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. 2026 Jun 1;12(2):e70270. doi: 10.1002/trc2.70270

Peripheral immune markers in dementia: A systematic review and meta‐analysis of risk, severity, and pathological correlates

Ahmad A Toubasi 1,✉, Thuraya N Al‐Sayegh 2
PMCID: PMC13239776  PMID: 42253438

Abstract

INTRODUCTION

Systemic inflammation and innate immune activation have been implicated in dementia; however, prior studies were limited by small sample sizes. We conducted this study to evaluate the association between peripheral immune markers and the future risk of mild cognitive impairment (MCI) and dementia, their ability to differentiate between dementia causes, and their correlation with disease severity and pathological biomarkers.

METHODS

On September 8, 2025, we searched PubMed, Embase, Scopus, and the Cochrane Central Register of Controlled Trials (CENTRAL). Eligible articles included cohort and case–control studies assessing the association between neutrophil‐to‐lymphocyte ratio (NLR), platelet‐to‐lymphocyte ratio (PLR), or systemic inflammatory index (SII) and the risk of developing MCI or dementia, as well as their association with disease severity and pathological biomarkers. Outcomes were the risk of MCI and dementia (Alzheimer's disease, vascular dementia, and Parkinson's disease dementia), dementia severity assessed by the Mini‐Mental State Examination, and cerebrospinal fluid or pathological markers including amyloid beta (Aβ)42, Aβ40, and tau. Hazard ratios (HRs) with 95% confidence intervals (CIs) were pooled as effect estimates.

RESULTS

Our analysis included 947,020 participants from 23 studies. NLR and SII, but not PLR, were associated with increased risk of dementia (NLR: HR = 1.24, 95% CI: 1.15−1.34; SII: HR = 1.08, 95% CI: 1.03−1.13) and MCI (NLR: HR = 2.78, 95% CI: 1.57–4.90; SII: HR = 1.28, 95% CI: 1.04–1.56). No differential associations were observed across dementia causes. NLR and SII were correlated with worse dementia (NLR: r = −0.22, p = 0.033; SII: r = −0.17, p = 0.041) and MCI severity (NLR: r = −0.30, p = 0.010; SII: r = −0.23, p = 0.025). NLR, but not SII, was associated with Aβ42, Aβ40, and tau.

DISCUSSION

NLR and SII may provide valuable insights when advanced biomarker testing is limited by technological or financial barriers.

Keywords: dementia, neutrophil‐to‐lymphocyte ratio, peripheral immune markers, platelet‐to‐lymphocyte ratio, systemic inflammatory index

Highlights

  • We evaluated the association between neutrophil‐to‐lymphocyte ratio (NLR), platelet‐to‐lymphocyte ratio (PLR) and systemic inflammatory index (SII) and risk of mild cognitive impairment (MCI) and dementia.

  • Our analysis included 947,020 participants from 23 studies.

  • NLR and SII were associated with risk of dementia and MCI.

  • NLR and SII were correlated with the severity of dementia and MCI.

  • NLR was correlated with amyloid beta and tau proteins levels.

1. INTRODUCTION

Worldwide, it is estimated that > 50 million people are currently living with dementia with projections estimating an increase to 131 million by 2050. 1 Dementia also accounts for 1.62 million deaths annually. 2 The prevalence of the disease and its severity imposes a substantial burden on patients, caregivers, and health‐care systems. It was estimated that the economic burden of the disease is > $818 billion per year. 3 , 4 Dementia encompasses a range of neurological disorders characterized with memory loss and cognitive impairment including Alzheimer's disease dementia (ADD), vascular disease dementia (VDD), Parkinson's disease dementia (PDD), Lewy body dementia (LBD), and frontotemporal dementia (FTD). 5 Typically, patients with dementia progress through intermediate stage known as mild cognitive impairment (MCI). 6

