Diagnostic value of a logistic model of occupational lead poisoning using hematological parameters

Abstract

Objective

We investigated the predictive value of a logistic model utilizing hematological parameters in diagnosing occupational lead poisoning.

Methods

This retrospective study (September 2020–December 2022) included patients with occupational lead poisoning. Differences in hematological parameters were compared between individuals with occupational blood lead poisoning and healthy individuals. We used logistic regression analysis to develop a diagnostic prediction model for occupational blood lead poisoning. Receiver operating characteristic (ROC) curves and corresponding area under the ROC curve values were used to assess the diagnostic value of hematological parameters and logistic models.

Results

Compared with controls, several indicators were significantly higher in the group with blood lead poisoning, but others were significantly lower. Logistic regression analysis showed that the red blood cell distribution width coefficient of variation (RDW-CV), neutrophil/lymphocyte ratio (NLR), and percentage of small red blood cells (Micro%) were independent factors in diagnosing occupational blood lead poisoning. The logistic regression model constructed based on these three parameters had sensitivity 78.7% and specificity 83.8% for diagnosing occupational lead poisoning.

Conclusion

We identified RDW-CV, NLR, and Micro% as independent predictors in the diagnosis of occupational lead poisoning. A logistic regression model that includes these may contribute to better detection of occupational lead poisoning.

Keywords

Hematological parameter blood lead poisoning logistic model diagnosis prediction occupational hazard retrospective study

Introduction

Lead (Pb) is a potentially toxic element that can enter the human body in a variety of ways, including via the skin, inhalation, or ingestion. Lead that enters the body accumulates in tissues and organs such as the bones, liver, kidneys, brain, and skin, which mainly affects the hematopoietic, nervous, digestive, and genitourinary systems.^1–3 Exposure to lead-contaminated soil and dust is the main exposure pathway for lead in occupational populations.^4–6 Lead accumulation does not produce any physiological effects, and any detectable level of lead in the body is considered abnormal.⁷ The current diagnostic criteria for lead poisoning outlined in China National Occupational Health Standards GBZ37-2015, Diagnosis of Occupational Chronic Lead Poisoning include indicators such as blood and urine lead levels, clinical symptoms, serum zinc protoporphyrin, and urine delta-aminolevulinic acid, among others.⁸ However, limitations such as the need for specialized equipment and technical expertise, high testing costs, and time constraints make it challenging to conduct diagnostic testing in primary health care settings. These limitations result in missed, delayed, or non-standardized diagnoses and treatment for chronic occupational lead poisoning.^9,10

Hematological analysis, a traditional and widely available blood test performed in hospitals, offers convenience, speed, and affordability. Using whole blood screening parameters, primary health care facilities can identify elevated lead levels associated with occupational exposure, facilitating early detection and treatment of chronic occupational lead poisoning. There are few relevant studies focusing on this approach. Therefore, in this study, we investigated differential parameters of whole blood cells including white blood cells (WBC), neutrophil granulocyte side-directional scattered light intensity (Neu-X), neutrophil fluorescence intensity (Neu-Y), neutrophil forward scattered light intensity (Neu-Z), lymphocyte side-directional scattered light intensity (Lym-X), lymphocyte fluorescence intensity (Lym-Y), lymphocyte forward scattered light intensity (Lym-Z), monocyte side-directional scattered light intensity (Mon-X), monocyte fluorescence intensity (Mon-Y), monocyte forward scattered light intensity (Mon-Z), red blood cells (RBC), hemoglobin (HGB), mean corpuscular volume (MCV), red blood cell distribution width coefficient of variation (RDW-CV), platelets (PLT), mean platelet volume (MPV), platelet distribution width (PDW), percentage of small red blood cells (Micro%), percentage of large red blood cells (Macro%), percentage of large platelets (P-LCR), blood neutrophil/lymphocyte ratio (NLR), and platelet/lymphocyte ratio (PLR). These parameters were used to construct a logistic regression model, and its diagnostic effectiveness was verified in a validation cohort and receiver operating characteristic (ROC) analysis.

