The relationships of blood lead level, body mass index, and osteoarthritis in postmenopausal women

The association between heavy metal exposure and obesity: A systematic review and meta-analysis

Background

Obesity and metabolic syndrome are global health concerns associated with development of different types of diseases and serious health threats in the long term. Their metabolic imbalance can be attributable to inherited and environmental factors. As a considerable environmental agent, heavy metals exposure can predispose individuals to diseases like obesity. This systematic review and meta-analysis aimed to evaluate the association between heavy metals exposure and the risk of obesity.

Methods

PubMed/MEDLINE, EMBASE and Web of Science were systematically searched until December 17, 2022. Only observational studies that evaluated heavy metals exposure and obesity were included. Studies were excluded if they assessed maternal or prenatal exposure, the mixture of heavy metals and other chemicals, reported the association with overweight or other diseases, and undesirable study designs. The Joanna Briggs Institute checklist was used for quality assessment. The pooled adjusted odds ratio (aOR) and the pooled standardized mean difference (SMD) with their 95% confidence intervals (CIs) were calculated, respectively. The publication bias was evaluated using Egger's and Begg's tests.

Results

Twenty studies (n = 127755), four case-control and sixteen analytical cross-sectional studies, were included. Lead exposure was significantly associated with a lower risk of obesity (aOR: 0.705, 95% CI: 0.498-0.997), while mercury (aOR: 1.458, 95% CI: 1.048-2.031) and barium (aOR: 1.439, 95% CI: 1.142-1.813) exposure increased the risk of obesity. No significant publication bias was found and the studies had a low risk of bias.

Conclusion

Overall, lead exposure reduced obesity risk, while mercury and barium exposure raised it. Further large-scale observational studies are recommended to determine the roles of heavy metals in obesity.Study registration ID: CRD42023394865.

Supplementary information

The online version contains supplementary material available at 10.1007/s40200-023-01307-0.

Cross-cultural validation of the Lower Extremity Functional Scale in Serbian postmenopausal women with knee osteoarthritis

OBJECTIVE

Translation and cross-cultural validation of the Serbian version of the Lower Extremity Functional Scale (Srb-LEFS).

METHODS

This prospective cross-sectional study initially included 186 postmenopausal women aged 60 to 75 years who underwent an x-ray examination of both knees, along with body mass, body height, waist circumference, knee range of motion, and blood pressure measurements. The laboratory analyses included the evaluation of fasting glucose levels and lipid profile. All participants completed the LEFS, Lequesne index, and 36-Item Short-Form Survey-RAND.

RESULTS

The analyses revealed good internal consistency (α = 0.95), good test-retest reliability, and a two-factor structure of the Srb-LEFS. Concurrent validity analysis confirmed a significant positive correlation between Srb-LEFS scores and the 36-Item Short-Form Survey-RAND Physical Functioning subscale ( r = 0.889, P < 0.0001), the Lequesne index ( r = -0.976, P < 0.0001), and the Numeric Rating Scale for pain ( r = -0.762, P < 0.0001). Convergent validity analyses revealed a statistically significant negative correlation between the Srb-LEFS scores and age ( r = -0.25, P = 0.006), body mass index ( r = -0.31, P < 0.01), and waist circumference ( r = -0.37, P < 0.0001). The Srb-LEFS scores were statistically significantly higher among participants that reported moderate physical activity levels, as well as those that had fewer comorbidities, minor structural knee damage, greater knee range of motion, and greater quadriceps femoris muscle strength.

CONCLUSIONS

The Serbian version of the LEFS is feasible, valid, and reliable for use in both clinical practice and clinical studies to assess self-reported physical functioning in older individuals with knee osteoarthritis.

Mediation Effect of Body Mass Index on the Association of Urinary Nickel Exposure with Serum Lipid Profiles

The objective of this study was to determine the relationship of urinary nickel (U-Ni) exposure to serum lipid profiles and the mediation effect of body mass index (BMI) in a US general population. We analyzed the cross-sectional data from 3517 participants in the National Health and Nutrition Examination Survey (NHANES) (2017-March 2020). Multivariable linear regression and restricted cubic spline (RCS) regression were conducted to explore the association of U-Ni with four serum lipids and four lipids-derived indicators. Mediation analysis was performed to examine the effect of BMI on the relationship between U-Ni levels and serum lipid profiles. Compared with the lowest quartile, the β with 95% confidence intervals (CIs) in the highest quartile were - 12.83 (- 19.42, - 6.25) for total cholesterol (TC) (P for trend < 0.001), - 12.76 (- 19.78, - 5.74) for non-high-density lipoprotein cholesterol (non-HDL-C) (P for trend = 0.001) and - 0.29 (- 0.51, - 0.07) for TC/HDL-C (P for trend = 0.007) in the fully adjusted model. RCS plots showed the linear association of log₂-transformed U-Ni levels with TC, non-HDL-C and TC/HDL-C (P for nonlinearity = 0.294, 0.152, and 0.087, respectively). Besides, BMI decreased monotonically in correlation with increasing U-Ni levels (P for trend < 0.001). Mediation analysis revealed that BMI significantly mediated the relationship of U-Ni to TC, non-HDL-C and TC/HDL-C with mediated proportions of 11.17%, 22.20% and 36.44%, respectively. In summary, our findings suggest that BMI mediates the negative association of U-Ni with TC, non-HDL-C, and TC/HDL-C in the US general population.

