Triin Ojakäär
Ivan Koychev- 1Sharp Therapeutics Ltd., London, United Kingdom
- 2Department of Psychiatry, University of Oxford, Oxford, United Kingdom
Alzheimer's disease (AD) is a progressive neurodegenerative disorder that is the most common cause of dementia. Over a third of dementia cases are estimated to be due to potentially modifiable risk factors, thus offering opportunities for both identification of those most likely to be in early disease as well as secondary prevention. Diabetes, hypertension and chronic kidney failure have all been linked to increased risk for AD and dementia and through their high prevalence are particularly apt targets for initiatives to reduce burden of AD. This can take place through targeted interventions of cardiovascular risk factors (shown to improve cognitive outcomes) or novel disease modifying treatments in people with confirmed AD pathology. The success of this approach to secondary prevention depends on the availability of inexpensive and scalable methods for detecting preclinical and prodromal dementia states. Developments in blood-based biomarkers for Alzheimer's disease are rapidly becoming a viable such method for monitoring large at-risk groups. In addition, digital technologies for remote monitoring of cognitive and behavioral changes can add clinically relevant data to further improve personalisation of prevention strategies. This review sets the scene for this approach to secondary care of dementia through a review of the evidence for cardiovascular risk factors (diabetes, hypertension and chronic kidney disease) as major risk factors for AD. We then summarize the developments in blood-based and cognitive biomarkers that allow the detection of pathological states at the earliest possible stage. We propose that at-risk cohorts should be created based on the interaction between cardiovascular and constitutional risk factors. These cohorts can then be monitored effectively using a combination of blood-based biomarkers and digital technologies. We argue that this strategy allows for both risk factor reduction-based prevention programmes as well as for optimisation of any benefits offered by current and future disease modifying treatment through rapid identification of individuals most likely to benefit from them.
Introduction
Alzheimer's disease (AD) is a progressive neurodegenerative disorder that is the most common cause of dementia accounting for 60–80% of all dementia cases (1). In the UK, the projected prevalence rate for dementia for individuals aged 65 and over has reported to be 7.2% (2). This constitutes 1 in every 14 individuals over the age of 65. Worldwide, it has been estimated that 46.8 million people are living with dementia, with the prevalence rates nearly doubling every 20 years (1). Continuous advances in healthcare have increased life expectancy, but as a result the number of individuals suffering from age-related diseases such as AD is on the rise. 2021 was a watershed moment for the field with the approval of the first, ostensibly, disease modifying treatment—an amyloid targeting therapy (aducanumab). This has given further impetus to the need to identify AD pathology with a focus on detecting disease in its earliest possible stages. Decades of research into the constitutional and environmental risk factors of AD have revealed the complexity of its pathology, but also highlighted the significant contribution of modifiable risk factors. It has been estimated that ~40% of AD cases worldwide could be attributable to 12 potentially modifiable risk factors (3). Controlling for these risk factors could prevent up to 1–3 million cases globally (4).
Cardiovascular risk factors such as hypertension and diabetes have been identified amongst the 12 potentially modifiable risk factors for dementia in the 2020 Lancet Commission on dementia prevention (3). Hypertension is estimated to carry a relative risk of 1.6 (95% CI 1.2–2.2) but interventional studies have shown that controlling it through therapy reduces the risk by ~10% (5). Diabetes, which also affects the cardiovascular system has been identified among the major risk factors for dementia with relative risk of 1.5 (95% CI 1.2–1.8) (3). The effect of diabetic control on the risk of dementia is less clear with studies reporting mixed results. A meta-analysis of cohort studies has reported that individuals taking metformin were less likely to develop cognitive impairment compared to those taking other medications or no treatment at all (6). However, other studies have reported no benefits of diabetes control on cognitive health (7). Hypertension and diabetes are known to cause multi-organ damage and have been identified as the primary causes of chronic kidney disease (CKD) (8, 9) which in turn has been associated with risk for cognitive decline (10). Taken together, diabetes, hypertension and CKD are inter-linked cardiovascular disorders that can help identify those most at risk for cognitive decline.
Prospective studies of at-risk populations have revealed that AD pathology is present decades before a clinical diagnosis is made (11, 12). The long pre-clinical phase offers a window of opportunity for secondary prevention of dementia through risk factor control and/or aetiological treatment. The amyloid hypothesis of AD argues that amyloid-β (Aβ) plaque accumulation is the initiating event, triggering a cascade of tau protein hyperphosphorylation (creating neurotoxic neurofibrillary tangles), synaptic loss, neurodegeneration and eventually cognitive decline (13). Crucially, when clinical symptoms of cognitive impairment appear, the underlying AD pathology has already entered its advanced stage, arguably limiting the impact of any interventions attempted at that phase (14). The long pre-clinical phase of AD provides the opportunity for detecting underlying pathology before clinical symptoms appear (15). Diagnosing the biological state of AD has accordingly become a major focus of the research field.
Incremental improvements in the cerebrospinal fluid (CSF) and positron emission tomography (PET) methodology have provided the means to detect early signs of amyloid and tau pathology in the living patient. However, the utility of these methods to detect AD at scale is precluded by their invasiveness, expensiveness and dependence on expertise and technology typically confined to major academic centers. Thus, to take full advantage of the opportunity offered by the protracted preclinical and prodromal dementia disease stages, more appropriate tools are needed to monitor at-risk groups. Recent advancements are proving new opportunities for this through blood-based biomarkers and digital technologies. Blood-based biomarkers have significant advantage over CSF and PET methods due to their time and cost-efficiency, reduced invasiveness and infrastructure availability to support large scale testing. There is substantial evidence that shows the utility of blood-based biomarkers in predicting dementia progression (16), conversion from Mild Cognitive Impairment (MCI) to AD (17) and detecting risk for future AD in healthy aging adults (18). Further data is accumulating on their usefulness in distinguishing different dementia-causing pathologies (19). Concurrently there has been significant investment in the development of digital biomarkers for monitoring cognitive, sensory and motor changes in individuals at risk of AD. Sensory and motor changes can predict AD onset 10–15 years before clinical symptoms appear (20, 21) making them an important complement to fluid biomarkers. The deep societal penetration of digital device use across age strata offers an until now unavailable opportunity for individuals to monitor their risk of AD without having to visit a specialized clinic. Active monitoring devices allow users to measure their cognitive abilities through digital assessments that target specific metrics previously been associated with AD (22). In contrast, passive monitoring devices, record users' activity and engagement with their smart device without having to perform any explicit tasks. For instance, the typing speed and number of pauses during typing on a smartphone can discriminate between individuals with cognitive impairment and healthy controls (23). Therefore, between the sensitivity of blood biomarker assays and the low implementation cost of digital technologies, a realistic opportunity has emerged to conduct secondary prevention programmes.
The purpose of this review is to make the case for the secondary prevention of dementia through (i) defining the chronic physical conditions most appropriate for active AD monitoring through the strength of evidence and prevalence and (ii) to propose scalable and cost-effective tools for monitoring high-risk populations for dementia.
Type II Diabetes
Epidemiology
Diabetes mellitus (DM) is a chronic metabolic disease affecting ~463 million adults globally (24). With the numbers rising significantly, it is estimated that the global prevalence of diabetes will reach 548 million by 2045 (24). A growth rate this high makes diabetes mellitus one of the most significant health challenges of the 21st century, with type II diabetes mellitus (T2DM) being the fastest growing chronic disorder worldwide (25). Type 2 diabetes is characterized by persistent hyperglycaemia (26) and can be attributed to multifactorial integrating factors such as genetics (27), age, socioeconomic status, education (28), as well as, modifiable risk factors including diet (29), smoking (30), and levels of physical activity (31).
The effectiveness of treatment options for diabetes have improved incrementally over the past few decades (32), thus increasing the lifespan of patients. Despite this positive trend, a growing body of epidemiological research suggests that individuals with type II diabetes are at an increased risk of developing neurodegenerative diseases such as AD (33). The Rochester dementia incident cohort study (34) conducted between 1970 and 1984 was among the first to demonstrate a significant association between diabetes and cognitive decline. It found that individuals with diabetes have a significantly higher risk for AD than controls. Similar findings were demonstrated by the Rotterdam Study (35) where individuals with diabetes were at a 2-fold risk of Alzheimer's disease compared to controls. Similarly, in the Finnish Vantaa cohort Ahtiluoto et al. (36) reported that diabetes doubled the incidence of dementia, AD and vascular dementia, and increased mortality. In this post-mortem study, individuals with diabetes had lower levels of amyloid plaques and tangles relative to non-diabetics but were more likely to have cerebral infarcts. Individuals with diabetes were shown to be more prone to extensive vascular pathology, which independently or combined with AD-type pathology (especially in APOE e4 carriers) results in a higher risk for dementia. These findings have also been confirmed in a meta-analysis of cross-cultural studies looking at the association between T2DM and AD (37). It found that the AD risk for individuals with diabetes was significantly higher relative to those without diabetes. When looking at ethnic differences, the relative risk for Caucasian populations was slightly lower compared to Asian populations. In summary, there is consistent evidence that points to a link between T2DM and increased risk for cognitive decline and dementia, with risk being approximately double for those with a diabetes diagnosis. In terms of dementia etiology, T2DM appears to be more tightly associated with vascular than AD causes.
Mechanism
There are many possible interacting mechanisms that contribute to the risk of dementia in individuals with T2DM. Firstly, T2DM associates with a pro-coagulation state which increases the risk for cerebrovascular events. Vascular risk factors and vascular events are an important determinant for brain atrophy and vascular brain lesions (38) that consequently increase the rate of cognitive decline (39). A systematic review of brain imaging studies has shown that T2DM is consistently associated with cerebral and lacunar infarcts (40) with the associated brain volume loss rate being equivalent to 3–5 years of healthy aging (41). Brain atrophy and brain lesions have been shown to accelerate in patients with DM even when cognitive differences are not detected (42). Furthermore, the presence of hypertension in diabetic patients has been associated with even greater cerebral atrophy than diabetes alone (43).
Chronic hyperglycaemia, which is central to T2D, is measured via glycosylated hemoglobin (HbA1c) and has been proposed as a direct mechanism linking T2D and cognitive decline. Research has shown that higher average glucose levels are associated with increased risk of dementia in both individuals with and without diabetes (44). Marden, Mayeda (45) reported that each percentage point increase in HbA1c was associated with a 0.052 unit decrease on a custom memory score per decade even in individuals without clinical diabetes. In a population-based cohort study HbA1c of 6.2% or greater predicted faster cognitive decline in individuals aged between 65 and 88 even after adjusting for age, sex, education, and APOE ε4 status (46). A cross-sectional study of patients with T2D showed a non-linear association between HbA1c levels and cognitive function that indicated a bell-shaped relationship with low and high HbA1c levels affecting cognitive decline (47). There is consistent evidence that links HbA1c levels with cognitive decline, but more research is needed to determine the temporal order of the effect.
