Progress on early diagnosing Alzheimer’s disease

Abstract

Alzheimer’s disease (AD) is a progressive neurodegenerative disorder that affects both cognition and non-cognition functions. The disease follows a continuum, starting with preclinical stages, progressing to mild cognitive and behavioral impairment, ultimately leading to dementia. Early detection of AD is crucial for better diagnosis and more effective treatment. However, the current AD diagnostic tests of biomarkers using cerebrospinal fluid and/or brain imaging are invasive or expensive, and mostly are still not able to detect early disease state. Consequently, there is an urgent need to develop new diagnostic techniques with higher sensitivity and specificity during the preclinical stages of AD. Various non-cognitive manifestations, including behavioral abnormalities, sleep disturbances, sensory dysfunctions, and physical changes, have been observed in the preclinical AD stage before occurrence of notable cognitive decline. Recent research advances have identified several biofluid biomarkers as early indicators of AD. This review focuses on these non-cognitive changes and newly discovered biomarkers in AD, specifically addressing the preclinical stages of the disease. Furthermore, it is of importance to explore the potential for developing a predictive system or network to forecast disease onset and progression at the early stage of AD.

Keywords

Alzheimer’s disease / early diagnosis / non-cognitive symptoms / biomarkers

1 Introduction

Alzheimer’s disease (AD) is the most common neurodegenerative disease in older individuals and is characterized by a gradual decline of cognition as a significant clinical symptom [1]. The increasing global aging population has elevated AD to a prominent social issue, leading to a substantial burden on social and economic fronts [2]. AD cases are projected to surge from 50 million in 2010 to 113 million by 2050 [3]. AD progresses through three stages: cognitively normal, mild cognitive impairment (MCI), and dementia [4].

Recent studies highlight the importance of addressing cognitive decline in its preclinical stages to prevent irreversible neuron damage [5]. Notwithstanding the notable cognitive decline-delaying effects exhibited by drugs such as lecanemab and donanemab, a substantial corpus of clinical research consistently underscores their potential to engender more favorable therapeutic responses in the nascent phases of AD. This emphasizes the paramount imperative for precise diagnosis and expeditious intervention during the preclinical stages of the disease [6,7]. Clinical diagnostic methods mainly rely on medical history, cognitive function, and biomarkers, including lumbar puncture for cerebrospinal fluid (CSF) collection and positron emission tomography (PET) imaging [8–10]. However, these approaches suffer from limitations such as invasiveness or high costs, which restrict their widespread implementation in a larger population, including monitoring cognitively healthy individuals well before disease onset. Therefore, developing affordable, rapid, and highly accurate preclinical detection methods is crucial. In lieu of biomarker screening, the need for a clinical marker arises to effectively identify or predict individuals at high risk during the preclinical stages of AD [11].

Some non-cognitive signs have already been presented before cognitive dysfunction appears in AD patients [12]. However, with studies up to date, it is difficult to determine whether those non-cognitive symptoms are predictive of cognitive decline [13]. Therefore, it is necessary to diagnose AD early to identify whether non-cognitive symptoms or factors can be correlated with AD pathologies and predict ongoing cognitive decline. Considering the complex and multifaceted nature of AD pathogenesis and the heterogeneity of individuals, multiple biomarkers or diagnostic networks are necessary to monitor the disease more sensitively and specifically [14]. Healthcare providers face several barriers in clinical practice, including identifying a convenient therapy window and achieving precise identification of AD pathology. Additionally, recognizing early AD symptoms is crucial instead of dismissing them as a natural part of the aging process [15]. Therefore, non-cognitive symptoms, biomarkers in the preclinical stage of AD, and the correlation between non-cognitive symptoms and clinical biomarkers play essential roles in managing dementia-related diseases in the future.

This review presents the non-cognitive alterations of AD in the preclinical stage, including behavioral and psychological symptoms, sleep disorders, sensory dysfunctions, physical changes, some novel imaging techniques, and biofluid biomarkers (Fig.1). Identifying non-cognitive symptoms can help predict MCI or AD early, preventing disease onset or slowing disease progression [16]. We also summarize the association between non-cognitive alterations and AD pathophysiological biomarkers. By integrating various non-cognitive signs associated with the same pathological biomarkers, establishing a network to identify dementia in its preclinical stages holds great potential for enhancing disease management and providing substantial benefits (Fig.2).

