Age Moderates Associations of Hypertension,White Matter Hyperintensities,and Cognition

Abstract

Background:

Hypertension and white matter hyperintensities (WMH) are mutually associated risk factors for cognitive impairment. However, age may modify the associations between hypertension and WMH, and their links to cognitive impairment.

Objective:

We evaluated the interaction between age and hypertension on WMH, and the age-stratified associations of hypertension and WMH with cognition.

Methods:

Key measures include systolic blood pressure (SBP), WMH (modified Fazekas visual ratings of cranial MRI), and the Montreal Cognitive Assessment (MoCA). Participants (N = 488) with prodromal and mild dementia were age-stratified (≤49, 50–59, 60–69,≥70), and considered hypertensive if their SBP≥140 mmHg. The interaction between age strata and hypertension on WMH, and age-stratified associations of hypertension and WMH with cognition, were evaluated using multiple linear regression analyses. Analyses controlled for other risk factors for WMH and cognitive impairment.

Results:

Age moderated the association between SBP and WMH. Hypertension was associated with higher WMH only in those aged 60–69, and WMH trends across age bands differed between those with and without hypertension. Finally, WMH and SBP≥140 were independently associated with lower MoCA scores within the 50–59 age band, while WMH alone was associated with poorer MoCA scores in the≥70 age band.

Conclusion:

In adults with prodromal or mild dementia, hypertension was associated with WMH specifically in the 60–69 age strata. Associations between hypertension and WMH with poorer cognition also differed across age bands. Future studies will be needed to investigate whether blood pressure management to slow cognitive decline by targeting WMH may be age dependent.

Keywords

Age cerebrovascular disease cognition hypertension white matter hyperintensities

INTRODUCTION

Hypertension and cerebral small vessel disease (SVD), particularly white matter lesions or white matter hyperintensities (WMH) on cranial magnetic resonance imaging (MRI), are closely linked to cognitive decline [1, 2], and have close mutual links [3]. Hypertension influences numerous mechanisms in WMH pathogenesis [3, 4], including cerebral autoregulation impairment, vascular collagenosis, blood-brain barrier disruption [5], and neuroinflammation [6]. Given the heterogeneity and age-dependence of these mechanisms, we hypothesize that associations between hypertension and WMH differ in different age groups [7].

By extension, the cross-sectional associations of hypertension and elevated WMH with poorer cognition are likely to be age-dependent. While poorer cognition has been associated with higher blood pressure in adult samples aged above 45 years [8, 9], these associations may be stronger in midlife (around 50 years of age) compared to older age bands [10, 11]. Similarly, the age-dependence of WMH-linked cognitive impairment [12 –15] remains controversial; one study concluded that greater WMH was associated with poorer cognition only in patients older than 60 years (but not younger) [16], while another observed this only in those older than 80 years [17].

Given the conflicting findings from these studies adopting varying age cut-off points, the associations between hypertension, WMH, and cognition across different age groups remain unclear. Additionally, cerebrovascular disease has also been proposed as a potential mechanism through which hypertension impacts cognition [18]; thus, further understanding the nature of age-specific associations between hypertension, WMH, and cognition may shed light on the mechanisms through which hypertension influences cognition. Finally, from a precision medicine perspective, delineating associations between age, blood pressure, and SVD is crucial to ascertain if blood pressure management to prevent WMH progression and cognitive decline may be more effective if targeted to specific age bands [2]. As such, the objective of this study is twofold: to evaluate if associations between hypertension and WMH depend on age groups, and to examine if WMH influences the age-stratified associations between hypertension and cognitive function.

Hence, in a sample of adults above 35 years of age with prodromal or mild dementia, we 1) assess WMH severity in participants with and without hypertension; 2) evaluate the interaction between age and hypertension on WMH, and 3) analyze the age-stratified associations of hypertension and WMH with cognitive function. We additionally conduct sub-analyses to investigate whether these associations can be replicated using a composite SVD burden score [19] in place of WMH.

METHODS

Participants

488 subjects from the Multimodal Connectome Study (2013–2017) and the Singapore Young Onset Dementia Cohort (SYNC, 2015–2018) study were included in this analysis. Both studies received approval from the SingHealth Centralized Review Board. Written informed consent was obtained from all patients prior to data collection. Study participants were recruited from an outpatient memory clinic in a tertiary neurology center in Singapore, or from the community via outreach and public awareness events about age-related memory disorders. Inclusion criteria for the current study were: 1) ages 35 and above; 2) availability of blood pressure data; and 3) completion of study protocol MRI. Additionally, included participants had prodromal dementia, indicated by presence of cognitive symptoms, Clinical Dementia Rating (CDR) score of 0.5 and not meeting DSM-IV criteria for dementia; or mild dementia indicated by fulfilling DSM-IV criteria for dementia and having a CDR score of 1.

Upon recruitment into their respective studies, participants’ demographic information and clinical histories were documented via interviews and clinical survey forms. Participants’ blood pressure was measured during their baseline visits, as well as subsequent clinic visits. They were then contacted via telephone for separate visits to complete neuroimaging at a neuroradiology laboratory, and were administered the Montreal Cognitive Assessment (MoCA) as a measure of global cognition [20] in the memory clinic. Cognitive evaluations were performed by trained psychologists blinded to clinical and neuroimaging findings.