The aim of this study was to investigate the use of hematological parameters to predict occupational lead poisoning. Our findings will enable primary health care facilities that lack blood lead testing capabilities to identify individuals with occult occupational lead poisoning and chronic lead poisoning. This approach has important clinical and social implications in safeguarding the occupational health of relevant workers by providing appropriate medical advice and ensuring timely treatment and referral.

Methods

Participants

This retrospective study was conducted among employees who underwent occupational health screening between September 2020 and December 2022. The sample size was estimated using G*Power version 3.1.9.2. The minimum sample size required for power analysis (β-error 95%; effect size 0.5) was 105 samples. Workers were included in the lead poisoning group if their blood lead level exceeded 40 μg/dL and they developed clinical symptoms associated with lead poisoning. An equal number of healthy individuals with normal blood lead levels were included in a control group. The inclusion criteria were at least 6 months of service in a lead-related occupation and no serious acute infections, anemia from other causes, liver or kidney dysfunction, tumors, pregnancy, autoimmune disease, high cholesterol, or high bilirubin.

This study used retrospective data and was approved by the Hospital Medical Ethics Committee of West China Fourth Hospital (approval number: HXSY-EC-2023025). All patient details were de-identified. Owing to the retrospective nature of this study, written/verbal informed consent was waived. The reporting of this study conforms to TRIPOD (Transparent Reporting of a multivariable prediction model for Individual Prognosis or Diagnosis) guidelines.¹¹

Clinical laboratory parameter detection

Lead concentration testing was performed following the standard procedure outlined by Subramanian.¹² A 2-mL fasting venous blood sample was collected with heparin anticoagulation and diluted at a ratio of 1:10 with 20 mL 1% nitric acid solution, 10 mL 1% Triton X-100 solution, and 70 mL water. The PE-900T Graphite Furnace Atomic Absorbance Meter (PerkinElmer, Shelton, CT, USA) was used to measure blood lead concentrations.¹²

National Clinical Laboratory Practice (4th edition) was used to guide the testing of hematological parameters, as follows. Participants fasted for at least 8 hours. Then, 2 mL of venous blood was collected using EDTA-K2 anticoagulated capillary tubes. The collected samples were mixed rapidly by inversion of the tube and then analyzed using a BC-6800PLUS hematological analyzer (Mindray, Shenzhen, China). All testers were properly trained and standardized procedures were strictly followed.¹³ The NLR was calculated as the neutrophil count divided by the lymphocyte count, and the PLR was calculated as the PLT count divided by the lymphocyte count. The meaning and clinical importance of all experimental parameters can be found in Table S1.

Statistical analysis

Data processing and statistical analysis were conducted using Excel (Microsoft Corporation, Redmond, WA, USA) and R (The R Foundation for Statistical Computing, Vienna, Austria). The logistic regression model was constructed and optimized using the glm() and step() functions in R. Plotting was performed using the ggplot2 and pROC packages. Categorical variables were compared using the chi-square test, and an independent samples t-test was used to compare normally distributed continuous variables. The diagnostic effectiveness of logistic regression models and individual blood cell parameters was assessed using ROC curves and the area under the ROC curve (AUC). A two-sided P < 0.05 was considered statistically significant in this study.

Results

Clinical features of patients and differences in hematological parameters

This study included 136 employees in whom blood lead poisoning was detected (111 men and 25 women) and 136 controls (111 men and 25 women). Among the 136 patients with lead poisoning, 65 experienced symptoms of abdominal pain or bloating (47.7% of the total), 11 had nausea/vomiting (8.1%), 7 had malaise (5.1%), 11 developed constipation (8.1%), and 5 patients experienced other symptoms (3.7% of the total) (data not shown). The remaining 62 patients had no available data or no notable symptoms. As for the occupation of patients with lead poisoning, 13 worked in the color painting industry, 12 in battery production, 2 worked in metal fabrication, 1 worked in the welding industry, and 4 patients worked in other industries (Table S2). No statistical differences were observed between the group with lead poisoning and the control group in terms of age, WBC, Neu-X, Neu-Y, Neu-Z, Lym-X, Lym-Y, Lym-Z, Mon-X, Mon-Z, Macro%, PLT, MPV, and P-LCR. However, the lead poisoning group exhibited significantly higher values for RDW-CV, Micro%, NLR and PLR and significantly lower values for Mon-Y, RBC, HGB, MCV, and PDW (P < 0.05) (Table 1).