Mechanisms for induction of AD-specific processes have also been proposed. Oxidative stress, mitochondrial dysfunction and chronic inflammation have been proposed to be key contributors (48). AD pathology is explained by the formation of neurofibrillary tangles and the build-up of extracellular β amyloid plaques, which both are facilitated by insulin resistance, the main characteristic of T2DM. Insulin resistance leads to a reduction in the activation of protein kinase B, a protein that holds an important role in glucose metabolism and the inhibition of glycogen synthase kinase 3 beta (GSK3β), a main kinase that phosphorylates the tau protein. An escalation in the activation of GSK3β may lead to the over-phosphorylation of the tau protein, which could explain the formulation of neurofibrillary tangles seen in individuals with AD (48). Furthermore, advanced glycation end products (AGE) which play a critical role in diabetes may increase in the brain due to hyperglycaemia and increase neuronal cell death (49).
Receptor for advanced glycation end products (RAGE) is a member of the immunoglobulin superfamily of cell surface molecules and has been proposed as an important link between T2DM and neurodegeneration. Research suggests that RAGE acts as an inflammatory intermediary and an inducer of oxidative stress leading to pathophysiological changes in the brain (50). An increase in oxidative stress with reduced antioxidant capacity can also lead to mitochondrial damage (51, 52). RAGE contributes to the production and accumulation of Aβ and neurofibrillary tangles, as well as overall neuronal degeneration. Furthermore, RAGE plays an important role in the pathogenesis of Aβ and increased tau phosphorylation, which are both associated with AD pathology (53).
Insulin resistance, a key contributor to diabetes, is also associated with inflammation (54). Systemic inflammation is thought to play a critical role in neurodegeneration and AD pathology. Systemic inflammation is characterized by release of pro-inflammatory cytokines and chemokines from the immune-related cells into the blood. These cytokines can lead to a pro-inflammatory environment in the central nervous system by entering the brain through the blood-brain barrier. Systemic inflammation can give rise to reactive, pro-inflammatory microglia and astrocytic phenotypes, that can also bring about tau hyperphosphorylation and Aβ amyloid oligomerization (55). A pro-inflammatory state has also been shown to directly contribute to the risk of coagulation and may additionally contribute to the risk for cerebrovascular events observed in T2DM (56).
Interaction With AD Factors
Knowing the pathophysiological changes triggered by diabetes, it is important to distinguish individuals at highest risk of AD pathology for early prevention and treatment. Diabetic APOE ε4 carriers have a significantly higher risk for AD compared to individuals with either factor and individuals with both factors have demonstrated a higher number of hippocampal neuritic plaques and hippocampal and cortical neurofibrillary tangles, as well as, an increased risk for cerebral amyloid angiopathy (57). The interactive effect of type 2 diabetes and AD risk factors on the rate of functional decline has been investigated in cognitively healthy individuals through the Alzheimer's Disease Neuroimaging Initiative (58). The interaction between diabetes and AD features (cognitive decline APOE ε4 carriership, cerebrospinal fluid β-amyloid, total tau (t-tau) and hyperphosphorylated-tau (p-tau) showed that individuals with both diabetes and at least one AD feature had a faster functional decline rate than those without both factors. Of the individual AD features, subtle objective cognitive decline, APOE ε4 carriership, p-tau and tau but not CSF β-amyloid all accelerated functional decline. This study indicated that while diabetes likely accelerates AD pathology, it may be that it does so primarily through tau mediated mechanisms.
Finally, age and sex specific incident rates of AD in diabetic individuals show demographic differences: the risk is reportedly higher in diabetic women vs. diabetic men (59) and the risk is further exacerbated for older women (60).
Effect of Treatments
Provided there are shared mechanisms involved in T2D and dementia, diabetes treatment could potentially provide an avenue to secondary prevention of cognitive decline. Results from interventional studies supporting this rationale have been mixed. Monotherapy with sulfonylurea has been found to decrease the risk of AD while combination therapy using non-sulfonylurea insulin secretagogue showed the opposite effect (59). However, after adjusting for underlying risk factors and duration of diabetes since diagnosis, neither monotherapy nor combination therapy with oral antidiabetic medications were associated with AD risk.
Rosiglitazone, an antidiabetic pharmacotherapy that optimizes endogenous insulin use and has systemic anti-inflammatory effects has also been shown to improve scores on cognitive measures after 6 months of treatment (61). Risner et al. (62) recruited mild to moderate AD patients to a randomized placebo-control trial and followed up for 24 weeks. There was a significant improvement in the cognitive assessments scores of APOE ε4 non-carriers who were given the rosiglitazone treatment compared to controls. APOE ε4 carriers not only did not improve cognitively but actually declined at the lowest treatment dose. These results suggest that the benefit of anti-diabetic medication may be limited in individuals at risk for AD-specific neurodegeneration.
Intranasal insulin has also been investigated as a potential protective treatment against further cognitive decline in those with memory impairments. Reger et al. (63) administered insulin treatment to individuals with memory-impairments and controls and found that the positive effects of treatment were stronger for memory impaired subjects who were APOE ε4 non-carriers compared to cognitively impaired non-carriers and cognitive healthy controls. Furthermore, APOE ε4 carriers showed poorer recall following intranasal insulin on one of the memory tests. These findings provide further support to the concept that diabetes agents may be more effective in those with non-AD pathology.
Hypertension
Epidemiology
Hypertension refers to systolic blood pressure above 140 mmHg and/or diastolic blood pressure above 90 mmHg which both increase with age (64). Hypertension is a major risk factor for cardiovascular disease as well as vascular dementia (65, 66). There is also evidence to suggest that hypertension increases the risk of AD-specific neurodegeneration. To understand AD prevalence in individuals with hypertension, Guan et al. (67) carried out a meta-analysis of longitudinal studies and found that out of 9 studies only one showed an association between hypertension and increased AD risk. Similarly, Power et al. (68) carried out a systematic review and meta-analysis to investigate the link between hypertension and AD. No clear relationship between hypertension and AD was found in the 18 studies analyzed. The lack of association in these studies have later been attributed to methodological flaws such as short follow-up times and lack of mid-life blood pressure measures. In a meta-analysis, Norton et al. (69) demonstrated that individuals with high mid-life blood pressure are at an increased risk for developing AD later in life. More recently, Walker et al. (70) examined the association of mid- to late-life blood pressure patterns with subsequent dementia, MCI and cognitive decline in prospective population-based cohort and showed that individuals combining mid-life hypertension and late-life hyper- or hypo-tension had significantly higher risk of dementia relative to those who maintained normal blood pressure levels during their adult years. In addition, systolic but not diastolic blood pressure shows a significant association with AD risk, while diastolic shows no association (71). These results have been interpreted as evidence that mid-life hypertension is the critical risk factor for dementia with late-life hypotension being a feature of prodromal dementia thus obfuscating the relationship between the two in older age.
Mechanism
The link between dementia and hypertension is often explained by micro- and macrovascular complications arising through chronically elevated blood pressure. Accordingly, hypertension is one of the main risk factors for cerebrovascular events such as stroke, cerebral infarcts and the development of ischemic white matter lesions. While the more dramatic events constitute medical emergencies and are easy to detect, cerebral infarcts can also happen without focal symptoms making early detection and treatment difficult. Undiagnosed cerebrovascular disorders increase with age and can play an important role in the development of AD in individuals with hypertension. Untreated hypertension can predict the severity of neurofibrillary tangles and neuritic plaques in the brain (72) further suggesting a direct link between AD and hypertension. It has been proposed that hypertension aggravates Aβ- induced cerebromicrovascular impairments in AD and accelerates its progression. Tau pathology has also been linked to hypertension in a mouse model where experimentally induced hypertension worsened tau-related motor dysfunction (73). A study in patients demonstrated that after adjusting for Aβ levels in the CSF, lobar micro bleeds associated with high blood pressure are linked to faster cognitive decline and higher levels of p-tau in CSF samples (74). Moreover, in a mouse model it has been demonstrated that Aβ and tau may interact to compromise brain vascular function in AD (75). Thus, suggesting that tau may further aggravate microvascular Aβ deposition and its effects on AD.
Over time hypertension has been shown to cause hypertrophy and stiffening of arterial walls which associates with reduced blood flow in the microvasculature (76). This in turn affects the oxygen and nutrient supplies to tissues which may initiate or accelerate the AD pathophysiological cascade e.g., by impairing microglial function (77). Moreover, capillary loss behind the lack of cerebral blood flow can negatively affect the clearance of Aβ. Cerebral blood vessels play an important role in exchanging Aβ between blood and the brain, and therefore, changes in the cerebrovascular function can negatively affect its clearance from cortex (78). In support to the role of hypertension early in the AD process, reduced cerebral perfusion has been demonstrated in preclinical stages of AD (79). Furthermore, it has been hypothesized that hypertension leads to blood-brain barrier (BBB) breakdown by interactive mechanisms involved in inflammation, oxidative stress and vasoactive substances. The increased BBB permeability disrupts central nervous system homeostasis, exposing it to potentially cytotoxic factors such as inflammatory cytokines which has been argued to associate with accelerating neurodegeneration (80).
A close interactive relationship between hypertension and inflammation has also been proposed. The interaction between inflammation and hypertension may be more than additive leaving individuals with comorbidities at an increased risk for AD (81). In a mouse model, hypertension triggered hypoperfusion and neuroinflammation in both cortex and hippocampus. Inflammatory response was even higher as Aβ deposition became more detectable (82). Moreover, to understand the role of neuroinflammation in hypertension induced Aβ pathology, immune system activating and inhibiting treatments were compared. The former but not latter reduced amyloid load, indicating that controlling inflammation with immune system stimulation might provide an effective approach to limit AD pathology in people at-risk through cardiovascular risk factors.
Interaction With AD Factors
Hypertension is often co-morbid with other metabolic risk factors such as diabetes, obesity and dyslipidemia, with <20% of all cases occurring in isolation (83). It is thought that this grouping can be attributed to an insulin resistance syndrome promoted by obesity and the closely related metabolic cardiovascular syndrome (84). Approximately 30% of coronary events in men and 70% in women have been attributed to clusters of two or more cardiovascular risk factors with hypertension being only one component of a complex interplay of risk factors (83). Risk factors for coronary complications are also associated with AD, and therefore, it is likely that the higher the number of risk factors for coronary events is, the higher the risk is for AD. For example, smoking and hypertension when comorbid with T2DM confer a higher risk for AD relative to individuals with T2DM only (85). Thus, the clustered risk profile of an individual with hypertension makes them more susceptible to AD through shared risk factors.
The modifying role of APOE genotype in the relationship between hypertension and AD has been studied by Kester et al. (86) in a patient population. It was found that the link between hypertension and levels of CSF tau and p-tau 181 was modulated by APOE genotype but differed between individuals depending on the characteristics of the genotype present. Homozygous APOE ε4 carriers with hypertension had significantly higher levels of CSF tau and ptau-181 compared to individuals of the same genotype, but no hypertension. Furthermore, APOE ε4 genotype did not interact with the relationship between hypertension and Aβ42, suggesting that tau pathology alone is directly modified by genotype. Data from the Seattle Longitudinal study spanning over a 21-year period revealed that hypertension synergises with the effects of APOE ε4 on the rate of cognitive decline (87). These findings demonstrate the potential benefit of combining hypertension and APOE genotype factors in identifying high risk for cognitive decline.
