Next Article in Journal
Advancing Biosensing and Imaging with DNA-Templated Metal Nanoclusters: Synthesis, Applications, and Future Challenges—A Review
Next Article in Special Issue
Evaluation of Corrosion Potential Stability of Stainless Steels in Dilute Electrolyte Solution for Application to a Quasi-Reference Electrode Used in Electrochemical Sensing System
Previous Article in Journal / Special Issue
A Simple Green Method for the Determination of Hydrogen Peroxide and Fe(III)/Fe(II) Species Based on Monitoring the Decolorization Process of Polymethine Dye Using an Optical Immersion Probe
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Chemosensors
Volume 12
Issue 12
10.3390/chemosensors12120269
chemosensors-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Review

Recent Progress in Saliva-Based Sensors for Continuous Monitoring of Heavy Metal Levels Linked with Diabetes and Obesity

by
Liliana Anchidin-Norocel
1,*,
Wesley K. Savage
1,*,
Alexandru Nemțoi
1,
Mihai Dimian
2,3 and
Claudiu Cobuz
1,4
1
Faculty of Medicine and Biological Sciences, Stefan cel Mare University of Suceava, 720229 Suceava, Romania
2
Integrated Center for Research, Development and Innovation in Advanced Materials, Nanotechnologies, and Distributed Systems for Fabrication and Control, Stefan cel Mare University of Suceava, 720229 Suceava, Romania
3
Department of Computers, Electronics and Automation, Stefan cel Mare University of Suceava, 720229 Suceava, Romania
4
Department of Diabetes, Nutrition and Metabolic Diseases, Sfântul Ioan cel Nou Clinical Hospital of Suceava, 720224 Suceava, Romania
*
Authors to whom correspondence should be addressed.
Chemosensors 2024, 12(12), 269; https://doi.org/10.3390/chemosensors12120269
Submission received: 13 November 2024 / Revised: 10 December 2024 / Accepted: 16 December 2024 / Published: 19 December 2024
(This article belongs to the Special Issue Low-Cost Chemosensors for Applications in Environment, Health, Food, and Industry Process Control)
Browse Figures
Versions Notes

Abstract

Sensors are versatile technologies that provide rapid and efficient diagnostic results, making them invaluable tools in public health for measuring and monitoring community exposure to environmental contaminants. Heavy metals such as lead, mercury, and cadmium, commonly found in food and water, can accumulate in the body and have toxic effects, contributing to the development of conditions like obesity and diabetes. Traditional methods for detecting these metals often require invasive blood samples; however, sensors can utilize saliva, offering a noninvasive and simplified approach for public health screening. The use of saliva as a diagnostic fluid represents a major advance in population health monitoring due to its low cost, noninvasiveness, and ease of collection. Recent advances in sensor technology have enabled the development of diagnostic tests that link heavy metal levels in saliva with the risk of developing obesity and diabetes. Optimizing these sensors could facilitate the identification of individuals or groups at risk, enabling targeted, personalized preventive measures. Sensors that use saliva for detecting heavy metals hold promise for diagnosing and preventing metabolic diseases, providing valuable insights into the link between heavy metal exposure and metabolic health.

1. Introduction

With rising living standards, there is an increasing emphasis on medical care focused on early disease prevention through painless screening methods [1]. Noninvasive sensors for detecting specific biomarkers are emerging as promising technologies, and with the advent of cloud medicine, the healthcare paradigm is shifting toward the integration of population-wide screening methods that can rapidly and cost-effectively generate public health assessments [2,3].
Among various diagnostic approaches, monitoring and assessing physiological status through biomarkers in saliva has been extensively studied [4]. Saliva, primarily secreted by the parotid, sublingual, and submandibular glands, is easily accessible in the oral cavity and requires no specialized skills, techniques, or equipment for sampling [5]. It contains a complex array of organic and inorganic biomarkers, including glucose, nitrogen-containing products, enzymes, antibodies, hormones, cytokines, and electrolytes, all of which correlate strongly with immune, hormonal, nutritional, metabolic, and emotional states [6,7].
Salivary biomarker sensors have advanced health diagnostics significantly [8]. However, many laboratory studies still face challenges in meeting clinical standards required for saliva to become a reliable biofluid for routine screening [2]. In contrast, traditional clinical diagnostics typically rely on blood samples to measure biomarkers [9]. While blood testing is a well-established method, it is invasive and can be uncomfortable for patients, especially children and the elderly. It also requires trained technicians, specialized equipment, and protocols, posing handling risks for healthcare workers.
In contrast, saliva sampling offers a noninvasive alternative, providing a stress-free, accessible method for detecting various disease-related biomarkers [10]. While there are some challenges with saliva collection and assay, continuous advancements in detection technology are enhancing its reliability as a diagnostic fluid [11,12].
One of the key advantages of saliva sampling is that it is relatively easy to collect in sufficient quantities with minimal training, making it a highly accessible option for a wide range of patients. Unlike blood sampling, it does not require specialized equipment, personnel, or facilities such as medical offices, clinics, or hospitals [13]. This makes saliva an attractive diagnostic fluid for both routine screenings and preventive health measures.
Salivary biomarker detection sensors hold great promise as noninvasive, easy-to-use, cost-effective tools for personal health monitoring. They significantly improve the applicability of point-of-care diagnostics, early disease detection, and overall health monitoring, offering valuable insights into disease risk assessment and management [14,15].
Diabetes, a well-recognized chronic disease, presents major public health and economic challenges, affecting around 10.5% of the global population, with prevalence on the rise [16]. It was notably linked with poorer outcomes in hospitalized patients during the COVID-19 pandemic [17]. Individuals with diabetes face increased risks of various health issues, resulting in prolonged hospital stays, diminished quality of life, and higher mortality rates [18]. One significant complication associated with diabetes is metabolic syndrome, characterized by persistent high blood sugar levels and associated with multiple comorbidities [19].
Mounting evidence indicates a connection between environmental factors and type 2 diabetes. Daily exposure to metals in food, water, and air can lead to health problems from environmental pollutants. Heavy metals such as arsenic (As), cadmium (Cd), copper (Cu), nickel (Ni), lead (Pb), and mercury (Hg) are particularly concerning due to their persistence in the environment, potentially contributing to the development of diabetes and other metabolic disorders [20,21].
Obesity, another critical public health challenge, significantly elevates the risk of numerous conditions including hypertension, myocardial infarction, type 2 diabetes (T2DM), fatty liver disease, stroke, dementia, osteoarthritis, obstructive sleep apnea, and various cancers, all of which adversely affect life expectancy and quality of life [22,23]. Moreover, obesity was a significant factor in the increased severity and higher mortality rates seen during the COVID-19 pandemic. In vivo studies indicate that exposure to heavy metals such as mercury, cadmium, lead, and arsenic can markedly influence body adiposity, suggesting that ingestion of these metals might contribute to obesity [24].
While previous research has explored the links between heavy metal exposure and metabolic conditions, this review highlights a novel approach involving saliva-based sensors. These sensors provide a noninvasive and rapid method to assess heavy metal exposure and its correlation with metabolic diseases. This innovative approach contributes significantly to understanding the underlying mechanisms and offers potential for developing more effective prevention and intervention strategies.

2. The Links Between Heavy Metals, Obesity and Type 2 Diabetes

The impact of heavy metal exposure on type 2 diabetes mellitus (T2DM), obesity, and metabolic syndrome has been extensively studied, yet the findings remain inconsistent. Metals such as cadmium, mercury, and the metalloid arsenic are believed to play a role in the development of T2DM; however, the evidence supporting these associations is often conflicting and inconclusive [25]. Despite these inconsistencies, there is sufficient evidence to suggest that individual heavy metals may contribute to an increased risk of T2DM through distinct biological mechanisms:
  • Oxidative Stress Induction—Toxic metals such as arsenic (As), cadmium (Cd), mercury (Hg), and lead (Pb) stimulate the production of reactive oxygen species (ROS), including superoxide radicals, hydrogen peroxide, and nitric oxide, leading to oxidative stress [26].
  • β-Cell Dysfunction—Oxidative stress induced by toxic metals impairs insulin gene expression in pancreatic β-cells, reducing insulin secretion, disrupting glucose uptake, and altering glucose regulation pathways, which may contribute to insulin resistance and T2D development [27].
  • Essential Trace Metal Imbalance—Imbalanced levels of essential trace metals, whether due to deficiency or overexposure, disrupt pancreatic islet cell function, impairing glucose metabolism and insulin signaling. While normal levels of these metals enhance insulin sensitivity and action, imbalances increase the risk of diabetes [28].
  • Antioxidant Effects of Essential Metals—Essential trace metals, such as zinc and copper, help counteract oxidative stress caused by toxic metals, protecting β-cells, maintaining insulin homeostasis, and reducing the risk of T2D through their antioxidant properties [29].
  • Competition between Metals—Toxic metals compete with essential metals for absorption, transport, protein binding, and metabolism. Both toxic metals (e.g., As, Cd, Hg, Pb) and some essential metals (e.g., cobalt (Co), copper (Cu), chromium (Cr), nickel (Ni), selenium (Se)) act as metalloestrogens, disrupting endocrine pathways and increasing the risk of T2D [30].
  • Effects on Body Weight and Lipid Metabolism—Heavy metals influence body weight and lipid metabolism. Lead exposure, for example, increases food intake, body weight, and insulin response, while mercury (Hg), manganese (Mn), and cobalt (Co) can disrupt adipose tissue metabolism, potentially exacerbating obesity-related diseases. Additionally, nickel allergies and low-nickel diets have been shown to affect weight management [31].
These mechanisms highlight the multifaceted role of heavy metals in the pathophysiology of T2DM, emphasizing the need for further research into how environmental exposures contribute to metabolic disorders.
Obesity is a multifactorial condition associated with a range of complex pathophysiological mechanisms [32]. Recent research has emphasized the significant roles of factors such as food quality, lifestyle choices, genetic and epigenetic influences, gut dysbiosis, and environmental exposures in the development and progression of obesity [33]. Numerous epidemiological and experimental studies have demonstrated that exposure to certain metals can induce “adipogenic” effects, with many heavy metals correlating with the anthropometric and metabolic parameters characteristic of obesity. Trace elements, often contaminants found in food and water, are now recognized as regulators of various biological processes that contribute to the onset and progression of obesity, diabetes, and related metabolic disorders [34]. Elevated levels of arsenic, lead, cadmium, and mercury have been significantly linked to the development of metabolic syndrome [35].
Recent studies have further suggested that exposure to heavy metals such as mercury, cadmium, lead, arsenic, nickel, and copper is associated with an increased risk of developing both obesity and type 2 diabetes (Table 1). In contrast, essential trace elements like chromium, which are naturally present in the diet, have shown potential in reducing fasting blood glucose levels in individuals with type 2 diabetes [36,37]. This growing body of evidence underscores the need to better understand the mechanisms through which these metals influence metabolic health. Moreover, it highlights the importance of developing effective screening strategies to mitigate the public health impacts of environmental metal exposure (Figure 1).

2.1. Mercury (Hg)

Salivary mercury levels can serve as an indicator of the body’s overall mercury burden, particularly reflecting recent exposure. Common nonoccupational sources of mercury contamination, such as dental amalgams and seafood consumption, are significant contributors to mercury exposure in populations not otherwise exposed through occupational hazards. Given the bioaccumulative nature and toxicity of mercury, it is critical to monitor its concentrations in humans, and salivary screening offers a viable solution for public health surveillance [58].
Mercury exposure has profound toxic effects across several biological systems, including the cardiovascular, pulmonary, digestive, hematological, immune, renal, endocrine, neurological, and reproductive systems [59]. In particular, mercury has been implicated in cardiovascular toxicity, which can manifest as elevated blood pressure and altered heart rate variability. In the lungs, exposure to mercury may lead to respiratory distress and impaired lung function [60].
Emerging evidence highlights mercury’s role in the pathogenesis of components of metabolic syndrome (MetS), such as dyslipidemia, hypertension, and insulin resistance, and to a lesser extent, obesity [61]. Mercury disrupts insulin signaling pathways and induces oxidative stress, which are pivotal mechanisms contributing to these metabolic disturbances. Additionally, mercury exposure can alter lipid metabolism, leading to modifications in cholesterol and triglyceride levels, which are key factors in the development of MetS [62].
In the context of diabetes, mercury has been shown to affect pancreatic beta-cell function, leading to cell dysfunction and apoptosis. Proposed mechanisms include disruptions in calcium homeostasis, activation of the Akt pathway via phosphatidylinositol 3-kinase, and the generation of reactive oxygen species [63]. Studies have correlated higher mercury levels in biological samples, such as hair, urine, and blood, with increased fasting glucose and decreased insulin sensitivity in patients with diabetes [25]. Mercury exposure further disrupts carbohydrate and lipid metabolism, promotes oxidative stress, and induces systemic inflammation, collectively increasing the risk of metabolic disorders associated with obesity [64].
Additionally, mercury may enhance adipogenesis, contributing to increased fat storage and obesity. Chronic mercury exposure also exacerbates inflammatory responses, which can further fuel metabolic imbalances leading to weight gain [65]. Furthermore, mercury’s neurotoxic effects should not be overlooked, as they may indirectly influence metabolic health. Neuroinflammation and neuroendocrine disruptions caused by mercury exposure can affect appetite regulation and energy expenditure, potentially leading to obesity and related metabolic disorders [66].
Monitoring salivary mercury levels through advanced sensor technologies offers a noninvasive, practical means for assessing exposure and its potential health risks. Early detection through salivary diagnostics may facilitate timely interventions to mitigate the adverse health effects of mercury exposure.

