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July 21, 2025
New research has uncovered important links between certain blood metabolites and ADHD by using a genetic method called Mendelian randomization. This approach leverages natural genetic differences to help identify which metabolites might actually cause changes in ADHD risk, offering stronger clues than traditional observational studies.
Key Metabolic Pathways Involved:
The study found 42 plasma metabolites with a causal relationship to ADHD. Most fall into two major groups:
Since many metabolites come from dietary sources like proteins and fats this supports the idea that diet could influence metabolic pathways involved in ADHD. However, because the study focused on genetic influences on metabolite levels, it doesn’t directly prove that dietary changes will have the same effects.
Notable Metabolites:
Five metabolites showed bidirectional links with ADHD, meaning genetic risk for ADHD also affects their levels which suggests a complex interaction between brain function and metabolism.
Twelve ADHD-related metabolites are targets of existing drugs or supplements, including:
While these findings highlight biological pathways, they don’t prove that changing diet will directly alter ADHD symptoms. Metabolite levels are shaped by genetics plus environment, lifestyle, and health factors, which require further study.
Conclusion:
This research provides stronger evidence of metabolic pathways involved in ADHD and points to new possibilities for diagnosis and treatment. Future work could explore how diet or drugs might safely adjust these metabolites to help manage ADHD.
While this study strengthens the link between amino acid and fatty acid metabolism and ADHD risk, suggesting that diet could play a role, ultimately more research is still needed before experts could use this research to give specific nutritional advice.
Shi S, Baranova A, Cao H, Zhang F. Exploring causal associations between plasma metabolites and attention-deficit/hyperactivity disorder. BMC Psychiatry. 2025 May 16;25(1):498. doi: 10.1186/s12888-025-06951-9. PMID: 40380147; PMCID: PMC12084988.
A relatively new area of ADHD research has been examining the association between ADHD and eating disorders (i.e., anorexia nervosa, bulimia nervosa, and binge-eating disorder). Nazar and colleagues conducted a systematic review and meta-analysis of extant studies.
They found only twelve studies that assessed the presence of eating disorders among people with ADHD and five that examined the prevalence of ADHD among patients with eating disorders. Although there were few studies, the total number of people studied was large, with 4,013 ADHD cases and 29,404 controls for the first set of studies and 1,044 eating disorder cases and 11,292 controls for the second set of studies. The meta-analyses of these data found that ADHD people had a 3.8-fold increased risk for an eating disorder compared with non-ADHD controls. The level of risk was similar for each of the eating disorders. Consistent with this, their second meta-analysis found that people with eating disorders had a 2.6-fold increased risk for ADHD compared with controls who did not have an eating disorder. The risk for ADHD was highest for those with binge-eating disorder (5.8-fold increased risk compared with controls).
This bidirectional association between ADHD and eating disorders provides converging evidence that this association is real and, given its magnitude, clinically significant. The results were similar for males and females and pediatric and adult populations.
We cannot tell from these data why ADHD is associated with eating disorders. Nazar et al. note that other work implicates both impulsivity and inattention in promoting bulimic symptoms, whereas inattention and hyperactivity are associated with craving. The association may also be due to the neurocognitive deficits of ADHD, which could lead to a distorted sense of self-awareness and body image.
Given that ADHD is also associated with obesity, some obese ADHD patients may have an underlying eating disorder, such as binge-eating, which has been associated with obesity in prospective studies. Also, lisdexamfetamine is FDA-approved for treating both binge eating and ADHD, which suggests the possibility that the two conditions share an underlying etiology involving the dopamine system. We do not know if treating ADHD would reduce the risk for eating disorders, as that hypothesis has not yet been tested. But such an effect would seem likely if ADHD behaviors mediate the association between the two disorders.
If we are to read what we believe on the Internet, dieting can cure many of the ills faced by humans. Much of what is written is true. Changes in dieting can be good for heart disease, diabetes, high blood pressure, and kidney stones to name just a few examples. But what about ADHD? Food elimination diets have been extensively studied for their ability to treat ADHD. They are based on the very reasonable idea that allergies or toxic reactions to foods can have effects on the brain and could lead to ADHD symptoms.
Although the idea is reasonable, it is not such an easy task to figure out what foods might cause allergic reactions that could lead to ADHD symptoms. Some proponents of elimination diets have proposed eliminating a single food, others include multiple foods, and some go as far as to allow only a few foods to be eaten to avoid all potential allergies. Most readers will wonder if such restrictive diets, even if they did work, are feasible. That is certainly a concern for very restrictive diets.