Effects of Treatment
Antihypertensive treatment has shown promising results in lowering risk of future cognitive decline. In a 3-year community cohort study, subjects taking antihypertensive medication (primarily diuretics) had a lower incidence of dementia compared to untreated controls (88). The use of other antihypertensive medication (calcium antagonists or β-blockers) only showed a reduction in AD risk in a subpopulation with pre-treatment systolic blood pressure >160 mm Hg or diastolic blood pressure >95 mm Hg. Furthermore, untreated subjects with dementia had twice the rate of cognitive decline compared to dementia patients receiving antihypertensive therapy. A retrospective national cohort study on antihypertensive drug use and AD risk in diabetic individuals demonstrated that the most effective treatment for lowering AD risk is seen with angiotensin II receptor blocker use (24% lower risk of dementia), followed by diuretics (14%), angiotensin-converting-enzyme inhibitors (11%) and b-blockers (4%) (79). More recently, a meta-analysis of prospective cohort studies revealed that individuals with treated hypertension had a reduced risk for developing dementia compared to those not taking medication (89). Overall, there is consistent evidence that points to the benefits of antihypertensive therapy as both primary (i.e., reducing risk of new dementia diagnoses in hypertensive patients) and secondary prevention of AD (i.e., reducing rate of cognitive decline in established dementia).
Chronic Kidney Disease
Epidemiology
Chronic kidney disease (CKD) is characterized by impaired kidney function as defined by glomerular filtration rate (GFR) of <60 mL/min per 1.73 m2, or signs of kidney damage of 3 months or longer duration. Two of the major causes of CKD are diabetes and hypertension with diabetes accounting for 30–50% of all CKD cases (90). Based on a survey of non-institutionalized adults in the USA, it was estimated that hypertension is present in 23% of adults without CKD, 36% in those with stage 1 CKD, 48% in stage 2, 60% in those with stage 3 CKD and 84% in those with stages 4 and 5 CKD (91). The relationship appears to be bi-directional as kidney function is a critical mechanism for regulating blood pressure and therefore CKD typically results in further deterioration of blood pressure control (92).
Individuals with chronic kidney disease have been found to have an elevated risk for cognitive decline. Etgen et al. (93) carried out a systematic meta-analysis to investigate the relationship between CKD and cognitive decline. Their analysis included over 54,000 participants and concluded that individuals with CKD are more likely to experience cognitive decline than those without CKD. Findings from the Chronic Renal Insufficiency Cohort Cognitive Study (94) showed that individuals with advanced CKD were more likely to show clinically significant cognitive impairment in most cognitive domains compared to those with mild to moderate CKD. Furthermore, a meta-analysis including 54,779 individuals showed that for each 10 mL decline in the GFR value below the clinical threshold for impairment (60 mL/min per 1.73 m2), the risk for cognitive decline increases by 11% (95). A study of 1,015 postmenopausal women with diagnosed coronary heart disease reported a 15–25% increase in risk of cognitive decline per 10 ml/min/1.73 m2 decrement n eGFR measured (96). Similarly, findings from the Rush Memory and Aging Project reported an association between CKD and dementia where global cognitive decline was comparable to 3 years of aging for each eGFR reduction of 15 ml/min/1.73 m2 (97). These studies suggest a linear relationship between loss of kidney function and subsequent cognitive decline.
Mechanism
Despite strong evidence pointing to the association between CKD and cognitive decline, the mechanisms involved in the interaction remain unclear with several interacting mechanisms having been proposed. Firstly, the failure to eliminate metabolic waste products through kidney failure adversely impacts multiple organs, including the brain. It has been proposed that of the many uremic toxins normally excreted through the kidneys, uric acid, parathyroid hormone (PTH) and indoxyl sulfate are most likely to contribute to the development of cognitive decline in individuals with CKD (10). Urinary toxins such as PTH can accumulate and pass through the blood-brain barrier. Elevated PTH levels have been shown to associate with to hyperparathyroidism, which can in turn impair cognitive performance (98, 99).
Vascular injury is a key clinical characteristic of CKD which has been hypothesized to accelerate AD progression (100). Similar to the mechanism proposed in hypertension, the reduced vascular reactivity and permeability seen in CKD may initiate or accelerate the core AD pathophysiological process (101). A relationship between albuminuria, a marker of microvascular dysfunction in both kidney and brain tissue, and AD has been demonstrated by Oh et al. (102), thus arguing that as both organs are low resistance end organs, they are particularly prone to injury through high blood flow.
The renin-angiotensin system (RAS) activation plays a key role in the development of CKD (103) and has also been found to be linked to AD progression. Continuous RAS activation in rodent models results in a reduction in cognitive functioning which in turn is linked to a reduction in cerebral surface blood flow and higher levels of oxidative stress (104).
Interaction With AD Factors
APOE ε4 is a major risk factor for AD, but surprisingly has been found to slow disease progression in CKD. While APOE ε2 genotype has been associated with lowered glomerular filtration rate and CKD, the ε4 allele provides protection against CKD progression (105). To investigate this further Chu et al. (106) examined the role of these two APOE alleles in CKD progression in a prospective cohort. There is consistent evidence linking APOE ε4 carriership to decreased risk of CKD, although the relationship appears to be strongest in Caucasians (107). The exact mechanisms for this unexpected interaction remain unclear.
The risk of AD is known to increase with age and such effects also contribute to AD risk in individuals with CKD. Cheng et al. (108) carried out a cohort study and found that AD risk in CKD patients was similar for both men and women, however, the age specific relative risk was the highest for the youngest group and lowest for the elderly. Taken together these findings highlight the importance of dementia monitoring of CKD to identify cognitive decline in younger individuals as well as those without genetic risk.
Effects of Treatment
The various health implications of CKD have led researchers to investigate whether CKD treatment could slow down the detrimental effects it has on brain functioning. Various studies have looked at the effects of renal replacement therapies on Aβ clearance. Peritoneal dialysis, a treatment for CKD, has been found to reduce Aβ plasma levels in both CKD patients and APP/PS1 mice model associated with early-onset AD suggesting Aβ clearance might be a promising therapeutic strategy to prevent the accumulation of amyloid plaques (109). Lower levels of serum Aβ in CKD patients receiving dialysis have been confirmed in another study (110) which the authors interpreted as evidence for protective action of dialysis through peripheral Aβ clearance. This interpretation is however potentially controversial, as reduction of Aβ in CSF and blood is also a hallmark of amyloid being deposited in cortical amyloid plaques. Renal transplantation, a treatment for end-stage kidney disease has also been shown to reduce the likelihood of developing dementia. For instance, a successful renal transplantation has been shown to significantly improve cognitive and psychomotor performance on measures of processing speed, attention and executive functioning (111). Improvements in cognitive performance have also been demonstrated in dialysed patients after kidney transplantation with the degree of cognitive improvement linked to factors such as duration of CKD, age and renal function post-surgery (112).
Prevention Strategy Based on Comorbidities
The added risk that comorbidities pose on healthy cognitive aging has received substantial attention from researchers, however, the application of this knowledge into active prevention strategies has been held back by underinvestment and difficulties in stratifying risk at scale. Monitoring populations based on high-risk comorbidities for AD and controlling their risk factors but also fast-tracking individuals into disease-modifying treatments as they become available could limit the burden of AD significantly.
Control of Risk Factors
The modifiable risk factors that deserve the highest level of attention are mostly linked to either cardiovascular and metabolic risk factors such as diabetes, hypertension and chronic kidney disease, and lifestyle factors such as smoking, physical activity, diet, mental and social stimulation. The evidence for the impact of interventions aimed at reducing modifiable risk factors on dementia risk is accumulating thus paving the way for prevention programmes. The Finnish Geriatric Intervention Study to Prevent Cognitive Impairment and Disability (113) was carried out to investigate the effects of lifestyle intervention on slowing cognitive decline in individuals with cognitive abilities at mean levels or lower than expected for their age according to Finnish population norms. They found that a multi-domain intervention including diet, exercise, cognitive training and control of vascular risk factors can improve or maintain cognitive abilities in individuals aged 60–77. It has been suggested that such findings indicate the possibility of lowering dementia risk in individuals with cardiovascular risk factors through control of their underlying cardiovascular disease (114). Similarly, Santos et al. (115) carried out a multidisciplinary rehabilitation program to study the effects of cognitive rehabilitation, computer assisted cognitive training, speech therapy, occupation therapy, art therapy, physical training and cognitive stimulation on cognitive decline. They found that patients with mild AD in the intervention arm experienced improvements in cognition, quality of life and a reduction in depressive symptoms. No improvement was seen in individuals with moderate AD, supporting the idea that risk reduction interventions hold the best chances of success if implemented in the earliest stages of AD.
Disease Modification Therapies
Excess accumulation and deposition of Aβ and intracellular neurofibrillary tangles composed of tau protein are at the heart of AD pathology, and therefore, have led to numerous secondary disease prevention strategies aiming to target these key components. The cortical Aβ burden is determined by the balance between its production and clearance, and an offset in this balance can lead to the accumulation of Aβ as seen in AD (116). Aβ is synthesized from amyloid precursor protein by γ-secretase, and thus, the blocking of these enzymes has been a major focus of drug development. However, it is now understood that Aβ clearance and degradation rather than synthesis are more critical in the accumulation of Aβ, and therefore, pharmacological targets have shifted focus (117). Despite the efforts, therapies targeted against Aβ clearance have been unsuccessful until recently. Aducanumab, a human monoclonal antibody that selectively targets Aβ build-ups has been shown to decrease the levels of aggregated Aβ in early-stage AD in a dose dependent fashion with an associated marginal effect on cognition and functional ability (118). After its successful approval with the FDA in the US, it is likely that aducanumab will follow similar trajectories in other regions including Europe.
Early tau targeting therapies have primarily focussed on either inhibition of kinases, tau aggregation or stabilization of microtubules, however, trials have been mostly unsuccessful due to toxicity and lack of efficacy (119). The majority of tau targeted therapies currently in clinical trials are immunotherapies. For instance, a phase 2 study began in January 2021 for passive immunization with JNJ-63733657, which has so far been found to eliminate pathogenic tau seeds in cell-based assays and to inhibit the spread of tau pathology in a mouse model. It has been argued that tau is likely to be a better target than Aβ once cognitive impairment is detectable, because it correlates better with clinical symptoms than Aβ accumulation (119).
Monitoring of At-Risk Groups
Monitoring Blood-Based Biomarkers
In a healthcare system of limited resources, a key challenge to either treatment strategy is the effective identification of preclinical dementia risk and the assessment of the efficacy of potentially toxic therapies in otherwise cognitively healthy or minimally impaired individuals.
