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Abstract

French

Objective

Changes in angiogenesis have been shown to contribute to cognitive decline and dementia. We aimed to identify angiogenesis blood markers associated with cognitive performance in older adults with mild cognitive impairment (MCI), remitted major depressive disorder (rMDD), or both (rMDD + MCI) who are at risk for dementia.

Method

We analyzed data from participants with MCI, rMDD, or rMDD + MCI in the Prevention of Alzheimer's Dementia with Cognitive Remediation plus Transcranial Direct Current Stimulation in Mild Cognitive Impairment and Depression study. Elastic net regression was used to select variables associated with cognitive performance among 19 angiogenesis markers and 6 covariates. Linear regressions were used to determine which of the selected angiogenesis markers were associated with cognitive performance, controlling for the selected covariates. Significant angiogenesis markers were independently analyzed without other angiogenesis markers, controlling for covariates, with subgroup analyses in those with and without rMDD.

Results

Angiogenin was the only selected marker associated with cognitive performance (β = 0.28, P_adj = 0.03, f² = .02) when controlling for other selected markers (endothelial cell-specific molecule 1, e-selectin, interleukin-33 [IL-33], oncostatin M, platelet-derived growth factor-AB, IL-33 receptor, and tissue inhibitor of metalloproteinases-1) and selected covariates (age, education, apolipoprotein E ε4 status, diagnosis, and cardiovascular risk factors). When independently analyzed, angiogenin remained positively associated with cognitive performance (β = 0.21, P = 0.01, f² =.02), controlling for the covariates. In subgroup analyses, angiogenin was also associated with cognition in rMDD and rMDD + MCI participants (β =0.50, SE = 0.14, P < 0.001, f² = 0.08) and in MCI-only participants (β= 0.20, SE = 0.09, P = 0.02, f² = 0.02).

Conclusion

The association of angiogenin with cognitive performance highlights a potentially novel biological pathway that could influence cognition in older adults at risk for dementia. Angiogenin may protect against cognitive decline, opening new avenues for innovative preventive, or therapeutic interventions.

Plain Language Summary

The relationship between blood markers and thinking/memory in older adults at risk for dementia.

This study aimed to identify blood markers related to angiogenesis (the process of new blood vessel formation) that could predict cognitive function (memory and thinking abilities), in older adults at risk for dementia. Angiogenesis has been linked to dementia, and understanding its associated biological factors may help in predicting cognitive decline, particularly in individuals who are at risk for dementia. Participants included adults over 50 years of age with cognitive impairment and/or a history of depression. We used the elastic net regression model to select eight out of 19 angiogenesis markers while considering five out of the six other factors (covariates) that might influence cognition, such as age, education level, apolipoprotein E genotype, diagnosis, and cardiovascular health. Among the markers studied, angiogenin, a marker that can increase angiogenesis, stood out as the only one associated with cognitive function. When considering other factors, higher levels of angiogenin were linked to better cognition. This association was present even when angiogenin was examined on its own, independent of other angiogenesis markers. Angiogenin might play a protective role in cognition in older adults at risk for dementia. Overall, the findings highlight angiogenin as a potential biomarker for cognitive health in aging adults, who were at risk with early signs of cognitive impairment and a history of depression. This suggests that angiogenin could be a target for future research into treatments aimed at preventing or slowing cognitive decline in vulnerable populations.

Keywords

angiogenesis angiogenin cognition major depressive disorder mild cognitive impairment dementia angiogenèse angiogénine trouble dépressif majeur déficit cognitif léger (DCL)démence

Introduction

Both mild cognitive impairment (MCI) and remitted major depressive disorder (rMDD) contribute to an increased risk of dementia in older adults.¹ MCI is often considered a transitional stage between normal aging and dementia, where cognitive decline becomes apparent, with no or minimal functional deficits. About 32% of those diagnosed with MCI progress to dementia within 2 years.² A history of major depressive disorder (MDD), even when it occurs early in life or in midlife, has been recognized as an independent risk factor for developing Alzheimer's disease (AD) and related dementias (ADRD).³ Late-life depression has been associated with a twofold to fivefold increased risk of dementia.⁴ The deleterious effect of depression may persist even after remission of MDD (rMDD, defined as being free or almost free of depressive symptoms for at least 2 months⁵): older individuals with rMDD have a three-time higher risk of cognitive decline compared to non-rMDD controls.⁶ With lifetime prevalence rates of MDD reaching 20%⁷ and the prevalence of MCI in older adults being over 15%,⁸ rMDD and MCI define large groups of individuals at risk for dementia. Thus, it is important to identify underlying biological factors that may contribute to brain health and cognitive performance in older individuals with MCI or rMDD.

