Introduction

Aging is a time-dependent biological phenomenon that encompasses various morphological and functional changes associated with health decline, disease increase and death (Harman 1981). It is also an inevitable process that is characterized by a cognitive decline associated with structural and functional alterations in the brain (Albinet et al. 2012; Eyler et al. 2011).

Trace elements (TE) homeostasis is crucial for normal brain functioning. While deficiency of essential metals/metalloids can compromise biological processes, excessive concentrations may induce detrimental intracellular events (e.g., oxidative stress, mitochondrial dysfunction, protein misfolding, autophagy dysregulation, DNA fragmentation, apoptosis activation), to which the aging brain is increasingly sensitive (Chen et al. 2016). On the other hand, some non-essential elements can cause severe toxicity even at very low concentrations (e.g., As, Cd, Hg and Pb), as they disrupt the activity of essential elements and have no biological value (Caito and Aschner 2015; Huat et al. 2019). These impact normal neurotransmission and lead to neurodegeneration, which frequently manifests as cognitive deterioration (Ijomone et al. 2020). Indeed, the relationship between TE and neurogenerative diseases has been studied, with literature findings supporting an interaction between specific metals and key Alzheimer’s Disease (AD) proteins (Bakulski et al. 2020; Lippi et al. 2022).

Human exposure to metals and metalloids has also been discussed. Typically environmental or occupational, exposure to metals/metalloids is a relevant factor that contributes to TE imbalances and dysregulations on the human body (Yegambaram et al. 2015). Common sources of hazard come from anthropogenic activities. They include mining and mining tailings, industrial activity and waste, welding and smelting operations, agricultural runoff, and use of fertilizers and pesticides, as well as domestic metal-containing compounds (He et al. 2005).

Given the involvement of TE in multiple physiological processes and the impact that dysregulation can have on brain health, studying the relationship between TE and cognition becomes relevant to understand how different elements may benefit or compromise individuals who are becoming increasingly susceptible to cognitive decline, mild cognitive impairment (MCI) and dementia. Different reviews have been conducted on TE. However, they were either (1) focused on the biological impact of metals in the human brain and disregarded cognition (e.g., Ijomone et al. 2020), (2) they only included clinical patients with neurodegenerative conditions, ignoring all healthy individuals who may have cognitive deficits despite not having a disease (e.g., Cicero et al. 2017), (3) they focused on other populations such as children (e.g., Heng et al. 2022), or (4) they referred only few selected elements (e.g., Bakulski et al. 2020), sometimes as few as one (Pereira et al. 2022). To the best of our knowledge, no systematic review has already addressed the associations between TE and the cognition of adults and elderly.

Therefore, this review aims to gather, summarize, and present findings on the relationship and the potential impact of several TE on the cognitive performance of adults and older adults, while also touching on exposure and cognitive impairment-related clinical conditions.

Methodology

This systematic review was carried out based on the Pattern of Reporting Systematic Review and Meta-Analysis (PRISMA) (Page et al. 2021a, b).

Literature Search

Appropriate studies were searched on PubMed, Scopus and Science Direct databases between May 1st and May 31st, 2023. Search terms included the keywords “cognition” or “cognitive performance” or “cognitive function” or “cognitive impairment” or “cognitive decline” or “neuropsychological assessment” and “metal” or “metals” or “trace elements” or “potentially toxic elements”. Scopus database allowed for the exclusion of the keyword “animal”. Articles were searched through titles and abstract with no limitations regarding the date of publication. Searches were limited to peer-reviewed publications.

Eligibility Criteria

The current review comprises studies on adults and older adults, with no reference to research performed with children and adolescents (age < 18 years). No restrictions were imposed regarding study populations and neurodegenerative diseases linked with cognitive decline, but all other diseases were excluded (e.g., cancer, attention deficit and hyperactivity disorder, schizophrenia).

Considering the aim of our study, we only included articles that reported evaluation of cognitive performance through standardized neuropsychological tools [e.g. Mini-Mental State Examination (MMSE), Montreal Cognitive Assessment (MoCA), Trail Making Test (TMT), Wechsler Memory Scale (WMS)], as well as quantitative analytical techniques [i.e., inductively coupled plasm mass spectrometry (ICP-MS), inductively coupled plasma optical emission spectroscopy (ICP-OES), cold vapor atomic absorption spectrometry, flame or graphite furnace atomic absorption spectroscopy (FAAS and GFAAS, respectively), or electrothermal atomic absorption spectroscopy, fluorimetry, chromatography] to measure TE in biological human samples [i.e., whole blood, serum, plasma, cerebral spinal fluid (CSF), urine, nails, hair]. Studies with no formal assessment of cognitive functioning and studies focusing on subjective cognitive complaints were excluded. The same was true for studies using qualitative or semi-quantitative measurements of TE in the human body.

Finally, meta-analyses, systematic reviews, conferences, and workshops were discarded. Appropriate studies were limited to the English and Portuguese languages.

Study Selection and Data Extraction

Papers selected for this review were managed using the Mendeley Reference Manager (Elsevier BV, Amsterdam, The Netherlands). After duplicates were removed, two authors (BG and JN) independently read the titles and abstracts of the articles in relation to the eligibility criteria. Upon discrepancies, papers with just one approval would be deemed suitable for the next screening phase. In the following stage, the full text of all potentially eligible studies was read considering the eligibility criteria. This time, discrepancies were solved by a third reviewer (SF). The selection process is depicted in Fig. 1.

Fig. 1
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Flow diagram of the literature review

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Data extraction included the following data: first author and year, study design and statistical approach, the country where the study was conducted, sample characteristics (e.g., population, exposure conditions, clinical diagnosis, sample size, age, educational level and, gender distribution), assessment protocol (e.g., analytical techniques and biological samples for TE measurement, evaluated TE, neuropsychological tools and respective cognitive domains), key findings and conclusions. The data was organized in an Excel spreadsheet (Microsoft Corp, Washington, WA, USA) and is summarized in Tables 1 and 2.

Table 1 Characterization of the included studies (n = 60)
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Table 2 Characterization of cognitive and TE assessments of the included studies (n = 60)
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Quality Assessment

Independent quality assessment and rating was performed by tow authors (BG and JN) for all included studies, using a similar classification method to the one implemented by Heng et al. (2022). This method was based on the National Heart, Lung, and Blood Institute Study Quality and Assessment Tools (NHLBI, 2021), which provide useful criteria to assess the internal validity of studies and suggest “good”, “fair” and “poor” as the three general categories of rating. Cross-sectional, longitudinal, and controlled intervention studies (clinical trials) were classified as “good” if scores were 9/14 and higher and as “fair” if scores were 7/14 and 8/14. Any other scores were rated “poor”. For case–control studies, scores 9/12 and higher were classified as “good”, scores 7/12 and 8/12 were classified as “fair” and any other scores were rated “poor”. Case series were rated “good” if scores 7/9 and above, “fair” if scores 5/9 and 6/9, and “poor” if any other score. Rating discrepancies were discussed toward consensus.

Results

A total of 1591 papers were identified across databases, with publication dates between 1994 and 2023. After duplicates were removed, 1072 studies were screened through title and abstract, of which 987 were excluded based on eligibility criteria and three were not possible to retrieve. The remaining 82 studies were considered for full-text analysis, upon which 22 were excluded for the following reasons: (1) absence of standardized neuropsychological tools for cognitive assessment (n = 11); (2) absence of quantitative analytical techniques for TE measurement (n = 10); retraction of paper (n = 1). A total of 60 studies were included in this systematic review. The selection process is depicted in Fig. 1. To facilitate readability, studies were numbered from 1 to 60 throughout the text, according to Tables 1 and 2.

Studies Characteristics

Summarization of the following characteristics can be found in Table 1. All studies were conducted in a single country, the most frequent being China (n = 14), the United States (n = 9) and Italy (n = 8), followed by Portugal and Egypt (n = 4), and finally Brazil (n = 3). Finland, Malaysia, Pakistan and Iran contributed with 2 studies each, while the United Kingdom, Sweden, Germany, Poland, Romania, Kazakhstan, Thailand, South Korea, Mexico, and Chile contributed with only 1. Regarding the design of the studies, 36 studies were cross-sectional, 18 case–control designs, and only 3 assumed a longitudinal design. Study no. 31 included both cross-sectional and longitudinal analyses. Additionally, 2 studies were case series and 2 referred to clinical trials.

Targeted populations included community (n = 34), specific occupational activities (welding workers—n = 5, aluminum factory workers—n = 4, farmers—n = 1), smokers (n = 1), nursing home residents (n = 1), and clinical groups (n = 15). Of the 15 studies on clinical groups, there were 4 MCI studies, 8 AD studies, 4 MCI and AD studies (of which 1 also included a vascular dementia group), 2 dementia studies and 1 study focusing on manganism. Among the studies including AD/dementia conditions, studies no. 7, 42, 53 and 54 discriminated degrees of severity. Only 3 studies (no. 7, 8 and 23) did not include healthy controls. Exposure to TE was addressed by 22 studies, 10 focusing on occupational exposure (studies no.1, 8, 17, 18, 20, 29, 35, 40, 46, 59), 11 on environmental exposure (studies no. 9, 10, 11, 16, 25, 32, 37, 43, 44, 47, 49) and 1 on exposure through consumption (study no. 30). All environmental exposure studies were conducted on community samples, while occupational exposure studies were based on specific occupational activities. Exposure through consumption was studied among smokers (study no. 30).

Sample sizes ranged from 11 subjects (study no. 8) to 3814 subjects (study no. 13), with gender distributions being reported by 54 of the 60 studies. Most studies included both males and females, except for studies no. 1, 8, 18, 20, 30, 46, which contemplated only males. These studies were performed on welders, factory workers (study no. 46) and smokers (study no. 30). In terms of age, most studies reported means of years and standard deviations (SD; n = 50), ranging from 26.25 ± 0.82 (study no. 4) to 86.7 ± 3.4 (study no. 24). Studies no. 6, 13, 20, 32 and 60 failed to report standard deviations, studies no. 13, 41 and 55 reported stratified ages and studies no. 37 and 45 only reported minimum and maximum ages. Finally, study no. 9 exclusively reported the inclusion age criteria. Regarding education, only 20 of the 60 studies reported the mean years of formal education (ranging from 1.81 ± 2.48 to 16.9 ± 0.67), whereas 23 studies reported stratified educational levels (ranging from illiteracy to PhD). Study no. 20 specified the included school degrees without reporting its distribution and 16 studies did not report any information on formal education.

Lastly, 28 studies were classified as being of good quality, 25 as being of fair quality, and 7 as being of poor quality.

Statistical Approaches

Summarization of the following characteristics can be found in Table 2. Potential relationships between TE and cognitive performance were analyzed either directly (n = 42), or via group comparison based on clinical diagnosis/cognitive performance (n = 30) or TE levels (n = 10). Forty-one studies conducted analyses based on one approach, whereas 18 studies selected two. Studies no. 9 and no. 33 conducted all three types of analyses.

