Skip main navigation

Circulating Metabolome and White Matter Hyperintensities in Women and Men

Originally published 2022;145:1040–1052



White matter hyperintensities (WMH), identified on T2-weighted magnetic resonance images of the human brain as areas of enhanced brightness, are a major risk factor of stroke, dementia, and death. There are no large-scale studies testing associations between WMH and circulating metabolites.


We studied up to 9290 individuals (50.7% female, average age 61 years) from 15 populations of 8 community-based cohorts. WMH volume was quantified from T2-weighted or fluid-attenuated inversion recovery images or as hypointensities on T1-weighted images. Circulating metabolomic measures were assessed with mass spectrometry and nuclear magnetic resonance spectroscopy. Associations between WMH and metabolomic measures were tested by fitting linear regression models in the pooled sample and in sex-stratified and statin treatment–stratified subsamples. Our basic models were adjusted for age, sex, age×sex, and technical covariates, and our fully adjusted models were also adjusted for statin treatment, hypertension, type 2 diabetes, smoking, body mass index, and estimated glomerular filtration rate. Population-specific results were meta-analyzed using the fixed-effect inverse variance–weighted method. Associations with false discovery rate (FDR)–adjusted P values (PFDR)<0.05 were considered significant.


In the meta-analysis of results from the basic models, we identified 30 metabolomic measures associated with WMH (PFDR<0.05), 7 of which remained significant in the fully adjusted models. The most significant association was with higher level of hydroxyphenylpyruvate in men (PFDR.full.adj=1.40×10−7) and in both the pooled sample (PFDR.full.adj=1.66×104) and statin-untreated (PFDR.full.adj=1.65×106) subsample. In men, hydroxyphenylpyruvate explained 3% to 14% of variance in WMH. In men and the pooled sample, WMH were also associated with lower levels of lysophosphatidylcholines and hydroxysphingomyelins and a larger diameter of low-density lipoprotein particles, likely arising from higher triglyceride to total lipids and lower cholesteryl ester to total lipids ratios within these particles. In women, the only significant association was with higher level of glucuronate (PFDR=0.047).


Circulating metabolomic measures, including multiple lipid measures (eg, lysophosphatidylcholines, hydroxysphingomyelins, low-density lipoprotein size and composition) and nonlipid metabolites (eg, hydroxyphenylpyruvate, glucuronate), associate with WMH in a general population of middle-aged and older adults. Some metabolomic measures show marked sex specificities and explain a sizable proportion of WMH variance.

Clinical Perspective

What Is New?

  • This is the first large-scale study to identify circulating metabolomic measures associated with white matter hyperintensities (WMH) volume, which is the most common brain imaging marker of small vessel disease and a major risk factor for incident stroke, dementia, and all-cause mortality.

  • The metabolomic profile of WMH volume appears largely sex specific.

  • The most significant metabolite, hydroxyphenylpyruvate, explains up to 6% and 14% of variance in WMH volume in the pooled sample and in men, respectively, which is comparatively more than the proportions of variance explained by hypertension (1%), type 2 diabetes (1% to 3%), or smoking (<0.1%).

What Are the Clinical Implications?

  • Hydroxyphenylpyruvate is a potentially useful clinical biomarker explaining more variance in WMH volume than the established WMH risk factors.

  • The marked sex specificities in the metabolomic associations of WMH volume add to the growing body of research suggesting that sex-specific molecular pathways underlie the associations between risk factors and cerebrovascular outcomes.

  • Some associated metabolites support vessel-related pathophysiology of WMH; others suggest the involvement of myelin disruption and neuron injury.

Editorial, see p 1053

White matter hyperintensities (WMH)—areas of high intensity in the periventricular and deep cerebral white matter on T2-weighted (T2W) or T2 fluid-attenuated inversion recovery images—are among the most commonly encountered signal alterations on brain magnetic resonance imaging (MRI). The exact neuropathologic mechanisms are not fully understood, but the proposed fundamental features of WMH are loss of myelin and axons and mild gliosis (reviewed in references 1 and 2). These features might arise from chronic ischemia and dysfunction of the blood–brain barrier (BBB) related to cerebral small vessel disease.2

Evidence from a recent meta-analysis of prospective cohort studies indicates that WMH are of major clinical significance.3 Data obtained from 14 529 individuals show that higher volume of WMH is associated with higher risk (hazard ratio [HR]) of incident stroke (HR, 2.45 [95% CI, 1.93–3.12]), intracerebral hemorrhage (HR, 3.17 [95% CI, 1.54–6.52]), ischemic stroke (HR, 2.39 [95% CI, 1.65–3.74]), dementia (HR, 1.84 [95% CI, 1.40–2.43]), Alzheimer disease (HR, 1.50 [95% CI, 1.22–1.84]), and all-cause mortality (HR, 2.00 [95% CI, 1.69–2.36]).3 WMH are common in the general population: the prevalence is ≈20% at 60 years of age and >90% at ≥80 years of age.4 Hypertension, type 2 diabetes, and smoking are the key cardiometabolic disease risk factors for WMH.5–7

We and others have shown that aberrations in the blood metabolome are associated with brain health: circulating levels of multiple lipid measures are associated with Alzheimer disease8,9 and cognitive functioning10 as well as structural properties of the brain, such as T1-weighted (T1W) signal intensity of white matter11,12 and thickness of the cerebral cortex.13 The metabolomic associations of WMH, however, are incompletely understood and only 2 small studies have been published to date.14,15 We used metabolomics technologies to provide a comprehensive characterization of variations in circulating metabolomic measures as a function of WMH volume assessed in the general population. We further explored whether these associations are independent of the key risk factors of WMH, including hypertension, type 2 diabetes, and smoking.


The summary-level results are available within the article and its Supplemental Material. The individual-level data analyzed in this study are available by application to the respective cohort committees.

Study Populations

This is a large collaborative work incorporating data from 8 population-based cohort studies: Age, Gene/Environment Susceptibility–Reykjavik Study,16 Framingham Heart Study,17,18 Insight 46 (a substudy of the British 1946 birth cohort),19 Lothian Birth Cohort 1936,20,21 Rotterdam Study,22–24 Saguenay Youth Study,25 Study of Health in Pomerania,26,27 and Southall and Brent Revisited Study.28 Study-specific descriptions are provided in the Supplemental Methods. After excluding individuals with dementia, stroke, multiple sclerosis, brain surgery, or gross morphologic abnormalities of the brain (eg, cysts, brain tumors), and individuals with poor quality of MRI scans, we analyzed up to 9290 individuals of mostly European ancestries with brain MRI and blood metabolomic data. All cohort studies were approved by local ethics committees, and all participants provided written informed consent (Supplemental Methods).

