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Aim
To investigate the medium-term associations of serum protein subfractions derived from proton nuclear magnetic resonance (1H-NMR) spectroscopy with periodontitis and tooth loss.
Materials and Methods
A total of 3031 participants of the cohort Study of Health in Pomerania (SHIP-TREND) were included. In addition to conventional serum testing, serum lipoprotein contents and subfractions were analysed by 1H-NMR spectroscopy. Confounder-adjusted associations of lipoprotein variables with periodontitis and the number of missing teeth variables were analysed using mixed-effects models with random intercepts for time across individuals, accounting for multiple testing.
Results
While only spurious associations between lipoprotein levels from conventional blood tests were found—that is, triglycerides were associated with mean clinical attachment level (CAL) and low-density lipoprotein cholesterol/high-density lipoprotein cholesterol (LDL-C/HDL-C) ratio with the number of missing teeth - several associations emerged from serum lipoprotein subfractions derived from 1H-NMR analysis. Specifically, elevated LDL triglycerides were associated with higher levels of mean probing depth (PD), mean CALs, and increased odds of having <20 teeth. HDL-4 cholesterol levels were inversely associated with mean PD. Systemic inflammation (C-reactive protein) might mediate the effects of LDL and HDL triglyceride contents on periodontitis severity.
Conclusions
Several associations between serum lipoprotein subfractions and periodontitis were observed. As the underlying biochemical mechanisms remain unclear, further research is needed.
Abstract
Aim
Observational research suggests that periodontitis affects psoriasis. However, observational studies are prone to reverse causation and confounding, which hampers drawing causal conclusions and the effect direction. We applied the Mendelian randomization (MR) method to comprehensively assess the potential bi‐directional association between periodontitis and psoriasis.
Materials and Methods
We used genetic instruments from the largest available genome‐wide association study of European descent for periodontitis (17,353 cases, 28,210 controls) to investigate the relationship with psoriasis (13,229 cases, 21,543 controls), and vice versa. Causal Analysis Using Summary Effect (CAUSE) estimates and inverse variance‐weighted (IVW) MR analyses were used for the primary analysis. Robust MR approaches were used for sensitivity analyses.
Results
Both univariable methods, CAUSE and IVW MR analyses, did not reveal any impact of periodontitis on psoriasis (CAUSE odds ratio [OR] = 1.00, p = 1.00; IVW OR = 1.02, p = .6247), or vice versa (CAUSE OR = 1.01, p = .5135; IVW OR = 1.00, p = .7070). The null association was corroborated by pleiotropy‐robust methods with ORs close to 1 and p‐values >.59. Overall, MR analyses did not suggest any effect of periodontitis on psoriasis. Similarly, there was no evidence to support an effect of psoriasis on periodontitis.
Conclusions
Within the limitations of this MR study, the outcomes supported neither periodontitis affecting psoriasis nor psoriasis affecting periodontitis.
Genetic risk factors play important roles in the etiology of oral, dental, and craniofacial diseases. Identifying the relevant risk loci and understanding their molecular biology could highlight new prevention and management avenues. Our current understanding of oral health genomics suggests that dental caries and periodontitis are polygenic diseases, and very large sample sizes and informative phenotypic measures are required to discover signals and adequately map associations across the human genome. In this article, we introduce the second wave of the Gene-Lifestyle Interactions and Dental Endpoints consortium (GLIDE2) and discuss relevant data analytics challenges, opportunities, and applications. In this phase, the consortium comprises a diverse, multiethnic sample of over 700,000 participants from 21 studies contributing clinical data on dental caries experience and periodontitis. We outline the methodological challenges of combining data from heterogeneous populations, as well as the data reduction problem in resolving detailed clinical examination records into tractable phenotypes, and describe a strategy that addresses this. Specifically, we propose a 3-tiered phenotyping approach aimed at leveraging both the large sample size in the consortium and the detailed clinical information available in some studies, wherein binary, severity-encompassing, and “precision,” data-driven clinical traits are employed. As an illustration of the use of data-driven traits across multiple cohorts, we present an application of dental caries experience data harmonization in 8 participating studies (N = 55,143) using previously developed permanent dentition tooth surface–level dental caries pattern traits. We demonstrate that these clinical patterns are transferable across multiple cohorts, have similar relative contributions within each study, and thus are prime targets for genetic interrogation in the expanded and diverse multiethnic sample of GLIDE2. We anticipate that results from GLIDE2 will decisively advance the knowledge base of mechanisms at play in oral, dental, and craniofacial health and disease and further catalyze international collaboration and data and resource sharing in genomics research.