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Machine-learning models predicting osteoarthritis associated with the lead blood level

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Abstract

Lead is one of the most hazardous environmental pollutants in industrialized countries; lead exposure is a risk factor for osteoarthritis (OA) in older women. Here, the performance of several machine-learning (ML) algorithms in terms of predicting the prevalence of OA associated with lead exposure was compared. A total of 2224 women aged 50 years and older who participated in the Korea National Health and Nutrition Examination Surveys from 2005 to 2017 were divided into a training dataset (70%) for generation of ML models, and a test dataset (30%). We built and tested five ML algorithms, including logistic regression (LR), a k-nearest neighbor model, a decision tree, a random forest, and a support vector machine. All afforded acceptable predictive accuracy; the LR model was the most accurate and yielded the greatest area under the receiver operating characteristic curve. We found that various ML models can be used to predict the risk of OA associated with lead exposure effectively, using data from population-based survey.

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Data availability

The data that support the findings of this study are available from the corresponding author upon reasonable request.

References

  • Abbott JH, Usiskin IM, Wilson R, Hansen P, Losina E (2017) The quality-of-life burden of knee osteoarthritis in New Zealand adults: a model-based evaluation. PLoS One 12:e0185676

    Article  Google Scholar 

  • Battineni G, Sagaro GG, Chinatalapudi N, Amenta F (2020) Applications of machine learning predictive models in the chronic disease diagnosis. J Pers Med 10:21

    Article  Google Scholar 

  • Choi YH, Park SK (2017) Environmental exposures to lead, mercury, and cadmium and hearing loss in adults and adolescents: KNHANES 2010-2012. Environ Health Perspect 125:067003

    Article  Google Scholar 

  • Counter SA, Buchanan LH, Ortega F (2005) Neurocognitive impairment in lead-exposed children of Andean lead-glazing workers. J Occup Environ Med 47:306–312

    Article  CAS  Google Scholar 

  • Farzan SF, Howe CG, Chen Y, Gilbert-Diamond D, Cottingham KL, Jackson BP, Weinstein AR, Karagas MR (2018) Prenatal lead exposure and elevated blood pressure in children. Environ Int 121:1289–1296

    Article  CAS  Google Scholar 

  • Frank JJ, Poulakos AG, Tornero-Velez R, Xue J (2019) Systematic review and meta-analyses of lead (Pb) concentrations in environmental media (soil, dust, water, food, and air) reported in the United States from 1996 to 2016. Sci Total Environ 694:133489

    Article  CAS  Google Scholar 

  • Galatzer-Levy IR, Ma S, Statnikov A, Yehuda R, Shalev AY (2017) Utilization of machine learning for prediction of post-traumatic stress: a re-examination of cortisol in the prediction and pathways to non-remitting PTSD. Transl Psychiatry 7:e1070

    Article  CAS  Google Scholar 

  • Harari F, Barregard L, Östling G, Sallsten G, Hedblad B, Forsgard N, Borné Y, Fagerberg B, Engström G (2019) Blood lead levels and risk of atherosclerosis in the carotid artery: results from a Swedish cohort. Environ Health Perspect 127:127002

    Article  Google Scholar 

  • Holz JD, Beier E, Sheu TJ, Ubayawardena R, Wang M, Sampson ER, Rosier RN, Zuscik M, Puzas JE (2012) Lead induces an osteoarthritis-like phenotype in articular chondrocytes through disruption of TGF-beta signaling. J Orthop Res 30:1760–1766

    Article  CAS  Google Scholar 

  • Hu H, Aro A, Payton M, Korrick S, Sparrow D, Weiss ST, Rotnitzky A (1996a) The relationship of bone and blood lead to hypertension: the Normative Aging Study. JAMA 275:1171–1176

    Article  CAS  Google Scholar 

  • Hu H, Payton M, Korrick S, Aro A, Sparrow D, Weiss ST, Rotnitzky A (1996b) Determinants of bone and blood lead levels among community-exposed middle-aged to elderly men. Am J Epidemiol 144:749–759