Participants were stratified into age bands (≤49, 50–59, 60–69, ≥70) based on their age at the baseline visit. Age bands, rather than age as a continuous variable, were used for to allow for more meaningful clinical application of the findings. They were also classified as hypertensive if their average systolic blood pressure (SBP)≥140 mmHg for two consecutive clinical visits [21], which were conducted at an interval of at least 4 weeks apart. Diastolic blood pressure (DBP) was not used to classify participants as its associations with WMH as the nature of its association with WMH is complex and inconsistent, with both high and low DBP having previously been associated with WMH [18].

Imaging protocol

Participants underwent MRI using a 1.5T whole-body MRI system (Achieva 1.5; Philips Medical Systems, Best, The Netherlands; or Sonata; Siemens Medical, Erlangen, Germany) or a 3.0-T scanner (Achieva 3.0 T TX Series; Philips Medical System, Best, the Netherlands; Siemens TIM Trio 3T; Siemens, Erlangen, Germany; 3T Siemens Prisma; Siemens, Erlangen, Germany). T2-weighted Fluid-Attenuated-Inversion-Recovery (T2-FLAIR) sequences were used to assess the severity of WMH, chronic lacunes, and enlarged perivascular vascular spaces. Microbleeds were visualized on T2 gradient-recalled echo sequences or susceptibility weighted imaging.

Imaging variables

The primary imaging variable was WMH, defined as hyperintensities without cavitation on T2-FLAIR, following the recommendations of Standards for Reporting Vascular Changes on Neuroimaging [22]. The modified Fazekas rating system scores periventricular WMH (PVH) and deep subcortical WMH (DSH) on a 0–3 points rating scale separately for the right and the left side. For PVH, 0 = absence of WMH; 1 = caps or pencil-thin lining around the lateral ventricle; 2 = smooth halo along the edges of the lateral ventricle; and 3 = irregular hyperintensities extending into the deep white matter. For DSH, 0 = absence of WMH; 1 = punctuate foci of WMH in the deep subcortical region; 2 = beginning confluence of WMH foci; 3 = large confluent areas. The PVH and DSH scores from both hemispheres are summed to obtain a total score of 0–12 [23]. Presence of confluent WMH was indicated by Fazekas scores of 3 in the periventricular regions and/or 2 to in the deep subcortical areas [19].

The Staals scoring system was used to capture participants’ overall small vessel cerebrovascular burden. Participants were given a score ranging from 0–4, with one point awarded each for presence of WMH confluence, lacunes, perivascular spaces and microbleeds, based on criteria described by Staals et al. [19].

Statistical analysis

Demographic, cardiovascular, and imaging variables were compared between the hypertensive and non-hypertensive groups using two-sided independent-samples t-tests for continuous variables (reported as mean±standard deviation) and Chi-squared tests for categorical variables (reported as frequency and percentage). Multiple linear regression was conducted to test the interaction between age group and hypertension on WMH, and adjusted means (with 95% confidence interval) of WMH were computed and reported. Similar analyses were run for age group and continuous SBP on total WMH outcome. Covariates for these analyses included education, sex, body mass index (BMI), treatment for hypertension, presence of cardiac disease, hyperlipidemia, and diabetes, as these factors have previously been associated with cerebrovascular disease. Age-stratified multiple regression analysis of hypertension and WMH on MoCA score outcome was carried out to further investigate the effects of these variables on global cognition within age groups. Covariates for these analyses included education, BMI, treatment for hypertension, presence of cardiac disease, hyperlipidemia, and diabetes, as these factors have previously been associated with cognitive decline.

These analyses were repeated with Staals et al.’s total SVD burden score in place of WMH, to verify if composite cerebrovascular burden may show similar patterns of associations. Participants with missing data were not included in the analysis. SAS software version 9.4 for Windows (SAS, Inc. Cary, NC) was used for data analysis. Statistical significance was set at p < 0.05.

RESULTS

Participant characteristics

Out of 571 screened participants, 488 (47% female, mean age 63.5±8.8 years (range 36–88), mean education 11.1±4.3 years) met inclusion criteria and were included in analyses. Of these participants, 266 (54.5%) had prodromal dementia, 168 (34.4%) had mild Alzheimer’s dementia, and 54 (11.1%) had mild Parkinsonism and other related dementias. Of those with mild Alzheimer’s dementia, N = 71(42%) had concomitant cerebrovascular disease (Fazekas score≥8). Those who did not meet inclusion criteria were excluded as they did not complete study protocol MRI (N = 40), BP measurements (N = 33), or did not meet CDR criteria (N = 10).

Of the total cohort, 131 participants (27%) had hypertension. Hypertensive participants were significantly older than non-hypertensives (p = 0.012). They also had higher mean WMH Fazekas score (p < 0.001) and greater frequency of confluent WMH (p = 0.001). Finally, hypertensive participants also had poorer global cognition, indicated by lower MoCA scores (p = 0.001).

Demographic, clinical and cognitive characteristics of the participants, stratified by age, are summarized in Table 1. Participants were stratified into ages≤49 (N = 26), 50–59 (N = 181), 60–69 (N = 191), and≥70 (N = 90). As the≤49 group (N = 26) may be too small for meaningful estimates to be obtained, the main analyses and results presented below exclude this group, and include only the 50 –50, 60 –69, and≥70 age groups. We repeat analyses on the whole sample, including the≤49 age group, and describe findings of interest from the repeated analysis at the end of each subsection.