Evaluation of the concentration of selected elements in serum patients with intervertebral disc degeneration

Quantitative analysis of the trace element content of human intervertebral discs (IVDs) is essential because it can identify specific enzymes or metabolites that may be related to human intervertebral disc degeneration (IVDD). The goal of this study was to assess the concentrations of copper (Cu), iron (Fe), manganese (Mn), lead (Pb), zinc (Zn), sodium (Na), magnesium (Mg), potassium (K), phosphorus (P), and calcium (Ca) in serum samples obtained from patients with IVDD in comparison to healthy volunteers (a control group). The study group consisted of 113 Caucasian patients qualified by a specialist neurosurgeon for microdiscectomy. The control group consisted of 113 healthy volunteers who met the eligibility criteria for blood donors. The examined clinical material was the serum samples obtained from both groups.Based on the quantitative analysis of selected elements, there were statistically significantly (p 0.05) higher concentrations of Cu (1180 μg/L±800 μg/L vs. 1230 μg/L±750 μg/L), Zn (790 μg/L±300 μg/L vs. 850 μg/L±200 μg/L), and Mg (21730 μg/L±4360 μg/L vs. 23820 μg/L±4990 μg/L) in the serum of healthy volunteers compared to those in the study group. In addition, statistically significant changes were not detected in the concentrations of any elements among either sex in either the study or control group or in their body mass index (BMI) values (p > 0.05). In the serum samples from the study group, the strongest relationships were noted between the concentrations of Zn and Pb (r = 0.61), Zn and P (r = 0.69), Zn and Ca (r = 0.84), Zn and Cu (r = 0.83), Mg and Ca (r = 0.74), and Ca and P (r = 0.98).It has been indicated that, above all, the concentrations of Cu, Zn, Ca, and Mg depend on the advancement of radiological changes, according to the Pfirrmann scale. However, no influence on pain intensity was found, depending on the concentration of the assessed elements.The analysis indicates that the determination of serum Cu, Zn, Ca, and Mg concentrations may have diagnostic significance in predicting the onset of lumbosacral IVDD. The predictive evaluation of changes in the concentrations of selected elements in patients with degenerative lumbar IVD lesions appears to be a promising, cost-effective strategy.

The combined effect of heavy metals and polycyclic aromatic hydrocarbons on arthritis, especially osteoarthritis, in the U.S. adult population

The evaluation of heavy metals (HMs) and polycyclic aromatic hydrocarbons (PAHs) impact on arthritis is usually limited to the analysis of the arthritis subtype (rheumatoid arthritis, RA), whereas studies on osteoarthritis (OA) are relatively sparse. Furthermore, the combined effect of HMs and PAHs co-exposure on arthritis also has rarely been analyzed. Herein, we aimed to comprehensively estimate the association between HMs and PAHs (three blood HMs and six urinary PAHs metabolites) co-exposure and arthritis. Using data from the National Health and Nutrition Examination Survey (NHANES), 2003-2016, we included 9735 adults, of whom 2464 had total arthritis, 1371 had OA, and 468 had RA. The logistic regression model was conducted to estimate the single effect of HMs and PAHs on arthritis. Moreover, weighted quantile sum (WQS) regression, quantile-based g computation (qgcomp), and Bayesian kernel machine regression (BKMR) were separately performed to assess the combined effect of HMs and PAHs co-exposure on arthritis. In the single-exposure analyses, cadmium (Cd) and lead (Pb) statistically grew the risk of total arthritis, OA, and RA. Among PAHs, 1-hydroxynaphthalene (1-NAP) and 3-hydroxyfluorene (3-FLU) showed a positive association with total arthritis, OA, and RA. Meanwhile, 2-NAP also was significantly associated with total arthritis. 2-NAP, 2-FLU, and 1-hydroxyphenanthrene (1-PHE) also were significantly associated with RA. Furthermore, the three complementary models consistently demonstrated that co-exposure to high levels of HMs and PAHs was positively associated with total arthritis, OA, and RA risk. The above associations were more obvious in young and medium-aged people. Interestingly, BKMR analyses indicated that 1-NAP might interact with Cd and 3-FLU in total arthritis, while Pb might interact with Cd in OA. Therefore, this study provided novel evidence that co-exposure to HMs and PAHs positively correlated with arthritis, especially OA, and these results were worthy of further prospective studies.

