The Relationship Between Fluoride Exposure and Child IQ

Post by Lila Metko 

The takeaway

Researchers have yet to determine to what extent fluoride exposure could cause neurotoxic effects. The authors examined multiple studies that measured the relationship between prenatal and child fluoride exposure and child IQ scores. They reported an inverse association between fluoride exposure and child IQ, meaning that IQ went down as fluoride exposure levels went up. 

What's the science?

It is estimated that on average the largest percentage of an American’s fluoride consumption comes from fluoridated drinking water. In 2006, the National Research Council issued a report outlining the possible neurotoxic effects of high fluoride exposure from drinking water. Multiple meta-analyses in the past decade have suggested an inverse relationship between fluoride exposure and child IQ. This week in JAMA Pediatrics, Taylor and colleagues conducted a meta-analysis of 74 studies on this topic, including a study quality assessment (also called risk of bias). 

How did they do it?

The authors systematically searched eight large network databases including PubMed, Scopus, and PsycINFO. The criterion for inclusion in the meta-analysis necessitated that the study “estimated the association between exposure to fluoride…and a quantitative measure of children’s intelligence.” Each study in the meta-analysis was evaluated with the OHAT risk of bias tool, a method developed by the National Toxicology Program. The OHAT risk of bias tool is an 11-question assessment including key questions that evaluate how well individual studies address potential confounding, exposure characterization, and outcome assessment. The majority of studies included reported group averages but 19 reported individual-level exposure, typically determined through fluoride content in drinking water or fluoride concentration in urine. The authors did a mean effects meta-analysis and a regression slopes meta-analysis that evaluated group-level and individual-level fluoride exposures respectively. A mean effects meta-analysis estimates standardized mean differences, a summary statistic that calculates the difference in IQ between children living in areas with high fluoride exposure and children living in areas with low fluoride exposure. A regression slopes meta-analysis uses regression coefficients from individual studies to estimate the change in IQ per a 1 mg/L increase in fluoride exposure. Some studies were excluded from these primary analyses because of factors such as a lack of reported mean IQ scores for outcome measures and overlapping populations. 

What did they find?

The authors found an inverse relationship between fluoride exposure and IQ in both the mean effects and regression slopes meta-analyses. Findings were consistent across high-risk of bias and low-risk of bias studies. Associations remained inverse when the exposure groups were exposed to less than 4 mg/L and less than 2 mg/L in drinking water. In the regression slopes meta-analysis the authors found that for every 1 mg/L increase in urinary fluoride concentration, there is a decrease of 1.63 points in a child’s IQ. While this study only assesses associations, it is significant to note that the inverse relationship between fluoride level exposure and child IQ was intact across different study designs, methods of assessing fluoride exposure, and IQ assessments. 

What's the impact?

This study is one of several in the past decade to find an inverse relationship between fluoride exposure and child IQ. This meta-analysis is notable because it used a rigorous and transparent process to identify all studies relevant to the specific research question, extract data from each study, and assess each of the 74 studies for risk of bias based on pre-specified criteria. Interestingly, associations remained inverse even when the exposure groups were exposed to less than 4 mg/L and less than 2 mg/L in drinking water. The EPA enforces that drinking water cannot have more than 4 mg/L fluoride and recommends that drinking water should have less than 2 mg/L fluoride. 

Human-AI Interactions Can Amplify Human Bias

Post by Meagan Marks

The takeaway

When AI systems are trained on human biases, they can absorb and amplify them over time. When we interact with these biased systems, our biases may be subliminally strengthened and our perceptual, emotional, and social judgments can be affected.

What's the science?

Artificial Intelligence (AI) is rapidly becoming more prevalent in the workplace, with its use expanding across fields like healthcare, marketing, and education. While AI offers numerous benefits, it is crucial to recognize its potential flaws to improve the technology and maximize its effectiveness. One such flaw is the ability of AI to recognize and mimic human biases, which may influence human perceptual, social, and emotional judgments over time. However, the exact ways in which human biases are introduced into AI systems and, in turn, how these biases affect human judgment—both directly (when using AI as a tool) and indirectly (when passively encountering AI-generated content)— have not been extensively studied. This week in Nature Human Behavior, Glickman and Sharot explore how AI systems learn from human biases, how biased results can influence human judgment across different contexts, and how these AI interactions compare to human-to-human interactions. 

How did they do it?

To test how AI systems influence human judgment, the authors conducted a series of experiments involving emotional, perceptual, and social tasks with 1401 participants total. In the first series of tasks, participants were shown a group of 12 faces and asked if they, as a whole, appeared more happy or sad (emotional judgment). An AI algorithm was then trained on the participants’ trials to perform the same task. A new pool of participants was then asked to perform this same task, however, this time participants were presented with an AI-generated judgment after they had submitted their initial judgment. These participants were then given the option to adjust their responses (human-AI interaction). This same test was conducted with human feedback for comparison (human-human interaction).  

In a second series of tasks, participants were shown a group of dots on a screen and estimated the percentage of which were moving from left to right (perceptual judgment). Again, participants first performed this task on their own. The researchers then developed an accurate, unbiased algorithm and a biased algorithm to perform the task. Participants then performed the task again, and after submitting their answers, some were shown the response of the accurate algorithm, while others were shown the results of the biased one. 