Table 1

Demographic, clinical, and cognitive differences between subjects of different age strata

Variables	Missing	≤49	50–59	60–69	≥70	p
	N	(n = 26)	(N = 181)	(N = 191)	(N = 90)
Demographic
Age (y)	–	46.1±4.1	56.0±2.88	65.0±2.7	75.6±4.1	–
Sex (Female)	–	7 (27)	97 (54)	101 (53)	41 (46)	0.051
Education (y)	6	13.8±2.93^cd	11.9±4.0^d	11.1±4.2^ad	11.2±4.2^acd	<0.001
SBP (mm Hg)	–	129.8±23.3	130.7±17.1	131.8±15.6	135.6±19.7	0.156
DBP (mm Hg)	–	75.2±17.0	75.5±11.0^d	73.1±10.2	70.0±9.6^b	0.001
Medical History
Cognitive Status
Prodromal dementia	–	16 (62)	101 (56)^d	116 (61)^d	33 (37)^bc	0.002
Mild dementia (Alzheimer’s dementia)	–	5 (19)^d	54 (30)^d	56 (29)^d	53 (59)^abc	<0.001
Mild dementia (Parkinsonism and related dementia)	–	5 (19)	26 (14)^d	19 (10)	4 (4)^b	0.046
Cardiac Disease	7	0 (0)	9 (5)	10 (5)	9 (10)	0.182
Hyperlipidemia	7	9 (35)	76 (43)^d	101 (54)	57 (63)^b	0.004
Diabetes	6	25 (15)	33 (19)	36 (19)	25 (28)	0.264
BMI (kg/m2)	10	23.9±4.3	24.5±4.74	23.6±3.9	23.3±3.4	0.097
SBP≥140	–	9 (35)	41 (23)	48 (25)	33 (25)	0.068
Neuroimaging
WMH Fazekas score (0–12)	25	3.6±3.3^cd	4.3±3.8^cd	5.9±3.8^abd	8.0±3.2^abc	<0.001
WMH Confluence	25	6 (23)^cd	68 (39)^cd	96 (53)^abd	66 (81)^abc	<0.001
Cognition
MoCA (0–30)	45	22.0±6.9	22.4±7.0	22.9±6.5	20.7±6.7	0.085

Values for continuous variables reported as mean±standard deviation and compared using one-way ANOVAs with Bonferroni-adjusted pairwise comparisons. Values for categorical variables reported as frequency (percent), and compared using Kruskal-Wallis tests with Bonferroni-adjusted Dunn’s tests for pairwise comparisons. ^asignificantly different from≤49 group, p < 0.05; ^bsignificantly different from 50–59 group, p < 0.05; ^csignificantly different from 60–69 group, p < 0.05; ^dsignificantly different from≥70 group, p < 0.05. SBP, systolic blood pressure; DBP, diastolic blood pressure; BMI, body mass index; WMH, white matter hyperintensities; MoCA, Montreal Cognitive Assessment.

Effect of hypertensive status within and between age groups

Multiple linear regression analysis revealed that participants with hypertension had a higher WMH load compared to those without (M_SBP ≥140 (95% CI) = 6.0 (5.1, 6.8) versus M_SBP <140 = 4.8 (4.1, 5.5), p = 0.008), for covariates fixed at Treatment for hypertension = 0 (None), Treatment for hypertension = 0 (None), Diabetes mellitus = 0 (None), Cardiac disease = 0 (None), Hyperlipidemia = 0 (None), Sex = 0 (male), Education = 11.20 years, BMI = 23.9 kg/m². WMH differed significantly between age groups (p < 0.001). Mean WMH scores were higher, although non-significant, in the≥70 age band compared to the 60–69 age band (M≥₇₀ = 6.8 (5.8, 7.9) versus M _60–69 = 5.7 (4.9, 6.5), p = 0.08), which was in turn significantly higher than that of the 50–59 age band (M _50-59 = 3.7 (2.8,4.5), p < 0.001).

However, the interaction between age band and hypertension was significant (p = 0.007), suggesting that hypertension has different effects on WMH within age bands (Fig. 1). Hypertension was associated with WMH only in the 60–69 age band (Beta coefficient = 2.81 (1.49, 3.93), p < 0.001). The beta coefficient indicates that hypertensives in the 60–69 age group had an average WMH score that was 2.71 points greater than that of non-hypertensives (Table 2). We further verified the effects of age and blood pressure on WMH by testing the interaction of continuous SBP and age groups in another multiple linear regression model (Table 3). Consistent with findings when hypertension was treated as a binary variable, the interaction between age group and SBP on WMH load was significant (p = 0.008). Post-hoc analyses also revealed that the association between SBP and WMH was statistically significant only in the 60–69 age group, but not in the other age groups (Beta coefficient = 0.55 (0.21, 0.90), p = 0.002). The coefficient indicates that a +10 mmHg difference in SBP is associated with a +0.55 points difference in WMH Fazekas score within this age group.

Fig. 1

Interaction between age group and hypertensive status on WMH load. Least square means with 95% confidence intervals of WMH load based on the interaction of age group and hypertensive status, multiple linear regression model. Covariates were fixed at Treatment for hypertension = 0 (None), Diabetes mellitus = 0 (None), Cardiac disease = 0 (None), Hyperlipidemia = 0 (None), Sex = 0 (male), Education = 11.2 years, BMI = 23.9 kg/m². ^* p < 0.05 in contrasts for WMH between consecutive age bands within hypertensives or non-hypertensives; ^# p < 0.05 in contrasts for WMH between hypertensives and non-hypertensives within 60 –69 age band. WMH, white matter hyperintensities.

