In this study of U.S. adults age 20–59 years, higher levels of urinary cadmium were associated with worse performance on each of four neuropsychological tests when the analyses were adjusted only for urinary creatinine. These relationships were not significant after adjustment for the full set of potential confounding variables (model 2). Adjustment for age in particular mitigated the effect estimates for urinary cadmium. However, the relationship between higher urinary cadmium levels and poor SDST scores was significant in the multivariable-adjusted model that was not adjusted for smoking variables (model 3). Smoking is a source of cadmium exposure  and smoking has been associated with neurocognitive deficiencies . Therefore cadmium may mediate some of the effect of smoking on neurocognitive performance. If so, including smoking variables in the models could underestimate the magnitude of associations with cadmium, because cadmium-independent and cadmium-dependent effects of smoking would be difficult to disentangle.
To further explore this issue we performed analyses restricted to never smokers with no evidence of occupational cadmium exposure. These exclusions removed potentially confounding co-exposures in tobacco smoke and certain occupational environments from the analyses, thus allowing us to evaluate associations that should be related primarily to dietary cadmium exposure [1, 20, 31, 32]. In these restricted analyses we found that the association between higher urinary cadmium and worse SDST score was still significant after multivariable adjustment (βCd = 0.0193, 95%CI: 0.0005, 0.0381). Because the SDST assesses attention and perception, this data suggests that elevated cadmium exposure in adults may be associated with reduced capacity in these cognitive functions.
The effect estimate for urinary cadmium among never smokers without occupational exposure (βCd = 0.0193) predicts a 1.93% (95%CI: 0.05, 3.81) increase in SDST score for a 1 μg/L increase in urinary cadmium. This suggests that moving from the 25th to the 75th percentile of urinary cadmium (0.19 to 0.82 μg/L) would correspond to 1.22% increase in SDST score. Higher scores indicate worse performance. While this effect size is small in magnitude, low-level cadmium exposure is nearly ubiquitous . Thus if this association reflects a causal relationship, there may be a public health impact . Furthermore, as we discuss below, this effect size and it’s public health impact may be underestimated due to the adjustment for age.
We also note here that the fully adjusted effect estimate for SDLT total error score was borderline significant when restricting to never smokers with no known occupational cadmium exposure (ORCd = 1.45, 95%CI: 0.99 - 2.14). This provides evidence for a potential association with decreased performance in learning recall and short-term memory as well.
For the 4 outcomes evaluated, adjusting for age produced the largest change in the urinary cadmium effect estimates, and in each case, age adjustment resulted in a decrease in the effect estimate. Because urinary cadmium concentration is a marker of cumulative exposure it tends to increase with age (bivariate model with log transformed urinary cadmium: βage = 0.0262, 95%CI: 0.0227, 0.0296). Cognitive performance on each of the four tests decreased with age in our study population and this is consistent with the findings of Kreig et. al. in 2001  (bivariate models: SRTT βage = 0.0012 [95%CI: 0.0008, 0.0017]; SDST βage = 0.0097 [95%CI: 0.0091, 0.0102]; OR for having a poor [above median] SDLT score associated with a 1 year increase in age: SDLT trials-to-criterion: 1.04, 95%CI: 1.03-1.05, SDLT total-error-score: 1.03, 95%CI: 1.02-1.04). The relationship between age and urinary cadmium concentration makes it difficult to determine if a portion of the age-associated cognitive changes may be due to cumulative cadmium exposure (i.e. cadmium may be on the causal pathway between age and neurocognitive performance). For this reason, adjusting for age may result in the underestimation of cadmium-test score associations. We nevertheless observed a significant association between urinary cadmium concentration and SDST scores despite adjusting for age (in the analysis restricted to never smokers with no known occupational exposure), and thus the association could be larger in magnitude than reported here. Though there is some instability in the urinary cadmium effect estimates within age strata in the SDST analysis (learning recall/short-term memory), these results (Table 4) are consistent with interpretation that adjusting for age may result in the underestimation of the urinary cadmium-SDST association.