2.2. Cadmium (Cd)

Cadmium (Cd) is a toxic environmental pollutant primarily acquired through tobacco smoke and dietary sources, with a strong association with several diseases, including diabetes, chronic kidney disease, osteoporosis, obesity, liver disease, cardiovascular disease, and cancer [63,64]. The toxic effects of cadmium are driven by mechanisms such as increased inflammation, oxidative stress, mitochondrial dysfunction, endoplasmic reticulum stress, and genomic instability [65].
Cadmium induces diabetes through mitochondrial toxicity, which results in oxidative stress, inflammation, ATP depletion, and insulin resistance in both insulin-dependent and insulin-independent tissues [66]. Additionally, cadmium exposure can indirectly affect diabetes by inducing hyperuricemia [67]. Smokers typically exhibit higher cadmium concentrations, whereas nonsmokers mainly acquire it from food sources such as grains, vegetables, mollusks, and organ meats. Certain groups, including women, the elderly, and individuals with iron deficiency, are particularly vulnerable to higher cadmium accumulation due to increased absorption with age [68].
Several studies indicate a linear relationship between diabetes risk and urinary cadmium concentrations, with blood cadmium levels only elevating risk at high exposure levels [69]. Prediabetes risk rises at lower levels of cadmium exposure, plateauing at higher urinary cadmium concentrations. Some research also suggests that cadmium acts as a human obesogen, potentially contributing to obesity through mechanisms such as altered adipocyte function and increased fat deposition [70,71,72].

2.3. Lead (Pb)

Lead (Pb) exposure is widespread in urban environments and correlates with elevated rates of obesity and diabetes [73]. Although extensive epidemiological studies have examined the impact of low-level exposure to environmental toxins on chronic metabolic diseases, the effects of lead exposure on the onset of diabetes have been less explored [74]. Lead is a well-known endocrine disruptor, affecting the hypothalamic–pituitary–adrenal axis, thyroid hormones, and bone metabolism. Animal studies suggest that lead exposure may contribute to increased body weight, with even low levels of gestational exposure potentially leading to obesity later in life [75].

2.4. Arsenic (As)

The relationship between arsenic exposure and the development of diabetes has been extensively investigated [50]. Arsenic disrupts metabolic processes in skeletal muscle and the liver, increasing free fatty acid synthesis and inducing insulin resistance through dyslipidemia [76]. Additionally, arsenic triggers oxidative stress, impairs glucose metabolism, and induces mitochondrial dysfunction, which promotes anaerobic metabolism and the formation of reactive oxygen species [77,78]. Recent studies have indicated that arsenic exposure can alter the gut microbiome, further contributing to metabolic disorders and obesity, and increasing the risk of developing type 2 diabetes [79,80].
Epidemiological studies consistently demonstrate a link between oral exposure to inorganic arsenic and a range of diseases, including cancers (e.g., skin, bladder, lung, kidney, and liver), as well as developmental, dermatological, neurological, respiratory, immune, cardiovascular, endocrine, and metabolic disorders [81,82].

2.5. Nickel (Ni)

Nickel, an essential trace element important for enzyme activity and several physiological processes, can become toxic when exposure exceeds safe levels. Overexposure to nickel has been linked to pulmonary fibrosis, skin dermatitis, renal and cardiovascular diseases, and cancer. Emerging research suggests that nickel exposure may interfere with glucose metabolism and insulin secretion, potentially contributing to insulin resistance. Nickel is believed to disrupt glucose regulation through mechanisms such as reactive oxygen species (ROS) modulation, interference with the nitric oxide (NO) pathway, and competition between calcium and nickel ions [52].
Although some studies suggest a positive correlation between urinary nickel exposure and diabetes risk, the relationship with blood nickel levels remains inconclusive [83,84]. Additionally, individuals with obesity or overweight status may exhibit symptoms related to allergic contact dermatitis and systemic nickel allergy syndrome, with recent findings suggesting a higher prevalence of nickel allergy in overweight women compared to the general population [54].

2.6. Copper (Cu)

Copper plays a critical role in glucose metabolism, and imbalances in copper levels have been associated with impaired glycemic control and diabetic complications, such as diabetic kidney disease [85]. Several studies have shown a strong correlation between elevated serum copper levels and poor glycemic control in diabetic patients [86,87,88]. Copper imbalances disrupt the antioxidant activity of copper-containing enzymes, contributing to oxidative stress, a key factor in the pathogenesis of diabetes. The progression of diabetes also affects copper metabolism, with aging being associated with increased copper levels in diabetic individuals, while younger individuals are at an increased risk of developing type 1 diabetes [89].

3. Saliva-Based Sensors: Principles, Advantages, and Recent Progress

Saliva is an accessible and informative biological fluid that contains a diverse range of biomarkers. It serves as a potential medium for diagnosing various diseases and monitoring health conditions. Its composition, which reflects systemic physiological and pathological states, provides significant opportunities for noninvasive diagnostics. Over 20% of proteins in saliva are derived from blood, providing a reliable mirror of systemic health and enabling the detection of biomarkers associated with diseases such as diabetes, cardiovascular disorders, and certain cancers [90,91,92,93]. Moreover, the salivary microbiome provides additional insight into environmental exposures, including heavy metals, and is an emerging area of research in diagnostic applications [94,95,96] [Figure 2].

3.1. Challenges in Traditional Saliva Biomarker Detection Methods

Conventional techniques for biomarker detection in saliva, such as enzyme-linked immunosorbent assay (ELISA), radio-immunoassays, chemiluminescent immunoassays, and electrophoretic methods, have proven effective for numerous applications. However, these methods face significant drawbacks that limit their widespread use, particularly in point-of-care (POC) settings. These limitations include slow processing times, high costs, and the requirement for specialized laboratory equipment and highly trained personnel [97,98,99]. Furthermore, traditional methods typically involve complex procedures that may not be suitable for rapid, real-time diagnostic applications. Therefore, there has been growing interest in developing advanced sensor technologies that can provide faster, more cost-effective, and user-friendly alternatives for saliva analysis [100].

3.2. Principles of Saliva-Based Sensors

Saliva-based sensors operate on the principle of molecular recognition, where the sensor detects the presence of specific analytes by using a recognition element that selectively binds to the target biomarker. For instance, when heavy metals or other biomarkers in saliva interact with a sensing surface, they induce a measurable change, which can be quantified. These sensors typically utilize various transduction mechanisms to generate a measurable signal, such as optical, electrochemical, or piezoelectric methods. The most commonly used methods are electrochemical and optical sensing techniques, which are well suited for detecting biomarkers in small, noninvasive samples like saliva [8].
Upon analyte recognition, the sensor generates a signal that is proportional to the concentration of the target biomarker. This signal is processed and analyzed to provide either qualitative or quantitative data about the concentration of the analyte. The advantage of saliva-based sensors lies in their noninvasive nature, rapid response time, and the ability to perform continuous, real-time monitoring of various physiological states.

3.3. Advances in Nanomaterials for Saliva-Based Sensors

The integration of nanomaterials into saliva-based sensors has led to significant advancements in sensitivity, specificity, and sensor performance. Nanomaterials, particularly carbon-based nanomaterials such as carbon nanotubes, graphene, and graphene oxide, offer remarkable properties due to their small size, high surface area, and unique electrical, optical, and chemical characteristics. These materials are ideal for enhancing the sensitivity and detection limits of sensors, making them particularly suitable for detecting low concentrations of analytes in complex biological fluids like saliva [101,102].
Carbon-based nanomaterials have been employed in various types of sensors, including electrochemical sensors, for detecting a broad range of clinically relevant biomarkers, including glucose, amino acids, hormones, and even heavy metals [103,104]. Their ability to interact with biomolecules at the nanoscale enables the development of highly sensitive and selective sensors that can detect target analytes at concentrations that were previously undetectable using conventional methods.
Recent studies have demonstrated that the use of these advanced nanomaterials can improve the detection of heavy metals such as lead, mercury, and cadmium in saliva, which is crucial for understanding the relationship between metal exposure and diseases like diabetes and obesity [103,105]. In addition, nanomaterials allow for the miniaturization of sensors, facilitating the development of portable, wearable devices that can monitor real-time changes in biomarker levels, providing a valuable tool for personalized healthcare.

3.4. Sensor Design: Overcoming Challenges in Sensitivity and Reproducibility

While the integration of nanomaterials has greatly enhanced the performance of saliva-based sensors, several challenges remain. One of the primary obstacles is the issue of biofouling, where proteins and other biological components from saliva adhere to the sensor’s surface, potentially interfering with analyte detection. Biofouling can lead to signal interference, reducing the accuracy and reproducibility of the sensor over time. To mitigate this, extensive research is focused on developing novel surface coatings and materials that resist biofouling and improve sensor longevity and performance [106,107].
Additionally, environmental factors such as changes in pH, temperature, and ionic strength in saliva can affect sensor performance. Therefore, designing sensors that are robust and capable of maintaining stable and reliable performance across a range of physiological conditions is crucial. Research is ongoing to optimize sensor designs by incorporating materials and coatings that reduce environmental interference, ensuring the reliability of saliva-based sensors in both laboratory and real-world settings.

3.5. Saliva-Based Wearable Sensors: Real-Time Monitoring and Integration with Intraoral Devices

Recent advancements in wearable technology have opened new possibilities for real-time, noninvasive monitoring of health parameters using saliva [108]. Wearable sensors, which can be integrated into devices such as mouthguards, pacifiers, and toothbrushes, offer the advantage of continuous, in situ sampling of saliva for the detection of biomarkers [109,110]. These devices can collect saliva samples directly from the mouth, allowing for real-time analysis of biomarkers without the need for external sample collection. This capability is particularly useful for monitoring dynamic changes in biomarkers, such as heavy metal concentrations, over time [111,112,113].
The integration of saliva-based sensors into intraoral devices has the potential to revolutionize the way diseases are monitored and managed. For example, sensors embedded in dental appliances could provide ongoing monitoring of patients exposed to environmental toxins or undergoing treatment for chronic conditions. Such technologies would not only improve the convenience of monitoring but also offer a more direct and consistent method for tracking health changes, particularly in populations at high risk for heavy metal exposure [114,115,116,117,118,119,120,121,122,123,124].

3.6. Point-of-Care (POC) Saliva Sensors

Point-of-care devices, which enable quick and on-site diagnostic testing, are increasingly being developed for saliva analysis. These devices, often designed using microfluidic or paper-based technologies, allow for the rapid detection of biomarkers directly from biofluids like saliva, without the need for complex laboratory procedures [125,126,127]. The advantages of POC sensors include their portability, ease of use, and ability to provide immediate results, making them ideal for applications in disease prevention, health monitoring, and personalized medicine [128].
Microfluidic devices, in particular, represent a significant advancement in POC sensor technology. These compact platforms are capable of integrating multiple functions, such as sample preparation, mixing, and detection, into a single device. Microfluidics enable the detection of heavy metals and other analytes with high sensitivity and specificity, using small sample volumes and minimal reagents. This technology is particularly valuable in settings where resources are limited, and rapid diagnostic results are needed to guide clinical decisions or assess environmental exposures [129].

3.7. Integration with Smartphones and Artificial Intelligence (AI)

The integration of smartphone technology with saliva-based sensors has further expanded the potential for real-time health monitoring. Smartphones, equipped with sensors and imaging capabilities, can be used to capture and analyze data from saliva sensors, providing immediate feedback to the user [130,131]. For example, smartphones can integrate with colorimetric or electrochemical detection methods to measure heavy metal concentrations or other biomarkers in saliva. The portability and ubiquity of smartphones make them an ideal platform for delivering diagnostic results directly to patients, enabling them to track their health status in real time [132,133,134].
Incorporating AI into saliva-based sensors is an exciting development that promises to improve the accuracy and efficiency of data analysis. AI algorithms, such as machine learning and deep learning, can be used to process complex sensor data, identify patterns, and predict health outcomes. AI has already been employed to analyze large datasets generated by sensors, improving the detection of heavy metals and other biomarkers. AI-driven models, such as support vector machines (SVM), neural networks (NN), and random forest (RF), enhance the interpretation of sensor data, reducing the potential for errors and increasing the precision of diagnostic results [135].

