Left Ventricular Hypertrophy and Cerebral Small Vessel Disease: A Systematic Review and Meta-Analysis

Article information

J Stroke. 2020;22(2):206-224
Publication date (electronic) : 2020 May 31
doi : https://doi.org/10.5853/jos.2019.03335
aMedical School, National and Kapodistrian University of Athens, Athens, Greece
bCardiovascular Prevention and Research Unit, Department of Pathophysiology, National and Kapodistrian University of Athens, Athens, Greece
cCognitive and Movement Disorders Unit and Unit of Neurochemistry and Biological Markers, First Department of Neurology, Eginition University Hospital, Medical School, National and Kapodistrian University of Athens, Athens, Greece
dSecond Department of Neurology, Attikon Hospital, School of Medicine, National and Kapodistrian University of Athens, Athens, Greece
eDepartment of Neurology, University of Tennessee Health Science Center, Memphis, TN, USA
fInstitute for Stroke and Dementia Research, LMU University Hospital, Ludwig-Maximilians-University (LMU), Munich, Germany
Correspondence: Marios K. Georgakis Institute for Stroke and Dementia Research, LMU University Hospital, Ludwig-Maximilians-University (LMU), Feodor-Lynen-Str. 17, 81377 Munich, Germany Tel: +49-89-4400-46126 Fax: +49-89-4400-46040 E-mail: marios.georgakis@med.uni-muenchen.de
*These authors contributed equally to the manuscript as first author.
Received 2019 November 27; Revised 2020 January 9; Accepted 2020 March 6.

Abstract

Background and Purpose

Left ventricular hypertrophy (LVH) is associated with the risk of stroke and dementia independently of other vascular risk factors, but its association with cerebral small vessel disease (CSVD) remains unknown. Here, we employed a systematic review and meta-analysis to address this gap.

Methods

Following the MOOSE guidelines (PROSPERO protocol: CRD42018110305), we systematically searched the literature for studies exploring the association between LVH or left ventricular (LV) mass, with neuroimaging markers of CSVD (lacunes, white matter hyperintensities [WMHs], cerebral microbleeds [CMBs]). We evaluated risk of bias and pooled association estimates with random-effects meta-analyses.

Results

We identified 31 studies (n=25,562) meeting our eligibility criteria. In meta-analysis, LVH was associated with lacunes and extensive WMHs in studies of the general population (odds ratio [OR]lacunes, 1.49; 95% confidence interval [CI], 1.12 to 2.00) (ORWMH, 1.73; 95% CI, 1.38 to 2.17) and studies in high-risk populations (ORlacunes: 2.39; 95% CI, 1.32 to 4.32) (ORWMH, 2.01; 95% CI, 1.45 to 2.80). The results remained stable in general population studies adjusting for hypertension and other vascular risk factors, as well as in sub-analyses by LVH assessment method (echocardiography/electrocardiogram), study design (cross-sectional/cohort), and study quality. Across LV morphology patterns, we found gradually increasing ORs for concentric remodelling, eccentric hypertrophy, and concentric hypertrophy, as compared to normal LV geometry. LVH was further associated with CMBs in high-risk population studies.

Conclusions

LVH is associated with neuroimaging markers of CSVD independently of hypertension and other vascular risk factors. Our findings suggest LVH as a novel risk factor for CSVD and highlight the link between subclinical heart and brain damage.

Introduction

Cerebral small vessel disease (CSVD) describes any pathological processes affecting the perforating arterioles, capillaries, and venules of the brain [1,2]. CSVD is the leading cause of vascular cognitive impairment [3], accounts for 25% of all ischemic strokes [4] and the majority of intracerebral hemorrhage cases [5], and is an independent predictor of mortality [6,7]. Manifestations of CSVD are further associated with physical and psychological sequalae in the elderly including gait [8], functional [9], and mood [10] disturbances. CSVD can be defined by neuroimaging markers including lacunes, white matter hyperintensities (WMHs), cerebral microbleeds (CMBs) and enlarged perivascular spaces (EPVSs) [11]. Despite the very high prevalence of CSVD in the ageing population (≥90% in individuals ≥65 years [12]), the underlying mechanisms are incompletely understood, thus impeding the development of effective prevention strategies.

Left ventricular hypertrophy (LVH), a pathological increase in left ventricular mass (LVM) [13], has been proposed as an independent risk factor for cardiovascular disease [14] and is included in the original 10-year Framingham stroke risk score for incident stroke prediction in the elderly [15]. LVH and increased LVM are clinical markers of hypertension-mediated organ damage and constitute surrogate indicators of the duration of exposure to hypertension and other vascular risk factors [14]. In large-scale population-based cohort studies, LVH and increased LVM have been associated with the risk of incident stroke in the elderly, independently of hypertension presence or duration and other traditional vascular risk factors [16-18]. Furthermore, in a recent meta-analysis, we showed similar associations of LVH with cognitive decline and risk of incident dementia in both the general and high-risk populations [19].

These associations could be explained by effects of LVH on the microvasculature. Although several studies explore the associations between LVH or increased LVM and subclinical neuroimaging markers of CSVD [20-22], the results vary widely, probably because of heterogeneity in the populations examined, small sample sizes, variable methodologies for LVH assessment or LVM indexing, and differences in CSVD neuroimaging definitions. Furthermore, the studies differ regarding their methods for adjustment for hypertension and other vascular risk factors. Hence, it remains unknown if LVH is independently associated with subclinical CSVD neuroimaging markers.

Here, we leveraged data from published literature and performed a systematic review of studies exploring associations between LVH with neuroimaging markers of CSVD, aiming to: (1) critically evaluate the methodology of the included studies and identify limitations of the existing literature; (2) quantify in meta-analyses the associations of LVH and LVM with lacunes, WMHs, CMBs, and EPVSs in general population and high-risk individuals; and (3) explore if these associations are independent of the presence and/or duration of hypertension and other vascular risk factors.

Methods

This systematic review was based on a predefined protocol registered to PROSPERO (30 October 2018, registration number: CRD42018110305, available from: http://www.crd.york.ac.uk/PROSPERO/display_record.php?ID=CRD42018110305 ), compliant with the Meta-analysis of Observational Studies in Epidemiology (MOOSE) guidelines [23].

Literature search

Two independent reviewers (A.P. and K.P.) systematically screened the Medline (through PubMed), Scopus and Cochrane databases from inception to December 28, 2019 to identify studies investigating the association between LVH and CSVD neuroimaging markers. The detailed search strategy is available in the Supplement (Supplementary methods). All reference lists of the derived eligible articles were hand-searched for potential eligible studies not identified through the database search (“snowball” procedure). No language or publication year restrictions were applied. Eligible studies were evaluated for possible population overlap according to geographical setting, chronological period, sample size, outcome under study, and type of statistical analysis. In case of overlap, we opted for the most recent study. We further contacted the corresponding authors of articles presenting evidence that relevant data were available but not quantifying the associations under study, in order to request supplementary analyses. Differences between the two reviewers were solved through team consensus.

Eligibility criteria

We considered as eligible cohort, cross-sectional, and case-control studies, as well as secondary analyses of randomized controlled trials exploring the association between LVH and neuroimaging markers of CSVD. Cases series, case reports, systematic or narrative reviews, animal and in vitro studies were excluded. We included studies of the general population or studies focused on specific high-risk populations, such as patients with stroke, hypertension, cardiovascular disease, diabetes mellitus, and chronic kidney disease. All analyses were performed separately for the general population and high-risk population studies. We excluded studies examining populations with genetic diseases predisposing to CSVD (e.g., Cerebral Autosomal Dominant/Recessive Arteriopathy with Subcortical Infarcts and Leukoencephalopathy [CADASIL, CARASIL], Fabry disease), autoimmune diseases and vasculitis, primary cardiomyopathies (e.g., dilatative or hypertrophic) and those including solely dementia individuals. Studies without a non-LVH comparison group were also excluded.

The exposure variables of interest included: (1) dichotomously defined LVH, diagnosed by electrocardiography (ECG), transthoracic echocardiography (TTE), or cardiac magnetic resonance imaging (MRI), and (2) continuous LVM measures, indexed (LVMI) or not to body surface area, assessed by TTE or cardiac MRI. For TTE-assessed LVH, we preferably included studies defining LVH as LVM ≥115 g/m2 in males and ≥95 g/m2 in females [13], but other cut-off points were also considered. ECG-assessed LVH should be defined by validated (e.g., Sokolow-Lyon indices or Cornell voltage criteria) [24,25] methods in standard 7 or 12-lead ECG. LVM should be calculated by TTE parameters according to the method of Devereux et al. [26].

The primary outcomes of our study included the following neuroimaging markers of CSVD, in accordance with the STandards for ReportIng Vascular changes on nEuroimaging (STRIVE [11]): lacunes, WMHs, CMBs, and EPVSs. Eligible were considered MRI or computed tomography (CT) studies assessing lacunes and WMHs, as previous literature has described compliance validity between the two methods [27,28], and MRI studies evaluating CMBs and EPVSs. We included studies defining lacunes as round or ovoid, subcortical, fluid-filled cavities, measuring between 3 and 15-mm in maximal diameter, consistent with a previous acute small deep brain infarct or hemorrhage in the territory of one perforating arteriole. Studies exploring lacunar strokes, defined as lacunes with acute clinical manifestations, were also included. We further post hoc decided to include studies examining “silent infarcts” provided that >80% of the included events were lacunes. WMHs should be identified as hyperintense areas on T2-weighted MRI sequences, isointense or hypointense on fluid-attenuated inversion recovery (FLAIR) imaging or as CT hypodensities. The studies should assess WMHs presence or severity through semiquantitative visual rating scales (e.g., Fazekas) or WMH volume via automated or semi-automated methods. Due to the high prevalence of WMHs in the elderly [12], the individual studies dichotomized WMH outcome based on specific burden levels (either based on a scale or a volumetric measurement) instead of mere presence. For simplicity, we use the term “extensive WMHs” to refer to this outcome although the individual studies used different methods for its assessment. CMBs had to be visualized as small (≤10 mm) areas of signal void with associated blooming on T2*-weighted MRI sequences. EPVSs should be defined as fluid-filled spaces following the course of a vessel with cerebrospinal fluid-like signal intensity.

