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A new investigational treatment targets the root cause of a life-threatening liver condition connected to obesity and diabetes, offering hope for millions of patients.

Scientists at the University of California, San Diego School of Medicine have developed an investigational drug that could provide a new treatment for metabolic dysfunction-associated steatohepatitis (MASH). This severe type of fatty liver disease is associated with obesity and type 2 diabetes and can progress to cirrhosis, liver failure, or even liver cancer.

Published in The Lancet, the study reports that the drug, known as ION224, blocks a liver enzyme called DGAT2. This enzyme is essential for the liver’s process of producing and storing fat. Inhibiting it reduces both fat accumulation and inflammation, the two main contributors to liver damage in MASH.

Blocking fat accumulation and inflammation

“This study marks a pivotal advance in the fight against MASH,” said Rohit Loomba, MD, principal investigator of the study and chief of the Division of Gastroenterology and Hepatology at UC San Diego School of Medicine. “By blocking DGAT2, we’re interrupting the disease process at its root cause, stopping fat accumulation and inflammation right in the liver.”

A Phase IIb clinical trial was carried out at multiple centers across the United States, enrolling 160 adults diagnosed with MASH and showing early to moderate fibrosis. Over the course of a year, participants were given monthly injections of either varying doses of the investigational drug or a placebo. Results revealed that at the highest dose, 60% of patients experienced significant improvements in liver health compared to those who received the placebo. Importantly, these benefits were observed regardless of changes in body weight, indicating that the treatment could be combined effectively with other therapies. The study also reported no serious side effects attributable to the drug.

A widespread and silent condition

MASH, formally known as nonalcoholic steatohepatitis (NASH), affects people with metabolic conditions like obesity and type 2 diabetes. It is often called a “silent” disease because it can progress for years without symptoms.

More than 100 million people have some form of fatty liver disease in the U.S., and as many as 1 in 4 adults worldwide may be affected, according to the Centers for Disease Control and Prevention. If left untreated, MASH can progress to liver failure and often may require a transplant.

Looking ahead to future therapies

“This is the first drug of its kind to show real biological impact in MASH,” Loomba said. “If these findings are confirmed in Phase III trials, we may finally be able to offer patients a targeted therapy that halts and potentially reverses liver damage before it progresses to life-threatening stages.”

Loomba, who is also director of the metabolic-dysfunction-associated steatotic liver disease (MASLD) research center at UC San Diego School of Medicine, and a gastroenterologist and hepatologist at UC San Diego Health, adds that for patients and families affected by this serious condition, these results bring new hope for better care and outcomes. He emphasizes that early intervention and targeted therapies may also help reduce the burden on health care systems by preventing costly and complex liver disease down the line.

Next steps include a larger clinical trial to move closer to making this treatment widely available.

Reference: “Antisense oligonucleotide DGAT-2 inhibitor, ION224, for metabolic dysfunction-associated steatohepatitis (ION224-CS2): results of a 51-week, multicentre, randomised, double-blind, placebo-controlled, phase 2 trial” by Rohit Loomba, Erin Morgan, Keyvan Yousefi, Dan Li, Richard Geary, Sanjay Bhanot and Naim Alkhouri, 23 August 2025, The Lancet.
DOI: 10.1016/S0140-6736(25)00979-1

Funding for this research came from Ionis Pharmaceuticals (ION224-CS2).

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The characteristics of type 2 diabetes vary from patient to patient and it has been proposed that the condition is made up of four subtypes. Now, new research being presented at the annual meeting of the European Association for the Study of Diabetes (EASD) in Vienna, Austria (15-19 September) shows that smoking increases the risk of the condition, regardless of subtype.

The researchers in Sweden, Norway and Finland also found that people with a genetic susceptibility to develop diabetes seem more vulnerable to the adverse effects of smoking.

It has previously been suggested that type 2 diabetes (T2D) can be divided into the following subtypes: SIRD (severe insulin-resistant diabetes), characterised by insulin resistance (where the body’s cells don’t respond to properly to insulin and can’t easily take up glucose from the blood); SIDD (severe insulin-deficient diabetes), characterised by a lack of insulin; MOD (mild obesity-related diabetes), associated with obesity and younger age of onset; and MARD (mild age-related diabetes) which develops later in life.

Severity, prognosis and risk of complications differ across the subtypes but it is not clear if they have different risk factors.

To find out more, Emmy Keysendal, a PhD student at Karolinska Institutet, in Stockholm, Sweden, and colleagues examined the link between smoking, which is already known to be a potent risk factor for T2D in general, and other forms of tobacco use, and the various subtypes of T2D.

The researchers used data on 3,325 individuals with T2D (495 SIDD, 477 SIRD, 693 MOD and 1660 MARD) and 3,897 controls from a long-running diabetes study in Norway (average follow-up time of 17 years) and a case-control study in Sweden.

They found that ever smokers (current and past smokers) were at higher risk of all four sub-types of T2D than those who had never smoked. The link between smoking and SIRD was particularly strong. 

Ever smokers were more than twice as likely (2.15 times) as never smokers to develop SIRD. This compares with increases in risk of 20% for SIDD, 29% for MOD and 27% for MARD.

