A systematic review and meta-analysis of metalloproteinases in patients with Alzheimer's disease and mild cognitive impairment

Abstract

Background

Various studies have reported altered expression of metalloproteinases in Alzheimer's disease (AD); however, expression profiles of each metalloproteinase during cognitive decline have not yet been fully characterized.

Objective

The purpose of this systematic review was to generate a comprehensive overview of metalloproteinases and their cognate inhibitors expression in AD and mild cognitive impairment (MCI), across sample matrices, to determine whether metalloproteinases are dysregulated in AD and may have predictive power in individuals with cognitive decline.

Methods

An electronic literature search was conducted in PubMed, EMBASE, Scopus and MEDLINE from inception to December 2024. Sixty-one publications reporting metalloproteinase and inhibitor levels in 8576 patients with AD or MCI, and 7333 controls were included in the systematic review, twenty-one of which were extracted for meta-analysis. Standardized mean difference (SMD) was used to illustrate comparisons, and the Newcastle-Ottawa scale to assess bias.

Results

Higher levels of cerebrospinal fluid (CSF) tissue inhibitor of metalloproteinase-2 (TIMP-2; p = 0.0003) were observed in the AD group and in patients with MCI (p = 0.0009) compared to cognitively healthy controls. Following sensitivity analysis, significantly higher levels of CSF MMP-10 (p = 0.0005) and lower plasma TIMP-2 (p = 0.004) were also noted in patients with AD. TIMP-3, across all sample matrices, was decreased in patients with MCI versus controls (p = 0.01).

Conclusions

Significantly altered levels of metalloproteinases and their inhibitors were verified between patients with AD and MCI, representing potential biomarkers and prospective therapeutic targets for cognitive decline. This study was registered with PROSPERO, CRD42024628202.

Keywords

Alzheimer's disease matrix metalloproteinase mild cognitive impairment systematic review tissue inhibitors of matrix metalloproteinases

Introduction

Alzheimer's disease (AD) is the most common type of neurodegenerative disorder, characterized by irreversible memory loss and cognitive dysfunction. Plaque accumulation of amyloid-β (Aβ) peptides, derived from amyloid-β protein precursor (AβPP), and the presence of neurofibrillary tangles, formed by hyperphosphorylation of tau protein, are the primary pathological hallmarks of AD. Increasing evidence from in vitro studies indicate that metalloproteinases are associated with AD. Some A Disintegrin and Metalloproteinase Domain proteins (ADAMs) are known to degrade AβPP, with ADAM10 identified as the primary α-secretase responsible for degradation of AβPP via the non-amyloidogenic pathway.^1,2 While other metalloproteinases, including matrix metalloproteinase-2 (MMP-2), matrix metalloproteinase-9 (MMP-9), neprilysin and insulin-degrading enzyme may degrade Aβ directly, contributing to amyloid clearance.^3,4 The wide array of proteins within the metalloproteinase family, including the 24 mammalian matrix metalloproteinases (MMPs), their endogenous tissue inhibitors (TIMPs), ADAMs, and A Disintegrin and metalloproteinase with thrombospondin motifs (ADAMTS) reflects their wide variety of physiological functions. For instance, TIMPs, as cognate inhibitors of metalloproteinases can not only reduce enzymatic activity but may also facilitate metalloproteinase activation, highlighting their role in balancing metalloproteinase activity and reducing the potential for uncontrolled enzymatic function.⁵

Metalloproteinases play important roles in remodeling the extracellular matrix, thereby influencing neuroinflammatory pathways and neurodegeneration in AD, through increasing blood-brain barrier (BBB) permeability via degradation of tight junction proteins such as claudin-5 and occludins, permitting subsequent B and T cell entry observed in patients with AD.^6–9 Pro-inflammatory cytokines such as IL-1β and nitric oxide (NO) have been shown to act as stimulators of MMPs^10–15 and reciprocally, MMPs, including MMP-9 and MT4-MMP, have been shown to promote the expression of pro-inflammatory cytokines such as IL-1β and TNFα,^16–19 elevating inflammatory responses. As ADAMs can act as sheddases of TREM2, a receptor responsible for phagocytic functions in microglia,²⁰ they also impact immune cell function in AD, while MMP-3, secreted from neurons, have also been shown to trigger microglial activation and subsequent pro-inflammatory cytokine release.¹⁶ As a result, metalloproteinases are implicated in neuroinflammatory-linked neurotoxicity. MMP-7 can proteolytically shed Fas ligand (FasL), reducing the availability of membrane-bound Fas ligand (FasL) for apoptotic signaling, this contributes to neuronal survival²¹ whereas, TIMP-3 exerts an opposing effect, facilitating neuronal death via stabilization of FasL.²²

Various MMPs and TIMPs, including MMP- 2, −3, −9, −10 and TIMP-4 are shown to be altered in patients with AD.^23,24 There remains a gap in the understanding of the complex landscape regarding the interaction of metalloproteinases with other signaling molecules and family members. Further, while several reviews have reported on the role of metalloproteinases in neurological conditions, these have primarily focused on molecular and in vitro insights.^25–28 Therefore, the aim of this systematic review was to comprehensively compile literature that measured metalloproteinase concentrations in patients diagnosed with AD and mild cognitive impairment (MCI), to help elucidate the role of metalloproteinases in cognitive decline, offering translation of the molecular interactions into clinical application. Additionally, we aim to assess how metalloproteinase and their cognate inhibitor levels differ between cerebrospinal fluid (CSF), plasma, serum, and saliva samples of patients with AD, MCI, and cognitively healthy control participants in order to assess the biomarker potential of metalloproteinases for AD.