Table 1.

Comparison of whole blood cell parameters between the group with blood lead poisoning and healthy controls.

Parameters	Healthy controls (n = 136)	Lead poisoning group (n = 136)	T value	P value
Age (y)	39.95 ± 15.36	39.35 ± 15.11	−0.32	0.75
Pb (μg/dL)	2.13 ± 0.60	53.41 ± 13.59	43.95	<0.05
WBC (10⁹/L)	6.4 ± 1.67	6.82 ± 2.29	1.72	0.09
Neu-X	346.53 ± 30.71	344.28 ± 33.45	−0.58	0.56
Neu-Y	430.69 ± 46.92	422.1 ± 39.33	−1.64	0.10
Neu-Z	1798.55 ± 136.13	1789.52 ± 129.75	−0.56	0.57
Lym-X	90.71 ± 5.98	91.9 ± 5.64	1.68	0.09
Lym-Y	662.74 ± 65.93	657.08 ± 59.12	−0.75	0.46
Lym-Z	962.98 ± 70.6	948.94 ± 55.45	−1.82	0.07
Mon-X	200.21 ± 10.7	201.52 ± 9.94	1.04	0.30
Mon-Y	980.13 ± 100.83	944.26 ± 84.06	−3.19	<0.05
Mon-Z	1289.42 ± 90.05	1282.17 ± 71.62	−0.73	0.46
RBC (10¹²/L)	4.97 ± 0.48	4.52 ± 0.9	−5.18	<0.05
HGB (g/L)	151.03 ± 15.17	131.38 ± 26.24	−7.56	<0.05
MCV (fL)	93.36 ± 4.35	90.79 ± 6.2	−3.96	<0.05
RDW-CV	13.06 ± 0.84	14.47 ± 1.75	8.45	<0.05
Micro%	1.47 ± 1.85	2.76 ± 3.82	3.56	<0.05
Macro%	2.93 ± 1.04	2.95 ± 2.11	0.12	0.91
PLT (10⁹/L)	217.51 ± 56.81	232.26 ± 69.77	1.91	0.06
MPV (fL)	10.91 ± 1.52	10.57 ± 1.85	−1.63	0.10
PDW (fL)	16.37 ± 0.34	16.05 ± 0.45	−6.69	<0.05
P-LCR (%)	32.35 ± 10.46	30.16 ± 12.72	−1.55	0.12
NLR	2.31 ± 2.17	3.19 ± 2.65	2.99	<0.05
PLR	120.86 ± 48.66	157.83 ± 96.8	3.98	<0.05

P < 0.05 is considered statistically significant.

Pb, lead; WBC, white blood cells; Neu-X, neutrophil granulocyte side-directional scattered light intensity; Neu-Y, neutrophil fluorescence intensity; Neu-Z, neutrophil forward scattered light intensity; Lym-X, lymphocyte side direction of scattering light; Lym-Y, lymphocyte fluorescence intensity; Lym-Z, lymphocyte forward scattered light intensity; Mon-X, monocyte side-directional scattered light intensity; Mon-Y, monocyte fluorescence intensity; Mon-Z, monocyte forward scattered light intensity; RBC, red blood cells; HGB, hemoglobin; MCV, mean corpuscular volume; RDW-CV, red blood cell distribution width coefficient of variation; Micro%, percentage of small red blood cells; Macro%, percentage of large red blood cells; PLT, platelets; MPV, mean platelet volume; PDW, platelet distribution width; P-LCR, percentage of large platelets; NLR, neutrophil/lymphocyte ratio; PLR, platelet/lymphocyte ratio.

