Understanding Mild Cognitive Impairment in Alzheimer Disease

By Clinical Content Hub

Alzheimer disease (AD) is a progressive neurodegenerative disorder characterized by neuronal death, leading to cognitive decline and functional impairment.1 Early symptoms often include memory loss, language difficulties, and impaired thinking. Brain changes begin at least 20 years before clinical symptoms appear.2

Brain Changes in AD

Key brain changes in AD include the accumulation of beta-amyloid (Aβ) plaques outside neurons and tau tangles inside neurons. These accumulated proteins interfere with interneuron communication and nutrient transport, leading to neurodegeneration. The presence of toxic Aβ and tau proteins activates microglia (ie, immune cells in the brain), resulting in chronic inflammation.3 Atrophy and impaired glucose metabolism constitute additional important brain changes observed in patients with AD.1 Figure 1 illustrates the rates of mortality, prevalence, and diagnostic challenges associated with AD.2

AD Continuum

AD progresses along a continuum beginning with preclinical changes that can manifest up to 30 years before symptom onset, advancing through mild cognitive impairment (MCI) due to AD, and culminating in AD dementia. The preclinical phase involves asymptomatic disease with biomarker evidence of Aβ accumulation.1

MCI is characterized by noticeable cognitive decline that does not significantly interfere with daily life. It is frequently regarded as a bridge between normal cognitive aging and dementia, particularly AD.4 Annually, approximately 10% to 12% of individuals with MCI exhibit progression to AD, but some people with an MCI diagnosis do not follow this trajectory. Many patients experience varied outcomes: some remain at the MCI stage and others return to a cognitively normal state. This variability presents challenges for clinical practice and research efforts.5 Importantly, individuals with MCI do not meet the diagnostic criteria for dementia.4

AD dementia represents the final stage of cognitive decline, characterized by substantial cognitive and functional impairments. This phase is further divided into mild, moderate, and severe stages based on the extent of impairment and dependency.1

Epidemiology of AD and MCI

AD remains the 5th leading cause of death among individuals aged 65 years and older in the United States. From 2000 to 2021, AD-related deaths in the US increased by more than 140%.2

In the US, an estimated 6.9 million Americans aged 65 years and older currently have AD dementia; this number is projected to double by 2060.2 The prevalence of AD dementia increases with age, affecting 3% to 4% of adults in their late working or retirement years; the rate is higher in women (7.13%) than in men (3.31%).3 The combined prevalence estimates of AD-related dementia and MCI in the US suggest that 10 to 12 million older Americans live with AD-related cognitive impairment. Considering that MCI can develop years before dementia onset, the actual number of affected individuals is likely to be higher.2

Unmet Needs in Diagnosing MCI

Efforts to improve the screening, detection, and diagnosis of MCI have received considerable attention because MCI represents an identifiable transitional stage before AD onset. Assuming that the prevention of MCI and/or its conversion to AD can also prevent subsequent AD onset,6 MCI should be a primary target for AD prevention.

The emphasis on diagnosing MCI has revealed several challenges. A key concern is whether patients with mild depression should be considered to have MCI, given that depression can be a risk factor for memory impairment. Another challenge involves distinguishing between dementia and MCI, based on the difficulty in defining functional impairment. Additionally, MCI does not always progress to AD; in some cases, it persists without progression, and it may be reversible in certain individuals.4

Accurate diagnosis of MCI is crucial because it represents a predementia phase; it can simultaneously be a risk factor for AD and a prodromal phase of AD. It is essential to precisely define criteria for identifying patients at risk for disease progression, those whose condition will remain stable, and those whose cognitive function may return to a normal state.4

There is evidence that approximately 8% of expected MCI cases are diagnosed, suggesting that up to 8 million people have undiagnosed MCI. A survey of more than 200,000 clinicians and practices showed that only 0.1% had diagnosis rates within the expected range. This diagnostic gap can be attributed to limited expertise, training, and confidence among primary care clinicians, who are often the initial point of contact for patients experiencing cognitive decline.2