Measured TE and Analytical Techniques

Concerning the selected TE, 11 studies focused on only one element, 9 studies focused on two elements, 9 studies focused on three elements and 31 studies focused on four or more elements. The maximum number of TE evaluated in one single study was 36 TE (by study no. 31). The most frequently studied TE was Pb (n = 33), followed by Mn (n = 31) and Cu (n = 30). Biological samples for TE measurement included whole blood (n = 24), serum (n = 23), plasma (n = 5), red blood cells concentrate (n = 1, study no. 54), CSF (n = 2), urine (n = 11), hair (n = 2) and nails (n = 5). Of the 60 studies, 10 utilized two different types of samples and study no. 25 was the only to utilize three. Regarding analytical techniques, 31 studies resorted to ICP-MS, 4 studies resorted to ICP-OES, 19 studies resorted to AAS (GFAAS: n = 16; FAAS: n = 4; CVASS: n = 1; ETAAS: n = 1; not specified: n = 3) and 5 studies resorted to colorimetry. Fluorimetry, spectrophotometry and chromatography were implemented by 2 studies each.

Assessment Procedures of Cognitive Performance

The present review aims to present the existing findings on the relationship and potential impact of different TE on the cognitive performance of adults and older adults. In the following sections, assessment procedures of cognitive performance are specified by cognitive domain.

As expected, although there was relative homogeneity for some of the included psychometric tools, there was also considerable heterogeneity that led to instruments being reported only once. In addition, while some studies used different instruments to assess one cognitive function, a single psychometric tool could also be classified as measuring multiple functions. This is because neuropsychological tools are not pure, i.e., they do not exclusively assess one specific function. To achieve consistency, the most prevalent categorization was considered.

Global Cognition

Global cognitive functioning was evaluated through performance-based instruments by 44 studies (see Table 2). Forty studies used screening tools, the most reported ones being the Mini-Mental State Examination (MMSE; n = 32), and the Montreal Cognitive Assessment (MoCA; n = 8). The Telephone Inventory for Cognitive Status—modified (TICS-m), the Abbreviated Mental Test Score (AMTS), and the Addenbrooke’s Cognitive Examination—Revised (ACE-R) were reported only once (studies no. 23, 33 and 50, respectively). Two studies incorporated comprehensive batteries, namely the Loewenstein Occupational Therapy Cognitive Assessment (LOTCA) battery (study no. 2) and the Alzheimer's Disease Assessment Scale—Cognitive (ADAS-Cog; study no. 5). Three studies calculated a composite z-score based on several individual tests (studies no. 14, 15 and 27). Study no. 18 assessed neurocognitive functioning based on the ranking of performance on three domain-specific instruments. Several studies included the evaluation of different targeted cognitive domains through specifically oriented psychometric tools. These will be presented in the following sections.

Orientation

Only 4 studies evaluated orientation (see Table 2) in relation to TE. Study no. 2 reported orientation scores of the LOTCA battery, while studies no. 46 and 47 based its analysis on the orientation scores derived from MoCA. Study no. 57 resorted to the orientation subscores derived from MMSE.

Attention

Twenty-three studies assessed the relationship between TE and attention (see Table 2). Several components of attention were measured such as alertness, concentration, processing speed, attention span, sustained attention, divided attention, and selective attention.

The most reported instruments were the Digit Span (forward trial; WAIS-R—Wechsler Adult Intelligence Scale—Revised; WMS-III) (studies no. 1, 4, 20, 29, 31, 32, 44 and 50), the Digit Symbol Substitution Test/Digit Symbol Coding Test (DSST/DSCT; WAIS, WAIS-III) (studies no. 1, 6, 27, 32, 45 and 55) and the TMT Part A (TMT-A) (studies no. 4, 23, 29, 31, 32, 43 and 44). Three studies assessed Reaction Times (studies no. 20, 25 and 32) and three studies applied the Stroop Color-Word Test (congruous conditions—word reading and color naming; studies no. 1, 8, 18 and 29). Two studies implemented a Dual Task to assess attention (studies no. 1 and 25).

Learning and Memory

Learning and memory were addressed by 25 studies (see Table 2). Instruments assessed episodic visual and verbal learning, short-term memory, long-term memory, recognition, and working memory. The most frequently used tool to assess verbal and visual memory were the Word List Learning and Recall (Consortium to Establish a Registry for Alzheimer’s Disease; CERAD) (studies no. 4, 6, 15, 27, 39 45, and 55) and the Constructional Praxis Recall (CERAD) (study no. 4, 39)/Immediate Visual Memory (Mental Deterioration Battery; MDB) (studies no. 50 and 51), respectively. The Digit Span (backward; WAIS-R or WMS-III) (studies no. 1, 4, 20, 29, 31, 32, 44 and 50) was the instrument most reported regarding working memory. Only two studies used comprehensive batteries: study no. 8 applied the WMS-III and study no. 18 the WMS.

Language

In turn, language was assessed in 13 studies addressing several subdomains, including auditory comprehension, language conceptualization, naming and writing (see Table 2). The Boston Naming Test (and its modified version) was the most frequently used (studies no. 8, 31 and 39), followed by the Similarities subtest (WAIS) and Synonyms (studies no. 1 and 20).

Executive Functioning

Executive functioning was evaluated by 23 studies (see Table 2) addressing mental flexibility, shifting ability, inhibition, planning, reasoning, self-regulation and self-monitoring. The TMT Part B (TMT-B; studies no. 4, 23, 26, 29, 32, 43 and 44) and Verbal Fluency tasks (studies no. 6, 15, 23, 26, 27, 29, 31, 39, 44, 50, 51 and 55) were the most applied, followed by the Stroop Color-Word Test (incongruent color-word condition; studies no. 1, 8, 18, 29).

Visuospatial and Visuoconstructive Abilities

Only 10 studies assessed visuospatial and visuoconstructive abilities and the psychometric tools applied were highly diverse (see Table 2). Embedded Figures and Block Design (WAIS-I) were reported twice (studies no. 1 and 20) and the same was true for the Copy Drawing—Freehand and With Landmarks (MDB) (studies no. 50 and 51), and for the Visuospatial/Executive subscore from MoCA (studies no. 46 and 47). The Visual Perception, Spatial Perception and Visuomotor Organization subscores of LOTCA battery, the Line Orientation and the Figure Copy (of RBANS - Repeatable Battery for the Assessment of Neuropsychological Status), the Constructional Praxis (CERAD) and the Clock Drawing Test (CDT) were reported only once (studies no. 2, 29, 39, and 40, respectively).

Intelligence

Two studies considered intelligence (see Table 2). Study no. 8 applied the totality of WAIS-III, while study no. 25 selected the subtests Information, Similarities, Picture Completion and Block Design from WAIS-R.

Dementia Staging

Considering that the focus of this review is the study of cognition, the inclusion of cognitive impairment conditions and related medical diagnoses was frequent. Consequently, some studies considered instruments that assess cognition and functionality based on performance and on reports of a reliable informant to evaluate dementia conditions (n = 11; see Table 2). The Clinical Dementia Rating (CDR) scale was the most frequently used tool, applied by 9 studies (studies no. 9, 11, 28, 31, 36, 42, 53, 54 and 57). The Community Screening Instrument for Dementia (CSID) and the Milan Overall Dementia Assessment (MODA) were reported only once (studies no. 15 and 48 respectively).

Outcomes on the Relationships Between TE and Cognition

Global Cognition

Global cognitive functioning was the most widely evaluated ability, being addressed by 44 studies. Alghadir et al (2015) (study no. 2) observed higher concentrations of serum Cu and Fe (p = 0.01) and lower concentrations of serum Zn and Zn/Cu ratio (p = 0.01) in individuals with moderate cognitive impairment (CI; Cu: M = 123.4, SD = 4.7 μg/dL, Fe: M = 98.2, SD = 3.5 μg/dL, Zn: M = 65.4, SD = 1.6 μg/dL, Zn/Cu: M = 0.53, SD = 0.62) and severe CI (Cu: M = 142.3, SD = 6.9 μg/dL, Fe: M = 114.2, SD = 1.9 μg/dL, Zn: M = 48.9, SD = 3.4 μg/dL, Zn/Cu: M = 0.34, SD = 0.78), when compared to subjects with normal cognitive performance (Cu: M = 102.3, SD = 6.3 μg/dL, Fe: M = 68.5, SD = 6.3 μg/dL, Zn: M = 78.3, SD = 2.5 μg/dL, Zn/Cu: M = 0.78, SD = 0.95). Notwithstanding, the authors also found positive associations between serum Cu, Fe, Zn and Zn/Cu ratio and LOTCA total scores (p < 0.01—p < 0.05) when controlling for sociodemographic and clinical variables. Pu et al (2017) (study no. 42) revealed significant positive correlations between serum Zn and MMSE scores, as well as negative correlations between serum Cu and MMSE scores across healthy subjects and patients with AD (mild, moderate, and severe), but reported no significant associations regarding Fe. In the same line, study no. 26 (Lam et al. 2008) showed absence of variations of MMSE scores with the various Cu, Fe and Zn levels in plasma.

In studies no. 50 and 51, Squitti et al (2002a, b) identified higher levels of serum Cu in AD versus controls (CTR) (p < 0.001 and p < 0.004, respectively). In study no. 50 authors also reported an inverse relationship between serum Cu and MMSE scores among AD patients (r = −0.331, p = 0.012), as well as the discriminatory utility of Cu levels (OR = 1.82; Cut-off—16 μmol/L, specificity = 95%, sensitivity = 60%) and Cu/Fe ratio to distinguish between these two groups. In study no. 51, in spite of the trend for increasing levels of Cu in the placebo-treated AD group, compared to the stable Cu levels of the Cu-chelating treated AD patients (p = 0.061), the differences had no impact on the rate of cognitive decline (indexed by the MMSE and the MDB). In study no. 52, Squitti et al (2011) expanded the clinical study groups and included MCI patients. Once again, Cu and free Cu levels in serum were higher in AD (Cu: M = 16, SD = 3.7 μmol/L; free Cu: M = 2.8, SD = 2.8 μmol/L) than in MCI (Cu: M = 15, SD = 2.8 μmol/L, p = 0.001; free Cu: M = 1.2, SD = 2.1 μmol/L, p < 0.001), and higher in MCI than in CTR (Cu: M = 14, SD = 2.7 μmol/L, p = 0.008; free Cu: M = 0.5, SD = 2.0 μmol/L, p = 0.010), with free Cu being able to discriminate MCI from CTR (relative risk ratio = 1.22, p = 0.010) and from AD (relative risk ratio = 1.34, p < 0.001). The authors also reported a significant association between higher serum levels of free Cu and worse MMSE scores (p < 0.05). In study no. 58, Yu et al (2023) reported higher Zn blood levels (p = 0.045), higher Cu/Se (p < 0.001) and Zn/Se (p < 0.001) ratios, and lower Se blood levels (p < 0.001) in MCI patients compared to normal individuals. Additionally, high blood levels of Zn and high Cu/Se and Zn/Se ratios were associated with an increased risk of MCI only in women (Zn: OR = 1.873, p = 0.015; Cu/Se: OR = 1.805, p = 0.035; Zn/Se: OR = 2.323, p = 0.002). In turn, both high and moderate blood Se and Cu/Zn ratio were associated with a reduced risk of MCI, the first in men (Se: OR = 0.388 and 0.495, p = 0.004 and 0.027) and women (Se: OR = 0.338 and 0.588, p < 0.001 and = 0.040), the latter only in women (Cu/Zn: OR = 0.544 and 0.576, p = 0.020 and 0.047). Study no. 33 (Markiewicz-Zukowska et al. 2015) showed a positive association between serum Zn (< 0.7 mg/L, 0.7–1.2 mg/L, > 1.2 mg/L) and the AMTS. The authors observed higher levels of Zn (p = 0.001) in subjects with normal cognitive functioning (M = 0.89, SD = 0.20 mg/L) compared to individuals with impairment (M = 0.76, SD = 0.19 mg/L) and reported that higher ATMS scores were explicative of the variations in serum Zn concentrations (β = 0.303, p = 0.019).

