How being uninsured in N.C. can make you sicker – WUNC

How being uninsured in N.C. can make you sicker – WUNC

Two years ago, North Carolina expanded Medicaid. Since then, 670,000 people have signed on to the health insurance program.
But now, after Congress passed the megabill that President Donald Trump will almost certainly sign in to law, those people, and maybe even more, could see their health insurance coverage declined.
This threatens more than inconvenience: Studies have shown that disruptions to insurance coverage can have serious health consequences.
Ciara Zachary, assistant professor in the Department of Health Policy and Management at UNC Gillings School of Global Public Health, says that people in the expansion population — those who gained access in 2023, and who earn between 100% to 138% of the federal poverty line — will be the first affected. That includes adults without children. About 34.5% of that population are ages 19-29. Another quarter fall in the next age bracket, ages 30-39.
Medicaid was designed to assist the most vulnerable among us, including pregnant people, children, those with low income, and those with disabilities, says Zachary.
Without it, people will suffer health consequences; some might even lose their lives. Estimates vary, but all are dire: losing Medicaid and ACA coverage might result in anywhere from 650 to 20,000 preventable deaths per year, depending on who or how you count, according to public health professionals at City University of New York, Hunter College, Cambridge Health Alliance, and University of Pennsylvania.
Otherwise, losing access to Medicaid can impact health in other ways.

Cancer, heart disease, and diabetes are examples of chronic conditions that require intensive management.
“Medicaid expansion has been shown to really help folks with substance use and behavioral health or mental health conditions,” said Zachary.
Without insurance, patients might not be able to access their medications, leading to behaviors like prescription rationing, like skipping doses or splitting pills. This behavior is harmful, too. One study found that people with cardiovascular disease who rationed their medication due to cost had higher rates of heart attacks and strokes.

If you manage to regain insurance once you lose it, that is not the end of the story. Half of one study’s respondents who had a gap in coverage said that losing insurance for any amount of time caused their health to worsen. The same amount of people said that the quality of their care also got worse after changing providers. Some 17% reported needing to change medications.

“We know that when people lose relationships with their provider, that can also cause them to delay care,” said UNC’s Zachary. That might look like not getting checked out when you’re sick and allowing a condition to worsen.
Delaying care might ultimately backfire, too. If a condition worsens, it might mean needing to take time off work down the line. Or, the disease might progress to a point that the required treatment is much more expensive.

Vaccination and screening rates will suffer without regular primary care visits. This is especially consequential for young children, for whom regular checkups are key to growing up healthy.
Preventative care is important at all ages. “Having accessible, reliable care means you’re getting your screenings for various cancers or other health issues,” said Zachary. “Even just getting education on healthy behaviors and reducing risk that could impact your health in the future.”

The burden of a medical bill is heavy when you’re uninsured or underinsured, and stress is a health concern on its own. “We all know that everything is getting more expensive, housing is getting more expensive, food is more expensive. So people just have all these stressors of ‘Am I going to make it financially?'” said Zachary.
Darcy Guill has lived in Pitt County, south of Greenville, for 17 years. Before that, she was an elementary school teacher in California and Colorado. She lives with her mom, Milly, who was also a teacher, and many pets around the house.
Darcy says that growing up under her mom’s health insurance, which she bought through her employer, she worried for naught. After all, she never had anything worse than allergies. And, when she started teaching, too, her insurance was “fairly good.”
Once, after a vacation, Guill noticed some tingling numbness in her feet. It persisted, and she thought she should go check it out. She had health coverage, so she didn’t think twice.
A few weeks later, she got her diagnosis: Multiple sclerosis, an autoimmune disease that impacts the nervous system. There were nine lesions in her brain.
There was a silver lining. “My neurologist says he never diagnosed anybody that early on,” she said. Right away, she got on medication. She stayed in education even when she moved to Colorado.
In 2009, she came to North Carolina. She began pursuing a certificate to teach exceptional children in the general curriculum – it was her way to stay involved with education despite her symptoms. While taking classes, she was a part-time substitute teacher.
However, without full-time work, Guill was left without employer-sponsored insurance, and couldn’t afford to pay for any out of pocket. She could no longer pay to see a neurologist. She began rationing her stockpile of medications until they ultimately ran out. By 2014, Guill felt too ill to work. Without a job, she could no longer afford health insurance.
For a decade, Guill was completely uninsured. In that time, she sought medical care at a free clinic, sponsored by a local church. Through vocational rehab, she was able to start on a pharmaceutical program that provided her with free medications.
In 2021, the churn began. “Churning” is a term that public health experts use to describe transitions between insurance, including losing and gaining insurance altogether. Guill gained access to Blue Cross, but then couldn’t afford the premium. She switched to Friday Health Plans, where the copay was only $11, until they went bankrupt.
The churn continued.
In February 2022, Guill was involved in a severe car accident, and was admitted to the trauma ward for a month. Combined with her unmanaged MS, recovery was grueling. In 2023, she also had an emergency hernia surgery, incurring a $5,000 hospital bill. It turns out she would never need to pay it in totality. In North Carolina, Medicaid expansion was underway.
“It didn’t quite dawn on me at the time, but I was going to be one of the people that benefited. I was going to have insurance again,” she said.
By December 1, on the first day of the expansion, Guill had her Medicaid card. It marked the end of 13 years of churn. Finally, with healthcare coverage, Guill was able to see a neurologist and consider different medication options. At the beginning of this year, she took an MRI to assess whether a certain treatment plan was appropriate.
“I had 30 lesions on my brain,” Guill said. The doctors knew that they had to change course on her treatment. “We have to do something far more aggressive.”
“Had I been consistently on medication, yeah, I would have gotten a few more (lesions), but it wouldn’t have tripled,” reflected Guill. “And the truth is, I thought I was eventually going to be able to go back to work part time. I don’t know if I’m going to be able to.”
Today, looking forward into the future of health care policy, Guill is angry. “I resent the fact that I might be uninsured again or underinsured again,” she said.
Speaking about policy makers, Guill says “they have this preconceived notion about who’s on Medicaid, and they’re wrong. It’s people like me, and I know so many people whose stories are not all that different from mine.”