Most specific diagnoses occur at moderate or severe stages of MCI, when independence has already been substantially reduced. Factors contributing to these delayed diagnoses include the misconception that cognitive decline is a normal part of aging, denial or lack of recognition among patients and caregivers, and the absence of simple, reliable biomarkers for MCI. Additionally, there is a mistaken belief that diagnostic delays have minimal consequences for patients and families.1

Reconceptualizing AD

Recent advancements have shifted the understanding of AD from a dementia-focused condition to a long-term pathophysiological continuum. This new perspective includes early asymptomatic changes identifiable via biomarkers, emphasizing the importance of early detection and intervention.1

Classification Schemes for AD

The differentiation between cognitive decline due to AD and cognitive decline due to normal aging is a key challenge in clinical practice. Accordingly, the National Institute on Aging-Alzheimer’s Association (NIA-AA) and the International Working Group (IWG) have developed comprehensive classification systems to guide research and clinical diagnosis.1

The 2011 NIA-AA guidelines delineate AD progression through 3 stages: preclinical AD (early pathological changes in cognitively normal individuals), MCI (symptomatic predementia), and dementia. The IWG guidelines use clinical and biological markers to categorize AD into preclinical and symptomatic stages. Both systems utilize biomarkers such as Aβ and tau levels in cerebrospinal fluid (CSF) as well as imaging techniques (eg, positron emission tomography [PET]) to track disease progression.3

In 2018, the NIA-AA introduced the AT(N) classification, a research-oriented biological definition of AD that assesses amyloid pathology (A), tau pathology (T), and neuronal injury (N). In contrast, the IWG emphasizes a clinical-biological approach to AD diagnosis, cautioning against routine biomarker testing in cognitively unimpaired individuals and highlighting the need for careful clinical judgment when utilizing advanced diagnostic tools.3

New Criteria for AD Diagnosis and Staging

In 2024, the Alzheimer’s Association Workgroup released updated criteria for AD diagnosis and staging, with a focus on incorporating recent scientific advancements to improve both clinical practice and research-oriented methods. The comprehensive update integrates new biomarkers and emphasizes a biological approach to AD diagnosis and progression monitoring.7

A key update in the revised criteria is the stratification of biomarkers into 3 broad categories: core biomarkers of AD neuropathologic change, nonspecific biomarkers important in AD pathogenesis but also present in other brain diseases, and biomarkers of common non-AD co-pathologies. This classification scheme acknowledges the complexity of AD and its frequent coexistence with other brain pathologies, especially in older adults. The scheme also distinguishes between imaging and fluid biomarkers, recognizing their different roles in diagnosing AD.7

A particularly notable advancement in the updated criteria is the integration of blood-based markers, which have the potential to revolutionize clinical care by simplifying biological diagnoses of AD. The criteria incorporate blood-based markers along with conventional CSF and PET biomarkers, enabling a more comprehensive and accessible diagnostic approach. This integration is expected to facilitate earlier and more accurate detection of AD, particularly in settings where advanced imaging technologies are not readily available.7

The revised criteria also introduce 3 new biomarker categories: inflammatory/immune mechanisms, vascular brain injury, and alpha-synucleinopathy. These additions are consistent with the typical pattern of AD occurrence along with other pathologies, especially in older adults.7 The inclusion of these biomarkers is intended to support a more holistic understanding of the disease and its interactions with other neurodegenerative conditions.

The fundamental principles underlying these criteria emphasize the separation of clinical impairment (syndrome) from biological etiology. AD is defined by its unique neuropathologic findings, and the detection of these changes through biomarkers is considered equivalent to a diagnosis of the disease itself. AD is regarded as a continuum, with pathophysiologic mechanisms that are potentially identifiable long before clinical symptoms appear. Accordingly, asymptomatic individuals with abnormal biomarker results are considered to have AD, rather than merely exhibiting a risk of AD onset.7

