A prescription drug traditionally used for cancer treatment and anaemia, leucovorin, has shown potential in helping some non-verbal autistic children develop speech, according to emerging research. Although not officially approved for autism treatment, some families have reported improvements in communication skills after its off-label use. Leucovorin, a form of vitamin B9, supports folate absorption and is believed to aid neurological function in children with autism who may have a deficiency.
Dr Richard Frye, a behavioural child neurologist, has led research into leucovorin’s effects, noting that it may also enhance social interaction, reduce repetitive behaviours, and improve attention. Studies suggest a significant number of autistic children have antibodies that block folate transport to the brain, and leucovorin bypasses this issue by using an alternative pathway. While small-scale studies have shown promising results, experts stress that larger clinical trials are needed to confirm its effectiveness.
Medical professionals warn that while leucovorin has been used safely for decades, it is not suitable for all autistic children. Some experience increased hyperactivity or no benefit at all, making professional medical guidance essential before considering treatment. Researchers are working towards gaining FDA approval, which could lead to standardised dosing, greater medical recognition, and potential insurance coverage.
Experts emphasise that leucovorin is not a standalone solution but could complement behavioural and speech therapies. Autism Speaks’ chief science officer, Dr Andy Shih, cautioned that individual successes cannot yet be applied to the broader autistic community, reinforcing the need for further research. Families are encouraged to consult healthcare professionals before exploring off-label treatments.
Researchers at Loughborough University have developed an advanced artificial intelligence (AI) model designed to predict how long individuals with learning disabilities may need to remain in hospital. The breakthrough, part of the DECODE project, aims to improve patient care, enhance resource planning, and address healthcare inequalities for those with complex health conditions.
The study, published in Frontiers in Digital Health, identified cancer as the primary reason for hospital admissions among patients with learning disabilities, while epilepsy was the most commonly treated condition during inpatient stays. On average, hospital stays last three days, but those exceeding 129 days are often linked to mental health conditions. Longer hospitalisations are more common in patients over 50, those living in deprived areas, individuals with obesity, or those with multiple long-term health conditions.
By analysing GP and hospital records from over 9,600 patients in Wales, the AI model demonstrated 76% accuracy in predicting whether a patient would experience a prolonged hospital stay. Professor Georgina Cosma, an expert in AI for healthcare, explained that the model evaluates factors such as age, medical history, and lifestyle to provide early predictions, allowing hospitals to improve planning and deliver personalised care.
The findings will support NHS efforts to develop risk assessment tools for clinical decision-making. Dr Satheesh Gangadharan, co-lead of the DECODE project, highlighted the potential to reduce hospital admissions by identifying earlier intervention opportunities and encouraging patient involvement in their care. The next phase of research will apply the model to hospital datasets across England to assess its wider applicability.