Data extraction

A predefined spreadsheet was used to extract the following data: publication details (authors, year), study information (geographical region, recruitment period, design, population under study, sample size, follow-up parameters), study sample characteristics (age, gender, smoking, body mass index, hypertension history, diabetes mellitus, stroke, coronary artery disease), LVH/LVMI ascertainment (assessment method, definition, method/scale of quantification), CSVD assessment (marker under study, imaging modality, definition, method/scale of quantification, number of cases), and statistical analysis details (analysis type, effect estimates, 95% confidence intervals [CIs], adjusting variables). The corresponding author was contacted in case of missing data.

Quality assessment

We evaluated studies for risk of bias using the Newcastle-Ottawa scale [29]. As the vast majority of eligible studies were of cross-sectional or cohort design, we applied the nine-item cohort subscale to all studies. The following criteria were assessed: representativeness of the exposed population; selection of the non-exposed group; LVH ascertainment; outcome absence at study onset; comparability of the exposed and non-exposed group for age and hypertension; CSVD markers assessment; follow-up period length and completion. Cross-sectional studies, by definition, did not receive any points for longitudinal assessment items (outcome absence at study onset, follow-up period length and completion). The detailed pre-defined handling of each criterion for the purposes of this systematic review is outlined in the Supplementary Table 1.

Statistical analysis

For each eligible study, we extracted association estimates and 95% CIs between presence of LVH and presence or incidence of neuroimaging CSVD markers. In 21 out of the 27 studies in our meta-analysis, the association estimates were odds ratios (ORs) derived from logistic regression analyses. Two prospective studies presented relative risks (RRs), but as the prevalence of the examined outcome was <10% in their population we considered RRs to be comparable to ORs [30] and pooled them with the other studies. Where ORs were not presented, we hand-calculated unadjusted ORs using 2×2 tables, based on data from the published articles. In studies presenting only ORs stratified by LVMI increments, we obtained the OR for the presence or absence of LVH by applying the method described by Hamling et al. [31]. For studies examining WMH volume or WMH severity measured as continuous outcomes, we transformed the presented beta coefficients to standardized mean differences and then used the latter to estimate the OR for a dichotomized WMH measure, based on validated formulae with the use of an online tool (https://campbellcollaboration.org/research-resources/effect-size-calculator.html ) [32].

We then performed random-effects meta-analyses of the derived association estimates to obtain pooled ORs and 95% CIs for each outcome. The method described by DerSimonian and Laird [33] was our primary meta-analytical approach. For our main analyses we also performed alternative random-effects meta-analytical approaches (ORs calculated via the Paule-Mandel between-study variance estimator [34], 95% CIs with the Hartung-Knapp [35] and modified Hartung-Knapp [36] methods), as detailed in the supplement (Supplementary methods) [34,36-46]. All analyses were performed separately for the general population and high-risk population studies. The presence of heterogeneity was evaluated by the I2 and the Cochran Q statistics. We defined low, moderate and high heterogeneity as an I2 of <25%, 25% to 75%, and >75%, respectively (significance threshold: P<0.10) [47]. To explore potential sources of heterogeneity, we performed sensitivity and subgroup analyses stratified by study design (cross-sectional, cohort), LVH assessment method (TTE, ECG), LVH definition criteria (ECG: only ↑QRS voltage-based criteria; TTE: LVMI ≥115 g/m2 in males and ≥95 g/m2 in females, body surface indexed), CSVD assessment method (MRI, CT), level of adjustment (studies adjusted for age, sex, hypertension, and other vascular risk factors), and fulfilment of the quality criteria of the Newcastle-Ottawa scale. Where possible, we further performed analyses for different left ventricular (LV) morphology patterns: normal LV geometry, concentric remodeling, eccentric hypertrophy, and concentric hypertrophy [13]. In order to explore the effect of each individual study in the overall estimate we conducted “leave-one-out” sensitivity analyses.

The results of our main analyses were graphically presented with funnel plots. The effect of potential publication bias (small-study effects) was explored in cases of ≥10 pooled studies using the Egger’s test (significance threshold: P<0.10) [48]. In case of statistically significant small-study effects, we adjusted the pooled effect estimates for publication bias using a “trim and fill” analysis [49].

Statistical significance for the main analyses was set at a two-sided P<0.05. All analyses were conducted with the STATA Software version 13.0 (Stata Corporation, College Station, TX, USA).

Results

Review of literature

Figure 1 summarizes the study selection process. Following screening of 1,456 articles yielded by the literature search, we identified 34 articles meeting our eligibility criteria (60 studies were excluded after full-text screening as described in Supplementary Table 2). Three of them were excluded due to population overlap (Supplementary Table 3) [50-52]. Of the 31 studies [20-22,53-80] (n=25,562) included in our systematic review, only 27 [21,22,53-60,62-69,72-80] (n=21,010) provided appropriate data to also be used in the meta-analysis. Quantitative synthesis of articles examining associations between LVMI and WMH severity or volume as continuous variables was not possible because of the highly heterogeneous statistical methodologies. Of the studies included in meta-analysis, 13 examined presence of lacunes, 16 assessed extensive WMHs and three examined presence of CMBs. No eligible articles investigating EPVSs were identified.

Figure 1.

Flowchart of the study selection process. The included articles for each of the outcomes do not sum up to the total number of included articles because several studies provided data for more than one outcome.

Study characteristics

The descriptive characteristics of the included studies are presented in Table 1.

Characteristics of studies investigating the associations between left ventricular hypertrophy or left ventricular mass (index) and lacunes, white matter hyperintensities, or cerebral microbleeds

Assessment of left ventricular hypertrophy

The most commonly used definition for ECG-based diagnosis of LVH was the Sokolo-Lyon and/or Cornell definition, but some older studies [56,57,65-67,77] considered isolated QRS changes “normal” and defined LVH only if additional ST-segment and/or T-wave changes were present. Regarding TTE-defined LVH, the diagnosis was usually made according to internationally accepted standards, i.e., LVMI ≥115 g/m2 for men and ≥95 g/m2 for women (indexed to body surface area). Some studies, however, used different cut-off points [55,68,69,78], no indexing (g) [71], or height-based indexes (e.g., g/m2.7) [59,72,76]. Six studies (n=1,279) [22,53,68,72,76,78] also evaluated LV morphology, classifying it as normal LV geometry, concentric remodeling, eccentric hypertrophy, or concentric hypertrophy.

Lacunes

Of the 13 studies [21,22,56,57,64,65,67,68,73,74,76,79,80] examining lacunes (n=13,529), nine were of cross-sectional (n=6,272), three of cohort (n=7,020), and one of case-control (n=237) design. Overall, lacunes were identified in 1,588 individuals. Five of the studies (n=6,650) were based on the general population and the remaining eight (n=6,879) on high-risk population subsets. Mean age of all the individuals was 67 years (range, 57 to 76). Lacunes were assessed by MRI in eight studies (n=6,091), by CT in two (n=844), whereas the three studies examining clinically manifest lacunar stroke utilized either MRI or CT (n=6,594).

White matter hyperintensities

Twenty-two studies [20-22,53-55,58,59,61-64,66,68-72,75,77,78,80] investigated WMHs (n=15,636). In 14 of these studies (n=8,540) the outcome was presence of extensive WMHs, whereas six studies (n=6,319) examined WMHs severity or volume as continuous outcomes. Two studies (n=777) presented both types of data. Eighteen studies were of cross-sectional (n=13,494) and three of cohort design (n=2,011), whereas one study presented both a cross-sectional (n=131) and a cohort analysis (n=113). Overall, nine of the studies (n=10,432) were based on the general population and the remaining 13 (n=5,204) on high-risk population subsets. Mean age of all individuals was 65 years (range, 30 to 76). WMHs were assessed by MRI in 21 studies (n=15,026) and CT in one (n=610). Presence of extensive WMHs was defined by the Fazekas-scale in seven studies (n=816). There was, however, heterogeneity regarding the cutoff point used to define extensive WMHs as well as the location of WMHs assessment (periventricular, deep, or both). Regarding continuous WMH data, either semi-quantitative scales were used to assess WMH severity, or semi-automated and automated computer-based algorithms calculated WMH volume. Two recent studies [20,54] examined the association between LVMI and diffusion tensor imaging parameters of WM integrity.

Cerebral microbleeds

All three studies [60,63,69] (n=493) examining presence of CMBs were of cross-sectional design and based on high-risk population subsets. CMBs were identified in 151 individuals. Mean age of all the individuals was 63 years (range, 52 to 72). All studies utilized the same MRI-based definition.

Quality assessment of included

The overall study quality was moderate. Only one study [79] (3%) fulfilled all nine criteria of the Newcastle-Ottawa scale (Supplementary Table 4). The median quality score was 5/9 for studies examining lacunes and WMHs and 4/9 for those examining CMBs. This could be explained by the cross-sectional design employed by 25 studies (81%), thus inherently limiting their maximum score to 6/9. Furthermore, only 10 studies (32%) were based on the general population, thus fulfilling the representativeness of the exposed cohort criterion. Most studies fulfilled the criteria for exposure and outcome assessment methods (87% and 94%, respectively), despite the between-study heterogeneity. Regarding the comparability criteria for age and hypertension, 15 (49%) studies controlled for both, 10 (32%) controlled for age but not hypertension, while only six studies (19%) presented unadjusted results. Lastly, concerning the cohort-specific criteria, three of the six cohort studies assessed CSVD markers at study onset, all six had follow-ups longer than 3 years, and attrition rates were <20% for three studies.

Meta-analysis: associations between LVH and CSVD

In studies of the general population we found presence of LVH to be associated with the odds of lacunes (OR, 1.49; 95% CI, 1.12 to 2.00; five studies; 6,650 individuals; 1,037 cases) and extensive WMHs (OR, 1.73; 95% CI, 1.38 to 2.17; five studies; 4,432 individuals) (Figure 2). Similar results were also obtained from studies in high-risk populations (lacunes: OR, 2.39; 95% CI, 1.32 to 4.32; eight studies; 6,879 individuals; 551 cases) (extensive WMHs: OR, 2.01; 95% CI, 1.45 to 2.80; 11 studies; 4,885 individuals) (Figure 2). A meta-analysis of the three high-risk population studies with data on CMBs also showed a significant association between LVH and presence of CMBs (OR, 2.54; 95% CI, 1.04 to 6.22; three studies; 493 individuals; 151 cases) (Supplementary Figure 1). When using various alternative meta-analytical approaches the associations for lacunes and extensive WMHs remained statistically significant, indicating the robustness of our findings (Supplementary Table 5).

Figure 2.

