Maha Alshahrani ¹ Serena Sabatini ² Devi Mohan ³ Jacob Brain ⁴ Eduwin Pakpahan ⁵ Eugene Y. Tang ⁶ Louise Robinson ⁶ Mario Siervo ¹ Aliya Naheed ⁷ Blossom C. Stephan ^1,4*

• ¹ Curtin University, Perth, Western Australia, Australia
• ² University of Surrey, Guildford, South East England, United Kingdom
• ³ The University of Queensland, Brisbane, Queensland, Australia
• ⁴ University of Nottingham, Nottingham, England, United Kingdom
• ⁵ Northumbria University, Newcastle upon Tyne, North East England, United Kingdom
• ⁶ Newcastle University, Newcastle upon Tyne, North East England, United Kingdom
• ⁷ International Centre for Diarrhoeal Disease Research (ICDDR), Dhaka, Dhaka, Bangladesh

Dementia is a leading cause of death and disability with over 60% of cases residing in Low-and Middle-Income Countries (LMICs). Therefore, new strategies to mitigate risk are urgently needed. However, despite the high burden of disease associated with dementia in LMICs, research into dementia risk profiling and risk prediction modelling is limited. Further, dementia risk prediction models developed in high income countries generally do not transport well to LMICs suggesting that context-specific models are instead needed. New prediction models have been developed, in China and Mexico only, with varying predictive accuracy. However, none has been externally validated or incorporated variables that may be important for predicting dementia risk in LMIC settings such as socio-economic status, literacy, healthcare access, nutrition, stress, pollutants, and occupational hazards. Since there is not yet any curative treatment for dementia, developing a context-specific dementia prediction model is urgently needed for planning early interventions for vulnerable groups, particularly for resource constrained LMIC settings.