Perhaps the most well-known ADHD diet is the Feingold diet(named after its creator). This diet eliminates artificial food colorings and preservatives that have become so common in the western diet. Some have claimed that the increasing use of colorings and preservatives explains why the prevalence of ADHD is greater in Western countries and has been increasing over time. But those people have it wrong. The prevalence of ADHD is similar around the world and has not been increasing over time. That has been well documented but details must wait for another blog.
The Feingold and other elimination diets have been studied by meta-analysis. This means that someone analyzed several well-controlled trials published by other people. Passing the test of meta-analysis is the strongest test of any treatment effect. When this test is applied to the best studies available, there is evidence that the exclusion of fool colorings helps reduce ADHD symptoms. But more restrictive diets are not effective. So removing artificial food colors seems like a good idea that will help reduce ADHD symptoms. But although such diets ‘work’, they do network very well. On a scale of one to 10where 10 is the best effect, drug therapy scores 9 to 10 but eliminating food colorings scores only 3 or 4. Some patients or parents of patients might want this diet change first in the hopes that it will work well for them. That is a possibility, but if that is your choice, you should not delay the more effective drug treatments for too long in the likely event that eliminating food colorings is not sufficient. You can learn more about elimination diets from Nigg, J. T., and K.Holton (2014). "Restriction and elimination diets in ADHD treatment."Child Adolesc Psychiatr Clin N Am 23(4): 937-953.
Keep in mind that the treatment guidelines from professional organizations point to ADHD drugs as the first-line treatment for ADHD. The only exception is for preschool children where medication is only the first-line treatment for severe ADHD; the guidelines recommend that other preschoolers with ADHD be treated with non-pharmacologic treatments, when available. You can learn more about non-pharmacologic treatments for ADHD from a book I recently edited: Faraone, S. V. &Antshel, K. M. (2014). ADHD: Non-Pharmacologic Interventions. Child AdolescPsychiatr Clin N Am 23, xiii-xiv.
A Swedish-Danish-Dutch team used the Swedish Medical Birth Register to identify the almost 1.7 million individuals born in the country between 1980 and 1995. Then, using the Multi-Generation Register, they identified 341,066 pairs of full siblings and 46,142 pairs of maternal half-siblings, totaling 774,416 individuals.
The team used the National Patient Register to identify diagnoses of ADHD, as well as neurodevelopmental disorders (autism spectrum disorder, developmental disorders, intellectual disability, motor disorders), externalizing psychiatric disorders (oppositional defiant and related disorders, alcohol misuse, drug misuse), and internalizing psychiatric disorders (depression, anxiety disorder, phobias, stress disorders, obsessive-compulsive disorder).
The team found that ADHD was strongly correlated with general psychopathology overall (r =0.67), as well as with the neurodevelopmental (r = 0.75), externalizing (r =0.67), and internalizing (r = 0.67) sub factors.
To tease out the effects of heredity, shared environment, and non-shared environment, a multivariate correlation model was used. Genetic variables were estimated by fixing them to correlate between siblings at their expected average gene sharing (0.5for full siblings, 0.25 for half-siblings). Non-genetic environmental components shared by siblings (such as growing up in the same family) were estimated by fixing them to correlate at 1 across full and half-siblings. Finally, non-shared environmental variables were estimated by fixing them to correlate at zero across all siblings.
This model estimated the heritability of the general psychopathology factor at 49%, with the contribution of the shared environment at 7 percent and the non-shared environment at 44%. After adjusting for the general psychopathology factor, ADHD showed a significant and moderately strong phenotypic correlation with the neurodevelopmental-specific factor (r = 0.43), and a significantly smaller correlation with the externalizing-specific factor (r = 0.25).
For phenotypic correlation between ADHD and the general psychopathology factor, genetics explained 52% of the total correlation, the non-shared environment 39%, and the shared familial environment only 9%. For the phenotypic correlation between ADHD and the neurodevelopmental-specific factor, genetics explained the entire correlation because the other two factors had competing effects that canceled each other out. For the phenotypic correlation between ADHD and the externalizing-specific factor, genetics explained 23% of the correlation, shared environment 22%, and non-shared environment 55%.