With respect to visual motor speed (SRTT) the age interaction analysis is intriguing. Interestingly, a trend of worse performance with higher urinary cadmium was observed only in the youngest age group (20–29 years of age), suggesting that age modifies the relationship between cadmium and visual motor speed. The direction of this effect estimate suggests that young adults may be vulnerable to an adverse effect of cadmium on visual motor speed, perhaps because their baseline performance speed is slightly faster than the older age groups.
None of the cadmium-sex interaction terms were significant, but future studies may still benefit from considering cadmium-sex interactions, in light of the evidence that cadmium may mimic sex hormones, and have different toxicokinetics in males and females .
Comparison to previous findings
As described in the introduction, there are a limited number of epidemiologic studies which looked specifically for associations between cadmium exposure and neurocognitive outcomes in adults. One study in Navy recruits (n = 40) found that high hair cadmium levels were significantly correlated with lower reading levels and behavioral problems but the analysis did not consider potential confounding . Another small study (n = 31) evaluated occupationally exposed workers from a refrigerator coil manufacturing plant and found that workers with higher urinary cadmium levels had worse performance in tests of attention/psychomotor speed, and memory . However, an alternate analysis found no significant relationships between urinary cadmium and performance after accounting for age and education. In a study of 89 workers (42 exposed and 47 control), urinary cadmium was significantly associated with poor visuomotor performance (symbol digit substitution and simple reaction time tests), even after adjusting for age, alcohol, exposure to other neurotoxicants, neuroactive medications, and years of schooling . Case control studies suggest that elevated cadmium exposure or differences in cadmium processing may be associated with violent criminal behavior  and dementia/Alzheimer’s disease [35, 36], but the results have been inconsistent [37, 38].
In addition to these small studies, three population-based studies have been reported in elderly adults. One of these studies involved the evaluation of trace minerals in drinking water and dementia/cognitive screening of elderly people in China (n =1,016) . This study did not detect an association between cognitive score and water cadmium levels but did identify a significant zinc-cadmium interaction. Participants with high levels of both zinc and cadmium in their water had lower cognitive scores. This group published another population-based study of elderly people in China that evaluated metal biomarkers in blood plasma rather than in drinking water (base study population: n = 2000, blood sample subpopulation: n = 188) . Here the authors reported an association between higher plasma cadmium levels and lower composite cognitive scores after adjusting for age, gender, education, APOE genotype and BMI. A third study of elderly people was conducted in Stockholm (study population: n = 804, subsample analyzed for blood cadmium: n = 763) . In this study no association was found between blood cadmium and mini-mental status exam (MMSE) scores, but in this report the composition of their regression models is unclear, and thus it is difficult to assess how potential confounding was addressed.
The previous work most comparable to our own in terms of outcomes measures (NES computerized neurobehavioral testing) is the study by Viaene et al . The primary difference in the Viene et. al. study is that it was done in an occupational study population with higher exposure levels. In this work the authors found significant relationships between urinary cadmium and poor performance in both a simple reaction time test (visual motor speed) and a symbol digit substitution test (attention and perception). The results of our unadjusted SRTT analyses were consistent with their findings, although we did not detect a significant association between urinary cadmium and SRTT after multivariable adjustment. We found a similar relationship between urinary cadmium and SDST scores, and this association was significant when we made our models more similar to those of Viaene et al., by not adjusting for smoking. In our study this relationship with SDST was also significant in the subpopulation of never smokers with no known occupational cadmium exposure. Our study primarily involved low-level non-occupational cadmium exposures, and we controlled for different (as well as more) potential confounding variables. These factors may explain the differences in the findings of the studies.
The other study which assessed similar neurocognitive testing outcomes (Hart et. al 1989) involved the evaluation of 31 occupationally exposed workers, and the authors reported decreased attention, psychomotor speed, and memory in workers with higher urinary cadmium levels . Though these associations were no longer significant after adjusting for age and education, the similarity of identified domains is worth noting.
The exposure metrics, exposure sources, exposure levels, sample sizes, age of participants, ethnicity of participants, outcome metrics, and consideration of potential confounders vary greatly among the prior epidemiologic studies. Despite these differences, 3 of the 4 large population-based studies, including our own, provide evidence linking cadmium exposure to worse neurocognitive performance in adults [5–7]. Additionally, the studies which attempted to evaluate neurocognitive domains, including our own, have identified similar domains: attention/perception (and perhaps memory) [3, 4]. Furthermore, there is evidence that cadmium exposure may be associated with adverse neurodevelopmental outcomes in children (briefly reviewed in ).