Construction of logistic model based on variance parameters

A total of 272 individuals in the lead poisoning group and control group formed the training cohort. The analysis revealed that the AUC for WBC, RDW-CV, NLR, and Micro% was greater than 0.6. After being included in the logistic regression model, non-significant indicators were removed in stepwise regression. As a result, logistic regression analysis showed that RDW-CV, NLR and Micro% independently predicted the diagnosis of occupational lead poisoning (P < 0.01) (Table 2). The logistic regression model constructed can be expressed as logit P/(1−P) = −19.541 + 1.426 × RDW-CV + 0.189 × NLR–0.158 × Micro%, and predicted values are obtained by incorporating relevant indicators into the model.

Table 2.

Analysis of logistic regression model for the diagnosis of occupational blood lead poisoning using whole blood cell parameters.

Parameters	Β value	Wald value	OR (95% CI)	P value
RDW-CV	1.406	43.871	4.079 (2.757–6.347)	<0.001
NLR	0.185	6.106	1.203 (1.048–1.415)	0.014
Micro%	−2.879	5.17	0.056 (0.005–0.875)	0.023
Intercept	−19.286	47.033	–	<0.001

The symbol – indicates N/A.

OR, odds ratio; CI, confidence interval; RDW-CV, red blood cell distribution width coefficient of variation; Micro%, percentage of small red blood cells; NLR, neutrophil/lymphocyte ratio.

Diagnostic effectiveness of the logistic model

To evaluate the diagnostic performance of the logistic regression model and compare it with individual indicators, we plotted ROC curves for RDW-CV, NLR, Micro%, and the logistic model (Figure 1a). The maximum Youden index was used to determine the optimal cutoff value and the AUC (95% confidence interval, [CI]), sensitivity, and specificity for each indicator (Table 3). ROC analysis demonstrated that the logistic model had a significantly higher AUC value than the other indicators. At a cutoff value of 0.48, the sensitivity was 78.7% and the specificity was 83.3%.

Figure 1.

ROC curve and AUC for the logistic model. (a) Comparison of differences in the AUC between single indicators and logistic models. (b) Diagnostic efficacy of logistic model in blood lead level groups >20 μg/dL, (c) >30 μg/dL, and (d) >40 μg/dL. ROC, receiver operating characteristic; AUC, area under the ROC curve; RDW-CV, red blood cell distribution width coefficient of variation; Micro%, percentage of small red blood cells; NLR, neutrophil/lymphocyte ratio.

Table 3.

Comparison of diagnostic effectiveness between single whole blood cell parameters and logistic model.

Parameters	AUC (95% CI)	Cutoff	Spe (%)	Sen (%)	Acc (%)	Youden index	P value
RDW-CV	0.76 (0.71–0.84)	13.55	69.85	87.5	63.7	0.574	<0.05
NLR	0.67 (0.60–0.73)	2.12	66.18	64.71	64.6	0.309	<0.05
Micro%	0.62 (0.55–0.68)	2.75	32.35	92.65	71.7	0.250	<0.05
Logistic model	0.85 (0.80–0.90)	0.48	83.8	78.7	80.1	0.625	<0.05

AUC, area under the receiver operating characteristic curve; Spe, specificity; Sen, sensitivity; Acc, accuracy; CI, confidence interval; RDW-CV, red blood cell distribution width coefficient of variation; Micro%, percentage of small red blood cells; NLR, neutrophil/lymphocyte ratio.

To explore the performance of the logistic model based on RDW-CV, NLR, and Micro% at lower blood lead levels, we further analyzed data from exposed populations with blood lead levels of 20 to 40 μg/dL. The results showed that the AUC (95% CI) of the logistic model was 0.71 (95% CI 0.66–0.76), 0.75 (0.71–0.80), and 0.85 (0.80–0.90) in the populations with blood levels >20 μg/dL, >30 μg/dL and >40 μg/dL with a Youden index of 0.34, 0.42, and 0.63, respectively (Figure 1b–1d, Table 4). Figure 2 illustrates the differential expression of the three model components (RDW-CV, NLR, Micro%).

Table 4.

Comparison of diagnostic efficacy of the logistic model in populations with blood lead levels >20 μg/dL, >30 μg/dL, and >40 μg/dL.