Associations of left ventricular hypertrophy with (A) lacunes, and (B) extensive white matter hyperintensities in general and high-risk population studies. Odds ratios (ORs) of each study are depicted as data markers; shaded boxes around the data markers indicate the statistical weight of the respective study; 95% confidence intervals (CIs) are indicated by the error bars; pooled-effect estimates for general and high-risk populations along with their 95% CI are reflected as a diamond.

Of note, the results for lacunes and extensive WMHs in the general population were also stable across studies adjusting their analyses for hypertension and other vascular risk factors on top of age and sex (lacunes: adjusted OR, 1.50; 95% CI, 1.09 to 2.06) (extensive WMHs: adjusted OR, 1.74; 95% CI, 1.34 to 2.25) (Figure 3).

Figure 3.

Associations of left ventricular hypertrophy with lacunes and extensive white matter hyperintensities in general population studies adjusting for age, sex, hypertension, and other vascular risk factors. Odds ratios (ORs) of each study are depicted as data markers; shaded boxes around the data markers indicate the statistical weight of the respective study; 95% confidence intervals (CIs) are indicated by the error bars; pooled-effect estimates for general populations along with their 95% CI are reflected as a diamond.

When exploring LV geometry patterns and LVH subtypes, we documented different magnitudes of associations with lacunes and extensive WMHs (Figure 4). Specifically, in both studies of the general and high-risk-populations, we found gradually increasing associations estimates for concentric remodeling, eccentric hypertrophy, and concentric hypertrophy with the odds of lacunes and extensive WMHs.

Figure 4.

Associations of left ventricular morphology patterns (normal geometry, concentric remodeling, eccentric and concentric hypertrophy) with (A) lacunes and (B) extensive white matter hyperintensities (WMHs) in general (red lines) and high-risk (black lines) population studies. Odds ratios (ORs) are depicted as data markers and 95% confidence intervals (CIs) are indicated by the error bars. All comparisons use “normal geometry” as the reference group (total number: lacunes general population, 665; lacunes high-risk population, 345; extensive WMHs general population, 665; extensive WMHs high-risk population, 419).

Table 2 summarizes the results derived from eight studies (five in the general, three in high-risk populations) exploring associations between LVM or LVMI and heterogeneous methods for a continuous or ordinal assessment of WMHs severity or volume, which could not be included in the meta-analysis. In accordance with our main results, five of the eight studies showed statistically significant associations between higher LVM or LVMI and higher WMH severity or volume, while the association estimates were directionally consistent across all studies.

Review of the results of studies examining the association between left ventricular mass or left ventricular mass index and white matter hyperintensity severity or volume that were not included in the meta-analysis

Heterogeneity, subgroup and sensitivity analyses

In studies of the general population, meta-analyses of WMHs studies showed no heterogeneity (I2=0%, P=0.77), while those of lacunes only moderate heterogeneity (I2=46%, P=0.12) (Figure 2). In subgroup analyses the results were stable for both cross-sectional and cohort studies, as well as for studies assessing LVH by either ECG or TTE (Table 3). Additionally, when restricting our analyses to studies defining LVH with the currently considered most optimal approaches (ECG: only ↑QRS voltage-based criteria; TTE: LVMI ≥95 g/m2 for women and ≥115 g/m2 for men, body surface indexed) the results remained stable (Table 3). Yet, we found moderate heterogeneity in studies of high-risk populations for both lacunes and WMHs (I2=71%, P=0.001 and I2=53%, P=0.02, respectively) (Figure 2). Although none of the sub-analyses entirely resolved the heterogeneity, the results were stable across the examined subgroups (Table 3). Overall, sensitivity analyses restricted to studies fulfilling each one of the Newcastle-Ottawa criteria showed consistent associations of LVH with both lacunes and WMHs (Supplementary Table 6). In “leave-one-out” sensitivity metaanalyses, we found no evidence that any single study significantly influenced the results of the main analyses (Supplementary Figure 2).

Sensitivity and subgroup analyses for the associations between left ventricular hypertrophy and lacunes or extensive white matter hyperintensities in general and high-risk population studies stratified by study type, exposure and outcome assessment methods, and specific population subsets

Assessment of publication bias

Funnel plots for the main analyses are presented in the supplement (Supplementary Figure 3). We did not perform the Egger’s test for meta-analyses of studies of the general population, or for high risk populations for the outcome lacunes due to <10 pooled studies. The Egger’s test showed statistically significant small-study effects for the 11 high risk WMHs studies (P=0.01). After adjusting this analysis for publication bias with the “trim and fill” method [49] the association remained statistically significant (OR, 1.45; 95% CI, 1.03 to 2.04) (Supplementary Figure 4).

Discussion

Polling data from 31 studies and >20,000 individuals, we found LVH to be associated with neuroimaging markers of CSVD in both the general population and specific high-risk populations. Specifically, LVH, defined by TTE or ECG, and increased LVM, assessed by TTE, were associated with lacunes, WMHs, and CMBs. Both eccentric and concentric LVH were associated with CSVD manifestations, but the latter presented larger association estimates. The results remained stable after adjustments for age, hypertension, and other vascular risk factors, in both cross-sectional and cohort studies, as well as in sensitivity analyses controlling for study quality. Among studies of the general population, there was no evidence of heterogeneity for studies assessing WMHs and only moderate heterogeneity for studies assessing lacunes.

Our results demonstrate an association of LVH with lacunes, WMHs, and CMBs, independently of age, hypertension, and other vascular risk factors. This is in accordance with studies exploring other vascular beds and endpoints. Particularly, both electrocardiographic and echocardiographic LVH has been previously associated with the risk of incident adverse coronary events [14], ischemic stroke [16-18], and all-cause mortality [14,81] in studies of the general population. Similar to our results, these associations appear to be independent of hypertension, and taken all together, suggest that LVH is an independent risk factor for global vascular disease.

However, the underlying hemodynamic mechanisms remain largely elusive. During the course of LVH there is initially preserved systolic function and only mild diastolic dysfunction [13], but over time both systolic and diastolic dysfunction ensue [82]. The decrease in stroke volume with its accompanying systemic hypoperfusion could predispose to cerebral ischemia and CSVD [83,84]. Additionally, the concomitant increased fibroblastic activity in the cardiac extracellular matrix can induce arrhythmias [13], which may cause hypotensive episodes, cerebral hypoperfusion, and CSVD [85]. Yet, it remains unknown if LVH could also influence the risk of CSVD during its earlier stages, when no systolic or diastolic dysfunction has developed.

Apart from LVM itself, when further exploring different LV geometry patterns, we documented that concentric hypertrophy showed the strongest association with lacunes and WMHs. In patients with hypertension and abnormal LV geometry, concentric patterns appear to be more common than eccentric, due to pressure but not volume overload [13]. Concentric hypertrophy has been associated with the highest risk of both ischemic stroke [86], as well as cardiovascular and all-cause mortality [81], when compared to other abnormal LV geometry patterns. A possible explanation for this could be related to the fact that concentric hypertrophy, in comparison to eccentric, is generally associated with higher LVM, as was also observed in some of the included studies in the current review [22,68]. Furthermore, specific LV geometry patterns reflect not only differences in hemodynamic load but also genetic predisposition [87,88]. It is therefore plausible that our observations could result from a common genetic predisposition to both cerebral microvascular disease and cardiac maladaptive remodelling in response to hemodynamic load. Regardless of the potential mechanism(s), our results highlight the need for further exploration of LV geometry patterns in future CSVD studies.

Our study finding for an association between LVH and CSVD could explain previous observations regarding the effects of LVH on other endpoints [18,19]. Specifically, in a previous meta-analysis, we found LVH to be strongly associated with cognitive impairment and decline [19], whereas more recent longitudinal studies have shown LVH to be associated with the risk of incident dementia independently of known vascular risk factors [89,90]. Furthermore, multiple studies have shown that LVH is an independent risk factor for stroke [16-18]. With CSVD being a well-established cause of vascular cognitive impairment, ischemic and hemorrhagic stroke, our findings implicate LVH as a potential mediator in these associations. Future longitudinal studies utilizing serial assessments of LVH, CSVD, cognitive, and vascular endpoints should formally explore this hypothesis.

According to current guidelines, patients with hypertension may undergo brain imaging for assessment of hypertension-mediated organ damage only if neurological symptoms or cognitive decline are present [91]. Future large studies should explore the potential benefit of performing brain imaging for all hypertensive patients diagnosed with LVH. Notably, it has been demonstrated that LVH regression via antihypertensive medications leads to risk reduction for future major cardiovascular events [92,93]. On the basis of our findings, future randomized-controlled clinical trials exploring pharmacological LVH regression should include CSVD neuroimaging assessment as a secondary outcome.

Despite the consistency of our findings when controlling for hypertension, our results could still be explained by residual confounding due to insufficient adjustments for high blood pressure duration in the individual studies. Hypertension is the primary risk factor for both LVH [13] and CSVD [1,2,94], increasing the risk in a time-dependent manner. In our study set, the cross-sectional design of the majority of the included studies precluded serial blood pressure measurements. Although some studies variably adjusted for hypertension duration [58,69], this also does not entirely capture its actual duration, as a highly variable subclinical period of high blood pressure often precedes the clinical diagnosis. Future studies should address this critical issue.

Our study also has limitations. First, the studies used highly heterogeneous ECG and TTE-based LVH definitions, and assessed CSVD markers, especially WMHs, with variable approaches. Yet, only moderate heterogeneity was identified in studies of the general population and the results remained stable across sub-analyses grouped by different methods of LVH or CSVD assessment. Second, the risk of bias assessment identified key methodological limitations among the included studies. These limitations were mainly related to the cross-sectional design the majority of the included studies employed and to inadequate adjustments for major confounding factors. For several of the included studies it was only possible to use unadjusted or minorly adjusted effect estimates in the meta-analysis, which are biased by confounding. Yet, sensitivity analyses, where possible, demonstrated consistency of our results among cohort studies and studies controlling for age, hypertension and other vascular risk factors. Third, the heterogeneous statistical methods applied across studies did not allow us to include all studies in the meta-analysis. However, the individual findings from these studies consistently support our pooled results. Fourth, no study utilized a composite CSVD score, which could add information regarding the entire spectrum of CSVD manifestations. Fifth, the lack of prospective studies did not allow us to dynamically explore the association between LVH progression and neuroimaging markers of CSVD.

Conclusions

Our results support an association of echocardiographically or electrocardiographically-defined LVH and echocardiographically-assessed LVM increase with a broad range of CSVD neuroimaging markers, including lacunes, WMHs and CMBs, independently of hypertension and other vascular risk factors. As such, our findings highlight a link between subclinical heart disease and CSVD and indicate LVH as a potential novel risk factor for CSVD and its clinical sequelae.

