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(https://www.psypost.org/does-your-partners-drinking-hurt-your-mental-health-men-may-feel-it-most/) Does your partner’s drinking hurt your mental health? Men may feel it most
Sep 8th 2024, 10:00

A new study published in (https://doi.org/10.1111/pere.12543) Personal Relationships explores how perceptions of a romantic partner’s drinking habits are related to relationship satisfaction and mental health among young adults. The study finds that when young men perceive their partner as having problematic drinking behavior, they are more likely to report increased symptoms of depression. The findings also suggest that men’s mental health and relationship satisfaction are more impacted by the quantity and nature of drinking within the relationship than women’s.
Alcohol consumption is prevalent among young adults, especially in college environments. Over half of individuals aged 18 to 25 report drinking alcohol in the past month, and around 30 percent engage in binge drinking. The college environment, with its social norms and opportunities for drinking, exacerbates alcohol use, which peaks during this period of life. Young adults’ romantic relationships are another critical context that can influence their drinking habits and overall well-being.
The researchers aimed to examine the intersection of romantic relationships, alcohol use, and mental health. Specifically, they were interested in understanding how individuals’ perceptions of their partner’s drinking behavior influenced their own mental health and relationship satisfaction. This area has been underexplored, particularly in non-married, young adult populations, as most research has focused on older, married couples.
The study involved 239 undergraduate students from a large southwestern university in the United States, all of whom were between the ages of 18 and 25, unmarried, and in a romantic relationship for at least three months. The participants were primarily female (76 percent) and predominantly white (87 percent). They were asked to report their own drinking habits and their perceptions of their partner’s drinking over the past two weeks and three months. The researchers also measured their levels of anxiety, depression, and relationship satisfaction.
To assess drinking behaviors, participants completed questionnaires that asked about how much alcohol they and their partner consumed, including how often they drank, the number of drinks they typically consumed, and whether they engaged in binge drinking. The study also used established scales to measure symptoms of anxiety and depression, as well as relationship satisfaction.
The researchers grouped the participants into categories based on their drinking behaviors and drinking problems, which are defined as negative consequences related to alcohol consumption. Two drinking quantity partnerships were identified: “concordant low” and “concordant heavy,” where both partners either drank lightly or heavily, respectively. For drinking problems, three partnerships emerged: “concordant low” (both partners reported low drinking problems), “discordant female high” (the female partner had high drinking problems, while the male partner did not), and “discordant male high” (the male partner had high drinking problems, while the female partner did not).
The most significant result was that men who perceived their partner as having drinking problems tended to report higher levels of depression. This pattern was not observed in women, which may indicate that men could experience a stronger association between their partner’s problematic drinking and depressive symptoms.
In contrast, perceived partner drinking behavior was not significantly linked to symptoms of anxiety for either men or women. This finding was somewhat unexpected, considering the known association between drinking problems and other mental health issues. However, men reported significantly higher levels of anxiety when both they and their partner were heavy drinkers, while this effect was not observed in women.
The researchers also identified differences in how drinking patterns were related to relationship satisfaction. Men’s relationship satisfaction tended to decrease when they perceived their own drinking behavior as problematic, particularly in relationships where both partners drank heavily. In these cases, men reported lower relationship satisfaction. For women, relationship satisfaction appeared less influenced by these factors, suggesting that men may be more sensitive to how their own and their partner’s drinking behavior relates to their feelings of relationship satisfaction.
While the study provides valuable insights, it has some limitations that should be considered. First, the study relied on self-reported data, which can be influenced by biases in how participants perceive their behavior and their partner’s. People might overestimate or underestimate their partner’s drinking, which could affect the results.
Additionally, the study used a cross-sectional design, meaning the data was collected at one point in time. As a result, it is difficult to establish cause-and-effect relationships. For example, it is unclear whether perceived partner drinking problems cause depression or whether individuals with depression are more likely to perceive their partner’s drinking as problematic.