Table 2

Adjusted Means of WMH Fazekas scores by age groups and hypertensive status, multiple linear regressions

Group		Mean WMH Fazekas Score
		(95% CI))
50–59	SBP < 140	3.7 (2.9, 4.6)
	SBP≥140	3.6 (2.3, 4.9)
60–69	SBP < 140	4.3 (3.5, 5.1)
	SBP≥140	7.0 (5.8, 8.2)
≥70	SBP < 140	6.5 (5.3, 7.6)
	SBP≥140	7.2 (5.8, 8.7)

Values for least square means with 95% confidence intervals of WMH load based on the interaction of age group and hypertensive status, multiple linear regression model. Covariates were fixed at Treatment for hypertension = 0 (None), Diabetes mellitus = 0 (None), Cardiac disease = 0 (None), Hyperlipidemia = 0 (None), Sex = 0 (male), Education = 11.2 years, BMI = 23.9 kg/m². SBP, systolic blood pressure; WMH, white matter hyperintensities.

Table 3

Coefficients for WMH Fazekas scores against continuous SBP by age groups, multiple linear regressions

Age Band	Adjusted Beta Coefficient⁺	Unadjusted Beta Coefficient
50–59	–0.19 (–0.52, 0.13)	–0.10 (–0.42, 0.22)
60–69	0.55^*** (0.21, 0.90)	0.56*** (0.22, 0.91)
≥70	0.20 (–0.21, 0.59)	0.29 (–0.11, 0.67)

Beta Coefficients are reported with 95% confidence intervals in parentheses. Covariates included treatment for hypertension, presence of diabetes mellitus, cardiac disease, hyperlipidemia, sex, education (years), and BMI (kg/m²). ⁺ Coefficients can be interpreted as increase or decrease in WMH score per 10 mmHg increase in SBP. ^** p < 0.01. SBP, systolic blood pressure; WMH, white matter hyperintensities.

Post-hoc analyses of pairwise differences in WMH between consecutive age groups within hypertensives and non-hypertensives were conducted. Within both groups, WMH increased from younger to older age bands. However, for those with hypertension, only the difference between the 60–69 and 50–59 age groups was statistically significant (Beta coefficient = 3.44 (1.51, 5.37), p < 0.001; M _{SBP ≥140,60-69} = 7.0 (5.8, 8.2) versus M _{SBP ≥140,50-59} = 3.6 (2.3, 4.9)), where WMH ratings in the 60–69 age band was 3.4 points higher than that of the 50–59 age band. On the other hand, in those without hypertension, only the difference between the≥70 and 60–69 age groups was significant (Beta coefficient = 2.15 (0.70, 3.59); M _{SBP <140, ≥70} = 6.5 (5.3, 7.6) versus M _{SBP <140, 60-69} = 4.3 (3.5, 5.1), p = 0.001), where the adjusted mean WMH score in the≥70 age band was 2.2 points higher than that of the 60–69 age band.

We further repeated the above analyses including the≤49 age band. We found that the≤49 age band had the lowest WMH among age groups (M≤₄₉ = 3.3 (1.8, 4.9)). WMH in this group was significantly lower than the 60–69 and≥70 age bands (ps < 0.01), although it did not differ significantly from that of the 50–59 age band. Further, WMH did not significantly differ between those with hypertension and those without for this age group. Finally, the interaction between age group and hypertension remained significant even when this age group was added (p = 0.030). Nonetheless, these findings should be interpreted with caution due to the small sample size of this group.

Effects of SBP and WMH on cognition

Age-stratified multiple linear regression analysis was performed to examine the effects of age band, hypertension and WMH on cognitive outcomes. SBP, WMH, and other risk factors for cognitive decline (BMI, treatment for hypertension, hyperlipidemia, diabetes, history of cardiac conditions, education, age group) were included in the regression model. In the 50–59 age band, WMH and SBP≥140 were associated with poorer MoCA scores (B_WMH = –0.34 (–0.63, –0.06), p = 0.020; B_SBP ≥140 = –4.46 (–6.96, –1.97, p = 0.001) while in the≥70 age band, only WMH (but not SBP) was associated with poorer MoCA scores (B = –0.59 (–1.00, –0.18), p = 0.006) (Table 4). These coefficients indicate that in the 50–59 age band, a one-point increase in WMH was associated with a 0.3-point decrement in MoCA, while those with SBP≥140 scored 4.5 points poorer on MoCA. In the 70–79 age band, a one-point increase in WMH corresponded to a 0.6-point drop in MoCA score. Neither WMH nor SBP were significantly associated with MoCA scores in the 60–69 age band. We also found that neither WMH nor SBP were significantly associated with MoCA scores in the ≤49 age band, although the estimates for this age band should be interpreted with caution due to the small sample size. Statistical significance for the associations between WMH and SBP with MoCA remained unchanged in models where either SBP or WMH were omitted. Effect sizes of WMH and SBP on MoCA, indicated by standardized beta coefficients, also changed minimally (<3%) in models where either SBP or WMH were omitted, respectively. These suggest that the effect of SBP and WMH on MoCA in the 50–59 and≥70 age bands were independent of each other.