Methods

Literature search

This systematic review and meta-analysis was conducted according to the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) 2020 guidelines.29 The protocol was developed and registered with the International Prospective Register of Systematic Reviews (PROSPERO; registration number CRD42024628202).

Electronic databases including PubMed, MEDLINE (OvidSP), EMBASE (OvidSP) and Scopus were systematically searched for publications containing “Alzheimer's disease” AND “metalloproteinases” AND sample matrices keywords: “CSF” or “cerebrospinal fluid” or “serum” or “plasma” or “saliva”, from database inception until 12^th December 2024. The Boolean operator “AND” was used to combine the terms and “OR” was used to broaden the search to include all relevant terms. Filters for English language and human species were applied. Peer-reviewed publications, conference abstracts, and preprints were included in the screening process. Reviews and articles written in a language other than English were excluded. The complete search strategy is listed in Supplemental Table 1. Search results for the databases were collated, and duplicate articles manually removed. Abstracts were identified and screened by two authors (GM^cN & LM^cC), and any disagreements were resolved by a third author (MC).

Selection criteria

Studies were deemed eligible if they met the following criteria: (1) peer-reviewed, full text studies published in English; (2) participants were over the age of 18 years old and met the principles of AD or MCI diagnosis based on either clinical diagnosis or at the authors discretion through the use of a cognitive assessment tool; (3) studies compared metalloproteinase levels measured in any of the following sample matrices: CSF, plasma, serum, saliva, human tissue and/or cells derived from patients with AD or MCI; (4) an appropriate, cognitively healthy control group was included within the study.

Exclusion criteria

Studies were excluded for the following reasons: (1) measured metalloproteinase levels in animals; (2) included controls with a medical history of cognitive dysfunction; (3) did not include controls; (4) did not report on any metalloproteinases; (4) AD or MCI was not their primary diagnosis. Studies were also excluded when no full access was available. In the case multiple publications were reported from the same center, the most comprehensive publication was included. In four cases, the authors were contacted directly to verify this. No other restrictions on sex, race, setting of study, or other conditions were made. The inclusion and exclusion criteria for this study were prespecified.

Data extraction

The following data was manually extracted from each of the included studies: (1) first author; (2) year of publication; (3) study design; (4) sample size for each group; (5) location; (6) age; (7) sex; (8) diagnosis; (9) diagnostic criteria used; (10) MMP analysis method, as detailed in the Characteristics of included studies table (Supplemental Table 2). Where incomplete data was reported, three study authors were contacted directly.

Risk of bias assessment

The Newcastle-Ottawa Scale (NOS) was used for quality assessments of all potentially eligible studies. This scale ranges from 0 to 9 stars with study selection, comparability, and outcome used to appraise the methodological quality of the included studies. Study selection comprises four key topics: (1) adequate case definition with independent clinical evaluation; (2) representativeness of cases; (3) selection of controls and (4) definition of controls, whereby controls are representative of the community and have no history of AD or MCI. Comparability assesses how well cases and controls are matched in terms of age, gender and other confounders while outcome includes ascertainment of exposure, for example via secure record or structured interview. In addition, whether the method used to ascertain cognitive outcome is the same for both cases and controls, and non-response rate, the percentage of individuals who did not respond in the case of follow-up studies, is reported within each study. Quality assessment for each study was performed by two independent reviewers (GMcN & LMcC) and results reported in Supplemental Table 3. Sensitivity analysis was conducted to evaluate the impact of including studies with a NOS score < 6.

Data collection and statistical analysis

Quantified expression levels of metalloproteinases were of interest in our study. Twenty-one studies contained data relating to metalloproteinase concentration, which was manually extracted and tabulated. Metalloproteinase and their inhibitor concentrations were recorded as detailed within each publication, and the data converted to mean and standard deviation using Wan's method.³⁰ Standard deviation was determined using standard error of the mean (SEM), where required.³¹ Mean ± SD values for each metalloproteinase reported were inputted into RevMan (version 5.4.1) for statistical analysis.³² Calculation of mean difference, standardized mean difference with 95% confidence intervals and determination of heterogeneity via Chi-square and inconsistency squared, was performed using RevMan. The pooled standardized mean difference (SMD) between AD and controls for each metalloproteinase, calculated using Hedges g, represented the fixed effect. A random effects model was applied throughout, as between-study variability was expected to be high due to variation in study design, participant demographics, disease severity, and outcome measurement. Three studies contained multiple AD groups, each compared to a single shared control group.^33–35 To account for the resulting dependence between effect sizes arising from these multi-arm, shared control studies, a multivariate random-effects meta-analysis was conducted using the metafor package in R.³⁶ In these models, random effects were specified at the study level to model between-study variability and at the within-study level to account for heterogeneity between AD subgroups within the same study. Correlation between effect sizes arising from shared control groups was incorporated via a sampling variance- covariance matrix. This was constructed as a block-diagonal matrix, with one block per study. For single-arm studies, studies with only one AD group, blocks contained only the sampling variance of the SMD whereas for multi-arm studies, off-diagonal covariance terms were included to account for dependent effect sizes. Covariances were estimated using the method described by Gleser and Olkin³⁶:

c o v (g_{i}, g_{j}) = \frac{s_{c t r l}^{2} / n_{c t r l}}{s_{p o o l e d (i)} \times s_{p o o l e d (j)}}

Similarly, where multiple studies originated from the same research group, multivariate random-effects models incorporating group-level clustering were employed to account for correlation between studies arising from shared methodologies, recruitment strategies or cohort overlap. To assess publication bias, Egger's test (number of studies ≤ 3) was conducted in R using the metafor package. Subgroup analysis was performed to assess each sample matrix individually. p-values of 0.05 or less were considered significant.

Results

A total of 2242 records were retrieved from PubMed (n = 276), EMBASE (n = 721), MEDLINE (n = 904), and Scopus (n = 341). Following de-duplication and initial title and abstract screening, 101 full-text articles were evaluated for eligibility according to the specified inclusion and exclusion criteria. A total of 61 studies were included in the systematic review, and meta-analysis (Figure 1). Participant and study characteristics are listed in Supplemental Table 2 which included 6410 patients diagnosed with AD, 2166 patients with MCI, and 7333 controls. In total ten metalloproteinases were analyzed across all sample types, in patients with AD compared to controls, and patients with MCI versus controls, and are presented alongside effect sizes, heterogeneity analysis and publication bias in Supplemental Tables 5 and 6.

Figure 1.

PRISMA 2020 flow diagram of study inclusion and exclusion. A total of 2242 records were identified. Studies were excluded if they did not meet the selection criteria above. After literature searching, selection and deduplication, 61 studies measuring metalloproteinases were included in the systematic review while 21 studies were included in the meta-analyses.

Comparison of metalloproteinases measured in patients with AD and controls

Metalloproteinase levels measured in multiple sample matrices from patients with AD were consistently lower in CSF than plasma and serum

When meta-analysis was performed with all sample matrices included for each metalloproteinase, none displayed a significant effect size as reported in Supplemental Table 5. This may be due to their differences in biological origin and composition, juxtaposing effect sizes. Two studies, Duits et al., 2015²⁴ and Delaby et al., 2015,³⁷ reported metalloproteinase levels in multiple sample types for the same participants which yielded an opportunity to evaluate how metalloproteinase levels vary between sample types. A significant difference was observed between MMP-2, MMP-9, MMP-10 and TIMP-1 measured in both plasma and CSF of patients with AD, with consistently higher MMP levels in plasma than CSF, while the cognitively healthy control group exhibited no significant difference in CSF and plasma MMP-9 levels. TIMP-1 levels measured in serum and CSF were also significantly different (p < 0.00001), irrespective of clinical group, as shown in Figure 2 below, with higher levels of TIMP-1 in serum than CSF. See Supplemental Tables 12 and 13 for a summary of these analyses.

Figure 2.

Forest plot highlighting the significant difference in TIMP-1 levels (pg/ml) measured in CSF and serum. (a) serum and CSF TIMP-1 levels measured in patients with AD (b) serum and CSF TIMP-1 levels measured in the control group. Green squares indicate the effect size; the standardized mean difference (SMD) and error bars indicate 95% confidence intervals. Total refers to the total number of participants within each group per study (Color figure available online).

Accordingly, subgroup analysis was performed to investigate metalloproteinase levels measured in each respective sample matrix, yielding more insightful outcomes.

Subgroup analysis

Patients with AD have higher CSF TIMP-2 compared to healthy controls

Quantified (pg/ml) metalloproteinase levels were stratified by sample matrix. Significant differences in TIMP-2 measured in CSF were noted between AD and controls as highlighted in Figure 3 above, as were plasma TIMP-3 and TIMP-4 levels.

Figure 3.

Forest plot of significant CSF TIMP-2 (pg/ml) outcomes following subgroup analysis in AD versus controls. Green squares indicate the effect size; the standardized mean difference (SMD) and error bars indicate 95% confidence intervals. Total refers to the total number of participants within each group per study (Color figure available online).

Plasma TIMP-4 levels increase with disease severity in patients with AD

Plasma TIMP-3 (p = 0.01) and TIMP-4 (p < 0.0001) levels also differed significantly between the patients with AD and controls, however as only one study reported plasma TIMP-3 concentration in patients with AD this finding is not robust. Plasma TIMP-4 was also measured in one study, however Qin et al., 2015³⁴ reported metalloproteinase levels in three AD cohorts: mild AD, moderate AD and severe AD, observing significantly higher TIMP-4 in each AD group compared to controls (p < 0.00001), as shown in Figures 4(a)-4(c) below. Analysis was also performed to compare alterations in metalloproteinase levels of different AD groups reported within the same study, to investigate the relationship between metalloproteinases and disease progression. Results are reported in Supplemental Table 16.