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JNF-USA Sharing Daily Updates From Israel – Boulder Jewish News

JNF-USA Sharing Daily Updates From Israel – Boulder Jewish News

October 10, 2023 News Comments Off on JNF-USA Sharing Daily Updates From Israel
The unthinkable continues in Israel, and Jewish National Fund-USA is working around the clock in the USA and on the ground in Israel to take care of our friends, family, and colleagues. To update you on the many ways JNF-USA is directly making an impact, we’ve provided info below:
JNF-USA will continue to host Emergency Briefings every day this week and next (except on Saturday/Shabbat) at 10:00 am MT.
Join us for our next LIVE Zoom Briefing: Wednesday, 10/11, at 10:00 am.
A recording of the 10/10 informative briefing from earlier today can be found here:
10/10 Recorded Briefing
10/9 Recorded Briefing
10/8 Recorded Briefing
Here’s how JNF-USA is making an impact:
Evacuations and Housing
JNF-USA is currently operating to support the evacuation of over 2,500 residents of the Eshkol region. The entire Jewish National Fund-USA-supported community of Shlomit in Halutza was evacuated together to Kfar Etzion, where they were welcomed with all the necessities. That is 350 people, 55 families. 50 families were evacuated from Amioz in Eshkol to Eilat. 200 families, a total of 800 people, were evacuated from Ein Habsor and Shuva in Eshkol to Timna Park, a Jewish National Fund affiliate. We are supporting the evacuation of 200 Ukrainian Olim, most of whom do not speak Hebrew, from Sderot to Jerusalem. We are supporting the evacuation of 200 people and their pets from Moshav Yevul in Eshkol to Masada Guest House. We are ready to absorb more evacuees, with over 2,000 rooms prepared to receive those who need them. We will open the dorms of AMHSI campuses in Hod Hasharon and Be’er Sheva to meet these needs.
Protective and Firefighting Equipment
Jewish National Fund-USA’s Sderot Indoor Playground sustained a direct missile hit, thankfully the fire was contained and controlled. We continue to purchase necessary equipment for fire and rescue in partnership with the relevant authorities. We have purchased encrypted radios for Eshkol, Sderot, and Sha’ar Hanegev. 115 security kits were purchased for volunteers in Sderot, including bulletproof vests, helmets, tactical clothes, etc. Lighting for search and rescue efforts was provided to multiple southern communities. 100 cell phone chargers were distributed to soldiers in the IDF.
Supplies
Alexander Muss High School Students packed 1,300 packages of essentials for civilians and soldiers in the south, which will be distributed by MAKOM volunteers, Jewish National Fund-USA’s Lauder Employment Center South established an emergency room, Margalit Start-Up City Galil and Lauder Employment Center North are collecting necessities for soldiers who have been mobilized to the region.
Our needs are constantly evolving, and we will keep you apprised as things continue to develop. Make your gift count today: www.jnf.org/supportisrael 
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Fitness influencer Joey Swoll helping victim of Huntington Beach hit-and-run crash on his road to recovery – ABC7 Los Angeles

Fitness influencer Joey Swoll helping victim of Huntington Beach hit-and-run crash on his road to recovery – ABC7 Los Angeles

Anthony Challman's life changed in the blink of eye last month.
The 21-year-old is on the long road to recovery after nearly being killed in a hit-and-run on June 22.
"He's in so much pain. He's head to toe broken," Anthony's father John Challman said. "He pretty much workouts 3 to 4 hours a day. He's all into the gym and that's his whole life pretty much."
Anthony was in the crosswalk at the intersection of Beach Boulevard and Utica Avenue in Huntington Beach when a car barreled into him.
"I hope that none of you guys ever have to get that phone call. It is the most horrible thing to deal with," the victim's father said.
John immediately drove down from Seattle to be by his son's side.
"He's a great kid and he just needs all of our support at this time," John said.
The driver who hit Anthony took off and no arrests have been made. Huntington Beach police said there is no description of the car available.
To raise Anthony's spirits as he recovers, his dad – on a whim – turned to popular fitness influencer Joey Swoll.
"My son is a big fan of him so I reached out to him just hoping that he would give him some encouraging words and all Joey did, went beyond the call of duty," John said.
Swoll was touched by Anthony's story that he visited him in the hospital to inspire him to overcome this roadblock.
"Knocked on the door, walked in, he kind of just looked up with his big smile on his face like 'no way.' It was pretty cool," Swoll said. "This is an opportunity to gain perspective, to be a little more grateful for life. Not a lot of people walk away from what he went through and the fact that he's still alive, still breathing, he has a chance at life is something you can look at as a blessing or you could look at it as a crutch. Don't let that be a crutch for you."
They hope to see Anthony back in the gym sooner than later.
Anyone with any information on the hit-and-run is urged to call the Huntington Beach Police Department immediately.

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How has Covid-19 affected the coffee industry in Shanghai? – Perfect Daily Grind