ADD is considered the most common cause of dementia. The pathophysiology of the disease includes amyloid and tau aggregation. 7 The second most common cause of dementia is VDD, in which atherosclerosis, arteriolosclerosis, and microvascular disease are the leading pathophysiological mechanisms. 8 Other neurodegenerative causes of dementia such as PDD, 9 LBD, 10 and FTD have their characteristic pathophysiology. Eventually, these diseases lead to dementia through neuronal toxicity and synaptic loss. While the pathophysiology of dementia and MCI has been explored, the risk stratification is still very limited due to the complexity of the disease process. 11

Recent biomarkers have been developed to diagnose dementia and MCI such as tau and amyloid beta (Aβ) in the plasma and cerebrospinal fluid (CSF). 12 Neuroimaging studies such as advanced (positron emission tomography [PET]) imaging have also improved the risk stratification of dementia. 13 However, these techniques are advanced, expensive, and mainly used in specialized memory clinics, often at later stages of disease. 14

Peripheral inflammatory markers such as neutrophil‐to‐lymphocyte ratio (NLR), platelet‐to‐lymphocyte ratio (PLR), and systemic inflammatory index (SII) are widely studied and easily accessible markers. 15 , 16 These biomarkers were previously studied in several non‐neurological and neurological diseases such as PDD, 17 amyotrophic lateral sclerosis, 18 multiple sclerosis, 19 cancers, 16 and kidney diseases, 20 among others.

Although several systematic reviews and meta‐analyses investigated the association between peripheral inflammatory markers and dementia, 21 , 22 , 23 these studies were limited by sample size; their lack of prospective analysis did not assess the association across different causes of dementia; and there was insufficient evaluation of the association across dementia subtypes, severity, and pathological biomarkers.

We aimed to conduct this systematic review and meta‐analysis to evaluate the association between PLR, NLR, and SII and future risk of MCI and dementia and whether this association differs according to the cause of dementia. In addition, we aimed to assess the correlation between these peripheral biomarkers and severity of MCI and dementia and their pathological biomarkers. We hypothesized that these markers are associated with increased risk and severity of MCI and dementia and that these associations may vary across dementia subtypes.

2. METHODS

2.1. Registration and protocol

This systematic review and meta‐analysis was conducted in line with the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta‐Analyses) statement. The study protocol was prospectively registered with the International Prospective Register of Systematic Reviews (PROSPERO) under the identifier CRD420251142176.

2.2. Search strategy

On September 8, 2025, we searched PubMed, Embase, Scopus, and the Cochrane Central Register of Controlled Trials (CENTRAL). The search combined the following keywords: ((PLR) OR (NLR) OR (SII) OR (platelet to lymphocyte ratio) OR (neutrophil to lymphocyte ratio) OR (Systemic Immune‐Inflammation Index) OR (granulocyte to lymphocyte ratio) OR (GLR)) AND ((Dementia) OR (Alzheimer Disease) OR (Dementia with LBD) OR (Parkinson Disease) OR (Vascular dementia) OR (FTD) OR (MCI) OR (MCI)). No filters were applied regarding language or publication date.

2.3. Exposures and outcomes of interest

The exposures of interest were NLR, PLR, and SII. Outcomes of interest included (1) risk of MCI defined as cognitive decline in one or more domains that does not interfere with functional capacity; (2) risk of dementia defined as significant cognitive decline in one or more domains along with interference with functional activity including ADD, VDD, PDD, LBD, and FTD; (3) severity of MCI and dementia measured using Mini‐Mental State Examination (MMSE); and (4) markers of dementia pathology including CSF and plasma Aβ42, Aβ40, total and phosphorylated tau, and neurofilament light chain (NfL).