To build on these findings, future research could examine how romantic partner drinking influences mental health over time. Longitudinal studies, which follow participants over months or years, would help clarify the direction of the relationship between partner drinking and mental health. For instance, does a partner’s drinking behavior worsen an individual’s mental health over time, or do pre-existing mental health issues lead to changes in perceptions of a partner’s drinking?
Finally, interventions could be developed based on these findings. If men are particularly vulnerable to anxiety and dissatisfaction in relationships where heavy drinking is present, then targeted mental health and relationship counseling for couples may help mitigate these effects. These interventions could focus on communication skills and strategies for managing drinking behaviors within the relationship.
The study, “(https://onlinelibrary.wiley.com/doi/10.1111/pere.12543) Anxiety and depression in young adults: The role of perceived romantic partner drinking,” was authored by Katie P. Himes, Sarah E. Victor, Adam T. Schmidt, and Andrew K. Littlefield.

(https://www.psypost.org/people-tend-to-exaggerate-the-immorality-of-their-political-opponents/) People tend to exaggerate the immorality of their political opponents
Sep 8th 2024, 08:00

A series of eight studies conducted in the United States found that people generally tend to overestimate their political opponents’ willingness to accept basic moral wrongs. This tendency to exaggerate the immorality of political opponents was observed not only in discussions of hot political topics but also regarding fundamental moral values. Many people believe that the opposing political side finds blatant wrongs acceptable. The research was published in (https://academic.oup.com/pnasnexus/article/3/7/pgae244/7712370) PNAS Nexus.
Political animosity in the U.S. has been steadily growing over the past 40 years. Many Americans report that they hate the opposing political party more than they love their own, a sentiment associated with rising support for political violence. Studies show that both Democrats and Republicans believe their opponents are more extreme, harbor more prejudice, and conform more closely to demographic stereotypes than they actually do. They even tend to overestimate how much they disagree with the other side on specific policy issues.
Study author Curtis Puryear and his colleagues propose an even more significant misperception of political opponents, which they call the “basic morality bias.” This bias refers to the exaggerated perception that outgroup members, in this case, political opponents, lack basic moral values—that they accept fundamental moral wrongs. The authors describe this bias as “basic” because it is not about contentious political issues or nuanced moral dilemmas but about widely agreed-upon moral wrongs in society (e.g., theft or wrongful imprisonment).
The authors clarify that this bias does not mean individuals believe the other side is completely devoid of all moral capacities. Instead, individuals tend to overestimate the other group’s willingness to accept basic moral wrongs.
To investigate the existence of the basic morality bias and explore potential remedies, the researchers conducted a series of eight studies. The first study analyzed 5.8 million tweets from 5,800 partisans. The authors examined how often words denying the other side’s basic moral values were used to describe political opponents. These words included terms like “rapist,” “pedophile,” “felon,” “thief,” “sociopath,” “murderer,” “molest,” “homicidal,” and “psychopath.”
The second study surveyed 346 MTurk workers (240 Democrats and 106 Republicans), who rated the immorality of various moral issues (e.g., fraud, child pornography, homicide, embezzlement, animal abuse, cheating on a spouse, wrongful imprisonment). They then rated how they believed the average Democrat and the average Republican would rate these issues. Participants also completed assessments of their willingness to engage with political opponents and how much they dehumanize them. The third study was similar but used a larger group recruited through Prolific. Participants were told they would receive a bonus for correctly guessing the views of the average Democrat or Republican.
Studies 4 through 8 were survey experiments that explored how correcting the basic morality bias influenced participants’ views of the other side. In these experiments, participants interacted with or were informed about the views of a fictional character with opposing political beliefs. Study 4 was conducted in person at a public university in the southeastern U.S., where participants were compensated with an ice pop. The remaining studies were conducted online, using MTurk workers or participants recruited through Qualtrics panels.