How has Covid-19 affected the coffee industry in Shanghai? – Perfect Daily Grind

There’s no denying that the Covid-19 pandemic has had a lasting effect on coffee shops, roasters, traders, producers, and plenty of other coffee businesses around the world. But through 2021 and 2022, while many other countries were loosening their pandemic restrictions, China continued to impose a strict zero-Covid policy across its larger cities, such as Beijing and Shanghai.
This policy forced around half of the population in these cities to remain at home at any given time as a way to decrease the number of Covid-19 cases. Naturally, this placed immense pressure on local businesses (including coffee shops) which had to adapt to the sudden reduction in footfall.
This has only stopped in early June 2022, when the majority of hospitality businesses in Shanghai were allowed to reopen, including the 6,500 coffee shops in the city. However, many businesses are still unable to operate at full capacity, which raises concerns for many coffee shop owners and baristas.
Felipe Cabrera is the CEO and founder of Ad Astra Coffee Consulting in Shanghai. He has been working in the Chinese coffee industry since 2015.
In this article, he explores how China’s zero-Covid policy has affected Shanghai’s coffee industry, as well as how the sector is looking to regain its economic footing.
You may also like our article on how coffee shops can draw customers back after Covid-19.
As many other countries around the world continued to loosen pandemic restrictions in 2022, the Chinese government announced that theirs would return in an attempt to reduce the number of Covid-19 cases in the country.
At any one time, full or partial lockdown measures meant that millions of people in cities like Shanghai and Beijing had to remain at home for weeks on end. Naturally, this meant that coffee shops also had to close their doors.
As a means of reducing pressure on local businesses (and citizens), Shanghai authorities cordoned the city into three types of zones. Areas with recorded positive Covid-19 cases were lockdown zones, while areas without recorded positive cases in the previous seven days were classified as control zones. 
The third type covered areas in Shanghai with no recorded positive Covid-19 cases in the previous 14 days: precaution zones. In these areas, residents were allowed to go outside for a few hours each day – mainly to supermarkets, pharmacies, hospitals, and even work places (if they were located close to their homes).
In early May 2022, according to the Shanghai Daily newspaper, very few districts in the city had low (or “zero”) levels of recorded positive Covid-19 cases. What’s more, the zone status of an area could change suddenly – and lead to an immediate shutdown.
Even in the city’s neighbourhoods which were categorised as precaution zones, coffee shops were unable to provide delivery services, and therefore remained closed. 
These Covid-19 restrictions had severe financial implications for Shanghai’s coffee culture – even larger chains like Starbucks and Luckin Coffee. Both Starbucks China and Yum China (which owns national KFC and Pizza Hut brands) expressed concern over declining sales if the zero-Covid lockdowns were to continue.
During the height of the lockdown in Shanghai, around 900 Starbucks stores were forced to close. In early May 2022, Starbucks reported that its store sales in China decreased 23% during the second quarter of the year – demonstrating why the company was concerned over zero-Covid measures.
So with larger chains like Starbucks and Luckin still very feeling the impact of the lockdown, how were smaller, independent coffee shops affected?
Many specialty cafés have been experiencing the impact of the pandemic even before China’s zero-Covid policy was enforced.
For example, coffee brand Yuzhou had to close all five of its Shanghai locations from mid-March 2022, continuing from four closures when the pandemic first hit the country.
Towards the end of March 2022, other coffee shops, such as haru.ESP Coffee, were unable to source enough delivery drivers (外卖骑手 in Chinese) to keep up with orders. If delivery times could not be guaranteed for the customer, many coffee shops were concerned that it would be detrimental to business in the longer term, and instead chose to close altogether.
Waste has also been an issue for some coffee shops in Shanghai. Towards the beginning of the lockdown, café owners were continuing to order regular levels of stock, such as milk and coffee beans.
However, as footfall abruptly decreased, many coffee shops were forced to dispose of the high volumes of milk and coffee they had bought. Others, meanwhile, were able to sell stock to customers and members of WeChat groups – mostly to people who were already isolated at home.
Local company Radar Coffee also reported selling stock through WeChat as a means of continuing business and reducing waste.
During lockdown, many smaller coffee shops in Shanghai were keeping a close eye on how larger chains were operating. This is because any news about reopening Starbucks or Luckin stores was a likely sign of a wider reopening for independent coffee shops in the city.
Following a two-month lockdown, Covid-19 restrictions in Shanghai eased in early June 2022 – reviving the city’s coffee culture. This allowed residents in “low-risk” areas to leave their homes – meaning hospitality businesses were able to reopen at reduced capacity.
Initially, customers were not allowed to eat or drink inside coffee shops. To accommodate for these restrictions, baristas took orders and served drinks from a small table placed at the front door of coffee shops.
But for many specialty coffee shops, the ongoing decline in foot traffic was still detrimental for sales. 
According to Radar Coffee, it was more sustainable to fully close its stores throughout the various phases of lockdown until conditions had stabilised, as opposed to opening locations on a more sporadic basis.
While some coffee shops chose to close their doors, others remained open and found new ways to boost sales, such as focusing on roasting. In one particular case, a 800 yuan bottle of natural wine (worth around US $120) was served in a coffee shop in an effort to attract more customers.
However, indoor dining restrictions in Shanghai have since been lifted, meaning customers can now visit coffee shops again. On 29 June 2022, Starbucks resumed indoor dining across 800 locations in the city, but government rules only allow a 50% to 70% seating capacity at any time (depending on the size of the store).
Much like other global cities, there will likely be a sharp increase in consumer spending as Covid-19 restrictions in Shanghai loosen – undoubtedly benefitting local coffee culture.
According to an online survey conducted by boutique market intelligence company STCAVISH + CO, Shanghai’s residents are keen to visit restaurants, coffee shops, and bars after the two-month city-wide lockdown. Around 52% of respondents stated they wanted to visit a restaurant as soon as they possibly could.
For Shanghai’s coffee shops, it’s safe to assume that recovery will be quick and positive, particularly for larger chains like Starbucks and Luckin.
Starbucks China reports it is still on track to open a total of 6,000 stores in the country by the end of the year. In the third quarter of 2022 alone, the brand was operating 5,135 locations – giving it a strong basis from which to grow.
But whether or not this recovery will be sustainable in the long-term remains to be seen. China’s wider coffee industry has already taken a substantial hit after shipments of imported green coffee were left stranded at ports and warehouses in Shanghai as a result of lockdown restrictions. For reference, Shanghai accounts for around 30% of China’s total green coffee imports – which could potentially have devastating impacts on the country’s coffee sector in the medium and long term.
For now, some local specialty coffee shops are experimenting with new ways to attract customers, such as selling instant coffee products, coffee concentrates, and single-serve filter coffee bags. 
There will undoubtedly be an increasing focus on delivery services and ecommerce in the Chinese coffee market over the following months. And with more rental vacancies soon to be available in the city (similar to the growth of Shanghai’s rental market in 2020), we could see more coffee shops opening across the city in the near future.
There’s no denying that lockdown measures have already had serious economic consequences for many coffee shops in Shanghai, but it’s difficult to predict how China’s Covid-19 restrictions will the affect city’s coffee culture in the long term. 
However, as the city steadily reopens, it’s likely that consumer spending will increase – boosting sales for many coffee shops. Whether or not this recovery will indicate further sustainable growth remains to be seen.
Enjoyed this? Then read our article on why coffee delivery is so popular in China.
Perfect Daily Grind
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Felipe is a coffee consultant based in Shanghai, China. He specialises in consulting services for a wide range of topics related to Chinese the coffee market and the foodservice industry.
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Jefferson County Leisure Center opens updated fitness center – The Augusta Chronicle

Jefferson County Leisure Center opens updated fitness center – The Augusta Chronicle

Jefferson County’s Leisure Center has gone through a number of changes making it a better place offering more opportunities to the senior citizens of Jefferson County. One new change that adds not only an additional aesthetic appeal but also a practical one is updating the fitness room.
One of the best ways to reduce the negative aspects associated with aging and improve preventative health concerns is following a good exercise and wellness plan.
“We have done a lot of work on the outside and the inside of the leisure center and it’s an effort that the whole county can be proud of, but for the seniors that come here and utilize the center, they love it. The one thing many of the seniors wanted was to update the equipment in our fitness room,” said Jefferson County Leisure Center Director Tammy Bennett.
Technology has changed the way we exercise the way the smartphone has changed communications. The pedometer was replaced by the Fitbit and shows how new fitness equipment can provide more effective exercise opportunities to a larger group.
“When we started to tackle the project of updating the equipment we found that some of the treadmills were really out of date. The new equipment was much more user friendly than the older equipment had been,” Bennett said.
One of the hallmarks of local government stresses providing constituents the best service possible while remaining good financial stewards of taxpayer dollars. Bennett began scouring organizations looking for grants and funding mechanisms that would help modernize the fitness room. Then she suddenly got a call from Wrens City Manager Arty Thrift about a grant that the city of Wrens received from American Association of Retired Persons (AARP).
Thrift originally was going to use the AARP to create more opportunities that focused on seniors in Wrens because of the specifics of the grant and knowing what Bennet was trying to do, the AARP grant could be used to help add equipment to the fitness center.
“Mr. Arty had called me about the AARP grant and he said that this grant would really help us and fit what we were trying to do. Then we were able to really start looking at what do our seniors need and want to really help them exercise and practice wellness,” Bennet said.
Bennet said we started to get some new treadmills, exercise bicycles and some dumb bells. The county also put in some speakers and a new TV.
“Right now we have three different classes we offer and usually have about 8-9 people per class. Caleb Moore, son of Mayor of Wadley Harold Moore plays college basketball and has to maintain his exercise routine in the summers is our instructor. It’s great to have a young guy with a lot of energy encouraging our seniors to keep exercising and remain active,” Bennett said.