Preclinical Stages of AD

Early identification of preclinical AD stages is essential for timely intervention. This phase, characterized by the accumulation of Aβ peptides, can begin 25 to 30 years before clinical symptoms appear. During this phase, affected individuals often show biomarker evidence of Aβ accumulation, supporting a classification of asymptomatic preclinical AD. Biomarkers that aid early detection include reduced levels of Aβ in CSF and increased retention of amyloid tracers in PET scans. After Aβ deposition, additional markers (eg, neuronal injury, glucose hypometabolism on neuroimaging, and brain atrophy) become evident.1

MCI and Its Progression

MCI represents an early stage of AD, and it often progresses to dementia. Within 6 years after diagnosis, rates of progression to dementia can reach 80% to 90%. The diagnosis of MCI involves clinical evaluations, neuropsychological assessments, CSF biomarkers, and neuroimaging techniques. Changes in biomarker levels within body fluids and specific brain regions may signify cognitive alterations even before MCI onset. Proteomic and genetic markers may improve the prediction of individuals at risk for dementia. Recent advancements in biofluid assays and multimodal machine learning algorithms have shown promise in enhancing diagnostic accuracy through the integration of biomarkers from CSF, peripheral blood, and saliva with the results of clinical assessments.8

MCI is characterized by cognitive impairments that do not substantially reduce independence. Prevalence ranges from 6% among individuals aged older than 60 years to 25% among those aged 80 to 84 years. Early diagnosis is crucial for the identification of individuals likely to experience progression to dementia as well as the implementation of timely interventions to delay disease progression.8

Key biomarkers in CSF for diagnosing MCI due to AD
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These biomarkers include Aβ42, t-tau, and p-tau. Aβ42 levels decrease as amyloid plaques accumulate in the brain.

Identifying MCI

The identification of MCI due to AD involves recognizing a spectrum of cognitive decline that ranges from normal aging to early AD.3 The Petersen criteria define MCI as performance 1.5 standard deviations below norms on memory tasks, with preserved activities of daily living. The Winblad criteria broaden this definition to include nonmemory deficits, distinguishing between amnestic MCI and non-amnestic MCI.3 In the amnestic subtype, the most prominent symptom is memory impairment. Conversely, the non-amnestic form typically does not impact memory function but may involve other cognitive deficits, including problems with attention, language, or executive function.4

Patients with amnestic MCI have an increased risk for AD progression, whereas patients with non-amnestic MCI are more likely to develop other forms of dementia, such as frontotemporal dementia (FTD) or dementia with Lewy bodies (DLB). MCI can be further classified based on whether the cognitive impairment affects a single domain or multiple domains. Single-domain MCI impacts only 1 cognitive area, whereas multiple-domain MCI involves several cognitive areas.4 Compared with single-domain MCI, multiple-domain MCI is considered a stronger risk factor for progression to full dementia. Specifically, multi-domain amnestic MCI increases the risk for AD onset and may lead to vascular dementia or AD with vascular complications. Moreover, individuals with non-amnestic MCI have a greater likelihood of experiencing progression to other dementias, including FTD, DLB, or Parkinson disease dementia (PDD).4

Table 1 summarizes the subtypes of MCI and their associated risks.4

Screening for MCI

There is evidence that 40% to 75% of patients with MCI develop AD dementia. The risk increases with Aβ accumulation and neurodegeneration. Lifetime risk estimates show statistically significant sex differences: women and men have respective risks of 41.9% and 33.6% in the presence of Aβ accumulation and neurodegeneration.3

Risk Factors for Progression From MCI to AD Dementia

Several other risk factors influence the progression from MCI to AD dementia. Age is the primary risk factor, and the prevalence of AD dementia significantly increases among older adults. Genetic factors, especially the APOE e4 variant, play crucial roles.2 The APOE e4 allele is the strongest genetic risk factor for late-onset AD, significantly increasing the risks of MCI onset and subsequent progression to AD. The presence of a single APOE e4 allele can increase the risk of amnestic MCI by 6-fold compared with the risk displayed by individuals with the e3/e3 genotype. Additionally, the presence of APOE e4 is associated with worse memory function, such as delayed recall and recognition memory, in patients with MCI.4