Table 4

Coefficients for WMH Fazekas scores and hypertension on MoCA s cores by age groups, multiple linear regressions

Age	Beta Coefficients (Combined)^a		Beta Coefficients (Separate)^b		Unadjusted Beta Coefficients
	SBP⁺	WMH^#	SBP	WMH	SBP	WMH
50–59	–4.03^** (–6.47, –1.58)	–0.30^* (–0.58, –0.02)	–4.12^** (–6.60, –1.65)	–0.32^* (–0.61, –0.03)	–4.27** (–6.91, –1.63)	–0.39* (–0.69, –0.08)
60–69	–0.13 (–2.40,2.13)	–0.05 (–0.33,0.22)	–0.26 (–2.42,1.90)	–0.06 (–0.32,0.20)	–0.65 (–2.93,1.90)	–0.18 (–0.45,0.09)
≥70	–0.35 (–2.93, 2.23)	–0.54^** (–0.92, –0.16)	–1.12 (–3.76. 1.51)	–0.55^** (–0.92, –0.18)	–0.48 (–3.62, 2.66)	–0.58* (–1.03, –0.12)

Beta Coefficients are reported with 95% confidence intervals in parentheses. Covariates included treatment for hypertension, presence of diabetes mellitus, cardiac disease, hyperlipidemia, education (years), BMI (kg/m²). ^aCoefficients for SBP and WMH were evaluated in the same model, including covariates. ^bCoefficients for SBP and WMH were evaluated in separate models, including covariates. ⁺Coefficients can be interpreted as difference in MoCA scores of hypertensive minus non-hypertensive groups. ^#Coefficients can be interpreted as change in MoCA score per one-point increase in WMH. ^* p < 0.05. ^** p < 0.01. SBP, systolic blood pressure; WMH, white matter hyperintensities.

Secondary analysis: Trends in Staals scores

In the subset of participants who had the required data (N = 256), we evaluated how composite SVD burden had was associated with age, hypertension, and global cognition. Multiple linear regression analyses were used to evaluate the interaction between age bands and hypertension on Staals’ composite SVD burden score [18], including the same covariates used in the earlier analyses. This revealed that SVD burden was significantly elevated in older age groups (p < 0.001), and elevated (although statistically non-significant) in the hypertensive (M = 1.1 (0.8, 1.4)) compared to the non-hypertensive group (M = 0.8 (0.5, 1.0), p = 0.061). Although the interaction between hypertension and age group was not significant (p = 0.224), post-hoc evaluations revealed that only in the 60–69 age band were the least square means of Staals scores significantly elevated in the hypertensive (M = 1.1 (0.7, 1.5)) compared to the non-hypertensive group (M = 0.7 (0.4, 0.9), p = 0.049), consistent with what we observed in WMH. Trends in findings remained unchanged when the <49 age group was included in analyses, although these findings should be interpreted with caution owing to the small number of participants in this group with Staals scores available (N = 16).

We further replicated earlier age-stratified multiple linear regression analyses with MoCA scores as outcomes, replacing WMH with Staals scores as a predictor variable. Staals scores were not significantly associated with MoCA scores in any of the age groups. Thus, while hypertension and age bands appear to have similar patterns of association with WMH and composite SVD burden, WMH scores may be more strongly associated with concurrent global cognition compared to Staals scores.

DISCUSSION

In this study, we aimed to evaluate if associations between hypertension and WMH depend on age groups, and to examine if WMH influences the associations between hypertension and cognitive function within these age groups. Our findings demonstrate that, for our sample with prodromal and mild dementia, those with hypertension had higher total WMH compared to those without hypertension. The interaction between age bands and hypertension was significantly associated with WMH, congruent with our hypothesis that associations between hypertension and WMH would differ between age groups. Specifically, the association between hypertension and WMH was statistically significant only in the 60–69 age band. Global cognition, measured by the MoCA, was associated with both hypertension and WMH in 50–59 age band, and only with WMH in the≥70 age group. Neither hypertension nor WMH influenced MoCA in the ≤49 age band or 60–69 age band.

To our knowledge, few studies have directly investigated how age bands moderate associations between hypertension, WMH, and cognition. Our findings may help to explain the marked inconsistencies in research on cross-sectional associations between hypertension and WMH. Although we showed that those with systolic hypertension (defined here as SBP≥140) had higher WMH on average, consistent with a few earlier reports [3, 24], others have also previously found non-associations [22, 25]. These inconsistencies could be attributed to the varying ages of samples used in different studies, since the association between WMH and SBP varies depending on age bands, as observed in this study.

We found that hypertension was associated with WMH specifically in the 60–69 age group, but not in the≥70 age group. Thus, within this age range, white matter may be especially vulnerable to alterations due to hypertension in patients with prodromal or mild dementia. Non-significant associations in the≥70 age group concur with findings of the Rotterdam Scan Study, in which participants from the 60–69, 70–79, and 80–89 age bands showed respectively smaller associations of higher BP with greater WMH progression [26]. This also aligns with Wardlaw et al.’s study of the Lothian Birth Cohort (mean age 72.5), which found that vascular risk factors (including hypertension) failed to explain 98% of the variance in WMH [27]. Similarly, our findings support the interpretation that risk factors other than hypertension may account for WMH in the≥70 age bracket.

However, while findings from the Rotterdam Scan Study may suggest that hypertension should be even more strongly associated with WMH in the 50–59 age group, we did not observe an association between SBP and WMH severity for this age band. Given that previous studies have shown that elevated blood pressure precedes WMH progression [3], this may be a manifestation of the latency of the effects of hypertension on WMH in the 50–59 age group that reveals itself mainly at the 60–69 age bracket. It is essential to highlight that firstly our cohort consists of subjects with largely prodromal and mild dementia, and, secondly, we had a substantial number of subjects below age 65 years. One of the main findings in this study was that there was no significant difference in WMH load between hypertensive and non-hypertensive subjects in the 50–59 and younger age groups. It is possible that in the younger age groups, a proportion of subjects may have had pre-hypertension with all the biological sequalae similar to those with hypertension [28, 29], but categorized into the non-hypertensive group by virtue of their normal blood pressure. Furthermore, younger subjects may have additional pathobiology such as neuroinflammation [30], which may account for WMH independent of their hypertension status. These reasons may account for the non-significant association between SBP and WMH in these age groups”.