Rapid developments in the methodology of blood-based assays now represent a viable option for the task of monitoring risk and assessing treatment effects in early-stage AD. Blood-based testing is significantly less expensive and less invasive than both CSF (requires lumbar puncture) and PET (requires administration of radioactive ligands). It also represents a routine clinical procedure that does not require specialist skills or equipment. Blood sampling is already used to screen large populations for other conditions due to its availability in a variety of settings from clinics to home based testing. Accumulating research demonstrates the effectiveness of use of blood-based biomarkers in distinguishing individuals with biological AD from controls across disease stages (120). The Amyloid/Tau/Neurodegeneration (ATN) network has been proposed as a framework for the assessment of the biological trajectory of AD irrespective of clinical symptoms (121). While the ATN network groups biomarkers from imaging to biofluids, the ease of testing and low cost of blood-based biomarkers, make them an ideal candidate for assessing biological change in large cohorts. In terms of amyloid, until recently its low concentrations in blood and the high affinity of albumin to it meant that well-established assays such as the enzyme-linked immunosorbent assay (ELISA) did not offer a reliable method for its detection. However, novel methods such as immunomagnetic reduction IMR have been shown to consistently separate AD patients from controls (120). In a recent study, a high-precision plasma β-amyloid 42/40 ratio assay (immunoprecipitation and liquid chromatography-mass spectrometry), accurately predicted amyloid cortical burden with an area under the curve (AUC) of 0.88. The performance was further improved when age and APOE4 carriership were added to the model (AUC 0.94) (122). Tau biomarker blood assays have also seen dramatic improvement, especially through the single molecule arrays (SiMOA) technology. In AD CSF p-tau181 has been shown to be abnormal even before Aβ markers (CSF or PET) reach abnormal levels, with elevated levels correlating with cortical Aβ pathology (123). Although both t-tau and p-tau are elevated in CSF levels in individuals with AD pathology, t-tau is non-specific as it is increased in any condition involving neural injury e.g., stroke, traumatic brain injury (124, 125). In contrast, p-tau change appears to be specific to AD pathology (126, 127). Encouragingly, ptau-181 and 217 in blood-based assays also demonstrate utility in differentiating between AD and non-AD pathology, thus offering an option for differential diagnosis at the earliest stages of disease. Neurofilament light (NfL) is a structural protein released through any demyelinating process. Its levels in CSF have been shown to be consistent AD neurodegeneration biomarkers and can be used effectively to distinguish individuals with AD from controls (128). More recently, plasma NfL concentration has also been demonstrated to differentiate AD from non-demented controls and to have a strong significant correlation with CSF NfL (129). Taken together, the blood biomarker assays currently available offer an opportunity to detect the biological process underlying AD (120). In addition, p-tau plasma assays appear to have a role in the differential diagnosis of AD from other dementia causing pathologies (130) while NFL has already been shown to be a dynamic marker of neurodegeneration which can be used to assess potential disease modification effects (131).
Monitoring Cognition
Digital technologies offer an until now unavailable opportunity to evaluate cognition remotely without a supervised clinician. Such novel digital technologies provide cost-effective, sensitive, objective and multidimensional alternatives to pen and paper cognitive tests that can be used at scale and at high density. They offer the definition of intra-individual trajectories in healthy aging which then can detect the subtle deviations in cognitive functioning part of the earliest disease manifestations.
Passive Monitoring by Wearable Devices
In addition to active cognitive monitoring, digital and wearable devices offer the option to derive cognitive information through gathering data on users' behavioral patterns and ability to interact with the devices. These technologies are widespread and do not require specialized equipment, offer immediate access to information, increase sensitivity, put an extremely low burden on the healthcare system and offer a unique approach to map cognition to biological changes in real time (132). Passive monitoring devices can collect rich high-frequency longitudinal data that is objective, inexpensive and is of low burden to the patient. Below we list some of the main areas of development in this field.
Monitoring Gait and Physical Activity
Wearable devices can track early signs of gait impairment, which can start up to 12 years before any cognitive impairment can be detected (133). By measuring gait speed, step variability, rhythm, asymmetry and postural control it has been possible to detect early signs of AD (134) and associations with AD biomarkers (135). The relevance of gait to AD detection lies in its complexity. It is a cognitive task that requires function from multiple domains such as attention, executive function, visuospatial function and motor processing for successful task execution (133). Wearables can also provide objective measures of physical activity levels through accelerometry which in turn allows the reconstruction of diurnal variation in activity. This offers an additional option for risk detection as specific patterns of physical activity and sleep associate with dementia risk (136, 137). The feasibility of gathering accelerometry data at scale has been demonstrated through studies such as the recording of data from 100 k UK Biobank participants (138).
Monitoring Fine Motor Movements
In addition to gait and physical activity, smart devices can also gather rich and valuable information about cognitive and motor abilities through the user's engagement with the device. These data on fine motor control, language abilities and processing speed, can be used to build predictive models for early disease detection. For example, Ntracha et al. (139) used touchscreen typing characteristics (participants were asked to type stories on the phone) to build a model with diagnostic ability that distinguishes MCI patients from controls. The best performing model had accuracy of 80% (AUC = 0.75), which is in a similar range to many other dementia prediction models that use large cohort data (140, 141).
Active Monitoring by Digital Devices
Monitoring Using Cognitive Tests
Prodromal AD can be detected by tests focusing on cognitive functions supported by structures affected first by the pathophysiological process e.g., temporal memory for bound features as a proxy of hippocampal function (142, 143). The sensitivity of this method can be improved further by high density longitudinal testing that generates an individual's expected trajectory which can be used to detect the subtle deviations that occur in early AD. Digital technologies have attempted to combine these two approaches. For example, the Mezurio app used a paired associate smartphone-based test of inanimate vs. animate objects presented visually (thought to reflect perirhinal function) to evaluate episodic memory at periods of up to 11 days of mid-life individuals at increased risk for AD. It found that longer term retention periods (5 days or longer) are needed to reliably uncover subtle cognitive deficits in people at risk for AD (144). The task also highlighted the value of short, frequent and flexible cognitive tests in terms of acceptability for the individual (145).
Monitoring Using Eye-Tracking Technologies
The oculomotor system is gaining increasing attention as a potential biomarker for dementia in AD. Individuals with MCI show deficits in executive functions e.g., inhibitory control (146). Eye-movement error-correction tasks (antisaccade tasks) can measure this in a simple paradigm and have been shown to detect early signs of AD before standard neuropsychological tests do (147). Novel digital technologies are emerging that combine eye-tracking technologies with cognitive tests and offer a multi-dimensional approach to detecting cognitive impairment. Data from built-in laptop web cameras show a strong correlation with high frame rate eye trackers on measures of visual memory (148), and therefore, have the potential to screen individuals without specialized laboratory equipment. A recent study has confirmed that eye-tracking can also be applied to smartphone cameras (149), providing an even more accessible tool. All in all, the rapid developments in digital monitoring are providing a broad gamut of options for cost-effective monitoring of individuals at risk at the comfort of their home.
Discussion
The failure of AD treatment strategies implemented in its clinical stages has shifted the focus of secondary prevention strategies to its preclinical stages (15). Improved understanding of the risk factors for AD has opened an opportunity to identify individuals at high risk for developing AD from mid-life (150). These individuals can then be monitored up using scalable technology to allow the identification of preclinical dementia and the subsequent implementation of prevention strategies, be it via addressing of risk factors or disease modification therapies as they become available. Developments in blood-based AD biomarkers are increasingly recognized as a mature option for scalable and low-cost alternatives to previous invasive diagnostic methods. Similarly, digital biomarkers offer the opportunity to monitor functional and behavioral changes in individuals through passive and active monitoring that lower the burden to the healthcare system and put the risk monitoring to patient's own hands.
In this paper, we focused on selecting cardiovascular diseases as the chief strategy for the identification of at-risk group. This is based on the strong evidence detailing the link between such comorbidities and the development of cognitive decline and dementia. The rationale is further strengthened by studies showing that targeted interventions of these risk factors can result in better dementia outcomes, especially when implemented early. In addition, conditions such as hypertension, diabetes and CKD tend to be looked after through close supervision by either primary and/or secondary care which makes them highly accessible for secondary prevention.
We propose that the mechanics of creating an at-risk cohort should be based on models estimating the risk of developing dementia in the next 5–10 years by combining demographic, genetic and cardiovascular severity data. We have previously shown that detecting Alzheimer's disease pathology among cognitively normal individuals can be achieved using models incorporating age, sex and APOE4 carriership (AUC 0.82) (155) and that this can be increased further (AUC 0.84), for example by adding body mass index, a proxy for insulin resistance (151). Individuals deemed to be at high risk can then be longitudinally (e.g., annually) monitored via plasma biomarkers for signs of AD pathophysiology being triggered (e.g., change in ptau-181 levels). In addition, digital biomarkers can be used to establish a cognitive baseline which through regular testing (e.g., 6 monthly) can detect subtle deviations in ability. Wearable technology can provide information on activity and sleep which in turn can further improve the risk prediction models. Taken together, such monitoring programmes can then focus efforts to those that would most benefit from targeted multi-domain interventions of specific risk factors (113), but also create the infrastructure for identifying individuals with specific AD pathology who would then be candidates for disease modification treatment using e.g., amyloid clearance therapies. The lack of such AD biomarker-informed research and healthcare infrastructure has been a major barrier to the development of novel dementia research therapies (152).
The creation of risk-based preclinical AD cohorts for the purposes of secondary prevention is not without its limitations. Firstly, the monitoring of risk would, in most cases, result in disclosure of information relating to personal and familial risk of a currently untreatable condition. The creation of such infrastructure therefore will require carefully designed risk disclosure protocols. Even so, it may be that a proportion of individuals choose not to engage with a programme that may result in risk disclosure which can limit the programme's impact. In addition, as shown in other screening programmes, the scale required will mean that the risk exists for psychological distress through false positives. Equally, there is the possibility that early disease cases may be missed through test inaccuracies or system errors, as demonstrated by a failure of primary care to follow-up abnormal screening mammograms in 27–73% of cases within 6 months (153). An AD secondary prevention programme may suffer an even exacerbated form of the problem due to the huge number of people who may fit the criteria for being at-risk. The disclosure of risk to a large population of people would need to be met with the availability of trained clinicians who can interpret, communicate the results and, arrange for follow-up through appropriately resourced clinical services. Secondly, large scale collection and linkage of sensitive health data places an emphasis on security and data privacy infrastructure that can withstand the ongoing global challenges in cybersecurity. Thirdly, data collection is likely to take place in collaboration with third parties that develop and maintain the digital technologies. This raises the issue of data ownership and intellectual property in regards to healthcare. A fourth factor is one of appropriate consent. Awareness of the implications of risk disclosure, data security and data ownership as well as other issues as they arise are going to be key to establishing ability for individuals to provide informed consent. This is likely to be a major challenge for any healthcare system and one that would be liable to legal risks in individuals deemed to be at risk of cognitive impairment.
