Abstract: PUB085
Exploring the Connection between Serum Lead Levels and Kidney Function among the Obese Population: Analysis of NHANES, 2017-2020
Session Information
Category: Augmented Intelligence, Digital Health, and Data Science
- 300 Augmented Intelligence, Digital Health, and Data Science
Authors
- Kookanok, Chutawat, University of California Irvine School of Medicine, Irvine, United States
- Thotsiri, Sansanee, Mahidol University Ramathibodi Hospital, Bangkok, Thailand
- Poochanasri, Methavee, Phramongkutklao Hospital, Bangkok, Thailand
- Rodsom, Kamonluk, University of California Irvine School of Medicine, Irvine, California, United States
- Kulthamrongsri, Narathorn, Mahidol University Faculty of Medicine Siriraj Hospital, Bangkok, Thailand
- Mongkolporn, Ariya, Thammasart University Faculty of Science and Technology, Khlong Nueng, Pathum Thani, Thailand
- Sinkajarern, Varissara, Vimut Hospital, Bangkok, Thailand
- Phoonakh, Thutpharritchn, Vimut Hospital, Bangkok, Thailand
- Noree, Wanprapit, University of Illinois Chicago, Chicago, Illinois, United States
- Chuenchaem, Urairat, Bumrungrad International Hospital, Bangkok, Bangkok, Thailand
- Tantisattamo, Ekamol, University of California Irvine School of Medicine, Irvine, California, United States
Background
Lead, a common toxic substance found in air pollution and consumer products, is known for its association with renal damage, notably lead nephropathy. National-scale evidence on how obesity influences this link is limited and uncertain. This study investigates whether obesity exacerbates the risk of kidney damage from lead toxicity.
Methods
A national study of 6,789 adults, using NHANES 2017-2020 data, analyzed the correlation between serum lead levels (Pb-S) and eGFR. Participants were categorized into non-obesity and obesity groups based on BMI, with three linear regression models employed (Table 1). Receiver operating characteristic (ROC) curve analysis was also conducted for each BMI category to predict advanced kidney disease (eGFR < 60) based on Pb-S levels.
Results
The study population had an average BMI of 29.9±7.3 kg/m2, with 41.5% classified as obese. The average Pb-S level was 1.04±0.68 µg/dL, and average eGFR was 96.1±19.8 mL/min/1.73 m2. In the final adjusted model, eGFR declined significantly in the total group by 0.96 (95% CI: -1.81 to -0.11) and in the obese group by 1.97 (95% CI: -3.41 to -0.54) per unit increase in Pb-S (Table 1). Pb-S demonstrated a notable predictive ability for advanced kidney disease, with obesity achieving a sensitivity of 63%, specificity of 68%, and an ROC-AUC of 0.77.
Conclusion
A significant negative correlation between Pb-S and eGFR, amplified by obesity was founded among US adult, suggest the kidney function monitoring among those with history of lead exposure especially those with obesity in order to early detection of kidney injury.
| Model 1 | Model 2 | Model 3 | ||||
| β-Coefficients (95% CI) | P-value | β-Coefficients (95% CI) | P-value | β-Coefficients (95% CI) | P-value | |
| Total | -8.35 (-9.02,-7.67) | <0.001 | -1.08 (-1.92,-0.23) | 0.013 | -0.96 (-1.81,-0.11) | 0.027 |
| Non-obesity | -7.60 (-8.43,-6.76) | <0.001 | -0.89 (-1.95,0.17) | 0.099 | -0.70 (-1.76,0.37) | 0.200 |
| Obesity | -10.46 (-11.63,-9.29) | <0.001 | -2.13 (-3.56,-0.71) | 0.003 | -1.97 (-3.41,-0.54) | 0.007 |
Table 1: Table of correlation analysis between serum lead levels (Pb-S) and estimated glomerular filtration rate (eGFR) using three different logistic regression models. The first model included only Pb-S, the second model added age, race, smoking, and drinking, and the third model further included dyslipidemia, hypertension, diabetes, coronary artery disease, congestive heart failure, and stroke.