3.8. Consensus

Saliva-based sensors represent a promising tool for noninvasive diagnostic applications, offering numerous advantages over traditional methods. Their ability to detect a wide range of biomarkers, including heavy metals, makes them particularly valuable for monitoring diseases and assessing environmental exposures. However, challenges remain in optimizing sensor sensitivity, minimizing biofouling, and ensuring reproducibility in diverse environments. Ongoing research into advanced nanomaterials, microfluidic systems, and AI-driven data analysis holds the potential to address these challenges and establish the way for the widespread adoption of saliva-based sensors in clinical and everyday healthcare applications. Sensors for heavy metals in saliva are presented in Table 2.

4. Mechanism of Electrochemical Sensors for Heavy Metal Detection

A variety of analytical techniques have been developed for detecting heavy metal ions (HMIs), including atomic absorption spectroscopy (AAS), inductively coupled plasma mass spectrometry (ICP-MS), inductively coupled plasma atomic emission spectroscopy (ICP-AES), and X-ray fluorescence spectrometry (XRF) [139]. While these methods are highly effective, they are often time-consuming, expensive, and require complex instrumentation and specialized operation [140].
In contrast, electrochemical sensors offer a more efficient and cost-effective alternative for detecting HMIs. A typical electrochemical setup involves an electrolytic cell containing an ionic conductor (electrolyte) and an electronic conductor (electrode). The electrolyte is usually an aqueous solution containing the heavy metal ions, and the potential of the cell is measured at the interface between the electrode and the solution [141].
Within the electrolytic cell, various half-reactions occur, with a key reaction taking place at the working electrode (WE). A reference electrode (RE) is used to measure the overall cell potential. An external power source generates an excitation signal, which drives the electrochemical process and allows the system to determine the response function while maintaining constant variables. This process can be summarized as follows:
Excitation signal → electrode → response function
In a typical three-electrode configuration, current flows between the working electrode (WE) and the counter electrode (CE), with the CE serving as the third electrode. During electrochemical measurements, heavy metal ions undergo either oxidation or reduction reactions at the surface of the working electrode by gaining or losing electrons. This electron transfer converts the chemical information into an electrical signal, which can then be correlated with the concentration of heavy metals. The current is analyzed by an electrochemical workstation, which quantifies heavy metal concentrations in the sample.
The effectiveness of electrochemical detection is largely determined by the characteristics of the sensing or working electrode. Consequently, significant efforts have been made to enhance the performance of these electrodes, which has resulted in modified electrodes with a range of interface materials, including inorganic substances (such as metal and metal oxide nanoparticles, graphene, and carbon nanotubes), organic materials (like metal–organic frameworks), and biomaterials. These modifications improve the electrode’s surface area, conductivity, and selectivity, thereby increasing the sensitivity, reliability, and detection limits of heavy metal detection [142]. By optimizing the working electrode’s properties, electrochemical sensors have become a powerful tool for rapid, sensitive, and cost-effective heavy metal detection, offering a promising alternative to traditional methods.

5. Challenges and Limitations of Saliva-Based Sensors

Before saliva-based sensors can be widely implemented in clinical settings for heavy metal detection, several significant challenges must be addressed. These challenges primarily involve technical and biological issues that affect the accuracy, reliability, and reproducibility of sensor performance [143]. Ensuring consistent and dependable sensor function across different environments and user populations is critical for the successful deployment of these sensors in real-world diagnostic and monitoring applications. Despite the immense potential of saliva-based sensors to revolutionize diagnostics and personalized healthcare, overcoming these hurdles is essential to realizing the full benefits of this technology.
One of the key challenges in saliva-based sensing is maintaining sensor performance over extended periods of use. For these sensors to be effective in long-term monitoring and diagnostics, they must remain stable and reliable under varying environmental conditions. Researchers are exploring several strategies to enhance sensor stability, such as developing durable coatings and advanced materials that can withstand harsh conditions. Long-term stability is crucial to ensure that sensors provide accurate, consistent results throughout their operational life, which is necessary for effective patient monitoring and disease management [144].
In addition to improving sensor stability, there is a need to enhance several other aspects of sensor performance, including sensitivity, specificity, durability, and portability. Sensitivity and specificity are critical to accurately detecting target analytes, such as heavy metals, in saliva. Additionally, improving the portability and miniaturization of sensors will facilitate point-of-care applications, making them more accessible to patients. The identification of new biomarkers for disease detection is another promising area of research that could expand the utility of saliva-based sensors to detect a broader range of health conditions.
A particularly challenging issue in saliva-based sensing is the interference caused by the complex composition of saliva. As a biological fluid, saliva contains a variety of components, such as proteins, enzymes, electrolytes, and other biomolecules, which can interact with the sensor and negatively affect its performance. For example, the pH of saliva fluctuates depending on factors such as diet, hydration, health conditions, and time of day, typically ranging from 6.2 to 7.6 [121]. These pH variations can influence both the recognition elements and the transducer in electrochemical sensors, leading to changes in sensitivity and reliability. To mitigate this issue, ongoing research is focused on developing sensors that can function effectively across a broad pH range.
Saliva also contains proteins, including enzymes, mucins, and antibodies, which can adsorb nonspecifically onto the sensor surface, causing biofouling or signal interference [13]. Protein adsorption can block active sites or interfere with the function of the transducer, reducing sensitivity and increasing background noise. Some salivary enzymes can degrade the recognition elements, which can shorten the sensor’s lifespan and decrease its accuracy over time [145]. Additionally, the presence of electrolytes, such as Na+, K+, Cl−, and Ca2+, can alter the ionic strength and conductivity of the saliva, further complicating sensor detection. These electrolytes may interfere with electrochemical measurements and influence the binding affinity of the recognition elements, reducing both the sensitivity and selectivity of the sensors. Ions in saliva can also compete with the target analyte for binding sites, further hindering sensor performance.
To address these interference issues, the correct and standardized collection of saliva samples is essential for ensuring accurate results. Variations in collection protocols, such as the timing of collection, diet, hydration status, and other environmental factors, can lead to inconsistent results and make it difficult to compare saliva-based sensor data with other diagnostic methods, such as blood or urine tests. Comparative studies are needed to evaluate the performance of saliva-based sensors relative to other biological samples, ensuring that collection methods and conditions are standardized. Such studies should consider factors such as meal timing, hydration, and time of day to identify the optimal conditions for saliva collection, ensuring that the results are comparable to those obtained from traditional diagnostic fluids [146].
Table 3 summarizes several studies that have reported standardized collection approaches for saliva sampling in the context of heavy metal detection. These studies emphasize the importance of standardized protocols to minimize variations in saliva collection, which are crucial for ensuring the consistency and accuracy of heavy metal detection in saliva samples.
Advances in sensor technology, combined with the rigorous standardization of collection and testing protocols, will be crucial for improving the accuracy, reliability, and clinical applicability of saliva-based sensors for heavy metal detection. These advancements will help ensure that sensors perform consistently across various environmental conditions and among different user populations. By minimizing the impact of biological variables—such as pH fluctuations, protein interference, and electrolyte variations—standardized protocols will ensure that sensor readings are both accurate and reproducible. This will establish the way for the widespread adoption of saliva testing as a reliable, noninvasive tool for diagnostics and disease monitoring, offering a promising alternative to traditional blood and urine testing methods.
Despite the significant potential of saliva-based sensors for detecting heavy metals, their successful implementation will depend on overcoming several technical and biological challenges. Key areas that require attention include enhancing sensor sensitivity, specificity, and stability, while mitigating interference from the complex matrix of saliva. Addressing these challenges will be critical for unlocking the full potential of saliva-based sensors in clinical practice, enabling more accessible, cost-effective, and personalized healthcare solutions. Ultimately, resolving these issues will enhance the ability of saliva-based sensors to monitor heavy metal exposure and related health risks, such as diabetes and obesity, improving patient outcomes and advancing personalized medicine.
By addressing the challenges associated with saliva-based sensors and advancing their technological capabilities, these devices have the potential to transform the monitoring and management of metabolic health conditions. Their integration into clinical practice could provide invaluable insights into disease progression and treatment efficacy, ultimately improving patient outcomes and enhancing healthcare delivery. Furthermore, the commercialization of these sensors will play a critical role in their widespread adoption. Successful commercialization will require collaboration between scientists, industry stakeholders, and regulatory bodies to ensure that these devices meet clinical standards and are accessible to a broad range of healthcare providers and patients [100].
Table 4 presents a selection of commercially available sensors for detecting heavy metals in biological samples. These devices offer a range of methods and detection limits, providing a snapshot of the current landscape in heavy metal detection technology.

6. Clinical Applications and Potential Implications in Diabetes and Obesity Management

Saliva-based sensors present a novel approach for the noninvasive monitoring of environmental contaminants, particularly heavy metals, which have been implicated in the pathogenesis of metabolic disorders such as diabetes and obesity. These sensors offer significant clinical potential due to their ability to detect harmful substances continuously, providing real-time data on heavy metal exposure. This is of paramount importance, as frequent monitoring can prevent the accumulation of toxic metals that disrupt metabolic health. Unlike traditional methods that require blood or tissue samples, saliva-based sensors eliminate the need for invasive procedures, ensuring higher patient compliance and the possibility for long-term monitoring without risk to the patient’s well-being.
The role of heavy metals—such as lead, cadmium, arsenic, and mercury—in the development of metabolic diseases is well documented. Exposure to these toxins has been shown to impair insulin signaling, increase oxidative stress, and disrupt adipocyte function, all of which contribute to insulin resistance, inflammation, and other metabolic disturbances associated with obesity and diabetes [160,161]. The use of saliva-based sensors to detect these contaminants provides a practical solution to monitor their concentrations at early stages of exposure, potentially preventing the onset or exacerbation of metabolic diseases.
Integrating saliva-based sensors into routine clinical practice could offer substantial benefits in disease management. The ability to monitor heavy metals in real-time can facilitate timely interventions, enabling clinicians to alter treatment plans or suggest lifestyle modifications before the accumulation of these toxins results in significant metabolic damage. For example, studies have shown that lead exposure impairs insulin secretion, while cadmium and arsenic contribute to systemic inflammation and insulin resistance, both of which are critical in the development of obesity and diabetes. By identifying individuals with elevated heavy metal levels, healthcare providers can take preventive measures to reduce exposure and mitigate the associated risks [162,163].
Additionally, saliva-based sensors could be used in conjunction with other diagnostic tools for metabolic diseases, such as monitoring blood glucose, lipid profiles, and markers of inflammation. This comprehensive approach to patient care would allow for the development of personalized treatment plans tailored to the specific needs of individuals based on their metabolic and environmental risk factors [164].

7. Future Perspectives

Looking to the future, clinical trials examining the link between heavy metal exposure and the prevalence of diabetes and obesity are critical to validating the role of saliva-based sensors in disease monitoring. A potential clinical study could involve collecting saliva samples from patients diagnosed with obesity and/or diabetes, as well as a control group, to assess the concentrations of heavy metals like lead, cadmium, arsenic, and mercury. These concentrations would then be correlated with metabolic parameters such as body mass index (BMI), insulin sensitivity, and markers of inflammation [165].
To accurately measure heavy metal levels in saliva, advanced analytical techniques such as inductively coupled plasma mass spectrometry (ICP-MS) or X-ray fluorescence (XRF) could be employed, enabling highly sensitive and precise detection of trace metals. The integration of these technologies would allow for the detection of low levels of heavy metals, which are often associated with chronic, low-level exposure. Furthermore, statistical analyses will be essential to evaluate the correlation between heavy metal concentrations and the severity of metabolic conditions, and to identify specific thresholds of exposure that may be predictive of disease progression [160].
Such studies should also consider factors such as age, gender, lifestyle, and environmental exposures, all of which can influence heavy metal concentrations in saliva and their impact on metabolic health. These variables are critical in understanding the full scope of the relationship between heavy metal exposure and metabolic diseases. Longitudinal studies will also be important in assessing the long-term effects of chronic exposure to heavy metals, which may not be immediately apparent but could lead to the gradual development of obesity and diabetes.

8. Conclusions

Saliva-based sensors for the detection of heavy metals offer significant promise in monitoring and managing metabolic diseases linked to environmental exposures. These sensors provide a noninvasive, cost-effective, and real-time method for detecting harmful levels of heavy metals such as lead, cadmium, arsenic, and mercury. Early detection of these contaminants in saliva could help prevent or delay the onset of obesity and diabetes by allowing for timely intervention before irreversible metabolic damage occurs.
Despite the promising potential, further research is needed to optimize these sensors, improving their sensitivity, specificity, and reliability in clinical settings. As these sensors evolve, they may become integral tools in personalized medicine, offering clinicians the ability to tailor treatment plans based on real-time monitoring of heavy metal exposure and metabolic health. Additionally, these sensors could play a crucial role in public health initiatives aimed at reducing the global burden of obesity and diabetes by providing a simple, accessible method for monitoring environmental pollutants and preventing disease.
To fully realize the clinical potential of saliva-based sensors, further studies are needed to explore the relationships between heavy metal exposure and a broader range of metabolic biomarkers, including insulin resistance, glucose metabolism, and inflammatory markers. These studies will help clarify the mechanisms through which heavy metals contribute to the development of metabolic diseases and provide valuable insights into the prevention and treatment of these conditions [160,161,164].
The use of saliva-based sensors could revolutionize the way healthcare providers monitor and manage patients’ metabolic health, especially in populations with high levels of environmental contamination. By enabling the early detection of harmful heavy metals, these sensors could not only prevent the development of metabolic diseases but also provide a powerful tool for improving global public health outcomes. As research continues to advance, saliva-based sensors could become essential components in routine healthcare monitoring, reducing the incidence of obesity and diabetes and improving quality of life for individuals worldwide [163,165].