Negahdar et al (2015) (study no. 38) found no differences in serum Cu, Mn, and Zn between CTR, mild MCI, and moderate-severe MCI. Similarly, Aly et al (2013) (study no. 3) found no differences between AD patients and CTR regarding Cu, Fe and Zn levels in serum. In study no. 7, Bomboi et al (2005) compared mild AD patients with moderate-severe AD patients and observed trending-to-significant increases in blood Fe (p = 0.05) and serum Mn (p = 0.02), as well as decreases in blood levels of Ca (p = 0.05) and serum levels of Cd (p = 0.05) and Mo (p = 0.03) in the former group. No between-group differences were observed regarding serum Al, Ba, Be, Bi, Co, Cr, Hg, Li, Ni, Pb, Sb, Si, Sn, Sr, Tl, V and W nor blood Cu, Mg, Si, and Zn. In study no. 5 (Balmu et al. 2017), MMSE correlated negatively with Mn levels in serum (r =  − 0 585, p < 0.001) across CTR, MCI and AD patients. AD patients exhibited lower serum levels Fe (140.43 ± 16.02 μg/dL) than MCI patients (242.47 ± 18.06 μg/dL, p < 0.05), lower serum Mg levels (AD: 994.08 ± 69.04 μg/dL) than CTR (1316.46 ± 60.27μg/dL, p < 0.05), and higher serum levels of Mn (AD: 3.32 ± 0.07 μg/dL) than CTR (2.68 ± 0.16 μg/dL, p < 0.05). MCI patients followed the same pattern, exhibiting lower serum Mg levels (1051.40 ± 65.43 μg/dL, p < 0.05) and higher serum levels of Mn (3.10 ± 0.09 μg/dL,) than CTR (p < 0.001). Despite differences between MCI and AD patients being non-significant, Mg and Mn followed a gradual increased between groups (from CTR to MCI and AD). In study no. 44, Santos-Burgoa et al (2001) reported a higher risk for CI (indexed by MMSE scores below 17 points) with increasing concentrations of blood Mn (OR = 4.92), but no significant results for blood Pb. Meramat et al (2017) (study no. 34) reported an increased risk of cognitive impairment associated with higher levels of Cu (OR = 1.275) and Pb (OR = 2.471) in toenails (p < 0.05) (no significant results for Al, Ca, Cd, Co, Cr, Fe, Se and Zn).

In studies no. 10 and no. 11, Cabral Pinto et al (2019a, b) reported increased levels of hair Hg (p = 0.001) in people with dementia (M = 4.43, SD = 13.86 μg/g) versus healthy subjects (HS; M = 0.88, SD = 0.92 μg/g), as well as an association between higher levels of fingernail Mn (p < 0.01) and Zn (p < 0.05) and severe dementia conditions. Johansson et al (2002) (study no. 24), measured Hg levels in blood and did not find correlations with MMSE scores. Study no. 47 (Sirivarasai et al. 2021) reported lower MoCA scores of individuals with the 3rd tertile of blood Hg, compared to the 1st tertile (p < 0.05), as well as an increased risk of CI associated with elevated level of Hg in blood (OR = 2.07, p = 0.003). Cabral Pinto et al (2018) (study no. 9) identified urinary levels of Al, Cd and Zn as relevant predictors of worse MMSE scores (R2 = 0.55, p < 0.005) and revealed, through multiple correspondence analysis, existing associations between high levels of urinary Al, As, Cr, Fe, Hg, Ni, Pb and Zn, as well as low levels of urinary Al, and dementia/severe dementia scores on MoCA. They also revealed positive associations between high urinary levels of Cd, Cr, Mn, Se and dementia scores on MMSE. No significant results were obtained regarding Cu. In study no. 21, Iqbal et al (2018) assessed blood Al, Cd, Cu, Mn, Pb and Zn in relation to MMSE scores and found negative correlations for all TE (p < 0.001), with Al and Cu showing the strongest associations (r = −0.638 and r = −0.610, respectively). In studies with aluminum factory workers, Shang et al (2021) (study no. 46) detected negative correlations between plasma Al (β =  − 0.068, p < 0.001) and Li (β =  − 0.040, p = 0.006) and MoCA scores, as well as increased plasma levels of Al and Li (p = 0.008) in CI subjects (Al–Md = 72.794 μg/L; Li–Md = 4.972 μg/L) compared to normal individuals (Al–Md = 55.862 μg/L; Li–Md = 3.750 μg/L). No significant associations were found regarding Co, Cr, Cu, Fe, Pb, Mn and Zn. Similarly, Zawilla et al (2014) (study no. 59) verified an inverse relationship between serum Al and ACE-R total scores, where higher levels of this metal significantly predict worse performances (β = −2.27, p = 0.02), and observed increased serum Al in subjects with less than 83.5 points in ACE-R (M = 22.86, SD = 11.10 μg/L), compared to individuals with higher scores (M = 17.28, SD = 6.59 μg/L, p < 0.05). Mohammed et al (2020) (study no. 35) found negative associations between serum Al (β = −8.958), Mn (β = −0.286), and Pb (β = −0.165) and MoCA scores (p < 0.005), but no significant associations between serum Zn and MoCA scores. In turn, in study no. 40 (Polizzi et al 2002), serum Al was negatively associated with performance on MMSE of aluminum smelter workers (crude scores: β = −0.150; adjusted scores for age and education: β = −0.160; p < 0.0001), and positively associated with the time necessary to complete the test (β = 0.143, p < 0.0002), but serum Cu and Zn, and blood Fe, Mn and Pb did no show significant results. In study no. 18 (Giorgianni et al. 2014), serum Al showed a strong positive association with cognitive impairment (p < 0.001), as higher levels of Al were observed in welders with impairment in, at least, one of the three tests applied.

Study no. 14 (Gao et al. 2008) assessed Al, Ca, Cd, Cu, Fe, Pb and Zn in plasma in relation to a composite z-score indicative of global cognitive performance and reported that higher levels of Ca were associated with higher scores (p < 0.0001), whilst lower scores were associated with high levels of Cd (p = 0.0044) and Cu (p = 0.0121). Study no. 22 (Iqbal et al. 2020) reported that, among Al, Cd, Cu, Mn, Pb and Zn levels assessed in serum, Cu showed the best diagnostic accuracy to distinguish between mild CI and CTR (cut-off = 0.51 mg/L, sensitivity = 100%, specificity = 95%), moderate CI and CTR (cut-off = 0.51 mg/L, sensitivity = 95%, specificity = 95%), and severe CI and CTR (cut-off = 0.51 mg/L, sensitivity = 100%, specificity = 95%), followed by Al, Pb and Zn. Cd and Mn did not show satisfactory results. Study no. 30 (Li et al. 2021) assessed Al, Cu, Fe, Mn, Pb and Zn in the CSF of male active smokers and found a significant negative correlation between Mn and MoCA scores for subjects under 40 years of age (r =  − 0.373, p = 0.009).

In a study with only MCI patients, Jakubowski et al (2021) (study no. 23) showed that baseline serum levels of Fe predicted better 2-years follow-up MMSE scores (β = 0.25, p = 0.035), while baseline Al levels borderline predicted worse 2-years follow-up MMSE scores (β = −0.20, p = 0.050). No significant interactions were found for As and Cu, nor between any TE and TICS-m (Telephone Inventory for Cognitive Status-modified) scores. In study no. 17 (Ghazali et al. 2013), although fingernail Cu and Mn negatively correlated with MoCA scores (Cu: r = 0.330, p = 0.015; Mn: r = −0.496, p < 0.001), no correlations were found between TE and MMSE scores. Arsenic, Cd, Pb, and Zn also did not yield significant results. Lucchini et al (2014) (study no. 32) found no significant associations between MMSE scores and the levels of Mn and Pb measured in whole blood and urine. Study no. 27 (Laouali et al. 2022) showed that the increase of one quartile of blood Cd, Mn and Pb is related to a decrease in overall cognitive performance. When stratified by sex, these results pointed a strong negative relationship for men (β = −0.10, p = 0.03) and a slight positive relationship for women (β = 0.02, p = 0.03). In study no. 12 (Cheng et al. 2023), blood Cd (β =  −0.37, p = 0.045) and Pb (β =  −0.44, p = 0.042) were negatively associated with MMSE scores, while Se shared a positive relationship (β = 0.71, p = 0.001). In addition, As (PIP = 0.22), Cd (PIP = 0.29) and Pb mixture were negatively associated with MMSE scores in a dose–response pattern, where Pb (PIP = 0.49) was the greatest contributor, but this relationship was weakened by higher levels of Se (low Se: β =  −0.81; medium Se: β =  −0.45; high Se: β =  −0.28). No significant results were reported for Cu nor Zn. In study no. 53, Tong et al (2014) reported no significant correlations between blood Cu, Hg and Pb and MMSE scores, but highlighted a negative association between MMSE scores and Mn (R2 = 0.119, p = 0.034), regardless of group (CTR, MCI, mild dementia, and dementia). The authors also observed higher levels of blood Mn in dementia patients, compared to CTR and MCI, but found no significant differences regarding Cu, Hg and Pb. Park et al (2014) (study no. 39) reported no significant associations between As, Cd, Hg and Pb measured in serum and the MMSE scores of HS and AD patients, nor did they find significant differences between HS and AD patients regarding serum levels of As, Cd, Hg and Pb. Also, the risk of AD did not vary across different metals’ concentrations.