Angiogenesis, the process of forming new blood vessels from preexisting vessels,⁹ may play a critical role in the etiology of ADRD, as vascular dysfunction has been suggested as one of the earliest pathological events during the development of AD from normal aging.¹⁰ Angiogenesis is a complex and regulated process involving different growth factors, cell adhesion molecules, and metabolites in endothelial cells.¹¹ In animal studies, the effects of cognitive-enhancing treatments appear to be associated with angiogenesis signaling.^10,12,13 Some clinical studies have examined human angiogenesis at different stages of cognitive impairment; however, their findings have been inconsistent. For example, a study reported a similar direction of change for both plasma angiogenin (a potent angiogenic stimulator¹¹) and tissue inhibitor of matrix metalloproteinase-4 (which has been associated with indirectly inhibiting angiogenesis through matrix metalloproteinases).¹⁴ Both of these proteins were also associated with cognitive performance assessed by the Mini-Mental State Examination (MMSE) and AD severity.¹⁴ In another study, MMSE scores were correlated with angiogenin in AD.¹⁵ For pre-dementia stages, plasma vascular endothelial growth factor (VEGF), a key pro-angiogenic marker,¹¹ was reported to be higher than controls in persons with MCI, but not in those with AD.¹⁶ In contrast, serum VEGF has been found to be lower in individuals with amnestic MCI or AD than in controls,¹⁷ suggesting the need for more research in individuals with MCI specifically. Angiogenesis is also of interest for cognition in rMDD: an association was found in individuals with MDD between worse cognitive performance and increased immature circulating endothelial progenitor cell counts, a sign of pro-angiogenic state.¹⁸

Taken in toto, there is a lack of consistent clinical findings on the relationship between angiogenesis markers and cognitive performance, particularly in the pre-dementia stages, which could provide information on potential underlying biological factors. Examining blood angiogenesis markers in these populations may suggest new targets that may potentially prevent further cognitive decline or monitor the disease progression. In this context, we aimed to identify angiogenesis markers associated with cognitive performance in the pre-dementia stages of MCI, rMDD, and rMDD + MCI and to assess whether there were differences among these three diagnostic groups.

Methods

Participants

We included participants from the randomized clinical trial, Prevention of Alzheimer's Dementia with Cognitive Remediation plus Transcranial Direct Current Stimulation in Mild Cognitive Impairment and Depression (PACt-MD; NCT02386670),^19,20 that had baseline blood angiogenesis marker data with no history of cancer or concomitant cancer, as angiogenesis marker levels are affected by cancer.²¹ Details of the PACt-MD trial, inclusion criteria, and assessments have been described previously.^19,20 Briefly, all participants provided written informed consent as approved by the local Research Ethics Board and completed detailed baseline clinical and cognitive assessments between January 2015 and August 2018. Participants were classified as rMDD (with normal cognition), MCI, or rMDD + MCI.¹⁹ For the MCI group (without rMDD), the main eligibility criteria were age ≥ 60, meeting criteria for a diagnosis of mild neurocognitive disorder based on the Diagnostic Statistical Manual of Mental Disorders Fifth Edition (DSM-5)⁵ and a Montgomery–Åsberg Depression Rating Scale (MADRS)²² score ≤ 10. For the rMDD with or without MCI groups, the main eligibility criteria were age ≥ 65, a diagnosis of rMDD based on the Structured Clinical Interview for DSM (fourth edition, text revised),²³ a MADRS²² score ≤ 10, and either meeting (rMDD with MCI) or not meeting (rMDD without MCI) the criteria for a diagnosis of mild neurocognitive disorder based on DSM-5.⁵

Plasma Levels of Angiogenesis Markers

At baseline, blood was collected in EDTA tubes via venipuncture with fasting not required. The plasma was separated immediately after collection and aliquots were stored at −80°C. Using this blood, the following 19 markers that have been known to contribute to angiogenesis^11,24–26 and have been associated with dementia or cognition in literature were measured simultaneously using the Luminex^® LX200 platform (Luminex Corp., Austin, TX, USA) and a customized multiplex immunoassay from R&D/Biotechne (Minneapolis, MN, USA): angiogenin,¹⁵ angiopoietin-1,²⁷ angiopoietin-2,²⁸ endothelial cell-specific molecule-1 (endocan),²⁹ endostatin,³⁰ e-selectin,³¹ hepatocyte growth factor (HGF),³² interleukin-33 (IL-33),³³ suppression of tumorigenicity-2 (ST2),³³ oncostatin-M,³⁴ platelet-derived growth factor-AA (PDGF-AA),³⁵ platelet-derived growth factor AB (PDGF-AB),³⁶ placental growth factor (PlGF),³⁷ tissue inhibitor of metalloproteinases-1 (TIMP-1),³⁸ vascular cell adhesion molecule-1 (VCAM-1),³⁹ VEGF,^16,40 VEGF receptor-1(VEGFR1),^40,41 VEGF receptor-2 (VEGFR2),⁴⁰ and VEGF receptor-3 (VEGFR3).⁴¹ Laboratory analyses followed the manufacturer's instructions. Briefly, protein standards were homogenized and serially diluted, and protein-specific capture beads were prepared. For each test well, 25 μL of the bead mix was combined with 25 μL of sample or standard and incubated for 16–18 h. After incubation, plates were washed, and 25 μL of detection antibody was added for 2 h at room temperature. Following this, 50 μL of streptavidin was added, and plates were incubated for 30 additional minutes. After a final wash, wells were resuspended in 100 μL of fluid drive buffer and read on the LX200^® system with Exponent^® software (Luminex Corp.). Data were analyzed using Belysa^® 1.2.2 software (Merck Millipore, Darmstadt, Germany), with results expressed in pg/mL. The detection limits were as follows: angiogenin (3.17 pg/mL), angiopoietin-1 (9.43 pg/mL), angiopoietin-2 (17.1 pg/mL), endocan/ESM-1 (1.08 pg/mL), HGF (1.0 pg/mL), IL-33 (1.8 pg/mL), oncostatin M/OSM (44.3 pg/mL), PDGF-AA (0.747 pg/mL), PlGF (1.9 pg/mL), e-selectin/CD62E (18.8 pg/mL), TIMP-1 (3.42 pg/mL), VEGFR1/Flt-1 (5.68 pg/mL), VEGFR2/KDR/Flk-1 (7.4 pg/mL), VEGFR3/Flt-4 (2.7 pg/mL), endothelin-1 (1.31 pg/mL), PDGF-AB (3.28 pg/mL), ST2/IL-33R (29.9 pg/mL), VCAM-1/CD106 (238 pg/mL), and VEGF-A (0.99 pg/mL).