2 Historical overview of early diagnosis research in AD

Over the course of the last 40 years, there has been a notable transition in the diagnostic methods employed for AD, moving away from a sole reliance on clinical evidence towards a more prominent utilization of biological approaches [17,18]. For a considerable period of time following the introduction of the initial diagnostic criteria for AD by the National Institute of Neurological and Communicative Disorders and Stroke-Alzheimer’s Disease and Related Disorders Association (NINCDS-ADRDA) in 1984, AD diagnosis relied solely on clinical evidence and exclusionary criteria [19]. During that period, the NINCDS-ADRDA committee introduced the term “probable AD” as a result of the constraints posed by methods of diagnosis [19]. The establishment of a conclusive diagnosis of AD is contingent upon doing a postmortem pathological investigation, hence making it exceedingly challenging to get an early diagnosis of AD. The introduction of the International Working Group (IWG) criteria for AD diagnosis in 2007 marked a significant milestone in the field. It was during this time that the notion of the prodromal stage of AD was initially proposed, and the inclusion of AD biomarkers became an integral part of the diagnostic criteria [20]. This integration of new evidence regarding disease-specific underlying pathophysiology has greatly facilitated the development of early AD diagnosis [17]. In 2010, the updated IWG criteria further proposed the temporal ordering of biomarker changes, categorizing them into pathophysiological biomarkers and downstream biomarkers [21]. Subsequently, in 2011, the National Institute on Aging-Alzheimer’s Association’s (NIA-AA) diagnostic criteria introduced the concept of “preclinical AD” and incorporated biomarkers of the preclinical stage into the diagnostic process [22]. And in 2014, IWG supplemented their previous criteria by differentiating AD diagnostic biomarkers from biomarkers of disease progression [23]. In 2018, NIA-AA criteria introduced an AD research framework based on the A/T/N biomarkers. According to this framework, individuals with positive A/T/N biomarkers are defined as having AD, even in the absence of cognitive symptoms, further emphasizing the significance of biomarkers [24]. Due to the advancement of detection techniques, the diagnostic performance of blood-based biomarkers has been continuously enhanced [25]. In 2023, the NIA-AA recommended including blood biomarkers in the diagnostic criteria [26] (Fig.3). The future holds great potential for the advancement of artificial intelligence (AI), which may lead to the discovery of minimally invasive and cost-effective early biomarkers for AD that go beyond the use of blood-based indicators [27–29]. This significant advancement possesses the potential to substantially expedite the advancement of AD screening and early detection among a sizable population.

3 Non-cognitive signs preceding cognitive manifestations in AD

3.1 Behavioral and psychological manifestations as early symptoms of AD

Behavioral and psychological symptoms of dementia (BPSD) are often associated with dementia [30]. Almost 80% of dementia patients have BPSD 2 years before their formal diagnosis [30,31]. Although BPSD is common in dementia, some symptoms appear more frequently after the cognitive decline, such as delusions, disinhibition, and hallucinations [32]. Depression and irritability are the two most common psychological symptoms before cognitive decline [32].

Depression can appear over 9 years before the onset of cognitive symptoms [33,34]. A longitudinal study was conducted to follow up on normal cognitive subjects with depression for two years, and it was found that depression escalates the risk of developing AD within 2 years, with a twofold to threefold increase compared to non-depressed individuals [33]. However, the results were heterogeneous, which could be attributed to the different approaches used by investigators [13,35]. It is important to clarify whether depression causes dementia or whether depressive symptoms are comorbid with dementia in the preclinical stage. Meta-analyses revealed that irritability was a significant predictor of MCI about 2.5 years before diagnosis [36].

Before the onset of cognitive symptoms, anxiety, agitation, and apathy can also occur. However, these symptoms are less common than depression and irritability, accounting for 17%, 13%, and 14% of all the psychological symptoms before cognitive decline [32]. However, apathy is not specific to AD, as it is also observed in non-AD patients (58.2%) [37]. Psychological symptoms can be systematically evaluated in populations using standardized scales. However, BPSD can fluctuate in frequency and severity, and there is high heterogeneity between individuals and phases of the disease in AD [38]. Therefore, a better understanding of the relationship between BPSD and dementia will help predict potential cognitive decline in early-stage AD.

3.2 Circadian rhythm disruption (CRD) at the early stage of AD

Circadian rhythm disruption can manifest in various physiological processes, including sleep–wake cycles, blood pressure, core body temperature, and hormone levels [39]. Patients with AD often experience CRD before cognitive decline and even before the onset of pathological changes. Current studies on CRD in the preclinical stage of AD primarily focus on disturbances in the sleep–wake phase. A meta-analysis found that individuals with sleep disturbances risk developing AD more than those without such disturbances [40]. Sleep performance can be assessed using several measurable indexes. A longitudinal study involving participants without cognitive impairment before testing has demonstrated a positive correlation between insomnia-related symptoms, such as longer sleep latency and lower sleep efficiency, and cognitive and memory decline [41]. Insomnia leads to reduced sleep duration, mainly non-rapid eye movement (NREM) sleep, and an increased risk of dementia [41,42]. Middle-aged or older individuals who sleep less than six hours per day are more susceptible to developing AD [43]. Sleep quality data can be acquired using sleep monitoring devices or software, while the quantification of sleep structure can be measured through an electroencephalogram (EEG) with the utilization of wearable headband [44]. Observations of EEG patterns have shown that NREM sleep decreases during the light session and increases during the dark session in the stage preceding cognitive decline, accompanied by a corresponding opposite alteration in waking time [42]. However, this result still lacks clinical evidence.