Phenotype Quantifications

Brain MRI and WMH Assessment

Brain MRI was carried out in each cohort study separately (details provided in Table S1). In the current analyses, the total volume of WMH (or hypointensities on T1W images) was considered as a continuous variable.

Metabolomic Quantifications

We used nuclear magnetic resonance–based techniques (Nightingale Health Ltd, National Phenome Center, Bruker Biospin) and mass spectrometry–based technologies (Metabolon, Biocrates Life Sciences AG, Broad Institute) that are commonly used in epidemiologic research and described elsewhere29–33 (Table S1). Using these platforms, it was possible to measure 2217 different metabolomic measures; of these, 1174 were quantified in ≥2 of the study populations. We did not include unknown metabolites in our study. Metabolomic quantifications were completed using serum or plasma samples, typically extracted from blood samples drawn after overnight fasting (Table S1). Blood samples were drawn before or at the time of MRI in all cohorts except for the Rotterdam Study, in which MRI scans were conducted at multiple time points; metabolomic data closest in time to the MRI scans were analyzed (Table S2).

Statistical Analyses

Linear Regression Models

Cross-sectional associations between log-transformed WMH and circulating metabolomic measures were studied using linear regression. Before model fitting, the log-transformed WMH and metabolomic measures were scaled to SD units, which enables the comparison and meta-analysis of data in different units and varying numeric ranges that originate from the multiple quantification methods used (Table S1). We fitted the regression models in 5 analytic samples (ie, pooled sample and sex- or statin treatment–stratified subsamples) using a simple covariate structure (basic models) and a more complete covariate structure (fully adjusted models) to test whether the associations are independent of key risk factors. The study models were as follows.

Pooled Sample

Basic models: logWMH ~ metabolic measure + age + sex + age × sex + time (if applicable) + fasting duration (if applicable) + intracranial volume or brain size + cohort-specific covariates

Fully adjusted models: as above + statin treatment + hypertension + type 2 diabetes + body mass index + estimated glomerular filtration rate + current smoking status

Sex-Stratified Subsamples

Basic models: logWMH ~ metabolic measure + age + time (if applicable) + fasting duration (if applicable) + intracranial volume or brain size + cohort-specific covariates

Fully adjusted models: as above + statin treatment + hypertension + type 2 diabetes + body mass index + estimated glomerular filtration rate + current smoking status

Statin Treatment–Stratified Subsamples

Basic models: logWMH ~ metabolic measure + age + sex + age × sex + time (if applicable) + fasting duration (if applicable) + intracranial volume or brain size + cohort-specific covariates

Fully adjusted models: as above + hypertension + type 2 diabetes + body mass index + estimated glomerular filtration rate + current smoking status

Time indicates the time in years between blood sampling and brain MRI and fasting duration denotes the time in hours between the last meal and blood sampling. To investigate possible differences in the associations between the analytic subsamples, we fitted models to test for metabolomic measure × sex and metabolomic measure × statin use interactions in both basic and fully adjusted models. In the Southall and Brent Revisited Study, where multiple major ethnicities were present, all models were fitted separately in each ethnic group.

Meta-Analyses and Multiple Testing Correction

The association results for metabolomic measures that were present in ≥2 cohorts (n=1173) were meta-analyzed. We used inverse variance–weighted fixed-effect meta-analysis to combine the effect estimates and standard errors from each cohort. In case a cohort reported multiple association results for the same metabolic measure (ie, the metabolic measure was quantified using >1 metabolomic platform within the same cohort), the result that was obtained using a larger number of individuals was included in the meta-analysis.

Many metabolic measures are highly correlated and thus the number of independent tests is lower than the number of metabolomic measures tested. To correct for multiple testing, we estimated false discovery rate (FDR)–adjusted P values using a method developed by Benjamini and Hochberg.34 All original P values from the meta-analyses (including all analytic samples, both basic and fully adjusted models, and all metabolomic measures analyzed, including all lipid ratios) were included in the numeric vector of P values used for estimating FDR-adjusted P values (PFDR); all associations with PFDR<0.05 were considered significant. Testing for metabolomic measure × sex and metabolomic measure × statin treatment interactions were considered exploratory and no correction for multiple comparisons was applied.

All statistical analyses were conducted using R.35


Characteristics of the study populations are described in Tables S1 and S2 and Figures S1 and S2. In the meta-analysis, we identified 416 metabolomic measures showing nominally significant associations with WMH in at least 1 of the study models. Out of these, 30 (basic models) and 7 (fully adjusted models) associations remained significant after correction for multiple testing (PFDR<0.05). An overview of the associations between circulating metabolomic measures and WMH in all basic and fully adjusted models is given in Figure 1. The relative importance metrics for the fully adjusted model of the most significant metabolite—hydroxyphenylpyruvate (HPP)—are given in Table S3. All meta-analyzed results from the basic models and fully adjusted models are tabulated in Tables S4 and S5, respectively, and the results for metabolomic measure × sex and metabolomic measure × statin treatment interactions are given in Tables S6 and S7. Cohort-specific association results are given in Tables S8 through S22. Figure S3 illustrates cohort-specific results of the metabolomic measures showing FDR-significant association with WMH. Figure S4 shows a comparison of the meta-analyzed results reported here versus the meta-analyzed results obtained in participants of European ancestries only. We found the cohort-specific results to be highly similar across the cohorts, with very little heterogeneity observed (Figure S3 and Tables S4 and S5).

Figure 1.

Figure 1. Metabolomic associations of white matter hyperintensities.

The plot shows the effect sizes (x-axis) and statistical significance (y-axis) of the metabolomic associations of white matter hyperintensities as obtained in the meta-analysis of up to 9290 individuals from 15 populations of mostly European ancestry. Metabolomic associations of log-transformed white matter hyperintensities were determined by fitting linear regression models separately in a pooled sample and in sex- or statin treatment–stratified subsamples (columns). Population-specific effect sizes and standard errors were combined with inverse variance–weighted fixed-effect meta-analysis. The metabolomic measures showing a significant (PFDR<0.05) association with white matter hyperintensities are labeled. Primary study models (top row): associations were adjusted for age and intracranial volume or brain size and for sex and age × sex interaction in the pooled sample and in statin treatment–stratified subsamples.* Fully adjusted models (bottom row): associations were also adjusted for type 2 diabetes (T2D), hypertension, current smoking status, body mass index (BMI), estimated glomerular filtration rate (eGFR), and hypertension, as well as for statin treatment in the pooled sample and in sex-stratified subsamples.† Where relevant, all models were also adjusted for fasting duration, time between blood sampling and brain magnetic resonance imaging, and cohort study-specific covariates. C indicates cholesterol; CE, cholesteryl ester; FC, free cholesterol; FDR, false discovery rate; ICV, intracranial volume; IDL, intermediate-density lipoprotein; L, large; LDL, low-density lipoprotein; LPC, lysophosphatidylcholines; M, medium; MUFA, monounsaturated fatty acid; S, small; SM-OH, hydroxylated sphingomyelin; TG, triglycerides; and VLDL, very-low-density lipoprotein.