Gu et al (2021) (study no. 19) identified lower levels of blood Al (GM = 65.43), Ba (GM = 42.75) and V (GM = 0.71), as well as higher levels of As (GM = 1.99) and Se (GM = 90.16) in subjects with cognitive dysfunction versus normal individuals (Al: GM = 74.52, p = 0.016; As: GM = 1.71, p < 0.001; Ba: GM = 47.97, p = 0.002; Se: GM = 86.69, p = 0.002; V: GM = 0.83, p = 0.014). The authors also highlighted a dose–response relationship between As (OR = 2.06, p-trend = 0.002) and Se (OR = 1.947, p-trend = 0.007) and an increased risk for cognitive dysfunction, as well as a protective role of Al (OR = 0.63, p-trend = 0.040), Ba (OR = 0.460, p-trend = 0.002) and V (OR = 0.549, p-trend = 0.007) against it. Study no. 57 (Yang et al. 2018) reported that participants with high levels of urinary inorganic As had lower MMSE scores, while participants with high levels of urinary dimethylarsinic acid had higher MMSE scores, compared to participants with low levels of inorganic As and dimethylarsinic acid, respectively (p < 0.05). In study no. 13 (Cheng et al. 2022), urinary Mo (PIP = 0.07), Se (PIP = 0.56) and V (PIP = 0.16) were positively associated with MMSE scores, while Co (PIP = 0.12) shared a negative relationship and Sr (PIP = 0.03) a non-linear relationship. Se and V had a positive additive effect on the associations of other TE with the MMSE, while Co had a negative additive effect. Baierle et al (2014) (study no. 4) verified that higher levels of V in blood predicted worse MMSE scores (R2 = 0.443, β =  −0.225, p < 0.05), but no significant associations were found with serum Cu, Fe, Se and Zn, nor with blood As, Cd, Cr, Hg, Ni and Pb. In Gerardo et al (2020) (study no. 16) longitudinal study, higher baseline concentrations of Ni in fingernails predicted better 5-years follow-up MMSE scores (R2 = 0.288, p = 0.022), while higher baseline concentrations of Ni and Se predicted better 5-years follow-up MoCA scores (R2 = 0.679, p = 0.007). In this study, Ni [0.09–2.98 μg/g] and Se [0.60–1.03 μg/g] were within normal ranges. No significant interactions were observed between cognitive performance and Al, As, Ba, Cd, Co, Cr, Cu, Fe, Hg, Li, Mn, Pb, Sb, Sn, Sr, Ti, V and Zn. Study no. 15 (Gao et al. 2007) reported significant associations between high level of Se in nails and higher composite z-scores indicative of better global cognitive performance (p < 0.0001).

Lin et al (2022) (study no. 31) studied 36 different TE in plasma. The authors found lower levels of B, Bi, Th and U in amnesic MCI (aMCI) (B: Md = 53 μg/L; Bi: Md = 0.03 μg/L; Th: Md = 0.0 μg/L; U: Md = 0.011 μg/L) versus CTR (B: Md = 141 μg/L; Bi: Md = 0.05 μg/L; Th: Md = 4.0 μg/L; U: Md = 0.017 μg/L) and in AD (B: Md = 35 μg/L; Bi: Md = 0.02 μg/L; Th: Md = 0.0 μg/L; U: Md = 0.007 μg/L) versus CTR, as well as lower levels of Sb and Zr in a MCI (Sb: Md = 7.0 μg/L; Zr: Md = 0.50 μg/L) versus CTR (Sb: Md = 8.0 μg/L, p = 0.038; Zr: Md = 1.68 μg/L, p = 0.034). The authors also found higher levels of Ba (Md = 1.0 μg/L), Mn (Md = 1.48 μg/L) and Pt (Md = 0.02 μg/L) in aMCI versus CTR (Ba: Md = 0.8 μg/L; Mn: Md = 0.89 μg/L; Pt: Md = 0.00 μg/L; p < 0.04) and of Co (Md = 0.23 μg/L) and Cr (Md = 1.5 μg/L) in AD versus CTR (Co: Md = 0.15 μg/L; Cr: Md = 1.4 μg/L; p < 0.05). In addition, higher levels of B (r = −0.070, p = 0.001), Th (r = −0.58, p = 0.007) and Zr (r = −0.52, p = 0.020) were associated with a greater decline from baseline to 1-year follow-up in the MMSE scores of aMCI patients, and the inverse was true for Ca levels (r = 0.50, p = 0.026). Finally, higher levels of Mn were associated with a greater decline between the MMSE scores from baseline to 1-year follow-up for AD patients (r = −0.91, p = 0.035). In turn, Zhang et al (2022) (study no. 60) assessed 22 TE in plasma. They observed lower levels of Fe (Md = 998.6 μg/L), Rb (Md = 399.6 μg/L) and Se (Md = 106.0 μg/L) in CI versus normal individuals (Fe: Md = 1081.7, p = 0.002; Rb: Md = 415.1, p = 0.009; Se: Md = 110.6, p = 0.006). Higher levels of Al were borderline associated with 1.53 times increased risk of AD (p = 0.050). Both Al and Cu increased with the risk of CI (p < 0.0001), while Cd and Rb shared an inverted-U shape and L shape with CI (p < 0.0001), respectively. Xiao et al (2021) (study no. 56) also assessed 22 TE, this time in whole blood, and only found a positive association between MMSE and Rb (β = 0.384, p = 0.004) and a negative association between MMSE scores and Cd (β = −0.46, p = 0.02).

Orientation

Among the 4 studies addressing orientation, 3 reported significant associations between this ability and certain TE (see Table 2). Alghadir et al (2015) (study no. 2) identified both positive and negative correlations (p < 0.05) between TE concentrations in serum and LOTCA Orientation subscores—while Zn and Zn/Cu ratio shared a positive relationship with performance, Cu and Fe were inversely related. In turn, Shang et al (2021) (study no. 46) identified positive associations between MoCA orientation subscores and Co (β = 0.023, p = 0.005) and Zn levels (β = 0.207, p < 0.001) in plasma, in spite of normal (Co: Md = 0.765; Zn: Md = 719.367) and CI (Co: Md = 0.793; Zn: Md = 708.469) participants not differing in terms of their exhibited concentrations (p > 0.05). In this study, Al, Cr, Cu, Fe, Li, Mn, and Pb did not relate to the orientation ability. Yang et al (2018) (study no. 57) assessed different forms of As in urine and found that high levels of inorganic As were associated with lower orientation scores on the MMSE (p < 0.05), while high levels of dimethylarsinic acid were associated with higher orientation scores (p < 0.05). Sirivarasai et al (2021) (study no. 47) did not report any significant relationship between MoCA orientation subscores and blood Hg.

Attention

Twenty-two studies analyzed TE in relation to attention and its components. In study no. 26, Lam et al (2008) reported that plasma levels of Fe were negatively associated with scores on the Blessed Information-Memory-Concentration Test in women (p = 0.014), but found no significant results for men, nor regarding Cu and Zn. Study no. 2 (Alghadir et al. 2015) quantified the same TE in serum samples and found that Cu and Fe shared a negative correlation with attention (attention and concentration LOTCA subscores, p < 0.05), while Zn and Zn/Cu ratio shared a positive one (p < 0.05). Baierle et al (2014) (study no. 4) also addressed Cu, Fe, Se and Zn levels in serum, as well as As, Cd, Cr, Hg, Ni, Pb, V levels in blood and found that higher levels of Hg predict worse cognitive performance (i.e. longer times to complete TMT-A; R2 = 0.685, β = 0.177, p < 0.05), while higher levels of Se predict better cognitive performance (i.e. shorter times to perform TMT-A; R2 = 0.704, β =  − 0.222, p < 0.01).

Study no. 29 (Lee et al. 2017) reported no significant association between blood Fe and Mn and attention measures (Symbol-Digit Coding, Symbol Search, Stroop Color-Word Test (word reading and color naming conditions) and TMT-A). The same was true for study no. 32 (Lucchini et al. 2014), where Mn and Pb were measured in blood and urine (attention measures: reaction time, DSST), and study no. 25 (Kunert et al. 2004), which assessed Pb in blood, Se in serum and Cd and Hg in urine (attention measures: reaction time, dual task). Notwithstanding, in study no. 8, Bowler et al (2007) identified a negative dose–effect relationship between Stroop-Color naming and blood Mn (β = −0.494, p = 0.025), as well as negative associations between blood Mn and Auditory Consonant Trigrams (β from −1.150 to −1.192 and p from 0.013 and 0.049). Laouali et al (2022) (study no. 27) found that the joined increase of Cd, Mn and Pb levels in blood by one quartile was associated with a decrease on DSST scores in men (β = –0.17, p < 0.001), whereas this association were slightly positive in women (β = 0.02, p < 0.001). Study no. 6 (Barahona et al. 2022) found no significant associations between Mn in whole blood and in urine, and attention performance (DSST).

Giorgianni et al (2014) (study no. 18) reported worse cognitive performances (p < 0.05) in the Attention Matrixes Test among welders exposed to Al (serum Al–M = 24.19 μg/L, SD = 9.99) compared to non-exposed controls (serum Al–M = 6.39 μg/L, SD = 1.95). Based on an identical approach, Akila et al (1999) (study no. 1) reported negative associations between urinary and blood Al and attention performance. Individuals with high exposure to Al (urinary Al ≥ 4.1 µmol/L) attempted less (p = 0.025) on the Digit Symbol than the reference group (urinary Al ≤ 1.0 µmol/L) and had slower backward counting rates on the Dual Task (p < 0.01). Whilst urinary Al inversely correlated with performances on the Digit Symbol (p = 0.27–036), both urinary and blood Al inversely correlated with Dual Task (p = 0.016–0.039). In study no. 20 (Hanninen et al. 1994), urinary Al showed no significant results, but increasing levels of Al in serum were associated with higher variability of visual reaction times (p < 0.01). No associations were found for the Digit Symbol subtest. In study no. 23 (Jakubowski et al. 2021), higher baseline serum Al was associated with better performances on baseline TMT-A (β = −0.26, p = 0.004) and SDMT (β = 0.15, p = 0.046) as well as with better 2-years follow-up performances on SDMT (β = 0.18, p = 0.044), Map Search (β = 0.21, p = 0.026) and TMT-A (β = −0.20, p = 0.027). In addition, baseline serum Fe was negatively associated with Map Search (β = −0.15, p = 0.044). Baseline serum Fe predicted better 2-years follow-up performance on TMT-A (β = −0.23, p = 0.026), while baseline serum Cu predicted worse 2-years follow-up performance on Map Search (β = −0.25, p = 0.033). Serum As did not yield significant relationships with any of the referred attention measurements. In study no. 43 (Rafiee et al. 2020), among 19 different TE measured in hair, only 5 yield significant results - higher As, Hg, Mn, Pb and Zn levels were associated with longer times to complete the TMT-A (β = 0.175, 0.122, 0.201, 0.150 and 0.155, respectively; p < 0.05). Study no. 47 (Sirivarasai et al. 2021) reported lower attention subscores (MoCA) of individuals with the 3rd tertile of blood Hg, compared to the 1st tertile (p < 0.05). Namazbaeva et al (2018) (study no. 37) assessed As, Cd, Cr, Cu, Fe, Hg, I, Mn, Ni, Pb, Se and Zn in blood and reported positive correlations between Zn and Attentional Capacity (AC; r = 0.4, p < 0.05). Also, environmentally exposed individuals (with and without CI) exhibited increased levels of Pb and decreased levels of Fe, I, Se and Zn (p < 0.05), comparatively to reference values, and had 1.2 times lower AC scores in comparison to CTR subjects.