Additionally, our secondary analyses with composite cardiovascular disease burden revealed consistent age-specific associations between hypertension and cardiovascular disease burden, implying that the age-dependent hypertension-WMH link we observed may be common to different forms of SVD, and is worth further investigation. Future longitudinal studies following cohorts from a younger age will shed light on how cardiovascular and other risk factors affect WMH and SVD across the lifespan, and if the 60–69 age strata may be particularly vulnerable to the development of WMH due to hypertension.

We observed that WMH burden generally increased across age bands for both non-hypertensives and hypertensives with prodromal and mild dementia, consistent with current knowledge that WMH progresses with age [31, 32]. In non-hypertensives, WMH increased slightly (albeit not statistically significantly) between age bands≤49 to 60–69; however, there was a significant spike in WMH from the 60–69 to≥70 age bands. However, hypertensive participants demonstrated a sharp increase in WMH at younger age bands; specifically, between the 50–59 and 60–69 bands (compared to 60–69 versuss≥70 age bands in non-hypertensives). This aligns with current knowledge that hypertension is a risk factor for WMH [33]. Unexpectedly, WMH did not significantly differ between the≥70 and 60–69 age bands, contrary to what was observed in non-hypertensives [32]. These findings imply that there may exist an ideal age range for blood pressure control in patients with prodromal or mild dementia as a preventive treatment for WMH, which remains a key question for clinical trials [34]. If these findings can be replicated in cognitively normal individuals, drug trials targeting hypertension to treat WMH may see larger effects if they target persons 50–59 years old, before the sudden sharp increase in WMH we observed. Further, these treatments may only reveal their effects on WMH after the peak increment in WMH at 60–69 years of age. Thus, further studies of age-based associations between hypertension and WMH especially in cognitively normal samples are needed, and will have important implications for designing future clinical trials.

We also observed that hypertension was associated with poorer concurrent global cognitive impairment only in the 50–59 age group. This complements previous cross-sectional research suggesting that high blood pressure at middle age, but not at older age, predicts dementia [10 , 36], although these findings are not unanimous [18]. Similarly, research on how age affects the link between WMH and cognition remains divided. For instance, although Zamboni et al. reported an association between WMH and concurrent cognition only in those older than 80 years [17], Vannorsdall et al. observed this association only in patients older than 60 [16]. However, we found that WMH predicted global cognition only in the 50–59 and≥70 age bands, but not in the≤49 or 60–69 age bands. The use of different age cut-offs to group participants in previous studies may hence explain the disagreement amongst their findings. Finally, total SVD burden scores were not associated with cognition in any of the age bands, suggesting further that these WMH-cognition links are specific to WMH, rather than due to total SVD burden.

Additionally, we found that WMH and SBP were independently associated with concurrent global cognition in the 50–59 age band, indicating that SBP and WMH may affect concurrent global cognition in prodromal or mild dementia through at least some independent pathways. This observation contradicts the findings of the Cardiovascular Health Study that baseline WMH may mediate the effects of hypertension on cognition at 3 years follow-up [37], albeit in a population of mean age 74, and hence, our findings require further investigation in future studies. Further, these associations were specific only to the 50–59 age band. This suggests an elevated vulnerability to the effects of SBP and WMH on cognition for persons with prodromal or mild dementia in this relatively young age group. If our findings are confirmed in future studies, clinicians would have reason to suspect greater concurrent cognitive impairment if they observe elevated SBP or WMH in prodromal or mild dementia patients aged 50–59, and that blood pressure management for these patients will be even more crucial.

The present study included participants of a wide age range, and analyses included numerous cardiovascular risk factors, allowing the effects of SBP on WMH, and their effects on global cognition, to be isolated across a wide age range. Further, we were able to evaluate the role of total SVD burden as a competing explanation for our findings through secondary analyses using Staals et al.’s composite SVD burden score.

However, findings are qualified by the relatively small sample size and unbalanced numbers across the different age groups, which may limit generalizability. In particular, only 26 participants met eligibility criteria for the≤49 age band; thus, we presented findings primarily for age groups 50 and older. Nonetheless, it would be informative if findings for the≤49 age band is replicated in future studies. Similarly, Staals scores were available only for a subgroup of participants, which may have resulted in the analysis being underpowered to detect subtler effects. Additionally, as this is study was conducted in a sample of persons with prodromal and mild dementia, findings may not generalize to community cohorts. Another limitation is the use of only MoCA scores as a cognitive measure in the present study, as it is a screening tool for global cognitive function. Further replication of our findings with a comprehensive neuropsychological assessment will be informative. We were unable to examine the effects of potential confounders, namely the duration of hypertension, as we did not collect information on the time of hypertension diagnosis. However, duration of hypertension is unlikely to completely explain present findings, as significant associations between hypertension and WMH respectively with cognition were not found in the oldest (≥70) age band, which is likely to have participants with the longest duration of hypertension. Nevertheless, future work to address the role of duration of hypertension in these associations is needed. The cross-sectional design of this study also precludes the ability to control for time-dependent extraneous variables including earlier WMH levels, and premorbid cognitive function. Further, changes in WMH and cognition may not be contemporaneous with elevations in blood pressure, and may reflect changes in previous age strata. Nonetheless, our findings suggest the importance for investigating SBP, WMH and cognition longitudinally, and across a wider range of age bands. Such research will be crucial to determining whether clinical trials and BP interventions for WMH and cognitive decline may benefit from targeting specific age groups.