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Shared genetic architecture between eating disorders, mental health conditions, and cardiometabolic diseases: a comprehensive population-wide study across two countries – Nature

Shared genetic architecture between eating disorders, mental health conditions, and cardiometabolic diseases: a comprehensive population-wide study across two countries – Nature

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Nature Communications volume 16, Article number: 6193 (2025)
Eating disorders arise from a complex interaction of genetic and environmental influences. Here we provide comprehensive population-level estimates of the heritability of eating disorders and their genetic relationships with various mental health and cardiometabolic disorders (CMDs), expanding beyond genome-wide association studies. We examined the heritability of three eating disorders—anorexia nervosa (AN), bulimia nervosa (BN), and other eating disorders (OED)—and investigated shared familial and genetic risk factors with mental health disorders and CMDs. Using national register data from Denmark and Sweden (1972–2016), we analysed clinical diagnoses for over 67,000 individuals with eating disorders, their first-degree relatives, and matched controls from populations totalling 17 million. Heritability estimates were moderate, h2AN = 36%, h2BN = 39%, and h2OED = 30% and genetic correlations revealed substantial overlap between AN and obsessive-compulsive disorder (rg = 0.65) and moderate correlations with other mental health disorders such as autism (rg = 0.36). Significant genetic associations were also identified between eating disorders and CMDs, showing strong replication across both countries. These findings emphasise the genetic foundations of eating disorders and their shared genetic architecture with mental health and CMDs. This research enhances our understanding of comorbidity patterns and has important implications for developing integrated treatment approaches.
Eating disorders (ED) such as anorexia nervosa (AN), bulimia nervosa (BN), binge-eating disorder (BED), and other eating disorders (OED), are often chronic and debilitating disorders occurring in 5–10% of the general population1. As with all psychiatric disorders, both genes and the environment contribute to the risk of developing an ED. The role of genetics in eating disorders has been well-established using several study designs (e.g. family- and twin studies)2, and more recently via molecular techniques such as genome-wide association studies (GWAS)3,4,5. These different study designs have provided ample evidence of the heritability of eating disorders, the occurrence within families, the potential involvement of specific gene variants, and the genetic correlations with other disorders. Heritability estimates ascertained from twin studies vary across eating disorders6. For instance, of these three disorders, AN has the highest reported twin-based heritability but also a broad range of estimates (0.28–0.74)7,8,9, followed by BN (0.55–0.62)9,10,11,12, and BED (0.39–0.45)11,13,14. Although not the focus of this study, a recent study has shown high twin-based heritability (0.79; 95% CI: 0.70–0.85) of broad avoidant restrictive food intake disorder (ARFID)15. Moderate heritability estimates for individual symptoms of eating disorders have also been shown, such as weight and shape concern (0.43)16,17, binge eating (0.49)16,17, and self-induced vomiting (0.72)18, with some variability across studies19.
Historically, genetic studies, such as GWAS, have focused heavily on AN3,4,20,21,22. However, a recent GWAS identified multiple common genetic risk variants associated with a BED phenotype obtained via machine-learning23. The discovery of more risk variants is likely given that a larger AN GWAS and the first binge-eating GWAS are currently underway and nearing completion24. Both twin studies and GWAS (specifically those of AN) have demonstrated the genetic overlap across eating disorders10, and between eating- and other psychiatric disorders3,4,25,26. The strongest positive genetic correlations with AN have been identified for obsessive-compulsive disorder (OCD), major depressive disorder (MDD), schizophrenia, and anxiety. In contrast, negative genetic correlations have been shown with cardiometabolic- and anthropometric traits (e.g. body fat percentage, insulin resistance, and leptin)3,4. In a recent study, we showed that whilst AN, BN, and BED share genetic risk with other psychiatric disorders, they differ in their shared genetic risk with cardiometabolic and anthropometric traits5. For example, whereas BED has positive genetic similarity with waist circumference and obesity, opposite patterns were observed for AN.
Twin studies and GWAS are useful in estimating the heritability and genomic architecture of complex traits and diseases; however, they have methodological limitations. First, both methods are often applied to highly self-selected clinical samples or rely on self-reported data, leading to a study sample that is not representative of the general population and thus potentially over- or underestimating heritability. Second, single-nucleotide polymorphism (SNP)-based heritability estimates are consistently lower than twin-based ones27. This is in large part because GWAS exclusively assesses the association between common additive genetic variants and a disorder, and thus does not capture the effect of gene-environment interactions, rare variants, and other non-additive contributions. The greatest limitation of twin-based studies is reliance on the equal environment assumption, which assumes that mono- and dizygotic twins are similarly exposed to relevant environmental factors, biasing heritability estimates upwards. Indeed, a recent register-based study in Denmark supported the non-representativeness of twins as it showed that compared to singletons, twins carry a 40% increased risk, whilst triplets/quadruplets carry a 92% increased risk for developing AN28. Therefore, the estimates using twin populations only may not reflect the genomic architecture of the general population, as they are likely to be biased upwards.
Building on existing knowledge and recent methodological advances29,30, we leveraged nationwide healthcare registers and near-complete population genealogies of ~17 million individuals spanning four generations across Denmark and Sweden, to: (a) estimate the risk of eating disorders in first-degree relatives of probands with eating disorders; (b) determine the heritability of three eating disorders; and (c) investigate co-heritability (i.e. the genetic correlation) amongst specific eating disorders and, between eating disorders and multiple psychiatric disorders and cardiometabolic diseases (CMD).
The prevalence of AN and BN was close to 0.5% in both countries, but was higher in Denmark than in Sweden for individuals born between 1968 and 2000 (AN: 0.59% vs 0.53% and BN: 0.48% vs 0.32%). These differences were more pronounced within the younger (1985–2000) cohort (AN: 0.80% vs 0.72% and BN: 0.55% vs 0.37%). Conversely, the prevalence of OED in Sweden was higher than that of Denmark in both the older (1.02% vs 0.6%) and younger cohort (1.40% vs 0.79%). As expected, in both countries individuals who received an eating disorder diagnosis in were substantially more likely to be female than male (13:1). However, we observed small differences in sex-specific eating disorder prevalence between Denmark and Sweden; with BN having a higher prevalence in males in Sweden (3.4% vs 2.5%, p = 1.19 × 10−3) and OED being observed more commonly in Danish males (9.3% vs 7.6%, p = 1.01 × 10−8). We observed a significant difference in the use of in- and outpatient registration between the two countries (Table 1). For instance, Danish individuals with an AN or OED diagnosis were more likely to be treated exclusively as inpatients compared to Swedish individuals with the same diagnosis (ORAN = 3.83, OROED = 1.19), who were more likely to be treated via the outpatient hospital system (Supplementary Data 2A). The opposite relationship was observed for BN. Overall, Danish individuals were significantly more likely to have both an in- and outpatient recording for all three EDs, potentially suggesting a hospitalisation trajectory. Individuals in Sweden were significantly younger when receiving their first AN diagnosis (median age in years: 17.8 vs 18.6, p = 9.74 × 10−11) or OED (20 vs 21, p = 7.09 × 10−17), whereas Danish individuals received a BN diagnosis at a younger age (24.3 vs 26.3, p = 1.30 × 10−47). Across all eating disorder diagnoses, Danish individuals were, on average, born earlier than Swedish individuals and diagnosed earlier, based on calendar years (Table 1, Supplementary Figs. 13, and Supplementary Note 3). These differences in prevalence, age at first diagnosis, year of birth, and year of diagnosis were too small to be clinically impactful but statistically significant due to the large sample size.