Changes in modifiable risk factors (eg, physical activity, smoking cessation, education level, social engagement, blood pressure management, and dietary pattern) can potentially prevent up to 40% of dementia cases. Cardiovascular health is critical; conditions such as hypertension and diabetes increase dementia risk, whereas heart-healthy lifestyles mitigate this risk.2

Table 2 lists the risk factors for progression from MCI to AD dementia.2

Diagnostic Workup for MCI Due to AD

The diagnosis of MCI due to AD requires a comprehensive workup, including clinical evaluations, neuropsychological assessments, CSF biomarkers, and neuroimaging techniques. The goal of the workup is accurate detection of cognitive impairment and identification of its cause, particularly if it is associated with AD.

Screening and Clinical Evaluations

The American Academy of Neurology (AAN) guidelines emphasize the importance of active screening for MCI, rather than assuming that a patient’s memory loss is due to normal aging. Individuals reporting possible memory loss or cognitive decline should undergo further workup using validated screening tools during initial assessments; comprehensive evaluations should be performed for those with positive screening results.9

Clinical Evaluations and Neuropsychological Assessments

Clinical evaluations and neuropsychological assessments are crucial in the diagnosis of MCI. These assessments help to identify cognitive impairments and measure functional decline through tests of episodic memory, such as verbal and visual memory, and associative learning4,10:

  • Montreal Cognitive Assessment (MoCA): The MoCA is highly sensitive in the detection of cognitive decline across various populations. It has demonstrated efficacy in distinguishing normal cognition, MCI, and dementia, with high sensitivity for MCI and dementia (0.95 and 0.96, respectively) but lower specificity for both (0.63 and 0.88, respectively). It also displays good internal consistency and effectiveness in differentiating individuals with MCI from healthy controls.
  • Mini-Mental State Examination (MMSE): Although widely used, the MMSE has limitations regarding sensitivity and specificity, especially in terms of distinguishing MCI from normal cognition. There is variation in its sensitivity (23% to 89%) and specificity (40% to 94%), depending on the dementia subtype.
  • Comparison of MoCA and MMSE: The MoCA has shown superiority over the MMSE in MCI detection, particularly among older adults. The MoCA’s cut-off point of 24/25 exhibited higher sensitivity (80.48%) and specificity (81.19%) compared with the MMSE’s 27/28 cut-off (sensitivity, 66.34%; specificity, 72.94%).

CSF Biomarkers

CSF biomarkers are crucial in the diagnosis of MCI and prediction of progression to AD. These biomarkers can be categorized using the A/T/N system proposed by the NIA-AA. The A/T/N system includes4,8:

  • A (amyloid): Beta-amyloid 42 (Aβ42), which decreases with amyloid plaque accumulation in the brain.
  • T (tau): Total tau (t-tau) and phosphorylated tau (p-tau), which increase due to neuronal damage and tau pathology.
  • N (neurodegeneration): Indicators of neurodegeneration.

Each category can be rated as positive or negative, helping to predict AD progression. For example, individuals with positive results in all 3 categories (A+/T+/N+) are more likely to undergo progression from MCI to AD. A combination of Aβ42 and tau biomarkers in CSF can achieve high sensitivity (95%) and specificity (83%) in terms of identifying individuals with MCI who are likely to develop AD.4

Neuroimaging Techniques

Neuroimaging techniques provide valuable information about brain structure and function in individuals with MCI. These techniques include4:

  • Magnetic resonance imaging (MRI):
    • Volumetric MRI: Useful for structural imaging and brain atrophy detection in specific areas such as the hippocampus and entorhinal cortex, which are often affected first in individuals with MCI or AD.
    • T1-weighted MRI: Reveals the topographic distribution of cortical atrophy.
    • T2-weighted MRI: Provides quantitative analysis of atrophy, although it may be less suitable for differentiating between MCI and AD.
    • Functional MRI (fMRI): Detects disease-specific alterations in brain function, helping to predict AD onset in individuals with MCI.
  • Positron emission tomography (PET):
    • Amyloid PET imaging: Visualizes amyloid plaques.
    • Tau PET imaging: Visualizes tau tangles, which are associated with neuronal death and cerebral atrophy in AD.
  • Single-photon emission computed tomography (SPECT): May be used for additional imaging data in cases of preclinical dementia or MCI.