ApoE Genotyping

Apolipoprotein E (ApoE) variants were genotyped using standard TaqMan SNP genotyping assays (Life Technologies, Burlington, ON) at CAMH Biobank and Molecular Core Facility (Toronto, ON, Canada) according to standardized genotyping protocols.

Cognitive Measures

A composite cognitive score was created using results from a comprehensive neuropsychological battery, as described previously.¹⁹ Briefly, z scores for 18 measures from 12 chosen cognitive tests were computed using the scores of cognitively unimpaired comparators (Digit Symbol Subtest from Wechsler Adult Intelligence Scale-III: total correct responses; Trail Making Test Part A: seconds per correct connections; Continuous Performance Task: d’ across all three trials; Paced Auditory Serial Addition Test: total correct responses across both types of trials; Clock Drawing: total score; Trail Making Test Part B: ratio of seconds per correct connections for Trail Making Test Part B over Part A; Stroop Neuropsychological Screening: time for color word divided by time for words; California Verbal Learning Test: total correct responses for Trials 1–5, percent retained at long delay trial from Trial 5 at immediate recall condition, d’ hits and false alarms of recognition yes/no responses; Boston Visual Memory Test, Revised: total correct responses for Trials 1–3, percent retained on delayed recall trial; Boston Naming Test: total spontaneous correct responses; Category Fluency: total spontaneous correct responses; Letter Fluency: total score). These z scores were then averaged to produce six z scores corresponding to six cognitive domains (speed of processing, working memory, executive functioning, verbal memory, visual memory, and language), and subsequently, the six domain z scores were averaged to create the composite cognitive score, which was also the primary outcome of the PACt-MD study.²⁰

Statistical Analysis

Data were analyzed using R version 4.4.1. χ², or Fisher's exact test,⁴² or t-tests (t) were used to determine whether there were significant differences in the demographic, clinical, and cognitive characteristics between overall sample and the subset. First, we examine the sample overall and subset in MCI, rMDD, and rMDD + MCI diagnostic groups at baseline, using one-way analysis of variance (ANOVA) or χ² (or Fisher's exact test⁴²) to test for statistical significance Post hoc comparisons were performed using Tukey's test with Bonferroni correction to determine where significant differences lie.

Based on visual inspection, angiogenesis marker levels (pg/mL) were not normally distributed, so they were log-transformed to better approximate a normal distribution; as five of the markers (counts) had non-detectable values with zeros: IL-33 (98), VEGF (80), PIGF (8), PDGF-AB (1), and VEGFR3 (1), these values were imputed using half of the minimum value⁴³ for each marker prior to transformation. A priori covariates included age,^44,45 diagnosis,⁴⁶ gender,⁴⁷ highest level of education,⁴⁷ ApoE ε4 allele status (0, 1, or 2 alleles),⁴⁸ and cardiovascular risk factors.⁴⁹ A vascular risk factors count was based on the presence of the following conditions:⁴⁹ hypertension, hypercholesterolemia, smoker status, body mass index (BMI) ≥ 30 kg/m², heart disease, diabetes, and/or history of stroke/transient ischemic attack. Missing data in the outcomes and covariates were handled using available case analysis. Elastic net regression was performed on the 19 angiogenesis markers and six covariates against cognitive performance to identify variables relevant to the model (non-zero coefficients) under regularization,⁵⁰ using the add-on R packages glmnet and caret with a repeated cross-validation approach (fivefold cross-validation, repeated five times) to set hyperparameter combinations based on root mean squared error. Then, relevant angiogenesis markers and covariates from the elastic net were assessed in linear regression analyses for coefficient estimates (β) and standard error (SE); false discover rate correction based on the Benjamini–Hochberg method was applied to adjust P values. The effect size of each variable was calculated using Cohen's f². Post hoc analyses included pairwise comparisons using estimated marginal means and a 95% confidence interval (CI) with Tukey's method. Finally, the single significant angiogenesis marker was further examined in a linear regression analysis without other angiogenesis markers with the same covariates and followed by subgroup analyses in diagnostic groups as a sensitivity analysis to see the impact of the grouping definition (combining the two rMDD groups considering the sample size). The likelihood ratio test (χ²_LRT) was used to assess statistically significant differences in model fit between two models, examining the effects of adding other angiogenesis markers. Kruskal–Wallis’ test (χ²_KW) and Dunn's multiple comparisons (Z) with Bonferroni corrections were conducted to determine whether there were significant between-group differences in the angiogenesis marker levels.

