Body Mass Index and Caries: Machine Learning and Statistical Analytics of the Dental, Oral, Medical Epidemiological (DOME) Nationwide Big Data Study
Abstract
:1. Introduction
- To explore the associations of decayed teeth as a dependent variable with different BMI categories in various statistical models adjusted for potential confounding factors, such as (1) socio-demographic variables: age, sex, educational level, socio-economic status (SES), residency, and country of birth; and (2) health-related habits: smoking, teeth brushing, cariogenic nutrition, and sugary drinks as well as other diseases comprising the Metabolic Syndrome (MetS) including, hypertension, diabetes, hyperlipidemia, cardiovascular disease, nonalcoholic Fatty Liver Disease (NAFLD), and obstructive sleep apnea (OSA).
- To employ supervised machine learning (ML) algorithms that will explore the relative clinical features importance for two targets: (a) the dichotomous variable of decayed teeth and (b) obesity (BMI ≥ 30 kg/m2), while using the same set of clinical features that were used in the statistical models.
- To compare the results obtained by the statistical and ML models and discuss and summarize the conclusions.
2. Methods
2.1. Data Source
2.2. Ethical Approval
2.3. Data Collection
2.4. Eligibility Criteria
2.5. Definition of Variables
2.6. Data Analysis
2.6.1. Statistical Analysis
2.6.2. Sub-Section Clinical Features Importance Based on Machine Learning Algorithms
3. Results
3.1. Socio-Demographics across BMI Categories
Parameter | BMI Categories | Total (%) or Mean ± SD | p Value | ||||
Underweight | Normal Weight | Overweight | Obesity | ||||
Number (%) | 3113 (4.7) | 38,924 (59.2) | 16,966 (25.8) | 6736 (10.2) | 65,739 (100) | ||
Age (years) | 19.9 ± 3.2 | 21.5 ± 5.7 | 25.2 ± 8.5 | 26.4 ± 9.0 | 22.8 ± 7.1 | <0.001 * | |
Sex | Men | 1556 (50.0) | 28,397 (73.0) | 14,113 (83.2) | 5342 (79.3) | 49,408 (75.2) | <0.001 ˅ |
Woman | 1557 (50.0) | 10,527 (27.0) | 2853 (16.8) | 1394 (20.7) | 16,331 (24.8) | ||
Education | High school | 2910 (93.7) | 33,064 (85.1) | 11,716 (69.1) | 4417 (65.7) | 52,107 (79.4) | <0.001 ˅ |
Technician | 75 (2.4) | 1865 (4.8) | 1940 (11.4) | 1038 (15.4) | 4918 (7.5) | ||
Academics | 120 (3.9) | 3937 (10.1) | 3289 (19.4) | 1272 (18.9) | 8618 (13.1) | ||
SES | Low | 128 (4.1) | 1642 (4.3) | 951 (5.7) | 4141 (6.3) | 3135 (4.8) | <0.001 ˅ |
Medium | 1654 (53.5) | 19,419 (50.5) | 9071 (54.5) | 3917 (59.2) | 34,061 (52.6) | ||
High | 1307 (42.3) | 17,364 (45.2) | 6636 (39.8) | 2288 (34.6) | 27,595 (42.6) | ||
Locality of residence | Urban Jewish | 2828 (91.2) | 33,151 (85.6) | 14,276 (84.7) | 5831 (87.1) | 56,086 (85.8) | <0.001 ˅ |
Urban non-Jewish | 267 (8.6) | 5369 (13.9) | 2401 (14.2) | 792 (11.8) | 8829 (13.5) | ||
Rural | 5 (0.2) | 196 (0.5) | 181 (1.1) | 75 (1.1) | 557 (0.7) | ||
Birth Country | Western Europe | 38 (1.2) | 896 (2.3) | 526 (3.1) | 223 (3.3) | 1683 (2.6) | <0.001 ˅ |
Eastern Europe | 238 (7.7) | 2196 (5.6) | 982 (5.8) | 412 (6.1) | 3828 (5.8) | ||
FSU | 42 (1.4) | 450 (1.2) | 207 (1.2) | 102 (1.5) | 801 (1.2) | ||
