Cognitive impairment is common among older adults. Dementia, the most severe expression of cognitive impairment, represents the seventh leading cause of death among all diseases and one of the main causes of disability in older people, currently affecting over 55 million individuals worldwide [1]. As increasing age is the main risk factor for cognitive impairment and dementia, the worldwide aging of populations is driving the exponential growth in the number of affected individuals. Indeed, dementia cases are expected to reach 78 million by 2030 and 130 million in 2050 unless effective preventive and therapeutic interventions become widely available [2]. Dementia has long been considered a non-preventable condition, but a lot of evidence from observational and recent intervention studies has shown the potential for risk reduction and prevention of this disorder and the main underlying diseases, including cerebrovascular disease and Alzheimer´s disease (AD) [1]. The life-course model of prevention summarised by the Lancet Commission on Dementia Prevention, Intervention and Care indicated that twelve modifiable risk factors account for about 40% of all cases of dementia worldwide, which can thus be potentially prevented or delayed [3]. These factors are low schooling, hypertension, hearing impairment, smoking, obesity, depression, physical inactivity, diabetes, low social contact, excessive alcohol consumption, traumatic brain injury, and air pollution [3]. Observational studies from Western countries reported a decreasing trend in the age-specific incidence of dementia, probably related to improvements in education, healthcare, and lifestyle, further supporting the potential benefits of change in risk factor profiles [3]. Europe has been at the forefront of intervention studies, testing innovative preventive approaches for dementia risk reduction and prevention. The Finnish Geriatric Intervention Study to Prevent Cognitive Impairment and Disability (FINGER, ClinicalTrials.gov Identifier: NCT01041989) is the first large and long-term randomised clinical trial (RCT) that showed a positive effect on cognition after a 2-year multidomain intervention that targeted lifestyle and vascular risk factors simultaneously [4]. The FINGER RCT included two arms: multidomain intervention, which consisted of physical activity, diet, social stimulation, cognitive training, and vascular and metabolic risk factor control, and the control group, which received regular health advice. FINGER was the first RCT to show the feasibility of preventing cognitive decline using a multidomain intervention among older individuals at risk of dementia [4]. Long-term follow-ups with the FINGER study participants are ongoing (5- and 7-year follow-up completed; 11-year follow-up ongoing) to assess the long-term effects of the multidomain intervention on cognition. Other European, large multidomain prevention trials (MAPT: Multidomain Alzheimer Preventive Trial; PreDIVA: Prevention of Dementia by Intensive Vascular Care), despite failing to report beneficial cognitive changes in their respective primary outcomes, reported significant positive effects on cognition in secondary analyses for participants with specific risk profiles [5,6]. Overall, these RCTs have highlighted the need for accurate risk prediction and stratification to optimise the efficacy of multidomain preventive interventions. The recent guidelines from the World Health Organisation (WHO) for reducing the risk of cognitive decline and dementia represent a milestone in the field of dementia prevention and highlight the need to further test and develop the FINGER model to define, on a global scale, effective and feasible preventive strategies [7]. The evidence synthetised in the WHO guidelines indicates that risk reduction for dementia can be achieved at individual and population levels through multidomain interventions tailored to specific risk profiles. In this landscape, the availability of models for accurately predicting dementia risk is pivotal to identify and monitor at-risk groups that can benefit from specific interventions. The development of predictive modelling of the onset and progression of dementia can leverage on large multidimensional data, reflecting nonmodifiable (e.g., age, sex, genetics) and modifiable risk factors (e.g., lifestyle, vascular and metabolic factors), as well as clinical information, such as cognitive status, and biological parameters (e.g., neuroimaging, blood markers). Increasing the availability of information and communication technologies (ICT) has also prompted research on digital biomarkers which can support non-invasive longitudinal monitoring of risk and inform personalised preventive approaches to maximise adherence to and benefits of preventive interventions. Artificial Intelligence (AI) tools can handle large and complex data but have not yet been used to develop predictive modelling of the onset and progression of dementia based on the above-listed data.