Figure 4.

Forest plots highlighting the significant difference in TIMP-4 levels (pg/ml) between AD groups. (a) patients with mild AD versus controls, (b) patients with moderate AD versus controls, (c) patients with severe AD versus controls, (d) patients with moderate AD compared to severe AD and (e) patients with moderate AD compared to mild AD. Green squares indicate the effect size, the standardized mean difference (SMD) and error bars indicate 95% confidence intervals. Total refers to the total number of participants within each group per study (Color figure available online).

Plasma TIMP-4 levels vary significantly across different clinical stages of AD (Figure 4(d), (e)), with lower TIMP-4 during earlier disease stages than severe, although a significant effect was observed between all groups and controls. In contrast, comparison of serum MMP-9 levels measured in patients with moderate AD and severe AD by Yousif et al., 2024 found no significant effect (p = 0.14) between groups. Similarly, we compared MMP-2, −7 and −9 levels reported for clusters VI and VII, patients with neurodegeneration and severe AD, respectively, reported by Mlekusch et al., 2009 and found no significant differences, as exhibited in Supplemental Table 16.

Comparison of metalloproteinases measured in patients with MCI and controls

Patients with MCI have lower levels of TIMP-3 and higher TIMP-4, across all sample matrices, compared to healthy controls

Meta-analysis was also performed in studies measuring metalloproteinases across serum, CSF, plasma and saliva samples in patients with MCI and cognitively healthy controls, as highlighted in Figure 5. A significant effect size was observed for both TIMP-3 and TIMP-4. Figure 5(a) suggests significant reduction in TIMP-3 levels in patients with MCI compared to healthy controls, with no heterogeneity observed between these studies suggesting consistent findings, despite the fact TIMP-3 levels were measured in plasma³⁸ and CSF.²³ In contrast, TIMP-4 levels were significantly higher in patients with MCI compared to healthy controls. As both CSF and plasma levels were reported, we performed subgroup analysis on each sample matrix to assess this outcome, as before.

Figure 5.

Forest plots highlighting the significant metalloproteinases in patients with MCI compared to controls, across all sample matrices (a) significant effect size observed with TIMP-3 between MCI and controls (b) significant effect size observed with TIMP-4 between MCI and controls. Green squares indicate the effect size; the standardized mean difference (SMD) and error bars indicate 95% confidence intervals. Total refers to the total number of participants within each group per study (Color figure available online).

Patients with MCI had significantly higher levels of CSF TIMP-2 compared with cognitively healthy controls

Subgroup analyses proved challenging in many cases, as the number of studies reporting MMP levels in MCI patients were limited, and in all cases except TIMP-1, TIMP-2, logMMP-10, logMMP3 and MMP-2, the number of studies for each sample type were limited to one so a valid comparison could not be derived. Notably, a significant effect size was observed when comparing CSF TIMP-2 levels between MCI and controls, as observed in Figure 6. Figure 7 highlights the metalloproteinases and their cognate inhibitors that were significantly altered between MCI and controls. For any metalloproteinases other than CSF TIMP-2, where subgroup analysis was possible, the results were not significant, see Supplemental Tables 10 and 11.

Figure 6.

Forest plot highlighting the significant increase in CSF TIMP-2 in patients with MCI compared to controls. Green squares indicate the effect size; the standardized mean difference (SMD) and error bars indicate 95% confidence intervals (Color figure available online).

Figure 7.

Significant comparative outcomes of metalloproteinases in MCI versus controls stratified by sample matrix. Metalloproteinases with significant effect sizes (SMD) were displayed. Blue bars indicate the number of studies included for each protein. Black dots represent the standardized mean difference (SMD) between AD and controls, error bars indicate 95% confidence intervals (Color figure available online).

Publication biases

After conducting Egger's test, no significant publication bias was observed for any metalloproteinases measured in AD or MCI patients, nor within subgroup analysis highlighting the comprehensive and unbiased literature that has been generated in investigating metalloproteinase levels during cognitive decline. Results are reported in Supplemental Tables 5–11.

Sensitivity analysis

Risk of bias assessment was conducted and results reported in Figure 8 and Supplemental Table 3. Twenty-three studies included in the systematic review scored less than six stars, suggesting a high risk of bias for these studies, eight of which were studies included in the meta-analysis.

Figure 8.

A bar graph summarizing the outcomes of each measure within the Newcastle-Ottawa scale was used to assess the risk of bias. Risk of bias assessment was performed for each measure and presented as percentages across all sixty-one included studies. Green corresponds to the percentage (%) of studies assessed with low risk of bias for each measure, yellow to unclear risk of bias and red to high risk of bias. Studies where no information was provided to assess that outcome were reported as unclear risk of bias (Color figure available online).