9. Additional Considerations

  • Ethical and Regulatory Issues: The widespread use of saliva-based sensors in clinical practice requires careful attention to ethical issues, particularly regarding data privacy and patient consent. The continuous nature of monitoring may raise concerns about the handling and security of sensitive health data. Furthermore, these devices must undergo rigorous regulatory evaluation to ensure their safety and effectiveness, particularly in regard to compliance with standards set by regulatory agencies such as the FDA [161].
  • Global Health Impact: The increasing prevalence of obesity and diabetes, especially in developing countries where environmental contamination may be more common, underscores the potential value of saliva-based sensors in public health. These sensors could provide an affordable and accessible tool for monitoring heavy metal exposure and metabolic health, particularly in regions with limited access to traditional healthcare facilities.
  • Economic Considerations: Although initial costs for developing and implementing saliva-based sensors may be high, the long-term economic benefits of preventing or mitigating the onset of obesity and diabetes could outweigh these costs. By enabling early detection and intervention, these sensors could reduce the long-term healthcare costs associated with managing chronic complications such as cardiovascular disease, kidney failure, and neuropathy, which are commonly associated with diabetes and obesity [162].

Author Contributions

L.A.-N.: conceptualization, writing—original draft, and resources; W.K.S.: investigation, writing—review and editing, supervision, and visualization; A.N.: conceptualization and supervision; M.D.: review and editing and supervision; C.C.: conceptualization, resources, review and editing, and supervision. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Conflicts of Interest

The authors declare no conflicts of interest.