Hormones such as cortisol and melatonin are closely related to circadian rhythm. In this discussion, we focused on cortisol in section “Endocrine dysregulation as potential biomarkers for early AD diagnosis.” Melatonin, a neurohormone produced by the pineal gland, plays a vital role in regulating circadian rhythm [39]. The secretion of melatonin gradually decreases with age, and this decline is more pronounced in the progression of AD and the level of melatonin in the CSF has already decreased in the temporal cortex during the preclinical stage of AD [45]. With the desynchronization of physiological rhythms in the degenerative disease, the relative amplitude variability of melatonin is reduced [46]. Research has shown that decreased relative amplitude negatively affects sleep efficiency and contributes to subsequent AD pathology in the early stages of the disease [46]. In summary, CRD, particularly in sleep patterns, are commonly observed in the preclinical stages of AD. Understanding the intricate relationship between circadian rhythm and AD pathology could pave the way for developing innovative interventions and early diagnostic strategies.

3.3 Sensory impairments at the early stage of AD

The human sensory systems mainly comprise olfaction, hearing, vision, gustation, and tactile perception, which can be affected before a cognitive decline occurs and can be used as a marker to predict AD at the preclinical stages. Sensory impairments are independently associated with the brain’s structure, including atrophy and cortical reorganization, and influence cognitive ability [47]. Furthermore, sensory dysfunction may lead to information deficiency, exacerbating cognitive decline [48]. Among all the senses, olfaction holds the most promise as a potential early marker in AD [49]. While auditory impairment has also been associated with AD progression, the specificity and sensitivity of the hearing test are lower than the olfactory test [50]. Visual changes, especially alterations in retinal thickness, hold potential for the preclinical diagnosis of AD.

3.3.1 Olfactory disorders in AD

A longitudinal multivariable analysis conducted on 515 elderly individuals over ten years found that the speed of olfactory aggravation during normal cognition positively correlates with the incidence of cognitive impairment [51]. In MCI and AD patients, the olfactory function is associated with global cognition, which includes odor identification (OI), odor threshold, habituation, and odor discrimination [49]. OI is suitable for community screening due to less heterogeneity from outside factors [49]. Patients with subjective cognitive decline have lower OI scores than normal controls [52]. However, more evidence is necessary for preclinical identification before cognitive decline. A 3.5-year longitudinal investigation including 1630 cognitively intact individuals found that the brief smell identification test could predict the risk of MCI, confirming the diagnostic efficacy of OI for the preclinical stage of AD [53]. However, the OI test as a predictor for preclinical AD has low sensitivity and high specificity, necessitating the combination with other non-cognitive tests [52].

3.3.2 Retina as a window into the brain of early-stage AD

For the early identification of AD and MCI, retinal detection mainly focuses on the retinal structure, vessels, and electrophysiological markers (Tab.1) [54]. Optical coherence tomography (OCT) was used to assess the retinal thickness. A longitudinal study confirmed that the thickness of the retinal ganglion cell layer (GCL) was associated with the prevalence of AD, while the thickness of the macular retinal nerve fiber layer (RNFL) was associated with the risk of developing AD, which suggests that thinner GCL and RNFL has the potential to be an early diagnostic biomarker of AD [55]. Further studies are needed to support the alterations in retinal thickness in preclinical AD. OCT-angiography can assess retinal vascular anatomy and function. Several studies suggest that, compared to the AD biomarker-negative group, the positive biomarker group had a more significant foveal avascular zone and a thinner mean inner foveal thickness [56,57]. The visual function has the potential to be a marker to detect AD in preclinical stage. The retinal functional alterations induce an increase in electroretinography (ERG) amplitudes in mice [58]. These ERG amplitude changes reflect the elevated retinal neuron activity, which might serve as a compensative mechanism at the disease onset.

3.3.3 Hearing loss and AD: how are they linked?

Hearing loss is independently associated with cognitive impairment and dementia [66]. Hearing function testing, as a simple, non-invasive approach, can serve as a monitoring tool for the early detection of AD. Hearing loss has been found to have far-reaching consequences, including social disorder and cognitive decline, as well as an increased cognitive burden, resulting in structural changes in the brain [47]. Consequently, impaired hearing has been identified as contributing to the increased risk of dementia. Longitudinal studies have examined the association between hearing impairment and cognitive decline in individuals aged 60 years and older who did not have cognitive impairment at baseline. The results suggest that hearing impairment, particularly abnormal central auditory processing and speech-in-noise hearing impairment, increase the risk of dementia [66,67]. A longitudinal study of 2927 participants without cognitive impairment at baseline has demonstrated that dual sensory deficits in hearing and vision are associated with an increased risk of dementia, particularly AD [68]. Although the relationship between sensory impairment and cognition is still unknown, combining multiple sensory indexes is necessary to predict AD in the preclinical stage rather than relying on a single sensory factor.

3.4 Intestinal flora dysbiosis and potential role in early AD

While the exact cause of AD is still unknown, recent studies have explored the potential role of dysbiosis in the pathogenesis and progression of AD. Dysbiosis, an imbalance in the gut microbiota, can lead to increased intestinal permeability and translocation of bacterial components into systemic circulation [69]. These bacterial components can stimulate the immune system and activate pro-inflammatory pathways that can lead to neuroinflammation and accumulation of amyloid-beta (Aβ) in the brain [70].