Results

Participant Characteristics

Out of the 418 randomized PACt-MD participants,²⁰ 308 participants who had blood angiogenesis marker data and no indication of cancer were included in this analysis. This subset of participants did not differ from the overall sample in age (t(654.4) = −1.1, P = 0.3), self-reported gender (χ²(1) = 0.21, P = 0.6), self-reported race (Fisher's: P = 0.8), BMI (t(646.6) = 0.11, P = 0.9), diagnosis (χ²(2) = 0.06, P = 0.97), highest level of education (χ²(3) = 0.26, P = 0.97), APoE ε4 status (χ²(2) = 0.12, P = 0.9), number of co-morbid physical illness (t(648.2) = −0.50, P = 0.6), vascular risk factors (t(665.8) = −0.59, P = 0.6), and composite cognitive score (t(650.6) = 0.5, P = 0.9).

Demographic, clinical, and cognitive characteristics are summarized in Table 1. Age, gender, education, comorbidities, vascular risk factors, and ApoE ε4 status did not vary among diagnostic groups (all P's > 0.05). BMI was higher in the MCI group than in the rMDD group, and race distribution differed between the MCI and rMDD groups. As expected, MADRS, MMSE, MoCA, and composite cognitive scores (F (2, 302) = 31, P ≤0.001) also differed significantly across the diagnostic groups: MADRS scores were higher in rMDD and rMDD + MCI groups than in the MCI group. The cognitive scores were lower in MCI and rMDD + MCI groups than in the rMDD group.

Table 1.

Demographic, Clinical, and Cognitive Characteristics.

	Overall	MCI	rMDD	rMDD + MCI	F (df1, df2) orχ² (df)	P value
N	308	166	78	64
Age, years	71.6 (6.2)	72.2 (7.2)	70.2 (4.4)	71.8 (5.1)	2.6 (2, 301)	0.07
Self-reported gender, women	187, 61%	97, 58%	55, 71%	35, 55%	4.5 (2)	0.1
BMI	27.0 (5.1)	26.2 (4.3)	28.2 (6.1)	27.8 (5.2)	5.2 (2, 299)	0.006^a
Self-reported race					FE	0.006^b
White	237, 77%	115, 69%	71, 91%	51, 80%
African descent	18, 5.8%	12, 7.2%	1, 1.3%	5, 7.8%
Asian	30, 9.7%	24, 15%	2, 2.6%	4, 6.2%
Native North American	0	0	0	0
Pacific Islander	0	0	0	0
Other	23, 7.5%	15, 9.0%	4, 5.1%	4, 6.2%
Highest education level					4.9 (6)	0.6
High school or less	45, 15%	26, 16%	8, 10%	11, 17%
Partial university	34, 11%	17, 10%	8, 10%	9, 14%
University degree	145, 47%	79, 48%	35, 45%	31, 48%
Graduate degree	84, 27%	44, 27%	27, 35%	13, 20%
Number of comorbidities	2.7 (2.4)	2.5 (2.1)	2.9 (3.0)	3.1 (2.3)	1.7 (2, 305)	0.2
Hypertension	94, 30%	49, 30%	21, 27%	24, 38%	2.0 (2)	0.4
Hypercholesterolemia	84, 27%	45, 27%	18, 23%	21, 33%	1.7 (2)	0.4
Diabetes	38, 12%	23, 14%	10, 13%	5, 7.8%	1.6 (2)	0.5
Number of vascular risk factors	0.85 (0.99)	0.85 (1.0)	0.74 (0.93)	0.98 (1.1)	0.34 (2, 305)	.4
ApoE ε4 status^c					FE	0.3
No allele	199, 69%	109, 68%	50, 73%	40, 69%
1 allele	70, 24%	37, 23%	18, 26%	15, 26%
2 alleles	18, 6.3%	14, 8.8%	1, 1.4%	3, 5.2%
MADRS score	4.2 (3.0)	3.6 (2.7)	4.8 (3.2)	5.3 (3.2)	9.6 (2, 305)	<0.001^d
MMSE score	28.1(1.7)	27.7 (1.8)	29.0 (1.2)	27.8 (1.7)	16 (2, 302)	<0.001^e
MoCA score	24.5 (3.1)	23.6 (2.8)	27.5 (1.7)	23.4 (2.7)	69 (2, 303)	<0.001^e
Composite cognitive score	−0.80 (0.84)	−0.97 (0.83)	−0.21 (0.54)	−1.06 (0.83)	31 (2, 302)	<0.001^e

Note. Values are reported as mean (SD) or n, %, or n.

FE = Fisher's exact test applied instead of χ²; MADRS = Montgomery–Åsberg Depression Rating Scale; MCI = mild cognitive impairment; MMSE = Mini-Mental State Examination; MoCA = Montreal Cognitive Assessment; rMDD = remitted major depressive disorder.

Post hoc: only significantly different between rMDD and MCI (Fisher's: P_adj = 0.003).

Post hoc: only significantly different between rMDD and MCI (M = 2.0, 95% CI, 0.36 to 3.62, P_adj = 0.01).

n = 287 with ApoE genotyping data available.

Post hoc: only significantly different between MCI and rMDD (M = 1.19, 95% CI, 0.24 to 2.14, P = 0.01) or MCI + rMDD (M = 1.72, 95% CI, 0.70 to 2.74, P < 0.001).