References

  1. Azevedo, M.J. The State of Health System(s) in Africa: Challenges and Opportunities. In Historical Perspectives on the State of Health and Health Systems in Africa; Springer: Berlin, Germany, 2017; pp. 1–73. [Google Scholar]
  2. Zheng, X.; Zhang, F.; Wang, K.; Zhang, W.; Li, Y.; Sun, Y.; Sun, X.; Li, C.; Dong, B.; Wang, L.; et al. Smart Biosensors and Intelligent Devices for Salivary Biomarker Detection. TrAC Trends Anal. Chem. 2021, 140, 116281. [Google Scholar] [CrossRef]
  3. Bohr, A.; Memarzadeh, K. The Rise of Artificial Intelligence in Healthcare Applications. In Artificial Intelligence in Healthcare; Elsevier: Amsterdam, The Netherlands, 2020; pp. 25–60. [Google Scholar]
  4. Cui, Y.; Yang, M.; Zhu, J.; Zhang, H.; Duan, Z.; Wang, S.; Liao, Z.; Liu, W. Developments in Diagnostic Applications of Saliva in Human Organ Diseases. Med. Nov. Technol. Devices 2022, 13, 100115. [Google Scholar] [CrossRef]
  5. Varga, G. Physiology of the Salivary Glands. Surgery 2015, 33, 581–586. [Google Scholar] [CrossRef]
  6. Zürcher, C.; Humpel, C. Saliva: A Challenging Human Fluid to Diagnose Brain Disorders with a Focus on Alzheimer’s Disease. Neural. Regen. Res. 2023, 18, 2606–2610. [Google Scholar] [CrossRef]
  7. Basilicata, M.; Pieri, M.; Marrone, G.; Nicolai, E.; Di Lauro, M.; Paolino, V.; Tomassetti, F.; Vivarini, I.; Bollero, P.; Bernardini, S.; et al. Saliva as Biomarker for Oral and Chronic Degenerative Non-Communicable Diseases. Metabolites 2023, 13, 889. [Google Scholar] [CrossRef] [PubMed]
  8. Malon, R.S.P.; Sadir, S.; Balakrishnan, M.; Córcoles, E.P. Saliva-Based Biosensors: Noninvasive Monitoring Tool for Clinical Diagnostics. Biomed. Res. Int. 2014, 2014, 962903. [Google Scholar] [CrossRef]
  9. Jain, K.K. Role of Biomarkers in Health Care. In The Handbook of Biomarkers; Humana Press: Totowa, NJ, USA, 2010; pp. 115–188. [Google Scholar]
  10. Song, M.; Bai, H.; Zhang, P.; Zhou, X.; Ying, B. Promising Applications of Human-Derived Saliva Biomarker Testing in Clinical Diagnostics. Int. J. Oral Sci. 2023, 15, 2. [Google Scholar] [CrossRef]
  11. Dongiovanni, P.; Meroni, M.; Casati, S.; Goldoni, R.; Thomaz, D.V.; Kehr, N.S.; Galimberti, D.; Del Fabbro, M.; Tartaglia, G.M. Salivary Biomarkers: Novel Noninvasive Tools to Diagnose Chronic Inflammation. Int. J. Oral Sci. 2023, 15, 27. [Google Scholar] [CrossRef]
  12. Kumar, P.; Gupta, S.; Das, B.C. Saliva as a Potential Non-Invasive Liquid Biopsy for Early and Easy Diagnosis/Prognosis of Head and Neck Cancer. Transl. Oncol. 2024, 40, 101827. [Google Scholar] [CrossRef] [PubMed]
  13. Pandit, P.; Crewther, B.; Cook, C.; Punyadeera, C.; Pandey, A.K. Sensing Methods for Stress Biomarker Detection in Human Saliva: A New Frontier for Wearable Electronics and Biosensing. Mater. Adv. 2024, 5, 5339–5350. [Google Scholar] [CrossRef]
  14. Zhou, C.; Cai, Z.; Jin, B.; Lin, H.; Xu, L.; Jin, Z. Saliva-Based Detection of SARS-CoV-2: A Bibliometric Analysis of Global Research. Mol. Cell. Biochem. 2024, 479, 761–777. [Google Scholar] [CrossRef] [PubMed]
  15. Chen, C.; Tian, L.; Li, W.; Wang, K.; Yang, Q.; Lin, J.; Zhang, T.; Dong, B.; Wang, L. Recent Advances and Perspectives Regarding Paper-Based Sensors for Salivary Biomarker Detection. Chemosensors 2023, 11, 383. [Google Scholar] [CrossRef]
  16. Lin, C.-W.; Tsai, Y.-H.; Peng, Y.-S.; Yang, J.-T.; Lu, Y.-P.; Chen, M.-Y.; Tung, C.-W. A Novel Salivary Sensor with Integrated Au Electrodes and Conductivity Meters for Screening of Diabetes. Biosensors 2023, 13, 702. [Google Scholar] [CrossRef] [PubMed]
  17. Al-Azzam, N.; Al-Azzam, S.; Khassawneh, B.; Araydah, M.; Karasneh, R.A.; Aldeyab, M.A. Factors Contributing to Poor COVID-19 Outcomes in Diabetic Patients: Findings from a Single-Center Cohort Study. PLoS ONE 2023, 18, e0290946. [Google Scholar] [CrossRef] [PubMed]
  18. Sharma, K.; Poudyal, S.; Subba, H.K.; Khatiwada, S. Metabolic Syndrome and Life Style Factors among Diabetes Patients Attending in a Teaching Hospital, Chitwan. PLoS ONE 2023, 18, e0286139. [Google Scholar] [CrossRef] [PubMed]
  19. Pasquel, F.J.; Lansang, M.C.; Dhatariya, K.; Umpierrez, G.E. Management of Diabetes and Hyperglycaemia in the Hospital. Lancet Diabetes Endocrinol. 2021, 9, 174–188. [Google Scholar] [CrossRef] [PubMed]
  20. Cai, J.; Li, Y.; Liu, S.; Liu, Q.; Xu, M.; Zhang, J.; Wei, Y.; Mo, X.; Lin, Y.; Tang, X.; et al. Associations between Multiple Heavy Metals Exposure and Glycated Hemoglobin in a Chinese Population. Chemosphere 2022, 287, 132159. [Google Scholar] [CrossRef] [PubMed]
  21. Zhang, J.; Yin, H.; Zhu, X.; Xiang, R.; Miao, Y.; Zhang, Y.; Song, Y.; Chen, J.; Zhang, L. Effects of Multi-metal Exposure on the Risk of Diabetes Mellitus among People Aged 40–75 Years in Rural Areas in Southwest China. J. Diabetes Investig. 2022, 13, 1412–1425. [Google Scholar] [CrossRef] [PubMed]
  22. Khafagy, R.; Dash, S. Obesity and Cardiovascular Disease: The Emerging Role of Inflammation. Front. Cardiovasc. Med. 2021, 8, 768119. [Google Scholar] [CrossRef]
  23. Ansari, S.; Haboubi, H.; Haboubi, N. Adult Obesity Complications: Challenges and Clinical Impact. Ther. Adv. Endocrinol. Metab. 2020, 11, 204201882093495. [Google Scholar] [CrossRef] [PubMed]
  24. Tinkov, A.A.; Aschner, M.; Ke, T.; Ferrer, B.; Zhou, J.-C.; Chang, J.-S.; Santamaría, A.; Chao, J.C.-J.; Aaseth, J.; Skalny, A.V. Adipotropic Effects of Heavy Metals and Their Potential Role in Obesity. Fac. Rev. 2021, 10, 32. [Google Scholar] [CrossRef]
  25. Ghorbani Nejad, B.; Raeisi, T.; Janmohammadi, P.; Mehravar, F.; Zarei, M.; Dehghani, A.; Bahrampour, N.; Darijani, M.H.; Ahmadipour, F.; Mohajeri, M.; et al. Mercury Exposure and Risk of Type 2 Diabetes: A Systematic Review and Meta-Analysis. Int. J. Clin. Pract. 2022, 2022, 7640227. [Google Scholar] [CrossRef]
  26. Zheng, T. Current Understanding of the Relationship between Metal Exposures and Risk of Type 2 Diabetes. Curr. Res. Diabetes Obes. J. 2018, 7, 555710. [Google Scholar] [CrossRef]
  27. Ramírez-Cruz, A.; Rios-Lugo, M.J.; Soto-Sánchez, J.; Juárez-Pérez, C.A.; Cabello-López, A.; Jiménez-Ramírez, C.; Chang-Rueda, C.; Cruz, M.; Hernández-Mendoza, H.; Vazquez-Moreno, M. Overweight, Obesity, Hypertriglyceridemia, and Insulin Resistance Are Positively Associated with High Serum Copper Levels in Mexican Adults. Metabolites 2024, 14, 282. [Google Scholar] [CrossRef]
  28. Szablewski, L. Changes in Cells Associated with Insulin Resistance. Int. J. Mol. Sci. 2024, 25, 2397. [Google Scholar] [CrossRef]
  29. Chen, Y.W.; Yang, C.Y.; Huang, C.F.; Hung, D.Z.; Leung, Y.M.; Liu, S.H. Heavy Metals, Islet Function and Diabetes Development. Islets 2009, 1, 169–176. [Google Scholar] [CrossRef]
  30. Alonso, M.L.; Montaña, F.P.; Miranda, M.; Castillo, C.; Hernández, J.; Benedito, J.L. Interactions between Toxic (As, Cd, Hg and Pb) and Nutritional Essential (Ca, Co, Cr, Cu, Fe, Mn, Mo, Ni, Se, Zn) Elements in the Tissues of Cattle from NW Spain. BioMetals 2004, 17, 389–397. [Google Scholar] [CrossRef] [PubMed]
  31. González-Domínguez, Á.; Millán-Martínez, M.; Domínguez-Riscart, J.; Lechuga-Sancho, A.M.; González-Domínguez, R. Metal Homeostasis and Exposure in Distinct Phenotypic Subtypes of Insulin Resistance among Children with Obesity. Nutrients 2023, 15, 2347. [Google Scholar] [CrossRef] [PubMed]
  32. Cuciureanu, M.; Caratașu, C.-C.; Gabrielian, L.; Frăsinariu, O.E.; Checheriță, L.E.; Trandafir, L.M.; Stanciu, G.D.; Szilagyi, A.; Pogonea, I.; Bordeianu, G.; et al. 360-Degree Perspectives on Obesity. Medicina 2023, 59, 1119. [Google Scholar] [CrossRef] [PubMed]
  33. Lin, X.; Li, H. Obesity: Epidemiology, Pathophysiology, and Therapeutics. Front. Endocrinol. 2021, 12, 706978. [Google Scholar] [CrossRef]
  34. Amerikanou, C.; Kleftaki, S.-A.; Karavoltsos, S.; Tagkouli, D.; Sakellari, A.; Valsamidou, E.; Gioxari, A.; Kalogeropoulos, N.; Kaliora, A.C. Vanadium, Cobalt, Zinc, and Rubidium Are Associated with Markers of Inflammation and Oxidative Stress in a Greek Population with Obesity. Front. Endocrinol. 2023, 14, 1265310. [Google Scholar] [CrossRef] [PubMed]
  35. Guo, Y.; Lv, Y.; Liu, X.; Wang, G. Association between Heavy Metal Mercury in Body Fluids and Tissues and Diabetes Mellitus: A Systematic Review and Meta-Analysis. Ann. Transl. Med. 2023, 11, 114. [Google Scholar] [CrossRef]
  36. Tian, H.; Guo, X.; Wang, X.; He, Z.; Sun, R.; Ge, S.; Zhang, Z. Chromium Picolinate Supplementation for Overweight or Obese Adults. Cochrane Database Syst. Rev. 2013, 2013, CD010063. [Google Scholar] [CrossRef] [PubMed]
  37. Khodavirdipour, A.; Haddadi, F.; Keshavarzi, S. Chromium Supplementation; Negotiation with Diabetes Mellitus, Hyperlipidemia and Depression. J. Diabetes Metab. Disord. 2020, 19, 585–595. [Google Scholar] [CrossRef] [PubMed]
  38. Moon, M.K.; Lee, I.; Lee, A.; Park, H.; Kim, M.J.; Kim, S.; Cho, Y.H.; Hong, S.; Yoo, J.; Cheon, G.J.; et al. Lead, Mercury, and Cadmium Exposures Are Associated with Obesity but Not with Diabetes Mellitus: Korean National Environmental Health Survey (KoNEHS) 2015–2017. Environ. Res. 2022, 204, 111888. [Google Scholar] [CrossRef]
  39. Wang, G.; DiBari, J.; Bind, E.; Steffens, A.M.; Mukherjee, J.; Bartell, T.R.; Bellinger, D.C.; Hong, X.; Ji, Y.; Wang, M.-C.; et al. In Utero Exposure to Mercury and Childhood Overweight or Obesity: Counteracting Effect of Maternal Folate Status. BMC Med. 2019, 17, 216. [Google Scholar] [CrossRef]
  40. Cho, K.Y. Association of Blood Mercury Levels with the Risks of Overweight and High Waist-to-Height Ratio in Children and Adolescents: Data from the Korean National Health and Nutrition Examination Survey. Children 2021, 8, 1087. [Google Scholar] [CrossRef]
  41. Tsai, T.-L.; Kuo, C.-C.; Pan, W.-H.; Wu, T.-N.; Lin, P.; Wang, S.-L. Type 2 Diabetes Occurrence and Mercury Exposure—From the National Nutrition and Health Survey in Taiwan. Environ. Int. 2019, 126, 260–267. [Google Scholar] [CrossRef]
  42. Jeon, J.; Morris, J.S.; Park, K. Toenail Mercury Levels Positively Correlate with Obesity and Abdominal Obesity among Korean Adults. J. Trace Elem. Med. Biol. 2021, 64, 126678. [Google Scholar] [CrossRef]
  43. Yimthiang, S.; Pouyfung, P.; Khamphaya, T.; Kuraeiad, S.; Wongrith, P.; Vesey, D.A.; Gobe, G.C.; Satarug, S. Effects of Environmental Exposure to Cadmium and Lead on the Risks of Diabetes and Kidney Dysfunction. Int. J. Environ. Res. Public Health 2022, 19, 2259. [Google Scholar] [CrossRef] [PubMed]
  44. Stahr, S.; Chiang, T.; Bauer, M.A.; Runnells, G.A.; Rogers, L.J.; Do, H.V.; Kadlubar, S.A.; Su, L.J. Low-Level Environmental Heavy Metals Are Associated with Obesity Among Postmenopausal Women in a Southern State. Expo. Health 2021, 13, 269–280. [Google Scholar] [CrossRef] [PubMed]
  45. Choi, J.E.; Bae, M.-J.; Kim, M.-J.; Oh, S.S.; Park, K.S.; Lee, C.J.; Park, S.; Koh, S.-B.; Cho, J.; Kim, C. Heavy Metal Exposure Linked to Metabolic Syndrome in Korean Male Firefighters: FRESH Cohort Cross-Sectional Analysis. Sci. Rep. 2023, 13, 14016. [Google Scholar] [CrossRef] [PubMed]
  46. Ji, J.H.; Jin, M.H.; Kang, J.-H.; Lee, S., II; Lee, S.; Kim, S.-H.; Oh, S.Y. Relationship between Heavy Metal Exposure and Type 2 Diabetes: A Large-Scale Retrospective Cohort Study Using Occupational Health Examinations. BMJ Open 2021, 11, e039541. [Google Scholar] [CrossRef] [PubMed]
  47. Hernández-Mendoza, H.; Rios-Lugo, M.J.; Álvarez-Loredo, H.E.; Romero-Guzmán, E.T.; Gaytán-Hernández, D.; Martínez-Navarro, I.; Juárez-Flores, B.I.; Chang-Rueda, C. Serum Lead Levels and Its Association with Overweight and Obesity. J. Trace Elem. Med. Biol. 2022, 72, 126984. [Google Scholar] [CrossRef]
  48. Qu, Z.; Zhou, J.; Guo, P.; Wang, J.; Wang, P.; Liu, L.; Wu, M.; Wang, P.; Liu, N. Association between Environmental Lead/Cadmium Co-Exposure in Drinking Water and Soil and Type 2 Diabetes Mellitus/Obesity in Southern China. Front. Public Health 2022, 10, 941922. [Google Scholar] [CrossRef]
  49. Rangel-Moreno, K.; Gamboa-Loira, B.; López-Carrillo, L.; Cebrián, M.E. Prevalence of Type 2 Diabetes Mellitus in Relation to Arsenic Exposure and Metabolism in Mexican Women. Environ. Res. 2022, 210, 112948. [Google Scholar] [CrossRef] [PubMed]
  50. Brown, C. Arsenic Exposure Linked to Diabetes Risk in Canada. Can. Med. Assoc. J. 2015, 187, E438–E439. [Google Scholar] [CrossRef]
  51. Wen, W.-L.; Wang, C.-W.; Wu, D.-W.; Chen, S.-C.; Hung, C.-H.; Kuo, C.-H. Associations of Heavy Metals with Metabolic Syndrome and Anthropometric Indices. Nutrients 2020, 12, 2666. [Google Scholar] [CrossRef] [PubMed]
  52. Qu, Y.; Ji, S.; Sun, Q.; Zhao, F.; Li, Z.; Zhang, M.; Li, Y.; Zheng, L.; Song, H.; Zhang, W.; et al. Association of Urinary Nickel Levels with Diabetes and Fasting Blood Glucose Levels: A Nationwide Chinese Population-Based Study. Ecotoxicol. Environ. Saf. 2023, 252, 114601. [Google Scholar] [CrossRef]
  53. Titcomb, T.J.; Liu, B.; Lehmler, H.; Snetselaar, L.G.; Bao, W. Environmental Nickel Exposure and Diabetes in a Nationally Representative Sample of US Adults. Expo. Health 2021, 13, 697–704. [Google Scholar] [CrossRef]
  54. Watanabe, M.; Masieri, S.; Costantini, D.; Tozzi, R.; De Giorgi, F.; Gangitano, E.; Tuccinardi, D.; Poggiogalle, E.; Mariani, S.; Basciani, S.; et al. Overweight and Obese Patients with Nickel Allergy Have a Worse Metabolic Profile Compared to Weight Matched Non-Allergic Individuals. PLoS ONE 2018, 13, e0202683. [Google Scholar] [CrossRef] [PubMed]
  55. Yang, H.; Liu, C.-N.; Wolf, R.M.; Ralle, M.; Dev, S.; Pierson, H.; Askin, F.; Steele, K.E.; Magnuson, T.H.; Schweitzer, M.A.; et al. Obesity Is Associated with Copper Elevation in Serum and Tissues. Metallomics 2019, 11, 1363–1371. [Google Scholar] [CrossRef]
  56. Wang, W.; Liu, L.; Shan, R.; Wang, C. Associations between Dietary Copper Intake, General Obesity and Abdominal Obesity Risk: A Nationwide Cohort Study in China. Front. Nutr. 2022, 9, 1009721. [Google Scholar] [CrossRef]
  57. Vazquez-Moreno, M.; Sandoval-Castillo, M.; Rios-Lugo, M.J.; Klünder-Klünder, M.; Cruz, M.; Martínez-Navarro, I.; Romero-Guzmán, E.T.; Victoria-Campos, C.I.; Vilchis-Gil, J.; Hernández-Mendoza, H. Overweight and Obesity Are Positively Associated with Serum Copper Levels in Mexican Schoolchildren. Biol. Trace Elem. Res. 2023, 201, 2744–2749. [Google Scholar] [CrossRef] [PubMed]
  58. Yuan, C.-G.; Wang, J.; Jin, Y. Ultrasensitive Determination of Mercury in Human Saliva by Atomic Fluorescence Spectrometry Based on Solidified Floating Organic Drop Microextraction. Microchim. Acta 2012, 177, 153–158. [Google Scholar] [CrossRef]
  59. Wu, Y.-S.; Osman, A.I.; Hosny, M.; Elgarahy, A.M.; Eltaweil, A.S.; Rooney, D.W.; Chen, Z.; Rahim, N.S.; Sekar, M.; Gopinath, S.C.B.; et al. The Toxicity of Mercury and Its Chemical Compounds: Molecular Mechanisms and Environmental and Human Health Implications: A Comprehensive Review. ACS Omega 2024, 9, 5100–5126. [Google Scholar] [CrossRef]
  60. Tinkov, A.A.; Ajsuvakova, O.P.; Skalnaya, M.G.; Popova, E.V.; Sinitskii, A.I.; Nemereshina, O.N.; Gatiatulina, E.R.; Nikonorov, A.A.; Skalny, A.V. Mercury and Metabolic Syndrome: A Review of Experimental and Clinical Observations. BioMetals 2015, 28, 231–254. [Google Scholar] [CrossRef] [PubMed]