We observed a statistically significant difference between the number of eating disorder diagnoses per individual in each country. For instance, individuals diagnosed in Denmark were substantially more likely to have an exclusive diagnosis of AN (OR = 1.88, p < 1 × 10−99) or BN (OR = 2.54, p < 1 × 10−99), whilst individuals diagnosed with an eating disorder in Sweden were more likely to have exclusively an OED diagnosis (OR = 0.41, p < 1 × 10−99). If multiple eating disorder diagnoses were given, Swedish individuals were more likely than Danish individuals to receive a diagnosis of AN (OR = 0.65, p = 4.74 × 10−75) or BN (OR = 0.84, p = 4.27 × 10−7) together with an OED diagnosis (Fig. 1), whereas Danish individuals were more likely to have both a diagnosis of AN and BN (OR = 3.06, p = 1.22 × 10−83) compared with individuals in Sweden. More can be found in Supplementary Data 2A, B.
Percentages reported are calculated as the number of individuals in each diagram section divided by the total number of individuals diagnosed in each country.
To assess the familial risk of eating disorders, we compared CIFs between individuals with a first-degree family member (i.e. parents and siblings) diagnosed with an eating disorder relative to the risk in the general population. The incidences were calculated from the number of new cases occurring for each age year. We calculated cumulative incidences up to a maximum of age 49 for birth cohort 1968–2000 and age 32 for birth cohort 1985–2000. We observed that individuals were substantially more likely to receive an eating disorder diagnosis if they had a first-degree relative (i.e. parent or full sibling) diagnosed with the same disorder (Fig. 2). In general, the probability of an eating disorder increased on average three-fold for all disorders when having either a parent or full sibling with an eating disorder diagnosis. However, in Sweden, children were less likely to receive a BN diagnosis if they had a parent with a BN diagnosis, regardless of birth cohort, compared to Danish individuals, who had an increased risk of 1.5- to 8.3-fold. This might, in part, be due to differences in recording time and left-sided censoring of medical data, as well as children of BN-diagnosed individuals in Sweden being too young to be diagnosed themselves. All estimates can be found in Supplementary Data 3.
Cumulative incidences (%) and 95% confidence intervals (reported as cumulative incidence estimate ± 1.96 × standard error) for eating disorders were calculated for different birth cohorts a 1985–2000 (nDenmark = 1,026,609, nSweden = 1,698,409) and b 1968–2000 (nDenmark = 2,124,093, nSweden = 3,435,832) using medical records up to 2016. Cumulative incidences were calculated for: the general population (all individuals born in the birth window), individuals with at least one full sibling diagnosed with the same eating disorder, and individuals with at least one parent diagnosed with the same eating disorder. Individuals diagnosed before age 10 were excluded. Risk estimates were taken, if possible, at age 49 (1968–2000) and 32 (1985–2000). General population confidence intervals are narrow due to the large sample size and may appear overlapping or not visible.
To estimate the heritability of eating disorders, we combined familial risk estimates as described in the methods section. We calculated ({h}_{{AN}}^{2}) as 0.36 (95% CI: 0.30–0.41), ({h}_{{BN}}^{2}) as 0.39 (95% CI: 0.32–0.46), and ({h}_{{OED}}^{2}) as 0.30 (95% CI: 0.20–0.40, Fig. 3). The h2 of AN and BN were generally lower than previously reported estimates, however it should be noted that these estimates varied widely (0.25–0.8)2,12,31. The heritability of OED as defined in this study has, to our knowledge, not been studied enough to make an adequate comparison. We observed no statistically significant difference between country-specific estimates (Bonferroni p < 3.33 × 10−3) (Supplementary Data 4).
All within-country (Denmark and Sweden) h2 estimates reported per birth year were first meta-analysed using random effect inverse variance weighting, followed by a between-country meta-analysis using the same method. Confidence intervals are reported as a meta-analysis of Danish and Swedish narrow-sense h2 estimates per birth year ± 1.96 × standard error.
Due to the large sex difference in eating disorder prevalence (>90% female) and small sample size in the Danish cohort, the heritability of eating disorders in females was nearly identical to the non-sex-stratified heritability. In comparison, the male estimates presented substantially large confidence intervals, or no heritability could be calculated (BN). We did not observe significant differences in sex-stratified h2 estimates of eating disorders in the Swedish sample (Supplementary Data 4). Estimates of h2 of other mental health disorders and CMD, as well as all meta-analysed estimates of h2 including SE, CIs, and p-values, are reported in Supplementary Data 4.
We performed a sensitivity analysis to assess the effect of exclusion criteria on the heritability (Supplementary Data 4). First, we assessed the effect of inpatient and outpatient recording by estimating the h2 separately. The ({{{{rm{h}}}}}_{{{{rm{OED}}}}}^{2}) using inpatient ICD-10 diagnoses was 0.6 (95% CI: 0.42–0.77), significantly higher (Bonferroni p < 1.15 × 10−3) than the ({{{{rm{h}}}}}_{{{{rm{OED}}}}}^{2}) using outpatient ICD-10 diagnoses: 0.27 (95% CI: 0.19–0.36) using Danish individuals, and we observed no such difference using Swedish data. No significant differences between in- and outpatient heritability estimates of AN and BN were observed. Furthermore, no significant difference was observed between the ({{{{rm{h}}}}}_{{{{rm{AN}}}}}^{2}) diagnosed under ICD-8 or ICD-9 (exclusively inpatient) and the ({{{{rm{h}}}}}_{{{{rm{AN}}}}}^{2}) diagnosed as inpatient using ICD-10 (p = 0.26). All estimates of h2 using various exclusion criteria can be found in Supplementary Data 5 and Supplementary Note 4.
We first quantified the genetic relationship across the three eating disorders via genetic correlation analyses. Next, we calculated genetic correlations across the three eating disorders by performing multiple between-disorder comparisons using (a) seven other mental disorders, and (c) six CMDs. All analyses were conducted using data from Denmark and Sweden using both full-sibling and parent-offspring comparisons. All estimates of rg are reported in Supplementary Data 6 and 7.
Genetic correlation analysis between eating disorders using full-sibling information showed a large shared genetic contribution between eating disorders, ranging from rg = 0.62 to rg = 0.96 (Supplementary Data 6). Meta-analyses of Danish and Swedish estimates did not significantly change the estimates. The analysis was repeated by stratifying individuals by inpatient and outpatient eating disorder hospitalisation; we observed no significant change in genetic correlation estimates.