    Article  CAS  Google Scholar 

  • Kim JH, Kang JC (2015) The lead accumulation and hematological findings in juvenile rock fish Sebastes schlegelii exposed to the dietary lead (II) concentrations. Ecotoxicol Environ Saf 115:33–39

    Article  CAS  Google Scholar 

  • Kim R, Rotnitsky A, Sparrow D, Weiss S, Wager C, Hu H (1996) A longitudinal study of low-level lead exposure and impairment of renal function: the Normative Aging Study. JAMA 275:1177–1181

    Article  CAS  Google Scholar 

  • LeCun Y, Bengio Y, Hinton G (2015) Deep learning. Nature 521:436–444

    Article  CAS  Google Scholar 

  • Litwic A, Edwards MH, Dennison EM, Cooper C (2013) Epidemiology and burden of osteoarthritis. Br Med Bull 105:185–199

    Article  Google Scholar 

  • Manolagas SC (2000) Birth and death of bone cells: basic regulatory mechanisms and implications for the pathogenesis and treatment of osteoporosis. Endocr Rev 21:115–137

    CAS  Google Scholar 

  • Martini CN, Gabrielli M, Bonifacino G, Codesido MM, Vila MDC (2018) Lead enhancement of 3T3-L1 fibroblasts differentiation to adipocytes involves ERK, C/EBPβ and PPARγ activation. Mol Cell Biochem 437:37–44

    Article  CAS  Google Scholar 

  • Meng Q, Richmond-Bryant J, Davis JA, Cohen J, Svendsgaard D, Brown JS, Tuttle L, Hubbard H, Rice J, Vinikoor-Imler L, Sacks JD, Kirrane E, Kotchmar D, Hines E, Ross M (2014) Contribution of particle-size-fractionated airborne lead to blood lead during the National Health and Nutrition Examination Survey, 1999-2008. Environ Sci Technol 48:1263–1270

    Article  CAS  Google Scholar 

  • Miao H, Liu Y, Tsai TC, Schwartz J, Ji JS (2020) Association between blood lead level and uncontrolled hypertension in the US population (NHANES 1999-2016). J Am Heart Assoc 9:e015533

    Google Scholar 

  • Nash D, Magder L, Lustberg M, Sherwin RW, Rubin RJ, Kaufmann RB, Silbergeld EK (2003) Blood lead, blood pressure, and hypertension in perimenopausal and postmenopausal women. JAMA 289:1523–1532

    Article  CAS  Google Scholar 

  • Nelson AE, Shi XA, Schwartz TA, Chen JC, Renner JB, Caldwell KL, Helmick CG, Jordan JM (2011a) Whole blood lead levels are associated with radiographic and symptomatic knee osteoarthritis: a cross-sectional analysis in the Johnston County Osteoarthritis Project. Arthritis Res Ther 13:R37

    Article  Google Scholar 

  • Nelson AE, Chaudhary S, Kraus VB, Fang F, Chen JC, Schwartz TA, Shi XA, Renner JB, Stabler TV, Helmick CG, Caldwell K, Poole AR, Jordan JM (2011b) Whole blood lead levels are associated with biomarkers of joint tissue metabolism in African American and white men and women: the Johnston County Osteoarthritis Project. Environ Res 111:1208–1214

    Article  CAS  Google Scholar 

  • Olchowik G, Widomska J, Tomaszewski M, Gospodarek M, Tomaszewska M, Jagiełło-Wójtowicz E (2014) The influence of lead on the biomechanical properties of bone tissue in rats. Ann Agric Environ Med 21:278–281

    Article  CAS  Google Scholar 

  • Park S, Choi NK (2019) The relationships of blood lead level, body mass index, and osteoarthritis in postmenopausal women. Maturitas 125:85–90

    Article  CAS  Google Scholar 

  • Patrick L (2006) Lead toxicity, a review of the literature. Part I: Exposure, evaluation, and treatment. Altern Med Rev 11:2–22

    Google Scholar 

  • Payton M, Riggs KM, Spiro A 3rd, Weiss ST, Hu H (1998) Relations of bone and blood lead to cognitive function: the VA Normative Aging Study. Neurotoxicol Teratol 20:19–27