  61. Fabricio, G.; Malta, A.; Chango, A.; De Freitas Mathias, P.C. Environmental Contaminants and Pancreatic Beta-Cells. J. Clin. Res. Pediatr. Endocrinol. 2016, 8, 257–263. [Google Scholar] [CrossRef]
  62. Shin, Y.-Y.; Ryu, I.-K.; Park, M.-J.; Kim, S.-H. The Association of Total Blood Mercury Levels and Overweight among Korean Adolescents: Analysis of the Korean National Health and Nutrition Examination Survey (KNHANES) 2010–2013. Korean J. Pediatr. 2018, 61, 121–128. [Google Scholar] [CrossRef]
  63. Genchi, G.; Sinicropi, M.S.; Lauria, G.; Carocci, A.; Catalano, A. The Effects of Cadmium Toxicity. Int. J. Environ. Res. Public Health 2020, 17, 3782. [Google Scholar] [CrossRef] [PubMed]
  64. Fatima, G.; Raza, A.M.; Hadi, N.; Nigam, N.; Mahdi, A.A. Cadmium in Human Diseases: It’s More than Just a Mere Metal. Indian J. Clin. Biochem. 2019, 34, 371–378. [Google Scholar] [CrossRef] [PubMed]
  65. Ghosh, S.; Nukavarapu, S.P.; Jala, V.R. Effects of Heavy Metals on Gut Barrier Integrity and Gut Microbiota. Microbiota Host 2023, 2, e230015. [Google Scholar] [CrossRef]
  66. Buha, A.; Đukić-Ćosić, D.; Ćurčić, M.; Bulat, Z.; Antonijević, B.; Moulis, J.-M.; Goumenou, M.; Wallace, D. Emerging Links between Cadmium Exposure and Insulin Resistance: Human, Animal, and Cell Study Data. Toxics 2020, 8, 63. [Google Scholar] [CrossRef] [PubMed]
  67. Satarug, S. Is Environmental Cadmium Exposure Causally Related to Diabetes and Obesity? Cells 2023, 13, 83. [Google Scholar] [CrossRef]
  68. Filippini, T.; Wise, L.A.; Vinceti, M. Cadmium Exposure and Risk of Diabetes and Prediabetes: A Systematic Review and Dose-Response Meta-Analysis. Environ. Int. 2022, 158, 106920. [Google Scholar] [CrossRef]
  69. Xiao, L.; Li, W.; Zhu, C.; Yang, S.; Zhou, M.; Wang, B.; Wang, X.; Wang, D.; Ma, J.; Zhou, Y.; et al. Cadmium Exposure, Fasting Blood Glucose Changes, and Type 2 Diabetes Mellitus: A Longitudinal Prospective Study in China. Environ. Res. 2021, 192, 110259. [Google Scholar] [CrossRef]
  70. Park, S.S.; Skaar, D.A.; Jirtle, R.L.; Hoyo, C. Epigenetics, Obesity and Early-Life Cadmium or Lead Exposure. Epigenomics 2017, 9, 57–75. [Google Scholar] [CrossRef]
  71. Nguyen, J.; Patel, A.; Gensburg, A.; Bokhari, R.; Lamar, P.; Edwards, J. Diabetogenic and Obesogenic Effects of Cadmium in Db/Db Mice and Rats at a Clinically Relevant Level of Exposure. Toxics 2022, 10, 107. [Google Scholar] [CrossRef] [PubMed]
  72. Green, A.J.; Hoyo, C.; Mattingly, C.J.; Luo, Y.; Tzeng, J.-Y.; Murphy, S.K.; Buchwalter, D.B.; Planchart, A. Cadmium Exposure Increases the Risk of Juvenile Obesity: A Human and Zebrafish Comparative Study. Int. J. Obes. 2018, 42, 1285–1295. [Google Scholar] [CrossRef]
  73. Tyrrell, J.B.; Hafida, S.; Stemmer, P.; Adhami, A.; Leff, T. Lead (Pb) Exposure Promotes Diabetes in Obese Rodents. J. Trace Elem. Med. Biol. 2017, 39, 221–226. [Google Scholar] [CrossRef] [PubMed]
  74. Leff, T.; Stemmer, P.; Tyrrell, J.; Jog, R. Diabetes and Exposure to Environmental Lead (Pb). Toxics 2018, 6, 54. [Google Scholar] [CrossRef] [PubMed]
  75. Wang, N.; Chen, C.; Nie, X.; Han, B.; Li, Q.; Chen, Y.; Zhu, C.; Chen, Y.; Xia, F.; Cang, Z.; et al. Blood Lead Level and Its Association with Body Mass Index and Obesity in China—Results from SPECT-China Study. Sci. Rep. 2015, 5, 18299. [Google Scholar] [CrossRef]
  76. Pánico, P.; Velasco, M.; Salazar, A.M.; Picones, A.; Ortiz-Huidobro, R.I.; Guerrero-Palomo, G.; Salgado-Bernabé, M.E.; Ostrosky-Wegman, P.; Hiriart, M. Is Arsenic Exposure a Risk Factor for Metabolic Syndrome? A Review of the Potential Mechanisms. Front. Endocrinol. 2022, 13, 878280. [Google Scholar] [CrossRef]
  77. Kulshrestha, A. Arsenic-Induced Abnormalities in Glucose Metabolism: Biochemical Basis and Potential Therapeutic and Nutritional Interventions. World J. Transl. Med. 2014, 3, 96–111. [Google Scholar] [CrossRef]
  78. Kausar, S.; Wang, F.; Cui, H. The Role of Mitochondria in Reactive Oxygen Species Generation and Its Implications for Neurodegenerative Diseases. Cells 2018, 7, 274. [Google Scholar] [CrossRef]
  79. Lu, K.; Abo, R.P.; Schlieper, K.A.; Graffam, M.E.; Levine, S.; Wishnok, J.S.; Swenberg, J.A.; Tannenbaum, S.R.; Fox, J.G. Arsenic Exposure Perturbs the Gut Microbiome and Its Metabolic Profile in Mice: An Integrated Metagenomics and Metabolomics Analysis. Environ. Health Perspect. 2014, 122, 284–291. [Google Scholar] [CrossRef] [PubMed]
  80. Mondal, V.; Hosen, Z.; Hossen, F.; Siddique, A.E.; Tony, S.R.; Islam, Z.; Islam, S.; Hossain, S.; Islam, K.; Sarker, K.; et al. Arsenic Exposure-Related Hyperglycemia Is Linked to Insulin Resistance with Concomitant Reduction of Skeletal Muscle Mass. Environ. Int. 2020, 143, 105890. [Google Scholar] [CrossRef] [PubMed]
  81. Castriota, F.; Rieswijk, L.; Dahlberg, S.; La Merrill, M.A.; Steinmaus, C.; Smith, M.T.; Wang, J.-C. A State-of-the-Science Review of Arsenic’s Effects on Glucose Homeostasis in Experimental Models. Environ. Health Perspect. 2020, 128, 16001. [Google Scholar] [CrossRef]
  82. Shakya, A.; Dodson, M.; Artiola, J.F.; Ramirez-Andreotta, M.; Root, R.A.; Ding, X.; Chorover, J.; Maier, R.M. Arsenic in Drinking Water and Diabetes. Water 2023, 15, 1751. [Google Scholar] [CrossRef]
  83. Xia, W.; Guo, X.; Xie, P.; Feng, L.; Wu, B.; Gao, J.; Ma, S.; Liu, H.; Sun, C.; Qu, G.; et al. Associations of Nickel Exposure with Diabetes: Evidence from Observational Studies. Environ. Sci. Pollut. Res. 2023, 30, 100233–100247. [Google Scholar] [CrossRef]
  84. Zhang, Y.; Cai, J.; Yao, Z.; Zhang, H.; Wang, Z.; Lei, J.; Guo, H. The Relationship between Plasma Nickel Concentrations and Type 2 Diabetes Mellitus Risk: A Protective Effect within a Specific Range. J. Trace Elem. Med. Biol. 2024, 82, 127362. [Google Scholar] [CrossRef] [PubMed]
  85. Eljazzar, S.; Abu-Hijleh, H.; Alkhatib, D.; Sokary, S.; Ismail, S.; Al-Jayyousi, G.F.; Tayyem, R. The Role of Copper Intake in the Development and Management of Type 2 Diabetes: A Systematic Review. Nutrients 2023, 15, 1655. [Google Scholar] [CrossRef] [PubMed]
  86. Hamasaki, H.; Kawashima, Y.; Yanai, H. Serum Zn/Cu Ratio Is Associated with Renal Function, Glycemic Control, and Metabolic Parameters in Japanese Patients with and without Type 2 Diabetes: A Cross-Sectional Study. Front. Endocrinol. 2016, 7, 147. [Google Scholar] [CrossRef]
  87. Ruan, S.; Guo, X.; Ren, Y.; Cao, G.; Xing, H.; Zhang, X. Nanomedicines Based on Trace Elements for Intervention of Diabetes Mellitus. Biomed. Pharmacother. 2023, 168, 115684. [Google Scholar] [CrossRef]
  88. Uriu-Adams, J.Y.; Rucker, R.B.; Commisso, J.F.; Keen, C.L. Diabetes and Dietary Copper Alter 67Cu Metabolism and Oxidant Defense in the Rat. J. Nutr. Biochem. 2005, 16, 312–320. [Google Scholar] [CrossRef] [PubMed]
  89. Gembillo, G.; Labbozzetta, V.; Giuffrida, A.E.; Peritore, L.; Calabrese, V.; Spinella, C.; Stancanelli, M.R.; Spallino, E.; Visconti, L.; Santoro, D. Potential Role of Copper in Diabetes and Diabetic Kidney Disease. Metabolites 2022, 13, 17. [Google Scholar] [CrossRef] [PubMed]
  90. Shakeeb, N.; Varkey, P.; Ajit, A. Human Saliva as a Diagnostic Specimen for Early Detection of Inflammatory Biomarkers by Real-Time RT-PCR. Inflammation 2021, 44, 1713–1723. [Google Scholar] [CrossRef] [PubMed]
  91. Jiao, L.-L.; Dong, H.-L.; Liu, M.-M.; Wu, P.-L.; Cao, Y.; Zhang, Y.; Gao, F.-G.; Zhu, H.-Y. The Potential Roles of Salivary Biomarkers in Neurodegenerative Diseases. Neurobiol. Dis. 2024, 193, 106442. [Google Scholar] [CrossRef] [PubMed]
  92. Hajjo, R.; Sabbah, D.A.; Al Bawab, A.Q. Unlocking the Potential of the Human Microbiome for Identifying Disease Diagnostic Biomarkers. Diagnostics 2022, 12, 1742. [Google Scholar] [CrossRef] [PubMed]
  93. Kumari, S.; Samara, M.; Ampadi Ramachandran, R.; Gosh, S.; George, H.; Wang, R.; Pesavento, R.P.; Mathew, M.T. A Review on Saliva-Based Health Diagnostics: Biomarker Selection and Future Directions. Biomed. Mater. Devices 2024, 2, 121–138. [Google Scholar] [CrossRef]
  94. Leake, S.L.; Pagni, M.; Falquet, L.; Taroni, F.; Greub, G. The Salivary Microbiome for Differentiating Individuals: Proof of Principle. Microbes Infect. 2016, 18, 399–405. [Google Scholar] [CrossRef] [PubMed]
  95. Passalacqua, K.D.; Charbonneau, M.-E.; O’Riordan, M.X.D. Bacterial Metabolism Shapes the Host–Pathogen Interface. Microbiol. Spectr. 2016, 4, 15–41. [Google Scholar] [CrossRef] [PubMed]
  96. Davis, E.; Bakulski, K.M.; Goodrich, J.M.; Peterson, K.E.; Marazita, M.L.; Foxman, B. Low Levels of Salivary Metals, Oral Microbiome Composition and Dental Decay. Sci. Rep. 2020, 10, 14640. [Google Scholar] [CrossRef]
  97. Gug, I.T.; Tertis, M.; Hosu, O.; Cristea, C. Salivary Biomarkers Detection: Analytical and Immunological Methods Overview. TrAC Trends Anal. Chem. 2019, 113, 301–316. [Google Scholar] [CrossRef]
  98. Abrao Nemeir, I.; Saab, J.; Hleihel, W.; Errachid, A.; Jafferzic-Renault, N.; Zine, N. The Advent of Salivary Breast Cancer Biomarker Detection Using Affinity Sensors. Sensors 2019, 19, 2373. [Google Scholar] [CrossRef] [PubMed]
  99. Kaczor-Urbanowicz, K.E.; Martin Carreras-Presas, C.; Aro, K.; Tu, M.; Garcia-Godoy, F.; Wong, D.T. Saliva Diagnostics—Current Views and Directions. Exp. Biol. Med. 2017, 242, 459–472. [Google Scholar] [CrossRef] [PubMed]
  100. Bhatia, D.; Paul, S.; Acharjee, T.; Ramachairy, S.S. Biosensors and Their Widespread Impact on Human Health. Sens. Int. 2024, 5, 100257. [Google Scholar] [CrossRef]
  101. Khan, I.; Saeed, K.; Khan, I. Nanoparticles: Properties, Applications and Toxicities. Arab. J. Chem. 2019, 12, 908–931. [Google Scholar] [CrossRef]
  102. Jyotsna; Stanley Abraham, L.; Hanumant Singh, R.; Panda, R.C.; Senthilvelan, T. Biomedical Applications of Carbon-Based Nanomaterials. In Nanomaterials and Their Biomedical Applications; Springer: Berlin, Germany, 2021; pp. 157–174. [Google Scholar]
  103. Ehtesabi, H. Application of Carbon Nanomaterials in Human Virus Detection. J. Sci. Adv. Mater. Devices 2020, 5, 436–450. [Google Scholar] [CrossRef]
  104. Ehtesabi, H. Carbon Nanomaterials for Salivary-Based Biosensors: A Review. Mater. Today Chem. 2020, 17, 100342. [Google Scholar] [CrossRef]
  105. Malik, S.; Singh, J.; Goyat, R.; Saharan, Y.; Chaudhry, V.; Umar, A.; Ibrahim, A.A.; Akbar, S.; Ameen, S.; Baskoutas, S. Nanomaterials-Based Biosensor and Their Applications: A Review. Heliyon 2023, 9, e19929. [Google Scholar] [CrossRef] [PubMed]
  106. Zucolotto, V. Specialty Grand Challenges in Biosensors. Front. Sens. 2020, 1, 3. [Google Scholar] [CrossRef]
  107. Goldoni, R.; Farronato, M.; Connelly, S.T.; Tartaglia, G.M.; Yeo, W.-H. Recent Advances in Graphene-Based Nanobiosensors for Salivary Biomarker Detection. Biosens. Bioelectron. 2021, 171, 112723. [Google Scholar] [CrossRef] [PubMed]
  108. Ferreira, R.G.; Silva, A.P.; Nunes-Pereira, J. Current On-Skin Flexible Sensors, Materials, Manufacturing Approaches, and Study Trends for Health Monitoring: A Review. ACS Sens. 2024, 9, 1104–1133. [Google Scholar] [CrossRef]
  109. Alemdar, S.; Pekel Bayramgil, N.; Karakuş, S. Applications of Cutting-Edge Biosensors in Healthcare and Biomedical Research. In New Advances in Biosensing; Karakuş, S., Ed.; IntechOpen: London, UK, 2024. [Google Scholar]
  110. Liu, Z.; Cascioli, V.; McCarthy, P.W. Healthcare Monitoring Using Low-Cost Sensors to Supplement and Replace Human Sensation: Does It Have Potential to Increase Independent Living and Prevent Disease? Sensors 2023, 23, 2139. [Google Scholar] [CrossRef] [PubMed]
  111. Slaughter, G. Current Advances in Biosensor Design and Fabrication. In Encyclopedia of Analytical Chemistry; Wiley: Hoboken, NJ, USA, 2018; pp. 1–25. [Google Scholar]
  112. Feng, L.; Song, S.; Li, H.; He, R.; Chen, S.; Wang, J.; Zhao, G.; Zhao, X. Nano-Biosensors Based on Noble Metal and Semiconductor Materials: Emerging Trends and Future Prospects. Metals 2023, 13, 792. [Google Scholar] [CrossRef]
  113. El-Warrak, L.; Nunes, M.; Luna, G.; Barbosa, C.E.; Lyra, A.; Argôlo, M.; Lima, Y.; Salazar, H.; de Souza, J.M. Towards the Future of Public Health: Roadmapping Trends and Scenarios in the Post-COVID Healthcare Era. Healthcare 2023, 11, 3118. [Google Scholar] [CrossRef]
  114. Alanzi, T.M.; Alzahrani, W.; Almoraikhi, M.; Algannas, A.; Alghamdi, M.; Alzahrani, L.; Abutaleb, R.; Ba Dughaish, R.; Alotibi, N.; Alkhalifah, S.; et al. Adoption of Wearable Insulin Biosensors for Diabetes Management: A Cross-Sectional Study. Cureus 2023, 15, e50782. [Google Scholar] [CrossRef] [PubMed]
  115. Kubala, E.; Strzelecka, P.; Grzegocka, M.; Lietz-Kijak, D.; Gronwald, H.; Skomro, P.; Kijak, E. A Review of Selected Studies That Determine the Physical and Chemical Properties of Saliva in the Field of Dental Treatment. Biomed. Res. Int. 2018, 2018, 6572381. [Google Scholar] [CrossRef] [PubMed]
  116. Lynge Pedersen, A.M.; Belstrøm, D. The Role of Natural Salivary Defences in Maintaining a Healthy Oral Microbiota. J. Dent. 2019, 80, S3–S12. [Google Scholar] [CrossRef]
  117. Lee, J.; Garon, E.; Wong, D. Salivary Diagnostics. Orthod. Craniofac. Res. 2009, 12, 206–211. [Google Scholar] [CrossRef] [PubMed]
  118. Smith, A.A.; Li, R.; Tse, Z.T.H. Reshaping Healthcare with Wearable Biosensors. Sci. Rep. 2023, 13, 4998. [Google Scholar] [CrossRef] [PubMed]
  119. Haji Mohammadi, M.; Mulder, S.; Khashayar, P.; Kalbasi, A.; Azimzadeh, M.; Aref, A.R. Saliva Lab-on-a-Chip Biosensors: Recent Novel Ideas and Applications in Disease Detection. Microchem. J. 2021, 168, 106506. [Google Scholar] [CrossRef]
  120. Jung, C.; Kim, M.-G. Direct Use of a Saliva-Collected Cotton Swab in Lateral Flow Immunoassay for the Detection of Cotinine. Biosensors 2022, 12, 214. [Google Scholar] [CrossRef]
  121. Timpel, J.; Klinghammer, S.; Riemenschneider, L.; Ibarlucea, B.; Cuniberti, G.; Hannig, C.; Sterzenbach, T. Sensors for in Situ Monitoring of Oral and Dental Health Parameters in Saliva. Clin. Oral Investig. 2023, 27, 5719–5736. [Google Scholar] [CrossRef] [PubMed]
  122. Nguyen, T.T.-H.; Nguyen, C.M.; Huynh, M.A.; Vu, H.H.; Nguyen, T.-K.; Nguyen, N.-T. Field Effect Transistor Based Wearable Biosensors for Healthcare Monitoring. J. Nanobiotechnol. 2023, 21, 411. [Google Scholar] [CrossRef] [PubMed]
  123. Norris, H.L.; Friedman, J.; Chen, Z.; Puri, S.; Wilding, G.; Edgerton, M. Salivary Metals, Age, and Gender Correlate with Cultivable Oral Candida Carriage Levels. J. Oral Microbiol. 2018, 10, 1447216. [Google Scholar] [CrossRef]
  124. Mani, V.; Beduk, T.; Khushaim, W.; Ceylan, A.E.; Timur, S.; Wolfbeis, O.S.; Salama, K.N. Electrochemical Sensors Targeting Salivary Biomarkers: A Comprehensive Review. TrAC Trends Anal. Chem. 2021, 135, 116164. [Google Scholar] [CrossRef]
  125. Kulkarni, M.B.; Ayachit, N.H.; Aminabhavi, T.M. Biosensors and Microfluidic Biosensors: From Fabrication to Application. Biosensors 2022, 12, 543. [Google Scholar] [CrossRef]