Wang et al (2022) (study no. 55) evaluated urinary levels of Ba, Cd, Co, Cs, Mn, Mo, Pb, Tl and U in relation to performances on the DSST and identified significant associations between impairment (≤ 33 points) and Cd in the fourth quartile (OR = 2.444), Ba (OR = 0.412) and Cs (OR = 0.440) in the third quartile, and Co in the second quartile (OR = 0.461). Furthermore, the prevalence of low performances on DSST (≤ 33 points) increased with increasing Cd levels and decreased with increasing Ba, Cs and Mn levels. In turn, Sasaki and Carpenter (2022) (study no. 45) showed that higher levels of blood Se were significantly associated better DSST performances (β = 8038, p < 0.01), contrary to higher levels of blood and urine Cd (blood: β = −2.29, p < 0.01; urine: β = −1.42, p < 0.01) and Pb (blood: β = −1.08, p = 0.04; urine: β = −1.03, p = 0.04), which were associated with worse performances. No significant results were obtained for blood Hg and Mn, nor for urinary Ba, Co, Cs, Mn, Mo, Sn, Sr, Tl, U and W. In study no. 41 (Przybyla et al. 2017), blood levels of Pb but not Cd were negatively associated with DSCT scores (β =  −0.10, p = 0.04).

Yang et al (2018) (study no. 57) assessed Cd, Hg, Pb and Se in blood, as well as As (inorganic As, monomethylarsonous acid and dimethylarsinic acid) in urine, and found no differences regarding attention and calculation subscores of MMSE between participants of different quartiles. Shang et al (2021) (study no. 46) evaluated plasma Al, Co, Cr, Cu, Fe, Li, Mn, Pb and Zn in relation to the attention and calculation subscore of MoCA and found no significant correlations.

Studies no. 31 (Lin et al. 2022) and no. 44 (Santos-Burgoa et al. 2001) did not report any results on the relationship between attention and plasma/blood TE, despite assessing this ability through specific, performance-based psychometric tools.

Learning and Memory

A total of 25 studies evaluated learning and memory. Since working memory can be considered either a type of memory or an executive function, evidence on this regard will be presented separately at the end of this subsection.

Studies no. 1 and 20 examined Al in serum and urine. In both studies, significant results were found only for urinary Al. Study no. 1 (Akila et al. 1999) reported worse performance (p = 0.029) of low-exposure individuals (1.1 µmol/L ≤ urinary Al ≤ 4.0 µmol/L) in Memory for Designs, compared to the reference group (urinary Al ≤ 1.0 µmol/L), but no differences were found for high-exposure group (urinary Al ≥ 4.1 µmol/L). None of the other memory measures (i.e., Paired Associates, Interference Recall, Similarities Recall and Digit Symbol Recall) yield significant results. In study no. 20 (Hanninen et al. 1994), significant negative associations were found between urinary Al levels and scores on Memory for Design (r = −0.57, p < 0.05) and Associative Learning (r = −0.50, p < 0.05). On the other hand, serum Al yield significant relationships with memory in studies no. 18 and 23. Giorgianni et al (2014) (study no. 18) observed lower scores on the WMS (p < 0.01) in subjects exposed to Al (serum levels: M = 24.19, SD = 9.99 μg/L) compared to their non-exposed counterparts (serum levels: M = 6.93, SD = 1.95 μg/L). Jakubowski et al (2021) (study no. 23) assessed serum As, Cu and Fe alongside serum Al in relation to the Hopkins Verbal Learning Test (HVLT), Paired Associated Learning (PAL) and Spatial Recognition Memory (SRM). Higher baseline Al was associated with better total recall on baseline HVLT (β = 0.19, p = 0.029), and the same was true for baseline Cu in relation to baseline HVLT-delayed recall (β = 0.19, p = 0.036). Baseline Fe was associated with worse baseline HVLT-total recall (β = −0.17, p = 0.040), as well as 2-years follow-up performance on HVLT-delayed recall (β = −0.18, p = 0.045) and PAL (more errors; β = 0.18, p = 0.041). Baseline Al associated with worse 2-years follow-up delayed recall on HVLT (β = −0.19, p = 0.037). No significant results were found for SRM nor for As levels.

In study no. 50 (Squitti et al. 2002a), higher levels of serum Cu correlated with worse immediate (r = −0.412, p = 0.002) and delayed recall (r = −0.391, p = 0.003) on the Rey’s Auditory Verbal Learning Test but shared no significant relationship with Immediate Visual Memory. Notwithstanding, serum Cu seemed to not be related to the rate of cognitive decline in these tasks, according to study no. 51 (Squitti et al. 2002b). In study no. 37 (Namazbaeva et al. 2018), blood Cu negatively correlates with short-term memory for number (r = 0.43, p < 0.05) (blood As, Cd, Cr, Fe, Hg, I, Mn, Ni, Pb, Se and Zn, as well as short-term memory for numbers, and short- and long-term memory for words did not yield significant results).

Lam et al (2008) (study no. 26) assessed plasma Cu, Fe and Zn in relation to Buschke-Fuld Selective Reminding Test. The authors observed poor performances of long-term and total recall in men with high and low levels of Fe. They registered significantly worse performances of long-term recall (β = −0.94, p < 0.001) and total recall (β = −1.05, p < 0.001) in men with low Fe comparted to intermediate Fe, and significantly worse performances of long-term recall in men with high Fe versus men with intermediate Fe (β = −0.69, p = 0.020). Regarding women, Fe shared borderline linear inverse associations with long-term (β = −0.51, p = 0.051) and total recall (β = −0.55, p = 0.046), whilst Cu shared a linear inverse association with short-term (β = -0.80, p = 0.032) and long-term recall (β = -0.77, p = 0.031). No significant results were found regarding Zn. Study no. 29 (Lee et al. 2017) assessed Fe and Mn levels in blood and reported no significant correlations between these TE and the performance of welders in the Verbal List—Learning, Recall and Recognition, Story Learning and Recall and Figure Recall subtests. However, welders (Fe: M = 552 μg/mL, SD = 57; Mn: M = 10.8 ng/mL, SD = 3.2) exhibited worse performances in the Story Recall subtest (p = 0.032) and slightly better performances in the Verbal List—Recognition (p = 0.040) compared to controls [who had significantly lower levels (p < 0.001) of Fe (M = 498 μg/mL, SD = 76) and Mn (M = 8.9 ng/mL, SD = 2.5)]. In turn, although study no. 6 (Barahona et al. 2022) found an inverse association between urinary Mn levels and word immediate learning (but not recall) in the Word List Learning and Recall subtest (β = −2.0, p = 0.01), it did not find significant relationships for blood Mn. In study no. 32 (Lucchini et al. 2014), neither blood Mn and Pb, nor urine Mn and Pb shared significant associations with Story Recall. In study no. 8 (Bowler et al. 2007), blood Mn was positively associated with the WMS-III word list 1—contrast 1 score (β = 1.283, p = 0.047). Contrary results were found by Laouali et al (2022) (study no. 27), who verified that increasing blood levels of Cd, Mn and Pb by one quartile was associated with a decrease in the Word List Learning and Recall subtest (β = −0.04). In study no. 39 (Park et al. 2014), among serum As, Cd, Hg and Pb, only Pb negatively correlated with Word List Recall (p = 0.039) and Recognition (p = 0.037) (no significant results for the other TE nor regarding Constructional Praxis Recall). According to Baierle et al (2014) (study no. 4), performance on Word List Learning and Recall subtest does not significantly relate to blood levels of As, Cd, Cr, Hg, Ni, Pb or V, or serum levels of Cu, Fe, Se or Zn. Finally, Gao et al (2007) (study no. 15) identified significant, dose–response associations between increasing nail Se quintiles and better performances in the Word List Learning (p = 0.0029) and Recall (p = 0.0107) and Story Recall (p < 0.0001) subtests, but found no results for blood Se. Sasaki and Carpenter (2022) (study no. 45) found an association between increasing levels of blood Cd and Pb and worse immediate (Cd: β = −0.54, p < 0.01; Pb: β = −0.58, p < 0.01) and delayed recall (Cd: β = −0.19, p = 0.04; Pb: β = −0.19, p = 0.03) on the Word List Learning and Recall subtest. The inverse was true for blood Se, where higher levels associated with better immediate (β = 2.68, p < 0.01) and delayed recall (β = 0.87, p = 0.04). Blood total Hg, methylmercury, inorganic Hg and Mn did not yield significant results. In turn, increasing urine levels of monomethylarsonic acid and W were associated with worse immediate (mono. acid: β = −0.90, p < 0.01; W: β = −0.38, p = 0.04) and delayed recall (mono. acid: β = −0.37, p = 0.01; W: β = −0.19, p = 0.05), while higher urine levels of Cd (β = −0.31, p = 0.05) borderline associated with worse immediate recall, and higher urine levels dimethylarsinic acid (β = −0.23, p = 0.04) associated with worse delayed recall. No results were found for urinary total As, arsenous acid, arsenobetaine, arsenocholine, Ba, Co, Cs, Mn, Mo, Pb, Sn, Sr, Tl and U. In study no. 55 (Wang et al. 2022), the 3rd quartile of urinary Cd and Tl was linked to CI (Total ≤ 21 points) in the Word List Learning and Recall subtest (p < 0.05) (no significant results for urinary Ba, Co, Cs, Mn, Mo, Pb and U).

In study no. 57 (Yang et al. 2018), individuals with quartile 3 of urinary inorganic As had lower registration and recall scores on MMSE compared to subjects in the 1st quartile (p < 0.05), while individuals with quartile 3 of urinary monomethylarsonous acid exhibited lower recall scores only (p < 0.05). In turn, subject with quartile 3 of dimethylarsinic acid had higher recall scores than quartile 1 (p < 0.05). Studies no. 46 and 47 found no significant associations between the memory subscores of MoCA and blood Hg (Sirivarasai et al. 2021), or plasma Al, Co, Cr, Cu, Fe, Li, Mn, Pb, and Zn (Shang et al. 2021).

Working memory was assessed by most studies through the application of the Digit Span (verbal stimuli). Performances on this test were not significantly associated with serum levels of Al (study no. 1 and 20; Akila et al. 1999; Hanninen et al. 1994), Cu (studies no. 4 and 51; Baierle et al. 2014; Squitti et al. 2002b), Fe, Se and Zn (study no. 4; Baierle et al. 2014), blood levels of Fe (study no. 29; Lee et al. 2017), Mn (studies no. 29 and 32; Lee et al. 2017; Lucchini et al. 2014), As, Cd, Cr, Hg, Ni, V (study no. 4; Baierle et al. 2014) and Pb (studies no. 4 and 32; Baierle et al. 2014; Lucchini et al. 2014), nor urine levels of Al (study no. 1 and 20; Akila et al. 1999; Hanninen et al. 1994), Cu (study no. 51; Squitti et al. 2002b), Mn and Pb (study no. 32; Lucchini et al. 2014). Besides the Digit Span subtest, study no. 29 (Lee et al. 2017) also assessed working memory through Letter-Number Sequencing and Spatial Span subtests in relation to blood Fe and Mn. Once again, no significant correlations were found. Studies no. 8 and no. 50 were the only to evidence a significant (negative) association for Digit Span. Study no. 50 (Squitti et al. 2002a) reported a relationship between Digit Span and serum levels of Cu (r = −0.285, p = 0.037), as well as a significant negative correlation between serum Cu and performances on the Corsi Test (r = −0.288, p = 0.035). Study no. 51 (Squitti et al. 2002b) also resorted to the Corsi test to assess visual working memory but found no significant relationship between performance decline and Cu levels in serum or urine. Study no. 8 (Bowler et al. 2007) reported negative associations between blood Mn and forward digit span (β = −0.716, p = 0.047), backward digit span (β = −1.243, p = 0.003) and total digit span (β = −0.934, p = 0.005). It also reported a negative association with the working memory index of WAIS-III (β = −1.089, p = 0.015) and a negative trend with the working memory index of WMS-III (β = −0.753, p = 0.052).