RESEARCH IN CONTEXT

  1. Systematic review: The clinical diagnosis of dementia is challenging. Recent biomarkers of plasma and cerebrospinal fluid along with advanced imaging such as positron emission tomography were developed to aid in diagnosing and risk stratification of dementia. Systemic inflammation and innate immune activation have been linked to dementia. Peripheral immune markers such as neutrophil‐to‐lymphocyte ratio (NLR), platelet‐to‐lymphocyte ratio (PLR), and systemic inflammatory index (SII) are considered simple and easily accessible measures. A systematic search was conducted on PubMed and Embase using NLR, PLR, SII, dementia, and meta‐analysis as keywords yielded two previous meta‐analyses. These meta‐analyses were focused on Alzheimer's disease dementia and NLR and were limited by sample size, did not study other causes of dementia or the association with immune markers other than NLR, did not evaluate the association between these markers and severity of dementia, and did not explore whether they are associated with advanced pathological markers of dementia.

  2. Interpretation: In this paper we evaluated association between PLR, NLR, and SII and future risk of mild cognitive impairment (MCI) and dementia and whether this association differs according to the cause of dementia. In addition, we aimed to assess the correlation between these peripheral biomarkers and severity of MCI and dementia and their pathological biomarkers. We add to the current literature that NLR and SII were associated with higher risk of MCI and dementia with the association being consistent across different causes of dementia. We also add that these markers are associated with higher severity of dementia and were correlated with advanced pathological markers of dementia such as amyloid beta and tau proteins.

  3. Future directions: Our findings have several clinical implications. First, we demonstrate that NLR and SII are simple and easily accessible biomarkers that can be used to predict dementia, and monitor its severity and progression, especially in the settings where more advanced biomarkers and imaging are not available such as primary care clinics or low‐ and middle‐income countries. Second, our findings highlight the tight connection between inflammation and neurodegeneration highlighting that it should be considered a potential target of therapy.

2.4. Study selection

Eligible articles included cohort and case–control studies assessing the association between NLR, PLR, or SII with risk of developing MCI or dementia, their severity, or their pathological biomarkers. We excluded reviews, editorials, case reports, and studies exploring NLR, PLR, and SII in neurological conditions other than MCI or dementia.

Screening was performed using Rayyan, by evaluating the article titles and abstracts, followed by full‐text review. Two reviewers (A.A.T. and T.N.A.) independently assessed all records. Disagreements were resolved by discussion until consensus was reached.

2.5. Data extraction

Data were extracted using a predefined template, which captured study details (author information, year, design, participant characteristics, sex distribution, and sample size), as well as data regarding the exposures and outcomes of interest. Extraction was carried out independently by A.A.T. and T.N.A., and discrepancies were resolved through consensus.

2.6. Quality of evidence assessment

Study quality was assessed using the Newcastle–Ottawa Scale (NOS), which evaluates three domains: (1) selection of study groups, (2) comparability of groups, and (3) ascertainment of exposure (for case–control studies) or outcome (for cohort studies). The maximum score is 9; studies scoring < 5 were classified as low quality, scores of 5 or 6 as moderate quality, and ≥ 7 as high quality. Quality appraisal was performed by A.A.T.

2.7. Data analysis

All statistical analyses were conducted with MetaXL version 5.3 (EpiGear International). Effect sizes were expressed as hazard ratios (HRs) with 95% confidence intervals (CIs) for the associations between peripheral immune markers and risk of MCI or dementia and pathological biomarkers of dementia. Correlation coefficient (r) and its 95% CI was used as the effect size for the correlation between peripheral immune markers and severity of dementia. Conversions between different effect estimates were applied when required, using published methods. 24 Heterogeneity across studies was assessed with the I 2 statistic and the Cochrane Q test. A random‐effects model was applied when heterogeneity was  ≥ 50%, while a fixed‐effects model was used when heterogeneity was < 50%. When possible, subgroup analysis was conducted according to the cause of dementia including ADD, VDD, PDD, LBD, and FTD. A subgroup analysis based on the dementia markers based on source CSF or plasma and characteristic phosphorylated or total was conducted when feasible.