Supplementary materials

Supplementary materials related to this article can be found online at https://doi.org/10.5853/jos.2019.03335.

Supplementary methodsjos-2019-03335-suppl1.pdf
Supplementary Table 1.

Management of the quality scoring criteria of the cohort subscale of the Newcastle-Ottawa assessment scale for the purposes of the current study*

jos-2019-03335-suppl2.pdf
Supplementary Table 2.

Number of articles excluded after screening the full-text by reason

jos-2019-03335-suppl3.pdf
Supplementary Table 3.

Results of the assessment of potential population overlap between the studies meeting eligibility criteria

jos-2019-03335-suppl4.pdf
Supplementary Table 4.

Results of the quality assessment of eligible studies according to the cohort subscale of the Newcastle-Ottawa scale

jos-2019-03335-suppl5.pdf
Supplementary Table 5.

Alternative random-effect meta-analytical approaches for obtaining pooled OR and 95% CI for the main analyses exploring the associations between left ventricular hypertrophy and lacunes, extensive WMHs and CMBs in general and high risk population studies

jos-2019-03335-suppl6.pdf
Supplementary Table 6.

Sensitivity analyses by fulfilment of each specific criterion of the cohort subscale of the Newcastle-Ottawa assessment scale for the associations between left ventricular hypertrophy and lacunes, extensive WMHs, CMBs in general and high-risk population studies

jos-2019-03335-suppl7.pdf
Supplementary Figure 1.

Forest plot of the meta-analysis association estimates between left ventricular hypertrophy and cerebral microbleeds in high-risk population studies. Odds ratios (ORs) of each study are depicted as data markers; shaded boxes around the data markers indicate the statistical weight of the respective study; 95% confidence intervals (CIs) are indicated by the error bars; pooled-effect estimate along with its 95% CI is as a diamond.

jos-2019-03335-suppl8.pdf
Supplementary Figure 2.

Leave-one out sensitivity analyses for the primary meta-analysis association estimates between left ventricular hypertrophy and lacunes (A, B) or extensive white matter hyperintensities (C, D) in general (A, C) and high-risk population studies (B, D). Odds ratios (ORs) for the meta-analysis estimate after exclusion of each study are depicted as data markers. 95% Confidence intervals (CIs) are indicated as error bars. Low confidence interval (LCI), OR, and high confidence interval (HCI) mark the overall meta-analysis results presented in Figure 2.

jos-2019-03335-suppl9.pdf
Supplementary Figure 3.

Funnel plots of the meta-analyses for the associations between left ventricular hypertrophy and lacunes (A, B) or extensive white matter hyperintensities (C, D) in general (A, C) and high-risk population studies (B, D). Each study is depicted as a dot; the black vertical line indicates the overall fixed-effect estimate; pseudo 95% confidence intervals (CIs) are represented by the dashed lines; in cases where ≥10 studies were pooled, the Egger line is drawn in orange along with its accompanying P-value.

jos-2019-03335-suppl10.pdf
Supplementary Figure 4.

“Trim and fill method” (forest and funnel plot) for the association between left ventricular hypertrophy and extensive white matter hyperintensities in high-risk population studies, where significant small study effects were identified with the Egger’s method. (A) “Filled” forest plot, (B) “filled” funnel plot; a total of 5 “missing studies” were added, labelled as “Fill 1–5.” OR, odds ratio; CI, confidence interval.

jos-2019-03335-suppl11.pdf

Notes

The authors have no financial conflicts of interest.

Acknowledgements

We are grateful to Dr. Takuo Hirose (Sendai, Japan) and Dr. Marco R. Di Tullio (New York, USA) for taking the time to reply to our request for supplementary analyses based on their published data.