Asia | 3 (0.1) | 73 (0.2) | 64 (0.4) | 23 (0.3) | 163 (0.2) | ||
East Asia | 5 (0.2) | 57 (0.1) | 25 (0.1) | 7 (0.1) | 94 (0.1) | ||
Ethiopia | 129 (4.1) | 832 (2.1) | 152 (0.9) | 22 (0.3) | 1135 (1.7) | ||
Africa | 5 (0.2) | 99 (0.3) | 75 (0.4) | 29 (0.4) | 208 (0.3) | ||
North America | 39 (1.3) | 995 (2.6) | 441 (2.6) | 112 (1.7) | 1587 (2.4) | ||
South America | 8 (0.3) | 298 (0.8) | 168 (1.0) | 60 (0.9) | 534 (0.8) | ||
Oceania | 1 (0.0) | 47 (0.1) | 14 (0.1) | 3 (0.0) | 65 (0.1) | ||
Israel | 2603 (83.7) | 32,974 (84.7) | 14,306 (84.4) | 5741 (85.3) | 55,624 (84.6) |
3.2. Mean Number of Decayed Teeth across BMI Categories
3.3. Health-Related Practices and Medical Diagnoses Related to Metabolic Syndrome (MetS) across BMI Categories
Parameter | BMI Categories | Total (%) or Mean ± SD | p Value | ||||
Underweight | Normal Weight | Overweight | Obesity | ||||
Number (%) | 3113 (4.7) | 38,924 (59.2) | 16,966 (25.8) | 6736 (10.2) | 66,790 (100) | ||
Smoking | No | 3033 (97.4) | 37,210 (95.6) | 14,854 (87.6) | 5541 (82.3) | 60,638 (92.2) | <0.001 ˅ |
Yes | 80 (2.6) | 1714 (4.4) | 2112 (12.4) | 1195 (17.7) | 5101 (7.8) | ||
Brushing teeth at least once a day | No | 108 (10.2) | 1262 (10.6) | 607 (12.2) | 359 (17.7) | 2336 (11.7) | <0.001 ˅ |
Yes | 946 (89.8) | 10,700 (89.4) | 4365 (87.8) | 1167 (82.3) | 17,678 (88.3) | ||
Consumption of cariogenic nutrition | No | 440 (41. 8) | 5879 (49.2) | 2602 (52.4) | 1016 (50.3) | 9937 (49.7) | <0.001 ˅ |
Yes | 613 (58.2) | 6074 (50.8) | 2366 (47.6) | 1004 (49.7) | 10,057 (50.3) | ||
Consumption of sugary drinks | No | 452 (43.0) | 5509 (46.1) | 2346 (47.3) | 887 (43.9) | 9194 (46.0) | 0.014 ˅ |
Yes | 598 (57.0) | 6429 (53.9) | 2167 (52.7) | 1133 (56.1) | 10,777 (54.0) | ||
Hypertension | No | 3090 (99.3) | 38,283 (98.4) | 16,034 (94.5) | 5813 (86.3) | 63,220 (96.2) | <0.001 ˅ |
Yes | 23 (0.7) | 641 (1.6) | 932 (5.5) | 923 (13.7) | 2519 (3.8) | ||
Diabetes | No | 3111 (99.9) | 38,865 (99.8) | 16,854 (99.3) | 6614 (98.2) | 65,444 (99.6) | <0.001 ˅ |
Yes | 2 (0.1) | 59 (0.2) | 112 (0.7) | 122 (1.8) | 295 (0.4) | ||
Hyperlipidemia | No | 3110 (99.9) | 38,704 (99.4) | 16,614 (97.9) | 6555 (97.3) | 64,983 (98.8) | <0.001 ˅ |
Yes | 3 (0.1) | 220 (0.6) | 352 (2.1) | 181 (2.7) | 756 (1.2) | ||
Non-alcoholic fatty liver disease (NAFLD) | No | 3112 (100) | 38,837 (9.8) | 16,657 (98.2) | 6338 (94.1) | 64,944 (98.8) | <0.001 ˅ |
Yes | 0 (0) | 87 (0.2) | 309 (1.8) | 398 (5.9) | 795 (1.2) | ||
Obstructive sleep apnea (OSA) | No | 3113 (100) | 38,878 (99.9) | 16,867 (99.4) | 6638 (98.5) | 65,496 (99.6) | <0.001 ˅ |
Yes | 0 (0) | 46 (0.1) | 99 (0.6) | 98 (1.5) | 243 (0.4) | ||
Cardiovascular disease | No | 3038 (97.6) | 37,919 (97.4) | 16,162 (95.3) | 6297 (93.5) | 63,416 (96.5) | <0.001 ˅ |
Yes | 75 (2.4) | 1005 (2.6) | 804 (4.7) | 439 (6.5) | 2323 (3.5) |
3.4. Carious Teeth According to BMI Categories in Different Multivariate Analyses Models
3.5. Clinical Features Importance Based on Machine Learning Algorithms
3.5.1. Clinical Features Importance Based on XGBoost Machine Learning Model with the Dichotomous Target Variable of Decayed Teeth