3. RESULTS

3.1. Search results

The search results yielded 1455 articles, of which 398 were duplicates. The remaining 1057 articles were screened using their titles and abstracts and 1016 articles were excluded because they were irrelevant, reviews, or editorials. The remaining 41 articles were screened using their full‐text form and 18 articles were excluded because they did not include data about the outcomes of interest. Finally, 23 articles were included in the analysis. Figure S1 in supporting information describes the process of study selection. The supporting information cites the included papers in the supplementary references.

3.2. Characteristics of the included studies

A total of 947,020 participants were included from 23 articles. The mean age was 67.6 ± 6.8 while the mean male‐to‐female ratio was 0.95. The majority of the studies were retrospective cohort studies (65.2%) and were conducted in Europe (43.5%) or Asia (43.5%; Table 1). Table S1 in supporting information demonstrates the characteristics of the included studies. Table S2 in supporting information demonstrates the adjustment variables included in the studies. In summary, all of the included studies included age and sex as part of their adjusting variables. The majority of the studies (73.9%) included comorbidities as part of their adjustment for confounders.

TABLE 1.

Summary of the characteristics of the included studies.

Variable Count (%)/mean ± standard deviation
Study design
Prospective cohort 8 (34.8%)
Retrospective cohort 15 (65.2%)
Geographical region
Europe 10 (43.5%)
Asia 10 (43.5%)
America 3 (13.0%)
Male to female ratio 0.95 ± 0.33
Age 67.6 ± 6.81
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3.3. Peripheral immune markers and MCI

Four papers 25 , 26 , 27 , 28 assessed the association between NLR and risk of MCI. In the non‐adjusted model, NLR was associated with significantly higher MCI risk (Figure 1A: HR = 2.31; 95% CI: 1.65–3.24); the model had insignificant heterogeneity (I2  = 46%, P = 0.140). Adjusting for confounding variables yielded similar results (Figure 1B: HR = 2.78; 95% CI: 1.57–4.90); the model had significant heterogeneity (I2  = 75%, P = 0.010). No studies evaluated the association between PLR and risk of MCI. One paper evaluated the association between SII and risk of MCI. 29 The paper found a significant association between the two.

FIGURE 1.

FIGURE 1

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The association between peripheral immune markers and mild cognitive impairment. We present the association between neutrophil‐to‐lymphocyte ratio (NLR) and risk of mild cognitive impairment in the unadjusted (A) and fully adjusted models (B). The analysis is presented using hazard ratios (HRs) and 95% confidence intervals (95% CIs)

3.4. Peripheral immune markers and dementia

The models that evaluated the association between NLR and risk of dementia included 11 articles. 22 , 25 , 26 , 27 , 28 , 30 , 31 , 32 , 33 , 34 , 35 Both the non‐adjusted (Figure 2A: HR = 1.30; 95% CI: 1.20–1.41) and adjusted (Figure 2B: HR = 1.24; 95% CI: 1.15–1.34) models found a significant association between NLR and risk of dementia. Both models (I2  = 97% and 95%, respectively) had significant heterogeneity (p < 0.001). Subgroup analysis of studies among ADD included eight articles. 22 , 25 , 28 , 30 , 31 , 32 , 33 , 34 , 35 Both the non‐adjusted (Figure 2C: HR = 1.34; 95% CI: 1.18–1.51) and adjusted (Figure 2D: HR = 1.38; 95% CI: 1.20–1.59) models found a significant association between NLR and risk of dementia. Both models (I2  = 95% and 94%, respectively) had significant heterogeneity (p < 0.001). Subgroup analysis of studies among VDD included four articles. 22 , 25 , 32 , 34 The unadjusted (Figure 2E: HR = 1.20; 95% CI: 1.15–1.25) and adjusted (Figure 2F: HR = 1.16; 95% CI: 1.13–1.20) models showed a significant association between NLR and risk of VDD with both models showing insignificant heterogeneity (I2  = 0%, p ≥ 0.400). One paper 36 evaluated the association between NLR and risk of PDD. The paper showed a significant association between the two.

FIGURE 2.

