Smoking and alcohol were linked to breast cancer and irregular heartbeat in women aged 55 and over, a global analysis suggests.

Breast cancer and atrial fibrillation or flutter represent a growing global health burden, but the reasons for similar rates in some regions are not well understood.

Atrial fibrillation, also known as AFib, is an irregular heartbeat.

Study co-author Dr Shu Wang, director of the Breast Disease Center at Peking University People’s Hospital, said: “Identifying shared risk factors is important for developing interventions that support optimal health, such as smoking cessation and alcohol restriction, which could potentially reduce the global incidence of breast cancer and atrial fibrillation/flutter substantially.”

Researchers examined rates of breast cancer and atrial fibrillation or flutter among women aged 55 and over in 204 countries and territories.

They assessed exposure to 58 shared and distinct health, behavioural and lifestyle risk factors, including smoking, alcohol use, body mass index and physical activity.

The analysis found that 80 of 202 countries and territories, around 39 per cent, had similar rates of both conditions.

Breast cancer was the dominant condition in 65 countries, while atrial fibrillation or flutter was dominant in 57.

After accounting for multiple variables, smoking and alcohol use were linked to higher rates of both breast cancer and atrial fibrillation or flutter.

A further analysis estimated that reducing alcohol intake and smoking could potentially cut breast cancer risk by around 15 per cent and atrial fibrillation or flutter risk by about 12 per cent worldwide.

Alcohol use was estimated to contribute to 9.27 per cent of breast cancer cases and 7.57 per cent of atrial fibrillation or flutter cases.

High-income and developed countries, including the US, Canada, Australia, New Zealand and much of Europe, had elevated rates of both conditions.

The findings were consistent with previous research linking Western diets and sedentary lifestyles to greater risks of cardiovascular and metabolic conditions and cancer.

Wang said: “One of the most surprising aspects of our findings was how common both breast cancer and atrial fibrillation/flutter diagnoses were among women ages 55 and older in high-income regions, which highlights the influence of lifestyle.

“This is the first study combining global data with machine learning to show the relationship between the conditions, their location across the world and the shared risk factors of these two conditions.”

The highest-risk areas were mostly in Western countries, where exposure to smoking and alcohol was greater than in Eastern regions.

Researchers said the pattern could reflect lifestyle, social and community differences. Western countries were also more likely to have higher body mass index, sedentary lifestyles and greater exposure to Western diets.

Study co-authors Dr Zeye Liu and Dr Yi Shi said: “Nowadays, more and more people are paying attention to the link between cancer and cardiovascular health.

“Breast cancer and atrial fibrillation/flutter rise together across many regions of the world and share the same modifiable risk factors.

“From a cardiovascular perspective, this means that reducing smoking and alcohol use could help lower the risk of both conditions at the same time.”

Dr Laxmi Mehta, chair of the American Heart Association’s Council on Clinical Cardiology, was not involved in the research.

She said: “Many of the same modifiable factors, including smoking, alcohol use, poor diet, physical inactivity and obesity, contribute to both breast cancer and cardiovascular disease including atrial fibrillation/flutter, as confirmed by this study’s findings.

“This overlap underscores the importance of integrated lifestyle strategies to reduce risk of cardiovascular disease and cancer. The American Heart Association’s Life’s Essential 8 highlights key behaviours and health factors essential for prevention and reducing risk.”

The researchers created global risk maps that could help healthcare professionals and policymakers develop prevention strategies tailored to different regions.

They plan to add long-term research and genetic, metabolic and socioeconomic data to future analyses.

The study used information from the Global Burden of Disease 2021 database.

Machine learning was used to examine global patterns, links between the two conditions and risk factors specific to different regions. Machine learning uses computer systems to identify patterns in large amounts of data.

The research was based on national-level information and did not include data about individual patients, meaning it cannot prove cause and effect.

Differences in screening, healthcare resources, data collection and definitions between countries may also have affected the results.

By Claire Pettengill, science intern and Jade Anstine, clinical AI intern, Ema EQ

Every year, Gallup asks Americans which professions they trust most. Every year, nurses win. Not doctors. Not scientists. Nurses. And if you spend any time thinking about why, the answer is not hard to find.

Medicine runs on the nurse noticing first. In other words, the diagnosis follows the nurse sounding the alarm. They ask questions that feel human, not procedural. They explain what is happening in language you can understand.

And, critically, they know when something is beyond their scope and get you to the right person without making you feel like a burden for needing more.

That is the model we built Ema on.

When we set out to build an AI companion for women’s health, we could have just built something that answers questions efficiently. Pattern matching. Fast retrieval. Clinically accurate outputs.

Those things matter, and Ema does all of them. But accuracy alone does not build trust, and trust is the entire game in healthcare.

A woman asking about her postpartum recovery, her fertility, or her breastfeeding supply is not looking for a search engine. She is looking for someone who will take her seriously.

Women’s concerns don’t just need to be ‘validated’; they also need to be believed. Dismiss a woman’s pain as anxiety once, and you’ve taught her to doubt her own body.

The nursing model of care is built on exactly that premise. It is care that is shaped by her story. It asks about context and symptoms.

It treats the person as a whole, and it recognises that the right answer is sometimes a referral, not a response.

We trained Ema to escalate. That may sound like a small thing, but in AI, it is a deliberate design choice.