The authors concluded that "ADHD is more phenotypically and genetically linked to neurodevelopmental disorders than to externalizing and internalizing disorders, after accounting for a general psychopathology factor. ... After accounting for the general psychopathology factor, the correlation between ADHD and the neurodevelopmental-specific factor remained moderately strong, and was largely genetic in origin, suggesting substantial unique sharing of biological mechanisms among disorders. In contrast, the correlation between ADHD and the externalizing-specific factor was much smaller and was largely explained by-shared environmental effects. Lastly, the correlation between ADHD and the internalizing subfactor was almost entirely explained by the general psychopathology factor. This finding suggests that the comorbidity of ADHD and internalizing disorders are largely due to shared genetic effects and non-shared environmental influences that have effects on general psychopathology."
Stimulant medications have long been considered the default first-line treatment for attention-deficit/hyperactivity disorder (ADHD). Clinical guidelines, prescribing practices, and public narratives all reinforce the idea that stimulants should be tried first, with non-stimulants reserved for cases where stimulants fail or are poorly tolerated.
I recently partnered with leading ADHD researcher Jeffrey Newcorn for a Nature Mental Health commentary on the subject. We argue that this hierarchy deserves reexamination. It is important to note that our position is not anti-stimulant. Rather, we call into question whether the evidence truly supports treating non-stimulants as secondary options, and we propose that both classes should be considered equal first-line treatments.
Stimulants have earned their reputation as the go-to drug of choice for ADHD. They are among the most effective medications in psychiatry, reliably reducing core ADHD symptoms and improving daily functioning when properly titrated and monitored. However, when stimulant and non-stimulant medications are compared more closely, the gap between them appears smaller than commonly assumed.
Meta-analyses often report slightly higher average response rates for stimulants, but head-to-head trials where patients are directly randomized to one medication versus another frequently find no statistically significant differences in symptom improvement or tolerability. Network meta-analyses similarly show that while some stimulant formulations have modest advantages, these differences are small and inconsistent, particularly in adults.
When translated into clinical terms, the advantage of stimulants becomes even more modest. Based on existing data, approximately eight patients would need to be treated with a stimulant rather than a non-stimulant for one additional person to experience a meaningful benefit. This corresponds to only a 56% probability that a given patient will respond better to a stimulant than to a non-stimulant. This difference is not what we would refer to as “clinically significant.”
One reason non-stimulants may appear less effective is the way efficacy is typically reported. Most comparisons rely on standardized mean differences, a method of averages that may mask heterogeneity of treatment effects. In reality, ADHD medications do not work uniformly across patients.
For example, evidence suggests that response to some non-stimulants, such as atomoxetine, is bimodal: this means that many patients respond extremely well, while others respond poorly, with few in between. When this happens, average effect sizes can obscure the fact that a substantial subgroup benefits just as much as they would from a stimulant. In other words, non-stimulants are not necessarily less effective across the board, but that they are simply different in who they help.
In our commentary, we also highlight structural issues in ADHD research. Stimulant trials are particularly vulnerable to unblinding, as their immediate and observable physiological effects can reveal treatment assignment, potentially inflating perceived efficacy. Non-stimulants, with slower onset and subtler effects, are less prone to this bias.
Additionally, many randomized trials exclude patients with common psychiatric comorbidities such as anxiety, depression, or substance-use disorders. Using co-diagnoses as exclusion criteria for clinical trials on ADHD medications is nonviable when considering the large number of ADHD patients who also have other diagnoses. Real-world data suggest that a large proportion of individuals with ADHD would not qualify for typical trials, limiting how well results generalize to everyday clinical practice.
Standard evaluations of medication tolerability focus on side effects experienced by patients, but this narrow lens misses broader societal consequences. Stimulants are Schedule II controlled substances, which introduces logistical barriers, regulatory burdens, supply vulnerabilities, and administrative strain for both patients and clinicians.
When used as directed, stimulant medications do not increase risk of substance-use disorders (and, in fact, tend to reduce these rates); however, as ADHD awareness has spread and stimulants are more widely prescribed, non-medical use of prescription stimulants has become more widespread, particularly among adolescents and young adults. Non-stimulants do not carry these risks.
Non-stimulants are not without drawbacks themselves, however. They typically take longer to work and have higher non-response rates, making them less suitable in situations where rapid results are essential. These limitations, however, do not justify relegating them to second-line status across the board.
This is a call for abandoning a one-size-fits-all approach. Instead, future guidelines should present stimulant and non-stimulant medications as equally valid starting points, clearly outlining trade-offs related to onset, efficacy, misuse risk, and practical burden.
The evidence already supports this shift. The remaining challenge is aligning clinical practice and policy with what the data, and patient-centered care, are increasingly telling us.