Population	AUC (95% CI)	Cutoff	Spe (%)	Sen (%)	Acc (%)	Youden index	P value
>20 μg/dL	0.71 (0.66–0.76)	0.81	71.3	62.6	82.3	0.34	<0.05
>30 μg/dL	0.75 (0.71–0.80)	0.62	68.4	73.4	71.8	0.42	<0.05
>40 μg/dL	0.85 (0.80–0.90)	0.48	83.8	78.7	80.1	0.63	<0.05

AUC, area under the receiver operating characteristic curve; Spe, specificity; Sen, sensitivity; Acc, accuracy; CI, confidence interval.

Figure 2.

Analysis of blood parameters for different groups in the training cohort. (a–c) Comparison of RDW-CV, NLR, and Micro% levels in samples from the group with lead poisoning and controls. *P < 0.05, **P < 0.01, P < 0.001, ***P < 0.0001 and (d) Heat map of hematological parameters in the lead poisoning and control groups. RDW-CV, red blood cell distribution width coefficient of variation; NLR, neutrophil/lymphocyte ratio; Micro%, percentage of small red blood cells.

Diagnostic value of the logistic model in the validation cohort

To further validate the diagnostic efficacy of the logistic model, the same selection criteria as those used to select the training cohort were applied to select 120 individuals as a validation cohort (lead poisoning group: 60 individuals; control group: 60 individuals). The results of the validation cohort showed significantly higher levels of RDW-CV, NLR, and Micro% in the group with lead poisoning compared with controls, with significant differences in all three indicators (P < 0.05). The AUC for the validation cohort was 0.915 (Figure 3). At a cutoff value of 0.55, the logistic model showed a sensitivity of 86.4%, specificity of 78.3%, and accuracy of 82.4% for the diagnosis of occupational lead poisoning.

Figure 3.

Assessment of efficacy in validation cohort for the diagnosis of blood lead poisoning. (a) ROC curves of the validation cohort and (b) Lift diagram of the logistic model. ROC, receiver operating characteristic; AUC, area under the ROC curve.

Discussion

Lead, a metal widely used in smelting, battery production, printing, transportation, and electronics, mainly produces reactive oxygen species in the body.¹⁴ When the cell's ability to neutralize these free radicals is depleted, oxidative stress damage occurs, leading to damage to lipids, proteins, and DNA.^15,16 Lead also inhibits thiol-related enzymes in various organs and affects the body's antioxidant capacity. With economic development, occupational health issues related to lead are gaining increased attention from the government and enterprises.¹⁷

Although hematological parameters are routinely tested to assess hematopoietic and inflammatory stress status, their potential in clinical diagnosis and treatment remains largely unexplored. With advances in testing technology, next-generation hematology analyzers offer a wide range of information. New parameters such as Neu-X, Neu-Y, Neu-Z, Lym-X, Lym-Y, Lym-Z, Mon-X, Mon-Y, and Mon-Z have emerged as indicators in the diagnosis and prognosis of various diseases.^18,19 These parameters reflect subtle changes in peripheral blood leukocyte morphology based on lateral scattered light intensity, fluorescence intensity, and forward scattered light intensity.¹⁰ RDW-CV measures changes in red blood cell volume whereas Micro% and Macro% can be used to differentiate and monitor various types of anemia.²⁰ P-LCR reflects platelet maturation and regeneration.²¹ NLR is a recently discovered index of inflammation that has been widely used in the diagnosis and prediction of clinical disease owing to its simplicity of calculation. PLR is important in monitoring the status of certain tumors and determining prognosis.¹⁹

In this study, we investigated the effectiveness of logistic models of multiple indicators in the diagnosis of occupational lead poisoning using various hematological parameters with a next-generation blood analysis platform. Analysis of variance parameters showed that WBC, RDW-CV, Macro%, NLR, and PLR levels were significantly higher in the lead poisoning group, as compared with healthy controls (P < 0.05). These findings are consistent with the expected increase in WBC, NLR, and PLR levels with oxidative stress. Elevated Micro% and RDW-CV levels were also observed in the group with lead poisoning, suggesting an increase in erythrocyte variability. Oxidative stress may lead to a reduction in cell size and volume, reduced nucleus and granule complexity, reduced cell maturation, and lower intracellular nucleic acid content. To verify this trend, we compared whole blood cell experimental parameters (usually not included in routine blood reports). We found significantly higher levels of RDW-CV, Micro%, NLR, and PLR and significantly lower levels of Mon-Y, RBC, HGB, MCV, and PDW in the group with lead poisoning. Our findings further demonstrate the effect of lead on cell volume and the ratio of neutrophils to lymphocytes (NLR) (P < 0.05).

