ISSN: 2455-5479
Archives of Community Medicine and Public Health
Editorial       Open Access      Peer-Reviewed

What is the consequence of metals on human health?

Rubí Rodríguez-Díaz1*, Raquel Blanes-Zamora1, Jorge Gómez-Rodríguez1, Arturo Hardisson2, Soraya Paz2 and González-Dávila E3

1Obstetrics and Gynecology, Hospital Universitario de Canarias, Hospital, Universidad de La Laguna, Spain
2Toxicology, Human Reproduction Unit, Canary Islands University Hospital, Spain
3Mathematics, Statistics and Operations Research, University de La Laguna, Tenerife, Spain
*Corresponding author: Rubí Rodríguez-Díaz, Professor, Obstetrics and Gynecology, Hospital Universitario de Canarias, Hospital, Universidad de La Laguna, Avenida B. Pérez Armas, 6.3B, CP 38007, Santa Cruz de Tenerife, Spain, Tel: (+34) 677 021 762 14; E-mail: rubrod@ull.edu.es
Received: 30 December, 2021 | Accepted: 27 April, 2022 | Published: 28 April, 2022
Keywords: Pandemic; Infections, COVID-19; Extraordinary; Plausible; Simpy

Cite this as

Rodríguez-Díaz R, Rodríguez-Díaz R, Gómez-Rodríguez J, Hardisson A, Paz S, et al. (2022) What is the consequence of metals on human health? Arch Community Med Public Health 8(2): 068-069. DOI: 10.17352/2455-5479.000176

Copyright License

© 2022 Ashraf S, et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

related to health outcomes in the later stages of life, including cancer, heart disease, kidney disease, and neurological conditions, that may be influenced by epigenetic mechanisms triggered in intrauterine and neonatal life.

Some natural substances and xenobiotics, which are chemical substances synthesized by man and released into the environment, affect the endocrine system, that is, they would act as endocrine disruptors, which are a series of persistent, organohalogenated and bioaccumulated compounds that include synthetic compounds and that continue to enter the food chain using pesticides (fungicides, herbicides, and insecticides) such as Dichloro-Diphenyl-Trichloroethane (DDT), which are now, or via ingestion in the case of plastics, with compounds such as bisphenol A and phthalate that are still found in the environment, and certain metals.

At present, negative effects of some toxins such as heavy metals are currently being detected. Heavy metals are metallic and metalloid chemical elements that are toxic to the environment and humans, and that may be present in humans. In some cases, they are essential for the maintenance of biochemical systems, but in certain amounts, they can be toxic. Exposure to metals may be due to contamination of water, due to its presence in soil or dust, fumes, or aerosols that are emitted into the atmosphere by industrial discharges, such as those related to the consumption and production of gasoline. Certain characteristics such as easy assimilation and bioaccumulation in organisms mean that they represent a potential risk to human health, especially when consuming contaminated food. Their concentration in the environment, in water, as well as exposure time, pH, temperature, salinity, and intrinsic factors (body mass index: BMI, gender) can influence the accumulation of heavy metals in the organism.

Among their detrimental effects are the interaction with essential metals due to electronic similarity, the formation of metal-protein complexes with inactivation of their function, the enzymatic inhibition of proteins with sulfhydryl groups, and the affectation of cellular organelles: mitochondria, lysosomes, and microtubules.

As regards effects on reproduction, sperm counts decreased in the last thirty years. There are many factors affecting semen production, such as stress, trauma, obesity, nutrition, tobacco smoking, and chemical substances like polychlorinated biphenyls and saturated fats. In addition, attention has also been paid to ethnic, genetic, and environmental factors, prenatal chemical and adult pesticide exposure, environmental pollution, occupational exposure, and changes in habits or lifestyle, all of which have been the subject of extensive research.

Xenobiotics and other factors such as radiation, can act directly on the germ cells of the mature testicles, but they can also act indirectly, through the exposure of the woman during pregnancy, altering the development of the reproductive tract in the male, in such a way that it affects the germ cells and the somatic tissue producing testicular dysgenetic syndrome, characterized by poor seminal quality, hypospadias, testicular cancer, and cryptorchidism.

Of these toxins, pollutants, or metals, some can affect sperm quality, such as Cadmium (Cd), Lead (Pb), Zinc (Zn), and Iron (Fe), among others. It has been observed that Cd and Pb are two of the metals that exert a greater influence, with increased amounts being found in infertile men with a significant inverse correlation between concentration and sperm motility and count in oligoasthenozoospermic men.

