mass index (BMI) obesity Metabolic syndrome data-driven analytics |
| url | https://www.mdpi.com/2218-1989/13/1/37 |
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