Most AI systems are optimised to answer and maintain engagement. Ema is optimised to help, and sometimes helping means saying “you need to speak to a clinician” and making that path easy.

This matters especially in women’s health, where the clinical trust gap is well-documented.

In a 2022 nationally representative survey of over 5,000 women, nearly 1 in 3 reported that their doctor had dismissed their concerns, and 15 per cent said a provider simply didn’t believe them.

Women are more likely to have their symptoms dismissed, their concerns minimised, and their pain undertreated. Among women under 35, nearly half reported at least one of these experiences.

They have had to learn how to advocate within systems designed for efficiency, built on men’s health.

With Ema, every conversation is an opportunity to make a woman feel heard, informed, and directed to the right level of care, neither over-triaged nor undertreated.

The goal is not to replace clinicians. It is to create a trustworthy first point of support that listens carefully, explains clearly, recognises limits, and helps women move toward appropriate care.

The nurses who top those Gallup rankings every year earn that trust through consistency. They show up, listen, follow through, and know their limits.

Ema is simply that trust, built into technology. That is the standard we hold Ema to: a trustworthy presence that knows when to answer and when to hand off.

Medicine spent a long time teaching women not to expect to be believed. Ema is built by the people who never stopped listening.

Bios

Claire Pettengill is a psychiatric nurse and DNP-PMHNP candidate at Columbia University School of Nursing, specialising in women’s mental health across the lifespan and algorithmic justice – ensuring the AI tools shaping women’s care are built to actually listen. She joined Ema EQ as a science intern focusing on clinical safety standards for evaluating AI in women’s health.

Jade Anstine is a senior nursing student at Gustavus Adolphus College looking to bridge the gap between frontline medicine and digital health innovation. He joined Ema EQ as a Clinical AI Intern to assess the Ema AI model across different clinical populations, specifically pediatrics and LGBTQ+.

An AI blood test being trialled by the NHS could spare thousands of women a painful examination for suspected womb cancer.

Around 90,000 postmenopausal women in England are referred by their GP each year to be investigated for possible womb cancer because of heavy bleeding.

Around 10,000 women a year in England are diagnosed with the disease, also known as uterine or endometrial cancer, and 2,700 die from it.

The PinPoint blood test could save one in five of those women, around 18,000 a year, from undergoing a transvaginal ultrasound scan.

Dr Jacinta Walsh, a GP at King’s Medical Practice in Normanton, West Yorkshire, said: “It often takes up to six visits to a GP before we’re able to rule out cancer.

“PinPoint will help shortcut that process to deliver peace of mind earlier and free up our capacity to see other patients.”

The procedure involves inserting an ultrasound probe into the vagina to measure the thickness of the womb lining. Many women find it uncomfortable or painful.

Although 20 per cent of women referred turn out not to have the disease, all currently undergo a pelvic examination involving an ultrasound scan.

If doctors still suspect cancer, women may then have a tissue sample taken during a biopsy and a hysteroscopy, an examination of the inside of the womb.

Several NHS hospitals are introducing the blood test after a trial involving 16,481 patients referred by GPs at 170 practices in Yorkshire for nine different forms of cancer.

All the patients had the test, including 3,313 women referred because their bleeding raised concerns that they might have womb cancer.

The results showed that the test was 99 per cent accurate in detecting the gynaecological cancers found among the 3,313 women and ruling out their presence.

This was a higher success rate than conventional testing. About one in 10 of the 90,000 women referred because of heavy bleeding turned out to have cancer.

The findings have prompted Mid Yorkshire NHS Teaching Trust to plan to use the test for six types of gynaecological or upper gastrointestinal cancer.

Leeds Teaching Hospitals NHS Trust plans to use it for gynaecological cancer.

The test was developed by Leeds-based PinPoint Data Science, which specialises in the statistical analysis of medical data.

It uses machine learning to assess whether someone is at low, elevated or high risk of cancer by analysing 30 blood markers.

Professor Sean Duffy, the company’s chief medical officer and a former NHS England national clinical director for cancer, said the test’s 99 per cent accuracy for womb cancer “is remarkable by any clinical standards”.

He added: “But equally, its value lies in safely ruling out very low-risk women. This has the potential to spare thousands of patients from painful invasive procedures they do not need.”

Brent Kilmurray, chief executive of the Mid Yorkshire trust, said there was an “especially compelling” case for hospitals to use the PinPoint test to detect gynaecological cancers.

Tracy Jackson, a consultant gynaecologist and cancer unit lead at the Leeds trust, said women referred by GPs currently undergo a transvaginal scan and, if needed, a hysteroscopy.

She said: “But the reality is that most women we see do not have cancer and we are acutely aware that the investigations can be uncomfortable and, for some, distressing.

“The PinPoint test gives us a way to triage more intelligently. If we can confidently rule out low-risk women in primary care, we reduce unnecessary invasive procedures and shorten our waiting lists.

“That means the women who do have cancer can be seen, diagnosed and treated earlier, which is exactly where our focus should be.”

Cancer Research UK said the PinPoint test appeared “promising”.

Samantha Harrison, a spokesperson for the charity, said: “Spotting cancer early saves lives, but right now patients are not being diagnosed quickly enough.

“This test could help to rule out endometrial cancer in some women, through a simple blood test, without the need for further testing.

“More research is needed to understand the benefits for patients and the NHS, but the results of this study are promising.”