  126. Omidfar, K.; Ahmadi, A.; Syedmoradi, L.; Khoshfetrat, S.M.; Larijani, B. Point-of-Care Biosensors in Medicine: A Brief Overview of Our Achievements in This Field Based on the Conducted Research in EMRI (Endocrinology and Metabolism Research Institute of Tehran University of Medical Sciences) over the Past Fourteen Years. J. Diabetes Metab. Disord. 2020, 23, 1455–1459. [Google Scholar] [CrossRef]
  127. Ye, S.; Feng, S.; Huang, L.; Bian, S. Recent Progress in Wearable Biosensors: From Healthcare Monitoring to Sports Analytics. Biosensors 2020, 10, 205. [Google Scholar] [CrossRef]
  128. Filippidou, M.-K.; Chatzandroulis, S. Microfluidic Devices for Heavy Metal Ions Detection: A Review. Micromachines 2023, 14, 1520. [Google Scholar] [CrossRef] [PubMed]
  129. Gamboa, J.; Paulo-Mirasol, S.; Estrany, F.; Torras, J. Recent Progress in Biomedical Sensors Based on Conducting Polymer Hydrogels. ACS Appl. Bio. Mater. 2023, 6, 1720–1741. [Google Scholar] [CrossRef] [PubMed]
  130. Sivakumar, R.; Lee, N.Y. Recent Progress in Smartphone-Based Techniques for Food Safety and the Detection of Heavy Metal Ions in Environmental Water. Chemosphere 2021, 275, 130096. [Google Scholar] [CrossRef]
  131. Lan, Y.; He, B.; Tan, C.S.; Ming, D. Applications of Smartphone-Based Aptasensor for Diverse Targets Detection. Biosensors 2022, 12, 477. [Google Scholar] [CrossRef]
  132. Srivastava, S.; Sharma, V. Ultra-Portable, Smartphone-Based Spectrometer for Heavy Metal Concentration Measurement in Drinking Water Samples. Appl. Water Sci. 2021, 11, 177. [Google Scholar] [CrossRef]
  133. Pungjunun, K.; Yakoh, A.; Chaiyo, S.; Siangproh, W.; Praphairaksit, N.; Chailapakul, O. Smartphone-Based Electrochemical Analysis Integrated with NFC System for the Voltammetric Detection of Heavy Metals Using a Screen-Printed Graphene Electrode. Microchim. Acta 2022, 189, 191. [Google Scholar] [CrossRef]
  134. Zhang, A.-L. Smartphone-Based Colorimetric Detection of Heavy Metal Copper (II) Ion by Help of Surface Acoustic Wave. Ferroelectrics 2021, 583, 41–50. [Google Scholar] [CrossRef]
  135. Yadav, N.; Maurya, B.M.; Chettri, D.; Pooja; Pulwani, C.; Jajula, M.; Kanda, S.S.; Babu, H.W.S.; Elangovan, A.; Velusamy, P.; et al. Artificial Intelligence in Heavy Metals Detection: Methodological and Ethical Challenges. Hyg. Environ. Health Adv. 2023, 7, 100071. [Google Scholar] [CrossRef]
  136. Zheng, P.; Li, M.; Jurevic, R.; Cushing, S.K.; Liu, Y.; Wu, N. A Gold Nanohole Array Based Surface-Enhanced Raman Scattering Biosensor for Detection of Silver(i) and Mercury(Ii) in Human Saliva. Nanoscale 2015, 7, 11005–11012. [Google Scholar] [CrossRef]
  137. Ilea, A.; Andrei, V.; Feurdean, C.; Băbțan, A.-M.; Petrescu, N.; Câmpian, R.; Boșca, A.; Ciui, B.; Tertiș, M.; Săndulescu, R.; et al. Saliva, a Magic Biofluid Available for Multilevel Assessment and a Mirror of General Health—A Systematic Review. Biosensors 2019, 9, 27. [Google Scholar] [CrossRef]
  138. Kim, Y.; Jeon, Y.; Na, M.; Hwang, S.-J.; Yoon, Y. Recent Trends in Chemical Sensors for Detecting Toxic Materials. Sensors 2024, 24, 431. [Google Scholar] [CrossRef] [PubMed]
  139. Ur Rehman, A.; Hakim Shah, A.; Ur Rahman, A.; Ur Rahman, F.; Ali, S.; Ur Rehman, A.; Ullah, R.; Ullah, I.; Fayaz, M.; Shi, K. Electrochemical Techniques for the Detection of Heavy Metals. In Heavy Metals—Recent Advances; Almayyahi, B.A., Ed.; IntechOpen: London, UK, 2023. [Google Scholar]
  140. Anchidin-Norocel, L.; Gutt, G.; Tătăranu, E.; Amariei, S. Electrochemical Sensors and Biosensors: Effective Tools for Detecting Heavy Metals in Water and Food with Possible Implications for Children’s Health. Int. J. Electrochem. Sci. 2024, 19, 100643. [Google Scholar] [CrossRef]
  141. Shirsat, M.D.; Hianik, T. Electrochemical Detection of Heavy Metal Ions Based on Nanocomposite Materials. J. Compos. Sci. 2023, 7, 473. [Google Scholar] [CrossRef]
  142. Meng, R.; Zhu, Q.; Long, T.; He, X.; Luo, Z.; Gu, R.; Wang, W.; Xiang, P. The Innovative and Accurate Detection of Heavy Metals in Foods: A Critical Review on Electrochemical Sensors. Food Control 2023, 150, 109743. [Google Scholar] [CrossRef]
  143. Zeadally, S.; Siddiqui, F.; Baig, Z.; Ibrahim, A. Smart Healthcare. PSU Res. Rev. 2019, 4, 149–168. [Google Scholar] [CrossRef]
  144. Jaime, F.J.; Muñoz, A.; Rodríguez-Gómez, F.; Jerez-Calero, A. Strengthening Privacy and Data Security in Biomedical Microelectromechanical Systems by IoT Communication Security and Protection in Smart Healthcare. Sensors 2023, 23, 8944. [Google Scholar] [CrossRef] [PubMed]
  145. Wang, X.; Zhou, J.; Wang, H. Bioreceptors as the Key Components for Electrochemical Biosensing in Medicine. Cell Rep. Phys. Sci. 2024, 5, 101801. [Google Scholar] [CrossRef]
  146. Liu, J.; Tang, Y.; Cheng, Y.; Huang, W.; Xiang, L. Electrochemical Biosensors Based on Saliva Electrolytes for Rapid Detection and Diagnosis. J. Mater. Chem. B 2023, 11, 33–54. [Google Scholar] [CrossRef] [PubMed]
  147. Pradeep Kumar Agrawal, J.B. Application of Mass Spectrometry for the Discovery of Biomarker in Human Saliva: Quantification of Heavy Metals in Saliva of Smokers and Non-Smokers Using ICP-MS; University of Greenwich: London, UK, 2010. [Google Scholar]
  148. Bhowmick, S.; Kundu, A.K.; Adhikari, J.; Chatterjee, D.; Iglesias, M.; Nriagu, J.; Guha Mazumder, D.N.; Shomar, B.; Chatterjee, D. Assessment of Toxic Metals in Groundwater and Saliva in an Arsenic Affected Area of West Bengal, India: A Pilot Scale Study. Environ. Res. 2015, 142, 328–336. [Google Scholar] [CrossRef]
  149. Topal, S.; Şaylan, M.; Zaman, B.T.; Bakırdere, S. Determination of Trace Cadmium in Saliva Samples Using Spray Assisted Droplet Formation-Liquid Phase Microextraction Prior to the Measurement by Slotted Quartz Tube-Flame Atomic Absorption Spectrophotometry. J. Trace Elem. Med. Biol. 2021, 68, 126859. [Google Scholar] [CrossRef]
  150. Thaweboon, S.; Thaweboon, B.; Veerapradist, W. Lead in Saliva and Its Relationship to Blood in the Residents of Klity Village in Thailand. Southeast Asian J. Trop. Med. Public Health 2005, 36, 1576. [Google Scholar]
  151. Kumar, V.; Kumari, N.; Ealla, K.K.R.; Gour, S.; Srivastava, H.; Rallabhandi, S. Comparative Analysis of Trace Elements in the Saliva and Serum of Patients with Oral Submucous Fibrosis and Squamous Cell Carcinoma. Mol. Clin. Oncol. 2024, 20, 18. [Google Scholar] [CrossRef] [PubMed]
  152. Herman, M.; Golasik, M.; Piekoszewski, W.; Walas, S.; Napierala, M.; Wyganowska-Swiatkowska, M.; Kurhanska-Flisykowska, A.; Wozniak, A.; Florek, E. Essential and Toxic Metals in Oral Fluid–a Potential Role in the Diagnosis of Periodontal Diseases. Biol. Trace Elem. Res. 2016, 173, 275–282. [Google Scholar] [CrossRef]
  153. Heavy Metal Test Kit. Available online: https://vitalityplus.au/Product/Heavy-Metal-Test-Kit/ (accessed on 4 November 2024).
  154. Chuanyi Mark Lu, MD, PhD, Get Tested. Available online: https://www.testing.com/tests/heavy-metals/ (accessed on 4 November 2024).
  155. Available online: https://www.chemsee.com/Product/Total-Heavy-Metal-Detection-Kit-Low-1-Test/ (accessed on 4 November 2024).
  156. Moritz Jaax, What Is Heavy Metal Poisoning—Why Does It Happen? Available online: https://www.cerascreen.co.uk/blogs/health-portal/heavy-metal-poisoning?srsltid=AfmBOopthboXgMhQrMyP6vlcAfbHInh9hUktSJRhHhEAYPRZVQHcfbHr (accessed on 4 November 2024).
  157. Chauhan, S.; Dahiya, D.; Sharma, V.; Khan, N.; Chaurasia, D.; Nadda, A.K.; Varjani, S.; Pandey, A.; Bhargava, P.C. Advances from Conventional to Real Time Detection of Heavy Metal(Loid)s for Water Monitoring: An Overview of Biosensing Applications. Chemosphere 2022, 307, 136124. [Google Scholar] [CrossRef]
  158. Available online: https://www.instrumentstrade.com/sjb-801-portable-heavy-metal-ion-detector-for-water-quality-testing_p1171.html (accessed on 4 November 2024).
  159. Available online: https://biodevicetech.com/Materials/157603883747601.Pdf (accessed on 4 November 2024).
  160. Kim, J.; Valdés-Ramírez, G.; Bandodkar, A.J.; Jia, W.; Martinez, A.G.; Ramírez, J.; Mercier, P.; Wang, J. Non-Invasive Mouthguard Biosensor for Continuous Salivary Monitoring of Metabolites. Analyst 2014, 139, 1632–1636. [Google Scholar] [CrossRef] [PubMed]
  161. Lino, C.; Barrias, S.; Chaves, R.; Adega, F.; Martins-Lopes, P.; Fernandes, J.R. Biosensors as Diagnostic Tools in Clinical Applications. Biochim. Biophys. Acta (BBA) Rev. Cancer 2022, 1877, 188726. [Google Scholar] [CrossRef]
  162. Ahmed, M.U.; Saaem, I.; Wu, P.C.; Brown, A.S. Personalized Diagnostics and Biosensors: A Review of the Biology and Technology Needed for Personalized Medicine. Crit. Rev. Biotechnol. 2014, 34, 180–196. [Google Scholar] [CrossRef] [PubMed]
  163. Haidar, Z.; Fatema, K.; Shoily, S.S.; Sajib, A.A. Disease-Associated Metabolic Pathways Affected by Heavy Metals and Metalloid. Toxicol. Rep. 2023, 10, 554–570. [Google Scholar] [CrossRef] [PubMed]
  164. Johnston, L.; Wang, G.; Hu, K.; Qian, C.; Liu, G. Advances in Biosensors for Continuous Glucose Monitoring Towards Wearables. Front. Bioeng. Biotechnol. 2021, 9, 733810. [Google Scholar] [CrossRef]
  165. Mitra, S.; Chakraborty, A.J.; Tareq, A.M.; Emran, T.B.; Nainu, F.; Khusro, A.; Idris, A.M.; Khandaker, M.U.; Osman, H.; Alhumaydhi, F.A.; et al. Impact of Heavy Metals on the Environment and Human Health: Novel Therapeutic Insights to Counter the Toxicity. J. King Saud Univ. Sci. 2022, 34, 101865. [Google Scholar] [CrossRef]
Chemosensors 12 00269 g001
Figure 1. Noninvasive screening method for continuous monitoring of heavy metals using saliva sensors in diabetes and obesity.
Figure 1. Noninvasive screening method for continuous monitoring of heavy metals using saliva sensors in diabetes and obesity.
Chemosensors 12 00269 g001
Chemosensors 12 00269 g002
Figure 2. Saliva-based sensors: principles, advantages, and recent progress.
Figure 2. Saliva-based sensors: principles, advantages, and recent progress.
Chemosensors 12 00269 g002
Table 1. Heavy metals and association with diabetes, obesity, and metabolic syndrome (MetS): mercury (Hg), cadmium (Cd), lead (Pb), arsenic (As), nickel (Ni), copper (Cu). ↑ = Positive association; ↑* = U-shaped associations; - = no data; No = no association.
Table 1. Heavy metals and association with diabetes, obesity, and metabolic syndrome (MetS): mercury (Hg), cadmium (Cd), lead (Pb), arsenic (As), nickel (Ni), copper (Cu). ↑ = Positive association; ↑* = U-shaped associations; - = no data; No = no association.
Heavy MetalStudy PopulationLocationStudy PeriodDiabetes (T2DM) AssociationObesity AssociationMetS AssociationReference
Hg3787 adultsRepublic of Korea 2015–2017No↑-[38]
1442 mother–child pairsUS--↑-[39]
327 (10–18 years)Republic of Korea2010–2013-↑-[40]
646 adults Taiwan 2005–2008↑--[41]
495 adultsRepublic of Korea--↑-[42]
Cd177 adultsThailand2020–2021↑--[43]
270 adultsSouthern State--↑-[44]
965 adultsRepublic of Korea---↑[45]
200 adultsRepublic of Korea2002–2018No--[46]
Pb3787 adultsRepublic of Korea2015–2017-↑-[38]
177 adultsThailand2020–2021↑--[43]
85 adultsMexico--↑-[47]
1035 adultsRepublic of Korea2002–2018No--[46]
1274 adultsSouthern China--↑-[48]
965 adultsRepublic of Korea---No[45]
As270 adultsSouthern State--↑-[44]
227 adultsMexico2007–2011↑--[49]
3517 adultsCanada2012–2013↑--[50]
Ni2444 adultsTaiwan2016–2018--↑[51]
10,890 adultsChina2017–2018↑--[52]
1585 adultsU.S.2017–2018↑--[53]
1128 adultsItaly2010–2016↑--[54]
Cu2444 adultsTaiwan2016–2018--↑[51]
117 adultsU.S.2013–2015-↑-[55]
13,282 adultsChina1997–2011-↑*-[56]
191 schoolchildrenMexico--↑-[57]
Table 2. Sensors for heavy metals in saliva. SERS = surface-enhanced Raman scattering; TFs-based sensors = transcription factor-based sensors.
Table 2. Sensors for heavy metals in saliva. SERS = surface-enhanced Raman scattering; TFs-based sensors = transcription factor-based sensors.
Type of Sensor/TraducerMetal IonsLimit of Detection (LOD)Reference
SERSAg (I)0.17 nM[136,137]
SERSHg (II)2.3 pM[136,137]
Microfluidic device (colorimetric)Cu (II), Ni (II), Cr (VI)0.29 ppm, 0.33 ppm, 0.35 ppm[138]
Microfluidic device (colorimetric) Hg (II) -[138]
Naked eyes/UV-Vis spec.Sb (III), Hg (II), Pb (II)33.7 nM, 6.34 nM, 2.38 nM[138]
Naked eyes Hg (II) 0.5 mM[138]
Voltametric sensor Hg (II) 0.15 nM[138]
TFs-based sensors Cu (II) 10 nM [138]
TFs-based sensors As (III), As (V) 10 µg/L[138]
- no data.
Table 3. Methods of saliva sampling for heavy metal detection.
Table 3. Methods of saliva sampling for heavy metal detection.
Sampling ProtocolElementsEquipmentReference
Subjects refrained from any food consumption or at least 1 h after any food consumption. Cr, Co, Ni, Zn, As, Cd, In, La, Hg, PbICP-MS[147]
Fasted >/= 1 h, oral rinse with Milli-Q water, collected saliva into 15 mL bottles over 5 min, and stored the samples in a salt–ice mixture at −20 °C until analysis.As, Mn, Ni, Cr, Pb, Se, ZnICP-MS, HG-AAS[148]
Saliva samples were collected in the morning before breakfast, with volunteers rinsing their mouths with sterilized distilled water, and then 7.0–8.0 mL of saliva was collected, filtered, vortexed, centrifuged, and stored at –18 °C before analysis, with daily preparation and dilution using ultrapure water.CdSQT-FAAS[149]
Subjects refrained from eating, drinking, smoking, and oral hygiene for 2 h before morning saliva collection.Pb AAS[150]
Prior to the collection of saliva, patients were not allowed to eat or drink for 2 h. Cu, Zn, Se, Mo ICP-OES[151]
Unstimulated oral fluid (3 mL) was collected after rinsing with deionized water and centrifuged. Blood (8 mL) was drawn from a forearm vein into tubes without anticoagulant. All samples were stored at −80 °C. Ca, Cd, Cu, Fe, Mg, Mn, Pb, Zn ICP-MS[152]
ND, not described. ICP-MS, inductively coupled plasma mass spectrometry. HG-AAS, hydride generation–atomic absorption spectroscopy. AAS, atomic absorption spectrophotometry. ICP-OES, inductively coupled plasma optical emission spectrometry. SQT-FAAS, slotted quartz tube–flame atomic absorption spectrophotometry.
Table 4. Commercial sensors for detecting heavy metals from biological samples.
Table 4. Commercial sensors for detecting heavy metals from biological samples.
Device for Heavy Metal DetectionHeavy MetalsMethodLimit of DetectionTime of AnalysesCostReference
Heavy metals test for water, urine and salivaPb, Cu, Zn, Cd, Cr, HgColorimetricsemi-quantitative ppm–ppb level-USD 39.95-[153]
Heavy metals test for urine/hairAl, Pb, As, Cd, Cr, Co, Ni, Hg, ZnColorimetric--EUR 14,900/199[154]
THMLOW-01 detection kit for heavy metals and trace arsenicAs, Cd, Cu, Pb, Hg, Tl, ZnColorimetric-15–120 sUSD 25.95[155]
Heavy metal test for urineAl, As, Pb Cd, Cr, Co, Ni, Hg, Zn.Colorimetric--GBP 89[156]
ChemSee generic heavy metal detectorPb, Hg, Cd, Co, Ni, ZnColorimetric-15–60 sUSD 9.95–87.90[155]
AppliTraceCd, Pb, Zn, Cu, AsAnodic stripping voltammetry1 μg/L40 min-[157]
uMEDCd, Zn, PbSquare wave anodic stripping voltammetry4 μg/L-USD 25[157]
Portable heavy metal ion detector INE-SJB-801Cd, Zn, As, HgAnodic stripping voltammetry(0–100) μg/L-USD 11,800.00[158]
DEP-ChipCd, Zn, As, Pb, CuDifferential pulse voltammetry2.6 μg/L 14.4 μg/L
4.0 μg/L 5.0 μg/L
15.5 μg/L 		5 min<USD 1[159]
- no data.
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Share and Cite