Dementia is usually preceded by mild cognitive impairment (MCI), and even subjective cognitive symptoms have been associated with increased risk of later cognitive decline. Subjective cognitive decline (SCD) is characterised by self-experienced, persistent cognitive decline, which can evolve into the appearance of objective cognitive impairment. In the absence of objective neuropsychological dysfunction, older adults with SCD are increasingly viewed as at-risk for non-normative cognitive decline and potential progression to MCI and AD dementia. MCI might be considered a pre-stage of dementia, characterised by incipient cognitive dysfunction, as documented in neuropsychological tests, and occurring in up to a fifth of people aged older than 65 years [8]. MCI represents a heterogeneous syndrome, and therefore, the prognosis can differ between each individual. Persons with MCI can either progress to dementia, remain in a stable state, or reverse to normal functioning [8]. The annual conversion rate varies between 5 and 15 percent in the existing literature, with higher rates observed in studies carried out in clinical settings. MCI is seen as a great opportunity for an early targeted intervention, thereby delaying or even preventing the conversion to overt dementia. Increasing evidence from epidemiological, clinical, and biomarker studies suggests that the development of neuropathological changes leading to dementing diseases, especially AD, starts many years before clinical symptoms become apparent, thereby indicating that AD starts as a clinically silent disorder. For instance, research in persons with familial autosomal dominant AD has revealed pre-symptomatic changes in multiple markers of disease in blood, cerebrospinal fluid (CSF), and neuroimaging [9,10]. Different biomarkers seem to be a good proxy for incipient neuropathological changes. The accumulation of Amyloid-Beta (Aβ) or neurofibrillary tangles—the major hallmarks of AD—can either be depicted through positron emission tomography (PET)-Imaging or examination of cerebrospinal fluid (CSF) [10]. Magnetic resonance imaging (MRI) gives insights into neurodegenerative processes through structural brain changes, such as cortical thickness or specific atrophy patterns. Ongoing studies are examining the use of blood-based analysis for a cheaper and minimally invasive assessment of AD biomarkers [11,12], with the aim of providing tools for the early detection of at-risk individuals, who can benefit from preventative interventions. Subjects in asymptomatic at-risk stages can be identified through dementia risk scores, which are weighted composites of non-modifiable and modifiable risk factors that reflect the likelihood of an individual developing dementia [13]. Overall, focus has shifted towards detecting pre-clinical non-symptomatic (or early symptomatic) persons at risk of developing dementia, as they are believed to provide a unique target group for preventive and/or disease-modifying interventions. Given the role of modifiable factors related to lifestyle and vascular health, prevention trials have focused on multidomain approaches, where multiple risk factors are simultaneously addressed to maximise benefits. In the following section, we present a brief summary of evidence on the main modifiable risk and protective factors for late-life cognitive impairment and dementia, which are targeted in multidomain prevention studies and are in the focus of the LETHE project.

Engagement in regular physical activity has been linked to a lower risk of cognitive decline, dementia, and AD in many prospective studies. The association is observed when investigating physical activity in midlife, but older adults who exercise are also more likely to maintain cognition than those who do not exercise [1]. The results of one meta-analysis of 15 prospective cohort studies following up on 33,816 individuals without dementia for 1–12 years reported that physical activity had a significant protective effect against cognitive decline, with high levels of activity being the most protective (hazard ratio ((acrshorthr) 0.62, 95% CI 0.54–0.70) [14]. Another meta-analysis included 16 studies with 163,797 participants without dementia and found that the risk ratio (RR) of dementia in the highest physical activity groups compared with the lowest was 0.72 (95% CI 0.60–0.86), and the RR of AD was 0.55 (95% CI 0.36–0.84) [15]. From the point of view of preventive interventions, the WHO guidelines on physical activity for global health have been integrated into the guidelines for dementia risk reduction [7].

Several studies consistently reported an increased risk of dementia and AD in association with vascular and metabolic risk factors, such as hypertension, hypercholesterolaemia, and obesity at midlife (≤65 years) [1]. Adequate management of these cardiovascular risk factors is pivotal in reducing cardiovascular morbidity in older populations and is thus recommended in midlife, while specific considerations apply for more advanced ages, as in this age group, evidence on some pharmacological interventions (e.g., statins) is mixed. Active treatment of hypertension in middle-aged (45–65 years) and older people (aged older than 65 years) without dementia is recommended to reduce dementia incidence [1]. For diabetes, the association with the increased risk of dementia and AD has been shown for all of adult life, with the risk being stronger when diabetes occurs in midlife than in late-life [1]. At an older age, management of diabetes is recommended through standard glycaemic control rather than intense glycaemic control due to the increased vulnerability of older adults to hypoglycaemia, which can increase dementia risk.

Social isolation might be a prodrome or a part of the dementia syndrome [3]. However, growing evidence has shown that a lack of social engagement is also a risk factor for dementia, while social contact—from being in a relationship, having contact and exchanging support with family members or friends, participating in community groups, or engaging in paid work—can be beneficial [3]. Cognitive benefits of social engagement, although of modest size in some studies, seem to be consistent across studies conducted in diverse populations, supporting the significance of social activity in different settings and cultures, and highlighting the importance of considering social engagement in older people and not only their physical and mental health.