Study no. 49 (Souza-Talarico et al. 2017) assessed performance on the Counting Span Test in relation to blood Cd and Pb. The authors reported that scores in this test were negatively associated with Cd levels (r = −0.348, p < 0.001), as well as with the interaction between Cd and Pb (r = −0.319, p < 0.001). In study no. 25 (Kunert et al. 2004), although blood levels of Pb were positively correlated with variability of the reaction times in the working memory task (R2 = 0.15, p = 0.02), they did not correlate with the scores of the task. In addition, there were no significant correlations between performance and serum Se, urinary Cd or urinary Hg (all p > 0.16). Finally, in study no. 26 (Lam et al. 2008), high and low plasma levels of Fe were associated with poor performance on Serial 7 of MMSE in men (p = 0.004). Men with low plasma Fe also performed poorer in this task compared to men with intermediate Fe levels (β = −1.05, p = 0.001). No significant results were obtained for women, using the backward “world” spelling of MMSE, nor regarding plasma Cu or Zn.

Although studies no. 31 (Lin et al. 2022) and no. 44 (Santos-Burgoa et al. 2001) have evaluated learning and memory/working memory through specific, performance-based psychometric tools, the authors did not report results on the relationship between these abilities and plasma/blood TE.

Language

Studies addressing language abilities yield mixed results. Studies no. 8 (Bowler et al. 2007) and no. 39 (Park et al. 2014) found no significant associations between blood Mn levels or serum As, Cd, Hg and Pb levels and performances on the Boston Naming Test. Notwithstanding, study no. 8 found a negative association between blood Mn and comprehension (β = −1.142, p = 0.024). Similarly, study no. 29 (Lee et al. 2017) reported no significant associations between blood Fe or Mn and scores on Picture Naming (RBANS). In contrast, study no. 23 (Jakubowski et al. 2021) found that baseline levels of serum Fe were borderline negatively associated with 2-years follow-up scores of MCI patients on Graded Naming (β = −0.17, p = 0.049) but could predict better performance in association with other predictors (in this case, plasma total homocysteine; β = 0.10, p = 0.035). Although study no. 23 did not report any significant results on serum Al, As or Cu, study no. 46 (Shang et al. 2021) found negative correlations between plasma Al and the scores obtained on the naming task of MoCA (β = −0.091, p = 0.006). The inverse was true for Cr, which shared a positive association with this score (β = 0.333, p = 0.004). Notwithstanding, the authors could not find significant associations for plasma Co, Cu, Fe, Li, Mn, Pb and Zn nor for the language subscore of MoCA.

In study no. 57, Yang et al (2018) analyzed Cd, Hg, Pb and Se in blood, as well as different forms of As in urine, and found that participants in the Q3 of inorganic As had lower scores on the language and praxis subscore of MMSE (p < 0.05). Study no. 47 (Sirivarasai et al. 2021), reported no differences between tertiles of Hg in blood and the language subscore of MoCA. Gao et al (2007) (study no. 15), assessed Se in nails and blood and found that higher quintiles of nail Se were associated with better performances on the Indiana University Token Test in a dose–response fashion, whilst lower blood levels of Se were associated with worse scores.

In study no. 1 (Akila et al. 1999), serum Al was associated with slower selection of items in the Synonyms task (r = 0.256, p = 0.027). Consistently, individuals with high exposure to Al (urinary Al > 4.1 µmol/L) were slower than low exposure subjects (1.1 µmol/L—urinary Al—4.0 µmol/L) and reference subjects (urinary Al < 1.0 µmol/L) to choose the items. No significant results were found for the Similarities subtest (WAIS). In line with this last result, Hanninen et al (1994) (study no. 20) could not find any significant results relating urinary and serum Al and performance on Synonyms and Similarities subtest. Study no. 50 (Squitti et al. 2002a) also failed to report significant correlations, this time between Cu in serum and the ability to form sentences (MDB).

Studies no. 31 (Lin et al. 2022) and no. 44 (Santos-Burgoa et al. 2001) did not report any results on the relationship between language and plasma/blood TE, despite assessing these abilities through specific, performance-based psychometric tools.

Executive Functioning

Giorgianni et al (2014) (study no. 18) assessed serum Al in relation to the Stroop Color-Word Test and observed significantly worse performances (p < 0.05) in welders exposed to Al (serum Al: M = 24.19, SD = 9.99 μg/L) compared to non-exposed individuals (serum Al: M = 6.93, SD = 1.95 μg/L). Contrastingly, Akila et al (1999) (study no. 1) found no significant associations between the Stroop Color-Word Test and urine or serum Al. In study no. 40, Polizzi et al (2002) reported negative associations between serum Al and scores on the CDT (β = −0.068, p < 0.001), as well as a positive association between serum Al and the time to complete the task (β = 0.059, p < 0.03). There were no significant results for serum Cu and Zn, nor blood Fe, Mn and Pb. In study no. 23 (Jakubowski et al. 2021) however, none of the studied TE—serum Al, As, Cu and Fe—were significantly related to performances on the Executive clock drawing task. Instead, the authors found negative associations between baseline Fe and baseline Category Fluency, positive associations between baseline Al and baseline Category Fluency, positive associations between baseline Fe and 2-years follow-up Category Fluency and TMT-B, and negative associations between Al and 2-years follow-up TMT-B. Category Fluency was not significantly associated with blood and nails Se in study no. 15 (Gao et al. 2007). In study no. 2 (Alghadir et al. 2015) serum Cu and Fe negatively correlated with the Thinking Operation subscores from LOTCA (p < 0.05), while serum Zn and Zn/Cu ratio shared a positive correlation (p < 0.05). Lam et al (2008) (study no. 26) observed a negative quadratic association between plasma Cu and performances on TMT-B in men (p = 0.033) but found no significant relationships for plasma Fe and Zn, Category Fluency or women. Phonemic Fluency (r = −0.354, p = 0.009), as well as Raven’s Colored Progressive Matrices (RCPM; r = −0.439, p = 0.001), negatively correlated with serum Cu in study no. 50 (Squitti et al. 2002a), but no differences were found on the rate of cognitive decline in these tests between placebo-treated and Cu-chelating agent-treated AD patients (study no. 51, Squitti et al. 2002b).

Lee et al (2017) (study no. 29) found no significant correlations between blood Fe and Mn and executive functioning measured by the Stroop Color-Word Test, the TMT-B, Phonemic and Semantic Fluency, and the Visual Verbal test, despite welders with higher levels of blood Fe and Mn than CTR exhibiting worse performances on Phonemic Fluency (p = 0.002). Blood Mn, but not urinary Mn, was negatively associated with Semantic Fluency (β = −0.8, p = 0.03) in study no. 6 (Barahona et al. 2022). Contrastingly, semantic fluency was not associated with blood Cd, Mn or Pb in study no. 27 (Laouali et al. 2022). In study no. 8 (Bowler et al. 2007), blood Mn shared a negative dose–effect relationship with D-KEFS Design Fluency—total correct (β = −0.907, p = 0.008) and attempted designs (β = −0.986, p = 0.007), as well as inverse dose–effect trends with Stroop (incongruent color-word condition) (β = −0.657, p = 0.053) and D-KEFS Design Fluency—switching (β = −0.591, p = 0.066). No results were found for the Rey-Osterrieth Complex Figure test, or the Controlled Oral Word Association Test.

Lucchini et al (2014) (study no. 32) reported negative associations between urinary Mn and TMT performances (p = 0.007) but found no significant results regarding blood Mn, blood and urinary Pb, or the RCPM test. Kunert et al (2004) (study no. 25) also found no significant associations between mental flexibility and blood Pb, serum Se or urinary Cd and Hg. In turn, Baierle et al (2014) (study no. 4), found that higher levels of blood Hg significantly predicted worse cognitive performance in the TMT-B, translated into longer times to complete the task. This result was concordant to study no. 43 (Rafiee et al. 2020), which showed a significant association between higher hair levels of As, Hg, Mn and Pb and longer times in the TMT-B (β = 0.203, 0.198, 0.111 and 0.138, respectively; p < 0.05). This study also reported significant associations between higher hair levels of Sn and shorter times in TMT-B (β = −0.198, p < 0.05), between higher hair levels of Hg and Pb and higher TMT ratios (B/A) (β = 0.104 and 0.116, p < 0.05), and between higher hair levels of As, Hg and Pb and higher delta TMT (B-A) (β = 0.325, 0.084 and 0.104, p < 0.05). No significant results were found for hair Al, B, Ba, Be, Cd, Co, Cr, Cu, Fe, Li, Ni, Sb, V and Zn. Study no. 47 (Sirivarasai et al. 2021) reported no differences between tertiles of Hg in blood and the visuospatial/executive subscore of MoCA. Study no. 46 (Shang et al. 2021) reported a negative correlation between plasma Li and Visuospatial/Executive subscores of MoCA (β = −0.113, p = 0.014), as well as a positive correlation between plasma Co and Visuospatial/Executive subscores of MoCA (β = 0.083, p = 0.024), but no significant associations for plasma Al, Cr, Cu, Fe, Mn, Pb and Zn. While study no. 55 (Wang et al. 2022) reported a link between the 3rd and 4th quartiles of urinary Cd and cognitive impairment in Category Fluency (i.e., ≤ 13 words, p < 0.05) (with no significant results for urinary Ba, Co, Cs, Mn, Mo, Pb, Tl and U), study no. 39 (Park et al. 2014) found no significant correlations between serum Cd and the same task (and neither for serum As, Hg or Pb).

Finally, studies no. 31 (Lin et al. 2022) and no. 44 (Santos-Burgoa et al. 2001) assessed executive functioning through specific, performance-based psychometric tools but did not report any results on the relationship between this ability and plasma/blood TE.