Nonlipid Measures

The most robust association, in terms of both effect size and P value, from across all studied metabolites was observed between WMH and higher circulating concentration of the amino acid derivative hydroxyphenylpyruvate (HPP; Figure 2): this association was most significant in the male subsample (βbasic=0.20, PFDR.basic=1.65×106; βfull.adj=0.22, PFDR.full.adj=1.40×107), but it was also significant in the pooled sample (βbasic=0.13, PFDR.basic=0.002; βfull.adj=0.15, PFDR.full.adj=1.66×104) and in the subsample not treated with a statin (βbasic=0.15, PFDR.basic=7.04×105; βfull.adj=0.18, PFDR.full.adj=1.65×106). The HPP × sex interaction reached nominal significance in both the basic and fully adjusted models (PsexINT.basic=0.0094, PsexINT.full.adj=0.0077), suggesting that the positive effect size is larger in men than in women.

Figure 2.

Figure 2. White matter hyperintensities associations with circulating levels of selected nonlipid metabolites. A, The associations between white matter hyperintensities and circulating metabolomic measures were determined using linear regression. The primary study models (diamonds) were adjusted for age and intracranial volume (ICV) or brain size and, in the pooled sample and in statin treatment–stratified subsamples, also for sex and age × sex interaction.* The fully adjusted models (squares) were also adjusted for type 2 diabetes (T2D), hypertension, current smoking status, body mass index (BMI), and estimated glomerular filtration rate (eGFR) and, in the pooled sample and in sex-stratified subsamples, also for statin treatment.† Where relevant, all models were adjusted for fasting duration, time between blood sampling and brain magnetic resonance imaging, and possible cohort study-specific covariates. Blue indicates negative effect size and red indicates positive effect size. Error bars indicate 95% CIs. P values below the threshold for multiple testing correction (PFDR<0.05) are indicated with bold font. B, Catabolic pathway of phenylalanine and tyrosine into hydroxyphenylpyruvate and homogentisic acid by phenylalanine hydroxylase (PAH), tyrosine aminotransferase (TAT), and hydroxyphenylpyruvate dioxygenase (HPD). FDR indicates false discovery rate; and GlycA, glycoprotein acetylation.

In women, the only significant association, after correction for multiple testing, was observed between WMH and higher circulating concentration of glucuronate (βbasic=0.11, PFDR.basic=0.047; βfull.adj=0.11, PFDR.full.adj=0.047; Figure 2); this association also was significant in the subsample not treated with a statin (βbasic=0.10, PFDR.basic=0.025; βfull.adj=0.10, PFDR.full.adj=0.040). The glucuronate × sex interaction did not reach statistical significance (PsexINT.basic=0.053, PsexINT.full.adj=0.129).

Lipid Measures

WMH volume was associated with lower circulating concentrations of lysophosphatidylcholines (LPCs) and hydroxylated sphingomyelins (SM-OHs; Figure 3). Among the studied LPCs, the strongest association was seen with LPC(22:6), which was significant in the pooled sample (βbasic=−0.078, PFDR.basic=0.028; βfull.adj=−0.082, PFDR.full.adj=0.047) and in the male subsample (βbasic=−0.11, PFDR.basic=0.025; βfull.adj=−0.12, PFDR.full.adj=0.046). Among the studied SM-OHs, the strongest association was observed with SM (OH) C22:2; this association was significant only in the male subsample (βbasic=−0.11, PFDR.basic=0.017; βfull.adj=−0.10, PFDR.full.adj=0.042). The interactions between LPC measures and sex or statin treatment typically did not reach statistical significance (Tables S5 and S6); the exceptions were nominally significant interaction with sex for LPC(17:0) in the basic and fully adjusted models (PsexINT.basic=0.027, PsexINT.fully.adj=0.045, respectively) and with statin treatment for LPC(20:4) in the fully adjusted model (PstatinINT.fully.adj=0.015; Figure 3). The SM-OH species × sex interactions were nominally significant in both basic and fully adjusted models for SM (OH) C14:1 (PsexINT.basic=0.018, PsexINT.fully.adj=0.033), SM (OH) C16:1 (PsexINT.basic=0.027, PsexINT.fully.adj=0.044), and SM (OH) C22:2 (PsexINT.basic=0.0061, PsexINT.fully.adj=0.013). The SM (OH) C14:1 × statin treatment interaction was nominally significant in the fully adjusted model (PstatinINT.fully.adj=0.047).

Figure 3.

Figure 3. White matter hyperintensities associations with circulating levels of lysophosphatidylcholines and hydroxylated sphingomyelins.

The associations between white matter hyperintensities and circulating metabolomic measures were determined using linear regression. The primary study models (diamonds) were adjusted for age and intracranial volume (ICV) or brain size and, in the pooled sample and in statin treatment–stratified subsamples, also for sex and age × sex interaction.* The fully adjusted models (squares) were also adjusted for type 2 diabetes (T2D), hypertension, current smoking status, body mass index (BMI), and estimated glomerular filtration rate (eGFR) and, in the pooled sample and in sex-stratified subsamples, also for statin treatment.† Where relevant, all models were adjusted for fasting duration, time between blood sampling and brain magnetic resonance imaging, and possible cohort study-specific covariates. Blue indicates negative effect size and red indicates positive effect size. Error bars indicate 95% CIs. P values below the threshold for multiple testing correction (PFDR<0.05) are indicated with bold font. FDR indicates false discovery rate; LPC, lysophosphatidylcholine; and SM (OH), hydroxylated sphingomyelin.