FIGURE 2

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The association between neutrophil‐to‐lymphocyte ratio (NLR) and dementia. We present the association between NLR and risk of dementia (A & B), Alzheimer's disease dementia (C & D), and vascular disease dementia (E & F) in the unadjusted (A, C, E) and fully adjusted models (B, D, F). The analysis is presented using hazard ratios (HRs) and 95% confidence intervals (CIs)

The models that evaluated the association between PLR and risk of dementia included five articles. 22 , 30 , 32 , 34 , 37 Neither the non‐adjusted (HR = 0.99; 95% CI: 0.95–1.04) nor the adjusted model (HR = 1.03; 95% CI: 0.97–1.08) found a significant association between the two variables. Both models had significant heterogeneity (I2  = 67% and 83%, respectively, P < 0.001). Subgroup analysis of studies among ADD included four articles. 22 , 32 , 34 , 37 Neither model showed a significant association between PLR and risk of dementia (non‐adjusted: HR = 1.01; 95% CI: 0.95–1.07, adjusted: HR = 1.02; 95% CI: 0.95–1.10) with both models showing significant heterogeneity (I2  = 77%, P < 0.001). Subgroup analysis of studies among VDD included three articles. 22 , 32 , 34 While the non‐adjusted model did not demonstrate a significant association between PLR and risk of dementia (HR = 0.98; 95% CI: 0.86–1.12, I2  = 81%, P = 0.010), the adjusted model found a significant one (HR = 1.06; 95% CI: 1.01–1.11); the model had insignificant heterogeneity (I2  = 35%, P = 0.220). One paper 36 evaluated the association between PLR and risk of PDD. The paper did not find an association between the two variables.

The association between SII and risk of dementia included five articles in the non‐adjusted model and four articles in the adjusted one. 22 , 29 , 30 , 32 , 34 Both models showed a significant association between SII and higher risk of dementia (Figure 3A: non‐adjusted: HR = 1.10; 95% CI: 1.04–1.15, Figure 3B: adjusted: HR = 1.08; 95% CI: 1.03–1.13). Both models had significant heterogeneity (I2  = 85% and 83%, respectively, P < 0.001). The subgroup analysis among ADD included three articles. 22 , 32 , 34 Both the unadjusted (Figure 3C: HR = 1.06; 95% CI: 1.01–1.11) and adjusted (Figure 3D: HR = 1.06; 95% CI: 1.01–1.11) models demonstrated a significant association between SII and higher risk of ADD. Both models had insignificant heterogeneity (I2  = 0%, P ≥ 0.610). Subgroup analysis among VDD included three articles. 22 , 32 , 34 The unadjusted (Figure 3E: HR = 1.13; 95% CI: 1.07–1.19) and adjusted (Figure 3F: HR = 1.12; 95% CI: 1.08–1.16) models found a significant association between SII and risk of VDD. Both models had insignificant heterogeneity (I2  = 20% and 2%, respectively, P ≥ 0.280). One paper 36 investigated the association between SII and risk of PDD. The paper found a significant association between the two.

FIGURE 3.

FIGURE 3

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The association between systemic inflammatory index (SII) and dementia. We present the association between SII and risk of dementia (A & B), Alzheimer's disease dementia (C & D), and vascular disease dementia (E & F) in the unadjusted (A, C, E) and fully adjusted models (B, D, F). The analysis is presented using hazard ratios (HRs) and 95% confidence intervals (CIs)

3.5. Peripheral immune markers and dementia severity

Three papers evaluated the association between NLR and severity of MCI. 23 , 25 , 38 The model that pooled these studies found a significant correlation between the two (Figure 4A: r = −0.30; 95% CI: –0.09 to –0.49); the model had a significant heterogeneity (I2  = 93%, P < 0.001). One paper 23 evaluated the association between PLR and severity of MCI and the paper found a significant correlation between the two. Two papers evaluated the correlation between SII and severity of MCI. 23 , 29 The model found a significant correlation between the two variables (Figure 4B: r = –0.23; 95% CI: −0.28 to −0.17); the model had insignificant heterogeneity (I2  = 0%, P = 0.360).