Post hoc: only significantly different between MCI (MMSE: M = −1.23, 95% CI, −1.76 to −0.70, P < 0.001; MoCA: M = −3.88, 95% CI, −4.70 to −3.05, P < 0.001; composite cognitive: M = −0.76, 95% CI, −1.00 to −0.51, P < 0.001) or MCI + rMDD (MMSE: M = −1.21, 95% CI, −1.86 to −0.56, P < 0.001; MoCA: M = −4.04, 95% CI, −5.06 to −3.03, P < 0.001); composite cognitive: M = −0.84, 95% CI, −1.14 to −0.54, P < 0.001) and rMDD.

Angiogenesis Markers and Cognitive Performance

The log-transformed values of angiogenesis markers are shown in Figure S1. Among the 19 angiogenesis markers and the six covariates for cognitive performance, elastic net regression identified the following relevant variables (non-zero coefficients): angiogenin (0.13), endocan (0.04), e-selectin (−0.02), IL-33 (0.01), oncostatin M (−0.28), PDGF-AB (0.04), ST2 (−0.15), TIMP-1 (0.10), VCAM-1 (−0.02), age (−0.03), education (0.18), diagnosis (0.03), ApoE ε4 allele status (−0.29), and vascular risk factors (−0.04).

These nine angiogenesis markers and five covariates were analyzed in a linear model for cognitive performance (F (18, 267) = 10, P ≤0.001, R² = 0.41, R_adj² = 0.37; Table 2). Controlling for other variables in the model, only one angiogenesis marker was associated with cognitive performance: controlling for other variables in the model, lower angiogenin was associated with lower cognitive performance (Table 2; Figure 1). Controlling for other variables, the other angiogenesis markers identified in the elastic regression (ST2, IL-33, oncostatin M, E-selectin, VCAM-1, endocan, PDGF-AB, and TIMP-1) were not associated with cognitive performance. Higher education level was associated with better cognitive performance. Compared to participants with rMDD, those with MCI or rMDD + MCI had lower cognitive performance. Older age was associated with worse cognitive performance. The presence of ApoE ε4 was also negatively associated with cognitive performance, with differences found only between non-carriers and carriers. Increased vascular risk factors also showed a negative trend with cognitive performance.

Figure 1.

Partial regression plot of log angiogenin levels (residuals from its regression against other angiogenesis markers and covariates) and composite cognitive scores (residuals from its regression against other angiogenesis markers and covariates).

Table 2.

Multiple Linear Regression With Relevant Variables for Cognition.

Terms	β	SE	t-statistic	P	P _adj	f²
(Constant)	5.04	2.37	2.13	0.03	NA	NA
ST2	−0.15	0.11	−1.33	0.2	0.4	0.004
IL-33	0.03	0.04	0.70	0.5	0.5	0.001
Oncostatin M	−0.25	0.24	−1.07	0.3	0.5	0.003
E-selectin	−0.07	0.11	−0.62	0.5	0.5	0.001
VCAM-1	−0.08	0.10	−0.84	0.4	0.5	0.002
Endocan	0.11	0.13	0.86	0.4	0.5	0.002
PDGF-AB	0.08	0.05	1.61	0.1	0.4	0.007
Angiogenin	0.28	0.10	2.92	0.004	0.03	0.02
TIMP-1	−0.17	0.12	−1.40	0.16	0.4	0.005
Age	−0.04	0.01	−5.93	<0.001	NA	0.10
Education: partial university	0.32	0.16	2.02	0.045^a	NA	0.07
Education: university degree	0.44	0.12	3.69	<0.001^a	NA
Education: graduate degree	0.64	0.13	5.00	<0.001^a	NA
ApoE ε4: one allele	−0.39	0.10	−4.13	<0.001^b	NA	0.06
ApoE ε4: two alleles	−0.51	0.17	−3.02	0.003^b
Diagnosis: rMDD	0.53	0.12	4.54	<0.001^c	NA	0.06
Diagnosis: rMDD + MCI	−0.01	0.12	−0.12	0.9^c	NA
Vascular risk factors	−0.08	0.04	−1.92	0.056	NA	0.01

Note. Endocan = endothelial cell-specific molecule 1; IL-33 = interleukin-33; MCI = mild cognitive impairment; NA = not applicable; PDGF-AB = platelet-derived growth factor AB; rMDD = remitted major depressive disorder; ST2 = suppressor of tumorigenicity 2/interleukin-33 receptor; TIMP-1 = tissue inhibitor of metalloproteinases-1; VCAM-1 = vascular cell adhesion molecule 1.

Post hoc: only statistically significantly different between those with high school or less and university degree (estimate [SE] = −0.44 [0.12], P_adj = 0.002) or graduate degree (estimate [SE] = −0.64 [0.13], P_adj < 0.001); other pairwise comparisons were not significant (P_adj > 0.05).

Post hoc: only statistically significantly different between those with no allele and one allele (estimate [SE] = 0.39 [0.09], P_adj < 0.001) or two alleles (estimate [SE] = 0.51 [0.17], P_adj = 0.008).

Post hoc: only statistically significantly different between rMDD and MCI (estimate [SE] = −0.55 [0.12], P_adj < 0.001) or MCI + rMDD (estimate [SE] = −0.57 [0.14], P_adj < 0.001).