In a final series, the authors wanted to produce a set of tasks designed to mimic real-world interactions with AI and assess how they impact social judgments. Within the task, participants were first shown images of people of different races and genders and were asked who would more likely be a financial manager. Participants were then presented with real AI-generated images from a public and popular AI software for 1.5s—a time meant to reflect quick, genuine interactions—and were asked the same question again. 

What did they find?

In the face-labeling series, participants initially showed a slight bias toward labeling faces as sad, but this bias gradually corrected itself throughout the trials. However, when AI was trained on this biased, human data, it reflected and amplified the bias in its responses over time. As participants evaluated their answers in collaboration with this biased AI system, they were more likely to adjust their responses to align with the AI’s outputs, which, over time, increased their own biases. This amplification of bias did not occur when participants were shown responses from other humans, indicating that human biases were more impacted by the AI system than by human feedback. Interestingly, AI’s label contributed to this effect: when researchers labeled human responses as AI-generated, participants were more likely to trust the response as correct. Interestingly, when participants were told the AI responses were human, they absorbed the bias, but to a lesser extent. 

In the moving dot series, participants were initially unbiased but developed increasingly biased responses as they interacted with the biased AI algorithm. However, participants’ answers improved in judgment and accuracy when working with the unbiased AI system. Notably, the participants were reportedly unaware of the biased algorithm’s influence over their judgment. 

Finally, in the real-world task, the authors also showed that exposure to biased AI images altered the social judgments of human participants.

What's the impact?

This study is the first to show that AI systems can reflect and amplify subtle human biases, ultimately influencing our judgments in perceptual, emotional, and social contexts. This is particularly concerning in high-stakes areas like medical diagnoses, hiring decisions, and widely seen advertisements. Greater awareness of AI’s potential to influence human judgment is needed, as is the development of measures to mitigate bias. 

Access the original scientific publication here

The Role of Mitochondria in Age-Related Cognitive Decline

Post by Kelly Kadlec

The takeaway

This study investigated how mitochondria influence cognitive decline related to aging. In addition to illuminating the molecular link between synaptic excitation and mitochondrial gene transcription, the authors demonstrate how this molecular cascade could provide a basis for treatments to improve age-related cognitive decline.      

What's the science?

A loss of energy as we age is a nearly universal experience, and a decline in cognitive function is seen as largely inevitable. It is thought that this change in the aging brain is related to changes in mitochondrial function, but the molecular underpinnings of this process have remained largely unknown. Understanding the relationship between neuronal activity and mitochondrial DNA transcription may provide key insights into the aging brain and how we might counteract functional decline by developing treatments that target this interaction. Last week in Science, Li and colleagues uncovered the molecular cascade linking synaptic excitation and mitochondrial DNA transcription and demonstrated that targeting this cascade can improve age-related cognitive decline in rodents.  

How did they do it?

The authors investigated the molecular process of activity-dependent mitochondrial DNA transcription in mice using a broad range of in vivo and ex vivo techniques. First, the authors used RNAscope and optogenetics in hippocampal brain slices along with foot-shocks and quantitative real-time PCR in vivo to establish that neuronal activity interacts with mitochondrial transcription. Then, they compared this mitochondrial expression in young and aged mice. To better understand the cause of the age-related changes they observed, they again used optogenetic and pharmacological tools to isolate a critical role for activity-dependent calcium. 

Next, they conducted immunogold electron microscopy in the hippocampus to determine whether or not this calcium dependency is regulated by calmodulin-dependent protein kinase II (CAMKII). The authors then sought to determine whether mitochondria can decode calcium activity through CRE-like sequences. They used a DNA-affinity assay to identify the presence of mitochondrial CREB and derived CREB activity sensors to directly probe its function. 

Finally, the authors used whole-cell recordings, intracellular ATP measurements, and a variety of genetic techniques to measure and modulate neuronal activity. They examined the role that activity-coupled mitochondrial transcription plays in synaptic function and regulation. They tested these findings under the hypothesis of age-related changes by investigating how inhibiting or enhancing activity-dependent mitochondrial transcription impacts association-based learning in mice of different ages.

What did they find?

The authors first show a causal coupling between neuronal and synaptic excitation and mitochondrial DNA transcription. This expression was reduced in aged mice compared to young mice and was also associated with lower levels of activity-dependent mitochondrial calcium. The authors subsequently found that activity-coupled mitochondrial transcription relies on mitochondrial calcium.

The authors also probed the mechanisms that link neural activity with mitochondrial transcription and found that this process recruits the same molecules that have an established role in activity-transcription coupling in the nucleus. Specifically, activity-dependent mitochondrial transcription and calcium were regulated by CaMKII. Moreover, the translation from activity-dependent calcium to DNA transcription is mediated by mitochondrial CREB.

The authors also show how activity-coupled mitochondrial transcription regulates both synaptic and mitochondrial resilience, further demonstrating how this molecular process mediates both neuronal energy reserves and memory processes. Finally, they show that restoring activity-dependent mitochondrial transcription in aged mice enhances memory, suggesting a mitigation of age-related cognitive decline.

What's the impact?

Activity-dependent mitochondrial DNA transcription has long been suspected to play a critical role in maintaining neural energy reserves, and this study provides key insight into the molecular cascade underlying this process. The authors also show age-related alterations in this pathway that likely contribute to cognitive decline during aging. The authors also demonstrate that reinvigorating this pathway may reduce or even reverse age-related decline in brain function.  

Access the original scientific publication here.