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Simple Blood Test Can Predict Liver Disease 10 Years in Advance

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A simple blood test may soon become one of the most powerful tools to detect liver disease years before symptoms appear. Researchers in Sweden and Finland have developed a new model that can predict risk with striking accuracy – and the tool is already available online.

A breakthrough for early detection

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The CORE model, created at Karolinska Institutet, uses routine blood tests combined with basic patient information to forecast liver disease risk.

Five key factors determine risk

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Age, sex, and three common liver enzymes (AST, ALT, and GGT) form the basis of the calculation, providing an early snapshot of liver health.

Tested on nearly half a million people

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The study analyzed health data from more than 480,000 Swedes, followed for up to 30 years. About 1.5 percent later developed severe liver disease.

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Higher accuracy than current methods

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The CORE model correctly distinguished between patients at risk and those not at risk in 88 percent of cases, outperforming the widely used FIB-4 test.

Designed for everyday healthcare

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One of the key advantages is that the model can be implemented directly in primary care, where most patients first seek help.

Online calculator already available

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Doctors and nurses can already access the tool via a web-based platform at www.core-model.com, making it easy to integrate into practice.

Detecting cirrhosis and cancer earlier

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By identifying patients at high risk, the test could enable earlier diagnosis of conditions like cirrhosis and liver cancer, where late detection often means poor prognosis.

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Validated in multiple countries

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Beyond Sweden, the method has also been tested in Finland and the UK, showing consistently high accuracy across populations.

Opening doors for treatment

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With new drugs for liver disease becoming available, researchers believe the model could guide preventive care and better treatment strategies.

Next steps for research

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The team emphasizes the need to validate the model in high-risk groups, such as people with obesity or type 2 diabetes, and to integrate it into electronic medical records for broader use.

This article is based on information from ScienceDaily

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