Sensitivity analysis was performed to assess the impact of these studies. Raw data outcomes following sensitivity analysis are detailed in Supplemental Tables 17 and 18. In addition, Figure 9 shows that a significant effect size was observed in MMP-10 levels measured across all matrices (p = 0.009) and CSF MMP-10 alone (p = 0.0005), with higher levels of MMP-10 in patients with AD compared to controls which was not evident previously. Likewise, a significant decrease in plasma TIMP-2 levels (p = 0.004) in patients with AD compared to controls was observed following sensitivity analysis, with one study removed due to poor selection of controls, while CSF TIMP-2 remained significantly increased in AD patients compared to controls (p = 0.001). The overall significant outcomes, of metalloproteinases with significantly different levels between patients with AD and controls are represented in Figure 10 following sensitivity analysis. As only one study reported MMP-7 levels, sensitivity analysis prevented MMP-7 meta-analysis while MMP-2 and MMP-3 remained non-significant in both clinical groups. Plasma TIMP-3 and TIMP-4 meta-analyses remained unchanged. Sensitivity analysis in MCI versus controls highlighted that Aksnes et al., 2023 study alone exhibited a significant increase of CSF TIMP-2 in patients with MCI compared to controls (p = 0.008) although a significant effect for TIMP-3 in MCI is no longer noted.

Figure 9.

Forest plots highlighting the significant metalloproteinases in patients with AD compared to controls following sensitivity analysis (a) significant increase of CSF MMP-10 in AD versus controls (b) significantly decrease of plasma TIMP-2 in patients with AD compared to controls. Green squares indicate the effect size; the standardized mean difference (SMD) and error bars indicate 95% confidence intervals. Total refers to the total number of participants within each group per study (Color figure available online).

Figure 10.

Significant comparative outcomes of metalloproteinases in AD versus controls stratified by sample matrix following sensitivity analysis. Metalloproteinases with significant effect sizes (SMD) were displayed. Blue bars indicate the number of studies included for each protein. Black dots represent the standardized mean difference (SMD) between AD and controls, error bars indicate 95% confidence intervals (Color figure available online).

Studies with high risk of bias were typically due to selection of hospitalized controls and insufficient reporting of non-response rate. While the selection of community-based controls enhances the representativeness of cases, this is recognized as a limitation in many cross-sectional studies due to difficulty in recruitment and lack of availability to medical records within the community-based controls. In all instances where the non-response rate displayed unclear risk of bias, the studies lacked follow-up periods, rendering this metric inapplicable. Therefore, this does not reflect a methodological shortcoming, but rather the nature of the study design. A number of studies also reported metalloproteinase levels in other disease groups such as subcortical ischemic vascular dementia, frontotemporal dementia, dementia with Lewy bodies, and vascular dementia, with five of these studies included in the meta-analysis. We performed sensitivity analysis, confirming this had no impact on outcomes.

Discussion

Many papers have reported on the involvement of metalloproteinases in AD and while a few reviews have outlined the literature surrounding the molecular mechanisms metalloproteinases may influence, these have predominantly assessed their molecular interactions and in vitro findings.^25–28^,39 To our knowledge, no systematic overview of metalloproteinase expression in patients with AD and MCI has been performed. We hope that by summarizing all current quantitative data measuring metalloproteinases in patients diagnosed with AD and MCI, this may offer a clearer understanding of how mechanistic insights translate into a clinical environment, helping to elucidate the molecular interactions at play in patients with cognitive decline and determine the potential for metalloproteinases as future biomarkers, with the ultimate goal of improving diagnostic accuracy, patient stratification and treatment options for patients with AD.

This meta-analysis demonstrated multiple significant differences in metalloproteinases between patients with AD or MCI, and cognitively healthy controls. Firstly, our meta-analysis found CSF TIMP-2 levels were significantly higher in patients with AD and MCI. TIMP-2 is thought to primarily act as a regulator of MMP-2 activity. As enzymes responsible for amyloid-β degradation, the gelatinases MMP-2 and MMP-9 have been closely linked to AD pathology, and neuroinflammation in particular, since constant Aβ phagocytosis results in chronic microglial activation.⁴⁰ Both MMP-2 and MMP-9 are latent enzymes, requiring activation by other metalloproteinases. At low concentrations, TIMP-2 binds pro-MMP-2 and MT1-MMP (MMP-14) facilitating its activation but at high concentrations TIMP-2 inhibits pro-MMP-2 cleavage,⁴¹while MMP-3 can cleave MMP-9, highlighting the complex molecular interactions between MMPs. Alongside the significantly increased levels of CSF TIMP-2 in AD and MCI patients, we found plasma TIMP-2 levels were decreased in AD following sensitivity analysis. Lower plasma TIMP-2 in AD may reflect peripheral homeostasis, whereas increased TIMP-2 within the CNS could represent a compensatory inhibitory response to neuroinflammation and BBB disruption. As TIMP-2 also activates pro-MMP2, studies measuring MMP-2 and TIMP-2 levels via zymography offer insight into overall enzymatic activity resulting from the balance of MMP inhibition and activation. Eight studies^42,43–49 included within this review used zymography to measure MMP-2, of which only three also measured TIMP-2 levels in these patients, making it difficult to glean an overall picture of enzymatic activity. Even so, six of these studies reported no significant difference in enzymatic activity of MMP-2 between AD and control groups while Lim et al., 2011⁴³ and Horstmann et al., 2010⁴⁶ found a significant decrease in plasma and CSF MMP-2 activity, respectively, in patients with AD compared to controls, corroborating quantitative findings of reduced CSF MMP-2 levels in patients with AD and MCI.^24,42,50 Given that MMP-2 is known to degrade Aβ, reports of reduced MMP-2 and increased TIMP-2 levels in patients with AD suggest these metalloproteinases may contribute to amyloid pathology.