Visuospatial and Visuoconstructive Abilities

Ten studies assessed visuospatial and visuoconstructive abilities in relation to TE. In study no. 1 on welders, Akila et al (1999) observed a negative association trend between urinary Al and the identification of items in the Embedded Figures test (r = -0.219, p = 0.55). Also, individuals from the reference group (urinary Al < 1.0 µmol/l) scored higher than low-exposure subjects (urinary Al 1.1–4.0 µmol/l) in difficult items of the Block Design subtest from WAIS, with which serum Al shared a negative trend (r =  − 0.196, p = 0.083). Contrastingly to these results, Hanninen et al (1994) (study no. 20) found no significant relationships between serum or urine Al and the performance of welders on Block Design or Embedded Figures. Study no. 46 on aluminum factory workers assessing TE in plasma (Shang et al. 2021) also found no significant results between Al, Cr, Cu, Fe, Mn, Pb and Zn and the Visuospatial/Executive score of MoCA, only identifying positive (p = 0.024) and negative (p = 0.014) correlations between Co and Li, respectively, and this score. In study no. 40 on aluminum smelter workers (Polizzi et al. 2002), serum Al concentrations were negatively associated with CDT scores (β = −0.068, p < 0.001), and positively associated with the time it took participants to perform the test (β = 0.059, p < 0.03). Serum Cu and Zn and blood Fe, Mn and Pb showed no significant results.

In study no. 2 (Alghadir et al. 2015), serum Cu and Fe correlated negatively with visual perception, spatial perception and visuomotor organization scores (LOTCA) (p < 0.05), while Zn and Zn/Cu ratio shared a positive correlation (p < 0.05). Similarly, study no. 50 (Squitti et al. 2002a) reported an association between higher levels of serum Cu and worse performance on Copy Drawing (freehand: r = −0.376, p = 0.005; with landmarks: r = −0.427, p = 0.001). Study no. 51 (Squitti et al. 2002b) reported no effects of different Cu levels on the rate of decline on Copy Drawing.

Finally, study no. 29 (Lee et al. 2017) found no significant correlations between blood Fe and Mn and scores on the Line Orientation and Figure Copy (RBANS), and study no. 39 (Park et al. 2014) reported no significant associations between serum As, Cd, Hg, and Pb and performance on Constructional Praxis (CERAD).

Intelligence

Among the 2 studies assessing intelligence, neither of them reported significant relationships with TE (see Table 2). Bowler et al (2007) (study no. 8) conducted a clinical series study where they assessed the verbal and performance Intelligence Quotients (WAIS-III) of welders suffering from manganism. Not only the subjects did not exhibit intellectual impairment, but there was also no significant nor trending associations between their performance and blood-Mn (M = 9.93 μg/L, SD = 2.68). On study no. 25, despite Kunert et al (2004) having assessed intellectual performance, the authors did not explore how it related to blood-Mn, serum-Se or urinary Cd and Hg.

Dementia Staging

Most studies addressing dementia staging resorted to the CDR. Study no. 9 (Cabral Pinto et al. 2018) found, through multiple correspondence analysis, significant associations between high levels of urinary Al, As, Cr, Fe, Hg, Ni, Pb and Zn, as well as low levels of Al, and CDR scores of severe dementia. It also reported significant associations between high levels of urinary Cd, Cr, Mn and Se and CDR scores of mild dementia. Only urinary Cu yield no significant results. In turn, in study no. 11 (Cabral Pinto et al. 2019b), Al, As, Ba, Cd, Co, Cr, Cu, Fe, Hg, Li, Mn, Ni, Pb, Sb, Se, Sn, Sr, Ti, V and Zn were assessed in fingernails. Among these TE, only Mn (p < 0.01) and Zn (p < 0.05) showed significant results, sharing a positive association with severe dementia. Study no. 53 (Tong et al. 2014) found higher blood levels of Mn in dementia patients (M = 13.98, SD = 0.88 ng/mL) versus CTR individuals (M = 11.20, SD = 0.95 ng/mL, p = 0.0293) or patients with mild dementia (M = 9.63, SD = 1.11 ng/mL, p = 0.0309) and that Mn positively correlates with CDR scores regardless of group (R2 = 0.0989, p = 0.0482). In study no. 31, Lin et al (2022) assessed a pool of 36 TE in plasma. In terms of discriminatory utility, B (AUC > 69.6%), Hg (AUC > 79.9%) and Th (AUC > 67.4%) could differentiate all groups (p < 0.05), while Bi (AUC > 76.3%), Ca (AUC > 79.2%), Tl (AUC > 73.2%), U (AUC > 72.2%), W (AUC > 100%) and Zr (AUC > 95.8%) discriminated both aMCI and AD patients from CTR individuals (p < 0.05), Co (AUC > 72.3%), Ge (AUC > 66.7%) and Mn (AUC > 80.2%) discriminated AD patients from aMCI and CTR individuals (p < 0.05), Cu (AUC > 72.8%) discriminated aMCI from CTR and AD patients (p < 0.05), and Ba (AUC = 73.1%), Pb (AUC = 77.7%), Pt (AUC = 77.5%) and Se (AUC = 81.5%) differentiated between aMCI and AD patients (p < 0.05). Study no. 48 (Smorgon et al. 2004) reported positive correlations between serum Co (r = 0.634, p < 0.001), Cr (r = 0.840, p < 0.001), Fe (r = 0.364, p < 0.04) and Se (r = 0.776, p < 0.001) and MODA scores, and negative correlations between serum Al (r = -0.628, p < 0.001) and Cu (r = −0.913, p < 0.001) and MODA scores. Both AD and vascular dementia patients (VaD) exhibited lower levels (p < 0.005) of serum Co (AD: M = 0.001, SD = 0.001 mg/mL; VaD: M = 0.002, SD = 0.002 mg/mL), Cr (AD: M = 0.005, SD = 0.003 mg/mL; VaD: M = 0.007, SD = 0.004 mg/mL) and Se (AD: M = 0.157, SD = 0.046 mg/mL; VaD: M = 0.122, SD = 0.022 mg/mL), and higher levels of Cu (AD: M = 1.457, SD = 0.250 mg/mL; VaD: M = 1.406, SD = 0.210 mg/mL) than CTR (Co: M = 0.030, SD = 0.014 mg/mL; Cr: M = 0.017, SD = 0.003 mg/mL; Cu: M = 1.059, SD = 0.081 mg/mL; Se: M = 0.303, SD = 0.044 mg/mL) and MCI patients (Co: M = 0.014, SD = 0.004 mg/mL; Cr: M = 0.013, SD = 0.001 mg/mL; Cu: M = 1.170, SD = 0.118 mg/mL; Se: M = 0.246, SD = 0.039 mg/mL), but only AD patients exhibited increased serum Al levels (M = 0.735, SD = 0.158 mg/mL, p < 0.001), compared to CTR (M = 0.215, SD = 0.106 mg/mL), MCI (M = 0.352, SD = 0.145 mg/mL) and VaD patients (M = 0.303, SD = 0.183 mg/mL). No significant results were found for serum Mn, Mo and Zn.

Serum Cu, Fe and Zn were also studied by Pu et al (2017) (study no. 42), who reported lower Cu levels in HS (M = 16.32, SD = 6.54 μmol/L) and in mild AD (M = 16.90, SD = 6.37 μmol/L) compared to moderate AD (M = 20.31, SD = 6.74 μmol/L; HS: p = 0.037; mild AD: p = 0.050) and severe AD patients (M = 21.29, SD = 6.92 μmol/L; HS: p = 0.014; mild AD: p = 0.025), higher levels of Fe (M = 9.19, SD = 4.71 μmol/L) and Zn (M = 100.67, SD = 9.78 μmol/L) in HS compared to mild (Fe: M = 7.82, SD = 4.67 μmol/L, p = 0.41; Zn: M = 86.78, SD = 9.91 μmol/L, p = 0.49), moderate (Fe: M = 77.71, SD = 4.42 μmol/L, p = 0.034; Zn: M = 66.48, SD = 9.37 μmol/L, p = 0.11) and severe AD (Fe: M = 7.28, SD = 4.19 μmol/L, p = 0.027; Zn: M = 71.55, SD = 8.97 μmol/L, p = 0.20), and higher levels of Zn in mild AD compared to moderate AD (p = 0.39) and severe AD (p = 0.48). While higher levels of Cu predicted greater dementia severity (OR = 10.255, p = 0.016), increasing levels of Zn predicted lesser (OR = 0.158, p = 0.033). Accordingly, in study no. 36 (Mueller et al. 2012) serum Cu levels were higher in progressive MCI than in stable MCI (p < 0.05) and early AD subjects (p < 0.01), whilst Cu/non-heme Fe ratio was also higher in progressive MCI compared with CTR (p < 0.01), stable MCI (p < 0.05) and early AD (p < 0.001). Lavados et al (2008) (study no. 28) assessed Fe (total and redox-active) in CSF and did not find between group differences with regards to total Fe. Notwithstanding, redox-active Fe was lower in normal subjects (0.29 ± 0.04 µM) versus moderate MCI (M = 0.46, SEM = 0.04 µM)) (p < 0.05), and lower in AD (M = 0.26, SEM = 0.05 µM) versus moderate MCI (p < 0.05). Study no. 54 (Vaz et al. 2018) analyzed Cu, Fe and Se in red blood cells concentrate and observed higher levels of Cu and Fe in AD (Cu: Md = 0.060 mg/L; Fe: Md = 13.00 mg/L) than in CTR (Cu: Md = 0.048 mg/L, p < 0.0001; Fe: Md = 10.00 mg/L, p < 0.001), with no differences regarding Se. No differences were found across the different stages of AD (mil, moderate and severe), but moderate AD exhibited higher levels of Cu (Md = 0.060 mg/L) and Fe (Md = 12.000 mg/L) than CTR (Cu: p = 0.013; Fe: p = 0.009), while severe AD exhibited both higher levels of Cu (Md = 0.065 mg/L) and Fe (Md = 14.000 mg/L) and lower levels of Se (Md = 0.014 mg/L) than CTR individuals (Cu: p = 0.001; Fe: p = 0.017; Se: Md = 0.029 mg/L, p = 0.008). In study no. 15 (Gao et al. 2007), higher nail Se quintiles associated with better CSID scores in a dose–response fashion (p < 0.0001), while lower blood Se quintiles associated with lower CSID scores.

Finally, study no. 57 (Yang et al. 2018) revealed that participants with high levels of urinary inorganic As or high levels of urinary monomethylarsonous acid had increased risks of AD (p < 0.05), while participants with higher levels of urinary dimethylarsinic acid had a reduced risk of AD (p < 0.05). Furthermore, the associations between these three forms of As and the risk of AD were all significant dose–response relationships. Blood Se shared a borderline significant inverse association with the risk of AD (Q3: ≥ 230.1 μg/L vs Q1: < 198 μg/L; p = 0.0582). Additionally, people with low blood Se associated with high urinary inorganic As, or low blood Se associated with low urinary dimethylarsinic acid had a 2.88- and 2.33-fold risk of AD, respectively (compared to those with high blood Se and low urinary inorganic As or high blood Se and high urinary dimethylarsinic acid). No significant results were found regarding blood Cd, Hg and Pb.

Discussion

The pathways through which TE exert their influence in cognition are diverse and not fully understood. The present review gathers and summarizes the current state of research on the relationship between TE and the cognitive performance of adults and older adults.

