Enhancing Alzheimer’s Disease Diagnosis and Care by Focusing on Plasma Biomarkers for Identifying Mild Cognitive Impairment

The term “mild cognitive impairment” (MCI) describes a condition in which individuals demonstrate cognitive impairment with minimal impairment of instrumental activities of daily living (IADL). 1 MCI can be further subdivided into amnestic MCI (aMCI) where there is a memory impairment and non-amnestic MCI (naMCI) which is characterized by a subtle decline in functions not related to memory. 1 In 2018, the NIA-AA Research framework, introduced the concept of MCI due to Alzheimer’s disease (AD) or prodromal AD in the AD continuum between the pre-clinical and the clinical phase of AD. 2 This phase has gained increasing attention for early AD diagnosis as it provides a window for more effective treatment, making the identification of accurate biomarkers for MCI a crucial and highly researched area.3,4, 3,4 As a matter of fact, two passive amyloid-β (Aβ) immunotherapies (lecanemab and donanemab) are currently approved by the FDA for treatment initiation in early AD comprising patients with MCI. On the other hand, a large number of active clinical trials for AD enrolled or are currently enrolling participants with MCI. 5

To date, cerebrospinal fluid (CSF) has been the gold standard biofluid for AD and MCI biomarkers; however, due to their less invasive nature and very good diagnostic accuracy, blood-based biomarkers (BBB) for these conditions have sparked increasing interest.4,6, 4,6 In point of fact, the recent 2024 NIA-AA revised criteria recommend the use of specific plasma forms of phosphorylated tau, that provide highly accurate tests and are being used to support AD diagnosis. 7 We hence defend the importance of BBB for AD early diagnosis (e.g., MCI due to AD) and its use not only on clinical trials but also in clinical routine.

In their recent publication in the Journal of Alzheimer’s Disease, 8 Tao Wang and the team delve into the topic by investigating the potential of plasma long noncoding RNAs (lncRNAs) as potential biomarkers for diagnosing aMCI. The authors kicked off their investigation by exploring significant differences in lncRNAs expression between aMCI individuals (n = 5) and normal controls (NCs, n = 5) using microarray analysis (discovery group). Subsequently, these findings were confirmed in two analytical cohorts and ultimately validated in a larger cohort (Fig. 1). In both analytical and validation groups, real-time quantitative reverse-transcription polymerase chain reaction (RT-qPCR) was used to identify differentially expressed lncRNAs between groups. This technique offers a more feasible methodology for implementation in less specialized clinical settings with fewer resources. Indeed, RT-PCR has been commonly employed for confirming diagnoses during the COVID SARS-CoV-2 pandemic. 9 Concerning the study design, we would also like to emphasize the independent diagnosis of study participants by two psychiatrists and the use of a single-blind approach for statistical analysis to mitigate interpretation bias.

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Fig. 1

Flowchart demonstrating the selection of lncRNAs based on their capacity to distinguish aMCI from normal controls in various study groups. aMCI, amnestic mild cognitive impairment; AD, Alzheimer’s disease; NC, normal control.

In the discovery group, the authors identified 67 upregulated and 220 downregulated lncRNA transcripts. Based on their high fold change and p-values the authors selected 12 lncRNAs to be further studied in the analytical and validation groups. In the analytic groups 1 and 2, five out of the 12 lncRNAs were identified as statistically significant whereas in the validation group, only four exhibited significant differences in expression between the aMCI and NC cohorts (Fig. 1). These 4 last candidates (T324988, NR_024049, ENST00000567919, and ENST00000549762) were then used to construct a predictive model with a sensitivity of 92% and specificity of 84%. Additionally, the authors demonstrated that plasma expression levels of these 4 transcripts correlated significantly with MoCA scores.

The results presented in this paper highlight the ability of these lncRNAs to differentiate between aMCI and NCs. Despite the encouraging results, there are still many steps that need to be taken before we can confidently discuss effective biomarkers. As an illustration, the authors confirmed the presence of positive Aβ deposition only in the discovery group and only among the five aMCI patients in that group. For instance, those in this group who do not have cognitive impairment (NCs) and have not been screened for Aβ buildup may have amyloid deposition and be in the preclinical phase of AD. Additionally, we argue that only five Aβ positive aMCI cases in the discovery group may not be a true representation of the aMCI subjects in the subsequent study groups lacking Aβ validation. This has the potential to cause biases in the presented findings since we do not know the underlying pathology of these individuals.

It is then crucial to emphasize that MCI may stem from causes beyond AD and that it is recognized that only a portion of MCI patients progress to AD while others remain stable or even improve to normal cognitive function.1,10, 1,10 Indeed, a meta-analysis comprising 41 studies demonstrated that approximately only 35% of MCI patients progress to AD within a 3-year follow-up with an annual conversion rate of 5% – 10%. 11 Thus, in the pursuit of identifying AD patients in the early phase, it is also crucial to explore biomarkers and risk factors that can predict the conversion of MCI patients to AD. In a previous work of our group, we demonstrated that the APOE ɛ4-TOMM40^′ 523 L haplotype is associated with a higher risk and shorter times of conversion from MCI to AD along with CSF biomarkers compatible with AD. 12 Other studies conducted by our group have also examined the validity of different biomarkers in predicting the progression from MCI to AD in both CSF and plasma.13,14, 13,14 Exploring whether these 4 lncRNAs can predict the conversion of MCI patients to AD will be an interesting endeavor. Moreover, in addition to the requirement for validation in larger populations, the analysis of these lncRNAs should also involve other cohorts like non-amnestic but Alzheimerian MCIs.

In a different light, the data put forth in this paper also substantiates the importance of lncRNAs in AD pathology. Over the past decades, lncRNAs have been associated with DNA replication, transcription, epigenetic processes, and mRNA control. Concerning AD, lncRNAs have been shown to impact its pathophysiology through different molecular and cellular mechanisms, such as Aβ aggregation, neuronal cell death, and oxidative stress. 15 Thus, exploring the molecular and cellular mechanisms of the lncRNAs spotlighted in this work can provide not only further insights into the molecular and cellular mechanisms of AD but also the identification of therapeutic targets, as reviewed by Liu et al. 15

In conclusion, Tao Wang and his colleagues demonstrated that particular plasma lncRNAs can differentiate between MCI patients and controls with a minimally invasive and cost-efficient method. Despite the need for more robust studies to validate these lncRNAs as biomarkers of MCI, the paper by Wang et al. also brings attention to critical facets for advancing AD diagnosis and care, including the necessity of integrating BBBs as cost-effective biomarkers not only in clinical trials but also in routine clinical care.

AUTHOR CONTRIBUTIONS

Remy Cardoso (Conceptualization; Data curation; Writing – original draft; Writing – review & editing); Charlotte E. Teunissen (Data curation; Writing – review & editing); Catarina Resende Oliveira (Formal analysis; Writing – review & editing).