In summary, improved understanding of the risk factors for AD combined with novel scalable diagnostic methods provide an until now unavailable opportunity for the secondary prevention of the chief cause of death in the developed world. Our success would depend on the ability of healthcare decision makers to invest in the required infrastructure and on the reformulation of dementia and cognitive health in public discourse: from an untreatable condition that is part of aging to a brain health state which can be modified by the right intervention at the right time for the right person.
Author Contributions
TO and IK drafted and reviewed the manuscript. Both authors contributed to the article and approved the submitted version.
Funding
IK has current funding through grants from the Medical Research Council (Dementias Platform UK grant number MR/L023784/2), National Institute of Health Research (Development and Skills Enhancement Award grant number NIHR301616), and the NIHR Oxford Health Biomedical Research Centre.
Conflict of Interest
TO was employed by Sharp Therapeutics. IK was a medical advisor to Sharp Therapeutics.
Publisher's Note
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References
1. Prince M, Comas-Herrera A, Knapp M, Guerchet M, Karagiannidou M. World alzheimer report 2016: improving healthcare for people living with dementia: coverage, quality and costs now and in the future. London, UK: Alzheimer's Disease International (ADI) (2016).
2. Wittenberg R, Hu B, Barraza-Araiza L, Rehill A. Projections of Older People With Dementia and Costs of Dementia Care in the United Kingdom, 2019–2040. London: London School of Economics (2019).
3. Livingston G, Huntley J, Sommerlad A, Ames D, Ballard C, Banerjee S, et al. Dementia prevention, intervention, and care: 2020 report of the lancet commission. Lancet. (2020) 396:413–46. doi: 10.1016/S0140-6736(20)30367-6
4. Barnes DE, Yaffe K. The projected effect of risk factor reduction on Alzheimer's disease prevalence. Lancet Neurol. (2011) 10:819–28. doi: 10.1016/S1474-4422(11)70072-2
5. Peters R, Warwick J, Anstey KJ, Anderson CS. Blood pressure and dementia: What the SPRINT-MIND trial adds and what we still need to know. Neurology. (2019) 92:1017–8. doi: 10.1212/WNL.0000000000007543
6. Sabia S, Fayosse A, Dumurgier J, Schnitzler A, Empana JP, Ebmeier KP, et al. Association of ideal cardiovascular health at age 50 with incidence of dementia: 25 year follow-up of Whitehall II cohort study. BMJ. (2019) 366:l4414. doi: 10.1136/bmj.l4414
7. Sastre AA, Vernooij RW, Harmand MGC, Martínez G. Effect of the treatment of type 2 diabetes mellitus on the development of cognitive impairment and dementia. Cochrane Database Syst Rev. (2017) 6:CD003804. doi: 10.1002/14651858.CD003804.pub2
8. Ritz E, Rychlík I, Locatelli F, Halimi S. End-stage renal failure in type 2 diabetes: a medical catastrophe of worldwide dimensions. Am J Kidney Dis. (1999) 34:795–808. doi: 10.1016/S0272-6386(99)70035-1
9. Atkins RC. The epidemiology of chronic kidney disease. Kidney Int. (2005) 67:S14–8. doi: 10.1111/j.1523-1755.2005.09403.x
10. Zhang C-Y, He F-F, Su H, Zhang C, Meng X-F. Association between chronic kidney disease and Alzheimer's disease: an update. Metabolic Brain Dis. (2020) 35:883–94. doi: 10.1007/s11011-020-00561-y
11. Yau W-YW, Tudorascu DL, McDade EM, Ikonomovic S, James JA, Minhas D, et al. Longitudinal assessment of neuroimaging and clinical markers in autosomal dominant Alzheimer's disease: a prospective cohort study. Lancet Neurol. (2015) 14:804–13. doi: 10.1016/S1474-4422(15)00135-0
12. Villemagne V, Burnham S, Bourgeat P, Brown B, Ellis K, Salvado O, et al. Amyloid β deposition, neurodegeneration, and cognitive decline in sporadic Alzheimer's disease: a prospective cohort study. Lancet Neurol. (2013) 12:357–67. doi: 10.1016/S1474-4422(13)70044-9
13. Jack CR, Knopman DS, Jagust WJ, Petersen RC, Weiner MW. Tracking pathophysiological processes in Alzheimer's disease: an updated hypothetical model of dynamic biomarkers. Lancet Neurol. (2013) 12:207–16. doi: 10.1016/S1474-4422(12)70291-0
14. Reiman EM, McKhann GM, Albert MS, Sperling RA, Petersen RC, Blacker D. Alzheimer's disease: implications of the updated diagnostic and research criteria. J Clin Psychiatry. (2011) 72:1190–6. doi: 10.4088/JCP.10087co1c
15. Sperling RA, Jack CR, Aisen PS. Testing the right target and right drug at the right stage. Sci Transl Med. (2011) 3:111cm33. doi: 10.1126/scitranslmed.3002609
16. Nabers A, Perna L, Lange J, Mons U, Schartner J, Guldenhaupt J, et al. Amyloid blood biomarker detects Alzheimer's disease. EMBO Mol Med. (2018) 10:8763. doi: 10.15252/emmm.201708763
17. Hansson O, Zetterberg H, Vanmechelen E, Vanderstichele H, Andreasson U, Londos E, et al. Evaluation of plasma Abeta(40) and Abeta(42) as predictors of conversion to Alzheimer's disease in patients with mild cognitive impairment. Neurobiol Aging. (2010) 31:357–67. doi: 10.1016/j.neurobiolaging.2008.03.027
18. Chouraki V, Beiser A, Younkin L, Preis SR, Weinstein G, Hansson O, et al. Plasma amyloid-beta and risk of Alzheimer's disease in the Framingham Heart Study. Alzheimers Dement. (2015) 11:249–57.e1. doi: 10.1016/j.jalz.2014.07.001
19. Simrén J, Leuzy A, Karikari TK, Hye A, Benedet AL, Lantero-Rodriguez J, et al. The diagnostic and prognostic capabilities of plasma biomarkers in Alzheimer's disease. Alzheimer Dementia. (2021) 17:1145–56. doi: 10.1002/alz.12283
20. Buchman AS, Wilson RS, Boyle PA, Bienias JL, Bennett DA. Grip strength and the risk of incident Alzheimer's disease. Neuroepidemiology. (2007) 29:66–73. doi: 10.1159/000109498
21. Wilson RS, Arnold SE, Schneider JA, Boyle PA, Buchman AS, Bennett DA. Olfactory impairment in presymptomatic Alzheimer's disease. Ann N Y Acad Sci. (2009) 1170:730–5. doi: 10.1111/j.1749-6632.2009.04013.x
22. Doraiswamy PM, Narayan VA, Manji HK. Mobile and pervasive computing technologies and the future of Alzheimer's clinical trials. NPJ Digit Med. (2018) 1:1. doi: 10.1038/s41746-017-0008-y
23. Stringer G, Couth S, Brown LJE, Montaldi D, Gledson A, Mellor J, et al. Can you detect early dementia from an email? A proof of principle study of daily computer use to detect cognitive and functional decline. Int J Geriatr Psychiatry. (2018) 33:867–74. doi: 10.1002/gps.4863
24. Saeedi P, Petersohn I, Salpea P, Malanda B, Karuranga S, Unwin N, et al. Global and regional diabetes prevalence estimates for 2019 and projections for 2030 and 2045: Results from the International Diabetes Federation Diabetes Atlas. Diabetes Res Clin Practice. (2019) 157:107843. doi: 10.1016/j.diabres.2019.107843
25. Guariguata L, Whiting DR, Hambleton I, Beagley J, Linnenkamp U, Shaw JE. Global estimates of diabetes prevalence for 2013 and projections for 2035. Diabetes Res Clin Pract. (2014) 103:137–49. doi: 10.1016/j.diabres.2013.11.002
26. Kasuga M. Insulin resistance and pancreatic beta cell failure. J Clin Invest. (2006) 116:1756–60. doi: 10.1172/JCI29189
27. Meigs JB, Cupples LA, Wilson P. Parental transmission of type 2 diabetes: the Framingham Offspring Study. Diabetes. (2000) 49:2201–7. doi: 10.2337/diabetes.49.12.2201
28. Klimek M, Knap J, Tulwin T, Trojnar M, Dzida G. Evaluation of the relationship between the incidence of diabetes and selected demographic factors. Clin Diabetol. (2018) 7:145–50. doi: 10.5603/DK.2018.0010
29. Lindström J, Peltonen M, Eriksson JG, Louheranta A, Fogelholm M, Uusitupa M, et al. High-fibre, low-fat diet predicts long-term weight loss and decreased type 2 diabetes risk: the Finnish Diabetes Prevention Study. Diabetologia. (2006) 49:912–20. doi: 10.1007/s00125-006-0198-3
30. Rimm EB, Chan J, Stampfer MJ, Colditz GA, Willett WC. Prospective study of cigarette smoking, alcohol use, and the risk of diabetes in men. BMJ. (1995) 310:555–9. doi: 10.1136/bmj.310.6979.555