Evidence on the relationship between TE and Global Cognition is complex and provides highly mixed results, especially when it comes to essential elements. Copper and Mn (two of the most widely studied metals alongside Fe, Se and Zn) were frequently found to be associated with worse cognitive performances, with only one study reporting contradictory results. In turn, Fe and Se were mostly related to better global cognition, with only two studies stating the opposite. Zinc exhibited an equivalent body of evidence supporting both positive and inverse associations with cognitive performances, although positive relationships were observed in studies of higher methodological quality. For each TE, there was a significant set of evidence on the lack of a relationship with cognition. Less studied elements such as Co and Cr were related to worse cognitive performances, while Rb and Mg were associated with better cognition. Barium, Ca, Mo and V yield contradictory results. Again, there was also significant evidence on the lack of a relationship between global cognition and each TE. Strontium, Sn and Ti were consistently unrelated to cognition.

Regarding non-essential elements, As, Cd, Hg and Pb appeared to be associated with worse cognitive performance, although some studies failed to detect these relationships. Concerning As specifically, one study differentiated between different types, reporting that while inorganic As was associated with worse performances, dimethylarsinic acid was associated with better performances. In turn, Li, Ni and Sb were mainly unrelated to global cognition. Despite evidence on Al being more inconsistent, most findings (which also come from studies of higher quality) support an association between the TE and worse global cognition. Limited data (≤ 2 studies) on Ag, Au, Be, Ga, Ge, Si, Te, Tl, W reported no significant associations with global cognition, data on Pt and U reported negative and positive associations (respectively), and data on B, Bi, Th and Zr reported mixed results.

In this review, four studies examined interactions between TE. In one study, higher ratios of Cu and Zn in relation to Se were found to be associated with a higher risk for elderly women of developing MCI, while higher Cu/Zn levels were associated with reduced risk. One study showed higher levels of As-Cd-Pb mixture relating to worse cognitive performance in a dose–response manner, but that this relationship was weakened by higher levels of Se. Another study reported a positive additive effect of Se and V and a negative additive effect of Co in the relationships between other TE and global cognition. Finally, Cd, Mn and Pb levels were associated with overall cognitive performances, sharing a strong negative relationship for men and a slight positive relationship for women.

The distribution of research focus across specific cognitive domains is notably uneven, with certain abilities having been considerably investigated while others remain underexplored, yielding limited to no empirical evidence.

Orientation is the awareness of oneself in relation to their surroundings. It depends on the integrity and integration of mental activities such as attention, perception and memory and is exceedingly vulnerable to brain disorders (Lezak et al. 2012). Results on orientation were scarce and sparse and did not allow for conclusions on how this ability relates with TE in the human body. The same is true for the Intelligence domain, which was only addressed by 2 of the 60 included studies.

When it comes to the Attention domain, findings were more abundant, and while some elements appear to have more consistent associations with this ability, others show contradictory or non-significant results. Among essential elements, Se was the only TE to be consistently related to better performances (although some studies were not able to report significant results). The same was true for I, but this data came only from one study. While Cr was reported as being unrelated to attention by all studies addressing it, Ba, Co, Cu and Mn shared an inverse or otherwise non-significant relationship with performances. Among non-essential elements, As, Cd, Cs, Hg and Pb tended to be related to worse attention performances, although a similarly relevant set of evidence suggested no association between the two. These disparities do not seem to be related to differences in analytical techniques, assessment procedures, or methodological quality. Both sets of evidence originate from a comparable number of fair and good quality studies that utilize various attention measures and analytical techniques to quantify TE. In addition to this, different studies employing the same attention measures and analytical techniques yield both significant and non-significant results, suggesting that the observed disparities are likely due to inherent variability in the data.

In turn, evidences on Al, Fe and Zn were contradictory. Aluminum tended to be related to worse attention but had one study reporting contradictory results (higher concentrations being associated with better performances). This study was the only clinical trial addressing attention, and therefore assumed a distinct methodology that may have contributed to the contradictory results. An equivalent number of studies related Fe with better and worse attention, while others did not find significant associations. Studies on Zn using blood-based samples reported non-significant to positive relationships with attention, while the only study using hair samples reported an inverse association. Limited data exist on B, Be, Li, Mo, Ni, Sb, Sn, Sr, Tl, U, V and W. The few studies (≤ 3 studies) addressing these TE reported no significant associations with attention.

Data on the relationship between TE and Learning and Memory showed that while some elements shared negative associations, others had no significant impact, but only a few were related to better memory performance. Essential element Fe and non-essential elements Cd and Pb were consistently related to worse memory, while Se was associated with better performances. It is important to note that for each element, there is also a set of findings suggesting a lack of association. Aluminum and Cu mainly shared inverse associations with learning and memory, despite some contradictory results. In turn, Mn was mostly unrelated to performances of this cognitive domain, albeit contradictory results from three different studies. The same was true for As. Notwithstanding, studies addressing different kinds of As reported associations between worse memory and inorganic As, as well as monomethylarsonic acid, while dimethylarsinic acid showed mixed results. There were consistent findings of a lack of significant associations between memory and Co, Cr, Hg, and Zn, as well as Ba, Cs, I, Li, Mo, Ni, Sn, Sr, Tl, U, V, and W (this last set of elements being shortly address by one or two studies).

Concerning working memory, results were fewer but marginally more consistent. All TE—Al, As, Cd, Cr, Cu, Fe, Hg, Mn, Ni, Pb, Se, V, Zn—had evidence to support the absence of an association with working memory, but Cd, Cu, Fe and Mn were also related to worse performances.

Evidence on Executive Functioning also showed some coherency, although for every TE there was a significant set of evidence supporting a lack of association. Data on essential elements Cu, Fe, and Mn suggest negative associations between these metals and executive performances, while Co and Zn potentially share a positive relationship with this cognitive domain. Non-essential elements As, Cd, Hg, Li, Pb were related to worse executive functioning (albeit supported by fewer findings when compared to essential metals). One study reported a positive association between executive performances and Sn. Finally, the only TE to show mixed results was Al, whose evidence were more supportive of an inverse association than a positive relationship. There is limited data on B, Ba, Be, Cr, Cs, Mo, Ni, Sb, Se, Tl, U and V (≤ 3 studies), but existing evidence suggest no significant associations with executive functioning.

Similar to the orientation domain, limited-to-no data exists on the interaction between TE and Visuospatial and Visuoconstructive abilities. Elements such As, Cd and Cr were assessed by only one study and shared no significant relationships with this cognitive domain. The same was true for Hg, Mn and Pb, which were assessed by two or three different studies. In turn, although Al, Fe, Cu and Li were associated with worse visuospatial/visuoconstructive performances and Co and Zn related with better performances, such evidence was counteracted by findings of no significant associations provided by studies of similar methodological qualities. Significant relationships should be taken in carefully given the reduced amount of data that exists on this topic.

In the same line, few data exist on the relationship between Language and TE, with no element being addressed by more than four studies. Aluminum, As and Mn were both unrelated and related to worse language performances. The opposite was true for Se, and one study suggested a relationship between Cr and better language performances. From the most studied element to the least studied, Cd, Co, Cu, Hg, Li, Pb and Zn lacked significant associations with this cognitive domain. Only Fe yielded mixed results.

Finally, concerning Dementia Staging, Cu and Fe were the most studied elements, followed by Mn, Se, and Zn. Evidence suggested an inverse relationship between Cu and cognitive status, with individuals with greater impairments (e.g., AD versus CTR, moderate and severe AD versus CTR and mild AD) presenting higher levels of this metal. Similar findings were reported for Mn and a opposite relationship was observed for Se. In turn, Fe and Zn showed mixed results, as did Al and Cr. No conclusions could be drawn regarding As, Ba, Cd, Co, Hg, Li, Mo, Ni, Pb, Sb, Sn, Sr and V. On the case of As, one study addressed its different forms in in relation to Se. Finding suggested a relationship between the increased risk of AD and high levels inorganic As (alone and associated with low Se), as well as monomethylarsonous. Dimethylarsinic acid (alone and associated with Se) was in turn associated with a risk reduction. Only two studies addressed the discriminatory validity of TE, suggesting a potential utility of several elements (e.g., Cu, Hg, Pb) to distinguish between different diagnosis/stages of cognitive impairment. It is important to point out that the studies addressing different stages of dementia/cognitive impairment were very scarce and generally of low quality when compared to studies focused on global cognition or specific cognitive domains. Therefore, any tendency for a relationship should be considered with caution.

Relevant drawbacks were detected during this review. The studies herein included demonstrated a need for improved methodologies, with fewer than 50% meeting the criteria for “good quality”. Moreover, the validity of research concerning the relationships between TE in the human body and cognitive performances hinges on the adequate measurement and statistical control of potential confounding variables, notably age, sex and education. While most studies performed statistical control of potential confounding variables, a significant subset omitted one of these critical variables, thereby jeopardizing the integrity of the findings. Important considerations for upcoming research must be the prioritization of better methodological quality and implementation of robust statistical control of confounding variables. Furthermore, the assessment of cognitive performances should be based on valid and reliable psychometric tools. Although this was not a prominent issue in this review, some isolated studies restricted cognitive assessment of one specific domain to an overly simplistic task, which may have limited the depth of their findings. Significant gaps in research were observed within specific cognitive domains, such as orientation, language, visuospatial and visuoconstructive abilities and intelligence. In addition, there were limited studies that employed psychometric tools to measure dementia staging. A lack of studies assuming longitudinal designs was also detected. Longitudinal studies are essential for stablishing causality, as causal associations require exposures/predictors of interest to occur/be measured prior to the outcome. Cross-sectional analyses provide weaker evidence of potential causal relationships. It is imperative that future studies aim to fill these gaps.

This review is not without limitations. Firstly, only studies written in English or Portuguese were considered for review, potentially excluding important findings published in other languages. Secondly, although we included all available non-significant findings, studies with such results may be more difficult to publish. Therefore, this review may be subject to publication bias, despite having followed the PRISMA guidelines for systematic reviews and meta-analysis. Thirdly, studies of fair and poor quality (characterized by methodological flaws) were still included in this review. While we critically evaluated these studies and considered their results in light of their limitations, the inclusion of lower-quality studies may have introduced some degree of variability into our findings. At last, despite the considerable number of studies included in this review, the heterogeneity among biological samples, analytical techniques around TE and psychometric tools used to assess cognitive performance, did not allow for a meta-analysis. Future research would benefit from the development and implementation of standardized protocols to quantitatively assess how TE influence cognition.

Conclusions

Existing literature on trace elements and cognitive performance of adults and older adults was summarized in this systematic review, and while this relationship was consistent for some elements, others provided mixed or limited results.

Evidence on Cu, Mn and Se were consistent in relating the first two elements with worse cognitive performance, and the latter with better cognitive performance (global and across cognitive domains). This was also the case for Zn, although its association with better cognition was more fragile. Evidence on Fe associated this element with better global cognition, but findings regarding different cognitive domains were opposite or inconclusive. In turn, As, Cd, Hg and Pb, although not adequately assessed in relation to all cognitive domains, yield consistent evidence of their relationship with worse cognitive performances, both global and specific. Sturdy conclusions could not be drawn for any other TE.

Given the valuable insight it provides into the complex relationships between TE and cognition, this review may serve as a foundational reference for researchers as it identifies patterns, gaps, and inconsistencies in the current literature, and highlights methodological drawbacks, as well as priority areas for future studies.