A sizable proportion of the studied metabolomic measures were circulating concentrations of lipoproteins and lipoprotein lipids (≈24% of the meta-analyzed measures). Out of the 30 metabolomic measures showing FDR-significant associations with WMH in the basic models, 12 were with measures of the lipid composition of the intermediate-density lipoprotein and low-density lipoprotein (LDL) particles (lipid composition calculated as a ratio of a lipid concentration against total lipids concentration within individual lipoprotein subfractions). Higher WMH volume was associated with higher triacylglycerol to total lipids ratio and lower cholesteryl ester to total lipids ratio in the pooled sample and male subsample (Figure 4). The effect sizes were attenuated in the fully adjusted models (Figure 4). Higher WMH volume was associated with larger LDL particle diameter in the male subsample and this association remained FDR significant in the fully adjusted model (βbasic=0.074, PFDR.basic=0.049; βfull.adj=0.078, PFDR.full.adj=0.047). We observed a nominally significant LDL diameter × sex interaction (PsexINT.basic=0.0093, PsexINT.full.adj=0.036). Higher WMH volume was also associated with lower free cholesterol to total lipids ratio within the medium-sized very-low-density lipoprotein subfraction in the statin-treated subsample (Figure 1), but no other significant association with either very-low-density lipoprotein or high-density lipoprotein subfraction measures was seen (Tables S3 and S4).

Figure 4.

Figure 4. White matter hyperintensities associations with low-density lipoprotein particle size and lipid composition measures in intermediate-density lipoprotein and low-density lipoprotein subfractions.

The associations between white matter hyperintensities and circulating metabolic measures were determined using linear regression. The primary study models (diamonds) were adjusted for age and intracranial volume (ICV) or brain size and, in the pooled sample and in statin treatment–stratified subsamples, also for sex and age × sex interaction.* The fully adjusted models (squares) were also adjusted for type 2 diabetes (T2D), hypertension, current smoking status, body mass index (BMI), and estimated glomerular filtration rate (eGFR) and, in the pooled sample and in sex-stratified subsamples, also for statin treatment.† Where relevant, all models were adjusted for fasting duration, time between blood sampling and brain magnetic resonance imaging, and possible cohort study-specific covariates. Blue indicates negative effect size and red indicates positive effect size. Error bars indicate 95% CIs. P values below the threshold for multiple testing correction (PFDR<0.05) are indicated with bold font. FDR indicates false discovery rate; IDL, intermediate-density lipoprotein; L, large; LDL, low-density lipoprotein; M, medium; and S, small.


We investigated associations between WMH volume and circulating metabolomic measures in up to 9290 individuals. Several aspects of our metabolomic findings support a possible vessel-related pathophysiology of WMH and some suggest the involvement of myelin disruption and neuron injury. A number of the observed associations showed sex specificities, indicating that distinct metabolomic features accompany WMH in men and women.

In the present study, the most robust association was the positive association between WMH volume and HPP in male participants (PFDR.full.adj=1.40×10−7); the association was also significant in the pooled sample (PFDR.full.adj=1.66×104) and in individuals not treated with a statin (PFDR.full.adj=1.65×106). HPP is a potentially toxic compound derived from the catabolism of phenylalanine and tyrosine36 (Figure 2). Higher circulating levels of phenylalanine and tyrosine have been associated with higher risk of cardiovascular disease in previous studies,37,38 but HPP was not examined in those studies. We did not see strong associations with phenylalanine or tyrosine and thus the association between WMH and HPP likely arises downstream from phenylalanine hydroxylase (PAH; Figure 2). The enzymatic alterations possibly contributing to higher level of HPP could be higher activity of tyrosine aminotransferase (TAT) or lower activity of hydroxyphenylpyruvate dioxygenase (HPD). In tyrosine breakdown, TAT converts tyrosine and α-ketoglutarate to HPP and glutamate39 and thus higher TAT activity could contribute to lower α-ketoglutarate and to higher glutamate. In male and statin-treated participants, however, we found only a nominal association between WMH and circulating α-ketoglutarate, which was not in the anticipated direction, and no association with glutamate (Figure 2). Therefore, in the view of these results, lower HPD activity appears as the most likely mechanism driving the association between WMH and circulating HPP. HPD requires oxygen to convert HPP to homogentisic acid (Figure 2) and, consistent with this requirement, hypoxia promotes accumulation of HPP.40 The deep and periventricular white matter, which is the predilection site for WMH, is characterized by sparse vasculature consisting of long, narrow end arteries and arterioles that are vulnerable to oxygen desaturation.41 Thus, higher circulating HPP in association with higher WMH may be an indicator of ischemic hypoxia promoting WMH. As indicated previously, the association of HPP with WMH volume was present in the pooled sample, in men, and in the subsample not treated with a statin, but not in women. In men, HPP explained 3.3% and 14.3% of variance in WMH, respectively, in the 2 cohorts providing results for HPP in the fully adjusted model: Insight 46 and the Third Generation of the Framingham Heart Study (Table S3). Consistent with previous research demonstrating that vascular risk factors, including hypertension, explain only up to 2% of variance in WMH volume,7,42 we found the respective proportions of variance explained by hypertension, diabetes, and smoking to be 0.1%, 2.9%, and 0.03% in male participants from Insight 46 and 1.1%, 1.0%, and 0.1% in male participants from the Third Generation of the Framingham Heart Study (Table S3). Taken together, our results suggest that circulating HPP may be a strong biomarker of WMH in men, but understanding its potential in clinical use requires further research.

Glucuronate was the only metabolite demonstrating a robust association with WMH in women (PFDR.full.adj=0.047); the association was also significant in individuals not treated with a statin (PFDR.full.adj=0.040). Glucuronate is derived from glucose, and, in humans, it is involved in the elimination of toxic substances by making them more water-soluble in a process called glucuronidation.36 In addition, hormones can be glucuronidated to enable easier transport.36 A key enzyme of glucuronidation is uridine 5'-diphospho-glucuronosyltransferase, which is highly expressed in endothelial cells of the BBB and associated astrocytes, where the enzyme contributes to protection of the brain from systemic toxic substances (reviewed by Ouzzine et al43). Endothelial cells use glucuronate to synthesize glycosaminoglycans, which are constituents of the glycocalyx layer that tightens the endothelial barrier and limits vascular permeability.44 The observed WMH association with glucuronate may involve altered glucose metabolism in endothelial cells, which might compromise the molecular mechanisms enabling the protective functions of the BBB. The association between WMH and glucuronate remained significant after adjusting for type 2 diabetes in the fully adjusted model. In our study, WMH association with (mostly fasting) glucose level did not reach statistical significance, which is in line with some45 but not all46 previous reports.