We estimated genetic correlations for each of the 21-eating disorder and other mental disorder diagnostic pairs using full-sibling comparisons and observed, again, similar estimates between countries (Fig. 4). After meta-analysis, 25 genetic correlations were statistically significant (Bonferroni p < 1.39 × 10−3) (Fig. 4). Genetic correlations for all the ED-other mental disorder pairs were positive and statistically significant except AN-schizophrenia. In line with previous literature32, OCD showed the largest genetic correlation with both AN (rg = 0.65, 95% CI: 0.56–0.73) and OED (rg = 0.66, 95% CI: 0.53–0.79). Other large genetic correlations were observed between OED and other mental disorders such as MDD (rg = 0.57, p = 1.01 × 10−131), ADHD (rg = 0.43, p = 2.03 × 10−96), anxiety disorders (rg = 0.57, p = 1.59 × 10−79), and ASD (rg = 0.46, p = 6.30 × 10−65). Genetic correlations between AN and ADHD, and between OED and ASD, were not replicated using parent-offspring comparisons. Overall, estimates from Denmark replicated well with Swedish estimates, with one genetic correlation showing a significant difference between countries using full-sibling information and five using parent-offspring data. In general, our results suggest that the genetic architecture of eating disorders is widely shared (between 0.14 and 0.69) with other mental disorders.
All within-country (Denmark and Sweden) rg estimates reported per birth year were first meta-analysed using random effect inverse variance weighting, followed by a between-country meta-analysis using the same method. Top row panels rg estimates based on full-sibling comparison, bottom row panels rg based on parent-offspring comparisons. Bonferroni p = 1.28 × 10−3. Confidence intervals are reported as a meta-analysis of Danish and Swedish rg estimates ± 1.96 × standard error.
We observed significant positive genetic correlations between AN, BN and two CMDs (heart failure and peripheral artery disease) in full-sibling comparisons (Fig. 4), and five estimates were significantly different between Denmark and Sweden. The observed significant genetic correlations between AN, BN and two CMDs did not replicate using parent-offspring comparisons. Finally, no significant associations were observed between obesity and AN and BN. However, obesity shares a substantial genetic overlap with OED (rg = 0.13, p = 5.46 × 10−7), which was replicated using parent-offspring comparisons (rg = 0.16, p = 1.71 × 10−5). All but one rg (OED and obesity) using parental information were similar between countries.
As sex stratification did not seem to affect the heritability, or no (reliable) heritability could be estimated using the Danish data, we opted not to perform any sex stratified genetic correlation analysis.
Here we have shown that the h2 estimates were substantial, i.e. ({h}_{{AN}}^{2}) = 0.36, ({h}_{{BN}}^{2}) = 0.39 and ({h}_{{OED}}^{2}) = 0.30, and not significantly different across eating disorders. Reducing phenotypic heterogeneity by excluding individuals with prior eating disorder diagnoses other than the eating disorder of interest did not affect the h2. The h2 of ICD-10 OED in Denmark for individuals receiving inpatient care was two times larger (0.60) than the h2 of ICD-10 OED diagnosed in outpatient care (0.27), suggesting stronger genetic underpinnings for eating disorders with greater severity33.
The observed pattern of genetic correlations with mental disorders and CMD advances understanding of the underlying aetiology, comorbidities, and complexity of different eating disorders. The largest positive genetic correlation observed between AN and OCD (rg = 0.65, p = 6.68 × 10−48) is in line with previous family34,35, register26, and genetic studies32. Moreover, previous analyses grouped mental disorders based on shared genomics and clustered AN, OCD, and Tourette syndrome into one category36. These findings therefore point towards a higher-level genetic structure that underlies disorders characterised by compulsivity32 compared to other mental disorders. In addition, eating disorders are often comorbid with internalising disorders, e.g. multiple anxiety phenotypes37,38 and mood disorders39,40, which were also observed in this study.
The positive genetic correlations seen in this study between AN and ASD (rg = 0.36, p = 2.50 × 10−22) support the increasing hypothesis of a substantial genetic overlap between these disorders41. Several clinical studies have hinted at an overlap between eating disorders (predominantly AN) and ASD, given the overlapping features such as limited emotional expression, reduced social contacts, and cognitive rigidity42,43, yet no significant genetic associations have been observed previously4,44. Differences in full-sibling and parent-offspring genetic correlations are likely due to increased awareness and diagnosis of neurodevelopmental disorders.
In line with recent work5, we did not observe a significant negative genetic correlation between AN and type-2 diabetes using parent-offspring comparisons (rg = -0.06, p = 2.50 × 10−3) and when using sibling comparisons (rg = 0.02, p = 0.37). However, this warrants further investigation as other large GWAS using linkage-disequilibrium score regression have observed significant negative genetic correlations between the two outcomes3,4. Nor did we observe a significant negative genetic correlation between AN and obesity (rg = -0.11, p = 0.02)4; however this is likely due to the nature of our sample, as obesity is likely to be diagnosed in the registers only if relevant to pathological findings in the healthcare context, and will be under-represented in this context. The discrepancy between previously observed negative genetic correlations between AN and type-2 diabetes can partially be explained by differences in sampling of individuals across different types of studies. In general, GWAS are performed using non-population representative samples, whereas a national register-based study contains all individuals with diagnoses that have been in contact with the hospital at any time and therefore is mostly free from generally occurring biases (e.g. volunteering bias and selection bias). In this case, it is entirely plausible that observed negative genetic correlation using GWAS data may in fact be influenced by other hidden and co-occurring disorders. Our finding of a relationship between AN, BN and two CMDs (heart failure and peripheral artery disease) using (young) full-sibling comparisons is novel and warrants further investigation. The large overlap between OED and obesity potentially hints at an enrichment of binge-eating disorder diagnoses in OED, which is not defined as a separate diagnostic entity in ICD-10. Our findings overall highlight the need to implement current diagnostic manuals (ICD-11) in research that includes BED as a separate diagnosis, given the impact this has on a large group of the population. In contrast to the genetic correlations calculated between EDs and other disorders, the genetic correlations between the eating disorders were substantially larger and often not significantly different from one, suggesting a high degree of genetic similarity across all EDs. Stratification based on hospital admission type (i.e. in or outpatient) did not affect the genetic correlation estimates, which may rule out disorder severity affecting these genetic correlations.