Concentration of Selected Macronutrients and Toxic Elements in the Blood in Relation to Pain Severity and Hydrogen Magnetic Resonance Spectroscopy in People with Osteoarthritis of the Spine

Macronutrients and toxic elements may play an important role in the pathogenesis of osteoarthritis of the spine. The objective of this study was to evaluate the relationship between the concentrations of Ca, Mg, Pb, Cd and Hg in blood with the results of hydrogen magnetic resonance spectroscopy and the severity of pain. Patients with osteoarthritis of the spine (n = 90) and control subjects (n = 40) were studied. The concentrations of mineral components in blood were determined by atomic absorption spectrometry (ASA). Spinal pain severity was assessed using the Visual Analog Scale (VAS). Hydrogen magnetic resonance spectroscopy (¹H-MRS) was used to determine the fat/water ratio in the bodies of L1, L5 and the L4/5 intervertebral disc. The median concentration of Mg in the serum of subjects with spinal degenerative disease was significantly lower (p < 0.001) than that in healthy subjects. The median concentration of Cd in the blood of subjects with osteoarthritis of the spine was significantly higher (p < 0.05) than that in the control group. Significantly lower (p < 0.05) median molar ratios of Ca to Cd and Pb as well as Mg to Pb and Cd were observed among patients with osteoarthritis of the spine. Significant differences (p < 0.05) were observed in the value of the fat/water ratio in selected spinal structures, depending on normal or abnormal serum Ca and Mg concentrations. The study showed some abnormal macronutrient concentrations, as well as disturbed ratios of beneficial elements to toxic elements in the blood of people with osteoarthritis of the spine.

Serum lead levels and its association with overweight and obesity

BACKGROUND

Lead (Pb) exposure has been associated with cardiovascular diseases and metabolic syndrome, nevertheless its association with obesity, type 2 diabetes, and dyslipidemia markers has been little explored in Mexico. Therefore, we evaluated the association of serum Pb levels (Pb-S), with body mass index (BMI), fasting plasma glucose (FPG), total cholesterol (TC), and triglycerides (TG).

METHODS

A cross-sectional study was performed on 85 Mexican adults (57 women and 28 men). BMI was calculated, while FPG, TC, and TG were measured by the enzymatic colorimetric method. Total Pb-S levels were measured using inductively coupled plasma mass spectrometry (ICP-MS).

RESULTS

The study population was 20.3 ± 1.9 years old, showed an average of Pb-S of 0.0982 ± 0.068 μg dL^-1, and presented a frequency of overweight (OW) and obesity (OB) of 50.5% and 18.8%, respectively. Men had higher average FPG than women (Women= 83.930 ± 5.662 vs Men= 84.953 ± 6.495; p = 0.037). When we analyzed anthropometric and clinical variables, Pb-S and frequency of OW and OB were observed to increase within the categories of Pb-S tertiles (<0.001). The averages of Pb-S were 0.051 ± 0.035 µg dL^-1, 0.107 ± 0.067 µg dL^-1, and 0.151 ± 0.063 µg dL^-1 for individuals with normal weight (NW), OW, and OB, respectively. In addition, an analysis adjusted for age and sex shows Pb-S is positively associated with BMI (β = 2.76 ± 0.498, p = <0.001).

CONCLUSION

Our results evidence a significant association between Pb-S and the increase of BMI in Mexican adults and highlight the important health impact that may represent environmental Pb exposure.

Biological aging mediates the associations between urinary metals and osteoarthritis among U.S. adults

BACKGROUND

Osteoarthritis (OA) is a worldwide public health concern, mainly afflicting older adults. Although the etiology of OA remains unclear, environmental factors are increasingly considered as non-negligible risk factors. This study aims to evaluate the associations of urinary metals with OA risk and the mediated effect of biological aging.

METHODS

Nine urinary metal concentrations were detected among 12,584 U.S. adults based on the National Health and Nutrition Examination Survey (NHANES), including barium (Ba), cadmium (Cd), cobalt (Co), cesium (Cs), molybdenum (Mo), lead (Pb), antimony (Sb), thallium (Tl), and uranium (Tu). Multivariable logistic regression and weighted quantile sum (WQS) regression were used to explore the associations of single metal and mixed metals with OA risk, respectively. Furthermore, biological aging was measured from different perspectives, including cell senescence (telomere length) and whole-body aging (phenotypic age and biological age). Mediation analyses were conducted to investigate the mediated effects of aging on the associations of metals with OA risk.