In addition to the nonlipid measures, we found that WMH were associated with several circulating lipids—most notably with LPCs and SM-OHs. These associations were almost exclusively negative and reached statistical significance predominantly in male participants and, in some cases, also in the pooled sample. LPCs are phospholipids generated by partial hydrolysis of phosphatidylcholines, which are the building blocks of cell membranes, such as those of blood cells and endothelial cells. In circulation, LPCs modulate inflammation and oxidative stress,47,48 and they may alter the integrity of endothelial membranes, including the BBB.49,50 Lower circulating levels of LPCs, such as LPC(18:2), have been associated with higher cardiovascular disease risk51 and adverse cognitive outcomes.52 Consistent with these previous reports, we found that lower LPC(18:2) is associated with higher WMH volume, but similar to most other tested LPCs, the association reached only nominal significance in the fully adjusted model. LPC(22:6) was the only LPC that remained significantly associated with WMH in the fully adjusted model (male participants: PFDR.full.adj=0.046; pooled sample: PFDR.full.adj=0.047). Docosahexaenoic acid (DHA; 22:6n-3) is one of the 2 predominant fatty acids in the human brain and a structural component of neuronal cell membranes.53,54 DHA plays a crucial role in neuronal survival, neurogenesis, and synaptic function.53 Evidence obtained in mice suggests that oral administration of LPC-DHA, but not free DHA, increases DHA content of the brain and improves spatial learning and memory.55 Thus, the associations of circulating LPCs with WMH observed here suggest BBB and neuron injury–related pathobiologies of WMH.

Similar to LPCs, the associations between WMH and SM-OHs were negative, with the most significantly associated SM-OH being SM (OH) C22:2 in male participants (PFDR.full.adj=0.042). SM-OHs are important components of myelin sheaths,56 which are lipid-rich cell membranes wrapped around neuronal axons, protecting the axons physically and providing trophic support, as well as increasing the conduction speed of action potentials.57 The negative associations between WMH and SM-OHs could point toward disruption of myelin sheaths,56 which is one of the key features of WMH.1 In line with our findings, previous evidence suggests that low level of serum total sphingomyelin is associated with cross-sectional memory impairment.58

Regarding the associations with lipoprotein measures, we observed that WMH were associated with larger LDL particle diameter (PFDR.full.adj=0.047 in male participants), likely arising from altered LDL lipid composition, namely relatively higher triacylglycerol and lower cholesteryl ester content. An altered interaction between lipoproteins and enzymes modulating their lipid composition could play a role in these associations. For example, the endothelium-bound lipoprotein lipase, which catalyzes the hydrolysis of lipoprotein triglycerides, interacts directly with LDL receptor–related protein,59 which is involved in the regulation of BBB permeability60; as such, lipoprotein lipase displays functions related to both blood metabolome and WMH found in areas with enhanced permeability of the BBB.61 Statin treatment may have a small confounding effect on these associations, as statin-treated individuals have the highest WMH volumes together with the lowest cholesterol levels (Table). However, considering the facts that the effect estimates of these measures were consistent across all analytic subsamples, including individuals not treated with a statin, and that the effect estimates were only marginally attenuated in the statin use–adjusted models compared with the basic models (Figure 4), statin use does not seem to be a key factor driving the negative associations between WMH and lower cholesteryl ester content in the LDL subfractions.

Table. Sample Characteristics

CharacteristicCombinedFemaleMaleNot on statinOn statin
Individuals, n92904711457975651633
On statin17.714.421.00100
Type 2 diabetes7.
Age, y61.0±7.360.7±7.461.4±7.259.6±7.567.9±5.6
BMI, kg/m227.3±4.427.1±4.727.5±4.027.1±4.428.2±4.1
Log WMH5.35±0.955.17±0.935.35±0.955.05±0.916.78±1.07
Total triacylglycerol, mmol/L*1.38±0.651.37±0.621.39±0.651.36±0.631.45±0.69
Total cholesterol, mmol/L*4.95±1.005.10±0.974.81±0.975.18±0.934.17±0.89
LDL-C, mmol/L2.39±0.672.49±0.672.31±0.662.57±0.641.74±0.54
HDL-C, mmol/L1.51±0.361.58±0.341.43±0.311.54±0.631.39±0.33

Values are percentages or mean±SD of the pooled data of 15 populations from 8 cohort studies. The population-specific characteristics are given in Table S2. BMI indicates body mass index; HDL-C, high-density lipoprotein cholesterol; LDL-C, low-density lipoprotein cholesterol; and WMH, white matter hyperintensities.

* Pooled data of 9 populations for which total triacylglycerol and total cholesterol data were available.

† Pooled data of 10 populations for which LDL-C and HDL-C data were available.

In the present study, several of the FDR-significant associations in the fully adjusted models showed nominally significant metabolite × sex interactions, suggesting that the effect sizes vary depending on sex. These associations included the amino acid derivative HPP (PsexINT.full.adj=0.0077) and 2 lipid measures (SM [OH] C22:2 [PsexINT.full.adj=0.013] and LDL particle size [PsexINT.full.adj=0.036]): for all 3, the effect sizes were nominally greater in men than in women. These sex specificities were observed despite similar sample sizes of male and female participants and no major sex differences in the distributions of WMH and key traits, such as body mass index, cigarette smoking, or clinical lipid traits (Table 1). The metabolomic measures did not show major sex differences, except for the circulating levels of SM (OH) C22:2, which tended to be lower in male than in female participants (Figure S1), as reported previously.62 Considering that hydroxysphingomyelins are important for the maintenance of myelination,56 the lower levels of this lipid in men than in women could contribute to the nominally greater negative effect size of SM (OH) C22:2 on WMH in men than in women. Regarding HPP, the circulating levels were similar between men and women (Figure S1), and the biological mechanism of the nominally greater positive effect of HPP on WMH in men versus women remains inconclusive. It could be that circulating HPP is a marker of ischemic hypoxia that is of a similar magnitude in men and women (as reflected by similar circulating levels of HPP in the 2 sexes), but the ischemic hypoxia has a greater effect in men than in women. This is supported by previous research indicating that, in men compared with women, ischemic hypoxia in the brain induces greater oxidative stress and thus possibly greater tissue damage and more extensive WMH.63,64 With respect to the LDL particle size, this measure also did not show consistent sex differences across the analyzed cohorts (Figure S1), and the biological mechanisms of the nominally greater positive effect size of LDL particle size on WMH in men than in women are not clear. Previous literature on sex differences in WMH is not extensive.65–67 Sex hormones may play a role, as they show direct effects on the endothelium68 and multiple brain cells, including oligodendrocytes producing myelin69 and neurons.70 In our study, female hormonal status was not likely a major factor in the observed sex specificities, as female participants were likely menopausal (average age >60 years). We cannot exclude the possibility that the use of hormonal replacement therapy and a greater lifetime exposure to female sex hormones would modulate the associations examined in older age. Overall, our results indicating sex-specific associations between metabolomic measures and WMH add to the growing body of research reporting sex differences in the associations between risk factors and cerebrovascular outcomes.71–75