Smoking was estimated to be responsible for more than a third of SIRD cases, but less than 15% of the other diabetes subtypes (SIDD, MOD and MARD).

Smoking heavily (≥15 pack-years/20 cigarettes a day for 15 years or equivalent) further increased the risk of all four subtypes. Heavy smokers were 2.35 times more likely to develop SIRD than never smokers and 52%, 57% and 45% more likely develop SIDD, MOD and MARD, respectively.

Interestingly, the data on men in Sweden suggested that heavy use of snus, a smokeless tobacco product popular in Scandinavian countries, was linked to increased risk of the severe subtypes SIDD (19% higher risk) and SIRD (13% higher risk) compared to never users of snus.

The study also explored whether smoking further increased the risk in individuals with a genetic predisposition to T2D, insulin resistance or reduced insulin secretion.

This showed that heavy smokers with genetic predisposition to T2D or reduced insulin secretion were particularly vulnerable.

For example, those who smoked heavily and had a high genetic risk for impaired insulin secretion had more than three times the risk (3.52-fold) of developing SIRD compared to those without these risk factors.

The researchers concluded that smoking increases the risk of T2D, whatever the subtype.

It is clear that smoking increases the risk of type 2 diabetes whatever the subtype, that is, regardless, of whether the diabetes is characterised by insulin resistance, lack of insulin, obesity or old age.

The strongest association was seen for the subtype characterised by severe insulin resistance (SIRD), which suggests that smoking may contribute to diabetes by impairing the body’s ability to respond to insulin.

Our findings emphasize the importance of smoking cessation in the prevention of type 2 diabetes. They also indicate that genetic information may help identify individuals most likely to benefit from extra support in stopping smoking.”

Ms. Emmy Keysendal, a PhD student at Karolinska Institutet

An AI algorithm based only on routine mammogram images plus age can predict a woman’s risk of major cardiovascular disease as well as standard risk assessment methods, finds research published online in the journal Heart.

And because it uses existing health infrastructure, routine mammography may offer a cost-effective ‘two for one’ effective screening option for women, suggest the researchers.

Cardiovascular disease and its risk factors are underrecognized and undertreated in women, and risk prediction algorithms have underperformed in them, point out the researchers. And while newer risk scores perform better in women than in men, they are complex and their accuracy depends on extensive medical data, which isn’t always available, they add.

The extent of arterial calcium deposits (BAC) and tissue density in the breasts have been linked to cardiovascular disease risk, but BAC isn’t associated with obesity and is negatively associated with smoking, suggesting that it’s not effective by itself, they suggest.

They therefore set out to discover if an automated AI analysis of the full range of internal breast structure and characteristics from routine mammogram images might be more accurate at cardiovascular risk prediction.

They drew on 49,196 women with an average age of 59, enrolled between 2009 and 2020 in the Lifepool cohort registry, and living in Victoria, Australia. 

At enrolment, the women provided initial background health information on their age, smoking status, alcohol intake, weight (BMI), any history of diabetes and use of high blood pressure and/or high cholesterol drugs and blood thinners. 

Additional information included menopause status, reproductive history and use of hormone therapy, as well as factors potentially affecting the internal structure of the breast, such as radiation, surgery, and cancer.

Some 5% of the women were current smokers, 62% had a BMI of more than 25, 6% had type 2 diabetes, 33% were taking drugs for high cholesterol, 27% for high blood pressure, and 11% were taking a blood thinner.

During an average tracking period of nearly 9 years, 3392 of these women had a first cardiovascular ‘event’: coronary artery disease (2383); heart attack (656); stroke (434) or heart failure (731).

The researchers developed an AI algorithm based on the full complement of internal breast structures and features from the mammogram images plus the woman’s age to predict major cardiovascular disease risk over 10 years.

This AI algorithm was as good as modern risk scores based on age and various clinical factors, including the New Zealand ‘PREDICT’ tool and the American Heart Association ‘PREVENT’ calculator. And it was only slightly better when various clinical factors were added.

The researchers acknowledge several limitations to their findings, including that different scanners don’t produce exactly the same data; the cardiovascular risk factors used for comparison relied on self-report; and all deep learning models are entirely dependent on their training datasets.

But they say: “A key advantage of the mammography model we developed is that it did not require additional history taking or medical record data and leveraged an existing risk screening process widely used by women.”

They add: “Mammography has potential as a ‘two-for-one’ risk assessment tool, offering efficiencies for both community and the healthcare system.” 

They concede that “the use of mammography images to predict cardiovascular risk is novel, but the use of machine learning models to do cardiovascular risk prediction is gaining traction.”

In a linked editorial, Professor Gemma Figtree and Dr. Stuart Grieve of the University of Sydney, point out that the poor performance of traditional risk factor algorithms in women is compounded by lack of awareness of the threat posed by heart disease to women by both women themselves and the health system. 

“In contrast with what is commonly thought, breast cancer causes only about 10% of the total deaths globally compared with those resulting from cardiovascular disease,” they write.

“Mammography may therefore represent a “touch point” for raising awareness about cardiovascular risk and disease in women,” they suggest.

But they add: “One of the challenges with new tools that show promise for improved cardiovascular risk assessment remains implementation.”