Original abstract online at
https://www.tandfonline.com/doi/full/10.1080/09603123.2026.2667879
Adolescents are highly susceptible to fluoride toxicity, yet evidence on renal effects and safety thresholds is limited and inconsistent. Data from adolescents aged 12–19 years in the 2015–2016 National Health and Nutrition Examination Survey (NHANES) were used in this study. The sample sizes were 1,042 for urinary fluoride (UF), 1,062 for plasma fluoride (PF), and 916 for drinking-water fluoride (WF). We analyzed associations between fluoride exposure (UF, PF, and WF) and renal function. Methods included survey-weighted linear/logistic regression, restricted cubic splines (RCS), inverse probability treatment weighting, trend tests, and Bayesian Benchmark Dose modeling (BBMD). RCS indicated linear relationships (P-nonlinear >0.05). Weighted linear regression showed significant inverse associations of UF (B = -11.16 (2-0.10, -2.22), P = 0.029) and PF (B = -5.04 (-9.45, -0.62), P = 0.036) with estimated glomerular filtration rate; WF was non-significant (P = 0.585). The 10% benchmark concentration (BMC10) for UF was 1.63 mg/L (10% Benchmark Concentration Lower bound (BMCL10): 1.25 mg/L); for PF, BMC10 was 0.46 umol/L (BMCL10: 0.24 umol/L). These renal BMC values were lower than bone-derived benchmarks. Future regulations on environmental fluoride exposure should consider non-skeletal health effects in vulnerable populations.
Abbreviations
| Bayesian benchmark-dose |
= |
BBMD
|
| Benchmark concentrations |
= |
BMCs
|
| Benchmark Concentration Lower bounds |
= |
BMCLs
|
| Benchmark response |
= |
BMR
|
| Estimated glomerular filtration rate |
= |
eGFR
|
| Inverse probability of treatment weighting |
= |
IPTW
|
| National Health and Nutrition Examination Survey |
= |
NHANES
|
| Poverty-To-Income ratio |
= |
PIR
|
| Plasma fluoride |
= |
PF
|
| Posterior prediction probability |
= |
ppp
|
| Restricted cubic spline |
= |
Rcs
|
| Standardized mean difference |
= |
SMD
|
| Urinary fluoride |
= |
UF
|
| Water fluoride |
= |
WF
|
Acknowledgements
We would like to extend our heartfelt thanks to Gazala Zafar (Harbin Medical University) for her kind guidance and valuable suggestions on the preparation and revision of this manuscript. Special thanks are also due to Drew Weissmann, lead software developer of Bayesian BMD, for his generous help and advice during the data analysis.
Disclosure statement
No potential conflict of interest was reported by the author(s).
Data availability statement
The datasets generated and analyzed during the current study are available in the National Health and Nutrition Examination Survey (NHANES), https://wwwn.cdc.gov/nchs/nhanes/ (accessed on 15 May 2024).
Informed consent statement
Informed consent was obtained from all subjects involved in the study.
Institutional review board statement
Ethical review and approval were waived for this study because this study relied on free, publicly available datasets from the National Health and Nutrition Examination Survey.
Additional information
Funding
This research was funded by the National Natural Science Foundation of China [82273749] and [82473747].