The strengths and limitations of this study should be considered. We analyzed data from multiple population-based cohorts, which enables a large sample size, yielding the statistical power required to detect small effect sizes reliably and to replicate and meta-analyze the results. Although the study sample included individuals from multiple geographic areas and diverse ethnic backgrounds, the large majority of participants were of European ancestries and thus replication of the findings in other ethnicities would be of high value. The fact that we analyzed metabolomic data quantified with multiple platforms could be considered as both a strength and a limitation: despite covering a wide spectrum of circulating lipids and metabolites, some of the metabolic measures are underrepresented and therefore we may lack statistical power to detect associations with these measures. Furthermore, because of the multiple testing correction to minimize the risk of false-positive associations, some biologically relevant associations may be missed. For instance, many of the associations between WMH and lipid concentrations within intermediate-density lipoprotein and LDL subfractions were negative with non–null-containing 95% CIs; however, a minority of these associations reached FDR significance. Because lipoprotein metabolism is an interconnected system, it may be more meaningful to examine the big picture instead of interpreting the associations on the level of individual metabolic measures. In most cohorts, WMH were quantified with automated image analysis techniques. Therefore, we cannot exclude the possibility that small lacunar lesions (also a feature of small vessel disease2,76) were included in the quantified WMH volume. This potential misclassification is expected to be low, however, as lacune prevalence is not high in the general population77 and, when present, lacunes usually occur singly or in low numbers, and the volume of WMH surrounding small lacunes is low. Manual segmentation of WMH is extremely labor-intensive and therefore it is not frequently used in large-scale cohort studies including thousands of participants,6 such as the ones included here. In future studies, automatic segmentation of multimodal magnetic resonance images may allow one to compare metabolic profiles of different types of tissue abnormalities present in white matter. It would be of high value to assess the causal inferences between circulating metabolites and WMH in future studies once suitable genetic instruments are available. Considering the results of our study, it is likely necessary to test the causality in sex-specific analyses. Further insight into the pathophysiology of WMH awaits the results of LACI-2 (Lacunar Intervention Trial 2), testing 2 repurposed licensed drugs with effects on endothelium-dependent vasodilation and BBB integrity to prevent progression of cerebral small vessel disease.78

This is the first large-scale study to determine associations between WMH and circulating metabolomic measures. Although no causal associations can be inferred from the findings, they indicate that alterations in circulating metabolism are closely linked with WMH in a general population of middle-aged and older adults and suggest that the metabolomic profiles accompanying WMH in men and women may differ.

Article Information


The authors thank Manon Bernard (database architect, The Hospital for Sick Children) and Hélène Simard (Cégep de Jonquière) for contributions in data acquisition and storage in the Saguenay Youth Study; the participants of the Saguenay Youth Study; the study members who helped in the design of the Insight 46 study; the Medical Research Council Unit of Lifelong Health and Ageing at University College London and Insight 46 study teams for their contributions in data acquisition, analysis, and storage; the Lothian Birth Cohort 1936 participants and team members who contributed to these studies; and the study participants, general practitioners, specialists, researchers, institutions, and funders of the studies that formed the basis of this analysis.

Supplemental Material

Supplemental Methods

Figures S1–S3

Tables S1–S22

Excel Files S1 and S2

References 79–88

Nonstandard Abbreviations and Acronyms


blood–brain barrier


docosahexaenoic acid


false discovery rate


hydroxyphenylpyruvate dioxygenase




hazard ratio


low-density lipoprotein




magnetic resonance imaging


phenylalanine hydroxylase


hydroxylated sphingomyelin






tyrosine aminotransferase


white matter hyperintensities

Disclosures Drs Seshadri and Pausova conceptualized the study. Drs Sliz, Shin, and Pausova contributed to the analysis plan. Drs Sliz, Shin, Ahmad, Williams, Gauß, Harris, Henning, Hu, and Wittfeld, Frenzel, and Wang analyzed the data and generated results. Drs Sliz and Pausova interpreted the results and wrote the original manuscript. Drs Yang, Völzke, Schott, Richards, Nauck, Launer, Ikram, Hosten, Grabe, Deary, Cox, Lewis, Jiménez, Sudre, Wardlaw, Proitsi, Ghanbari, Barnes, Hernandez, Chaturvedi, Rotter, Fornage, Paus, Seshadri, and Pausova obtained funding, provided additional study resources, and contributed to metabolic or magnetic resonance imaging data. All authors contributed to revising the content and approved the final version.

Dr Grabe has received travel grants and speaker honoraria from Fresenius Medical Care, Neuraxpharm, Servier, and Janssen Cilag as well as research funding from Fresenius Medical Care. The other authors report no disclosures.


Supplemental Material, the podcast, and transcript are available with this article at

For Sources of Funding and Disclosures, see page 1049.

Circulation is available at

Correspondence to: Zdenka Pausova, MD, Departments of Physiology and Nutritional Sciences, Peter Gilgan Centre for Research and Learning, University of Toronto, Room 10-9705, 686 Bay Street, Toronto, ON M5G 0A4 Canada. Email