The first study revealed that words indicating basic morality bias were especially prevalent in political tweets. The proportion of tweets using such words has increased over time. In 2013, only about 0.5% of political tweets from both liberals and conservatives used these terms. This share increased significantly over time, particularly among liberals, and peaked in 2018. In that year, 3% of tweets by liberals used words reflecting basic morality bias, compared to about 1.25% of political tweets by conservatives. By 2022, a little more than 2% of tweets by liberals referencing conservative elites or identity used basic morality bias words, compared to just under 2% of tweets by conservatives mentioning liberal elites or identity.
The results of studies 2 and 3 showed that both Republicans and Democrats vastly overestimated the percentage of supporters from the other side who approve of basic moral wrongs. For example, Democrats estimated that more than 25% of Republicans supported wrongful imprisonment, while in reality, less than 4% of Republicans held such views. Similarly, Republicans in the third study believed that around 32% of Democrats approved of cheating on a spouse, while fewer than 5% of Democrats expressed such views.
Democrats also believed that over 30% of Republicans approved of tax fraud, when the actual percentage was under 5%. Similarly, Republicans estimated that around 25% of Democrats approved of tax fraud, but the real figure was less than 3%.
The findings from studies 4 through 8 showed that correcting the basic morality bias reduced dehumanization of political opponents and increased participants’ willingness to engage with them (in studies 4, 6, and 7). It also reduced participants’ inclination to opt out of collaborating with political opponents (study 5). Furthermore, correcting the basic morality bias for one member of the opposing party decreased dehumanization of the entire political group (study 8).
“The United States is witnessing historic levels of political hostility and gridlock. This animosity is partly grounded in misperceptions of opponents’ political beliefs, but we find many Americans overestimate political opponents’ willingness to accept even the most basic moral wrongs. These findings suggest individuals and practitioners working to foster cross-partisan interaction might first correct this basic morality bias. Specifically, we show that learning a single opponent condemns basic moral wrongs increases behavioral engagement with political opponents and decreases dehumanization of the entire political outgroup,” the study authors concluded.
The study sheds light on the psychological mechanisms influencing the perception of political opponents. However, the authors note that the studies were primarily conducted using online samples, which tend to be more politically engaged than the average American. It also remains unclear how long the corrections to the basic morality bias observed in these experiments will last.
The paper, “(https://doi.org/10.1093/pnasnexus/pgae244) People believe political opponents accept blatant moral wrongs, fueling partisan divides,” was authored by Curtis Puryear, Emily Kubin, Chelsea Schein, Yochanan E. Bigman, Pierce Ekstrom, and Kurt Gray.

(https://www.psypost.org/brain-imaging-reveals-the-neural-roots-of-curiosity/) Brain imaging reveals the neural roots of curiosity
Sep 8th 2024, 06:00

In a recent study published in the (https://doi.org/10.1523/JNEUROSCI.0974-23.2024) Journal of Neuroscience, researchers have made a groundbreaking discovery: the subjective feeling of curiosity is linked to specific brain activity, which varies according to uncertainty. Using brain imaging, the team pinpointed how certain regions of the brain respond to ambiguous visual stimuli, helping to explain why we sometimes feel curious when we encounter uncertainty. This research provides novel insights into the neural basis of curiosity and offers clues about how curiosity might arise in various contexts.
Curiosity is a fundamental human trait. It drives us to learn, explore, and seek out new information, even when there are no direct rewards involved. While previous research has shown that curiosity engages brain areas linked to motivation and memory, there is still much to understand about how feelings of curiosity are generated. Specifically, scientists have wondered how our brain represents uncertainty and how this uncertainty might trigger curiosity.
Previous studies on curiosity often focused on questions or trivia and how people’s confidence in knowing the answer influenced their curiosity. This research consistently showed that people tend to be more curious when they are less confident about the answer, suggesting a link between uncertainty and curiosity. However, the neural mechanisms behind this link remained largely unexplored. This new study set out to clarify these mechanisms by focusing on perceptual curiosity—curiosity triggered by ambiguous or distorted images rather than factual questions.