In conclusion, we found that age moderates the association between SBP and WMH in adults with prodromal or mild dementia. Their influence on concurrent global cognition also varies in different age bands. Thus, future studies and clinical trials will benefit from taking age bands into account.

Footnotes

ACKNOWLEDGMENTS

This work was funded by the Singapore National Medical Research Council [grant number CIRG14may025]; and the Biomedical Research Council [grant number ACP0113687]. We thank the dementia team at the National Neuroscience Institute for assistance with data collection and supporting this study.

Authors’ disclosures available online ().

References

Wolf

(2012) Contributions of the Framingham Heart Study to stroke and dementia epidemiologic research at 60 years. Arch Neurol 69, 567–571.

Peters

, Booth

, Rockwood

, Peters

, D’Este

, Anstey

(2019) Combining modifiable risk factors and risk of dementia: A systematic review and meta-analysis. BMJ Open 9, e022846.

Verhaaren

BFJ

, Vernooij

, de Boer

, Hofman

, Niessen

, van der Lugt

, Ikram

(2013) High blood pressure and cerebral white matter lesion progression in the general population. Hypertension 61, 1354–1359.

Debette

, Markus

(2010) The clinical importance of white matter hyperintensities on brain magnetic resonance imaging: Systematic review and meta-analysis. BMJ 341, c3666.

Lin

, Wang

, Lan

, Fan

(2017) Multiple factors involved in the pathogenesis of white matter lesions. Biomed Res Int 2017, 9372050.

Raj

, Yin

, Breur

, Doorduin

, Holtman

, Olah

, Mantingh-Otter

, Van Dam

, De Deyn

, den Dunnen

, Eggen

BJL

, Amor

, Boddeke

(2017) Increased white matter inflammation in aging- and Alzheimer’s disease brain. Front Mol Neurosci 10, 206.

Wardlaw

, Valdés Hernández

, Muñoz-Maniega

(2015) What are white matter hyperintensities made of? Relevance to vascular cognitive impairment. J Am Heart Assoc 4, 001140.

Wei

, Yin

, Liu

, Tan

, Jia

(2018) Association between hypertension and cognitive function: A cross-sectional study in people over 45 years old in China. J Clin Hypertens 20, 1575–1583.

Obisesan

, Obisesan

, Martins

, Alamgir

, Bond

, Maxwell

, Gillum

(2008) High blood pressure, hypertension, and high pulse pressure are associated with poorer cognitive function in persons aged 60 and older: The Third National Health and Nutrition Examination Survey. J Am Geriatr Soc 56, 501–509.

10.

, Rhew

, Shofer

, Kukull

, Breitner

JCS

, Peskind

, Bowen

, McCormick

, Teri

, Crane

, Larson

(2007) Age-varying association between blood pressure and risk of dementia in those aged 65 and older: A community-based prospective cohort study. J Am Geriatr Soc 55, 1161–1167.

11.

Legdeur

, van der Lee

, de Wilde

, van der Lei

, Muller

, Maier

, Visser

(2019) The association of vascular disorders with incident dementia in different age groups. Alzheimers Res Ther 11, 47.

12.

Hachinski

(1990) The decline and resurgence of vascular dementia. CMAJ 142, 107–111.

13.

Boyle

, Yu

, Fleischman

, Leurgans

, Yang

, Wilson

, Schneider

, Arvanitakis

, Arfanakis

, Bennett

(2016) White matter hyperintensities, incident mild cognitive impairment, and cognitive decline in old age. Ann Clin Transl Neurol 3, 791–800.

14.

Tosto

, Zimmerman

, Carmichael

, Brickman

(2014) Predicting aggressive decline in mild cognitive impairment. JAMA Neurol 71, 872.

15.

Benedictus

, van Harten

, Leeuwis

, Koene

, Scheltens

, Barkhof

, Prins

, van der Flier

(2015) White matter hyperintensities relate to clinical progression in subjective cognitive decline. Stroke 46, 2661–2664.

16.

Vannorsdall

, Waldstein

, Kraut

, Pearlson

, Schretlen

(2009) White matter abnormalities and cognition in a community sample. Arch Clin Neuropsychol 24, 209–217.

17.

Zamboni

, Griffanti

, Mazzucco

, Pendlebury

, Rothwell

(2019) Age-dependent association of white matter abnormality with cognition after TIA or minor stroke. Neurology 93, e272–e282.

18.

Iadecola

, Yaffe

, Biller

, Bratzke

, Faraci

, Gorelick

, Gulati

, Kamel

, Knopman

, Launer

, Saczynski

, Seshadri

, Zeki Al Hazzouri

, American Heart Association Council on Hypertension; Council on Clinical Cardiology; Council on Cardiovascular Disease in the Young; Council on Cardiovascular and Stroke Nursing; Council on Quality of Care and Outcomes Research; and Stroke Council (2016) Impact of hypertension on cognitive function: A scientific statement from the American Heart Association. Hypertension 68, e67–e94.

19.

Staals

, Makin

SDJ

, Doubal

, Dennis

, Wardlaw

(2014) Stroke subtype, vascular risk factors, and total MRI brain small-vessel disease burden. Neurology 83, 1228–1234.