References

1. Pantoni L. Cerebral small vessel disease: from pathogenesis and clinical characteristics to therapeutic challenges. Lancet Neurol 2010;9:689–701.
2. Wardlaw JM, Smith C, Dichgans M. Small vessel disease: mechanisms and clinical implications. Lancet Neurol 2019;18:684–696.
3. Dichgans M, Leys D. Vascular cognitive impairment. Circ Res 2017;120:573–591.
4. Sudlow CL, Warlow CP. Comparable studies of the incidence of stroke and its pathological types: results from an international collaboration. International Stroke Incidence Collaboration. Stroke 1997;28:491–499.
5. Qureshi AI, Tuhrim S, Broderick JP, Batjer HH, Hondo H, Hanley DF. Spontaneous intracerebral hemorrhage. N Engl J Med 2001;344:1450–1460.
6. Debette S, Schilling S, Duperron MG, Larsson SC, Markus HS. Clinical significance of magnetic resonance imaging markers of vascular brain injury: a systematic review and meta-analysis. JAMA Neurol 2019;76:81–94.
7. Georgakis MK, Duering M, Wardlaw JM, Dichgans M. WMH and long-term outcomes in ischemic stroke: a systematic review and meta-analysis. Neurology 2019;92:e1298–e1308.
8. de Laat KF, van Norden AG, Gons RA, van Oudheusden LJ, van Uden IW, Bloem BR, et al. Gait in elderly with cerebral small vessel disease. Stroke 2010;41:1652–1658.
9. Inzitari D, Pracucci G, Poggesi A, Carlucci G, Barkhof F, Chabriat H, et al. Changes in white matter as determinant of global functional decline in older independent outpatients: three year follow-up of LADIS (leukoaraiosis and disability) study cohort. BMJ 2009;339:b2477.
10. van Agtmaal MJM, Houben AJHM, Pouwer F, Stehouwer CDA, Schram MT. Association of microvascular dysfunction with late-life depression: a systematic review and meta-analysis. JAMA Psychiatry 2017;74:729–739.
11. Wardlaw JM, Smith EE, Biessels GJ, Cordonnier C, Fazekas F, Frayne R, et al. Neuroimaging standards for research into small vessel disease and its contribution to ageing and neurodegeneration. Lancet Neurol 2013;12:822–838.
12. de Leeuw FE, de Groot JC, Achten E, Oudkerk M, Ramos LM, Heijboer R, et al. Prevalence of cerebral white matter lesions in elderly people: a population based magnetic resonance imaging study. The Rotterdam Scan Study. J Neurol Neurosurg Psychiatry 2001;70:9–14.
13. Marwick TH, Gillebert TC, Aurigemma G, Chirinos J, Derumeaux G, Galderisi M, et al. Recommendations on the use of echocardiography in adult hypertension: a report from the European Association of Cardiovascular Imaging (EACVI) and the American Society of Echocardiography (ASE). Eur Heart J Cardiovasc Imaging 2015;16:577–605.
14. Levy D, Garrison RJ, Savage DD, Kannel WB, Castelli WP. Prognostic implications of echocardiographically determined left ventricular mass in the Framingham Heart Study. N Engl J Med 1990;322:1561–1566.
15. Wolf PA, D’Agostino RB, Belanger AJ, Kannel WB. Probability of stroke: a risk profile from the Framingham Study. Stroke 1991;22:312–318.
16. Bikkina M, Levy D, Evans JC, Larson MG, Benjamin EJ, Wolf PA, et al. Left ventricular mass and risk of stroke in an elderly cohort. The Framingham Heart Study. JAMA 1994;272:33–36.
17. O’Neal WT, Almahmoud MF, Qureshi WT, Soliman EZ. Electrocardiographic and Echocardiographic left ventricular hypertrophy in the prediction of stroke in the elderly. J Stroke Cerebrovasc Dis 2015;24:1991–1997.
18. Bots ML, Nikitin Y, Salonen JT, Elwood PC, Malyutina S, Freire de Concalves A, et al. Left ventricular hypertrophy and risk of fatal and non-fatal stroke. EUROSTROKE: a collaborative study among research centres in Europe. J Epidemiol Community Health 2002;56 Suppl 1:i8–i13.
19. Georgakis MK, Synetos A, Mihas C, Karalexi MA, Tousoulis D, Seshadri S, et al. Left ventricular hypertrophy in association with cognitive impairment: a systematic review and meta-analysis. Hypertens Res 2017;40:696–709.
20. Moore EE, Liu D, Pechman KR, Terry JG, Nair S, Cambronero FE, et al. Increased left ventricular mass index is associated with compromised white matter microstructure among older adults. J Am Heart Assoc 2018;7e009041.
21. Johansen MC, Shah AM, Lirette ST, Griswold M, Mosley TH, Solomon SD, et al. Associations of echocardiography markers and vascular brain lesions: the ARIC Study. J Am Heart Assoc 2018;7e008992.
22. Nakanishi K, Jin Z, Homma S, Elkind MS, Rundek T, Tugcu A, et al. Left ventricular mass-geometry and silent cerebrovascular disease: the cardiovascular abnormalities and brain lesions (CABL) study. Am Heart J 2017;185:85–92.
23. Stroup DF, Berlin JA, Morton SC, Olkin I, Williamson GD, Rennie D, et al. Meta-analysis of observational studies in epidemiology: a proposal for reporting. Meta-analysis Of Observational Studies in Epidemiology (MOOSE) group. JAMA 2000;283:2008–2012.
24. Soliman EZ, Howard G, Prineas RJ, McClure LA, Howard VJ. Calculating Cornell voltage from nonstandard chest electrode recording site in the Reasons for Geographic And Racial Differences in Stroke study. J Electrocardiol 2010;43:209–214.
25. Gosse P, Jan E, Coulon P, Cremer A, Papaioannou G, Yeim S. ECG detection of left ventricular hypertrophy: the simpler, the better? J Hypertens 2012;30:990–996.
26. Devereux RB, Alonso DR, Lutas EM, Gottlieb GJ, Campo E, Sachs I, et al. Echocardiographic assessment of left ventricular hypertrophy: comparison to necropsy findings. Am J Cardiol 1986;57:450–458.
27. Ferguson KJ, Cvoro V, MacLullich AMJ, Shenkin SD, Sandercock PAG, Sakka E, et al. Visual rating scales of white matter hyperintensities and atrophy: comparison of computed tomography and magnetic resonance imaging. J Stroke Cerebrovasc Dis 2018;27:1815–1821.
28. Simoni M, Li L, Paul NL, Gruter BE, Schulz UG, Küker W, et al. Age- and sex-specific rates of leukoaraiosis in TIA and stroke patients: population-based study. Neurology 2012;79:1215–1222.
29. Wells G, Shea B, O’Connell D, Peterson J, Welch V, Losos M, et al. The Newcastle-Ottawa Scale (NOS) for assessing the quality if nonrandomized studies in meta-analyses. Ottawa Hospital Research Institute http://www.ohri.ca/programs/clinical_epidemiology/oxford.Asp . 2011. Accessed March 26, 2020.
30. McNutt LA, Wu C, Xue X, Hafner JP. Estimating the relative risk in cohort studies and clinical trials of common outcomes. Am J Epidemiol 2003;157:940–943.
31. Hamling J, Lee P, Weitkunat R, Ambühl M. Facilitating metaanalyses by deriving relative effect and precision estimates for alternative comparisons from a set of estimates presented by exposure level or disease category. Stat Med 2008;27:954–970.
32. Polanin JR, Snilstveit B. Converting between effect sizes. Campbell Syst Rev 2016;12:1–13.
33. DerSimonian R, Laird N. Meta-analysis in clinical trials. Control Clin Trials 1986;7:177–188.
34. Paule RC, Mandel J. Consensus values, regressions, and weighting factors. J Res Natl Inst Stand Technol 1989;94:197–203.
35. Hartung J, Knapp G. On tests of the overall treatment effect in meta-analysis with normally distributed responses. Stat Med 2001;20:1771–1782.
36. Knapp G, Hartung J. Improved tests for a random effects meta-regression with a single covariate. Stat Med 2003;22:2693–2710.
37. Sterne JAC. Meta-Analysis in Stata: An Updated Collection from the Stata Journal 1st edth ed. College Station, TX: Stata Press; 2009.
38. Cornell JE, Mulrow CD, Localio R, Stack CB, Meibohm AR, Guallar E, et al. Random-effects meta-analysis of inconsistent effects: a time for change. Ann Intern Med 2014;160:267–270.
39. Sidik K, Jonkman JN. A note on the empirical Bayes heterogeneity variance estimator in meta-analysis. Stat Med 2019;38:3804–3816.
40. Veroniki AA, Jackson D, Viechtbauer W, Bender R, Bowden J, Knapp G, et al. Methods to estimate the between-study variance and its uncertainty in meta-analysis. Res Synth Methods 2016;7:55–79.
41. Röver C, Knapp G, Friede T. Hartung-Knapp-Sidik-Jonkman approach and its modification for random-effects meta-analysis with few studies. BMC Med Res Methodol 2015;15:99.
42. IntHout J, Ioannidis JP, Borm GF. The Hartung-Knapp-SidikJonkman method for random effects meta-analysis is straightforward and considerably outperforms the standard DerSimonian-Laird method. BMC Med Res Methodol 2014;14:25.
43. Jackson D, Law M, Rücker G, Schwarzer G. The HartungKnapp modification for random-effects meta-analysis: a useful refinement but are there any residual concerns? Version 2. Stat Med 2017;36:3923–3934.
44. Zeraatkar D, Han M, Ge L, Hanna SE, Guyatt GH. A comparison of the Hartung-Knapp-Sidik-Jonkman method for meta-analysis with conventional frequentist methods: a systematic review of simulation and empirical studies. Cochrane Colloquium Santiago https://colloquium2019.cochrane.org/abstracts/comparison-hartung-knapp-sidik-jonkman-method-meta-analysis-conventional-frequentist . 2019. Accessed May 13, 2020.
45. van Aert RCM, Jackson D. A new justification of the Hartung-Knapp method for random-effects meta-analysis based on weighted least squares regression. Res Synth Methods 2019;10:515–527.
46. Jackson D, Bowden J, Baker R. How does the DerSimonian and Laird procedure for random effects meta-analysis compare with its more efficient but harder to compute counterparts? J Stat Plan Inference 2010;140:961–970.
47. Higgins JP, Thompson SG, Deeks JJ, Altman DG. Measuring inconsistency in meta-analyses. BMJ 2003;327:557–560.
48. Egger M, Davey Smith G, Schneider M, Minder C. Bias in meta-analysis detected by a simple, graphical test. BMJ 1997;315:629–634.
49. Duval S, Tweedie R. Trim and fill: A simple funnel-plot-based method of testing and adjusting for publication bias in meta-analysis. Biometrics 2000;56:455–463.
50. Bezerra DC, Sharrett AR, Matsushita K, Gottesman RF, Shibata D, Mosley TH Jr, et al. Risk factors for lacune subtypes in the Atherosclerosis Risk in Communities (ARIC) Study. Neurology 2012;78:102–108.
51. Kohara K, Igase M, Yinong J, Fukuoka T, Maguchi M, Okura T, et al. Asymptomatic cerebrovascular damages in essential hypertension in the elderly. Am J Hypertens 1997;10:829–835.
52. Ohira T, Shahar E, Chambless LE, Rosamond WD, Mosley TH Jr, Folsom AR. Risk factors for ischemic stroke subtypes: the Atherosclerosis Risk in Communities study. Stroke 2006;37:2493–2498.
53. Butenaerts D, Chrzanowska-Wasko J, Slowik A, Dziedzic T. Left ventricular geometry and white matter lesions in ischemic stroke patients. Blood Press 2016;25:149–154.
54. Cermakova P, Muller M, Armstrong AC, Religa D, Bryan RN, Lima JAC, et al. Subclinical cardiac dysfunction and brain health in midlife: CARDIA (Coronary Artery Risk Development in Young Adults) brain magnetic resonance imaging substudy. J Am Heart Assoc 2017;6e006750.
55. Vedala K, Nagabandi AK, Looney S, Bruno A. Factors associated with leukoaraiosis severity in acute stroke patients. J Stroke Cerebrovasc Dis 2019;28:1897–1901.
56. Das RR, Seshadri S, Beiser AS, Kelly-Hayes M, Au R, Himali JJ, et al. Prevalence and correlates of silent cerebral infarcts in the Framingham offspring study. Stroke 2008;39:2929–2935.
57. Davis BR, Vogt T, Frost PH, Burlando A, Cohen J, Wilson A, et al. Risk factors for stroke and type of stroke in persons with isolated systolic hypertension. Systolic Hypertension in the Elderly Program Cooperative Research Group. Stroke 1998;29:1333–1340.
58. Ferreira JP, Kearney Schwartz A, Watfa G, Zohra L, Felblinger J, Boivin JM, et al. Memory alterations and white matter hyperintensities in elderly patients with hypertension: the ADELAHYDE-2 study. J Am Med Dir Assoc 2017;18:451.
59. Fox ER, Taylor HA Jr, Benjamin EJ, Ding J, Liebson PR, Arnett D, et al. Left ventricular mass indexed to height and prevalent MRI cerebrovascular disease in an African American cohort: the Atherosclerotic Risk in Communities study. Stroke 2005;36:546–550.
60. Görner A, Lemmens R, Schrooten M, Thijs V. is leukoaraiosis on CT an accurate surrogate marker for the presence of microbleeds in acute stroke patients? J Neurol 2007;254:284–289.
61. Haring B, Omidpanah A, Suchy-Dicey AM, Best LG, Verney SP, Shibata DK, et al. Left ventricular mass, brain magnetic resonance imaging, and cognitive performance: results from the strong heart study. Hypertension 2017;70:964–971.
62. Hénon H, Godefroy O, Lucas C, Pruvo JP, Leys D. Risk factors and leukoaraiosis in stroke patients. Acta Neurol Scand 1996;94:137–144.
63. Henskens LH, van Oostenbrugge RJ, Kroon AA, Hofman PA, Lodder J, de Leeuw PW. Detection of silent cerebrovascular disease refines risk stratification of hypertensive patients. J Hypertens 2009;27:846–853.
64. Hirose T, Hashimoto M, Totsune K, Metoki H, Hara A, Satoh M, et al. Association of (pro)renin receptor gene polymorphisms with lacunar infarction and left ventricular hypertrophy in Japanese women: the Ohasama study. Hypertens Res 2011;34:530–535.
65. Ikeda T, Gomi T, Kobayashi S, Tsuchiya H. Role of hypertension in asymptomatic cerebral lacunae in the elderly. Hypertension 1994;23:I259–I262.
66. Jeerakathil T, Wolf PA, Beiser A, Massaro J, Seshadri S, D’Agostino RB, et al. Stroke risk profile predicts white matter hyperintensity volume: the Framingham Study. Stroke 2004;35:1857–1861.
67. Kawamoto A, Shimada K, Matsubayashi K, Nishinaga M, Kimura S, Ozawa T. Factors associated with silent multiple lacunar lesions on magnetic resonance imaging in asymptomatic elderly hypertensive patients. Clin Exp Pharmacol Physiol 1991;18:605–610.
68. Kohara K, Zhao B, Jiang Y, Takata Y, Fukuoka T, Igase M, et al. Relation of left ventricular hypertrophy and geometry to asymptomatic cerebrovascular damage in essential hypertension. Am J Cardiol 1999;83:367–370.
69. Lee SH, Park JM, Kwon SJ, Kim H, Kim YH, Roh JK, et al. Left ventricular hypertrophy is associated with cerebral microbleeds in hypertensive patients. Neurology 2004;63:16–21.
70. Lee WJ, Jung KH, Ryu YJ, Kim JM, Lee ST, Chu K, et al. Association of cardiac hemodynamic factors with severity of white matter hyperintensities in chronic valvular heart disease. JAMA Neurol 2018;75:80–87.
71. Longstreth WT Jr, Manolio TA, Arnold A, Burke GL, Bryan N, Jungreis CA, et al. Clinical correlates of white matter findings on cranial magnetic resonance imaging of 3301 elderly people. The Cardiovascular Health Study. Stroke 1996;27:1274–1282.
72. Martinez-Vea A, Salvadó E, Bardají A, Gutierrez C, Ramos A, García C, et al. Silent cerebral white matter lesions and their relationship with vascular risk factors in middle-aged predialysis patients with CKD. Am J Kidney Dis 2006;47:241–250.
73. Mounier-Vehier F, Leys D, Rondepierre P, Godefroy O, Pruvo JP. Silent infarcts in patients with ischemic stroke are related to age and size of the left atrium. Stroke 1993;24:1347–1351.
74. Pirinen J, Eranti A, Knekt P, Lehto M, Martinez-Majander N, Aro AL, et al. ECG markers associated with ischemic stroke at young age: a case-control study. Ann Med 2017;49:562–568.
75. Ryu WS, Woo SH, Schellingerhout D, Chung MK, Kim CK, Jang MU, et al. Grading and interpretation of white matter hyperintensities using statistical maps. Stroke 2014;45:3567–3575.
76. Selvetella G, Notte A, Maffei A, Calistri V, Scamardella V, Frati G, et al. Left ventricular hypertrophy is associated with asymptomatic cerebral damage in hypertensive patients. Stroke 2003;34:1766–1770.
77. Shimada K, Kawamoto A, Matsubayashi K, Ozawa T. Silent cerebrovascular disease in the elderly: correlation with ambulatory pressure. Hypertension 1990;16:692–699.
78. Sierra C, de la Sierra A, Paré JC, Gómez-Angelats E, Coca A. Correlation between silent cerebral white matter lesions and left ventricular mass and geometry in essential hypertension. Am J Hypertens 2002;15:507–512.
79. Tanizaki Y, Kiyohara Y, Kato I, Iwamoto H, Nakayama K, Shinohara N, et al. Incidence and risk factors for subtypes of cerebral infarction in a general population: the Hisayama study. Stroke 2000;31:2616–2622.
80. van der Veen PH, Geerlings MI, Visseren FL, Nathoe HM, Mali WP, van der Graaf Y, et al. Hypertensive target organ damage and longitudinal changes in brain structure and function: the second manifestations of arterial disease-magnetic resonance study. Hypertension 2015;66:1152–1158.
81. Ghali JK, Liao Y, Cooper RS. Influence of left ventricular geometric patterns on prognosis in patients with or without coronary artery disease. J Am Coll Cardiol 1998;31:1635–1640.
82. Palmieri V, Wachtell K, Gerdts E, Bella JN, Papademetriou V, Tuxen C, et al. Left ventricular function and hemodynamic features of inappropriate left ventricular hypertrophy in patients with systemic hypertension: the LIFE study. Am Heart J 2001;141:784–791.
83. Shimizu A, Sakurai T, Mitsui T, Miyagi M, Nomoto K, Kokubo M, et al. Left ventricular diastolic dysfunction is associated with cerebral white matter lesions (leukoaraiosis) in elderly patients without ischemic heart disease and stroke. Geriatr Gerontol Int 2014;14 Suppl 2:71–76.
84. Vogels RL, van der Flier WM, van Harten B, Gouw AA, Scheltens P, Schroeder-Tanka JM, et al. Brain magnetic resonance imaging abnormalities in patients with heart failure. Eur J Heart Fail 2007;9:1003–1009.
85. Mok V, Kim JS. Prevention and management of cerebral small vessel disease. J Stroke 2015;17:111–122.
86. Di Tullio MR, Zwas DR, Sacco RL, Sciacca RR, Homma S. Left ventricular mass and geometry and the risk of ischemic stroke. Stroke 2003;34:2380–2384.
87. Arnett DK, Hong Y, Bella JN, Oberman A, Kitzman DW, Hopkins PN, et al. Sibling correlation of left ventricular mass and geometry in hypertensive African Americans and whites: the HyperGEN study. Hypertension Genetic Epidemiology Network. Am J Hypertens 2001;14:1226–1230.
88. Schunkert H, Bröckel U, Hengstenberg C, Luchner A, Muscholl MW, Kurzidim K, et al. Familial predisposition of left ventricular hypertrophy. J Am Coll Cardiol 1999;33:1685–1691.
89. Norby FL, Chen LY, Soliman EZ, Gottesman RF, Mosley TH, Alonso A. Association of left ventricular hypertrophy with cognitive decline and dementia risk over 20 years: the Atherosclerosis Risk In Communities-Neurocognitive Study (ARIC-NCS). Am Heart J 2018;204:58–67.
90. Moazzami K, Ostovaneh MR, Ambale Venkatesh B, Habibi M, Yoneyama K, Wu C, et al. Left ventricular hypertrophy and remodeling and risk of cognitive impairment and dementia: MESA (Multi-Ethnic Study of Atherosclerosis). Hypertension 2018;71:429–436.
91. Williams B, Mancia G, Spiering W, Agabiti Rosei E, Azizi M, Burnier M, et al. 2018 ESC/ESH Guidelines for the management of arterial hypertension. Eur Heart J 2018;39:3021–3104.
92. Pierdomenico SD, Cuccurullo F. Risk reduction after regression of echocardiographic left ventricular hypertrophy in hypertension: a meta-analysis. Am J Hypertens 2010;23:876–881.
93. Okin PM, Devereux RB, Jern S, Kjeldsen SE, Julius S, Nieminen MS, et al. Regression of electrocardiographic left ventricular hypertrophy during antihypertensive treatment and the prediction of major cardiovascular events. JAMA 2004;292:2343–2349.
94. SPRINT MIND Investigators for the SPRINT Research Group, Nasrallah IM, Pajewski NM, Auchus AP, Chelune G, Cheung AK, et al. Association of intensive vs standard blood pressure control with cerebral white matter lesions. JAMA 2019;322:524–534.