FIGURE 4.

FIGURE 4

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The correlation between peripheral immune markers and severity of dementia. We present the correlation between neutrophil‐to‐lymphocyte ratio (NLR) and severity of dementia (A), Alzheimer's disease dementia (B), vascular disease dementia (C), and Parkinson's disease dementia (D). In (E), we present the correlation between systemic inflammatory index (SII) and severity of dementia. The analysis is presented using rho correlation coefficient (r) and 95% confidence intervals (CIs)

Eight articles evaluated the correlation between NLR and severity of dementia indicated by MMSE scores. 23 , 25 , 29 , 38 , 39 , 40 , 41 , 42 The model showed a significant correlation between NLR and severity of dementia (Figure 4C: r = −0.22; 95% CI: −0.08 to −0.36); the model had a significant heterogeneity (I2  = 96%, P < 0.001). One paper 25 evaluated the association between NLR and severity of ADD. The paper did not find a significant correlation between the two variables. However, the same paper found a significant correlation between NLR and severity of VDD. Two papers assessed the correlation between NLR and severity of PDD. 39 , 41 The model that pooled these papers found a significant correlation between the two (Figure 4D: r = –0.18; 95% CI: −0.09 to −0.26); the model had insignificant heterogeneity (I2  = 1%, P = 0.320). One paper 39 evaluated the correlation between NLR and severity of LBD and the paper did not find a significant correlation between the two.

Four papers evaluated the correlation between PLR and severity of dementia. 23 , 29 , 39 , 40 The model that pooled these papers did not find a correlation between the two (r = −0.07; 95% CI: −0.21 to 0.06) and had significant heterogeneity (I2  = 76%, P = 0.01). No papers evaluated the correlation between PLR and severity of ADD or VDD. One paper 39 evaluated the correlation between PLR and severity of PDD and LBD and the paper did not find a significant correlation.

The model that evaluated the correlation between SII and severity of dementia included six papers. 23 , 29 , 39 , 40 , 43 , 44 The model found a significant correlation between the two variables (Figure 4E: r = −0.17; 95% CI: −0.23 to −0.11); the model had significant heterogeneity (I2  = 58%, P = 0.040). No papers evaluated the correlation between SII and severity of ADD or VDD. One paper 39 evaluated the correlation between SII and severity of PDD and LBD and the paper did not find a significant correlation.

3.6. Peripheral immune markers and dementia pathology markers

Three papers evaluated the association between NLR and Aβ42. 29 , 37 , 45 The model that pooled these papers found a significant association between the two (Figure 5A: HR = 1.05; 95% CI: 1.00–1.09); the model had insignificant heterogeneity (I2  = 34%, P = 0.220). One paper 46 evaluated the association between NLR and Aβ40 and the study found an association between the two. The model that evaluated the association between NLR and tau included four articles, 29 , 37 , 42 , 45 and it found a significant association between the two variables (Figure 5B: HR = 1.08; 95% CI: 1.01–1.17); the model had a significant heterogeneity (I 2 = 89%, P < 0.001). The model that evaluated the association between NLR and NfL included two papers 29 , 37 and the model did not find an association between the two variables (HR = 1.12; 95% CI: 0.89–1.40); the model had insignificant heterogeneity (I2  = 63%, P = 0.100).

FIGURE 5.

FIGURE 5

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The association between peripheral immune markers and pathological markers of dementia. We present the association between neutrophil‐to‐lymphocyte ratio (NLR) and amyloid beta 42 (A) and tau (B). The analysis is presented using hazard ratios (HRs) and 95% confidence intervals (CIs)

Two papers evaluated the association between PLR and Aβ42. 29 , 37 The model that pooled these papers did not find an association between the two variables (HR = 1.01; 95% CI: 0.99–1.02); the model had insignificant heterogeneity (I2  = 0%, P = 0.320). One paper evaluated the association between PLR and Aβ40 46 and the study did not find an association between the two variables. Two papers 29 , 37 evaluated the association between PLR and tau and the model that pooled these papers did not find an association (HR = 1.12; 95% CI: 0.88–1.42); the model had significant heterogeneity (I2  = 84%, P = 0.010). Similarly, the model that evaluated the association between PLR and NfL included two papers 29 , 37 and the model did not find an association between the two (HR = 0.84; 95% CI: 0.39–1.82); the model had significant heterogeneity (I2  = 93%, P < 0.001).