The gut microbiota has emerged as a critical factor in maintaining human health and has been implicated in the pathogenesis of various diseases, including AD. Numerous studies have examined the gut microbiota of individuals with MCI and AD compared to healthy controls. In particular, patients with AD have been shown to have a lower abundance of certain beneficial gut bacteria, such as Bifidobacterium, and a higher abundance of pathogenic bacteria, such as Escherichia/Shigella [71]. Moreover, preclinical studies in animal models of AD have shown that the bacterial diversity increased in 1-month-old triple-transgenic AD model (3×Tg) mice (the stage before cognitive symptoms occur) but decreased in 5-month-old 3×Tg mice (the stage after cognitive impairment onset) compared to non-transgenic AD model mice [72]. These findings are consistent with clinical studies, which show reduced microbial diversity in AD patients compared to cognitively normal controls [71]. These observations may provide insight into the role of intestinal flora disorders in the preclinical stages of cognitive decline in AD patients. However, further investigation is necessary to confirm whether the gut microbiome can be a reliable biomarker for early-stage AD.

3.5 Physical changes in the preclinical AD

Among various physical changes associated with AD, gait disorder is one of the most common behavioral markers that predict the risk of cognitive decline in early-stage AD, especially the change in walking speed [73]. Gait changes can be measured by the activity monitor. Several studies investigated gait speed in subjects from different cognitive stages [73,74]. The results showed that walking speed had already decreased before cognitive decline. Both usual speed and fast speed would slow down with cognitive decline. While activity fragmentation (frequency of activity) is not directly linked to the risk of AD, it is associated with gait speed significantly [73]. This is because individuals with slow gait may compensate for their physical function with higher levels of activity fragmentation, which may lead to higher cognitive function than those with lower levels of activity fragmentation. Therefore, combining gait speed with activity fragmentation is essential to accurately assess the risk of AD, as slow gait may be not only due to central nervous system impairment but also peripheral conditions such as osteoarthritis or cardiopulmonary diseases. Clinical practice is necessary to confirm the availability of gait disorders as markers to predict or diagnose AD in the preclinical stage.

Fractal regulation (FR) refers to the ability of healthy physiological systems to generate similar patterns of fluctuations in physiological outputs across different time scales. This regulation reflects the body’s plasticity and adaptability in both its internal function and external activity [75]. A study involves 1097 participants who are cognitively normal at baseline and monitors their FR in motor activity with wrist actigraphy [75]. The result indicates that a lower FR predicts a higher risk of MCI or AD independent of other physical activity or biological rhythms. However, the reason why these two indices predict cognitive impairment before MCI remains not fully understood. Additionally, given that FR declines with age, a standardized reference value is necessary for diagnosing AD before cognitive symptom onset.

Weight change is a crucial factor in the preclinical diagnosis of AD and should be considered with age. While weight loss in older people can be an early indicator of AD, mid-life obesity increases the risk of cognitive decline [76]. Weight changes may be associated with Aβ-mediated dysfunction of the hypothalamic, and may be accompanied by metabolic abnormalities induced by type 2 diabetes mellitus (T2DM) synchronously [76]. However, this age-dependent change complicates monitoring efforts, highlighting the need for a chronological index.

3.6 Cardiovascular dysfunction as a predictor for AD preclinical stage

Cardiovascular dysfunction is another non-cognitive impairment that can occur in individuals with AD. Studies have suggested that AD can also cause changes in blood pressure, heart rate, and other cardiovascular functions, which may indicate an early AD diagnosis beyond the cognitive decline symptoms. AD patients, for instance, may have a higher prevalence of hypertension and heart disease than those without AD [77,78]. A longitudinal study recruited cognitively unimpaired participants and found that hypertension was related to decreased brain functional connectivity, which might precede AD pathologic changes [79]. Moreover, recent studies showed that changes in blood pressure variability were associated with cognitive decline and progression to dementia in individuals with AD, suggesting that changes in blood pressure and heart rate variability may be used as biomarkers for early AD diagnosis [80]. A potential and convenient avenue for cardiovascular assessments in AD diagnosis is through the use of wearable devices. These devices can monitor blood pressure, heart rate, and other cardiovascular functions in real time, providing valuable data for early AD detection. A study found that wearable devices could detect subtle changes in cardiovascular function indicative of early AD in individuals with MCI [81]. These findings suggest that a multi-disciplinary approach involving neurological and cardiovascular assessments may be necessary for early AD detection and management (Tab.2).

4 Biomarkers for early detection of AD: recent advances and future direction

Before cognitive symptoms appear, AD biomarkers have already been detectable up to 20 years before the onset of cognitive symptoms [82]. It provides an extended window for timely prevention or treatment in the preclinical stages of AD. Imaging and CSF biomarkers are two common ways to measure AD-related biomarkers sensitively. As relatively non-invasive and convenient diagnostic methods, blood tests, pathogens, hormones, metabolomics, and exosomal microRNA (miRNA) have the potential for widespread monitoring in the cognitively normal group.

4.1 Innovative neuroimaging biomarkers for the preclinical assessment of AD

Different types of neuroimaging tests can show changes in the brain over time as AD progresses. AD-related pathologies appear first, followed by hypometabolism 14 years prior to the onset of cognitive symptoms. Brain structure atrophy occurs in the final stage [82]. Thus, compared with structural imaging, molecular imaging is more important for the early diagnosis of AD (Tab.1).