MDPI and ACS Style

Anchidin-Norocel, L.; Savage, W.K.; Nemțoi, A.; Dimian, M.; Cobuz, C. Recent Progress in Saliva-Based Sensors for Continuous Monitoring of Heavy Metal Levels Linked with Diabetes and Obesity. Chemosensors 2024, 12, 269. https://doi.org/10.3390/chemosensors12120269

AMA Style

Anchidin-Norocel L, Savage WK, Nemțoi A, Dimian M, Cobuz C. Recent Progress in Saliva-Based Sensors for Continuous Monitoring of Heavy Metal Levels Linked with Diabetes and Obesity. Chemosensors. 2024; 12(12):269. https://doi.org/10.3390/chemosensors12120269

Chicago/Turabian Style

Anchidin-Norocel, Liliana, Wesley K. Savage, Alexandru Nemțoi, Mihai Dimian, and Claudiu Cobuz. 2024. "Recent Progress in Saliva-Based Sensors for Continuous Monitoring of Heavy Metal Levels Linked with Diabetes and Obesity" Chemosensors 12, no. 12: 269. https://doi.org/10.3390/chemosensors12120269

APA Style

Anchidin-Norocel, L., Savage, W. K., Nemțoi, A., Dimian, M., & Cobuz, C. (2024). Recent Progress in Saliva-Based Sensors for Continuous Monitoring of Heavy Metal Levels Linked with Diabetes and Obesity. Chemosensors, 12(12), 269. https://doi.org/10.3390/chemosensors12120269

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop