In early times, elemental profiling analysis in various samples was mainly used in geoscience, environmental science, and ecological studies. In the last decade, ionomics has grown in popularity in different areas of life science as a powerful tool for studying the metabolism and homeostasis of diverse trace elements and minerals in different organisms, including plants and humans. The major techniques for the quantification of chemical elements include inductively coupled plasma mass spectrometry (ICP-MS), inductively coupled plasma atomic/optical emission spectroscopy (ICP-AES/ICP-OES), X-ray fluorescence (XRF), atomic absorption spectrometry (AAS), neutron activation analysis (NAA), and several other methods (23, 33). Currently, ICP-MS and ICP-AES are the two most widely used high-throughput analytical tools. ICP-MS has been the dominant technique for the analysis of multi-elemental concentrations due to its high accuracy, high sensitivity, wide measurement capability, and the ability for isotopic analysis (36). Although ICP-AES/ICP-OES is less sensitive than ICP-MS, it offers the advantages of lower cost and simplicity of operation (37). Both methods have been used for ionomics studies in yeast, plants, and animals, which demonstrate the potential benefits of ionomics in the exploration of new aspects of trace elements in biology and medicine (26, 27, 38–40). Other techniques, although used less frequently, may provide alternative approaches for determining various elements from different samples (41, 42).

With the increase in volume of data generated by ionomics studies, it becomes more and more important to develop appropriate information management systems for ionomics data acquisition, storage, retrieval, and analysis. However, publicly accessible platforms and databases that allow researchers to obtain such resources are very rare so far. The iHUB system is the first integrated workflow system, which contains ionomics data of yeast and important plants such as Arabidopsis thaliana, maize, rice, and soybean, and provides open access for raw data management, data mining and analysis, and knowledge discovery (43). The OPTIMAS Data Warehouse (OPTIMAS-DW) is a comprehensive and integrated data resource for maize, which contains ionomics and other omics data such as transcriptome, proteome, metabolome, and phenome, and allows users to extract information of particular interest (44). Unfortunately, an ionomics database associated with human disease is still absent.

On the other hand, with the rapid progress in the experimental and computational identification of novel trace element-related genes and their functions, relevant information should be well-maintained for further utilization. A known ionome gene (KIG) database was recently developed, which is a curated list of genes known to affect uptake, accumulation, and distribution of elements in several plant species (45). New systems and annotation tools are clearly required to obtain a better understanding of the function and regulation of genes affecting the ionome on a genome-wide scale.

The recent explosion of ionomics data has prompted the development of computational strategies and methods for investigation of the associations between ionomic profiles and complex traits. Bioinformatics approaches for ionomics data analysis include both basic statistical methods (such as statistical significance testing and analysis of variance) and advanced multivariate statistical methods (e.g., principal component analysis and logistic regression analysis), which could also be applied for other types of omics data (31). Moreover, network-based systems biology approaches and machine learning algorithms have been used to clarify the dynamic interplay between elements under different physiological or metabolic conditions and to build ionome-based prediction models or markers for a variety of diseases (46–49). Very recently, an integrated R-based tool called IonFlow was developed for the analysis of ionomics data sets, which may help researchers to process, explore, and interpret their data via multiple approaches (50).

Before the concept of ionomics was introduced, ICP-MS had been used to evaluate the concentrations of certain trace elements in biofluids, hair, nails, and some other human tissues for different diseases such as cancer, neurodegenerative diseases, and coronary heart disease, demonstrating that imbalance of trace elements may be one of the risk factors of many complex or common diseases (51–53). Most of these preliminary studies only focused on single or a very limited number of trace elements and lacked deep analysis of the interactions between elements. In recent years, ionome-based studies have been performed for a variety of complex diseases, which may assist in diagnosis and prognosis evaluation of disease. Considering that most ionomics results have been derived from cross-sectional or case-control studies, it is uncertain whether disrupted ion homeostasis is a potential cause of these diseases. Large-scale prospective, longitudinal, and interventional studies are necessary to solve these questions. In addition, the intrinsic limitation of current elemental analytical techniques and the validity and reliability of using ionomic information in the prediction of diseases imply that there is still a long way ahead of us before ionomics can be widely used in medical clinics. However, the application of ionomics in several types of diseases has provided evidence for its potential benefits, which suggests significant correlations between trace elements and the development of different diseases and may help early detection and improve therapeutic regimens for patients in the future.

DM comprises a group of metabolic diseases characterized by chronic hyperglycemia resulting from insufficient insulin secretion and/or insulin resistance in target tissues, which may lead to multi-organ dysfunction and failure (59). There are three main types of DM: type 1 DM (T1DM), T2DM, and gestational DM (GDM). T2DM accounts for more than 90% of all diabetic cases, which has become the most prevalent metabolic disease worldwide. It has been suggested that ion homeostasis may play an important role in T2DM and some other types of DM (3).

To date, the ionomes in blood (including whole blood, plasma, and serum), urine, hair, and even teardrops have been examined in different cohorts of patients with T2DM, implying that changes in elemental profiles represent a potential source of biomarkers of this complex disease (60–70). The majority of significantly changed elements in different biofluids (especially in the blood samples) are positively associated with T2DM, including several heavy metals (such as Cu and Cd) and Se whose accumulation in the body may be a high risk for T2DM. In contrast, concentrations of Zn and Mg in the blood and hair/nail samples were observed to generally have a negative association with the prevalence of T2DM. These trends are consistent with previous observations that environmental heavy metals and Se might affect insulin sensitivity and normal insulin regulatory function and cause pathoglycemia or even more serious illness such as T2DM, whereas deficiency of Zn, an essential trace metal for synthesis and secretion of insulin, may have a major impact on the pathogenesis of T2DM (3, 64, 67). Thus, such variations could be used for differentiating patients with T2DM from healthy controls. Moreover, significant correlations (|correlation coefficient| > 0.3 and p < 0.05) between elements and increased Cu/Zn ratio were also reported, suggesting possible benefits of the use of such information in detection and prognosis evaluation of T2DM (61, 63). By using machine learning algorithms [such as discriminate analysis, support vector machine (SVM), and random forest], several new approaches and models for the early detection of T2DM based on multi-elemental contents have been developed, which exhibit good prediction performance and may serve as a valuable tool of its diagnosis (66, 70–72). Very recently, the blood levels of several essential trace elements (such as V, Cr, Mn, and Se) were found to be negatively correlated with hemoglobin A1c (HbA1c) in diabetic subjects, especially in those with HbA1c ≥ 7.0%, suggesting mutual effects between them and metabolic abnormalities of blood glucose during the onset and progression of T2DM (69). On the other hand, although a significant number of studies only examined the concentrations of single or a small set of trace elements in biofluids of T2DM patients, similar or more complex patterns of changes were obtained for some of them in different regions of the world (73–80). For example, serum Cu, Zn, and Se levels were found to vary with the effect of glycemic control in T2DM patients, indicating a potential relationship between their homeostasis and the prognosis and treatment of T2DM (73). Long-term Ni and As exposures were also observed to be associated with elevated prevalence of T2DM in Chinese elderly and Mexican women, respectively, implying the harmfulness of more heavy metal contamination in the pathogenesis of T2DM (74, 75). Although González de Vega et al. reported lower plasma concentrations of total Se found in Spanish patients with T2DM which might be due to decreased levels of selenoprotein P (76), the majority of other studies on Se and DM suggest that Se may increase the risk of T2DM across a wide range of exposure levels (77). In addition, several recent ionomics studies have found that trace element imbalance is involved in the development of diabetic complications. Marín-Martínez et al. reported that decreased Co level in saliva and increased strontium (Sr) level in plasma were associated with the presence of chronic complications (both microvascular and macrovascular) in patients with T2DM, which provides some useful enlightenment for predicting diabetic complications (81). Other ionomics studies revealed that increased Sr and decreased Zn and Se levels in plasma have a significant association with incident cardiovascular disease (CVD) risk in patients with T2DM, whereas decreased Mg and Cr levels in blood might be involved in the progression of microvascular complications (such as diabetic retinopathy and diabetic nephropathy) (82, 83).

Ionomics studies have also been conducted for several other types of DM (84–87). One Italian group measured the blood levels of multiple trace elements in patients with T1DM and T2DM from Sardinia, and observed similar alternations of significantly changed elements between them (Cr, Mn, and Ni deficiency) in that area (84). Similar to T2DM, elevated serum level of Cu and lower levels of Zn and/or Mg were also found in subjects with T1DM from different regions, suggesting that perturbation in the balance of these essential metals may be related to the pathology of both types of DM (80, 85). Peruzzu et al. reported that whole blood levels of Cr and Cu were strongly correlated with glycemic control whereas Zn, Fe, and Se were associated with lipid metabolism in patients with T1DM, suggesting different effects of these elements on metabolic control in this disease (86). With regard to GDM, Roverso et al. carried out the first ionomics study of the placentas from women affected by GDM, and showed that a decreased level of Cd and increased level of Se might be linked to the molecular pathways of GDM (87). They further measured the ionomes of placenta, maternal whole blood, and umbilical cord blood samples from a larger cohort of GDM and normal pregnant women, which revealed that a number of elements detected in the umbilical cord blood of fetuses were significantly associated with the occurrence of GDM (such as increased Cu and decreased Fe levels) (88). This may indicate that elemental profiles in the cord blood provide more evidence for understanding the biochemical processes occurring during GDM. Moreover, several recent case-control and cohort studies as well as meta-analyses reported that exposure to toxic heavy metals [such as Ni, antimony (Sb), Cd, and Cu] and insufficient Zn content in the body in early pregnancy are associated with an increased risk of GDM, either individually or as a metal mixture (89–96). Overall, these studies demonstrate high complexity and tissue specificity of the ionomes in patients with different types of DM. However, elevated Cu and Se concentrations, decreased Zn concentrations, and increased Cu/Zn ratio in various biofluid samples might be one of the common risk factors for DM. Further effort is needed to investigate the molecular mechanisms underlying dyshomeostasis of trace elements in patients with different types of DM.

Obesity is a chronic and complex disease characterized by excessive amounts of fat within adipocytes, which may result from insulin resistance, dysregulation in lipid and other metabolic balances, and inflammatory and/or hormonal processes (97). It may increase the risk of other diseases and health problems, such as T2DM, hypertension, CVD, and certain cancers. Both genetic susceptibility and environmental factors contribute significantly to the development of obesity (98). Among them, trace element and mineral dyshomeostasis has already been known to be associated with the pathogenesis of this disease (99). On the other hand, obese patients may be at a greater risk of developing imbalance of trace elements.

Before the use of ionomics in obesity research, many studies have shown a possible relationship between obesity and disturbance of homeostasis of certain trace elements, such as Zn or Fe deficiency and Cu overload, although the exact metabolic roles of these elements remain unclear (100–102). In the recent decade, a variety of studies have been carried out to examine the concentrations of more elements or ionomes in different biological samples of patients with obesity and healthy controls, which demonstrate more complicated changes linked to the prevalence of this disease (60, 62, 103–117). Consistent with previous observations, significantly increased levels of Cu and decreased levels of Zn and Fe in multiple samples (especially in serum) appear to be the most common features for obesity. However, the relationship between obesity and the circulating levels of Se is still controversial. A recent meta-analysis revealed that overweight/obese individuals might have lower levels of Se in the urine and nails but higher levels in the hair (18). Compared to DM, deficiency of essential trace elements may play a more important role in the pathogenesis of obesity and obesity-related metabolic dysfunction, especially in women and children. The Cu/Zn ratio was also mainly increased in several types of samples of the obese groups (in particular in diabetic obese individuals), and was negatively associated with increased risk of metabolic abnormalities in the urine (108, 113). In addition, exposure to environmental toxic metals in the body may significantly influence the ionome and other metabolic parameters of obesity. For instance, Skalny et al. reported that Hg-exposed overweight/obese subjects had quite different hair elemental profiles (such as elevated Se and Zn and decreased Mn and Mg levels) and a more adverse effect on metabolic parameters when compared to unexposed obese adults, suggesting that metabolic alterations observed in obesity may be partially related to Hg-associated disturbances in trace element metabolism (118).

A significant number of studies with larger cohorts have been performed recently to improve our understanding of the relationship between obesity and single or few essential/non-essential elements in various biofluids and tissues, which showed generally similar changes in the concentrations of some of them in different populations, such as increased serum and hepatic tissue Cu levels (119, 120), decreased serum, hair, and salivary Zn levels (121–123), decreased fingernail Se levels (124), and increased toxic heavy metal (such as Al, Hg, Pb, and As) contents (125–128). Many of these significantly changed elements are positively or negatively correlated with body mass index (BMI) and waist circumference, which are commonly used to estimate the risk of overweight, obesity, and some other metabolic diseases such as MetS (128, 129). Moreover, levels of some elements are associated with insulin resistance in obese adults. Kim and Song examined the concentrations of several trace elements in the hair of viscerally obese adults, and found that Cr and Se levels were inversely associated with insulin resistance, whereas Cu level has a positive correlation with insulin resistance (130). Thus, imbalance of these micronutrients might be involved in the development of obesity, which could be used as potential indicators for its incidence. Interestingly, although serum levels of Cu and Zn are thought to be significantly related to obesity in both children and adults, a recent study showed that they did not differ significantly between pre-obese and obese children, implying that alternations of the two metals are earlier than the occurrence of obesity (131). Nevertheless, further research is needed to confirm these findings prospectively in larger study populations and to extend the understanding of the roles of different elements in the prevalence of obesity.

MetS is a group of metabolic disorders, including abdominal obesity, hypertension, high blood sugar levels (insulin resistance or glucose intolerance), and dyslipidemia [high triglyceride and low high-density lipoprotein (HDL)-cholesterol levels]. It may raise the risk of CVD, T2DM, stroke, and other serious health problems (4). The cause of MetS has been intensively studied, and many factors (such as genetic and metabolic susceptibility) may contribute to the development of MetS (132). Among these, an imbalance in trace element status is an important risk factor and potential biomarkers for MetS (133). The important roles of several micronutrients (such as Fe, Zn, and Cr) in carbohydrate and lipid metabolism and their alterations in MetS have been discussed in multiple reviews, which provide evidence for the involvement of trace element dyshomeostasis in the pathology of MetS (4, 134–136).

In early years, a limited number of studies have been conducted to explore the distribution and relationships of selected trace elements in different samples of patients with MetS and normal controls, which revealed that some of these elements might be related to the incidence and course of MetS (137–140). For instance, elevated circulating Cu levels and body Fe stores as well as reduced serum levels of Zn were reported in patients with MetS in different areas. On the other hand, controversy exists with regard to the relationship between MetS and Se levels in body, probably due to differences in study design, methodology, and population characteristics (141).

Several ionomic profiling studies with large cohorts of patients were carried out in the recent decade (62, 142–150), which basically support previous observations that accumulations of Cu and Fe in the body are strongly associated with the increased prevalence of MetS. Moreover, imbalance of additional elements were detected in different biological samples of MetS cases, implying a high degree of variability of the ionomic patterns in patients with MetS. Generally, the majority of significantly changed elements have positive associations with MetS, especially environmental toxic metals (such as Pb, Cd, and As), implying that exposure to these elements affects the body composition and metabolic profiles and further exacerbates the risk of MetS (142, 144–149). In contrast, decreased Mg levels were often observed in the blood (including plasma and serum) and hair samples from patients from different regions (62, 142, 143, 150), which could influence glucose metabolism and insulin sensitivity and action as well as promote chronic inflammation, linking Mg deficiency with developing MetS and other metabolic disorders such as T2DM and obesity (151). So far the ionomic information available for assessing the potential relationship between Zn status and the incidence of MetS is inconsistent and may vary by gender. It seems that increased Zn levels in the serum/plasma samples may lead to the occurrence of MetS in men but have a protective effect in women (143, 148, 152). In addition, some of the elements were found to be significantly associated with different MetS components. For example, Wen et al. have shown that urine Cu and blood Pb levels were positively associated with obesity-related indices such as body roundness index and body/visceral adiposity index, and urine Ni was positively correlated with lipid accumulation product in a large cohort of MetS cases (145). Increased Ca/Mg ratio in the hair was also found to have a positive correlation with insulin resistance in Korean adult males (153). Very recently, Zhang et al. investigated the relationship between the blood concentrations of several essential metals and MetS components in Chinese children, which revealed that different metals correlate with MetS components individually, such as Cu (positively associated with elevated waist and triglyceride levels) and Mg (positively associated with reduced HDL-cholesterol levels) (154). Such information may suggest a potential use of ionomic data in monitoring the progression of MetS components and helping determine the proper treatment strategies.

Besides the ionome-level studies mentioned above, more case-control studies and meta-analyses recently focused on single or several trace elements and explored their differences between MetS patients and healthy controls in different biological samples (17, 155–164). Although some of these results appear to be contradictory and ambiguous, it appears that elevations of Se, Cu, Zn (in males), and toxic metals (such as Cd and Hg) and reduction of Mg in blood samples are associated with increased risk of MetS and its components. Besides Zn, gender difference may affect the correlation between MetS and some other elements such as Se (165). Moreover, interactions between different elements may modify their own roles in MetS, e.g., the harmful effects of Hg exposure on MetS could be significantly attenuated by high levels of Se in toenails (166). Future studies should aim to unravel the mechanisms underlying the associations between these elements and the development of MetS to assist in the prevention and early intervention of this disease and its components.

NAFLD is the most common chronic liver disease worldwide, which is characterized by excessive fat accumulation (≥5%) in hepatocytes in the absence of excessive alcohol consumption (167). It encompasses a broad histological spectrum ranging from simple hepatic steatosis to non-alcoholic steatohepatitis (NASH), cirrhosis, and hepatocellular carcinoma (HCC) that may develop further (168). Because of the lack of efficient diagnostic methods and therapeutic targets, early detection and treatment of NAFLD still face many challenges. The underlying mechanism for the pathogenesis and progression of NAFLD is complex, and disturbance in trace element and mineral metabolism and interactions have been identified as one of the key risk factors of this disease (169, 170). As NAFLD is closely associated with the components of MetS, obesity, and T2DM, a change in nomenclature from NAFLD to metabolic-associated fatty liver disease (MAFLD) has been recently proposed, which may better reflect the underlying pathophysiology of this disease (171). Here, we still use NAFLD in the following sections.

The association between certain trace elements and NAFLD risk has been reported since 1990s, such as hepatic Fe overload and lower liver and serum Cu concentrations in different types of NAFLD patients (including pronounced hepatic steatosis, NASH, and even NASH-related cirrhosis with HCC) (172–177). The serum ferritin, a marker of Fe storage, has been considered as an independent predictor of histologic severity and fibrosis in patients with NAFLD, which may also indicate that changes in Fe speciation [say, the conversion between Fe(II) and Fe(III) species] are related to the pathogenesis of this disease (178). However, children with NASH exhibit decreased serum Fe levels with no significant Fe accumulation in the liver, implying a new mechanism for Fe deficiency in pediatric NASH (179). To date, very few ionomics studies have been carried out for NAFLD, which mainly used blood samples from patients. For the first time, Asprouli et al. measured plasma ionomes in different stages (mild, moderate, and severe) of NAFLD subjects in Greece, which demonstrated that Zn had a negative association with the severity of the disease whereas cesium (Cs) showed a positive correlation (180). Li et al. examined the relationship between NAFLD and serum contents of a variety of metals based on cross-sectional and longitudinal analyses in Chinese men, and found that depressed Mo and elevated Zn levels were associated with an increased risk of NAFLD (181). It should be admitted that ionomics in NAFLD is still at the very beginning of its development. So far ionomic profiling of other biological samples in NAFLD patients is not yet available.

In the last few years, a large number of studies have explored the correlation between single or several elements and the development of NAFLD. First, consistent with previous findings, Fe accumulation appears to be the most significant and common marker for NAFLD, and indicators of Fe status (e.g., serum Fe and ferritin levels) could be used for the prediction of disease severity and adverse outcomes (such as the risk of advanced hepatic fibrosis and HCC) in different populations (182–186). Second, decreased Cu and Mn concentrations in hepatic tissues, serum, and/or hair were reported in different cohorts of NAFLD patients, and higher Cu or Mn levels achieved significant protective effect against NAFLD, especially in men (187–192). In addition, recent Cu isotopic composition analysis showed that serum 65Cu/63Cu ratio was significantly lower in NAFLD patients and remained stable during disease progression, suggesting its potential for early diagnosis of NAFLD (193). Third, although the relationship between Zn deficiency and the incidence of NAFLD is still uncertain, low serum levels of Zn have been proposed to serve as an independent risk factor for poor prognosis in patients with NAFLD (194–197). In contrast, the roles of Se in different types of NAFLD are complex and conflicting (198). It seems that increased plasma/serum Se levels are positively associated with elevated prevalence of liver steatosis but negatively associated with that of hepatic fibrosis or HCC (198–201). Finally, exposure to toxic heavy metals (such as Pb, Cd, Hg, and Ni) was significantly associated with the onset and progression of NAFLD in different regions of the world, which is probably a causative factor of hepatic steatosis and fibrosis in some patients (202–208). Moreover, increased serum Ca and phosphorus (P) levels were also reported as independent risk factors for fatty liver in Korean population (209). Although not at the ionomic level, these single/few element-based studies might still provide useful information regarding the changes of trace elements in biological samples of NAFLD patients. Further investigation is necessary to evaluate the value of ionomic changes in the early detection of NAFLD individuals.

Hyperlipidemia (also referred to dyslipidemia) represents the condition of abnormally elevated levels of one or more lipids and/or lipoproteins in the blood, mainly including high levels of total cholesterol (hypercholesterolemia), triglycerides (hypertriglyceridemia), and low-density lipoprotein (LDL)-cholesterol or low levels of HDL-cholesterol. It is a significant risk factor for a variety of diseases such as fatty liver, CVD, DM, neurodegenerative diseases, and acute pancreatitis (210). Hyperuricemia (serum uric acid level ≥6.8 mg/dL), a key risk factor for the development of gout, is caused by increasing production and/or decreasing clearance of uric acid (the end product of purine metabolism) (211). In addition, hyperuricemia may also increase the risk of chronic kidney disease, CVD, metabolic disorders such as hyperlipidemia, obesity, and other insulin resistance-related syndromes (212). In recent years, the incidence of hyperlipidemia and hyperuricemia has increased significantly, which have become two of the most common health threatening disorders worldwide. Therefore, the prevention and treatment of the two pathological conditions are important for a reduced risk of more severe outcomes such as atherosclerosis, coronary heart disease, and renal disease (213, 214). It has been proposed that trace elements and minerals play an important role in the regulation of lipid and purine metabolism. Thus, investigation of the association between trace element imbalance and the occurrence of hyperlipidemia and hyperuricemia might be helpful to study the etiology and pathogenesis of the two metabolic diseases and to develop new lipid- and uric acid-lowering drugs.

The relationship between hyperlipidemia and trace element homeostasis has been analyzed in early times. Deficiency of several essential elements (Zn, Fe, Cr, and Se), Cu excess, and significant urinary excretion of Fe, Zn, and Cu were observed to be associated with the prevalence of hyperlipidemia (215–219). However, Cu deficiency and hepatic Fe overload were reported to contribute to hypertriglyceridemia and hypercholesterolemia in rats, and the genetic mechanisms driven to hypertriglyceridemia might favor Fe deposits (220–222). Moreover, some lipid-lowering agents (e.g., statins) could greatly influence the serum levels of multiple trace elements such as Zn and Cu (223). In the last decade, very few ionomics studies have been conducted for hyperlipidemia patients. Yerlikaya et al. found that lower levels of essential elements (Cr, Fe, Mn, Mo, and Se) as well as higher levels of Co, Ni, and Cd in serum are associated with an increased risk of primary hyperlipidemia, suggesting an important role of these elements in the regulation of lipid homeostasis (224). On the other hand, several studies have either confirmed previous observations or revealed additional elements that are associated with hyperlipidemia. For example, high Mn exposure could decrease the risk of high triglycerides (225), while elevated blood/serum levels of toxic heavy metals (such as Hg, As, and Pb) may increase the risk of different types of hyperlipidemia (226–228). Very recently, Barragán et al. examined the associations of plasma essential trace elements with hyperlipidemia in a Mediterranean population, which demonstrated that plasma Mg concentrations and combined effect of plasma Zn, Cu, and Se levels had a positive association with hypercholesterolemia whereas plasma Mn concentrations would have negative effects on the amount of plasma lipids (229, 230). A U-shaped link was suggested to exist between plasma Se levels and hypercholesterolemia, indicating that both low and high Se concentrations might have negative effects on plasma lipids (230).

Dyshomeostasis of trace elements has also been implicated in the incidence and progression of hyperuricemia for a long time. In general, circulating levels of several essential metals (such as Ca, Fe, and Cu) and toxic metals (Pb and As) showed positive correlations with the prevalence of hyperuricemia in different populations (231–238). In contrast, concentrations of Mg in serum and I in urine were inversely associated with the prevalence of hyperuricemia (239, 240). Although the relationship between Zn levels and hyperuricemia has not been explored, dietary Zn intake was reported to be inversely correlated with hyperuricemia in different regions, suggesting that Zn deficiency might increase the susceptibility of hyperuricemia (241, 242). Interestingly, there was a discrepancy of the association between blood Cd, serum uric acid levels, and hyperuricemia in different genders. Several studies have shown that blood Cd levels had a positive correlation with increasing risk of hyperuricemia in men (243, 244) or a negative correlation in women in Chinese and Korean populations (237). However, an opposite pattern was observed among populations in the U.S., in which serum Cd levels (in the acceptable range) were positively associated with increased prevalence of hyperuricemia in women, but inversely associated in men (245). This may suggest that different compositions of blood, race, and different ranges of blood Cd can affect the effect of Cd exposure on uric acid levels (246). So far the ionomics study on hyperuricemia is almost a blank. Wang et al. measured the plasma levels of 13 metals in hyperuricemia patients and controls in Chinese Han adults and found that higher plasma levels of several metal ions (particularly Zn and As) and lower levels of Co might increase hyperuricemia risk (247). Ma et al. reported that increased urinary levels of toxic metals (As and Cd) and decreased levels of essential metals (Co, Mn, and I) might have a positive combined effect on hyperuricemia (248). A systematic meta-analysis has been recently conducted to clarify the relationship between exposure to certain metals and the risk of hyperuricemia, which revealed that exposure to As, Ca, Cd, and Pb is associated with an increased risk of hyperuricemia whereas Mo exposure appears to be associated with a decreased prevalence of this disease (249). All these findings reinforce the importance of heavy metal (especially environmental toxic metal) accumulations in the risk of hyperlipidemia and hyperuricemia in general population. Moreover, alternations specific for each of the two metabolic diseases (e.g., elevated serum Co levels in hyperlipidemia and decreased plasma Co and urinary I levels in hyperuricemia) might help to provide potential markers for distinguishing them before clinical symptoms appear. Further ionome-level studies are required to validate the above results and to explore new patterns of elemental profiles in more biological samples and larger cohorts.