With respect to possible mechanisms, laboratory animal experiments have demonstrated that cadmium exposure can affect neurotransmitter function, electrophysiological parameters, and behavior [40–47]. Combined with the epidemiologic studies, these reports from experimental toxicology increase the public health concerns about cadmium exposure and neurocognition.
Implications for cadmium risk assessments
Recent risk assessments have established urinary cadmium reference levels to protect against kidney damage (EFSA 2009: 1 μg Cd/g creatinine, WHO/FAO 2011: 5.24 μg Cd/g creatinine), as this has been considered the most sensitive endpoint of cadmium toxicity [1, 10]. In our study we did not use creatinine-standardized urinary cadmium to assess exposure. We instead included urinary creatinine as an independent term in our regression models as recommended by Barr et al. 2005 , but we can compare our results to these reference levels by evaluating our models within the exposure ranges defined by these reference levels. We added a reference level-cadmium interaction term to our fully adjusted SDST models and extrapolated stratum specific effect estimates for urinary cadmium among participants with 1) less 1 μg Cd/g creatinine, 2) 1–5.24 μg Cd/g creatinine, and 3) greater than 5.24 μg Cd/g creatinine. Excluding the extreme outlier there were only 2 participants with urinary cadmium levels above 5.24 μg Cd/g creatinine in the full study population, and among the never smokers with no known occupational cadmium exposure there were no urinary cadmium concentrations above this level, thus the highest exposure category could not be evaluated in these analyses.
In the general study population we found evidence that the inverse relationship between urinary cadmium concentration and SDST performance may be present among those with 1–5.24 μg Cd/g creatinine (βCd = 0.0114, 95%CI: -0.0016, 0.0244) but not present among those with less 1 μg Cd/g creatinine (βCd = −0.0133, 95%CI: -0.0298, 0.0031). Among the never smokers with no known occupational cadmium exposure we saw a similar pattern, however the association magnitude was much greater and highly significant among those with 1–5.24 μg Cd/g creatinine (1–5.24 μg Cd/g creatinine: βCd = 0.0510, 95%CI: 0.0217, 0.0804, less than 1 μg Cd/g creatinine: βCd = −0.0223, 95%CI: -0.0509, 0.0064). If this relationship reflects a casual mechanism then our data suggests that neurocognitive performance may be a sensitive endpoint of cadmium toxicity in adults and that the WHO/FAO reference level may not protect against this effect.
Recent evidence suggests that decreased rates of smoking are contributing to decreases in cadmium exposure in the U.S . However, our study suggests that non-smoke, non-occupation based cadmium exposure may be associated with adverse neurocognitive outcomes in adults at levels below 5.24 μg Cd/g creatinine (the current WHO/FAO reference level). If this association reflects a causal relationship then common low level dietary cadmium exposures may be responsible decreased neurocognitive function in many U.S. adults. Further neurocognitive research should be conducted and new risk assessments for cadmium may be needed. It is important to note that associations with adverse neurodevelopmental outcomes in children have been recently been reported at cadmium levels below 1 μg Cd/g creatinine  and therefore the findings in children may have a larger impact on future risk assessments.
Limitations and strengths
Our study is limited by the cross-sectional nature of the data, which restricts our ability to assess the temporal relationships between variables. However the long half-life of cadmium in the body (> 10 years) and the cumulative nature of the urinary cadmium exposure metric  suggest that at least some component of the exposure measurement reflects cadmium exposure which occurred well before the neurocognitive outcome measurement. We also note that our findings, like those of any observational study, may reflect non-causal relationships related to uncontrolled confounding.
The main strengths of our analysis include: a large sample size, adjustment for multiple potential confounders, sufficient information on exposure routes to evaluate nonsmoking nonoccupational cadmium exposure, and the use of an objective computer-based neuropsychological evaluation that was designed for epidemiologic applications . Additional strengths of our analysis include use of penalized splines to assess and account for nonlinear relationships, as well as the use of a dataset that was designed to be representative of the U.S. population.