    Article  CAS  Google Scholar 

  • Pirkle JL, Brody DJ, Gunter EW, Kramer RA, Paschal DC, Flegal KM, Matte TD (1994) The decline in blood lead levels in the United States: the National Health and Nutrition Examination Surveys (NHANES). JAMA 272:284–291

    Article  CAS  Google Scholar 

  • Safiri S, Kolahi AA, Smith E, Hill C, Bettampadi D, Mansournia MA, Hoy D, Ashrafi-Asgarabad A, Sepidarkish M, Almasi-Hashiani A, Collins G, Kaufman J, Qorbani M, Moradi-Lakeh M, Woolf AD, Guillemin F, March L, Cross M (2020) Global, regional and national burden of osteoarthritis 1990-2017: a systematic analysis of the Global Burden of Disease Study 2017. Ann Rheum Dis 79:819–828

    Article  Google Scholar 

  • Sniekers YH, Intema F, Lafeber FP, van Osch GJ, van Leeuwen JP, Weinans H, Mastbergen SC (2008) A role for subchondral bone changes in the process of osteoarthritis; a micro-CT study of two canine models. BMC Musculoskelet Disord 9:20

    Article  Google Scholar 

  • Sniekers YH, Weinans H, van Osch GJ, van Leeuwen JP (2010) Oestrogen is important for maintenance of cartilage and subchondral bone in a murine model of knee osteoarthritis. Arthritis Res Ther 12:R182

    Article  Google Scholar 

  • Sommar JN, Hedmer M, Lundh T, Nilsson L, Skerfving S, Bergdahl IA (2014) Investigation of lead concentrations in whole blood, plasma and urine as biomarkers for biological monitoring of lead exposure. J Expo Sci Environ Epidemiol 24:51–57

    Article  CAS  Google Scholar 

  • Talukder A, Ahammed B (2020) Machine learning algorithms for predicting malnutrition among under-five children in Bangladesh. Nutrition 78:110861

    Article  Google Scholar 

  • Tchounwou PB, Yedjou CG, Patlolla AK, Sutton DJ (2012) Heavy metal toxicity and the environment. Exp Suppl 101:133–164

    Google Scholar 

  • Webber CE, Chettle DR, Bowins RJ, Beaumont LF, Gordon CL, Song X, Blake JM, McNutt RH (1995) Hormone replacement therapy may reduce the return of endogenous lead from bone to the circulation. Environ. Health Perspect 103:1150–1153

    Article  CAS  Google Scholar 

  • Weitzmann MN, Pacifici R (2006) Estrogen deficiency and bone loss: an inflammatory tale. J Clin Invest 116:1186–1194

    Article  CAS  Google Scholar 

  • Zhang X, Yuan Z, Ji J, Li H, Xue F (2016) Network or regression-based methods for disease discrimination: a comparison study. BMC Med Res Methodol 16:100

    Article  Google Scholar 

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Funding

This research was supported by the Keimyung University Research Grant of 2019.

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Authors and Affiliations

  1. College of Pharmacy, Keimyung University, Daegu, 42601, Republic of Korea

    Kisok Kim

  2. Department of International Healthcare Administration, Daegu Catholic University, Gyeongsan, 38430, Republic of Korea

    Hyejin Park

Authors
  1. Kisok Kim
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  2. Hyejin Park
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Contributions

K.K. contributed to conceptualization, data analysis and interpretation, and supervision of the study. H.P. contributed to design of the study, algorithm analysis, and drafting of the manuscript. All authors have read and agreed to the final manuscript.

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Correspondence to Kisok Kim.

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Ethics approval and consent to participate

The KNHANES received approval from the KNHANES Institutional Review Board (IRB approval # 2010-02CON-21-C), and written informed consent was obtained from all participants.

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Not applicable.

Conflict of interest

The authors declare no competing interests.

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Responsible Editor: Lotfi Aleya

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Kim, K., Park, H. Machine-learning models predicting osteoarthritis associated with the lead blood level. Environ Sci Pollut Res 28, 44079–44084 (2021). https://doi.org/10.1007/s11356-021-13887-6

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  • DOI: https://doi.org/10.1007/s11356-021-13887-6

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