Risk Factors of Dental Caries: Consumption of Sugary Snacks Among Indonesian Adolescents

Lendrawati, Lendrawati; Pintauli, Sondang; Rahardjo, Anton; Bachtiar, Adang; Maharani, Diah Ayu

doi:10.4034/pboci.2019.191.42

Cited by 7 publications

(5 citation statements)

References 15 publications

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“…Among the habits affecting oral health, tooth brushing frequency and consumption of sugary snacks were found to be among the factors associated with increased risk of caries according to our study. It is important to note that the consumption of sugary snacks is a significant risk factor for caries formation, with studies showing that it increases the risk of caries fivefold [ 62 ]. This finding is supported by the findings of other studies in the literature [ 29 , 30 , 32 , 58 ].…”

Section: Discussionmentioning

confidence: 99%

Utilization of machine learning models in predicting caries risk groups and oral health-related risk factors in adults

Çiftçi,

Aşantoğrol

2024

BMC Oral Health

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Background The aim of this study was to analyse the risk factors that affect oral health in adults and to evaluate the success of different machine learning algorithms in predicting these risk factors. Methods This study included 2000 patients aged 18 years and older who were admitted to the Department of Oral and Maxillofacial Radiology, Faculty of Dentistry, Gaziantep University, between September and December 2023. In this study, patients completed a 30-item questionnaire designed to assess the factors that affect the decayed, missing, and filled teeth (DMFT). Clinical and radiological examinations were performed, and DMFT scores were calculated after completion of the questionnaire. The obtained data were randomly divided into a 75% training group and a 25% test group. The preprocessed dataset was analysed using various machine learning algorithms, including naive Bayes, logistic regression, support vector machine, decision tree, random forest and Multilayer Perceptron algorithms. Pearson's correlation test was also conducted to assess the correlation between participants' DMFT scores and oral health risk factors. The performance of each algorithm was evaluated to determine the most appropriate algorithm, and model performance was assessed using accuracy, precision, recall and F1 score on the test dataset. Results A statistically significant difference was found between various factors and DMFT-based risk groups (p < 0.05), including age, sex, body mass index, tooth brushing frequency, socioeconomic status, employment status, education level, marital status, hypertension, diabetes status, renal disease status, consumption of sugary snacks, dry mouth status and screen time. When considering machine learning algorithms for risk group assessments, the Multilayer Perceptron model demonstrated the highest level of success, achieving an accuracy of 95.8%, an F1-score of 96%, and precision and recall rates of 96%. Conclusions Caries risk assessment using a simple questionnaire can identify individuals at risk of dental caries, determine the key risk factors, provide information to help reduce the risk of dental caries over time and ensure follow-up. In addition, it is extremely important to apply effective preventive treatments and to prevent the general health problems that are caused by the deterioration of oral health. The results of this study show the potential of machine learning algorithms for predicting caries risk groups, and these algorithms are promising for future studies.

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Section: Discussionmentioning

confidence: 99%

Utilization of machine learning models in predicting caries risk groups and oral health-related risk factors in adults

Çiftçi,

Aşantoğrol

2024

BMC Oral Health

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“…Menggosok gigi dua kali sehari serta menggunakan mouthwash mampu menghambat pertumbuhan bakteri penyebab karang gigi (Lendrawati et al, 2019). Akan tetapi adanya kandungan bahan abrasif, flourida, deterjen, pengawet dan perasa dalam pasta gigi dan mouthwash berbahaya bagi kesehatan (Putri et al, 2012).…”

Section: Pendahuluanunclassified

UJI AKTIVITAS ANTIBAKTERI GEL NANOPARTIKEL EKSTRAK ETANOL GAMBIR TERHADAP BAKTERI PENYEBAB KARIES GIGI (Streptococcus mutans)

Saputra¹,

Susanty²

2022

Human Care

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<em>Karang gigi, merupakan salah satu masalah yang banyak diderita oleh masyarakat yang ada diseluruh dunia. Pemilihan pasta gigi dan mouthwash yang tidak tepat dapat menyebabkan gigi rapuh dan kanker tulang. Salah satu solusi mengatasi permasalahan tersebut dengan pengolahan bahan alam seperti gambir menggunakan teknologi nanopartikel. Penelitian ini bertujuan untuk mengetahui aktivitas antibakteri variasi formulasi gel nanopartikel ekstrak etanol gambir terhadap bakteri Streptococcus mutans. Preparasi nanopartikel menggunakan metode gelasi ionik dengan bahan dasar kitosan dan NaTPP. Formulasi gel dibuat menggunakan variasi konsentrasi nanopartikel 0,5%, 1%, 3% dan 5% dalam basis gel carbopol. Produk nanopartikel selanjutnya dikarakterisasi dengan PSA. Untuk produk gel nanopartikel dilakukan uji aktifitas antibakteri. Dari hasil penelitian didapatkan ukuran partikel rata-rata nanopartikel ekstrak gambir yaitu 64 nm. Hasil uji zona hambat Formula 0, 1, 2, 3 dan 4 yaitu 0, 0, 0, 9 dan 19 mm. Hasil tersebut menunjukan Formula 4 memiliki aktivitas antibakteri yang kuat, hampir sama dengan kontrol positif yaitu 20 mm</em>

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“…More than 40% children and adolescents, and 90% adults in the US are being affected by dental caries, or more commonly known as tooth decay. Multiple factors are considered to contribute to the risk of having dental caries, such as some environmental factors and social behaviors [30][31][32]. Evidence has shown that some individuals are more susceptible to caries while some others are more resistant, almost irrelevant to the environmental risk factors they are exposed to, suggesting that genetic factors may play crucial roles in the risk of developing caries [33].…”

Section: Application 2: Whole Genome Association Study For Dental Cariesmentioning

confidence: 99%

A Constrained Generalized Functional Linear Model for Multi-Loci Genetic Mapping

Huang

Yang

et al. 2021

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In genome-wide association studies (GWAS), efficient incorporation of linkage disequilibria (LD) among densely typed genetic variants into association analysis is a critical yet challenging problem. Functional linear models (FLM), which impose a smoothing structure on the coefficients of correlated covariates, are advantageous in genetic mapping of multiple variants with high LD. Here we propose a novel constrained generalized FLM (cGFLM) framework to perform simultaneous association tests on a block of linked SNPs with various trait types, including continuous, binary and zero-inflated count phenotypes. The new cGFLM applies a set of inequality constraints on the FLM to ensure model identifiability under different genetic codings. The method is implemented via B-splines, and an augmented Lagrangian algorithm is employed for parameter estimation. For hypotheses testing, a test statistic that accounts for the model constraints was derived, following a mixture of chi-square distributions. Simulation results show that cGFLM is effective in identifying causal loci and gene clusters compared to several competing methods based on single markers and SKAT-C. We applied the proposed method to analyze a candidate gene-based COGEND study and a large-scale GWAS data on dental caries risk.

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Risk Factors of Dental Caries: Consumption of Sugary Snacks Among Indonesian Adolescents

Cited by 7 publications

References 15 publications

Utilization of machine learning models in predicting caries risk groups and oral health-related risk factors in adults

Utilization of machine learning models in predicting caries risk groups and oral health-related risk factors in adults

UJI AKTIVITAS ANTIBAKTERI GEL NANOPARTIKEL EKSTRAK ETANOL GAMBIR TERHADAP BAKTERI PENYEBAB KARIES GIGI (Streptococcus mutans)

A Constrained Generalized Functional Linear Model for Multi-Loci Genetic Mapping

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