3.5.2. Clinical Features Importance Based on XGBoost Machine Learning Model with Obesity Set as a Target Variable
4. Discussion
Strength and Limitations
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Variable | BMI Categories | |||
---|---|---|---|---|
Normal Weight | Underweight | Overweight | Obesity | |
1st Model without adjustment: carious teeth across BMI categories | ||||
OR and 95%CI | 1 | 1.40 (1.26–1.56) | 1.05 (1.00–1.11) | 1.46 (1.35–1.57) |
2nd Model: 1st Model adjusted for age | ||||
OR and 95%CI | 1 | 1.33 (1.19–1.48) | 1.19 (1.12–1.26) | 1.70 (1.58–1.84) |
3rd Model: 2nd model parameters with sex | ||||
OR and 95%CI | 1 | 1.42 (1.27–1.58) | 1.16 (1.09–1.22) | 1.68 (1.56–1.82) |
4th Model: 3rd model parameters with educational level | ||||
OR and 95%CI | 1 | 1.39 (1.24–1.55) | 1.15 (1.09–1.21) | 1.63 (1.50–1.76) |
5th Model: 4th model parameters with socio-economic status (SES) | ||||
OR and 95%CI | 1 | 1.35 (1.21–1.50) | 1.10 (1.05–1.17) | 1.53 (1.41–1.65) |
6th model: 5th model 5 parameters with residence location | ||||
OR and 95%CI | 1 | 1.32 (1.18–1.47) | 1.11 (1.05–1.17) | 1.51 (1.40–1.64) |
7th model: 6th model parameters with birth countries | ||||
OR and 95%CI | 1 | 1.29 (1.16–1.44) | 1.11 (1.05–1.17) | 1.51 (1.40–1.64) |
8th model: 7th model parameters with hypertension | ||||
OR and 95%CI | 1 | 1.29 (1.16–1.44) | 1.11 (1.05–1.17) | 1.51 (1.39–1.63) |
9th model: 8th model parameters with diabetes mellitus | ||||
OR and 95%CI | 1 | 1.29 (1.16–1.44) | 1.11 (1.05–1.17) | 1.51 (1.39–1.63) |
10th model: 9th model parameters with hyperlipidemia | ||||
OR and 95%CI | 1 | 1.29 (1.16–1.44) | 1.11 (1.05–1.17) | 1.51 (1.39–1.63) |
11th model: 10th model parameters with nonalcoholic Fatty Liver Disease (NAFLD) | ||||
OR and 95%CI | 1 | 1.29 (1.16–1.45) | 1.11 (1.05–1.17) | 1.51 (1.40–1.64) |
12th model: 11th model with Obstructive Sleep Apnea (OSA) | ||||
OR and 95%CI | 1 | 1.30 (1.16–1.45) | 1.11 (1.05–1.17) | 1.52 (1.40–1.64) |
13th model: 12th model with cardiovascular disease | ||||
OR and 95%CI | 1 | 1.30 (1.16–1.45) | 1.11 (1.05–1.17) | 1.52 (1.40–1.64) |
14th model: 13th model parameters with smoking | ||||
OR and 95%CI | 1 | 1.30 (1.16–1.45) | 1.11 (1.05–1.17) | 1.50 (1.39–1.63) |
15th model: 14th model parameters and tooth brushing | ||||
OR and 95%CI | 1 | 1.30 (1.16–1.45) | 1.11 (1.05–1.17) | 1.50 (1.39–1.63) |
16th model: 15th model parameters with cariogenic nutrition and sugary drinks | ||||
OR and 95%CI | 1 | 1.18 (1.004–1.39) | 1.04 (0.96–1.13) | 1.56 (1.39–1.76) |
Parameter | B | Standard Error | p Value | Exp(B) and 95% Confidence Interval for Exp(B) | Collinearity Statistics | |
---|---|---|---|---|---|---|
Tolerance | VIF | |||||
(Intercept) | 3.78 | 0.14 | <0.001 | 44.06 (33.08–58.69) | ||
Underweight vs. normal weight | 0.16 | 0.08 | 0.045 | 1.18 (1.004–1.39) | 0.838 | 1.193 |
Overweight vs. normal weight | 0.04 | 0.04 | 0.309 | 1.04 (0.96–1.13) | 0.858 | 1.165 |
Obesity vs. normal weight | 0.45 | 0.06 | <0.001 | 1.56 (1.39–1.76) | 0.951 | 1.051 |
Age | −0.01 | 0.004 | 0.005 | 0.989 (0.981–0.997) | 0.288 | 3.467 |