Lead inhibits the expression of key enzymes involved in hemoglobin biosynthesis, resulting in reduced hemoglobin production.²² Moreover, lead ions increase fragile cells, reduce RBC lifespan, and cause anemia.²³ These lead to a decrease in RBC and HGB levels, which in turn stimulates the release of nucleated RBCs from the bone marrow into the circulating blood, leading to an increase in Micro% and RDW-CV levels. Our study also confirmed that RDW-CV and Micro% were independent predictors in the diagnosis of occupational lead poisoning (P < 0.05), with RDW-CV having the greatest influence. We also revealed that NLR was an independent factor (P < 0.05) influencing the diagnosis of occupational lead poisoning, which may be related to oxidative stress injury caused by lead poisoning. Oxidative stress damage increases apoptosis of peripheral lymphocytes, leading to increased NLR levels.²⁴

In the present study, we developed and optimized a logistic regression model using differential indicators with a sensitivity of 78.7%, specificity of 83.8%, and accuracy of 80.1%. The logistic model had a higher diagnostic performance than individual hematological parameters. These findings may provide a simple and effective diagnostic aid for occupational lead poisoning in clinical care settings, occupational disease treatment centers, and primary care settings with limited toxicology testing capabilities. This study also has some limitations, including a small sample size, unbalanced sex distribution (predominantly men), and possible limitations in terms of clinical applicability. Therefore, further validation and optimization in a large-scale, multi-center prospective study is necessary.

Conclusion

In the present study, we identified RDW-CV, NLR, and Micro% as independent factors in the diagnosis of occupational lead poisoning. A logistic regression model that includes these factors will contribute to the early detection of occupational lead poisoning.

Supplemental Material

sj-pdf-1-imr-10.1177_03000605231213221 - Supplemental material for Diagnostic value of a logistic model of occupational lead poisoning using hematological parameters

Supplemental material, sj-pdf-1-imr-10.1177_03000605231213221 for Diagnostic value of a logistic model of occupational lead poisoning using hematological parameters by Guokang Sun, Pinpin Xiang, Yiping Chen, Zheng Li, Bo Wu, Yanping Rao and Zheng Zhu in Journal of International Medical Research

Supplemental Material

sj-pdf-2-imr-10.1177_03000605231213221 - Supplemental material for Diagnostic value of a logistic model of occupational lead poisoning using hematological parameters

Supplemental material, sj-pdf-2-imr-10.1177_03000605231213221 for Diagnostic value of a logistic model of occupational lead poisoning using hematological parameters by Guokang Sun, Pinpin Xiang, Yiping Chen, Zheng Li, Bo Wu, Yanping Rao and Zheng Zhu in Journal of International Medical Research

Footnotes

Acknowledgements

The authors would like to sincerely thank all the patients and their families for participating in this project.

Authors’ contributions

GKS and ZZ conceived and designed the experiments; GKS and PPX analyzed the data; YPC and YPR collected the clinical data; ZL and BW offered technical and language support; GKS wrote the paper. All authors have read and approved the final manuscript.

Data availability statement

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

Declaration of conflicting interests

The authors declare that there is no conflict of interest.

Funding

This study was supported by the Medical Research Project of Chengdu Municipal Health Commission (2022365). The sponsors had no involvement in the experiment design, specimen collection, data analysis, or drafting of the manuscript.

ORCID iD

Guokang Sun

Supplementary Information

Table S1. Definition and clinical importance of blood laboratory parameters.

Table S2. Proportion of the population with lead poisoning by industry and year.

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