“The neural mechanisms that generate curiosity are very poorly understood. Perceptual curiosity — curiosity about ambiguous images — is particularly interesting, because it offers the possibility to understand how curiosity about an item (e.g., an image) arises from the way that the brain represents that item and specifically, about the uncertainty of that neural representation,” said corresponding author (https://zuckermaninstitute.columbia.edu/jacqueline-gottlieb-phd) Jacqueline Gottlieb, a professor of neuroscience and principal investigator at Columbia University’s Zuckerman Institute.
To investigate how uncertainty relates to curiosity, researchers recruited 32 participants who underwent brain scans using functional magnetic resonance imaging (fMRI), a noninvasive technology that tracks brain activity by measuring changes in blood oxygen levels. As participants viewed images, the researchers could observe how various brain regions responded.
The visual stimuli used in the study were special images called “texforms.” These texforms were images of animals or man-made objects, such as a walrus or a hat, that had been distorted to varying degrees. The distortion was designed to make the images more or less recognizable, thus creating different levels of uncertainty for the participants.
In each trial, participants viewed a texform and were asked to guess what the original image might be. They then rated both their confidence in their guess and their curiosity about seeing the undistorted image. Finally, the participants were shown the original image, giving them the “answer” to their guess.
Throughout the process, the researchers measured brain activity, focusing on specific regions like the occipitotemporal cortex (OTC), the ventromedial prefrontal cortex (vmPFC), and the anterior cingulate cortex (ACC). The OTC, located just above the ears, is a brain region involved in visual processing and object recognition, while the vmPFC and ACC are areas associated with subjective confidence, decision-making, and information-seeking behaviors.
The researchers found that participants’ curiosity was inversely related to their confidence: the less confident they were about what the distorted image depicted, the more curious they were to see the original image. This relationship was expected based on previous studies involving trivia questions, but the researchers demonstrated it with visual stimuli, expanding the understanding of curiosity to perceptual domains.
One of the most significant findings was how curiosity corresponded to activity in the OTC. When participants were less curious, the OTC showed brain activity patterns that distinctly identified the texform’s category, whether it was an animal or a man-made object. In other words, when participants were more curious, the OTC displayed more ambiguous activity patterns, suggesting that the brain couldn’t clearly categorize the image, which heightened curiosity.
“I was pleasantly surprised to find a significant correlation between visual activity and curiosity,” Gottlieb told PsyPost. “Of course, our study was conducted in the hope that we would find such a correlation. However, there were many reasons why we may have failed to find it — for instance, the fact that many processing stages intervene between the visual activity and the subjective feeling that activity generates. So I was very gratified — and a bit surprised — to get the result we were hoping for.”
Furthermore, brain scans revealed that two additional areas—the vmPFC and ACC—were active during the task. The vmPFC, which is involved in evaluating confidence and value, showed increased activity when participants felt more certain about the texform’s identity, and decreased activity when curiosity was higher. This region appeared to act as a kind of “bridge” between the uncertainty represented in the OTC and the participants’ feelings of curiosity. It seemed to “read” the uncertainty from the OTC and helped determine whether the person needed to be curious about the image.
Interestingly, while both the vmPFC and ACC responded to confidence, only the vmPFC appeared to mediate the relationship between curiosity and the uncertainty in the OTC. This suggests that different brain regions may play distinct roles in how we experience and act on curiosity.
The findings demonstrate “that the brain has sophisticated mechanisms, which involve interactions among several areas, of signaling when you are uncertain and should become curious (ask a question) about something,” Gottlieb explained.
The study sheds new light on the neural mechanisms of curiosity. But there are limitations to consider. The study focused exclusively on visual curiosity, specifically curiosity about ambiguous images. It remains to be seen whether these findings can be generalized to other forms of curiosity, such as curiosity about trivia questions or social information. Future research will need to explore whether the same brain mechanisms are involved when people are curious about different types of stimuli.