Article information Continued

Figure 1.

Flowchart of the study selection process. The included articles for each of the outcomes do not sum up to the total number of included articles because several studies provided data for more than one outcome.

Figure 2.

Associations of left ventricular hypertrophy with (A) lacunes, and (B) extensive white matter hyperintensities in general and high-risk population studies. Odds ratios (ORs) of each study are depicted as data markers; shaded boxes around the data markers indicate the statistical weight of the respective study; 95% confidence intervals (CIs) are indicated by the error bars; pooled-effect estimates for general and high-risk populations along with their 95% CI are reflected as a diamond.

Figure 3.

Associations of left ventricular hypertrophy with lacunes and extensive white matter hyperintensities in general population studies adjusting for age, sex, hypertension, and other vascular risk factors. Odds ratios (ORs) of each study are depicted as data markers; shaded boxes around the data markers indicate the statistical weight of the respective study; 95% confidence intervals (CIs) are indicated by the error bars; pooled-effect estimates for general populations along with their 95% CI are reflected as a diamond.

Figure 4.

Associations of left ventricular morphology patterns (normal geometry, concentric remodeling, eccentric and concentric hypertrophy) with (A) lacunes and (B) extensive white matter hyperintensities (WMHs) in general (red lines) and high-risk (black lines) population studies. Odds ratios (ORs) are depicted as data markers and 95% confidence intervals (CIs) are indicated by the error bars. All comparisons use “normal geometry” as the reference group (total number: lacunes general population, 665; lacunes high-risk population, 345; extensive WMHs general population, 665; extensive WMHs high-risk population, 419).

Table 1.

Characteristics of studies investigating the associations between left ventricular hypertrophy or left ventricular mass (index) and lacunes, white matter hyperintensities, or cerebral microbleeds