Up until now, three advanced amyloid-targeting imaging probes have been approved in the US and Europe: florbetapir (¹⁸F-AV-45, Amyvid), flutemetamol (¹⁸F-GE067, Vizamyl), and florbetaben (¹⁸F-BAY94-9172, NeuraCeq). In recent years, several improved probes have been developed. For example, ¹⁸F-D15-AV-45, which replaces the easily metabolized hydrogen in the body with more stable deuterium, exhibits superior Aβ-specific binding ability and reduced bone uptake compared to ¹⁸F-AV-45 [83]. Recent study has developed a near-infrared BF2-dipyrrolmethane fluorescent imaging probe. This probe, based on the hydrophobic structure of the C-terminus of Aβ, is capable of detecting both soluble and insoluble forms of Aβ, broadening the scope of Aβ detection [64]. Furthermore, probes such as ¹⁸F-FIBT, which exhibit superior binding affinity and lower bone uptake, as well as the ¹⁸F-labeled amyloid PET ligand NAV4694 (¹⁸F-AZD4694), are still under clinical trials and research [84,85]. Due to the lack of consistent evidence about the relationship between amyloid burden and cognitive function in preclinical AD, an increased Aβ level may also exist in cognitively normal older people with age, combining Aβ with other factors may improve diagnostic efficacy [86]. The astrocytic α7 nicotinic acetylcholine receptor (α7nAChR) emerges as a potential target for identifying reactive astrogliosis, which is associated with early impairment of neurotransmission in AD [87]. An increase in α7nAChR expression is linked to early Aβ pathology in the brain [61]. Utilizing α7nAChR-specific probes, such as ¹¹C-Kln83 or ¹⁸F-ASEM, combined with Aβ, can provide critical clinical evidence for the early diagnosis of AD [60]. Despite these encouraging results, it is essential to note that the clinical evidence for this probe remains lacking. Consequently, further research is needed to confirm the feasibility of this innovative technology for clinical purposes.

Currently, the utilization of PET-tau studies in the preclinical stage of AD remains limited [86]. Recent research using ¹⁸F-flortaucipir-PET has revealed that tau burden in different brain regions is associated with various cognitive functions, highlighting the potential of this imaging technique as an innovative neuroimaging biomarker for the pre-symptomatic assessment of AD [63]. Another promising PET radiotracer, ¹⁸F-MK-6240 PET, has demonstrated reliable performance in identifying Braak stages of tau accumulation, with research indicating that asymptomatic AD patients exhibit more positive ¹⁸F-MK-6240 PET results than their cognitively healthy counterparts [62]. Moreover, research using proton magnetic resonance spectroscopy to track metabolite levels in preclinical AD patients with Down syndrome has identified increased Myo-inositol levels before cognitive decline [65]. These findings provide valuable insights into the potential for innovative neuroimaging biomarkers to aid in the pre-symptomatic assessment of AD.

4.2 Blood-based biomarkers for preclinical AD

Compared to invasive and expensive CSF and PET biomarkers, blood-based markers could facilitate widespread screening in the early-stage AD. Daggett et al. used a soluble oligomer binding assay to detect the α-sheet structure of Aβ oligomers in the plasma. They found that this biomarker could identify cognitively normal individuals who later developed MCI [88]. A cross-sectional study revealed the sound diagnostic efficacy of plasma Aβ42/40 in individuals with normal cognitive function but positive Aβ-PET signals (AUC=0.86) [89]. Additionally, a longitudinal study further confirmed the predictive value of plasma Aβ42 [90]. However, it is imperative to conduct comprehensive longitudinal studies to augment the existing findings and ascertain their reliability and applicability. Given that the combination of Aβ and other factors may be a better choice to predict AD development (section “Innovative neuroimaging biomarkers for the pre-symptomatic assessment of AD”), Pascal and his team found that those who both had the amyloid burden and abnormally activated astrocyte in the blood were associated with the Braak stage of tau protein and may lead to further cognitive decline [91]. High plasma total-tau protein predicts the onset of AD cognitive symptoms within 8–16 years [92]. Plasma phosphorylated-tau protein (P-tau) T181 and P-tau T217 have significantly high levels in preclinical AD patients [93,94]. The simultaneous presence of activated microglia cells, playing a crucial role in neuroinflammation and the accumulation of tau protein, further strengthens its potential as a biomarker for the early diagnosis of AD [95]. The marker neurofilament light chain (NfL) and the glial fibrillary acidic protein (GFAP) have promising diagnostic efficacy in preclinical AD patients. Longitudinal analyses confirmed plasma NfL and GFAP in AD mutation carriers were higher than in non-carriers 10 years before the onset of cognitive symptoms [96].

One of the challenges associated with blood tests is the heterogeneity of different individuals, which necessitates the consideration of factors such as age, sex, and lifestyle [97]. Another obstacle is how to incorporate blood-based biomarkers into clinical use [97]. Plasma Aβ analysis showed no difference between AD cases and normal individuals [98]. However, with some novel technologies, such as soluble oligomer binding assay, biofluid markers like plasma Aβ could be monitored early in the disease. However, further study is still needed, using novel technologies to discover more biomarkers in the early-stage AD.

4.3 Early diagnosis using metabolomics profiling in AD

Metabolomics means a set of metabolites our bodies produce and their pathological changes, including amino acids, steroids, and fatty acids [99]. In recent years, metabolomics has gradually been used as a biomarker of early AD diagnosis. One of the metabolomics’ most variable co-expression modules was the astrocyte/microglia metabolic module. Protein levels of the astrocyte/microglia metabolic module are elevated in the prodromal phase of AD, and microglia protein markers in this module are biased toward an anti-inflammatory disease-related state, suggesting a protective or compensatory function against AD pathology [100].

AD has a close relationship with lipid metabolism. Lipid alterations can be measured in the preclinical stage of AD. Several studies found that sphingomyelin increased while glycerophospholipid and fatty acids (except docosahexaenoic acid) reduced in the asymptomatic AD compared with normal individuals [101]. Polyunsaturated fatty acids (PUFA) decreased with compensatory increase of phosphatidylcholine in the cognitively normal individuals with positive CSF P-tau/Aβ42 [102]. In contrast, ether glycerophospholipids are reduced at the same stage, which may increase the risk of AD. Sphingolipids, associated with neurogenesis and synaptogenesis, are derived from ceramides. The ceramides increased in the preclinical stage of AD, stimulating Aβ production and forming a feed-forward cycle to accelerate disease progression [102,103]. Furthermore, the urinary and fecal metabolic profiles reflect alterations in the metabolic pathways of amyloid protein. Some urinary and fecal metabolite biomarkers have the potential to predict AD before the onset of cognitive symptoms [104]. But the study was still on the animal level. Corresponding clinical research is necessary to confirm the ability of these metabolisms to identify AD in its preclinical stage.

4.4 The roles of exosomal miRNA in the early diagnosis of AD

Exosome is one of the extracellular vesicles produced from the cellular plasma membrane [105]. It acts as a carrier that transfers small molecules inside the body [1]. Neuron-derived exosomes are small vesicles secreted by brain cells [106]. They could convey AD-related molecular biomarkers stably through the blood–brain barrier to the whole body. Meanwhile, the level of those proteins in serum-based exosomes is highly consistent with the protein level in the CSF [107]. These characteristics allow exosomal biomarkers to be easily detected in the peripheral plasma, which is non-invasive. miRNAs are small strands of noncoding RNAs. Most of them come from exosomes, specifically [108]. As increasing evidence shows that miRNAs are related to AD pathology, miRNAs promise to be a specific predictor of AD in plasma before the onset of cognitive symptoms [108]. A multicenter study collects neuron-derived exosomes in the blood [108]. This study establishes the receiver operating characteristic curve of six miRNAs in preclinical AD patients. The data have a high area under the curve (AUC) values (AUC=0.852) to identify AD 5 to 7 years before the onset of clinical symptoms. Furthermore, the level of above six miRNAs does not change in other neurodegenerative diseases. Such results suggest that miRNAs have the potential to identify AD specifically before cognitive impairment [108]. But the regulation mechanism still needs further research.

4.5 Pathogen is a potential biomarker at the early stage of AD

The brain–blood barrier (BBB) is vital in preventing most of the pathogens outside. However, in AD patients, pathological factors would lead to a reduced protective effect of the BBB, which increases the risk of pathogen invasion into the brain. Several pathogens increase the risk of AD, such as herpes simplex virus (HSV), Toxoplasma gondii, and bacteria like Chlamydial pneumonia, Borrelia burgdorferi, and Porphyromonas gingivalis (P. gingivalis) [109]. Given the failure of clinical tests to target Aβ burden, the infectious hypothesis should be taken into consideration [110]. Pathogen origin may explain some early pathological changes of AD. The pathology during HSV infection is similar to specific chronic amplification of Aβ42 of AD patients in the early stage. Aβ42 has a similar structure to viral fusion domains and is akin to a sequence of bacterial family [111]. This evidence shows HSV’s potential to become an early biomarker for AD diagnosis. The expression of unfolded p53 in the peripheral blood increased several years prior to the onset of cognitive symptoms. This misfolded conformation is believed to be attributed to heightened oxidative stress in AD, mainly induced by lipopolysaccharide from bacteria, especially the P. gingivalis [112]. Further longitudinal studies are necessary to assess the accuracy of different pathogens as risk factors for AD in early diagnosis.

4.6 Endocrine dysregulation as potential biomarkers for early AD diagnosis

Due to their contribution to both cognitive and non-cognitive manifestations of the disease, endocrine disorders have been implicated as potential biomarkers for early AD diagnosis. Abnormalities in insulin resistance, cortisol, thyroid, and sex hormones have been associated with AD pathology and cognitive decline [113,114].

Insulin resistance has been identified as a risk factor for AD, potentially due to impaired glucose metabolism and reduced neuronal energy production [115]. An 11-year longitudinal study analyzed data from participants without dementia at baseline and found that patients with T2DM had a higher risk of AD, especially women [116]. Therefore, monitoring blood sugar levels and addressing insulin resistance may be necessary for early AD diagnosis and management. Cortisol, a hormone released in response to stress, may also be a biomarker for early AD diagnosis [117,118]. The cortisol level in the preclinical AD stage does not have consistent evidence. A longitudinal study indicated that elevated cortisol with abnormal Aβ level was related to higher risk from cognitively normal transition to MCI/AD [119]. Another Mendelian randomization study indicated that a genetic predisposition to higher plasma cortisol decreased the risk of AD, which may be attributed to the role of cortisol in regulating metabolism and reducing obesity [120]. The relationship between cortisol and dementia needs further study to confirm.