Sex: Men vs. women | 0.15 | 0.04 | <0.001 | 1.16 (1.07–1.26) | 0.922 | 1.084 |
Educational level: technicians vs. high school | −0.62 | 0.08 | <0.001 | 0.54 (0.46–0.63) | 0.564 | 1.774 |
Educational level: academic vs. high school | −0.50 | 0.07 | <0.001 | 0.60 (0.52–0.70) | 0.441 | 2.269 |
SES: medium vs. low | −1.03 | 0.08 | <0.001 | 0.35 (0.30–0.41) | 0.946 | 1.057 |
SES: high vs. low | −1.53 | 0.08 | <0.001 | 0.21 (0.18–0.25) | 0.937 | 1.068 |
Residence location: Urban Jewish vs. Urban non-Jewish | 0.34 | 0.05 | <0.001 | 1.41 (1.27–1.57) | 0.981 | 1.020 |
Residence location: Rural vs. Urban non-Jewish | 1.04 | 0.32 | 0.001 | 2.82 (1.49–5.33) | 0.985 | 1.015 |
Birth countries Western Europe vs. Israeli | 0.41 | 0.06 | <0.001 | 1.51 (1.33–1.70) | 0.983 | 1.017 |
Birth countries Eastern Europe vs. Israeli | 1.20 | 0.14 | <0.001 | 3.33 (2.49–4.45) | 0.980 | 1.021 |
Birth countries Asia vs. Israeli | 0.03 | 0.25 | 0.890 | 1.03 (0.63–1.69) | 0.995 | 1.005 |
Birth countries Ethiopia vs. Israeli | 0.26 | 0.13 | 0.052 | 1.30 ().98–1.69) | 0.986 | 1.015 |
Birth countries Africa vs. Israeli | 0.02 | 0.31 | 0.943 | 1.02 (0.55–1.88) | 0.986 | 1.015 |
Birth Countries North America vs. Israeli | −0.63 | 0.13 | <0.001 | 0.53 (0.40–0.69) | 0.991 | 1.010 |
Birth countries South America vs. Israeli | −0.25 | 0.19 | 0.202 | 0.77 (0.52–1.14) | 0.997 | 1.003 |
Hypertension | 0.08 | 0.08 | 0.362 | 1.08 (0.91–1.29) | 0.894 | 1.118 |
Diabetes Mellitus | 0.22 | 0.22 | 0.310 | 1.25 (0.81–1.93) | 0.946 | 1.057 |
Hyperlipidemia | −0.14 | 0.14 | 0.340 | 0.86 (0.65–1.16) | 0.957 | 1.045 |
Nonalcoholic Fatty Liver Disease (NAFLD) | −0.11 | 0.13 | 0.412 | 0.89 (0.67–1.17) | 0.907 | 1.103 |
Obstructive sleep apnea (OSA) | −0.37 | 0.23 | 0.117 | 0.69 (0.43–1.09) | 0.972 | 1.029 |
Cardiovascular disease | 0.07 | 0.08 | 0.409 | 1.07 (0.90–1.28) | 0.920 | 1.087 |
Smoking | 0.23 | 0.06 | <0.001 | 1.26 (1.11–1.44) | 0.750 | 1.333 |
Brushing teeth at least once a day | −0.62 | 0.04 | <0.001 | 0.53 (0.49–0.58) | 0.793 | 1.261 |
Consumption of cariogenic nutrition | 0.15 | 0.04 | 0.002 | 1.16 (1.06–1.27) | 0.589 | 1.679 |
Consumption of sugary drinks | 0.50 | 0.04 | <0.001 | 1.65 (1.50–1.81) | 0.578 | 1.731 |
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Ben-Assuli, O.; Bar, O.; Geva, G.; Siri, S.; Tzur, D.; Almoznino, G. Body Mass Index and Caries: Machine Learning and Statistical Analytics of the Dental, Oral, Medical Epidemiological (DOME) Nationwide Big Data Study. Metabolites 2023, 13, 37. https://doi.org/10.3390/metabo13010037
Ben-Assuli O, Bar O, Geva G, Siri S, Tzur D, Almoznino G. Body Mass Index and Caries: Machine Learning and Statistical Analytics of the Dental, Oral, Medical Epidemiological (DOME) Nationwide Big Data Study. Metabolites. 2023; 13(1):37. https://doi.org/10.3390/metabo13010037
Chicago/Turabian StyleBen-Assuli, Ofir, Ori Bar, Gaya Geva, Shlomit Siri, Dorit Tzur, and Galit Almoznino. 2023. "Body Mass Index and Caries: Machine Learning and Statistical Analytics of the Dental, Oral, Medical Epidemiological (DOME) Nationwide Big Data Study" Metabolites 13, no. 1: 37. https://doi.org/10.3390/metabo13010037