“The results are so far specific to the images we used,” Gottlieb noted. “It is my hope that they will generalize to other types of curiosity (for example, curiosity about other images, or about trivia questions) but this remains to be determined in future research.”
The study also raises important questions about individual differences in curiosity. For example, are some people more prone to curiosity because of differences in how their brains process uncertainty? Could curiosity be linked to personality traits or interests? These are open questions that future research could explore. Understanding how curiosity varies between individuals could have implications for education, where fostering curiosity is often a goal, and for mental health, where curiosity is sometimes diminished in conditions like depression.
“I would like to continue to understand the neural mechanisms that generate other forms of curiosity, and how they relate to individual interests and personality types,” Gottlieb said.
The study, “(https://www.jneurosci.org/content/44/33/e0974232024) Neural Representations of Sensory Uncertainty and Confidence are Associated with Perceptual Curiosity,” was authored by Michael Cohanpour, Mariam Aly, and Jacqueline Gottlieb.

(https://www.psypost.org/5-myths-about-antidepressants-you-should-stop-believing/) 5 myths about antidepressants you should stop believing
Sep 7th 2024, 14:00

During my work as a clinical psychologist and neurobiologist, I have spoken with many individuals who are considering taking (https://www.canada.ca/en/health-canada/services/drugs-medical-devices/antidepressant-drugs.html) antidepressant medications such as selective serotonin reuptake inhibitors (SSRIs). Many ask me for my thoughts on whether they need medication, whether the talk therapy will be enough or whether they are “strong enough” to get over it without medications.
I always make a point of listening to their reasons for taking medications versus their hesitations. While many are reasonable, such as potential interactions with other health conditions, I also hear many unfounded reasons over and over, suggesting that myths underlying antidepressant hesitancy exist deep in our collective psyches.
Given the (https://www.camh.ca/en/camh-news-and-stories/anxiety-depression-loneliness-among-canadians-spikes-to-highest-levels) rising rates in depression and anxiety, it is time to talk about how treatments work and why people hesitate so we can make informed treatment decisions — especially when hesitations may not be grounded in science.
Here are some of the most common myths I hear, along with my responses:
Myth 1: I am stronger if I do this without meds
Overcoming depression is like overcoming a broken leg. You could be an extremely strong competitive weightlifter, but if your leg is broken you cannot use it in the same way. You may be an incredibly strong person psychologically but if you have depression, (https://doi.org/10.1038/s41380-020-0685-9) your brain is no longer responding to everyday life in the same way and it needs to “heal” before you can expect it to function like it did pre-depression.
Myth 2: I will be dependent on antidepressants to be happy
Antidepressants don’t make people happy; they allow people to experience all emotions in an appropriate and balanced way. Antidepressants do not offer immediate symptom relief, in fact they take four to six weeks to take full effects. However, they are a long-term (typically at a minimum for a year) and (hopefully) curative treatment, much like chemotherapy for certain types of cancer. With chemotherapy, you typically have to do a certain number of treatments over a prescribed time in order to kill the cancerous cells and be considered in remission.
Similarly, most studies show that if you take antidepressant medications for a year before coming off of them, (https://doi.org/10.1177/2045125317737264) the majority of people will not relapse. That means you will likely need to take them for a certain period of time to maintain the effects, but the effects will often remain long after you stop taking them. However, a small portion of people have a more chronic form of depression and (https://doi.org/10.1038/s41380-020-0843-0) may need to remain on medications for longer periods.
Myth 3: Meds will change who I am, I will be different or feel high
Antidepressant medications (https://doi.org/10.1017/s1121189x00006254) do not make people feel “high.” They don’t change what you know, what you learn or who you are, but they do allow you to view things from a more balanced perspective. I once heard a patient describe taking antidepressant medications quite simply: “I still see the same good and bad things, but when I was depressed I seemed to only pay attention to the bad and now I pay attention to the good as well.”