However, some elements are considered essential, these are called essential metals. Therefore, a distinction can be made between Copper (Cu), Chromium (Cr), Manganese (Mn), Zn, Cobalt (Co), Fe, and the Vanadium (V), compared to Cadmium Cd, Mercury (Hg), and Lead (Pb) which are toxic.

These changes in seminal samples appear to be relatively recent and could be related to lifestyle or the increased concentration of pollutants and environmental toxins in developed countries. Evidence from toxicological, epidemiological, biochemical, and physiological studies shows that toxins and pollutants have adverse effects on human health and can cause male infertility, either by affecting endocrine function or spermatogenesis.

In conclusion, the detection of metals in semen opens up a new field in the study of male infertility, and many cases of unknown infertility might be due to the presence, absence, or alterations in the proper concentration of metals in semen, with the opportunity of providing treatments for these possible anomalies. No relationship has been found between spermiogram, sperm motility, and concentration with non-essential metal levels, although Ni levels tend to be lower in patients with oligozoospermia. The occupational exposure factor has a significant effect on metal concentrations in sperm as patients with occupational exposure to metals have a lower sperm concentration. There is a significant relationship between the level of occupational exposure to metals and Ni and an increase in the levels of Aluminum (Al) in the semen of workers with high occupational exposure. In addition, occupational exposure to metals and place of residence have some effects on Al and V levels in semen. No relationship has been reported between spermiogram, sperm motility, and concentration with metal levels, although Ni levels tend to be lower in patients with oligozoospermia.

On the other hand, normozoospermia is related to higher amounts of Ca, Fe, and Zn than pathologic stereograms. Increased levels of Fe in human semen appear to have a significant correlation with male fertility, suggesting that Fe in human seminal plasma is an important factor in male reproductive function. Fe acts as an antioxidant being a co-factor of catalase, which protects sperm. In addition, elevated Fe levels are also associated with sperm damage and continue to increase lipid peroxidation that will affect the plasma membrane and the sperm motility. Most authors associate Fe with sperm motility and higher estimated fertility potential, based on standard semen parameters for infertile men, which are associated with lower levels of Fe. Normozoospermia is associated with higher amounts of Fe. In males with pathological spermiogram, the percentage of men with Fe in semen was lower than expected.

There are few studies analyzing the effect of heavy metals in paternal semen on ART outcomes. Therefore, more studies are needed to understand the real impact of metals on ART results. The study here confirms the importance of Zn, Fe, Ca, Na, Al, Mg, V, and Pb in the positive-negative effects on reproduction and supports the analysis of metals in semen as a new field of study on male fertility with implications for reproductive outcomes.

Moving on to the repercussions of the presence of metals on female fertility at the ovarian follicular level, patients who are unable to conceive have elevated Pb levels in the follicular fluid that will result in a decreased probability of pregnancy. Cobalt (Co) produces a decrease in the number of embryonic blastomeres, which induces worse results in reproduction, and Zn concentration produces a beneficial effect for reproduction since its high levels in blood and urine favor follicular development. In the same way, elevated levels of Cd in follicular fluid have a positive relationship with fertilization.

On the other hand, there has been an increase in the number of studies recently aimed at detecting possible alterations in the fetus’s health that are related to environmental exposure to various elements. Their effects on fetal development not only can have immediate consequences but even long-term consequences for adult health. This environmental exposure can affect molecular reprogramming during the most critical periods of development, such as preconception, preimplantation, fetal period, and early childhood.

It is worth mentioning that among the most important causes of perinatal and infant morbidity and mortality in low birth weight, it has been observed that alterations in the concentrations of trace elements and minerals adversely affect the outcome of pregnancy. The presence of metals in pregnant women with premature rupture of membranes has also been demonstrated.

Regarding obesity, in obese patients (BMI ≥30.0kg/m2) the percentage of men with Fe in semen is less than expected, and obesity harms seminal quality and is associated with higher rates of asthenozoospermia and oligozoospermia. In addition, obese patients have low levels of Pb in semen probably due to the accumulation of Pb in the fatty tissue.

The importance of knowing the real impact of metals on health lies in the possibility of being able to prescribe preventive and therapeutic measures for certain conditions, as well as advising certain healthy consumption habits.