RESULTS

In the single-exposure model, Cd, Co, and Cs were identified to be positively associated with OA risk, with odds ratios (OR) ranging from 1.48 to 1.64 (all P < 0.05). Mixed-exposure analyses showed consistent associations (OR 1.23, 95%CI 1.10 to 1.37) and highlighted that Cd, Co, and Cs were responsible for the outcomes. Additionally, Cd, Co, Cs, Pb, and Tl were positively associated with biological aging markers, while all biological aging markers had significant associations with OA risk. Further mediation analyses showed that the associations of single metal (mainly Cd and Cs) and mixed metals with OA risk parallelly mediated by the above biological aging markers, with the proportion of mediation ranging from 16.89 to 69.39% (all P < 0.05). Moreover, such associations were also serially mediated through telomere length-biological age path and telomere length-phenotypic age path (the proportion of mediation: 4.17-11.67%), indicating that metals accelerated cell senescence to lead to whole-body aging and finally aggravated OA progress.

CONCLUSIONS

These findings suggested that exposure to metals increased OA risk, which was possibly and partly mediated by biological aging.

The Influence of Obesity on Pain and Function in Knee Osteoarthritis: Comparison of Body Mass Index With Seven Knee Function Scales and Two Pain Scales

Instruction: Obesity is a health problem that is rapidly increasing both in local societies and internationally. It is well known that obesity has a risk relationship with many different diseases. The scale for obesity is Body Mass Index (BMI), which has been widely accepted worldwide for many years. The relationship between BMI and disease is a frequently studied topic. This study aimed to evaluate and measure knee function and pain in patients with knee osteoarthritis.

Materials and Methods: A total of 100 patients in radiologically advanced stage (Kellgren/Lawrence grade 3-4) who were scheduled for knee arthroplasty were administered seven knee osteoarthritis scales (Timed up and Go (TUG), American Knee Society Score (AKSS), the Lequesne Knee Index, Knee injury and Outcome Subtotal Pain Score (KOOS-PS), Western Ontario and McMaster Universities Index (WOMAC), Oxford Knee Score, and International Knee Documentation Committee (IKDC)), and two pain scales, the McGill Pain Questionnaire and a visual analog pain scale (VAS), which were completed simultaneously on the same form. Data that did not show a normal distribution were analyzed with Spearman and Kendall correlation tests.

Results: The mean age of the 100 consecutive patients, 92% of whom were female, was 65.2 years (48-81 years). There was a strong correlation between BMI and all functional knee scales, but no significant association was found between pain scales and BMI.

Conclusion: In our hypothesis, we expected that all functional and pain scales would moderately or strongly correlate with BMI. However, while a strong correlation with the functional pain scales is an expected result, the expected strong positive correlation between pain scales and BMI was not found in the study.

Lead, mercury, and cadmium exposures are associated with obesity but not with diabetes mellitus: Korean National Environmental Health Survey (KoNEHS) 2015-2017

BACKGROUND

Associations of heavy metal exposures with obesity and obesity-related traits have been suggested, while those with nonalcoholic fatty liver disease (NAFLD) and diabetes mellitus (DM) are often inconsistent.

METHODS

This study included 3787 adults aged ≥19 years who participated in the Korean National Environmental Health Survey 2015-2017, and investigated the association of toxic heavy metals with metabolic diseases. Lead (Pb), mercury (Hg), and cadmium (Cd) were measured either in urine (uHg, uCd) or total blood (bPb, bHg). Body mass index (BMI) was calculated, and DM cases were identified through a self-answered medication history. Hepatic Steatosis Index (HSI) as a surrogating index of NAFLD, was calculated using hepatic enzyme measurements, including aspartate aminotransferase (AST) and alanine aminotransferase (ALT).

RESULTS

Adults in the highest quartile of bPb, bHg, and uHg showed significantly elevated odds of obesity (BMI ≥25 kg/m²), compared to the lowest quartile (OR 1.58 for bPb, 1.92 for bHg, and 1.81 for uHg). HSI was positively correlated with bHg, uHg, and uCd concentrations. The odds of NAFLD (HSI ≥36) were also increased with increasing quartile of bHg, uHg, and uCd concentrations. For DM, bPb showed a significant negative association, while bHg and uCd exhibited non-monotonic and inconclusive associations.

CONCLUSIONS

Among the general adult population of Korea, both Pb and Hg exposures were associated with an increased risk of obesity. In addition, both Hg and Cd exposures were associated with increased odds of NAFLD. These metals, however, were not associated with an increased risk of DM.

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

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.