Myth 4: I will become addicted
Antidepressants taken as prescribed (https://doi.org/10.2147%2FSAR.S37917) are generally not addictive and have a low potential for misuse. Antidepressants are not associated with things like cravings for the drug, as seen with addictive medications like opioids. Some patients report withdrawal symptoms such as headache or nausea when they stop taking certain antidepressants suddenly, but these are generally short-lived and can be minimised by tapering off treatment slowly.
Myth 5: Meds should only be used as a last resort
Reserving antidepressants only for extreme cases doesn’t make sense for several reasons. First, it is a matter of quality of life: depression hurts. It hurts (https://doi.org/10.1007/s40273-021-01040-7) the sufferer, the people around them, work productivity and has immense societal consequences. The financial repercussions that can be attributed to depression in terms of the number of work days missed, jobs lost, accidents caused, etc. are enormous.
We actually have medications that can help, are not addictive and have been around for long enough that long-lasting effects following treatment have been studied. To date, major long-term consequences of taking antidepressants as prescribed have rarely been observed in the short-term, though new evidence suggests that long-term antidepressant use (10 years or more) (https://doi.org/10.1192/bjo.2022.563) may be associated with increased cardiovascular disease risk. Though it is important to note that depression itself is (https://doi.org/10.1001/jama.2020.23068) also associated with increased cardiovascular disease risk.
So, if it improves someone’s quality of life — their concentration, their sleep, their relationships, their ability to work or to be present as a parent, decreases worry or helps them find the energy to do things they enjoy — why not consider the treatment?
Another factor in favour or treatment is that while major long-term negative consequences of taking antidepressants for a depressive episode have not been observed, the major long-term ramifications of living with depression have absolutely been observed. Depression significantly increases risk of (https://doi.org/10.1007/s11920-005-0055-y) cardiovascular disease, gastrointestinal disease, respiratory disease and (https://doi.org/10.1212/wnl.0000000000001684) Parkinson’s disease, to name a few. It also seems to worsen the outcomes for cancer.
If taking the medication is generally not associated with long-term health consequences but living with depression is, then the answer seems straightforward.
Treating depression
I am not suggesting that everyone with depression should take medications. Of course, this is something to be discussed with your doctor and there may be reasons why this would be a good or bad option for you.
Like any treatment, antidepressant medications do have side-effects and may pose risks to certain patients. If you are going to therapy or getting support in other ways and you see improvement, then by all means continue. But, if you are struggling and have held out on considering medications because of antidepressant hesitancy myths, maybe reconsider and discuss the possibility with your doctor.
It is also important to note that generally, the number of people that show improvement with talk therapy or by taking antidepressant treatments is similar (around 50-60 per cent). However, (https://doi.org/10.1002/wps.20701) combining antidepressant medication with talk therapy is associated with greater improvement and a significantly (https://doi.org/10.1177/2045125317737264) reduced likelihood of relapse.
One theory as to why this occurs is because (https://doi.org/10.1038/s41380-019-0615-x) antidepressants increase neuroplasticity, which then leaves the brain in a better position to retain and exercise the gains made in therapy. One might think of antidepressants as therapy boosters in this case.
Antidepressant medications have evolved extensively from the (https://doi.org/10.1177/2398212818812629) first-generation medications used in the 1950s. There are now lifetimes of data about the long-term effects and underlying functions. Newer medications are now largely designed based on scientific theory.
Debunking the myths surrounding antidepressants is critical to permitting educated treatment decisions for those who suffer.
 
This article is republished from (https://theconversation.com) The Conversation under a Creative Commons license. Read the (https://theconversation.com/debunking-5-myths-about-antidepressants-206915) original article.