Although the genetic correlations were comparable between the two familial relationships in our study, we observed substantially larger confidence intervals between specific clusters of disorder constellations related to differences in age of onset. For example, most mental disorders have a relatively early age of onset, whereas CMDs tend to be diagnosed later in life. This, in part, explains why the confidence intervals of the genetic correlations between eating disorders and CMDs are substantially smaller using parent-offspring information compared to full-sibling information, as parents generally have lived long enough to develop a CMD. The opposite pattern was observed for eating disorders and other mental disorders, apart from schizophrenia, which has an older average age at onset than other mental disorders. We believe that our findings might reflect differences in the underlying genetic architecture of disorders. For example, it is entirely plausible that individuals that receive a diagnosis of CMD (i.e. disorders that generally onset later in life) at a relatively young age might have a specific phenotype that has a higher genetic liability than individuals that develop the same disorder at an older age (with a larger environmental liability, related to lifestyle factors).
Our study has some limitations. First, we utilised secondary care hospital diagnoses from the 1960s onwards since the establishment of the inpatient registers in both countries. Therefore, some relatively newer diagnoses (e.g. BN) will be under-ascertained in older individuals and may skew the age at onset estimations. This underrepresentation might also result in misclassification (e.g. in Denmark) due to the non-adoption of ICD-9 and therefore, the “late” introduction of BN as a substantive diagnostic category. Second, the outpatient diagnoses were recorded decades later than the inpatient diagnoses in both countries, and diagnostic practices might have varied over time and between countries. However, we hypothesise that our calendar year-stratified analyses partially account for these time trends within and between countries. However, while we attempt to compensate for within-country differences by calculating the genetic correlation per birth-year and meta-analysing these estimates, we believe that some of the observed differences in rg between Denmark and Sweden are due to strong country-specific effects, such as differences in diagnostic and referral practices (temporal effects). Another limitation concerns undiagnosed individuals, for instance, only ~20-30% of individuals with an eating disorder in the general population seek or access medical services45,46,47. Therefore, our cases likely represent more severely affected individuals who sought treatment and, therefore, are not fully representative of the entire population of individuals affected by eating disorders, which in turn may have inflated our h2 and rg estimates. Due to diagnostic manuals used in the healthcare systems in Denmark and Sweden, we were not able to differentiate other eating disorders outside of AN and BN, therefore, our OED category is likely to be a heterogeneous group and might comprise multiple eating disorders that are undefined up to ICD-10 (e.g. BED). We did not account for the influence of shared environmental factors among close relatives on heritability and genetic correlation estimates. As a result, these estimates may be slightly inflated, although prior research suggests that such effects are minimal29. Regarding ancestry, in this study, to minimise the impact of left censoring, we excluded individuals born outside of Denmark and Sweden, as their medical diagnoses may have been recorded in other countries to which we did not have access. This exclusion applied both to Danish and Swedish citizens born abroad and to individuals who migrated to these countries. While no data on race, ancestry, or ethnicity were recorded in the registers, it is important to acknowledge that Denmark and Sweden have historically been predominantly composed of individuals of white individuals of European ancestry. However, both countries have experienced increasing migration from non-European regions in recent decades. As a result, by restricting our sample to individuals born in Denmark and Sweden, we likely captured a population primarily of white individuals of European ancestry while inadvertently reducing the representation of individuals from more recent immigrant backgrounds. While this study likely includes more individuals of diverse backgrounds, our sample is likely to be representative of individuals of white-European ancestry. This should be considered when interpreting the generalisability of our findings beyond those of European ancestry.
Nonetheless, our study has several important implications for research and clinical practice. By quantifying heritability in a non-twin, near-complete total population sample of two countries, we provide a more comprehensive estimate of the contribution of genetic factors to the variance of eating disorders. Second, we show an increased familial risk of eating disorders, namely that there is, on average, a threefold higher risk among individuals who have a first-degree relative with an ED compared to the general population. Quantification of familial risk can help prevention and early identification. Although the intergenerational transmission of eating disorders has been studied less than other mental disorders, ongoing studies will help understand risk markers and developmental trajectories. The identified genetic correlations between eating disorders and other somatic diseases and psychiatric disorders have supported both a deeper investigation of the pathophysiology of disorders5, and a reconceptualisation of eating disorders as disorders encompassing brain and body48. This study confirms the strong genetic relationship between eating disorders and other psychiatric disorders, particularly internalising disorders (e.g. OCD, anxiety, and depressive disorders). Clinically, these disorders are highly comorbid with eating disorders, and clarifying the nature of this overlap can improve clinical management and therapeutic approaches to comorbidities. For example, the overlap between ASD and eating disorders in clinical samples has received much attention recently49, leading to the development of specific care pathways, and this paper is the first to provide a likely genetic basis for this observed overlap50,51.
In summary, we derived population-wide genetic estimates by harnessing the power of data containing almost all hospital records and genealogical information of two entire Scandinavian countries (n = 17 million). Our results provide a unique perspective on the complex heritable nature of eating disorders, as well as the overlap in genetic aetiology underlying psychiatric and CMD.
This study was based on the Danish and Swedish civil and national health registries, which have been previously described in detail in refs. 52,53,54,55,56,57,58,59 (Supplementary Notes 1 and 2). Eating disorder cases included all individuals born in Denmark or Sweden who were clinically diagnosed with either AN, BN, and/or OED (Supplementary Data 1) according to inpatient and outpatient discharge records from all Danish and Swedish hospitals up to December 31, 2016, obtained from the Danish and Swedish National Patient Register or Psychiatric Central Research Register (for details about years of coverage of both registers see)59. We applied the same criteria to identify cases for attention-deficit/hyperactivity disorder (ADHD), anxiety disorders, autism spectrum disorders (ASD), bipolar disorder, MDD, obsessive compulsive disorder (OCD), schizophrenia, type 2 diabetes, heart failure, hypertension, coronary artery disease, obesity, and peripheral artery disease cases. The total number of cases is less than the sum of all individual disorder diagnoses because many individuals have more than one diagnosis. Most AN, BN, OED, MDD, schizophrenia, and bipolar disorder diagnostic codes translate poorly to individuals below the age of 10, therefore, we excluded individuals who received a diagnosis before this age. Family members (non-twin full-siblings and parents) were identified using the near-complete population genealogies derived from the multi-generation registers60. Information about being male or female was obtained from the civil registers, and for most individuals born in Denmark and Sweden refers to sex, which was assigned at birth. However, for some individuals, this information should be referred to as gender, as individuals are allowed to request a change to this information based on their preferred gender identity. Details regarding exclusion criteria and ICD-8, 9, and 10 codes used in this study are reported in Supplementary Data 1.