The inhibitory effect of TIMPs on CSF metalloproteinase levels within this review may account for the lack of significance in MMP-9 levels across biological matrices in patients with AD and MCI. Although multiple included studies^51–52 report significantly more peripheral MMP-9, measured in serum, plasma and saliva samples in patients with AD compared to controls, these findings were not corroborated within the meta-analysis. Notably, Duits et al., 2015²⁴ found CSF MMP-9 levels were lower than plasma MMP-9 from the same patients with AD, similar to findings in patients with MCI.^55–57 This may suggest heighted peripheral inflammation, a restriction of MMP-9 influx across the BBB, or more efficient clearance mechanisms within the CNS. Notably, five other studies included within the review that had measured MMP-9 using semi-quantitative methods found no significant difference in CSF or plasma MMP-9 levels for either patient group compared to controls, in agreement with our meta-analysis.^{35,42,43,49,58} The lack of significance observed for plasma MMP-2 is bolstered by results from Abe et al., 2020, Hanzel et al., 2014, Lorenzl et al., 2008, Martin-Aragon et al., 2009 and Horstmann et al., 2010^{42,43,46,49,62}; however, conflicting outcomes are also noted.^{43,46,50,58,63} Despite the fact MMP-2 is constitutively expressed, whereas MMP-9 requires upregulation, MMP-9 levels are expected to be higher than that of MMP-2 in AD patients,⁵⁹ confirmed by Lorenzl et al., 2008, Gu et al., 2020 and Whelan et al., 2019.^44,60–61 Given that MMP-9 is also implicated with cancer⁵³ and cardiovascular diseases,⁵⁴ co-morbid conditions in the included cohorts may be masking disease-specific effects within our meta-analysis.

The significant decrease of TIMP-3, a promoter of apoptosis^22,64 in patients with MCI, suggests a dysregulation of apoptotic pathways even at early stages of cognitive decline. However, because TIMP-3 inhibits a broad range of MMPs including MMP-1, −2, −3, −7, −9 in addition to the α-secretases ADAM10 and ADAM17,⁶⁵ its diverse biological functions make it challenging to establish a direct causative link to apoptosis alone. Park et al., 2022,³⁸ one of the studies included in the meta-analysis also acknowledge that their findings dispute previous findings in the brains of AD patients and APP transgenic mice⁶⁶ and propose that TIMP-3 may have aggregated alongside Aβ, preventing accurate detection, while contributing to AD pathology via its inhibitory effects on ADAM10 and ADAM17. Our meta-analysis found TIMP-4 was significantly increased in patients with AD and MCI compared to controls, confirming the outcomes reported by Qin et al., 2015, although as the functions of TIMP-4 remain unclear, deriving conclusions with regard to its role in cognitive decline is challenging regardless.³⁹

As there are multi-faceted associations between MMPs, with MMP-3 capable of cleaving MMP-9 while TIMP-1 is a known inhibitor, this complicates interpretation of the observed outcomes. As a result, MMP-9 levels may often be reported as an MMP-9/TIMP-1 ratio, endeavoring to summarize overall effects.^58,67–68 However, as TIMP-1 may also inhibit ADAM10,⁶⁹ it is debatable as to the suitability of this approach. Notably, Stomrud et al., 2010 reported MMP-1/TIMP-1, MMP-3/TIMP-1, and MMP-9/TIMP-1 to better determine the equilibrium across multiple different metalloproteinases. This group found that AD patients had higher MMP-9/TIMP-1 ratios and lower TIMP-1 levels compared to cognitively healthy individuals, in keeping with studies in patients with Multiple Sclerosis.⁷⁰ Our meta-analysis failed to extract any significant effects of MMP-3 in AD and MCI across all matrices, although this may be due to the limited number of studies available for inclusion. Craig-Shapiro et al., 2011⁷¹ reported log MMP-3 values, indicating skewness in the distribution of MMP-3 within this cohort, therefore other outcomes measuring MMP-3 were log-transformed for comparison.⁷² Yet other publications included within the review report an increased expression of MMP-3 in AD or MCI versus controls in CSF,^{42,55,73–74} plasma^46,75–77 and serum,³⁵ with CSF MMP-3 and −9 exhibiting opposing correlations with cognitive features.⁷⁸ Sex differences in MMP-3 and MMP-10, primarily, have been reported^23,77,79 which may aid justification of the observed increase in significance in CSF MMP-10 levels in AD compared to controls following sensitivity analysis on studies with a NOS score less than six. One study was removed during sensitivity analysis on MMP-10 levels in AD as minimal demographic information was provided.³³ As a result, their lack of adjustment for gender may have masked the significant outcomes for MMP-10 that are also authenticated through other publications included within this review.^{24,60,80–82} Kamalian et al., 2023 and Syed et al., 2020 also report significant differential expression of MMP-10 between MCI and controls; however, this effect is not observed within the meta-analysis.^83–84 Furthermore, MMP-10 has been previously identified as a prognostic biomarker of AD⁸⁵ yet further longitudinal studies measuring metalloproteinases are required for verification, with a comprehensive longitudinal study in middle-aged adults over a 25-year period identifying CSF MMP-12, rather than MMP-10 as a marker of dementia.⁸⁶