(https://www.psypost.org/new-machine-learning-model-predicts-parkinsons-disease-risk-up-to-15-years-in-advance/) New machine learning model predicts Parkinson’s disease risk up to 15 years in advance
Sep 7th 2024, 12:00

A recent study published in (https://doi.org/10.1212/WNL.0000000000209531) Neurology suggests that individuals at high risk of developing Parkinson’s disease could be identified years before symptoms arise. By using machine learning to analyze proteins found in blood samples and combining this data with simple clinical information, researchers developed a model capable of predicting Parkinson’s disease risk up to 15 years in advance. This early detection could help prevent or delay the progression of this neurodegenerative disorder, offering new hope for disease management and treatment.
Parkinson’s disease is the second most common neurodegenerative disorder after Alzheimer’s. It primarily affects movement, causing tremors, stiffness, and balance problems. However, by the time these symptoms appear, significant and irreversible brain damage has often occurred. Parkinson’s has a long “prodromal phase” that can last for decades before typical motor symptoms become evident. During this period, non-motor symptoms such as sleep disorders, depression, and loss of smell may emerge, but they are often not recognized as early warning signs of Parkinson’s.
The challenge in treating Parkinson’s lies in its late diagnosis, after extensive brain damage has already occurred. Current treatments focus on managing symptoms rather than halting disease progression. Scientists believe that identifying the disease at earlier stages—before noticeable motor symptoms—could allow for interventions that prevent or delay the onset of more severe symptoms.
The new study, led by (https://www.dcs.warwick.ac.uk/~feng/) Jian-Feng Feng, Dean of the Institute of Science and Technology for Brain-Inspired Intelligence at Fudan University, along with (https://wchenglab.com/) Wei Cheng, Principal Investigator at the same institute, was driven by the need for an accessible and non-invasive way to identify those at high risk for developing Parkinson’s years in advance.
The study team also included Lin-Bo Wang, a Young Associate Principal Investigator at Fudan University, and Jia You, a former postdoctoral researcher who has since been promoted to Young Principal Investigator. Their collective efforts focused on combining machine learning with blood biomarkers to create a predictive model that could detect Parkinson’s disease risk long before clinical symptoms appear.
“Parkinson’s disease is characterized by the irreversible loss of dopaminergic neurons, which is caused by α-synuclein aggregates,” the researchers told PsyPost. “In 2022, (https://doi.org/10.1212/wnl.0000000000200814) upon discovering that the brain volume of newly diagnosed patients correlates with future clinical progression, we developed an interest in predicting Parkinson’s disease years prior to clinical diagnosis. Early detection holds utmost significance for the development of treatments aimed at decelerating brain atrophy and postponing disease progression in the very early stage of the disease.”
To develop a model capable of predicting Parkinson’s risk, the researchers analyzed data from over 50,000 participants in the UK Biobank, a large health resource in the United Kingdom that collects data on genetic, clinical, and lifestyle factors. The study focused on the levels of 1,463 different proteins found in the blood and how these proteins might be linked to future Parkinson’s diagnoses.
The study included 52,503 participants who did not have Parkinson’s at the start of the study. These participants had their blood plasma analyzed to measure protein levels. The research team used machine learning, a form of artificial intelligence that identifies patterns in data, to analyze the blood protein levels alongside clinical and demographic information. This information included factors like age, education, history of head injuries, and other health markers.
During a median follow-up period of 14 years, 751 participants developed Parkinson’s disease. The researchers used this data to train a machine learning model that could predict who was at risk of developing Parkinson’s based on their protein levels and clinical characteristics. They then validated the model using a separate dataset from the Parkinson’s Progression Markers Initiative, a project that includes people diagnosed with Parkinson’s, those at high risk of developing the disease, and healthy individuals.
The model developed by the researchers achieved a high level of accuracy, correctly identifying individuals at risk for Parkinson’s disease in both the UK Biobank and the validation dataset. The analysis revealed that 22 specific proteins found in blood plasma were significantly associated with Parkinson’s risk. Some of the most important proteins identified included neurofilament light (NfL), a protein linked to brain cell damage, and several proteins involved in inflammation and muscle function.