Primary analyses were cumulative incidence functions (CIF) with age at first hospital contact as an inpatient or outpatient for all eating disorders, other mental health disorders, and CMD. CIFs were estimated for: (a) the general population, and for individuals with any (b) full sibling or (c) parent diagnosed with the studied disorder, and finally for individuals with any (d) full sibling or (e) parent diagnosed with a diagnosis different than their own disorder (e.g. all offspring with AN with at least one parent diagnoses with MDD). All CIFs were calculated using the Nelson-Aalen estimator for individuals born in the same calendar year to account for substantial changes over time in the underlying incidence, as well as censoring and competing risks.
We calculated the additive heritability (h2) under the liability threshold model based on the cumulative incidence as a function of pedigree relatedness. Briefly, we used the general population and full sibling CIFs at the last overlapping observed time point as estimates of the proportion of the population born in the same calendar year that were affected in their lifetime. Using these estimates, we calculated h2 estimates as described by Wray and Gottesman29,61,62 per given calendar year. All within-country h2 estimates were first meta-analysed using random effect inverse variance weighting, followed by a between-country meta-analysis using the same method.
We calculated the genetic correlation (rg) between two traits by deriving the general population cumulative incidence-based risk for both disorders (e.g. AN and SCZ) and full siblings’ cross disorder familial risk (i.e. the cumulative incidence risk for AN when having a full sibling with SCZ). We extracted the last possible time point for each birth-year-stratified CIF and calculated the rg by inputting the previously calculated h2 estimates using the formula described by Wray and Gottesman29,61,62. In line with the h2 calculations, all birth-year specific genetic correlation estimates were meta-analysed first within-country and followed by a between-country meta-analysis. We repeated the analyses using parent-offspring comparisons. Danish register data was stored on a PostgreSQL 13.3 database server information was extracted using the psql 16.2 database client. All register-based analyses were done in R v4.2.1 using the cmprsk v2.2 package.
Further information on research design is available in the Nature Portfolio Reporting Summary linked to this article.
This work is based on Danish register data that are not publicly available due to privacy protection, including the General Data Protection Regulation (GDRP). Only Danish research environments are granted authorisation. Foreign researchers can, however, get access to data under Danish research environment authorisation. Further information on data access can be found at https://www.dst.dk/en/TilSalg/Forskningsservice or by contacting Thomas Werge (thomas.werge@regionh.dk). The use of Swedish data was approved by the regional ethics review board in Stockholm, Sweden, with DNR 2012/1814-31/4. Data from Swedish registers is not available for sharing due to policies and regulations in Sweden. Swedish register data are available to all researchers through applications at Statistics Sweden (SCB, https://www.scb.se/en/) and the National Board of Health and Welfare (Socialstyrelsen, https://www.socialstyrelsen.se/). By both Danish and Swedish law, individual consent to use register data for register-based studies is not required.
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J.M., A.B., T.W., and F.F. received funding from the European Union’s Horizon 2020 Research and Innovation Programme: the “Predicting Comorbid Cardiovascular Disease in Individuals with Mental Disorder by Decoding Disease Mechanisms” project (CoMorMent, grant number 847776, to Ole Andreassen). J.M., J.P., T.W., and A.B. were supported by the US National Institutes of Health study on extreme MDD (R01 MH123724, to Patrick Sullivan). J.M., N.M., S.A., and H.D. were supported by the Laureate Grant Award from the Novo Nordisk Foundation (grant no: NNF22OC0071010, to N.M.). R.Z. was supported by the Swedish Research Council (Vetenskapsrådet, grant no. 2022-00242); J.P. received funding from the European Research Council (grant number 101042183). C.B. was supported by the National Institute of Mental Health (R56MH129437; R01MH120170; R01MH124871; and R01MH124871); Swedish Research Council (Vetenskapsrådet, award: 538-2013-8864); and Lundbeck Foundation (grant no. R276-2018-4581). A.B., F.F., and T.W. were supported by the Nordforsk grant 164218.
These authors contributed equally: Joeri Meijsen, Kejia Hu.
These authors jointly supervised this work. Alfonso Buil, Nadia Micali.
Institute of Biological Psychiatry, Mental Health Center Sct. Hans, Copenhagen University Hospital, Mental Health Services Copenhagen, Roskilde, Denmark
Joeri Meijsen, Stefana Aicoboaie, Helena L. Davies, Mischa Lundberg, Richard Zetterberg, Thomas Werge, Alfonso Buil & Nadia Micali
Center for Eating and feeding Disorders Research, Mental Health Center Ballerup, Copenhagen University Hospital, Mental Health Services Copenhagen, Copenhagen, Denmark
Joeri Meijsen, Stefana Aicoboaie, Helena L. Davies & Nadia Micali
Unit of Integrative Epidemiology, Institute of Environmental Medicine, Karolinska Institutet, Stockholm, Sweden
Kejia Hu, Dang Wei & Fang Fang
Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
Ruyue Zhang, Joëlle Pasman, Weimin Ye & Cynthia M. Bulik
Genetic Epidemiology, Department of Psychiatry, Amsterdam UMC location University of Amsterdam, Amsterdam, The Netherlands
Joëlle Pasman
Department of Clinical Medicine, University of Copenhagen, Copenhagen, Denmark
Thomas Werge
Department of Psychiatry, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
Cynthia M. Bulik
Department of Nutrition, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
Cynthia M. Bulik
Great Ormond Street Institute of Child Health, University College London, London, UK
Nadia Micali
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J.M., A.B., and N.M. conceived the study. J.M., A.B., C.B., R. Zhang, and N.M. contributed to the study design. J.M., S.A., and H.L.D., performed the literature search, and J.M., K.H., D.W., M.L., J.P., and R. Zetterberg performed programming and/or data analyses. J.M., A.B., R. Zhang, N.M., F.F., and T.W. contributed to data interpretation. F.F., W.Y., A.B., and T.W. provided access to data. J.M., C.B., and N.M. wrote the initial draft. N.M., F.F., and T.W. obtained the funding.
Correspondence to Joeri Meijsen or Nadia Micali.
N.M. receives an honorarium as associate editor on the European Eating Disorders Review. C.B. receives royalties from Pearson Education Inc. and has served as a consultant with Orbimed. All other authors declare no competing interests.
The use of Danish data was approved by the Danish Health Data Authority (project no. FSEID-00003339) and the Danish Data Protection Agency. By Danish law, informed consent is not required for register-based studies. The use of Swedish data was approved by the regional ethics review board in Stockholm, Sweden, with DNR 2012/1814-31/4.
Nature Communications thanks Shahram Bahrami and Sarah Cohen-Woods for their contribution to the peer review of this work. A peer review file is available.
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Meijsen, J., Hu, K., Wei, D. et al. Shared genetic architecture between eating disorders, mental health conditions, and cardiometabolic diseases: a comprehensive population-wide study across two countries. Nat Commun 16, 6193 (2025). https://doi.org/10.1038/s41467-025-61496-5
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