Angiogenin and Cognitive Performance

In the model excluding other angiogenesis markers and controlling for the same covariates (F (10, 275) = 17, P≤0.001, R² = 0.39, R_adj² = 0.37), angiogenin was still positively associated with cognitive performance (Table 3); the covariates contributed in a similar direction and magnitude as in the full model with all nine markers, except that vascular risk factors were negatively associated with cognitive performance (Table 3). Subgroup analyses showed significant models in diagnostic subgroups (MCI only: F (7, 152) = 11.8, P≤0.001, R² = 0.35; rMDD and rMDD + MCI; F (7, 118) = 7.2, P≤0.001, R² = .30) and revealed that angiogenin was associated with cognitive performance in the subgroups (rMDD and rMDD + MCI: β = 0.50, SE = 0.14, P < 0.001, f² = 0.08; MCI only: β = 0.21, SE = 0.10, P = 0.04, f² = 0.02), controlling for other covariates. There was no evidence of additional effects from the other eight angiogenesis markers on cognitive performance considering the same covariates (χ²_LRT(8) = 11.8, P = 0.16).

Table 3.

Multiple Linear Regression With Only Angiogenin and Covariates for Cognitive Performance.

Terms	β	SE	t-statistic	P	f²
(Constant)	−0.29	0.96	−0.30	0.8	NA
Angiogenin	0.21	0.08	2.55	0.01	0.02
Age	−0.04	0.01	−6.50	<0.001	0.11
Education: partial university	0.27	0.16	1.71	0.09^a	0.06
Education: university degree	0.41	0.12	3.51	0.001^a
Education: graduate degree	0.61	0.13	4.83	<0.001^a
ApoE ε4: one allele	−0.38	0.09	−4.17	<0.001^b	0.06
ApoE ε4: two alleles	−0.55	0.16	−3.38	0.001^b
Diagnosis: rMDD	0.60	0.10	6.24	<0.001^c	0.10
Diagnosis: rMDD + MCI	0.12	0.11	1.12	0.3^c
Vascular risk factors	−0.08	0.04	−2.02	0.045	0.01

Note. MCI = mild cognitive impairment; NA = not applicable; rMDD = remitted major depressive.

Post hoc: only statistically significantly different between those with high school or less and university degree (estimate [SE] = −0.41 [0.12], P_adj = 0.003) or graduate degree (estimate [SE] = −0.61 [0.13], P_adj < 0.001); other pairwise comparisons were not significant (P_adj > 0.05).

Post hoc: only statistically significantly different between those with no allele and one allele (estimate [SE] = 0.39 [0.09], P_adj < 0.001) or two alleles (estimate [SE] = 0.55 [0.16], P_adj = 0.002).

Post hoc: only statistically significantly different between rMDD and MCI (estimate [SE] = −0.60 [0.09], P_adj < 0.001) or MCI + rMDD (estimate [SE] = −0.48 [0.12], P_adj < 0.001).

Angiogenin and Different Clinical Characteristics

No differences in angiogenin were observed between gender (χ²_KW(1) = 0.55, P = 0.5), race (χ²_KW(3) = 5.3, P = 0.2), and education level groups (χ²_KW(4) = 4.0, P = 0.4). In diagnostic subgroups (Figure 2a), the MCI and rMDD groups had higher angiogenin levels than the rMDD + MCI group (MCI: Z = 7.2, P_adj < 0.001; rMDD: Z = 4.9, P_adj < 0.001). No differences were observed between the MCI and rMDD groups (Z = 1.7, P = 0.08, P_adj = 0.2), but the MCI group had higher angiogenin levels than the rMDD and rMDD + MCI groups (χ²_KW(1) = 5.3, P < 0.001; Figure 2b). There were no differences between t participants with rMDD (without MCI) and participants with MCI (either MCI or rMDD + MCI; χ²_KW(1) = 0.19, P = 0.7). ApoE ε4 carriers (i.e., carriers with either one or two alleles) had lower angiogenin levels than non-carriers (Z = 4.6, P_adj = 0.03; Figure 2c). Additional analyses considering different ApoE ε4 groupings were shown in Figure S2.

Figure 2.

Boxplots of log angiogenesis levels in subgroups by (a) diagnosis, (b) presence of rMDD, (c) ApoE ε4 carrier status. (a) MCI and rMDD had higher angiogenin levels than rMDD + MCI (MCI: Z = 7.2, P_adj < 0.001; rMDD: Z = 4.9, P_adj < 0.001). No differences were observed between MCI and rMDD (Z = 1.7, P = 0.08, P_adj = 0.2). (b) MCI alone had higher angiogenin levels than those with rMDD (both rMDD and rMDD + MCI groups; χ²_KW(1) = 5.3, P < 0.001). (c) APoE ε4 carriers (those with one or two alleles) had lower angiogenin levels than non-carriers (Z = 4.6, P = 0.03). *P < 0.05; *** P < 0.001.

Discussion

In this study of older adults with MCI, rMDD, or both, angiogenin, a pro-angiogenic factor was associated with cognitive performance when controlling for well-established covariates including age,^44,45 diagnosis,⁴⁶ education,⁴⁷ ApoE ε4 carrier status,⁴⁸ and number of vascular risk factors.⁴⁹ Specifically, higher angiogenin was associated with higher composite cognitive scores. However, the effect size was small;⁵¹ it was numerically smaller than the effect size of age but larger than that of total number of vascular risk factors. Each one unit increase in log-transformed angiogenin levels was associated with a 0.21 point higher composite cognitive score. Despite the small effect size of angiogenin on cognitive performance, its incremental value in risk prediction remains noteworthy, with potential for greater relevance in specific subgroups yet to be identified.