Three papers 29 , 37 , 43 evaluated the association between SII and Aβ42 and the model that pooled these studies did not find an association between the two variables (HR = 1.01; 95% CI: 0.98–1.04); the model had significant heterogeneity (I2  = 65%, P = 0.060). Two papers 37 , 43 evaluated the association between SII and Aβ40 and the model that pooled these studies did not find an association between the two variables (HR = 1.48; 95% CI: 0.94–2.34); the model had significant heterogeneity (I2  = 97%, P < 0.001). Similarly, three papers 29 , 37 , 43 evaluated the association between SII and tau and the model that pooled these studies did not find an association between the two (HR = 1.24; 95% CI: 0.92–1.67); the model had significant heterogeneity (I2  = 98%, P < 0.001). The model that evaluated the association between SII and NfL included two articles 29 , 37 and it did not find an association between the two variables (HR = 1.08; 95% CI: 0.85–1.37); the model had significant heterogeneity (I2  = 76%, P = 0.040).

3.7. Quality assessment of the evidence

The median NOS score among the included articles was 6 with a range from 5 to 9. In summary, 34.8% of the articles were of high quality while the rest were of intermediate quality (65.2%). Table S1 demonstrates the detailed results of the NOS assessment.

4. DISCUSSION

In this systematic review and meta‐analysis, we investigated the association between peripheral immune markers, specifically NLR, PLR, and SII, and future risk of MCI and dementia. In addition, we investigated the association between these biomarkers and severity of dementia and its pathological biomarkers. Our findings highlight several implications. First, NLR and SII were associated with future risk of dementia and MCI. These associations were consistent across all the causes of dementia including ADD, VDD, and PDD. Surprisingly, PLR was associated with VDD but not with other causes of dementia. Second, NLR and SII were correlated with the severity of dementia as indicated with MMSE scores. Third, NLR but not SII or PLR was associated with Aβ42 and tau of the CSF.

We found a significant association between NLR and SII and future risk of MCI and dementia. This association was consistent across different causes of dementia. As highlighted in the introduction, toxic substance accumulation that results in neurodegeneration such as Lewy bodies, Aβ, tau, and synuclein are the main pathological causes of neurodegenerative dementia. Immune responses are known to trigger the activation of microglial cells which are responsible for clearing these toxic substance to protect from neurodegeneration. 15 Several studies used translocator protein (TSPO) tracers to assess the activation of microglia and PET scans to quantify Aβ accumulation and found a significant correlation between the two. 47 , 48 As the inflammation continues, it triggers the release of more inflammatory cytokines, which increase the number of macrophages to combat the accumulation of toxic substances. 49 However, with time, the inflammatory cytokines start to produce a contradictory effect by increasing the accumulation of these toxic substances. 49 For example, interleukin 1 results in increase of the expression of amyloid precursor protein, which creates a cycle of accumulation of Aβ, activation of microglia, and increased cytokines eventually resulting in an increase of neutrophils. 50 As the inflammation continues, lymphocytes are also activated resulting in reduction of peripheral lymphocytes and increase in their presence in the central nervous system (CNS). 51 Moreover, neutrophils are able to secrete reactive oxygen species (ROS) and alter cell adhesion mechanisms impairing lymphocyte function and rerouting them into the CNS, which increase the lymphocytic presence in the CNS 52 compared to the neutrophils resulting in an increase in NLR in the peripheral blood. Similarly, SII increases through the same mechanism as it is calculated by multiplying NLR with the platelet count. Previous systematic reviews and meta‐analyses among patients with ADD found a significant association between NLR and ADD. 21 , 22 , 23 Here we add to the literature that this association exists in MCI and across different causes of dementia. We also highlight that SII is also associated with the risk of MCI and dementia. Interestingly, we found that PLR is associated with future risk of VDD but not with other types of dementia which could be due to the difference in the pathophysiology of VDD as it involves atherosclerosis and cerebrovascular events. Platelets play a fundamental role in the thrombotic/hemostatic mechanisms which are central mechanisms in stroke and atherosclerosis. 53

