# Sex/Gender influences on periodontal disease and diabetes: A population science approach, with software

> **NIH NIH R01** · VIRGINIA COMMONWEALTH UNIVERSITY · 2022 · $575,194

## Abstract

Periodontal Disease (PD) continues to remain a major public health burden in the United States. Manifestation
and progression of PD are multifactorial, and may vary across gender, with/without additional comorbidities, such
as Type-2 Diabetes (T2D), where comorbid subjects are at an elevated risk of compromised oral health. There
is an overall paucity of clinically interpretable and nationally representative cross-sectional summaries of
numerous risk factors (and their complex interactions) in assessing multi-comorbidity aspects (here, PD and
T2D), and precise estimation of associated causal treatments for PD in practice-based settings, factoring in the
interactions of sex/gender influences. Publicly available nationwide survey databases (such as the NHANES),
and large oral health databases (such as the HealthPartners®, HP) are important, but somewhat under-utilized
resources for such evaluations and practical interpretations, mainly due to several unique statistical and
epidemiological complexities, which are often beyond the capabilities of existing standard analytical tools and
software packages. Furthermore, how to prioritize patients for oral clinic visits based on their sex/gender
determinants, and multi-comorbidity risks continues to remain unresolved. In this project, we address these
challenges, and initially propose a stochastically-principled, nationally meaningful, summary risk index (Aim 1)
representing cross-sectional PD association from about 11,700 adult dentate subjects, who are part of the
NHANES 2009-2014 study, for the 4 target groups: (a) Males with T2D, (b) Males without T2D, (c) Females with
T2D, and (d) Females, without T2D. We then refine and validate this derived index, and propose a time-varying
PD index (Aim 2) for the four target subgroups, accommodating causality of periodontal treatment effects, via
application to the rich, longitudinal, observational HP database of about 25,000 subjects in a practice-based
setting, with further model fitting and cross-validation using the Kaiser Permanente Northwest database of about
1,17,000 subjects with similar characteristics. Next, we utilize the time-varying index to construct an optimal
policy (Aim 3) for prioritizing high-risk patients for quicker clinic visits. Finally, we produce a free, interactive,
web-application tool (Aim 4) via R Shiny, for estimation and computation of the personalized index and recall
decisions for any future patient. Our statistically principled, comprehensive, unique index for PD integrating
electronic medical records from two large HMOs will be the first of its kind to generate new knowledge in regards
to assessing sex/gender influences. Furthermore, the proposed methodology is readily generalizable to other
comorbidities across gender choices, such as cardiovascular disease, kidney and liver disease, etc. In the longer
term, pending rigorous model validation, the derived index has the potential to be integrated into popular
chairside software, ...

## Key facts

- **NIH application ID:** 10531704
- **Project number:** 1R01DE031134-01A1
- **Recipient organization:** VIRGINIA COMMONWEALTH UNIVERSITY
- **Principal Investigator:** Dipankar Bandyopadhyay
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $575,194
- **Award type:** 1
- **Project period:** 2022-09-01 → 2026-08-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10531704

## Citation

> US National Institutes of Health, RePORTER application 10531704, Sex/Gender influences on periodontal disease and diabetes: A population science approach, with software (1R01DE031134-01A1). Retrieved via AI Analytics 2026-05-25 from https://api.ai-analytics.org/grant/nih/10531704. Licensed CC0.

---

*[NIH grants dataset](/datasets/nih-grants) · CC0 1.0*