Thyroid hormones are critical in brain function, growth, and metabolism. Abnormal thyroid function, such as hypothyroidism or hyperthyroidism, has been associated with cognitive impairment and AD. A Framingham study, which recruited cognitively normal individuals with 12.7-year follow-up, demonstrated that both low and high levels of thyrotropin increased the risk of AD in women but not men [121]. Therefore, evaluating thyroid function could be useful in early AD diagnosis.

Sex hormones, including estrogen and testosterone, have also been implicated in AD pathology. A 7-year longitudinal study found that lower testosterone levels and higher sex hormone-binding globulin were associated with a higher incidence of AD independently [122]. Additionally, kallikrein-8, induced by estrogen, increased in the 3-month-old female AD mice even before the onset of amyloid plaque [123]. This evidence suggests sex-specific AD differences and provides clues for preclinical AD diagnosis.

In conclusion, endocrine disorders may serve as non-cognitive manifestations of early AD. Additionally, certain biomarkers associated with these disorders could potentially aid in early AD diagnosis. However, further research is necessary to fully understand the relationship between endocrine disorders and their potential as indicators in the diagnostic process of early AD (Tab.3).

5 Correlations between non-cognitive symptoms and biomarkers: insights and future directions

Psychological, sensory, sleep, and other behavioral and physical symptoms may help to screen for abnormalities in a large group of individuals. However, using the above methods to identify cognitive decline patients inevitably has significant heterogeneity in a very early stage of disease onset. AD-related biomarkers provide a long-time window for early identification and prevention of AD. But ethical and economic factors limit large-scale monitoring of cognitively normal individuals for such biomarkers [13]. Given these restraints, non-cognitive symptoms combined with biochemical biomarker detection may be optimal for the early identification of AD.

5.1 Correlation between behavioral and psychological symptoms of AD and pathological changes

The onset of BPSD may correlate with genes or some changes in brain structure and functions of dementia [11]. For example, BPSD may occur at an earlier age because of the presence of the apolipoprotein ε4 allele [124]. Cross-sectional or longitudinal studies following cognitively normal individuals and MCI subjects found that several neuropsychiatric symptoms, especially depression, were associated with Aβ deposition [125]. Exogenous Aβ would increase depressive symptoms and anxiety [126]. The above evidence suggests that amyloid burden may be one of the reasons for depression in the preclinical stage of AD. The cognitive debt hypothesis suggests that repetitive anxiety would have negative effect on brain structure and increase Aβ and tau proteins [35]. The above correlations point out that if those behavioral and psychological symptoms exist, measurement of those objective changes simultaneously may reflect the onset risk of cognitive impairment in an average cognitive individual or reveal the preclinical symptoms of AD.

5.2 Correlations between biological rhythm symptoms and pathological changes in AD

Sleep alterations correspond to AD pathology in a bidirectional manner [127]. Sleep would increase the clearance of AD-related proteins, such as Aβ and tau protein. Sleep disorders increase the risk of AD by exacerbating the Aβ burden and tau [128,129]. Amyloid deposits would, in turn, negatively affect sleep quality [76]. Some studies suggested that tau protein may contribute to sleep disorders, as pure tauopathies showed more severe sleep symptoms than AD [129,130]. Sleep disturbances appear before Aβ accumulation and may be associated with the first appearance of tau NFTs [130]. Locus coeruleus (LC) regulates the sleep–wake cycle and is also one of the first affected regions in AD before the entorhinal cortex [130]. Tau protein can be first detected in the LC-norepinephrine system of AD patients [131]. The spread of tau protein through neural networks (the tau Braak-like stages) may lead to the degeneration of correlative functions, including the sleep–wake cycle [130]. As cognitive symptoms appear after tau spreads to the neocortex in AD, the early identification of tau pathology in the LC and declining sleep quality is necessary for the early diagnosis of AD [95,132].

5.3 The correlation between sensory impairments and pathological changes in AD

Olfactory-related brain regions show neuropathology in an early stage of AD [49]. Previous investigations found that poor olfaction is associated with a rapid accumulation of amyloid and tau in olfaction-related regions in cognitively normal people [49]. The accumulation of Aβ in the olfactory bulb (OB) decreases the activation of OB oscillations, leading to the dysfunction of the OB network [133]. AD patients show severe tau pathology in olfactory information processing region before the appearance of NFTs in the cortices, which may damage olfactory function [49].

The research found that retinal thickness, especially the GCL layer, is significantly related to the tau protein level in CSF [134]. Aβ could also be found in the retinal layers in AD patients even earlier than accumulation in the brain, in the form of peptide oligomers but not fibrillary [135,136]. The accumulation of AD-related biomarkers contributes to decreased retinal thickness. And these deposits also affect the ERG amplitude [137].