  • 1. Moroni F, Ammirati E, Hainsworth AH, Camici PG. Association of white matter hyperintensities and cardiovascular disease: the importance of microcirculatory disease.Circ Cardiovasc Imaging. 2020; 13:e010460. doi: 10.1161/CIRCIMAGING.120.010460LinkGoogle Scholar
  • 2. Wardlaw JM, Smith C, Dichgans M. Small vessel disease: mechanisms and clinical implications.Lancet Neurol. 2019; 18:684–696. doi: 10.1016/S1474-4422(19)30079-1CrossrefMedlineGoogle Scholar
  • 3. 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. doi: 10.1001/jamaneurol.2018.3122CrossrefMedlineGoogle Scholar
  • 4. Debette S, Markus HS. The clinical importance of white matter hyperintensities on brain magnetic resonance imaging: systematic review and meta-analysis.BMJ. 2010; 341:c3666. doi: 10.1136/bmj.c3666CrossrefMedlineGoogle Scholar
  • 5. Power MC, Deal JA, Sharrett AR, Jack CR, Knopman D, Mosley TH, Gottesman RF. Smoking and white matter hyperintensity progression: the ARIC-MRI Study.Neurology. 2015; 84:841–848. doi: 10.1212/WNL.0000000000001283CrossrefMedlineGoogle Scholar
  • 6. Sargurupremraj M, Suzuki H, Jian X, Sarnowski C, Evans TE, Bis JC, Eiriksdottir G, Sakaue S, Terzikhan N, Habes M, et al; International Network against Thrombosis (INVENT) Consortium; International Headache Genomics Consortium (IHGC). Cerebral small vessel disease genomics and its implications across the lifespan.Nat Commun. 2020; 11:6285. doi: 10.1038/s41467-020-19111-2CrossrefMedlineGoogle Scholar
  • 7. Wardlaw JM, Allerhand M, Doubal FN, Valdes Hernandez M, Morris Z, Gow AJ, Bastin M, Starr JM, Dennis MS, Deary IJ. Vascular risk factors, large-artery atheroma, and brain white matter hyperintensities.Neurology. 2014; 82:1331–1338. doi: 10.1212/WNL.0000000000000312CrossrefMedlineGoogle Scholar
  • 8. Bernath MM, Bhattacharyya S, Nho K, Barupal DK, Fiehn O, Baillie R, Risacher SL, Arnold M, Jacobson T, Trojanowski JQ, et al; Alzheimer’s Disease Neuroimaging Initiative and Alzheimer’s Disease Metabolomics Consortium. Serum triglycerides in Alzheimer disease: relation to neuroimaging and CSF biomarkers.Neurology. 2020; 94:e2088–e2098. doi: 10.1212/WNL.0000000000009436CrossrefMedlineGoogle Scholar
  • 9. Varma VR, Oommen AM, Varma S, Casanova R, An Y, Andrews RM, O’Brien R, Pletnikova O, Troncoso JC, Toledo J, et al. Brain and blood metabolite signatures of pathology and progression in Alzheimer disease: a targeted metabolomics study.PLoS Med. 2018; 15:e1002482. doi: 10.1371/journal.pmed.1002482CrossrefMedlineGoogle Scholar
  • 10. van der Lee SJ, Teunissen CE, Pool R, Shipley MJ, Teumer A, Chouraki V, Melo van Lent D, Tynkkynen J, Fischer K, Hernesniemi J, et al. Circulating metabolites and general cognitive ability and dementia: evidence from 11 cohort studies.Alzheimers Dement. 2018; 14:707–722. doi: 10.1016/j.jalz.2017.11.012CrossrefMedlineGoogle Scholar
  • 11. Syme C, Pelletier S, Shin J, Abrahamowicz M, Leonard G, Perron M, Richer L, Veillette S, Gaudet D, Pike B, et al. Visceral fat-related systemic inflammation and the adolescent brain: a mediating role of circulating glycerophosphocholines.Int J Obes (Lond). 2019; 43:1223–1230. doi: 10.1038/s41366-018-0202-2CrossrefMedlineGoogle Scholar
  • 12. Sliz E, Shin J, Syme C, Patel Y, Parker N, Richer L, Gaudet D, Bennett S, Paus T, Pausova Z. A variant near DHCR24 associates with microstructural properties of white matter and peripheral lipid metabolism in adolescents.Mol Psychiatry. 2021; 26:3795–3805. doi: 10.1038/s41380-019-0640-9CrossrefMedlineGoogle Scholar
  • 13. Sliz E, Shin J, Syme C, Black S, Seshadri S, Paus T, Pausova Z. Thickness of the cerebral cortex shows positive association with blood levels of triacylglycerols carrying 18-carbon fatty acids.Commun Biol. 2020; 3:456. doi: 10.1038/s42003-020-01189-5CrossrefMedlineGoogle Scholar
  • 14. de Leeuw FA, Karamujić-Čomić H, Tijms BM, Peeters CFW, Kester MI, Scheltens P, Ahmad S, Vojinovic D, Adams HHH, Hankemeier T, et al. Circulating metabolites are associated with brain atrophy and white matter hyperintensities.Alzheimers Dement. 2021; 17:205–214. doi: 10.1002/alz.12180CrossrefMedlineGoogle Scholar
  • 15. Li D, Misialek JR, Jack CR, Mielke MM, Knopman D, Gottesman R, Mosley T, Alonso A. Plasma metabolites associated with brain MRI measures of neurodegeneration in older adults in the Atherosclerosis Risk in Communities– Neurocognitive Study (ARIC-NCS).Int J Mol Sci. 2019; 20:1–12. doi: 10.3390/ijms20071744CrossrefGoogle Scholar
  • 16. Harris TB, Launer LJ, Eiriksdottir G, Kjartansson O, Jonsson PV, Sigurdsson G, Thorgeirsson G, Aspelund T, Garcia ME, Cotch MF, et al. Age, Gene/Environment Susceptibility–Reykjavik Study: multidisciplinary applied phenomics.Am J Epidemiol. 2007; 165:1076–1087. doi: 10.1093/aje/kwk115CrossrefMedlineGoogle Scholar
  • 17. DeCarli C, Massaro J, Harvey D, Hald J, Tullberg M, Au R, Beiser A, D’Agostino R, Wolf PA. Measures of brain morphology and infarction in the framingham heart study: establishing what is normal.Neurobiol Aging. 2005; 26:491–510. doi: 10.1016/j.neurobiolaging.2004.05.004CrossrefMedlineGoogle Scholar
  • 18. Tsao CW, Vasan RS. Cohort profile: The Framingham Heart Study (FHS): overview of milestones in cardiovascular epidemiology.Int J Epidemiol. 2015; 44:1800–1813. doi: 10.1093/ije/dyv337CrossrefMedlineGoogle Scholar
  • 19. Lane CA, Parker TD, Cash DM, Macpherson K, Donnachie E, Murray-Smith H, Barnes A, Barker S, Beasley DG, Bras J, et al. Study protocol: Insight 46: a neuroscience sub-study of the MRC National Survey of Health and Development.BMC Neurol. 2017; 17:75. doi: 10.1186/s12883-017-0846-xCrossrefMedlineGoogle Scholar
