Dental Screening for Cardiovascular Disease Risk
Periodontal disease (PD) is one of the most prevalent multifactorial chronic infections, affecting 46% of US adults. Though PD occurs in the 30-40 year age range, the incidence peaks at age 38 years. Atherosclerotic cardiovascular diseases (ACVD) is caused by heredity, environment, and their interactions; conventional risk factors are mainly lifestyle risk factors, including smoking, obesity, hypertension, and diabetes, all of which can be managed by lifestyle improvements. PD is an independent risk factor for the development of ACVD.
Systematic reviews since the late 1980s have shown a consistent association between ACVD and PD which may be partially attributed to shared risk factors and the dissemination of periodontal pathogens into the bloodstream and cardiovascular system or an increase in systemic inflammation. For example, Lalla et al reported that oral infection with P gingivalis accelerates early atherosclerosis in Apolipoprotein E–Null Mice. Similarly, the DNA of periodontal pathogens was detected in the tissues of patients undergoing carotid endarterectomy.
Numerous studies reported that information on exposure and outcome data extracted from large volumes of healthcare insurance claims data are valid for clinical research purposes. In the current study, healthcare insurance claims data were used to investigate whether people with PD status have an increased risk of a nonfatal ACVD event compared to people without PD status.
A cohort of 1.2 million participants from Achmea, a Netherlands-based insurance claims database was studied longitudinally for 8 years, from O1 January 2007 through 31 December 2014. Individuals with censored follow-up were excluded and focused on cases of nonfatal ACVD in relation to PD.
The Dutch system allows the usage of dental care insurance claim codes only in a consecutive manner. Meaning PD treatment codes are only allowed to use after the use of a PD diagnosis code. In other words, this claim code is sufficient enough to define a participant as a PD patient.
For medical insurance claim codes, the system always uses one code including both the diagnosis and treatment of a specific disease. In this study, PD status was derived from dental care insurance claims, and ACVD status from medical care insurance claims. The documentation dates of PD and ACVD codes were extracted from the database to allow for survival analyses.
Age was calculated at the start of the insurance period (01 January 2007) for all participants. Status for Diabetes Mellitus (insulin-dependent and insulin-non-dependent) was derived from the codes for glucose-lowering agents. Prescriptions for antihypertensive and lipid-lowering agents were also extracted for further background characterization of the participants.
Person-time at risk (PTAR) was calculated from the start of follow-up (01 January 2007) for participants with and without PD status until ACVD or event-free point of censoring (31 December 2014) (exposed time). Incident density (per 1000 person-years) was calculated for participants with and without PD by dividing the total number of ACVD events by their PTAR and multiplying that by 1000.
Time-dependent Cox proportional hazard models were used to calculate the hazard ratio (HR) with their corresponding 95% confidence intervals (CI) and to adjust for shared risk factors (age, sex, socioeconomic position, and diabetes mellitus).
Previous studies showed strong evidence for overlapping genetic risk factors in individuals <35 years of age compared to the ones 35 years or older. Therefore the cut-off for subgroup analyses was set as 35 years of age.
E-values were calculated for the total study population and both age subgroups. All statistical analyses were performed using SPSS Statistics 27.0 Software.
- Of the 1224457 participants available for the univariable analysis, sex was missing for 54, and SEP for 102619 participants. Therefore, data of 1121790 (91.6%) participants were used for multivariate analyses. The mean age of participants with ACVD was 59.9 ± 12.6 and 44.5 ± 15.0 in the participants without ACVD. There were more females in the total study population and the group without ACVD, whereas males were more in the ACVD group.
- The percentage of participants with low, medium, and high SEP was more or less similar in both groups. However, the DM percentage was higher in the group with ACVD compared to the one without ACVD. The prescription of antihypertensive and lipid-lowering medication was higher for participants with ACVD compared to the participants without ACVD and the subgroup >35 years of age.
- The prevalence of PD was 20.1%, and the cumulative incidence of nonfatal ACVD was 7.5%.
- Cox-proportional-hazard models showed that participants with PD have a high risk of ACVD compared to the ones without PD. Furthermore, in the subgroup of participants ≤35 years of age, people with PD have an increased risk of ACVD compared to people without PD status, both in the univariable model and the two multivariable models.
- Both univariable and multivariable analyses showed a limited risk of ACVD for participants with PD status (HR: 1.12; 95% CI 1.10–1.14, HR: 1.06; 95% CI 1.04–1.08).
- A subgroup analysis of participants ≤35 and > 35 years of age showed that those ≤35 years of age with PD had a higher ACVD risk (univariable HR: 1.20; 95% CI 1.05–1.37, multivariable HR: 1.21; 95% CI 1.05–1.39). It was also observed that ACVD risk was not increased in participants >35 years of age with PD status (univariable HR: 0.92; 95% CI 0.91–0.94, multivariable HR: 0.96; 95% CI 0.94–0.98).
The relationship between PD and nonfatal ASCVD has important public health implications. The objective of this study was to assess this association using large-scale cohort data from the country’s largest healthcare insurance claims database. The results of this observational study showed that PD was only a weak factor for nonfatal ACVD events but it was a strong risk factor in the participants ≤35 of age nonetheless.
Buekers et al found associations between PD and ACVD using cross-sectional data extracted from electronic healthcare records of dental school. However, the longitudinal data in this study allowed to do survival analyses, thereby making it possible to study PD as a putative risk factor for ACVD. Earlier studies reported genetic overlaps between ACVD and aggressive PD, and the current study found that the risk of ACVD is stronger in younger people with PD. There is little evidence of genetic overlap between ACVD and PD in older people (>35 years of age). In other words, the risk of ACVD in people decreases as they age. This warrants further exploration of the role of variable lifestyle factors that may trigger inflammatory pathways in PD and ACVD.
Another important finding is that the number of participants using antihypertensive or lipid-lowering medication was significantly higher in the subgroup > 35 years of age. Because these medications are used to lower cardiovascular risk, this could provide some explanation for the lesser risk of ACVD in this subgroup.
The present study contains several limitations. Firstly, the shorter follow-up time (8 years). Since ACVD is a slowly progressive disease, a longer follow-up would have been preferable. Similarly, the average age could have been higher. ACVD occurs at an older age (59.9) so if the mean age at baseline was higher, the number of ACVD events and possible risk of PD could have been analyzed better. Second, no information was available on when the medication for cardiovascular risk reduction was prescribed for the first time. Thirdly, the SEP assessment was based on postal codes, which may have resulted in differential misclassification and ecological bias. Fourth, PD status was defined based on insurance and billing codes. Fifth, the participants voluntarily signed up for additional dental insurance.
The multivariable survival analysis of this large population-based cohort study showed that PD is a weak risk factor for nonfatal ACVD. The finding that PD status is a stronger risk factor in the participants <35 years of age may indicate genetic risk factor overlap.
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