As only one study reported quantified (pg/ml) levels of MMP-7 and MMP-12, we could not adequately perform meta-analysis of these proteins. MMP-7, as with all MMPs, has a wide variety of physiological functions such as wound healing,⁸⁷ bone remodeling,⁸⁸ angiogenesis,⁸⁹ tumor progression⁹⁰ and inflammation.²⁸ It can also shed FasL, reducing its effectiveness to mediate apoptosis.⁹¹ The lack of studies reporting quantitative levels of MMP-7 within this meta-analysis may be due to the fact MMP-7 is largely membrane-bound to surface proteoglycans, which may limit its detection in bodily fluids. Meanwhile, MMP-12 has been implicated in neuroinflammation via activation of pro-TNF in macrophages upon its own upregulation⁹² and via cleavage of PGRN, offering a potential mechanism for neuronal destruction.^93–94 Although MMP-1 was measured in two studies as CSF and serum levels were reported, subgroup analysis was restricted. As an inducible collagenase, MMP-1 has been implicated in arthritis⁹⁵ and breast cancer,⁹⁶ with reports of significant increases in MMP-1 expression following Aβ treatment in vitro,⁹⁷ exhibiting neuronal toxicity in culture.^98,99 Endogenous concentrations of MMP-1 and MMP-12 appear to be very low, with Mlekusch et al., 2009 reporting undetectable values for CSF MMP-1 while Aksnes et al., 2023 obtained an average MMP-12 concentration of 2.7 pg/ml for patients with AD.^23,33 Likewise, MMP-15 is reported minimally but appears to deviate significantly between AD and controls.¹⁰⁰ Although genetic mutations in ADAM10 and ADAM17 have been implicated in an increased risk of developing AD,^101–102 few studies have reported their concentrations in AD. As enzymes of the non-amyloidogenic pathway, quantification of AβPP products, sAβPPα and sAβPPβ has been utilized to measure activity levels.^35,103–104 While measurement of ADAM10 has yielded conflicting results, lack of protein quantification alongside qPCR prevents accurate conclusions.¹⁰⁵ Rather, measurement of mRNA and protein levels of neprilysin, an Aβ degrading metalloproteinase, consistently reported decreased activity in AD compared with controls.¹⁰⁶

To determine the diagnostic accuracy of metalloproteinases when combined with clinical data, many studies included within this review performed linear regression and receiver operating characteristic (ROC) curve analysis. While fluid biomarkers of AD such as Aβ₄₀, Aβ₄₂, Aβ₄₂/Aβ₄₀ ratio, total tau and phospho-tau-181 have been well established,^107–108 efforts to improve upon the prediction accuracy further still extend to investigation of metalloproteinases. Metalloproteinases such as MMP-10,^{60,62,71,73,78,81} MMP-2¹⁰⁹ MMP-9,^{51,57,67,109,110} MMP-3,^42,76 TIMP-3,³⁸ TIMP-4,³⁴ and ADAM10¹⁰⁵ showed significant correlation with these biomarkers and cognitive function, typically computed using Mini-Mental State Examination score.¹¹¹ In addition, TIMP-1 was found to positively correlate with hippocampal volume^62,68 which endorses reports of TIMP-1 involvement in learning and memory.¹¹² However, various included publications reported no correlation between metalloproteinase levels and Aβ or tau concentrations^24,62,68 nor with cognitive function.^{44,46,58,61,113} Intriguingly, a lack of significant correlation between cognitive decline and metalloproteinase levels was exclusive to plasma samples. Contradictory findings regarding metalloproteinase correlation with other AD biomarkers may be due to variation in methodology and sample matrix, with AD biomarkers typically measured in CSF samples using well-validated ELISA kits. As Delaby et al., 2015 and Duits et al., 2015 have shown, metalloproteinase levels rarely correlate between different sample matrices, therefore, it is difficult to infer diagnostic capabilities by comparing studies measuring metalloproteinases in differing sample types. Furthermore, as evidenced in a number of the included studies, CSF AD biomarkers are commonly measured by ELISA while metalloproteinases quantification uses a range of different assays.^24,68 Consequently, ROC curve analysis yielded a range of area under the curve (AUC) scores for individual metalloproteinases^{37,51,61,68,71,76,81} with the highest for MMP-9 (0.985)⁵⁷ and lowest for MMP-3 (0.678).⁷⁴ Even so, when combined with traditional AD biomarkers such as tau and Aβ₄₂, many studies observed the metalloproteinases increased biomarker prediction accuracy,^37,51,71,114 highlighting their biomarker potential in cognitive decline.