We also found that NLR and SII correlated with MMSE scores. Previous systematic reviews and meta‐analyses highlighted an association between NLR and MMSE scores among patient with ADD, which is consistent with our findings. 21 However, we add to the current literature that NLR is correlated with MMSE among patients with MCI and dementia across its different causes. We also add that SII was correlated with MMSE. The inflammation–toxic substance accumulation cycle highlighted above explains these findings as inflammatory changes seem to precipitate and precede toxic substances accumulation that result in neurodegeneration 50 and eventually cognitive decline.

We also found that NLR and SII were associated with Aβ42 and tau, which also highlight the tight connection between inflammation and neurodegeneration in the CNS. This also highlights that these biomarkers can be used in the settings where these advanced biomarkers of the CSF and plasma are not accessible or expensive.

A few limitations should be acknowledged before concluding. First, peripheral immune markers are also affected by demographic factors and comorbidities; however, it is important to highlight that our results in the adjusted models controlled for confounding variables. Second, some associations could not be assessed due to the low sample size, especially among subgroup analyses such as the association with severity of dementia and its pathological markers. In addition, only one paper investigated the role of these biomarkers with LBD 39 and no studies were found among patients with FTD. Third, subgroup analyses based on the markers of dementia in CSF compared to plasma and total compared to phosphorylated tau was not possible due to the low number of the included studies in these analyses. Finally, some of our models had high heterogeneity and while some of this heterogeneity was reduced in the subgroup analyses models, it remained high in other subgroup models. The high heterogeneity limits the generalizability and confidence in our conclusions. This heterogeneity could be due to differences in the study design, characteristics of the included studies, definitions of dementia and its types, and the severity of the dementia among the included populations. Future studies are needed to further assess the correlation between peripheral immune markers and severity of dementia and its pathological biomarkers among causes of dementia.

Despite the aforementioned limitations, our meta‐analysis demonstrates that NLR and SII are associated with the risk of MCI and dementia across its causes. Moreover, we found that PLR is associated with VDD specifically but not with other types of dementia. We also found that NLR and SII are associated with the severity of dementia and its pathological biomarkers. Accumulatively, NLR and SII offer accessible and cheap biomarkers that can be used to predict dementia, and monitor its severity and progression, especially in the settings where more advanced biomarkers and imaging are not available such as primary care clinics or low‐ and middle‐income countries. In addition, these findings highlight the tight connection between inflammation and neurodegeneration highlighting that it should be considered a potential target of therapy.

AUTHOR CONTRIBUTIONS

A.A.T. was involved in conceptualization; A.A.T. and T.A. were involved in project administration; A.A.T. and T.A. were involved in data acquisition; A.A.T. was involved in data analysis; A.A.T. and T.A. were involved in manuscript writing; A.A.T. was involved in editing and reviewing the manuscript.

CONFLICT OF INTEREST STATEMENT

The authors declare that there are no conflicts of interest. Author disclosures are available in the Supporting Information.

CONSENT STATEMENT

Not applicable. This study analyzes data from prior literature.

Supporting information

Supporting Information

TRC2-12-e70270-s001.pdf (436.6KB, pdf)

Supporting Information

TRC2-12-e70270-s002.pdf (195.3KB, pdf)

ACKNOWLEDGMENTS

The authors have nothing to report.

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