Today, most treatment guidelines recommend starting ADHD treatment with stimulant medications. These medicines often work quickly and can be very effective, but they do not help every child, and they can have bothersome side effects, such as appetite loss, sleep problems, or mood changes. Families also worry about long-term effects, the possibility of misuse or abuse, as well as the recent nationwide stimulant shortages. Non-stimulant medications are available, but they are usually used only after stimulants have not been effective.
This stimulant-first approach means that many patients who would respond well to a non-stimulant will end up on a stimulant medication anyway. This study addresses this issue by testing two different ways of starting medication treatment for school-age children with attention-deficit/hyperactivity disorder (ADHD). We want to know whether beginning with a non-stimulant medicine can work as well as the “stimulant-first” approach, which is currently used by most prescribers.
From this study, we hope to learn:
Our goal is to give families and clinicians clear, practical evidence to support a truly shared decision: “Given this specific child, should we start with a stimulant or a non-stimulant?”
Who will be in the study?
We will enroll about 1,000 children and adolescents, ages 6 to 16, who:
We will include children with common co-occurring conditions (such as anxiety, depression, learning or developmental disorders) so that the results reflect the “real-world” children seen in clinics, not just highly selected research volunteers.
How will the treatments be assigned?
This is a randomized comparative effectiveness trial, which means:
Parents and clinicians will know which type of medicine the child is taking, as in usual care. However, the experts who rate how much each child has improved using our main outcome measure will not be told which treatment strategy the child received. This helps keep their ratings unbiased.
What will participants be asked to do?
Each family will be followed for 12 months. We will collect information at:
At these times:
We will also track:
Data will be entered into a secure, HIPAA-compliant research database. Study staff at each site will work closely with families to make participation as convenient as possible, including offering flexible visit schedules and electronic options for completing forms when feasible.
How will we analyze the results?
Using standard statistical methods, we will:
All analyses will follow the “intention-to-treat” principle, meaning we compare children based on the strategy they were originally assigned to, even if their medication is later changed. This mirrors real-world decision-making: once you choose a starting strategy, what tends to happen over time?
Why is this study necessary now?
This study addresses a critical, timely gap in ADHD care:
In short, this study is needed now to move ADHD medication decisions beyond “one-size-fits-all.” By rigorously comparing stimulant-first and non-stimulant-first strategies in real-world settings, and by focusing on what matters most to children and families overall functioning, side effects, and long-term well-being, we aim to give patients, parents, and clinicians the information they need to choose the best starting treatment for each child.
This project was conceived by Professor Stephen V. Faraone, PhD (SUNY Upstate Medical University, Department of Psychiatry, Syracuse, NY) and Professor Jeffrey H. Newcorn, MD (Icahn School of Medicine at Mount Sinai, Department of Psychiatry, New York, NY). It will be conducted at nine sites across the USA.
EBI-ADHD:
If you live with ADHD, treat ADHD, or write about ADHD, you’ve probably run into the same problem: there’s a ton of research on treatments, but it’s scattered across hundreds of papers that don’t talk to each other. The EBI-ADHD website fixes that.
EBI-ADHD (Evidence-Based Interventions for ADHD) is a free, interactive platform that pulls together the best available research on how ADHD treatments work and how safe they are. It’s built for clinicians, people with ADHD and their families, and guideline developers who need clear, comparable information rather than a pile of PDFs. EBI-ADHD Database The site is powered by 200+ meta-analyses covering 50,000+ participants and more than 30 different interventions. These include medications, psychological therapies, brain-stimulation approaches, and lifestyle or “complementary” options.
The heart of the site is an interactive dashboard. You can:
The dashboard then shows an evidence matrix: a table where each cell is a specific treatment–outcome–time-point combination. Each cell tells you two things at a glance:
Clicking a cell opens more detail: effect sizes, the underlying meta-analysis, and how the certainty rating was decided.
EBI-ADHD is not just a curated list of papers. It’s built on a formal umbrella review of ADHD interventions, published in The BMJ in 2025. That review re-analyzed 221 meta-analyses using a standardized statistical pipeline and rating system.
The platform was co-created with 100+ clinicians and 100+ people with lived ADHD experience from around 30 countries and follows the broader U-REACH framework for turning complex evidence into accessible digital tools.
Why it Matters
ADHD is one of the most studied conditions in mental health, yet decisions in everyday practice are still often driven by habit, marketing, or selective reading of the literature. EBI-ADHD offers something different: a transparent, continuously updated map of what we actually know about ADHD treatments and how sure we are about it.
In short, it’s a tool to move conversations about ADHD care from “I heard this works” to “Here’s what the best current evidence shows, and let’s decide together what matters most for you.”
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