Study Region (recruitment period) Study type (follow-up) Population type No. Mean age (yr) Men (%) Ht (%) DM (%) CVD (%) No. of cases Exposure examined and ascertainment Outcome examined and ascertainment
Lacunes
Das et al. (2008) [56] US (1996–1998) Cross-sectional General population 2,040 62 47 37 9 Stroke: 0 220 LVH: (ECG) ↑QRS voltage (R in V5+S in V1 ≥3.5 mV)+ST segment depression or flat/diphasic T waves Lacunes*: (MRI) PD-W, T2-W, lesions ≥3 mm
CAD: 7.7
Hirose et al. (2011) [64] Japan (1998) Cross-sectional General population 659 66 32 40 14 Stroke: 0 190 LVH: (ECG) Sokolow-Lyon/Cornell Lacunes: (MRI) T1-W, T2-W, lesions 3–15 mm
CAD: NR
Ikeda et al. (1994) [65] Japan (1991–1992) Cross-sectional Hypertensive patients 249 69 42 100 0 Stroke: 0 51 LVH: (ECG) ↑QRS voltage (R in V5+S in V1 ≥3.5 mV)+ST segment depression (0–5 to 1.0 mV) and flat or diphasic T waves Lacunes: (CT) lesions ≤15 mm
CAD: 0
Johansen et al. (2018) [21] US (2011–2013) Cross-sectional General population 1,665 76 40 68 29 Stroke: 10 366 LVMI§: (TTE) measured as a continuous variable (mean=78.7 g/m2, SD=19.5 g/m2, body surface) Lacunes: (MRI) MP-RAGE, axial GRE T2*, axial FLAIR, axial DTI, lesions 3–20 mm
CAD: 5
Kawamoto et al. (1991) [67] Japan (NR) Cross-sectional Hypertensive patients 54 69 44 100 0 Stroke: 0 11 LVH: (ECG) ↑QRS voltage (R in V5+S in V1 ≥3.5 mV)+flat T waves (<10% R) or ST-segment depression and diphasic T waves Lacunes: (MRI) T1-W, T2-W, lesions ≤10 mm
CAD: 0
Kohara et al. (1999) [68] Japan (1992–1998) Cross-sectional Hypertensive patients 150 58 48 100 NR Stroke: 0 101 LVH: (TTE) LVMI ≥108 g/m2 for women and ≥118 g/m2 for men (body surface) Lacunes: (MRI) T1-W, T2-W, lesions 3–15 mm
CAD: 0
Mounier-Vehier et al. (1993) [73] France (1989–1992) Cross-sectional Stroke patients 595 66 50 56 19 Stroke: 100 116 LVH: (ECG and TTE), no criteria reported Lacunes*: (CT) lesions ≤15 mm
CAD: 6.6
Nakanishi et al. (2017) [22] US (2005–2010) Cross-sectional General population 665 71 41 78 28 Stroke: 0 94 LVH: (TTE) LVMI ≥95 g/m2 for women and ≥115 g/m2 for men (body surface) Lacunes*: (MRI) FLAIR, lesions ≥3 mm
CAD: 6.5
Selvetella et al. (2003) [76] Italy (2000–2002) Cross-sectional Hypertensive patients 195 61 44 100 21 Stroke: 0 62 LVH: (TTE) LVMI ≥ 50 g/m2.7 (height corrected) Lacunes: (MRI) T1-W, T2-W, lesions ≤10 mm
CAD: 0
Davis et al. (1998) [57] US (1985–1988) Cohort (4.5 yr) Hypertensive patients 4,736 72 43 100 10 Stroke: 1.4 66 LVH: (ECG) Minnesota codes (3.1 plus 4.1–4.3 or 5.1–5.3) or (3.3 plus 4.1–4.3 or 5.1–5.3) Lacunar strokes: (MRI or CT) clinical lacunar syndrome+lesion ≤20 mm or autopsy proven
CAD: 5.4
Tanizaki et al. (2000) [79] Japan (1961) Cohort (max 32 yr) General population 1,621 57 44 2 8 Stroke: 0 167 LVH: (ECG) Minnesota code 3–1 Lacunar strokes: (MRI or CT) focal neurological deficit+lesion ≤15 mm
CAD: 3.1
van der Veen et al. (2015) [80] Holland (2001–2005) Cohort (3.9 yr) CVD patients 663 57 81 61 13 Stroke: 23 60 LVH: (ECG) Sokolow-Lyon/Cornell Lacunes: (MRI) T1-W, T2-W, FLAIR, lesions 3–15 mm
CAD: 62
Pirinen et al. (2017) [74] Finland (1994–2007) Case-control NA 237 43** 64 85†† 5 Stroke: 100 84 LVH: (ECG) Sokolow-Lyon/Cornell Lacunar strokes: (MRI or CT) lesion ≤15 mm (verified stroke cases from the Helsinki Young Stroke Registry)
CAD: 2.4
White matter hyperintensities
Butenaerts et al. (2016) [53] Poland (2014) Cross-sectional Stroke patients 155 62** 49 71 26 Stroke: 100 61 LVH: (TTE) LVMI ≥95 g/m2 for women and ≥115 g/m2 for men (body surface) Occurrence of severe WMH: (MRI) FLAIR, Fazekas total score ≥3 (dWMHs+pWMHs; scale range 0–6)
CAD: 25.2
Fox et al. (2005) [59] US (1993–1994) Cross-sectional General population 667 62 37 68 21 Stroke: 2.7 92 LVH: (TTE) LVMI ≥121 g/m for women and ≥163 g/m for men (height corrected) Occurrence of severe WMH: (MRI) PD-W, T2-W, grade ≥4 on self-designed scale (scale range, 1–10)
CAD: 4.2
Hénon et al. (1996) [62] France (1991–1993) Cross-sectional Stroke patients 610 64 57 49 14 Stroke: 100 88 LVH: (ECG), no criteria reported WMH severity§§: (CT) Inzitari's criteria (definition), Blennow's scale (extension range, 0–3; severity range, 0–3). Total score=(extension+severity)/2
CAD: 16.4
Henskens et al. (2009) [63] Netherlands (2004–2006) Cross-sectional Hypertensive patients 192 52 51 100 0 Stroke: 0 39 LVH: (TTE) LVMI ≥95 g/m2 for women and ≥115 g/m2 for men (body surface) Occurrence of severe WMH: (MRI) T2-W, FLAIR, Fazekas scale; dWMHs grade ≥2 or pWMHs grade 3 (scale range, 0–3)
CAD: 0
Hirose et al. (2011) [64] Japan (1998) Cross-sectional General population 659 66 32 40 14 Stroke: 0 274 LVH: (ECG) Sokolow-Lyon/Cornell Occurrence of severe WMH: (MRI) T1-W, T2-W, large caps (≥5×10 mm)
CAD: NR
Jeerakathil et al. (2004) [66] US (1991–1995) Cross-sectional General population 1,814 53 47 18 5 Stroke: 0 240 LVH: (ECG) ↑QRS voltage (R in V5+S in V1 ≥3.5 mV)+ST segment depression or flat/diphasic T waves Occurrence of severe WMH: (MRI) T2-W, >1 age-specific SD of WMHV
CAD: 5.8
Johansen et al. (2018) [21] US (2011–2013) Cross-sectional General population 1,665 76 40 68 29 Stroke: 10 NA‡‡ LVMI: (TTE) continuous variable (mean=78.7 g/m2, SD=19.5 g/m2, body surface) WMH volume: (MRI) axial FLAIR, quantification by semi-automated algorithm
CAD: 5
Kohara et al. (1999) [68] Japan (1992–1998) Cross-sectional Hypertensive patients 150 58 48 100 NR Stroke: 0 25, NA‡‡ LVH: (TTE) LVMI ≥108 g/m2 for women and ≥118 g/m2 for men, also used as continuous variable (mean=122.8 g/m2, SD=24.8 m2, body surface) Occurrence of severe WMH: (MRI) T2-W, Fazekas scale for pWMHs ≥2 (scale range, 0–3)
CAD: 0
Lee et al. (2004) [69] South Korea (1998–2000) Cross-sectional Hypertensive patients with stroke 102 64 59 100 17 Stroke: 100 NA‡‡ LVMI: (TTE) continuous variable (mean=156.7 g/m2, SD=50.6 g/m2, body surface) WMH severity: (MRI) T2-W, Fazekas scale for pWMHs (scale range, 0–3)
CAD: 0
Lee et al. (2018) [70] South Korea (2008–2016) Cross-sectional VHD patients 217 66 44 46 20 Stroke: 11.6 NA‡‡ LVMI: (TTE) continuous variable (mean=109.9 g/m2, SD=32.5 g/m2, body surface) WMH severity: (MRI) T2-W, Fazekas scale for pWMHs (scale range, 0–3)
CAD: 0
Longstreth et al. (1996) [71] US (1989–1990) Cross-sectional General population 3,301 75 42 45 10 Stroke: 0 NA‡‡ LVM: (TTE) continuous variable (mean, SD not reported) WMH volume: (MRI) FLAIR, manually identified hyperintense lesions semi-automatically drawn
CAD: 23
Martinez-Vea et al. (2006) [72] Spain (NR) Cross-sectional CKD patients 52 49 73 100 0 Stroke: 0 17 LVH: (TTE) LVMI ≥47 g/m2.7 for women and ≥49 g/m2.7 for men (height corrected) WMH severity: (MRI) PD-W, self-designed scale (scale range, 1–8) Occurrence of severe WMH: (MRI) T1-W, T2-W, FLAIR, Fazekas scale; dWMHs grade ≥2 or pWMHs grade ≥2 (scale range, 0–3)
CAD: 9.7
Moore et al. (2018) [20] US (2012–2014) Cross-sectional General population∥∥ 313 73 58 53 17 Stroke: 0 NA‡‡ LVMI: (CMR) continuous variable (mean=51 g/m2, SD=10 g/m2, body surface) Alterations in white matter microstructure: (MRI) DTI, parameters measured; fractional anisotropy, mean, radial, and axial diffusivity
CAD: 4
Nakanishi et al. (2017) [22] US (2005–2010) Cross-sectional General population 665 71 41 78 28 Stroke: 0 166 LVH: (TTE) LVMI ≥95 g/m2 for women and ≥115 g/m2 for men (body surface) Occurrence of severe WMH: (MRI) FLAIR, upper quartile of WMHV
CAD: 6.5
Ryu et al. (2014) [75] South Korea (2011–2012) Cross-sectional Stroke patients 2,669 67 60 66 32 Stroke: 100 NA‡‡ LVH: (ECG or TTE), no criteria reported WMH volume§§: (MRI) FLAIR, lesions were segmented and registered semi-automatically
CAD: 11.7
Shimada et al. (1990) [77] Japan (NR) Cross-sectional Hypertensive patients 34 69 33 100 0 Stroke: 0 11 LVH: (ECG) ↑QRS voltage (R in V5+ in V1 ≥3.5 mV)+flat T waves (<10% R) or ST-segment depression and diphasic T waves Occurrence of severe WMH: (MRI) T2-W, Fazekas scale for pWMHs ≥2 (scale range, 0–3)
CAD: 0
Sierra et al. (2002) [78] Spain (NR) Cross-sectional Hypertensive patients 62 54 63 100 0 Stroke: 0 26 LVH: (TTE) LVMI 110 g/m2 for women and 130 g/m2 for men (body surface) Occurrence of severe WMH: (MRI) no sequence reported, van Swieten scale grade ≥1 (scale range, 0–2)
CAD: 0
Vedala et al. (2019) [55] US (2010-2014) Cross-sectional Stroke patients 167 62 46 73 37 Stroke: 100 NA‡‡ LVH: (TTE) LVMI 122 g/m2 for women and 149 g/m2 for men (body surface) WMH severity§§: (MRI) FLAIR, Wahlund scale for WMHs (scale range, 0–15; only hemisphere contralateral to stroke was assessed)
CAD: NR
Cermakova et al. (2017) [54] US (1990) Cohort (20 yr) General population 627 30 48 -¶¶ -¶¶ Stroke: NR 269 LVMI§: (TTE) continuous variable (mean=79.9 g/m2, SD=18.4 g/m2, body surface) Occurrence of severe WMH: (MRI) T1 & T2-FLAIR, WMHV >0.3 cm3 Alterations in white matter microstructure: (MRI) DTI, parameter measured; fractional anisotropy
CAD: NR
Ferreira et al. (2017) [58] France (2003–2005) Cross-sectional, cohort (7.7 yr) Hypertensive patients 131/113*** 68 48 100 12 Stroke: 2.3 83 LVH: (ECG) Sokolow-Lyon/Cornell (1) Cross-sectional: Occurrence of severe WMH: (MRI) T2-W, Fazekas total score ≥2 (dWMHs+pWMHs; scale range, 0–6)
CAD: 6.9 (2) Cohort: WMH severity§§: (MRI) T2-W, change in Fazekas score from baseline
Haring et al. (2017) [61] US (1993–1995) Cohort (17.3 yr) General population 721 56 31 31 29 Stroke: 0 NA‡‡ LVM: (TTE) continuous variable (mean=150.1 g, SD=32.1 g) WMH volume/severity: (MRI) FLAIR; (1) quantitative volumetric brain data using automated software, (2) self-designed semi-quantitative 10-point scale
CAD: 11.9
van der Veen et al. (2015) [80] Holland (2001–2005) Cohort (3.9 yr) CVD patients 663 57 81 61 13 Stroke: 23 NA‡‡ LVH: (ECG) Sokolow-Lyon/Cornell WMH volume§§: (MRI) T1-W, T2-W, FLAIR, automatically measured and visually checked
CAD: 62
Cerebral microbleeds
Görner et al. (2007) [60] Belgium (2003–2004) Cross-sectional Stroke patients 199 72 59 48 18 Stroke: 100 56 LVH: (ECG) Sokolow-Lyon/Cornell Microbleeds: (MRI) GRE T2*, ≤5 mm
CAD: NR
Henskens et al. (2009) [63] Netherlands (2004–2006) Cross-sectional Hypertensive patients 192 52 51 100 0 Stroke: 0 29 LVH: (TTE) LVMI ≥95 g/m2 for women and ≥115 g/m2 for men (body surface) Microbleeds: (MRI) GRE T2*, ≤5 mm
CAD: 0
Lee et al. (2004) [69] South Korea (1998–2000) Cross-sectional Hypertensive patients with stroke 102 64 59 100 17 Stroke: 100 66 LVH: (TTE) LVMI ≥110 g/m2 for women and ≥135 g/m2 for men (body surface Microbleeds: (MRI) GRE T2*, ≤5 mm
CAD: 0

Ht, hypertension; DM, diabetes mellitus; CVD, cardiovascular disease; CAD, coronary artery disease; LVH, left ventricular hypertrophy; ECG, electrocardiogram; MRI, magnetic resonance imaging; PD-W, proton density weighted; NR, not reported; CT, computed tomography; LVMI, left ventricular mass index; TTE, transthoracic echocardiogram; SD, standard deviation; MP-RAGE, magnetization prepared-rapid gradient echo; GRE, gradient echo; FLAIR, fluid attenuated inversion recovery; DTI, diffusion tensor imaging; NA, not applicable; WMH, white matter hyperintensity; dWMH, deep white matter hyperintensity; pWMH, periventricular white matter hyperintensity; WMHV, white matter hyperintensity volume; VHD, valvular heart disease; LVM, left ventricular mass; CKD, chronic kidney disease; CMR, cardiac magnetic resonance.