Previous literature has not found a consistent relationship between angiogenin and cognitive performance. For example, the relationship between angiogenin and cognition has been assessed in AD with conflicting findings;^15,52 with a small study (AD: n = 20) reporting a positive correlation between angiogenin and MMSE¹⁵ and a larger study (AD: n = 205) reporting a negative correlation.⁵² Other angiogenesis markers have been shown to be different in individuals with MCI or AD.^16,17 Angiogenin has not previously been assessed in individuals with rMDD or MDD, but other pro-angiogenic factors have been associated with worse cognitive performance in MDD, contrary to the current findings.¹⁸ There may be several reasons for these inconsistent findings. With the AD studies, the larger study⁵² probably presented more generalizable and robust findings, but no clinical characteristics were considered in that study. In the second study with a positive association with MMSE,¹⁵ angiogenin concentrations were about tenfold lower than those in the current study and existing literature, where reported mean plasma^53–55 or serum levels⁵⁶ generally ranged from 150 to 370 ng/mL.¹⁵ The MDD study had mostly young adults,¹⁸ and the results may not be comparable to the current study considering the different demographics. It also only analyzed two specific markers; analyzing more angiogenesis markers at the same time can provide a more accurate representation of the effects on angiogenesis as many factors can be involved. The relationship between angiogenin and cognition may also vary across levels of cognitive impairment. A study of angiogenin levels in a small number (n = 13) of individuals with MCI found lower serum angiogenin and VGEF in participants with AD than in those with MCI and controls.⁵⁷ While previous studies used a single cognitive screening test (e.g., the MMSE), our cognitive assessment was comprehensive, with 18 measures from 12 tests used to create a composite score.

The exact mechanisms linking angiogenesis and cognition have not been elucidated. In AD models, amyloid beta can trigger angiogenesis leading to hypervascularity and blood–brain barrier leakiness. Yet, hyper-induction of angiogenesis could ameliorate cognitive deficits and reduce AD-related neuropaththologies.¹¹ In support of this potentially beneficial effect, a deficiency in angiogenesis has been linked to cognitive impairment.^58,59 Angiogenesis in AD has been thought to occur as a compensatory mechanism for impaired cerebral blood flow,^60,61 further supporting angiogenesis’ suggested neuroprotective effects. Angiogenin belongs to the ribonuclease A superfamily, and it works with actin on endothelial and smooth muscle cell surfaces to stimulate plasmin catalytic activity, promoting angiogenesis.¹¹ By complexing with fibulin-1, it can enhance the proliferation, migration, and stabilization of the new vessel walls.¹¹ It contributes to vascularization in both healthy and disease states.¹¹ Thus, the positive association found between angiogenin and cognitive performance in our study aligns with the existing literature in animal studies connecting angiogenesis to neuroprotection.^62,63

Angiogenesis has been linked to neurogenesis in animal and in vitro models. For example, in ischemic mice, increased angiogenin in the neurogenic subventricular zone due to physical exercise was associated with the migration of derived neuroblasts, linking angiogenin to the post-stroke neurogenesis processes.⁶⁴ Similarly, human angiogenin has been associated with neurite extension and pathfinding and survival of motor neurons.⁶⁵ In vitro, human angiogenin has been shown to prevent cell death due to oxidative stress and play a role in cell migration.⁶⁶ Vascular disease, while interacting with other pathological processes, such as inflammation and angiogenesis,⁶¹ can lead to changes in brain function associated with depression, such as dorsal hypometabolism and ventral hypermetabolism;⁶⁷ this connection is consistent with the relationship between cognitive performance and angiogenin found in the subgroup with rMDD. On the other hand, older men with higher serum levels of endostatin, an anti-angiogenic protein, had greater odds of depression in a large cross-sectional study; they predicted that the probability of depression could be increased with serum endostatin levels to as high as 20–25% in older men,⁶⁸ suggesting that the relationship between angiogenic balance and depression is complex. Angiogenesis is a multi-faceted process potentially contributing to both depression and cognition.

This analysis has some limitations. First and foremost, it has a cross-sectional design, which prevents us to make any causal interpretation of the results. Future longitudinal studies will need to assess the trajectory of angiogenesis marker and cognition over time, which will provide more information to better understand the relationship between angiogenesis and cognition. While the MADRS scores for all participants were lower than 10, the mean scores were 1–2 points higher in the rMDD and rMDD + MCI groups than in the MCI group. While we cannot rule out the residual effects of depressive symptoms on cognition,⁶⁹ the MADRS scores of the two rMDD groups were typically within the non-depressed range (i.e., 0–6) and a 2-point difference is well below the 6-point minimal clinically important difference for the MADRS.⁷⁰ Due to the small sample size, the rMDD and rMDD + MCI groups could not be compared in subgroup analysis; the ApoE ε4 status groups also could not be compared. Nevertheless, the current findings support further investigation of the potential mediating role of angiogenesis, or specifically angiogenin, in older adults at risk of dementia, particularly those with rMDD orMCI.