20.

Nasreddine

, Phillips

, Bedirian

, Charbonneau

, Whitehead

, Collin

, Cummings

, Chertkow

(2005) The Montreal Cognitive Assessment, MoCA: A brief screening tool for mild cognitive impairment. J Am Geriatr Soc 53, 695–699.

21.

Carey

, Whelton

(2018) Prevention, detection, evaluation, and management of high blood pressure in adults: Synopsis of the 2017 American College of Cardiology/American Heart Association Hypertension Guideline. Ann Intern Med 168, 351.

22.

Wardlaw

, Smith

, Biessels

, Cordonnier

, Fazekas

, Frayne

, Lindley

, O’Brien

, Barkhof

, Benavente

, Black

, Brayne

, Breteler

, Chabriat

, DeCarli

, de Leeuw

F-E

, Doubal

, Duering

, Fox

, Greenberg

, Hachinski

, Kilimann

, Mok

, Oostenbrugge

R van

, Pantoni

, Speck

, Stephan

BCM

, Teipel

, Viswanathan

, Werring

, Chen

, Smith

, van Buchem

, Norrving

, Gorelick

, Dichgans

(2013) Neuroimaging standards for research into small vessel disease and its contribution to ageing and neurodegeneration. Lancet Neurol 12, 822–838.

23.

Fazekas

, Chawluk

, Alavi

, Hurtig

, Zimmerman

(1987) MR signal abnormalities at 1.5 T in Alzheimer’s dementia and normal aging. Am J Roentgenol 149, 351–356.

24.

van Dijk

, Breteler

MMB

, Schmidt

, Berger

, Nilsson

L-G

, Oudkerk

, Pajak

, Sans

, de Ridder

, Dufouil

, Fuhrer

, Giampaoli

, Launer

, Hofman

, CASCADE Consortium (2004) The association between blood pressure, hypertension, and cerebral white matter lesions: Cardiovascular determinants of dementia study. Hypertension 44, 625–630.

25.

Dickie

, Ritchie

, Cox

, Sakka

, Royle

, Aribisala

, Valdés Hernández

M del C

, Maniega

, Pattie

, Corley

, Starr

, Bastin

, Deary

, Wardlaw

(2016) Vascular risk factors and progression of white matter hyperintensities in the Lothian Birth Cohort 1936. Neurobiol Aging 42, 116–123.

26.

van Dijk

, Prins

, Vrooman

, Hofman

, Koudstaal

, Breteler

MMB

(2008) Progression of cerebral small vessel disease in relation to risk factors and cognitive consequences: Rotterdam Scan study. Stroke 39, 2712–2719.

27.

Wardlaw

, Allerhand

, Doubal

, Hernandez

, Morris

, Gow

, Bastin

, Starr

, Dennis

, Deary

(2014) Vascular risk factors, large-artery atheroma, and brain white matter hyperintensities. Neurology 82, 1331–1338.

28.

Giannotti

, Doerries

, Mocharla

, Mueller

, Bahlmann

, Horvàth

, Jiang

, Sorrentino

, Steenken

, Manes

, Marzilli

, Rudolph

, Lüscher

, Drexler

, Landmesser

(2010) Impaired endothelial repair capacity of early endothelial progenitor cells in prehypertension: Relation to endothelial dysfunction. Hypertension 55, 1389–1397.

29.

Hoth

, Tate

, Poppas

, Forman

, Gunstad

, Moser

, Paul

, Jefferson

, Haley

, Cohen

(2007) Endothelial function and white matter hyperintensities in older adults with cardiovascular disease. Stroke 38, 308–312.

30.

Raz

, Yang

, Dahle

, Land

(2012) Volume of white matter hyperintensities in healthy adults: Contribution of age, vascular risk factors, and inflammation-related genetic variants. Biochim Biophys Acta 1822, 361–369.

31.

Sachdev

, Wen

, Chen

, Brodaty

(2007) Progression of white matter hyperintensities in elderly individuals over 3 years. Neurology 68, 214–222.

32.

Huang

C-C

, Yang

, Chou

K-H

, Liu

M-E

, Fang

S-C

, Chen

C-C

, Tsai

S-J

, Lin

C-P

(2018) Nonlinear pattern of the emergence of white matter hyperintensity in healthy Han Chinese: An adult lifespan study. Neurobiol Aging 67, 99–107.

33.

Prins

, Scheltens

(2015) White matter hyperintensities, cognitive impairment and dementia: An update. Nat Rev Neurol 11, 157–165.

34.

van Middelaar

, Argillander

, Schreuder

FHBM

, Deinum

, Richard

, Klijn

CJM

(2018) Effect of antihypertensive medication on cerebral small vessel disease: A systematic review and meta-analysis. Stroke 49, 1531–1533.

35.

Knecht

, Wersching

, Lohmann

, Berger

, Ringelstein

(2009) How much does hypertension affect cognition?. J Neurol Sci 283, 149–152.

36.

Kritz-Silverstein

, Laughlin

, McEvoy

, Barrett-Connor

(2017) Sex and age differences in the association of blood pressure and hypertension with cognitive function in the elderly: The Rancho Bernardo Study. J Prev Alzheimers Dis 4, 165–173.

37.

Hajjar

, Quach

, Yang

, Chaves

PHM

, Newman

, Mukamal

, Longstreth

, Inzitari

, Lipsitz

(2011) Hypertension, white matter hyperintensities, and concurrent impairments in mobility, cognition, and mood. Circulation 123, 858–865.