*

Due to definition inadequacy, a proportion of lesions categorized as “lacunes” were in fact silent cortical infarcts, but there were no available data selectively for lacunes. The % of cortical infarcts was <20% in each of these studies;

Percentage of individuals under antihypertensive medication(s);

Myocardial infarction specifically;

§

Effect size provided by these studies (odds ratio for LVMI increments) was appropriately converted to an overall odds ratio using cut off values of 95 g/m2 for women and 115 g/m2 for men;

ST-depression (Minnesota code 4-1,2,3 except for 3-1);

Small vessel disease cases (n=84)+their controls (n=153);

**

Median;

††

Refers only to cases;

‡‡

White matter hyperintensities examined as a quantitative or scaled outcome;

§§

Continuous effect sizes provided by these studies were appropriately converted to odds ratios in the meta-analysis;

∥∥

A 48% of the individuals were categorized as having either early mild cognitive impairment (n=27) or mild cognitive impairment (n=122);

¶¶

Only data for the follow-up visit are reported (2010) (systolic blood pressure, 117 mm Hg [SD=17 mm Hg]; diastolic blood pressure, 73 mm Hg [SD=11 mm Hg]; fasting plasma glucose, 96 mg/dL [SD=29 mg/dL]);

***

Study number is 131 for cross-sectional and 113 for cohort analyses.

Table 2.

Review of the results of studies examining the association between left ventricular mass or left ventricular mass index and white matter hyperintensity severity or volume that were not included in the meta-analysis

Study Population (total no.) Association examined Adjustments Results
Cermakova et al. (2017) [54] General population (n=627) LVMI (per 1 SD, g/m2) with the DTI metric of white matter fractional anisotropy* Age, sex, hypertension, diabetes, smoking, alcohol, BMI, TC, education, race, study site, sedentary time, intracranial volume, ApoE-ε4 genotype Exposure standardized beta coefficient β=–0.001 (–0.003 to 0.0003), P=0.11
Haring et al. (2017) [61] General population (n=721) LVM (per 25 g) with (1) WMH volume (%), normalized to total intracranial volume; (2) graded using a 10-point scale Age, sex, hypertension, diabetes, smoking, alcohol, BMI, aFib, study site, education, income, anxiety, ApoE-ε4 genotype, follow-up duration (1)_Unstandardized beta coefficient β=0.019 (–0.017 to 0.054), P=0.30,
(2) Unstandardized beta coefficient β=0.077 (–0.001 to 0.155), P=0.05
Johansen et al. (2018) [21] General population (n=1,665) LVMI (per 10 g/m2) with WMH volume (cm3), modelled by generalized linear models with γ families and identity links Age, sex, hypertension, diabetes, smoking, alcohol, BMI, LDL-C, MI, education, total intracranial volume Unstandardized beta coefficient β=0.64 (0.19 to 1.08), P<0.01
Kohara et al. (1999) [68] Hypertensive patients (n=150) LVMI (g/m2) with WMH grade (scale 1–4) Age, hypertension, BMI, relative wall thickness Partial correlation coefficient r=0.33 (0.195 to 0.465), P<0.01
Lee et al. (2018) [70] Valvular heart disease patients (n=217) LVMI (g/m2) with WMH volume (mL) Unadjusted Correlation coefficient r=0.072 (–0.061 to 0.205), P=0.29
Lee et al. (2004) [69] Hypertensive patients with stroke (n=102) LVMI grade (scale 0–3) with WMH grade (scale 0–3) Age, sex, hypertension, duration of hypertension, diabetes, glucose, smoking, BMI, total cholesterol, haematocrit, creatinine, anti-platelet use, prior stroke Ordinal logistic regression OR, 1.51 (1.07 to 2.12), P<0.05
Longstreth et al. (1996) [71] General population (n=3,301) LVM (g) with WMH grade (scale 1–8) Age, sex Partial correlation coefficient r=0.067 (0.021 to 0.113), P<0.01
Moore et al. (2018) [20] General population (n=313) LVMI (per 1 SD, g/m2) with DTI metrics (per 1 SD) of white matter microstructure (fractional anisotropy*, mean, radial, axial diffusivity) Age, sex, hypertension, anti-hypertensive drug usage, diabetes, smoking, CVD, aFib, education, race/ethnicity, cognitive status, ApoE-ε4 genotype Standardized beta coefficients provided, all P-values corrected for multiple comparisons <0.05

LVMI, left ventricular mass index; SD, standard deviation; DTI, diffusion tensor imaging; BMI, body mass index; TC, total cholesterol; LVM, left ventricular hypertrophy; WMH, white matter hyperintensities; aFib, atrial fibrillation; ApoE, apolipoprotein E; LDL-C, low density lipoprotein cholesterol; MI, myocardial infarction; CVD, cardiovascular disease.

*

Lower values of fractional anisotropy indicate loss of white matter integrity;

Results indicate statistical significance.

Table 3.

Sensitivity and subgroup analyses for the associations between left ventricular hypertrophy and lacunes or extensive white matter hyperintensities in general and high-risk population studies stratified by study type, exposure and outcome assessment methods, and specific population subsets

Sensitivity and subgroup analyses Lacunes
WMHs
LVH vs. no LVH k* Total no. OR (95% CI) Heterogeneity, I2, P P for subgroup difference k* Total no. OR (95% CI) Heterogeneity, I2, P P for subgroup difference
General population
Overall analysis 5 6,650 1.49 (1.12–2.00) 46%, 0.12 5 4,432 1.73 (1.38–2.17) 0%, 0.77
Study type 0.12 0.77
Cross-sectional 4 5,029 1.40 (0.97–2.01) 47%, 0.13 4 3,805 1.81 (1.41–2.32) 0%, 0.79
Cohort 1 1,621 1.80 (1.25–2.59) NA 1 627 1.37 (0.77–2.44) -
Exposure assessment 0.12 0.77
ECG 3 4,320 1.59 (1.19–2.12) 0%, 0.53 2 2,473 1.87 (1.26–2.80) 0%, 0.33
Only ↑QRS voltage-based criteria 2 2,280 1.57 (1.13–2.19) 21%, 0.26 0.06§ 1 659 1.67 (1.05–2.65) - 0.73§
TTE 2 2,330 1.49 (0.76–2.91) 82%, 0.02 3 1,959 1.67 (1.27–2.20) 0%, 0.73
LVMI ≥95 g/m2 (F), ≥115 g/m2 (M) 2 2,330 1.49 (0.76–2.91) 82%, 0.02 2 1,292 1.67 (1.23–2.28) 0%, 0.42
Outcome assessment - -
CT 0 - - - 0 - - -
MRI 4 5,029 1.40 (0.97–2.01) 47%, 0.13 5 4,432 1.73 (1.38–2.17) 0%, 0.77
High-risk populations
Overall analysis 8 6,879 2.39 (1.32–4.32) 71%, 0.00 11 4,867 2.01 (1.45–2.80) 53%, 0.02
Study type 0.00 0.02
Cross-sectional 6 1,480 3.20 (1.75–5.87) 61%, 0.02 10 4,222 1.74 (1.36–2.22) 12%, 0.34
Cohort 2 5,399 0.97 (0.27–3.53) 75%, 0.05 2 776 2.90 (0.42–19.84) 92%, 0.00
Exposure assessment 0.00 0.06
ECG 6 6,534 1.73 (0.85–3.55) 71%, 0.00 4 1,420 2.41 (0.98–5.90) 78%, 0.00
Only ↑QRS voltage-based criteria 2 900 0.92 (0.27–3.16) 68%, 0.08 - 2 776 2.90 (0.42–19.84) 92%, 0.00 0.00§
TTE 3 940 3.33 (1.53–7.24) 54%, 0.11 6 778 2.31 (1.57–3.39) 0%, 0.91
LVMI ≥95 g/m2 (F), ≥115 g/m2 (M) 0 - - - 2 347 1.84 (1.02–3.31) 0%, 0.51
Outcome assessment 0.00 0.02
CT 2 844 1.97 (1.09–3.55) 0%, 0.37 1 610 1.54 (0.71–3.33) NA
MRI 4 1,062 3.62 (1.00–13.14) 85%, 0.00 10 4,257 2.11 (1.47–3.04) 58%, 0.01
Specific high-risk population subsets 0.02 0.02
Hypertensive patients 5 5,384 3.67 (1.97–6.86) 63%, 0.03 5 551 3.18 (1.70–5.97) 41%, 0.15
Stroke patients 1 595 1.22 (0.37–4.09) NA 4 3,601 1.57 (1.17–2.11) 21%, 0.28

LVH, left ventricular hypertrophy; WMH, white matter hyperintensity; OR, odds ratio; CI, confidence interval; ECG, electrocardiogram; TTE, transthoracic echocardiogram; LVMI, left ventricular mass index; F, female; M, male; CT, computed tomography; MRI, magnetic resonance imaging; NA, not applicable.

*

Numbers of studies in each category do not always add up to the total for a number of different reasons, e.g., article presents additional analyses, article does not fit in any group, etc.;

Results indicate statistical significance;

Subgroup comparison: “ECG” with “TTE”;

§

Subgroup comparison: “Only ↑QRS voltage-based criteria” (i.e., Sokolow-Lyon/Cornell/Minessota code 3–1) with “LVMI ≥95 g/m2 (female), ≥115 g/m2 (male)”.