Conclusion

Our results provide some preliminary support for angiogenin as a biomarker associated with cognitive impairment. If replicated, these results suggest that interventions that promote angiogenesis may offer a novel approach to prevent or slow cognitive decline in individuals with rMDD, MCI, or both who are at risk for dementia. Further research is needed to explore angiogenesis longitudinally, and specifically angiogenin, to assess its potential as a therapeutic target for cognitive decline.

Supplemental Material

sj-docx-1-cpa-10.1177_07067437251337627 - Supplemental material for Blood Angiogenesis Markers and Cognition in Older Adults at Risk for Dementia: Marqueurs sanguins de l’angiogenèse et cognition chez les personnes âgées à risque de démence

Supplemental material, sj-docx-1-cpa-10.1177_07067437251337627 for Blood Angiogenesis Markers and Cognition in Older Adults at Risk for Dementia: Marqueurs sanguins de l’angiogenèse et cognition chez les personnes âgées à risque de démence by Bing Xin Song, PhD, Erica Vieira, PhD, Damien Gallagher, MD, Breno S. Diniz, MD, PhD, Corinne E. Fischer, MD, Alastair J. Flint, MD, Nathan Herrmann, MD, Linda Mah, MD, MHSc, Benoit H. Mulsant, MD, MS, Tarek K. Rajji, MD, Clement Ma, PhD, Krista L. Lanctôt, PhD and on behalf of the PACt-MD Study Group in The Canadian Journal of Psychiatry

Footnotes

Acknowledgments

PACt-MD Study Group: Benoit H. Mulsant, MD, MS (principal investigator); Tarek K. Rajji, MD (co-PI; site PI, Centre for Addiction and Mental Health, lead, neurostimulation and neurophysiology); Nathan Herrmann, MD (co-PI; site PI, Sunnybrook Health Sciences Centre), Bruce G. Pollock, MD, PhD (co-PI); Daniel Blumberger, MD, MSc (co-investigator); Christopher Bowie, PhD, C.Psych (co-investigator; lead, cognitive remediation and neuropsychology); Meryl Butters, PhD (xonsultant, neuropsychology); Corinne Fischer, MD (co-investigator; site PI, St. Michael's Hospital); Alastair Flint, MD (co-investigator; site PI, University Health Network); Angela Golas, MD (lead, CSF); Ariel Graff, MD (lead, neurochemistry); James L. Kennedy, MD (lead, genetics); Sanjeev Kumar, MD (co-investigator); Krista Lanctot, PhD, (site PI, Sunnybrook Health Sciences Centre), Lillian Lourenco, MPH (study co-manager), Linda Mah, MD, MHS (co-investigator; site PI, Baycrest Health Sciences); Shima Ovaysikia, MA (study co-manager); Mark Rapoport, MD (co-investigator); Kevin Thorpe, MSc (biostatistician); Nicolaas P.L.G. Verhoeff, MD, PhD (co-investigator); Aristotle Voineskos, MD, PhD (lead, neuroimaging). We also acknowledge the contribution of Kathleen Bingham, MD; Lina Chiuccariello, PhD; Tiffany Chow, MD; Pallavi Dham, MD; Breno Diniz, MD, PhD; Dielle Miranda, Carmela Tartaglia, MD; and the PACt-MD Research Staff. Genotyping was performed by the CAMH Biobank and Molecular Core Facility (previously CAMH Microarray Facility) and funded by the Discovery Fund. ApoE variants rs7412 and rs429358 were genotyped using standard TaqMan SNP genotyping assays (Life Technologies, Burlington, ON) as per the manufacturer's directions. The authors would like to thank Huijue Wang and Myuri Ruthirakuhan for their statistical analysis support.

Data Availability

The data generated and analyzed in the current study is available from the corresponding author and the PACt-MD Study Group upon request.

Declaration of Conflicting Interests

The authors declared the following potential conflicts of interest with respect to the research, authorship, and/or publication of this article: KLL has received consulting fees from Boehringer Ingelheim, Bristol Meyers Squibb, Eisai Co. Ltd., Exciva, Ironshore Pharmaceuticals, H Lundbeck A/S, Novo Nordisk, Otsuka and Praxis Therapeutics.

Funding

The authors disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This project has been made possible by Brain Canada through the Canada Brain Research Fund, with the financial support of Health Canada and the Chagnon Family and the CAMH Discovery Fund. BXS has received funding from the Ontario Graduate Scholarship and CAN-TAP-TALENT Doctoral Award supported by the Canadian Institutes of Health Research (CIHR). KLL holds the Bernick Chair in Geriatric Psychopharmacology and has received grants from CIHR (PJT 183584, PJ2 179753, CNA 163902, PJT153079, PJ8 159823, and PJ8 169698), Alzheimer's Drug Discovery Foundation (GC-201808-2016354), Alzheimer's Association (PTCG-20–700751, PTC18-543823), and Weston Brain Institute (CT190002). CEF has received funding from Novo Nordisk, ADDF, NIH, NIA, CCNA, CIHR, and the Hilary and Galen Weston Foundation.

ORCID iDs

Bing Xin Song

Nathan Herrmann

Benoit H. Mulsant

Krista L. Lanctôt

Supplemental Material

Supplemental material for this article is available online.

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