Diet across the whole lifespan has a major effect on health and is linked to late-life cognition and dementia risk, both directly and through its role on cardiovascular risk factors related to dementia, such as diabetes mellitus, obesity, and hypertension. Observational studies and RCTs have reported a reduced risk of cognitive impairment, dementia, and AD in subjects with high adherence to specific diets, including the Mediterranean Diet, the Nordic Diet, DASH (Dietary Approaches to Stop Hypertension), and the hybrid MIND (Mediterranean-DASH Intervention for Neurodegenerative Delay) diet. Common elements of these diets are the high intake of vegetables, nuts, and legumes; preference for whole grains; and low consumption of red meat and high-saturated-fat foods. Differences in the three dietary profiles entail specific indications of the quantity/quality of fruit and vegetable oils (in general high consumption), as well as fish, poultry, and dairy products (low-moderate intake) [1,3].

Mentally stimulating activities across the lifespan, including education, occupational mental demands, and cognitively stimulating leisure activities, have been associated with better late-life cognition and decreased risk of cognitive impairment, AD, and dementia. Such a protective effect might be attributed to mechanisms related to the reduced accumulation of AD neuropathology (i.e., resistance to AD), or the ability to delay or avoid the clinical expression of underlying neuropathology, or resilience to AD [16]. Cognitive stimulation can continue into late life and can be promoted through various activities, including cognitive stimulation therapy, which implies participation in a range of activities aimed at improving cognitive and social functioning, and/or cognitive training, consisting of a guided practice of specific standardised tasks designed to enhance particular cognitive functions, such as memory, attention, or problem solving [17]. Computerised cognitive training is increasingly available and evidence from intervention studies in cognitively healthy older adults and subjects with MCI is encouraging, although methodological limitations exist, and findings needs to be further verified [3,7].

Sleep disorders have received attention in recent years for their potential role in the development of cognitive impairment [18]. A main challenge in assessing the current evidence stems from the methodological heterogeneity of studies carried out so far, including different designs (cross-sectional, longitudinal), study populations (some cohorts included cases with cognitive impairment), and heterogeneity of sleep-related parameters assessed (quality, measured through different indicators, and duration). Two meta-analyses reported that sleep disturbances were associated with a higher risk of all-cause dementia (RR 1.2; 95% CI 1.1–1.3) and AD dementia (RR 1.6; 95% CI 1.3–1.9) compared with the absence of sleep disturbance [19,20]. Even if less evidence is available on the potential cognitive benefits of meditation, relaxation, and spirituality, there is growing interest in these factors, as they have been suggested to be related to AD risk [21].

Up to date, most models have applied regression methods to predict dementia [22]. Recently, Pekkala et al. developed a late-life dementia prediction index using supervised machine learning based on data from a population-based Cardiovascular Risk Factors, Aging, and Incidence of Dementia (CAIDE) study [23,24], and they reported good results for shorter-term dementia prediction [25]. It has also been shown that deep learning methods are promising for predicting cognitive decline [26]. Generally, predicting dementia or cognitive decline requires a quantitative parameter to derive information from. Accurate risk prediction of dementia can leverage on a wide range of data—lifestyle, health-related, etc.—which can be cumbersome to determine. Walters et al. attempted to circumvent this problem by using data routinely collected in primary care settings for dementia prediction using a regression model, which showed good results for an age range between 60 and 79 years [27].

Besides these global architectures targeted at the prediction of dementia, the detection of specific dementia-relevant early symptoms using wearable technology has also already be demonstrated. For example, it was shown that smart glasses can be used to monitor eye blinks [28], which can help to distinguish between essential tremor and essential tremor-Parkinson’s disease [29] and it was demonstrated that reliable wearable medical monitoring systems can nowadays easily be implemented on mobile devices [30].

A consequent extension of current methods would be a combination of modern machine learning methods and routinely—or even better, continuously—acquired data to improve predictions on an individual level.

AI and machine learning technologies require large, diverse, and high-quality data sets to ensure thorough training of the algorithms and to maintain the inclusion of all relevant aspects. Collaborative data from various sources therefore greatly fosters data-driven machine learning methods to yield robust and bias-free models which generalise well to new unseen data [31,32,33]. However, especially in the healthcare domain, sharing of health data for collaborative efforts is accompanied by numerous barriers [34,35] since the data are highly sensitive in terms of data protection and data privacy since confidential patient data are not to be shared with unwanted third parties under any circumstances. Therefore, the usage of these data are strongly regulated to preserve patient rights, and compliance to data protection must be carefully assessed before data sharing [36,37]. Besides regulatory considerations, sharing and subsequently merging data from multiple institutions also comprises technical challenges regarding data curation and data harmonisation [38]. A way to bypass this problem is to omit central storage of the data, perform calculations at the data source, and only communicate model updates based on the local data without ever transmitting the data itself. This approach, termed federated learning [39], has the potential to revolutionise future studies in the healthcare sector since the amount of training data can be drastically increased, while security issues can be handled easier.