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.

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.”

Changes in AI mammogram scores may help predict breast cancer years before diagnosis, research involving more than 54,000 women suggests.

Scores rose steadily among women who later developed the disease but remained broadly stable among those who did not.

The increase could be detected up to six years before diagnosis and became much steeper during the final two years.

Researchers led by Professor Constance Lehman, of Harvard Medical School and healthcare technology company Clairity, analysed screening mammograms taken between 2009 and 2019.

They used a validated, open-source deep learning model to calculate five-year breast cancer risk scores from the images alone.

Deep learning is a form of artificial intelligence trained to recognise complex patterns in large amounts of data.

The model examined the whole mammogram rather than relying on a limited, predetermined feature such as breast density.

Models of this kind have performed better than traditional risk models and breast density alone when estimating a woman’s five-year breast cancer risk.

The study initially included 239,703 consecutive two-dimensional screening mammograms from 89,882 patients across six imaging sites spanning urban tertiary, community-based and rural settings.

All were standard bilateral full-field digital mammography examinations, taken with or without digital breast tomosynthesis.

Digital breast tomosynthesis uses multiple low-dose X-ray images to create a three-dimensional view of the breast.

After exclusions, the final analysis involved 54,014 women with a median age of 61 and a total of 158,807 mammograms.

Each woman contributed one index examination and up to six previous annual mammograms. Women had a median of three scans each.

For women who developed cancer, the index examination was their final screening mammogram within the year before diagnosis. For the cancer-free group, it was their final mammogram during the five-year study period.

The model did not use demographic information, clinical records or historical imaging data when calculating each score.

Of the women included, 817, or one per cent, were diagnosed with breast cancer within 365 days of their index examination.

This included 451 women, or 55 per cent, with invasive breast cancer and 118, or 14 per cent, with ductal carcinoma in situ, known as DCIS.

DCIS occurs when abnormal cells are found inside a milk duct but have not spread into the surrounding breast tissue.

The cancer type was unknown for the remaining 248 patients, representing 30 per cent of the cancer group.

A total of 682 cancers, or 83 per cent, were detected through screening, while 135, or 17 per cent, were interval cancers diagnosed between routine mammograms.

The other 53,197 women were not diagnosed with breast cancer during follow-up and formed the cancer-free comparison group.

Professor Lehman said: “We observed clinically relevant differences in risk trajectories between women who did and did not develop cancer. The increase in scores among cancer patients was detectable as early as six years prior to diagnosis and became more pronounced over time.”

Among women later diagnosed with the disease, the median score rose from 2.1 five to six years before diagnosis to 6.6 at the index examination.

Scores among cancer-free women remained stable, with median values ranging from 1.8 to 2.2 throughout the study.

The rise among women who developed cancer was steepest during the two years before their index examination.

Professor Lehman said: “These findings demonstrate signals, invisible to the human eye, in the image alone can predict future risk. This is exciting, because 85 per cent of women diagnosed with breast cancer do not have a significant family history of breast cancer or known genetic mutations.”

Most breast cancers are considered sporadic, meaning they are not driven by inherited genetic changes or a family history of the disease.

Traditional risk models have a limited ability to distinguish between women who will and will not develop breast cancer when used across large screening populations.

Researchers said tracking how scores change over time could provide more information than calculating risk at a single appointment.

Professor Lehman said: “AI-derived risk scores can identify patients who are otherwise predisposed to the disease, and our findings demonstrate that image-based AI risk scores evolve over time and that changes in those scores may provide additional information about future breast cancer risk.”

The patterns remained consistent when women were grouped by age and breast density.

Breast density describes the amount of fibrous and glandular tissue visible on a mammogram. Dense tissue can make cancers harder to detect and is also associated with an increased risk of the disease.

Researchers said image-based scores could support personalised screening and risk-reduction strategies without relying on self-reported or inconsistent clinical information.

Professor Lehman said: “These trends remained robust across subgroups defined by age and breast density, further supporting the generalisability of our findings. This is particularly relevant given persistent disparities in screening performance across patient populations. A dynamic biomarker approach grounded in the imaging data could mitigate some of these disparities by enabling risk-based personalisation that does not rely on self-reported or inconsistent clinical data.”

A biomarker is a measurable sign that can indicate a person’s health, disease risk or response to treatment.

Changing scores could eventually help clinicians identify women who may benefit from additional imaging or measures intended to reduce their risk.

Professor Lehman said: “With the power of AI, computer vision, and the ability to extract predictive data, we are able to apply the power of imaging to risk assessment and preventing disease from developing. Having a dynamic risk score opens up a whole new domain of more effective preventive therapies for breast cancer, similar to how we screen for and treat patients with high cholesterol and hypertension.”

AI image-based risk scores are included in the 2026 National Comprehensive Cancer Network guidelines.

The guidelines recommend that, from the age of 35, women with an elevated five-year risk score of more than 1.7 per cent consider breast MRI alongside annual mammography.

An AI image-based model approved by the US Food and Drug Administration is already being used to calculate five-year breast cancer risk at selected US healthcare institutions.