By integrating clinical information such as age, history of traumatic brain injury, and blood creatinine levels (a marker of muscle mass and kidney function), the researchers were able to improve the accuracy of the model. The final version of the model, which included both protein and clinical data, was able to predict Parkinson’s risk with a high degree of accuracy, even up to 15 years before diagnosis.
“A combination of plasma proteins and clinical-demographic measures is capable of identifying individuals at high risk of developing Parkinson’s disease up to 15 years prior to the clinical diagnosis,” Feng and his colleagues said. “Our model can be integrated into routine health examinations to detect high-risk individuals of developing Parkinson’s disease, thereby presenting opportunities to explore and assess neuroprotective treatments.”
The study also revealed that certain proteins showed distinct changes over time in individuals who eventually developed Parkinson’s. For example, levels of the protein NfL began to rise about 12 years before diagnosis, while other proteins linked to inflammation and muscle function showed changes several years before diagnosis. These findings suggest that monitoring protein levels over time could provide valuable insights into an individual’s risk of developing Parkinson’s.
“We were surprised to discover that changes in several proteins could be observed more than a decade before clinical diagnosis,” the researchers explained. “For example, NfL, a marker of neuronal damage and the most significant predictive protein, showed increased levels 12 years before diagnosis, indicating early neuroaxonal damage.”
While the findings of this study are promising, there are several limitations that need to be addressed in future research. One limitation is the lack of diversity in the study population. Most participants in the UK Biobank and the Parkinson’s Progression Markers Initiative were of European descent, which may limit the generalizability of the findings to other populations. Future studies will need to validate the model in more diverse populations to ensure it is effective for everyone.
Another limitation is that the diagnosis of Parkinson’s in the UK Biobank was based on medical records, which may not always be accurate. Some participants could have been misdiagnosed, particularly in cases where specialists were not involved in the diagnosis. More accurate diagnostic methods, such as brain imaging, could help improve the reliability of future studies.
In addition, while the study identified several proteins that are associated with Parkinson’s risk, many of these proteins are also linked to other neurodegenerative diseases. For example, elevated levels of NfL have been found in Alzheimer’s disease and other conditions that involve brain cell damage. Therefore, these proteins may not be specific enough to Parkinson’s, and additional biomarkers may be needed to distinguish Parkinson’s from other diseases.
The study also used a semi-quantitative method to measure protein levels, which may limit the accuracy of the findings. Future studies that use more precise measurement techniques could help refine the model and improve its predictive power.
Finally, the model was trained using data collected at a single point in time, which may not capture biological fluctuations in protein levels. Repeated measurements of protein levels over time could provide more accurate predictions and help identify the most reliable biomarkers for early detection of Parkinson’s.
Despite these limitations, this study represents a significant step forward in the early detection of Parkinson’s disease. The model developed by the researchers offers a non-invasive, cost-effective way to identify individuals at high risk of developing Parkinson’s, potentially allowing for earlier interventions that could slow or prevent the progression of the disease. While further research is needed to validate the findings and refine the model, the results suggest that blood-based biomarkers, combined with clinical information, could be a valuable tool for predicting Parkinson’s risk in the general population.
“Our long-term objective is to develop a series of predictive models applicable in community sets,” the researchers said. “These models will utilize non-invasive, cost-efficient, and readily accessible features to detect PD and other neurodegenerative disorders years prior to clinical diagnosis, with the aim of slowing down or preventing their progression.”
“In addition, the testing fee for plasma proteins is currently high in high-throughput proteomics. We are working on collaborating with companies to conduct blood tests specifically targeting these biomarkers, which would significantly reduce application costs.”
The study, “(https://www.neurology.org/doi/10.1212/WNL.0000000000209531) Prediction of Future Parkinson Disease Using Plasma Proteins Combined With Clinical-Demographic Measures,” was authored by Jia You, Linbo Wang, Yujia Wang, Jujiao Kang, Jintai Yu, Wei Cheng, and Jianfeng Feng.

Forwarded by:
Michael Reeder LCPC
Baltimore, MD

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