These neuroimaging techniques are essential for the detection of early brain changes associated with AD, aiding in the early diagnosis and management of MCI.4

Conclusion

Timely diagnosis of AD is crucial for early intervention, optimal outcomes, and continued independence. The identification of MCI due to AD enables interventions that delay progression. Proactive cognitive assessments, especially for adults aged 65 and older, help to identify at-risk individuals who could benefit from early medical intervention. Medicare supports cognitive function assessments during annual wellness visits, and professional guidelines aim to reduce diagnostic delays.

Routine targeted cognitive assessments are essential. Annual evaluations, as recommended by the Alzheimer’s Association, ensure prompt diagnosis and care planning. The detection of MCI due to AD requires diagnostic tools such as clinical evaluations, neuropsychological assessments, and advanced imaging techniques. Efforts to enhance MCI detection are necessary to address the rising prevalence of AD, an important public health problem.

References

1. Liss JL, Seleri Assunção S, Cummings J, et al. Practical recommendations for timely, accurate diagnosis of symptomatic Alzheimer’s disease (MCI and dementia) in primary care: a review and synthesis. J Intern Med. 2021;290(2):310-334. doi:10.1111/joim.13244

2. The Journal of Alzheimer’s Association. 2024 Alzheimer’s disease facts and figures. Alzheimers Dement. 2024;20(5):3708-3821. doi:10.1002/alz.13809

3. Tahami Monfared AA, Byrnes MJ, White LA, Zhang Q. Alzheimer’s disease: epidemiology and clinical progression. Neurol Ther. 2022;11(2):553-569. doi:10.1007/s40120-022-00338-8

4. Giau VV, Bagyinszky E, An SSA. Potential fluid biomarkers for the diagnosis of mild cognitive impairment. Int J Mol Sci. 2019;20(17):4149. doi:10.3390/ijms20174149

5. Wang X, Ye T, Zhou W, Zhang J; Alzheimer’s Disease Neuroimaging Initiative. Uncovering heterogeneous cognitive trajectories in mild cognitive impairment: a data-driven approach. Alzheimers Res Ther. 2023;15(1):57. doi:10.1186/s13195-023-01205-w

6. Suo WZ. GRK5 deficiency causes mild cognitive impairment due to Alzheimer’s disease. J Alzheimers Dis. 2022;85(4):1399-1410. doi:10.3233/JAD-215379

7. Jack CR Jr, Andrews JS, Beach TG, et al. Revised criteria for diagnosis and staging of Alzheimer’s disease: Alzheimer’s Association Workgroup. Alzheimers Dement. 2024. doi:10.1002/alz.13859

8. Blanco K, Salcidua S, Orellana P, et al. Systematic review: fluid biomarkers and machine learning methods to improve the diagnosis from mild cognitive impairment to Alzheimer’s disease. Alzheimers Res Ther. 2023;15(1):176. doi:10.1186/s13195-023-01304-8

9. Tahami Monfared AA, Phan NTN, Pearson I, et al. A systematic review of clinical practice guidelines for Alzheimer’s disease and strategies for future advancements. Neurol Ther. 2023;12(4):1257-1284. doi:10.1007/s40120-023-00504-6

10. Mian M, Tahiri J, Eldin R, Altabaa M, Sehar U, Reddy PH. Overlooked cases of mild cognitive impairment: Implications to early Alzheimer’s disease. Ageing Res Rev. 2024;98:102335. doi:10.1016/j.arr.2024.102335

Posted by Haymarket’s Clinical Content Hub. The editorial staff of Neurology Advisor had no role in this content’s production.

Reviewed July 2024