The association between hearing loss and AD-related pathology still lacks consistent evidence. Hearing loss may increase the susceptibility of hippocampal synapses to Aβ-induced damage [138]. In contrast, Glymour and their team indicated that hearing impairment was related to accumulated NFT burden but not typical AD pathological changes [139].

5.4 Other non-cognitive changes in AD and their correlation with biomarkers: cardiovascular dysfunction and physical alterations

The link between cardiovascular dysfunction and preclinical AD biomarkers has not been consistently confirmed [140]. Several findings indicated a bidirectional relationship between AD and intestinal flora. In addition to triggering inflammation and Aβ accumulation, gut dysbiosis may also decrease the absorption of PUFA [70,141]. And short-chain fatty acids may decrease hypothalamic–pituitary–adrenal axis responsiveness through down-regulating gene expression [142]. It is also associated with cerebral glucose metabolism [143]. All these factors increase the risk of cognitive decline. The association between gait disorders and AD pathology is still rarely investigated, although studies found that Aβ level in CSF may be associated with lower walk speed, and tau protein may correlate with gait rhythm and complex walking tasks [74,144].

6 Conclusions and perspectives

Early detection is essential for implementing effective interventions that can slow or halt disease progression. Non-cognitive onset offers valuable insights into varying degrees of early-stage neuron damage and can be easily monitored in larger populations due to the emergence of novel portable tools and advancements in AI. However, as non-cognitive symptoms can also occur in other neurodegenerative diseases with heterogeneous preclinical symptom resembling AD, it becomes crucial to assess the extent of their influence from AD onset to cognitive impairment progression. It is necessary to develop a systematic method or scale to quantify the degree or impact of each non-cognitive symptom, specifically in the preclinical stages of AD. Meanwhile, recent advancements in multi-omics technologies hold great potential for integrating diverse data sources and facilitating the development of personalized therapies. These innovative approaches can aid in distinguishing AD from other diseases and significantly contribute to accurate and sensitive disease diagnosis. While the initial screening of non-cognitive symptoms is valuable, a comprehensive approach combining clinical biomarkers detection is necessary to enhance diagnostic accuracy (Fig.2). Novel biomarkers and imaging techniques have emerged to detect AD prior to the manifestation of symptoms, offering the potential to enhance patient outcomes and expedite the development of effective therapies. Investigating the correlation between non-cognitive symptoms and in vivo biochemical changes can further deepen our understanding of the connection between these symptoms and AD. Therefore, it is vital to develop systematic methods or scales for assessing non-cognitive symptoms and integrating novel biomarkers to achieve accurate and sensitive early-stage AD diagnosis. Furthermore, comprehending the associations between various non-cognitive symptoms is crucial for early AD detection. By integrating these techniques with machine learning, the intricate interrelationships among different diagnostic indicators can be captured, enabling personalized treatment approaches for individuals with AD.

7 Outstanding questions

- How can non-cognitive manifestations be utilized as early indicators for the detection of AD?

- What are the most promising emerging biofluid/image biomarkers for early detection of AD, and how do they compare to traditional diagnostic methods?

- What is the relationship between non-cognitive symptoms and the underlying pathology of AD, including the presence of amyloid-beta plaques and tau tangles?

- How can the integration of non-cognitive manifestations and biofluid biomarkers improve the accuracy and timeliness of AD diagnosis compared to current clinical diagnostics?

- What are the potential challenges and limitations associated with incorporating non-cognitive symptoms and emerging biomarkers into routine clinical practice for early detection of AD?

- What additional research is needed to establish the utility and reliability of biofluid biomarkers in the early detection of AD, and how can this knowledge be translated into clinical practice?

8 Searching strategy and selection criteria

In the literature review, we conducted a comprehensive literature search on the PubMed database within the past 5–10 years using the following search terms: “Alzheimer Disease” [Majr] AND (“Psychiatry”[Mesh] OR “Physiology”[Mesh] OR “Depression”[Mesh] OR “Irritable Mood” [Mesh] OR “Circadian Rhythm”[Mesh] OR “Melatonin”[Mesh] OR “Smell”[Mesh] OR “Retina”[Mesh] OR “Hearing Loss”[Mesh] OR “Gastrointestinal Microbiome”[Mesh] OR “Behavior”[Mesh] OR “Cardiovascular System” [Mesh] OR “Neuroimaging”[Mesh] OR “Biomarkers” [Mesh] OR “Viruses”[Mesh] OR “Bacteria”[Mesh] OR “Microbiology”[Mesh] OR “Parasites”[Mesh] OR “Endocrine System” [Mesh] OR “Insulin” [Mesh] OR “Thyroid Hormones” [Mesh] OR “Hypothyroidism”[Mesh] OR “Hyperthyroidism”[Mesh] OR “Hydrocortisone”[Mesh] OR “Gonadal Steroid Hormones”[Mesh] OR “Metabolomics”[Mesh] OR “Exosomes”[Mesh] OR “MicroRNAs”[Mesh]). We included studies on human or animal models prior to the onset of cognitive impairment and excluded the subjects with cognitive deficits in early diagnosis-related studies. Additionally, we reviewed the references cited in the selected articles to ensure a comprehensive overview of the literature.

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