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Record W4414556855 · doi:10.1186/s12903-025-06736-2

Dental caries and body mass index in adult and elderly lithuanians: a cross-sectional study exploring sex-specific patterns

2025· article· en· W4414556855 on OpenAlexaff
Lina Stangvaltaite‐Mouhat, Rasa Skudutyte‐Rysstad, Jolanta Aleksejūnienė, Vilma Brukienė, Indrė Stankevičienė, Алина Пуриене

Bibliographic record

VenueBMC Oral Health · 2025
Typearticle
Languageen
FieldDentistry
TopicDental Health and Care Utilization
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsBody mass indexObesityOral and maxillofacial surgeryOral healthConfidence intervalLithuanianPublic healthNational Health and Nutrition Examination SurveyBinomial regression

Abstract

fetched live from OpenAlex

BACKGROUND: WHO calls to end the global oral health crisis, as oral diseases impact more people than all other noncommunicable diseases (NCDs) combined. Obesity, a global epidemic, is the fifth leading cause of death worldwide. Both caries and obesity substantially contribute to the global NCDs burden and share common risk factors including commercial determinants of health. However, the relationship between caries and obesity has been inconsistently reported and it has been suggested that sex may play a role. Therefore, the aim of this study was to investigate the sex-specific association between body mass index (BMI) and dental caries in a national sample of Lithuanian adults and early elderly, adjusting for common risk factors. METHODS: The study was based on data from the Lithuanian National Oral Health Survey (2017–2019), comprising a stratified random sample of 1405 adults aged 34–78 from major cities and suburban/rural areas. Information on sociodemographic factors, health-related behaviors, height, and weight was collected using the WHO Oral Health Questionnaire, BMI index was calculated and categorized per WHO criteria. Caries experience was assessed at a surface level and recorded as intact, decayed, missing, or filled surfaces. The count of decayed-, missing-, and filled surfaces (D3MFS), and counts of individual components, namely, the total numbers of decayed surfaces (D3S), filled surfaces (FS) and missing teeth (MT) were also recorded. Negative binomial regression analyses were conducted. Ratios of means (RMs) with 95% confidence intervals (CI) for the associations were estimated, accounting for the natural logarithm of age and common risk factors. RESULTS: The study included 886 (67%) females, mean age 55.2 (SD 11.7) years and 441 (33%) males, mean age 54.0 (SD 12.1) years. Males with obesity had an average of 37% more missing teeth (adjusted RM 1.37; 95% CI 1.01, 1.85), and males with overweight had an average of 41% fewer D3S than normal-weight males (adjusted RM 0.59; 95% CI 0.39, 0.86). Overweight females had an average of 33% and obese 74% more missing teeth than normal-weight females (adjusted RM 1.33; 95% CI 1.10, 1.61 and adjusted RM 1.74; 95% CI 1.43, 2.15, respectively). Moreover, obese females had an average D3MFS that was 14% higher than normal-weight females (adjusted RM 1.14; 95% CI 1.05, 1.24). The crude inverse associations between obesity and FS in females disappeared after the adjustment for common risk factors. CONCLUSION: Sex-specific associations between caries and overweight/obesity were observed. The associations were stronger and more pronounced in females, with a notable BMI-related gradient in tooth loss. Some sex-specific associations diminished after adjusting for the common risk factors, partly supporting the common risk factor approach. Our findings may inform gender- and sex-responsive public health strategies simultaneously targeting overweight/obesity and caries. Future studies should confirm our findings, including a more comprehensive set of common risk factors and performing sufficiently powered age-stratified sex-specific analyses.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.224
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.062
GPT teacher head0.365
Teacher spread0.303 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

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Citations1
Published2025
Admission routes1
Has abstractyes

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