Osteoporosis is a systemic bone metabolic disease characterized by bone mass reduction and bone microstructure destruction, which increases the risk of fracture, particularly in older postmenopausal women. The pathogenesis of osteoporosis is complex and involves the interaction of genetic, hormonal, environmental, and nutritional factors (250). Osteomalacia (or rickets in children) is a metabolic bone disorder of decreased mineralization of newly formed bone due to the lack of available Ca, P, or vitamin D. The major causative factors of osteomalacia include nutritional deficiency, impaired absorptive capabilities, and renal insufficiency or dialysis (251). Trace elements have been known to be essential for maintaining bone health, whose imbalance may be significantly associated with the disorders of bone metabolism and further lead to osteoporosis and/or osteomalacia (252, 253). Analysis of the relationship between trace element contents and osteoporosis/osteomalacia may assist in the prevention, diagnosis, and treatment of bone metabolic diseases.

The correlation between certain trace element and mineral levels within the body and osteoporosis has been examined in a large number of population-based studies, case-control cohort studies, and meta-analyses since the early 1970s. In general, reduced circulating levels and/or elevated urinary excretion of a variety of essential metals [such as Ca, Cu, Zn, Mg, Se, and boron (B)] as well as accumulation of Fe, Mn, and toxic heavy metals (Cd, Pb, and As) are strongly related to the prevalence of osteoporosis (254–266). In the recent decade, with the widespread use of ionomics approaches in various complex diseases, several studies have been carried out to provide evidence of the relationship between trace elements in blood and bone tissues and osteoporosis. For the first time, Noor et al. analyzed the difference of the bone concentrations of 27 elements from Indonesian postmenopausal women with and without osteoporosis, which showed that increased B and As and decreased Se levels in bone tissues might directly contribute to microarchitectural abnormalities in osteoporotic bones (267). Another ionomics study suggested that low serum levels of Cu, Zn, Fe, and Mg are associated with the incidence of osteoporosis in Turkish postmenopausal women, implying that Fe deficiency is also one of the risk factors for the development of osteoporosis (268). Lin et al. measured multi-elemental contents in human bone tissues of older Chinese adults and found a significant correlation between decreased Cu, Zn, and Mn levels and increased risk of osteoporosis, indicating that both Mn exposure and deficiency (although very rare in human body) might lead to abnormal bone metabolism, osteoporosis, and even fractures (269). A SVM-based prediction model for bone mineral density loss and osteoporosis was recently proposed by using urinary concentrations of several toxic metals (such as As and Cd) and some other features, suggesting the possibility to develop new methods for the early detection of osteoporosis based on elemental distribution in biofluids (270). Additionally, lowering excessive toxic metals and/or supplementation of deficient essential elements might be valuable for the therapy of osteoporosis.

In the past several decades, besides low serum levels of Ca and P that have been proposed as a screening test in patients who have clinical symptoms suggestive of osteomalacia (271), elevated concentrations of several other elements have been observed in different biological samples of osteomalacia patients, such as Al, Fe, Cd, and Sr in bone or serum and Pb in blood or hair (childhood rickets) (272–275). In contrast, Fe deficiency was reported in autosomal dominant hypophosphatemic rickets, a rare form of rickets caused by mutations in the fibroblast growth factor 23 (FGF23) gene, implying that monitoring the Fe status is helpful for diagnosis, prognosis, and treatment of this genetic disease (276). Moreover, Ca/Zn ratio in bone was found to be related to the degree of osteomalacia in Cd-exposed subjects, indicating that interactions among these metals might be involved in the pathogenesis of osteomalacia (277). To date, ionomics studies on osteomalacia/rickets are extremely rare. Previously, Miekeley et al. reported that elevated concentrations of Ca, Cd, Al, Fe, and several other elements in hair were associated with the incidence of osteomalacia, which could be used as a complementary tool for its early detection (278). Dogan et al. examined multiple trace element levels in serum and found significantly decreased Zn concentrations in children with nutritional rickets associated with vitamin D deficiency, suggesting an important role of Zn in the early steps of bone maturation (279). However, the correlations between many other trace elements and the prevalence of osteomalacia/rickets are still unclear. Future investigations are necessary to identify more ionome-level variations for bone metabolic diseases.