  1. Mello IF, Squillante L, Gomes GO, Seridonio AC, de Souza M. Epidemics, the Ising-model and percolation theory: A comprehensive review focused on COVID-19. Physica A. 2021 Jul 1;573:125963. doi: 10.1016/j.physa.2021.125963. Epub 2021 Mar 29. PMID: 33814681; PMCID: PMC8006539.
  2. Ashraf S, Gao M, Chen Z, Kamran S, Raza Z. Efficient Node Monitoring Mechanism in WSN using Contikimac Protocol. Int J Adv Comput Sci Appl. 2017; 8.
  3. Ashraf S, Arfeen ZA, Khan MA, Ahmed T. SLM-OJ: Surrogate Learning Mechanism during Outbreak Juncture. Int J Mod Trends Sci Technol. 2020; 6:162–167.
  4. Ashraf S, Ahmed T. Sagacious Intrusion Detection Strategy in Sensor Network, in 2020 International Conference on UK-China Emerging Technologies (UCET), Glasgow, United Kingdom 2020; 1–4.
  5. Grassly NC, Fraser C. Mathematical models of infectious disease transmission. Nat Rev Microbiol. 2008 Jun;6(6):477-87. doi: 10.1038/nrmicro1845. PMID: 18533288; PMCID: PMC7097581.
  6. Ashraf S. A proactive role of IoT devices in building smart cities. Internet Things Cyber-Phys Syst. 2021; 1:8–13.
  7. Ashraf S, Muhammad D, Aslam Z. Analyzing challenging aspects of IPv6 over IPv4. J Ilm Tek Elektro Komput Dan Inform. 2020; 6:54.
  8. Ashraf S, Saleem S, Afnan S. FTMCP: Fuzzy based Test Metrics for Cosmetology Paradigm. Adv Comput Intell Int J ACII. 2020; 4:1-13.
  9. Ashraf S, Alfandi O, Ahmad A, Khattak A, Hayat B, et al. Bodacious-Instance Coverage Mechanism for Wireless Sensor Network. Wirel Commun Mob Comput. 2020; 1–11.
  10. Ashraf S, Muhammad D, Khan MA, Ahmed T. Fuzzy based efficient Cosmetology Paradigm. 8:513–520.
  11. Mohamadou Y, Halidou A, Kapen PT. A review of mathematical modeling, artificial intelligence and datasets used in the study, prediction and management of COVID-19. Appl Intell (Dordr). 2020;50(11):3913-3925. doi: 10.1007/s10489-020-01770-9. Epub 2020 Jul 6. PMID: 34764546; PMCID: PMC7335662.
  12. Ashraf S, Saleem S, Ahmed T, Aslam Z, Shuaeeb M. Iris and Foot based Sustainable Biometric Identification Approach, in 2020 International Conference on Software, Telecommunications and Computer Networks (SoftCOM), Split, Hvar, Croatia. 2020; 1–6.
  13. Ashraf S, Ahmed T. Machine Learning Shrewd Approach for An Imbalanced Dataset Conversion Samples. J Eng Technol. 2020; 11.
  14. Ashraf S, Aslam Z, Saleem S, Afnan S, Aamer M. Multi-biometric Sustainable Approach for Human Appellative. CRPASE Trans Electr Electron Comput Eng. 2020; 6:146-152.
  15. Ashraf S, Ahmed T, Saleem S. NRSM: node redeployment shrewd mechanism for wireless sensor network. Iran J Comput Sci. 2020.
  16. Ashraf S, Saleem S, Chohan AH, Aslam Z, Raza A. ‎Challenging strategic trends in green supply chain ‎management. J Res Eng Appl Sci. 2020; 5:71–74.
  17. Shahzad A. Towards Shrewd Object Visualization Mechanism. Trends Comput Sci Inf Technol. 2020; 097-102.
  18. Saleem S, Ashraf S, Basit MK. CMBA - A Candid Multi-Purpose Biometric Approach. ICTACT J. Image Video Process. 2020; 11:6.
  19. Python R. SimPy: Simulating Real-World Processes with Python – Real Python. 2021.
  20. Ashraf S, Gao M, Chen Z, Naeem H, Ahmad A, et al. Underwater Pragmatic Routing Approach Through Packet Reverberation Mechanism. IEEE. 2020; 8:163091-163114.
  21. Modeling and prediction of the 2019 coronavirus disease spreading in China incorporating human migration data.
  22. SEIR and Regression Model based COVID-19 outbreak predictions in India. 2021.
  23. MaskedFace-Net – A dataset of correctly/incorrectly masked face images in the context of COVID-19 - ScienceDirect.
  24. Guo XJ, Zhang H, Zeng YP. Transmissibility of COVID-19 in 11 major cities in China and its association with temperature and humidity in Beijing, Shanghai, Guangzhou, and Chengdu. Infect Dis Poverty. 2020 Jul 10;9(1):87. doi: 10.1186/s40249-020-00708-0. PMID: 32650838; PMCID: PMC7348130.
  25. Lalmuanawma S, Hussain J, Chhakchhuak L. Applications of machine learning and artificial intelligence for COVID-19 (SARS-CoV-2) pandemic: A review. Chaos Solitons Fractals. 2020 Oct;139:110059. doi: 10.1016/j.chaos.2020.110059. Epub 2020 Jun 25. PMID: 32834612; PMCID: PMC7315944.
  26. Chen YC, Lu PE, Chang CS, Liu TH. A Time-dependent SIR model for COVID-19 with Undetectable Infected Persons. ArXiv200300122 Cs Q-Bio Stat 2020.
  27. Real-Time Mask Identification for COVID-19: An Edge-Computing-Based Deep Learning Framework. IEEE Journals & Magazine. IEEE Xplore 2021.
  28. Wang L, Wong A. COVID-Net: A Tailored Deep Convolutional Neural Network Design for Detection of COVID-19 Cases from Chest X-Ray Images. 2020. ArXiv200309871 Cs Eess 2021.
  29. Ashraf S, Saleem S, Ahmed T, Aslam Z, Muhammad D. Conversion of adverse data corpus to shrewd output using sampling metrics. Vis. Comput Ind Biomed Art. 2020; 3:19.
  30. Ashraf S, Raza A, Aslam Z, Naeem H, Ahmed T. Underwater Resurrection Routing Synergy using Astucious Energy Pods. J Robot Control JRC. 2020:1.
  31. Ashraf S. Underwater Routing Protocols Analysis of Intrepid Link Selection Mechanism, Challenges and Strategies. Int J Sci Res Comput Sci Eng. 2020; 8:1-9.
  32. Ashraf S. Underwater routing protocols: Analysis of link selection challenges. AIMS Electron Electr Eng. 2020; 4:234-248.
  33. Ashraf S, Gao M, Mingchen Z, Ahmed T, Raza A, et al. USPF: Underwater Shrewd Packet Flooding Mechanism through Surrogate Holding Time. Wirel Commun Mob Comput. 2020; 1-12.
  34. Ashraf S. Avoiding Vulnerabilities and Attacks with a Proactive Strategy for Web Applications. 3: 9.
  35. Allahham MS, Khattab T, Mohamed A. Deep Learning for RF-Based Drone Detection and Identification: A Multi-Channel 1-D Convolutional Neural Networks Approach. in 2020 IEEE International Conference on Informatics, IoT, and Enabling Technologies (ICIoT). 2020; 112–117.
  36. Organic Power Generation and Utilization Using Anaerobic Digestion Process. J Appl Emerg Sci. 126–132.
  37. Sadiq EH, Ashraf S, Aslam Z, Muhammad D. Fuzzy based multi-line Power Outage Control System. J Crit Rev. 2021; 08: 11.
  38. Khan MA, Dharejo FA, Deeba F, Ashraf S, Kim J, et al. Toward developing tangling noise removal and blind inpainting mechanism based on total variation in image processing. Electron Lett. 2021; 57: 436-438.
  39. Ashraf S. Culminate Coverage for Sensor Network through Bodacious-Instance Mechanism. Manag J Wirel Commun Netw. 2019; 8:9.
  40. Ashraf S, Ahmed T, Aslam Z, Muhammad D, Yahya A, et al. Depuration‎ based Efficient Coverage Mechanism for ‎Wireless Sensor Network. J Electr Comput Eng Innov. 2020; 8:145-160.
  41. Ashraf S, Ahmed T, Raza A, Naeem H. Design of Shrewd Underwater Routing Synergy Using Porous Energy Shells. Smart Cities. 2020; 3: 74–92.
  42. Ashraf S, Ahmed T, Saleem S, Aslam Z. Diverging Mysterious in Green Supply Chain Management. Orient J Comput Sci Technol. 2020; 13:22–28.
  43. Shahzad A, Tauqeer A (2020) Dual-nature biometric recognition epitome. Trends Comput Sci Inf Technol. 2020; 5: 008-014.
  44. Ashraf S, Gao M, Chen Z. CED-OR Based Opportunistic Routing Mechanism for Underwater Wireless Sensor Networks. Wireless Personal Communications 2022.