Association Between Number of Missing Teeth and Hyperlipidemia: The TCLSIH Cohort Study

Abstract

Background

To explore the association between the number of missing teeth and the prevalence of hyperlipidemia in a Chinese adult population.

Methods

13,932 adults were investigated in the TCLSIH cohort study. The number of missing teeth was determined at baseline through a self-reported questionnaire, and then classified into three categories: 0, 1–2, and ≥3. We defined hyperlipidemia as total cholesterol (TC) ≥ 5.17 mmol/L or triglycerides (TG) ≥ 1.7 mmol/L or low-density lipoprotein (LDL) cholesterol ≥ 3.37 mmol/L or a self-report of physician-diagnosed hyperlipidemia during follow-up visits. Cox proportional-hazards regression models were employed to assess the relationship between the number of missing teeth and incident hyperlipidemia.

Results

A total of 6756 first-incident cases of hyperlipidemia occurred during 42,048 person-years of follow-up (median follow-up, 4.2 years). After adjusted confounders, multivariable HRs and 95% CI for incident of hyperlipidemia across the categories of missing teeth were as follows: in male participants, 1.00 (reference), 1.10 (0.98, 1.22), and 1.03 (0.91, 1.16) (P for trend = 0.30); in female participants, 1.00 (reference), 1.09 (0.99, 1.19), and 1.18 (1.04, 1.33) (P for trend < 0.01).

Conclusion

The number of missing teeth is associated with an increased risk of hyperlipidemia in female participants but not in male participants. Systemic chronic inflammation may potentially mediate this association.

Keywords:

• tooth loss
• dyslipidemia
• epidemiological studies
• population health
• inflammation

Data Sharing Statement

The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request.

Ethics Approval and Consent to Participate

The research protocol was granted approval by the Institutional Review Board of Tianjin Medical University (No: TMUhMEC201430), and all subjects participated voluntarily in the research work and provided written informed consent.

Acknowledgments

We would like to thank the authors Tingjing Zhang, Huiping Li, Xuena Wang, Juanjuan Zhang, Shaomei Sun, Xing Wang, Ming Zhou, Qiyu Jia who have contributed to this scientific literature. We are truly grateful for their unwavering support and collaboration throughout the research process.

Disclosure

The authors report no conflicts of interest in this work.

Additional information

Funding

This study was supported by grants from the National Natural Science Foundation of China (No. 81673166, 81372118, 81372467 and81302422); the key technologies R&D program of Tianjin (Key Project: No. 11ZCGYSY05700, 12ZCZDSY20400, 13ZCZDSY20200, and 15YFYZSY00020); The Science and Technology Project of Tianjin Health Commission (ZC20134).