31. Steinbrecher A, Erber E, Grandinetti A, Nigg C, Kolonel LN, Maskarinec G. Physical activity and risk of type 2 diabetes among Native Hawaiians, Japanese Americans, and Caucasians: the Multiethnic Cohort. J Phys Activity Health. (2012) 9:634–41. doi: 10.1123/jpah.9.5.634
32. Ali MK, Bullard KM, Saaddine JB, Cowie CC, Imperatore G, Gregg EW. Achievement of goals in US diabetes care, 1999-2010. N Engl J Med. (2013) 368:1613–24. doi: 10.1056/NEJMsa1213829
33. Jayaraman A, Pike CJ. Alzheimer's disease and type 2 diabetes: multiple mechanisms contribute to interactions. Curr Diabetes Rep. (2014) 14:476. doi: 10.1007/s11892-014-0476-2
34. Leibson CL, Rocca WA, Hanson V, Cha R, Kokmen E, O'brien P, et al. Risk of dementia among persons with diabetes mellitus: a population-based cohort study. Am J Epidemiol. (1997) 145:301–8. doi: 10.1093/oxfordjournals.aje.a009106
35. Ott A, Stolk RP, van Harskamp F, Pols HA, Hofman A, Breteler MM. Diabetes mellitus and the risk of dementia: The Rotterdam Study. Neurology. (1999) 53:1937–42. doi: 10.1212/WNL.53.9.1937
36. Ahtiluoto S, Polvikoski T, Peltonen M, Solomon A, Tuomilehto J, Winblad B, et al. Diabetes, Alzheimer disease, and vascular dementia: a population-based neuropathologic study. Neurology. (2010) 75:1195–202. doi: 10.1212/WNL.0b013e3181f4d7f8
37. Zhang J, Chen C, Hua S, Liao H, Wang M, Xiong Y, et al. An updated meta-analysis of cohort studies: diabetes and risk of Alzheimer's disease. Diabetes Res Clin Practice. (2017) 124:41–7. doi: 10.1016/j.diabres.2016.10.024
38. Geerlings MI, Appelman AP, Vincken KL, Algra A, Witkamp TD, Mali WP, et al. Brain volumes and cerebrovascular lesions on MRI in patients with atherosclerotic disease. The SMART-MR study. Atherosclerosis. (2010) 210:130–6. doi: 10.1016/j.atherosclerosis.2009.10.039
39. Rusinek H, De Santi S, Frid D, Tsui W-H, Tarshish CY, Convit A, et al. Regional brain atrophy rate predicts future cognitive decline: 6-year longitudinal MR imaging study of normal aging. Radiology. (2003) 229:691–6. doi: 10.1148/radiol.2293021299
40. Van Harten B, de Leeuw F-E, Weinstein HC, Scheltens P, Biessels GJ. Brain imaging in patients with diabetes: a systematic review. Diabetes Care. (2006) 29:2539–48. doi: 10.2337/dc06-1637
41. Biessels GJ, Reijmer YD. Brain changes underlying cognitive dysfunction in diabetes: what can we learn from MRI? Diabetes. (2014) 63:2244–52. doi: 10.2337/db14-0348
42. Kooistra M, Geerlings MI, Mali WP, Vincken KL, van der Graaf Y, Biessels GJ, et al. Diabetes mellitus and progression of vascular brain lesions and brain atrophy in patients with symptomatic atherosclerotic disease. The SMART-MR study. J Neurol Sci. (2013) 332:69–74. doi: 10.1016/j.jns.2013.06.019
43. De Bresser J, Tiehuis AM, Van Den Berg E, Reijmer YD, Jongen C, Kappelle LJ, et al. Progression of cerebral atrophy and white matter hyperintensities in patients with type 2 diabetes. Diabetes Care. (2010) 33:1309–14. doi: 10.2337/dc09-1923
44. Crane PK, Walker R, Hubbard RA, Li G, Nathan DM, Zheng H, et al. Glucose levels and risk of dementia. N Engl J Med. (2013) 369:540–8. doi: 10.1056/NEJMoa1215740
45. Marden JR, Mayeda ER, Tchetgen Tchetgen EJ, Kawachi I, Glymour MM. High hemoglobin A1c and diabetes predict memory decline in the health and retirement study. Alzheimer Dis Assoc Disord. (2017) 31:48–54. doi: 10.1097/WAD.0000000000000182
46. Ganguli M, Beer JC, Zmuda JM, Ryan CM, Sullivan KJ, Chang CH, et al. Aging, diabetes, obesity, and cognitive decline: a population-based study. J Am Geriatr Soc. (2020) 68:991–8. doi: 10.1111/jgs.16321
47. Janssen J, van den Berg E, Zinman B, Espeland MA, Geijselaers SLC, Mattheus M, et al. HbA1c, insulin resistance, and beta-cell function in relation to cognitive function in type 2 diabetes: The CAROLINA Cognition Substudy. Diabetes Care. (2019) 42:e1–3. doi: 10.2337/dc18-0914
48. Pugazhenthi S, Qin L, Reddy PH. Common neurodegenerative pathways in obesity, diabetes, and Alzheimer's disease. Biochim Biophys Acta. (2017) 1863:1037–45. doi: 10.1016/j.bbadis.2016.04.017
49. Ko S-Y, Ko H-A, Chu K-H, Shieh T-M, Chi T-C, Chen H-I, et al. The possible mechanism of advanced glycation end products (AGEs) for Alzheimer's disease. PLoS ONE. (2015) 10:e0143345. doi: 10.1371/journal.pone.0143345
50. Zhang K-L, Lou D-D, Guan Z-Z. Activation of the AGE/RAGE system in the brains of rats and in SH-SY5Y cells exposed to high level of fluoride might connect to oxidative stress. Neurotoxicol Teratol. (2015) 48:49–55. doi: 10.1016/j.ntt.2015.01.007
51. Evans JL, Goldfine ID, Maddux BA, Grodsky GM. Oxidative stress and stress-activated signaling pathways: a unifying hypothesis of type 2 diabetes. Endocrine Rev. (2002) 23:599–622. doi: 10.1210/er.2001-0039
52. Schmeichel AM, Schmelzer JD, Low PA. Oxidative injury and apoptosis of dorsal root ganglion neurons in chronic experimental diabetic neuropathy. Diabetes. (2003) 52:165–71. doi: 10.2337/diabetes.52.1.165
53. Cai Z, Liu N, Wang C, Qin B, Zhou Y, Xiao M, et al. Role of RAGE in Alzheimer's disease. Cell Mol Neurobiol. (2016) 36:483–95. doi: 10.1007/s10571-015-0233-3
54. Shoelson SE, Lee J, Goldfine AB. Inflammation and insulin resistance. J Clin Investig. (2006) 116:1793–801. doi: 10.1172/JCI29069
55. Walker KA, Ficek BN, Westbrook R. Understanding the role of systemic inflammation in Alzheimer's disease. ACS Chemical Neuroscience. (2019) 10:3340–3342. doi: 10.1021/acschemneuro.9b00333
56. Iannucci J, Renehan W, Grammas P. Thrombin, a mediator of coagulation, inflammation, and neurotoxicity at the neurovascular interface: Implications for Alzheimer's disease. Front Neurosci. (2020) 14:762. doi: 10.3389/fnins.2020.00762
57. Peila R, Rodriguez BL, Launer LJ. Type 2 diabetes, APOE gene, and the risk for dementia and related pathologies: The Honolulu-Asia Aging Study. Diabetes. (2002) 51:1256–62. doi: 10.2337/diabetes.51.4.1256
58. Thomas KR, Bangen KJ, Weigand AJ, Edmonds EC, Sundermann E, Wong CG, et al. Type II diabetes interacts with Alzheimer's disease risk factors to predict functional decline. Alzheimer Dis Associated Disord. (2020) 34:10. doi: 10.1097/WAD.0000000000000332
59. Huang C-C, Chung C-M, Leu H-B, Lin L-Y, Chiu C-C, Hsu C-Y, et al. Diabetes mellitus and the risk of Alzheimer's disease: a nationwide population-based study. PLoS ONE. (2014) 9:e87095. doi: 10.1371/journal.pone.0087095
60. Wang K-C, Woung L-C, Tsai M-T, Liu C-C, Su Y-H, Li C-Y. Risk of Alzheimer's disease in relation to diabetes: a population-based cohort study. Neuroepidemiology. (2012) 38:237–44. doi: 10.1159/000337428
61. Watson GS, Cholerton BA, Reger MA, Baker LD, Plymate SR, Asthana S, et al. Preserved cognition in patients with early Alzheimer disease and amnestic mild cognitive impairment during treatment with rosiglitazone: a preliminary study. Am J Geriatr Psychiatry. (2005) 13:950–8. doi: 10.1097/00019442-200511000-00005
62. Risner M, Saunders A, Altman J, Ormandy G, Craft S, Foley I, et al. Efficacy of rosiglitazone in a genetically defined population with mild-to-moderate Alzheimer's disease. Pharmacogenomics J. (2006) 6:246–54. doi: 10.1038/sj.tpj.6500369
63. Reger MA, Watson G, Green PS, Baker LD, Cholerton B, Fishel MA, et al. Intranasal insulin administration dose-dependently modulates verbal memory and plasma amyloid-β in memory-impaired older adults. J Alzheimer's Dis. (2008) 13:323–31. doi: 10.3233/JAD-2008-13309
64. Lloyd-Jones DM, Evans JC, Levy D. Hypertension in adults across the age spectrum: current outcomes and control in the community. JAMA. (2005) 294:466–72. doi: 10.1001/jama.294.4.466
65. Lin J, Hsu W, Hsu H, Fung H, Chen S. Risk factors for vascular dementia: a hospital-based study in Taiwan. Acta Neurol Taiwanica. (2007) 16:22.
66. Hayden KM, Zandi PP, Lyketsos CG, Khachaturian AS, Bastian LA, Charoonruk G, et al. Vascular risk factors for incident Alzheimer disease and vascular dementia: the Cache County study. Alzheimer Dis Assoc Disord. (2006) 20:93–100. doi: 10.1097/01.wad.0000213814.43047.86
67. Guan J-W, Huang C-Q, Li Y-H, Wan C-M, You C, Wang Z-R, et al. No association between hypertension and risk for Alzheimer's disease: a meta-analysis of longitudinal studies. J Alzheimer's Dis. (2011) 27:799–807. doi: 10.3233/JAD-2011-111160
68. Power MC, Weuve J, Gagne JJ, McQueen MB, Viswanathan A, Blacker D. The association between blood pressure and incident Alzheimer disease: a systematic review and meta-analysis. Epidemiology. (2011) 22:646. doi: 10.1097/EDE.0b013e31822708b5
69. Norton S, Matthews FE, Barnes DE, Yaffe K, Brayne C. Potential for primary prevention of Alzheimer's disease: an analysis of population-based data. Lancet Neurol. (2014) 13:788–94. doi: 10.1016/S1474-4422(14)70136-X
70. Walker KA, Sharrett AR, Wu A, Schneider AL, Albert M, Lutsey PL, et al. Association of midlife to late-life blood pressure patterns with incident dementia. JAMA. (2019) 322:535–45. doi: 10.1001/jama.2019.10575
71. Lennon MJ, Makkar SR, Crawford JD, Sachdev PS. Midlife hypertension and Alzheimer's disease: a systematic review and meta-analysis. J Alzheimer's Dis. (2019) 71:307–16. doi: 10.3233/JAD-190474
72. Petrovitch H, White L, Izmirilian G, Ross G, Havlik R, Markesbery W, et al. Midlife blood pressure and neuritic plaques, neurofibrillary tangles, and brain weight at death: the HAAS?. Neurobiol Aging. (2000) 21:57–62. doi: 10.1016/S0197-4580(00)00106-8
73. Diaz-Ruiz C, Wang J, Ksiezak-Reding H, Ho L, Qian X, Humala N, et al. Role of hypertension in aggravating a neuropathology of AD type and tau-mediatedmotor impairment. Cardiovasc Psychiatry Neurol. (2009) 2009:107286. doi: 10.1155/2009/107286
74. Chiang GC, Mao X, Kang G, Chang E, Pandya S, Vallabhajosula S, et al. Relationships among cortical glutathione levels, brain amyloidosis, and memory in healthy older adults investigated in vivo with 1H-MRS and Pittsburgh compound-B PET. Am J Neuroradiol. (2017) 38:1130–7. doi: 10.3174/ajnr.A5143
75. Castillo-Carranza DL, Nilson AN, Van Skike CE, Jahrling JB, Patel K, Garach P, et al. Cerebral microvascular accumulation of tau oligomers in Alzheimer's disease and related tauopathies. Aging Dis. (2017) 8:257. doi: 10.14336/AD.2017.0112
76. Mayet J, Hughes A. Cardiac and vascular pathophysiology in hypertension. Heart. (2003) 89:1104–9. doi: 10.1136/heart.89.9.1104
77. Skoog I, Gustafson D. Update on hypertension and Alzheimer's disease. Neurol Res. (2006) 28:605–11. doi: 10.1179/016164106X130506
78. Iadecola C. Neurovascular regulation in the normal brain and in Alzheimer's disease. Nat Rev Neurosci. (2004) 5:347–60. doi: 10.1038/nrn1387
79. Johnson ML, Parikh N, Kunik ME, Schulz PE, Patel JG, Chen H, et al. Antihypertensive drug use and the risk of dementia in patients with diabetes mellitus. Alzheimer's Dementia. (2012) 8:437–44. doi: 10.1016/j.jalz.2011.05.2414
80. Pires PW, Dams Ramos CM, Matin N, Dorrance AM. The effects of hypertension on the cerebral circulation. Am J Physiol Heart Circulatory Physiol. (2013) 304:H1598–614. doi: 10.1152/ajpheart.00490.2012
81. Nelson L, Gard P, Tabet N. Hypertension and inflammation in Alzheimer's disease: close partners in disease development and progression. J Alzheimer's Dis. (2014) 41:331–43. doi: 10.3233/JAD-140024
82. Carnevale D, Mascio G, Ajmone-Cat MA, D'Andrea I, Cifelli G, Madonna M, et al. Role of neuroinflammation in hypertension-induced brain amyloid pathology. Neurobiol Aging. (2012) 33:205. doi: 10.1016/j.neurobiolaging.2010.08.013
83. Kannel WB. Risk stratification in hypertension: new insights from the Framingham Study. Am J Hyperten. (2000) 13:3S−10. doi: 10.1016/S0895-7061(99)00252-6
84. Skoog I, Gustafson D. Hypertension, hypertension-clustering factors and Alzheimer's disease. Neurol Res. (2003) 25:675–80. doi: 10.1179/016164103101201986
85. Vagelatos NT, Eslick GD. Type 2 diabetes as a risk factor for Alzheimer's disease: the confounders, interactions, and neuropathology associated with this relationship. Epidemiol Rev. (2013) 35:152–60. doi: 10.1093/epirev/mxs012
86. Kester MI, van der Flier WM, Mandic G, Blankenstein MA, Scheltens P, Muller M. Joint effect of hypertension and APOE genotype on CSF biomarkers for Alzheimer's disease. J Alzheimers Dis. (2010) 20:1083–90. doi: 10.3233/JAD-2010-091198
87. de Frias CM, Schaie KW, Willis SL. Hypertension moderates the effect of APOE on 21-year cognitive trajectories. Psychol Aging. (2014) 29:431. doi: 10.1037/a0036828
88. Guo Z, Fratiglioni L, Zhu L, Fastbom J, Winblad B, Viitanen M. Occurrence and progression of dementia in a community population aged 75 years and older: relationship of antihypertensive medication use. Arch Neurol. (1999) 56:991–6. doi: 10.1001/archneur.56.8.991
89. Ding J, Davis-Plourde KL, Sedaghat S, Tully PJ, Wang W, Phillips C, et al. Antihypertensive medications and risk for incident dementia and Alzheimer's disease: a meta-analysis of individual participant data from prospective cohort studies. Lancet Neurol. (2020) 19:61–70. doi: 10.1016/S1474-4422(19)30393-X
90. Webster AC, Nagler EV, Morton RL, Masson P. Chronic kidney disease. Lancet. (2017) 389:1238–52. doi: 10.1016/S0140-6736(16)32064-5
91. Introduction to Volume One: USRDS Annual data report atlas of chronic kidney disease in the United States, American Journal of Kidney Diseases (2013) 63:e1–e22. doi: 10.1053/j.ajkd.2012.11.001
92. Judd E, Calhoun DA. Management of hypertension in CKD: beyond the guidelines. Adv Chronic Kidney Dis. (2015) 22:116–22. doi: 10.1053/j.ackd.2014.12.001
93. Etgen T, Chonchol M, Förstl H, Sander D. Chronic kidney disease and cognitive impairment: a systematic review and meta-analysis. Am J Nephrol. (2012) 35:474–82. doi: 10.1159/000338135
94. Yaffe K, Ackerson L, Tamura MK, Le Blanc P, Kusek JW, Sehgal AR, et al. Chronic kidney disease and cognitive function in older adults: findings from the chronic renal insufficiency cohort cognitive study. J Am Geriatr Soc. (2010) 58:338–45. doi: 10.1111/j.1532-5415.2009.02670.x
95. Tamura MK, Wadley V, Yaffe K, McClure LA, Howard G, Go R, et al. Kidney function and cognitive impairment in US adults: the Reasons for Geographic and Racial Differences in Stroke (REGARDS) Study. Am J Kidney Dis. (2008) 52:227–34. doi: 10.1053/j.ajkd.2008.05.004
96. Kurella M, Yaffe K, Shlipak MG, Wenger NK, Chertow GM. Chronic kidney disease and cognitive impairment in menopausal women. Am J Kidney Dis. (2005) 45:66–76. doi: 10.1053/j.ajkd.2004.08.044
97. Buchman AS, Tanne D, Boyle P, Shah R, Leurgans S, Bennett D. Kidney function is associated with the rate of cognitive decline in the elderly. Neurology. (2009) 73:920–7. doi: 10.1212/WNL.0b013e3181b72629
98. Walker MD, McMahon DJ, Inabnet WB, Lazar RM, Brown I, Vardy S, et al. Neuropsychological features in primary hyperparathyroidism: a prospective study. J Clin Endocrinol Metabolism. (2009) 94:1951–8. doi: 10.1210/jc.2008-2574
99. Babińska D, Barczyński M, Stefaniak T, Oseka T, Babińska A, Babiński D, et al. Evaluation of selected cognitive functions before and after surgery for primary hyperparathyroidism. Langenbeck's Arch Surg. (2012) 397:825–31. doi: 10.1007/s00423-011-0885-5
100. Kapasi A, Schneider J. Vascular contributions to cognitive impairment, clinical Alzheimer's disease, and dementia in older persons. Biochim Biophys Acta. (2016) 1862:878–86. doi: 10.1016/j.bbadis.2015.12.023
101. Rabkin SW. Is it time to utilize measurement of arterial stiffness to identify and reduce the risk of cognitive impairment? J Clin Hypertens. (2018) 20:31–2. doi: 10.1111/jch.13126
102. Oh Y-S, Kim J-S, Park J-W, An J-Y, Park SK, Shim Y-S, et al. Arterial stiffness and impaired renal function in patients with Alzheimer's disease. Neurol Sci. (2016) 37:451–7. doi: 10.1007/s10072-015-2434-4
103. Siragy HM, Carey RM. Role of the intrarenal renin-angiotensin-aldosterone system in chronic kidney disease. Am J Nephrol. (2010) 31:541–50. doi: 10.1159/000313363
104. Inaba S, Iwai M, Furuno M, Tomono Y, Kanno H, Senba I, et al. Continuous activation of renin-angiotensin system impairs cognitive function in renin/angiotensinogen transgenic mice. Hypertension. (2009) 53:356–62. doi: 10.1161/HYPERTENSIONAHA.108.123612
105. Hsu CC, Kao WH, Coresh J, Pankow JS, Marsh-Manzi J, Boerwinkle E, et al. Apolipoprotein E and progression of chronic kidney disease. JAMA. (2005) 293:2892–9. doi: 10.1001/jama.293.23.2892
106. Chu AY, Parekh RS, Astor BC, Coresh J, Berthier-Schaad Y, Smith MW, et al. Association of APOE polymorphism with chronic kidney disease in a nationally representative sample: a Third National Health and Nutrition Examination Survey (NHANES III) Genetic Study. BMC Med Genet. (2009) 10:108. doi: 10.1186/1471-2350-10-108
107. Liberopoulos EN, Miltiadous GA, Cariolou M, Kalaitzidis R, Siamopoulos KC, Elisaf MS. Influence of apolipoprotein E polymorphisms on serum creatinine levels and predicted glomerular filtration rate in healthy subjects. Nephrol Dialysis Transplant. (2004) 19:2006–12. doi: 10.1093/ndt/gfh349
108. Cheng K-C, Chen Y-L, Lai S-W, Mou C-H, Tsai P-Y, Sung F-C. Patients with chronic kidney disease are at an elevated risk of dementia: a population-based cohort study in Taiwan. BMC Nephrol. (2012) 13:1–8. doi: 10.1186/1471-2369-13-129
109. Jin WS, Shen LL, Bu XL, Zhang WW, Chen SH, Huang ZL, et al. Peritoneal dialysis reduces amyloid-beta plasma levels in humans and attenuates Alzheimer-associated phenotypes in an APP/PS1 mouse model. Acta Neuropathol. (2017) 134:207–20. doi: 10.1007/s00401-017-1721-y
110. Liu YH, Xiang Y, Wang YR, Jiao SS, Wang QH, Bu XL, et al. Association between serum amyloid-beta and renal functions: implications for roles of kidney in amyloid-beta clearance. Mol Neurobiol. (2015) 52:115–9. doi: 10.1007/s12035-014-8854-y
111. Radic J, Ljutic D, Radic M, Kovacic V, Dodig-Curkovic K, Sain M. Kidney transplantation improves cognitive and psychomotor functions in adult hemodialysis patients. Am J Nephrol. (2011) 34:399–406. doi: 10.1159/000330849
112. Harciarek M, Biedunkiewicz B, Lichodziejewska-Niemierko M, Debska-Slizien A, Rutkowski B. Cognitive performance before and after kidney transplantation: a prospective controlled study of adequately dialyzed patients with end-stage renal disease. J Int Neuropsychol Soc. (2009) 15:684–94. doi: 10.1017/S1355617709990221
113. Kivipelto M, Solomon A, Ahtiluoto S, Ngandu T, Lehtisalo J, Antikainen R, et al. The finnish geriatric intervention study to prevent cognitive impairment and disability (FINGER): study design and progress. Alzheimers Dement. (2013) 9:657–65. doi: 10.1016/j.jalz.2012.09.012
114. Crous-Bou M, Minguillon C, Gramunt N, Molinuevo JL. Alzheimer's disease prevention: from risk factors to early intervention. Alzheimers Res Ther. (2017) 9:71. doi: 10.1186/s13195-017-0297-z
115. Santos GD, Nunes PV, Stella F, Brum PS, Yassuda MS, Ueno LM, et al. Multidisciplinary rehabilitation program: effects of a multimodal intervention for patients with Alzheimer's disease and cognitive impairment without dementia. Arch Clin Psychiatry. (2015) 42:153–6. doi: 10.1590/0101-60830000000066
116. Murphy MP, LeVine H. Alzheimer's disease and the amyloid-beta peptide. J Alzheimers Dis. (2010) 19:311–23. doi: 10.3233/JAD-2010-1221
117. Yoon S-S, Jo SA. Mechanisms of amyloid-β peptide clearance: potential therapeutic targets for Alzheimer's disease. Biomol Therapeutics. (2012) 20:245. doi: 10.4062/biomolther.2012.20.3.245
118. Sevigny J, Chiao P, Bussiere T, Weinreb PH, Williams L, Maier M, et al. The antibody aducanumab reduces Abeta plaques in Alzheimer's disease. Nature. (2016) 537:50–6. doi: 10.1038/nature19323
119. Congdon EE, Sigurdsson EM. Tau-targeting therapies for Alzheimer disease. Nat Rev Neurol. (2018) 14:399–415. doi: 10.1038/s41582-018-0013-z
120. Koychev I, Jansen K, Dette A, Shi L, Holling H. Blood-based ATN Biomarkers of Alzheimer's disease: a meta-analysis. J Alzheimers Dis. (2021) 79:177–95. doi: 10.3233/JAD-200900
121. Jack CR J, Bennett DA, Blennow K, Carrillo MC, Dunn B, et al. NIA-AA research framework: toward a biological definition of Alzheimer's disease. Alzheimers Dement. (2018) 14:535–62. doi: 10.1016/j.jalz.2018.02.018
122. Schindler SE, Bollinger JG, Ovod V, Mawuenyega KG, Li Y, Gordon BA, et al. High-precision plasma beta-amyloid 42/40 predicts current and future brain amyloidosis. Neurology. (2019) 93:e1647–59. doi: 10.1212/WNL.0000000000008081
123. Moscoso A, Grothe MJ, Ashton NJ, Karikari TK, Rodriguez JL, Snellman A, et al. Time course of phosphorylated-tau181 in blood across the Alzheimer's disease spectrum. Brain. (2021) 144:325–39. doi: 10.1093/brain/awaa399
124. Hesse C, Rosengren L, Andreasen N, Davidsson P, Vanderstichele H, Vanmechelen E, et al. Transient increase in total tau but not phospho-tau in human cerebrospinal fluid after acute stroke. Neurosci Lett. (2001) 297:187–90. doi: 10.1016/S0304-3940(00)01697-9
125. Rubenstein R, Chang B, Yue JK, Chiu A, Winkler EA, Puccio AM, et al. Comparing Plasma Phospho Tau, Total Tau, and Phospho Tau-Total Tau Ratio as acute and chronic traumatic brain injury biomarkers. JAMA Neurol. (2017) 74:1063–72. doi: 10.1001/jamaneurol.2017.0655
126. Buerger K, Ewers M, Pirttilä T, Zinkowski R, Alafuzoff I, Teipel SJ, et al. CSF phosphorylated tau protein correlates with neocortical neurofibrillary pathology in Alzheimer's disease. Brain. (2006) 129:3035–41. doi: 10.1093/brain/awl269
127. Hampel H, Blennow K, Shaw LM, Hoessler YC, Zetterberg H, Trojanowski JQ. Total and phosphorylated tau protein as biological markers of Alzheimer's disease. Experi Gerontol. (2010) 45:30–40. doi: 10.1016/j.exger.2009.10.010
128. Olsson B, Lautner R, Andreasson U, Ohrfelt A, Portelius E, Bjerke M, et al. CSF and blood biomarkers for the diagnosis of Alzheimer's disease: a systematic review and meta-analysis. Lancet Neurol. (2016) 15:673–84. doi: 10.1016/S1474-4422(16)00070-3
129. Lewczuk P, Ermann N, Andreasson U, Schultheis C, Podhorna J, Spitzer P, et al. Plasma neurofilament light as a potential biomarker of neurodegeneration in Alzheimer's disease. Alzheimers Res Ther. (2018) 10:71. doi: 10.1186/s13195-018-0404-9
130. Janelidze S, Mattsson N, Palmqvist S, Smith R, Beach TG, Serrano GE, et al. Plasma P-tau181 in Alzheimer's disease: relationship to other biomarkers, differential diagnosis, neuropathology and longitudinal progression to Alzheimer's dementia. Nat Med. (2020) 26:379–86. doi: 10.1038/s41591-020-0755-1
131. Fyfe I. Neurofilament light chain-new potential for prediction and prognosis. Nat Rev Neurol. (2019) 15:557. doi: 10.1038/s41582-019-0265-2
132. Kourtis LC, Regele OB, Wright JM, Jones GB. Digital biomarkers for Alzheimer's disease: the mobile/wearable devices opportunity. NPJ Digit Med. (2019) 2:84. doi: 10.1038/s41746-019-0084-2
133. Buracchio T, Dodge HH, Howieson D, Wasserman D, Kaye J. The trajectory of gait speed preceding mild cognitive impairment. Arch Neurol. (2010) 67:980–6. doi: 10.1001/archneurol.2010.159
134. Mc Ardle R, Morris R, Hickey A, Del Din S, Koychev I, Gunn RN, et al. Gait in mild Alzheimer's disease: feasibility of multi-center measurement in the clinic and home with body-worn sensors: a pilot study. J Alzheimer's Dis. (2018) 63:331–41. doi: 10.3233/JAD-189003
135. Koychev I, Galna B, Zetterberg H, Lawson J, Zamboni G, Ridha BH, et al. Aβ 42/Aβ 40 and Aβ 42/Aβ 38 ratios are associated with measures of gait variability and activities of daily living in mild Alzheimer's disease: a pilot study. J Alzheimer's Dis. (2018) 65:1377–83. doi: 10.3233/JAD-180622
136. Spartano NL, Demissie S, Himali JJ, Dukes KA, Murabito JM, Vasan RS, et al. Accelerometer-determined physical activity and cognitive function in middle-aged and older adults from two generations of the Framingham Heart Study. Alzheimer's Dementia. (2019) 5:618–26. doi: 10.1016/j.trci.2019.08.007
137. Larson EB, Wang L, Bowen JD, McCormick WC, Teri L, Crane P, et al. Exercise is associated with reduced risk for incident dementia among persons 65 years of age and older. Ann Internal Med. (2006) 144:73–81. doi: 10.7326/0003-4819-144-2-200601170-00004
138. Doherty A, Jackson D, Hammerla N, Plötz T, Olivier P, Granat MH, et al. Large scale population assessment of physical activity using wrist worn accelerometers: The UK Biobank Study. PLoS ONE. (2017) 12:e0169649. doi: 10.1371/journal.pone.0169649
139. Ntracha A, Iakovakis D, Hadjidimitriou S, Charisis V, Tsolaki M, Hadjileontiadis L. Detection of mild cognitive impairment through natural language and touchscreen typing processing. Front Digit Health. (2020) 2:567158. doi: 10.3389/fdgth.2020.567158
140. Licher S, Yilmaz P, Leening MJG, Wolters FJ, Vernooij MW, Stephan BCM, et al. External validation of four dementia prediction models for use in the general community-dwelling population: a comparative analysis from the Rotterdam Study. Eur J Epidemiol. (2018) 33:645–55. doi: 10.1007/s10654-018-0403-y
141. Kivipelto M, Ngandu T, Laatikainen T, Winblad B, Soininen H, Tuomilehto J. Risk score for the prediction of dementia risk in 20 years among middle aged people: a longitudinal, population-based study. Lancet Neurol. (2006) 5:735–41. doi: 10.1016/S1474-4422(06)70537-3
142. Parra MA, Abrahams S, Logie RH, Méndez LG, Lopera F, Della Sala S. Visual short-term memory binding deficits in familial Alzheimer's disease. Brain. (2010) 133:2702–13. doi: 10.1093/brain/awq148
143. Parra MA, Calia C, García AF, Olazarán-Rodríguez J, Hernandez-Tamames JA, Alvarez-Linera J, et al. Refining memory assessment of elderly people with cognitive impairment: insights from the short-term memory binding test. Arch Gerontol Geriatrics. (2019) 83:114–20. doi: 10.1016/j.archger.2019.03.025
144. Lancaster C, Koychev I, Blane J, Chinner A, Chatham C, Taylor K, et al. Gallery Game: Smartphone-based assessment of long-term memory in adults at risk of Alzheimer's disease. J Clin Experi Neuropsychol. (2020) 42:329–43. doi: 10.1080/13803395.2020.1714551
145. Lancaster C, Koychev I, Blane J, Chinner A, Wolters L, Hinds C. Evaluating the feasibility of frequent cognitive assessment using the Mezurio smartphone app: observational and interview study in adults with elevated dementia risk. JMIR mHealth uHealth. (2020) 8:e16142. doi: 10.2196/16142
146. Johns EK, Phillips NA, Belleville S, Goupil D, Babins L, Kelner N, et al. The profile of executive functioning in amnestic mild cognitive impairment: disproportionate deficits in inhibitory control. J Int Neuropsychol Soc. (2012) 18:541–55. doi: 10.1017/S1355617712000069
147. Crawford TJ, Higham S. Distinguishing between impairments of working memory and inhibitory control in cases of early dementia. Neuropsychologia. (2016) 81:61–7. doi: 10.1016/j.neuropsychologia.2015.12.007
148. Bott NT, Lange A, Rentz D, Buffalo E, Clopton P, Zola S. Web camera based eye tracking to assess visual memory on a visual paired comparison task. Front Neurosci. (2017) 11:370. doi: 10.3389/fnins.2017.00370
149. Valliappan N, Dai N, Steinberg E, He J, Rogers K, Ramachandran V, et al. Accelerating eye movement research via accurate and affordable smartphone eye tracking. Nat Commun. (2020) 11:4553. doi: 10.1038/s41467-020-18360-5
150. Ritchie CW, Ritchie K. The PREVENT study: a prospective cohort study to identify mid-life biomarkers of late-onset Alzheimer's disease. BMJ Open. (2012) 2:e001893. doi: 10.1136/bmjopen-2012-001893
151. Calvin CM, de Boer C, Raymont V, Gallacher J, Koychev I. Prediction of Alzheimer's disease biomarker status defined by the 'ATN framework'among cognitively healthy individuals: results from the EPAD longitudinal cohort study. Alzheimer's Res Therapy. (2020) 12:1–16. doi: 10.1186/s13195-020-00711-5
152. Koychev I, Young S, Holve H, Yehuda MB, Gallacher J. Dementias Platform UK Clinical Studies and Great Minds Register: protocol of a targeted brain health studies recontact database. BMJ Open. (2020) 10:e040766. doi: 10.1136/bmjopen-2020-040766
Keywords: dementia, Alzheimer's disease, secondary prevention, diabetes, hypertension, chronic kidney disease, digital technology, blood-based biomarkers
Citation: Ojakäär T and Koychev I (2021) Secondary Prevention of Dementia: Combining Risk Factors and Scalable Screening Technology. Front. Neurol. 12:772836. doi: 10.3389/fneur.2021.772836
Received: 08 September 2021; Accepted: 11 October 2021;
Published: 15 November 2021.
Edited by:
Craig Ritchie, University of Edinburgh, United KingdomReviewed by:
Eline Pecho-Vrieseling, University of Basel, SwitzerlandAlexandra Economou, National and Kapodistrian University of Athens, Greece
Copyright © 2021 Ojakäär and Koychev. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
*Correspondence: Ivan Koychev, SXZhbi5rb3ljaGV2JiN4MDAwNDA7cHN5Y2gub3guYWMudWs=