Through conducting this systematic review of the literature and assessing bias and heterogeneity, we have amalgamated all research investigating metalloproteinases in human participants, generating unique and comprehensive conclusions. However, limitations to our meta-analysis should be addressed. Included studies had large variation in sample size, study design and inconsistent definitions used for cases and controls. This may be attributed to the various cognitive assessment tools used such as Montreal Cognitive Assessment, Mini-Mental State Examination, and Clinical Dementia Rating—Sum of Boxes, and the National Institute of Neurological and Communicative Disorders and Stroke and the Alzheimer's Disease and Related Disorders Association, National Institute on Aging and the Alzheimer's Association, and the Petersen criteria, as denoted for each study in Supplemental Table 2. Future studies should endeavor to be longitudinal in design, rather than cross-sectional, to better inform decisions regarding prediction accuracy over the lengthy time period associated with AD progression, while better definitions for each stage of the AD continuum would be beneficial to better deduce the role of metalloproteinases in disease progression, thus aiding optimization of patient stratification. Although some studies have attempted to overcome stratification limitations, for example, through classification based on Aβ and tau status,^{23,33,60,63,100} or by the presence of autosomal dominant mutations such as APP, PSEN1, and PSEN2,^51,83 without consistent nomenclature across all studies we cannot stratify in this manner for meta-analyses. Although study number prevented the inclusion of MMP-12 and salivary biomarkers in our meta-analysis, many techniques currently being developed, such as Luminex xMAP, Meso Scale Discovery, Simoa and Olink platforms are facilitating multiplex biomarker detection in low sample volumes and helping to overcome detectability issues associated with traditional assays. As a large number of publications (41%) included with this review utilized these assays, this offers greater insight into the role of this large family of proteins in AD and MCI. Future work should endeavor to measure both metalloproteinase protein levels using these technologies alongside zymography techniques to assess enzymatic activity yielding a more complete picture of the relationship between MMPs and TIMPs in cognitive decline.

Conclusions

To summarize, this systematic review and meta-analysis demonstrated that a range of metalloproteinases are associated with AD and MCI. From the analysis, MMP-10, TIMP-2, TIMP-3, and TIMP-4 were found to be differentially expressed in AD and MCI. This review has provided evidence for alterations in metalloproteinase levels during cognitive decline, however, is limited by the small number of studies that have reported quantitative data. Nonetheless, this has offered an opportunity to retrieve multiple sample types for direct comparisons of the diagnostic potential of metalloproteinases in AD and MCI while contributing to the delineation of their potential roles in disease monitoring and cognitive decline.

Supplemental Material

sj-docx-1-alz-10.1177_13872877261456729 - Supplemental material for A systematic review and meta-analysis of metalloproteinases in patients with Alzheimer's disease and mild cognitive impairment

Supplemental material, sj-docx-1-alz-10.1177_13872877261456729 for A systematic review and meta-analysis of metalloproteinases in patients with Alzheimer's disease and mild cognitive impairment by Gillian McNaugher, Leah McColgan, Mia Church, Paula L. McClean, David S. Gibson, Elaine K. Murray, Sophie Coyle and Victoria McGilligan in Journal of Alzheimer's Disease

Footnotes

Acknowledgements

This study was funded by a DfE CAST Award in collaboration with Biosynth who sponsored this work.

For the purpose of open access, the author(s) has applied a Creative Commons Attribution (CC BY) licence to any Author Accepted Manuscript version arising from this submission.

ORCID iDs

Gillian McNaugher

Leah McColgan

Mia Church

Paula L. McClean

David S. Gibson

Elaine K. Murray

Sophie Coyle

Victoria McGilligan

Ethical considerations

Not applicable

Consent to participate

Not applicable

Consent for publication

Not applicable

Author contribution(s)

Gillian McNaugher: Conceptualization; Data curation; Formal analysis; Investigation; Methodology; Software; Writing – original draft.

Leah McColgan: Methodology; Writing – review & editing.

Mia Church: Methodology; Writing – review & editing.

Paula L. McClean: Conceptualization; Supervision; Writing – review & editing.

David S. Gibson: Conceptualization; Writing – review & editing.

Elaine K. Murray: Conceptualization; Writing – review & editing.

Sophie Coyle: Conceptualization; Writing – review & editing.

Victoria McGilligan: Conceptualization; Funding acquisition; Project administration; Supervision; Writing – review & editing.

Funding

The authors disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This study was funded by a DfE CAST Award in collaboration with Biosynth who sponsored this work.

Declaration of conflicting interests

The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Data availability statement

No datasets were generated or analyzed during the current study.

Supplemental material

Supplemental material for this article is available online.

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