  • 20. Taylor AM, Pattie A, Deary IJ. Cohort profile update: the Lothian Birth Cohorts of 1921 and 1936.Int J Epidemiol. 2018; 47:1042–1042r. doi: 10.1093/ije/dyy022CrossrefMedlineGoogle Scholar
  • 21. Wardlaw JM, Bastin ME, Valdés Hernández MC, Maniega SM, Royle NA, Morris Z, Clayden JD, Sandeman EM, Eadie E, Murray C, et al. Brain aging, cognition in youth and old age and vascular disease in the Lothian Birth Cohort 1936: rationale, design and methodology of the imaging protocol.Int J Stroke. 2011; 6:547–559. doi: 10.1111/j.1747-4949.2011.00683.xCrossrefMedlineGoogle Scholar
  • 22. Hofman A, Breteler MM, van Duijn CM, Krestin GP, Pols HA, Stricker BH, Tiemeier H, Uitterlinden AG, Vingerling JR, Witteman JC. The Rotterdam Study: objectives and design update.Eur J Epidemiol. 2007; 22:819–829. doi: 10.1007/s10654-007-9199-xCrossrefMedlineGoogle Scholar
  • 23. Ikram MA, Brusselle GGO, Murad SD, van Duijn CM, Franco OH, Goedegebure A, Klaver CCW, Nijsten TEC, Peeters RP, Stricker BH, et al. The Rotterdam Study: 2018 update on objectives, design and main results.Eur J Epidemiol. 2017; 32:807–850. doi: 10.1007/s10654-017-0321-4CrossrefMedlineGoogle Scholar
  • 24. Ikram MA, Brusselle G, Ghanbari M, Goedegebure A, Ikram MK, Kavousi M, Kieboom BCT, Klaver CCW, de Knegt RJ, Luik AI, et al. Objectives, Design and Main Findings Until 2020 From the Rotterdam Study. Springer: Netherlands; 2020.CrossrefGoogle Scholar
  • 25. Pausova Z, Paus T, Abrahamowicz M, Bernard M, Gaudet D, Leonard G, Peron M, Pike GB, Richer L, Séguin JR, et al. Cohort profile: the Saguenay Youth Study (SYS).Int J Epidemiol. 2017; 46:e19. doi: 10.1093/ije/dyw023CrossrefMedlineGoogle Scholar
  • 26. John U, Greiner B, Hensel E, Lüdemann J, Piek M, Sauer S, Adam C, Born G, Alte D, Greiser E, et al. Study of Health In Pomerania (SHIP): a health examination survey in an east German region: objectives and design.Soz Praventivmed. 2001; 46:186–194. doi: 10.1007/BF01324255CrossrefMedlineGoogle Scholar
  • 27. Völzke H, Alte D, Schmidt CO, Radke D, Lorbeer R, Friedrich N, Aumann N, Lau K, Piontek M, Born G, et al. Cohort profile: the study of health in Pomerania.Int J Epidemiol. 2011; 40:294–307. doi: 10.1093/ije/dyp394CrossrefMedlineGoogle Scholar
  • 28. Jones S, Tillin T, Park C, Williams S, Rapala A, Al Saikhan L, Eastwood SV, Richards M, Hughes AD, Chaturvedi N. Cohort profile update: Southall and Brent Revisited (SABRE) study: a UK population-based comparison of cardiovascular disease and diabetes in people of European, South Asian and African Caribbean heritage.Int J Epidemiol. 2020; 49:1441–1442e. doi: 10.1093/ije/dyaa135CrossrefMedlineGoogle Scholar
  • 29. Würtz P, Kangas AJ, Soininen P, Lawlor DA, Davey Smith G, Ala-Korpela M. Quantitative serum nuclear magnetic resonance metabolomics in large-scale epidemiology: a primer on -omic technologies.Am J Epidemiol. 2017; 186:1084–1096. doi: 10.1093/aje/kwx016CrossrefMedlineGoogle Scholar
  • 30. Dona AC, Jiménez B, Schäfer H, Humpfer E, Spraul M, Lewis MR, Pearce JT, Holmes E, Lindon JC, Nicholson JK. Precision high-throughput proton NMR spectroscopy of human urine, serum, and plasma for large-scale metabolic phenotyping.Anal Chem. 2014; 86:9887–9894. doi: 10.1021/ac5025039CrossrefMedlineGoogle Scholar
  • 31. Römisch-Margl W, Prehn C, Bogumil R, Röhring C, Suhre K, Adamski J. Procedure for tissue sample preparation and metabolite extraction for high-throughput targeted metabolomics.Metabolomics. 2012; 8:133–142. doi: 10.1007/s11306-011-0293-4CrossrefGoogle Scholar
  • 32. Jiménez B, Holmes E, Heude C, Tolson RF, Harvey N, Lodge SL, Chetwynd AJ, Cannet C, Fang F, Pearce JTM, et al. Quantitative lipoprotein subclass and low molecular weight metabolite analysis in human serum and plasma by 1H NMR spectroscopy in a multilaboratory trial.Anal Chem. 2018; 90:11962–11971. doi: 10.1021/acs.analchem.8b02412CrossrefMedlineGoogle Scholar
  • 33. Harris SE, Ritchie SJ, Correia GDS, Jiménez B, Fawns-Ritchie C, Pattie A, Corley J, Maniega SM, Hernández MV, Starr JM, et al. Plasma lipid and lipoprotein biomarkers in LBC1936: do they predict general cognitive ability and brain structure?bioRxiv. Preprint posted online July 17, 2020. doi: 10.1101/2020.07.09.194688CrossrefGoogle Scholar
  • 34. Benjamini Y, Hochberg Y. Controlling the false discovery rate: a practical and powerful approach to multiple testing.J R Stat Soc Ser B. 1995; 57:289–300. doi: 10.1111/j.2517-6161.1995.tb02031.xCrossrefGoogle Scholar
  • 35. R Core Team. R: A language and environment for statistical computing.2021. Scholar
  • 36. Wishart DS, Feunang YD, Marcu A, Guo AC, Liang K, Vázquez-Fresno R, Sajed T, Johnson D, Li C, Karu N, et al. HMDB 4.0: the human metabolome database for 2018.Nucleic Acids Res. 2018; 46(D1):D608–D617. doi: 10.1093/nar/gkx1089CrossrefMedlineGoogle Scholar
  • 37. Würtz P, Havulinna AS, Soininen P, Tynkkynen T, Prieto-Merino D, Tillin T, Ghorbani A, Artati A, Wang Q, Tiainen M, et al. Metabolite profiling and cardiovascular event risk: a prospective study of 3 population-based cohorts.Circulation. 2015; 131:774–785. doi: 10.1161/CIRCULATIONAHA.114.013116LinkGoogle Scholar
  • 38. Würtz P, Raiko JR, Magnussen CG, Soininen P, Kangas AJ, Tynkkynen T, Thomson R, Laatikainen R, Savolainen MJ, Laurikka J, et al. High-throughput quantification of circulating metabolites improves prediction of subclinical atherosclerosis.Eur Heart J. 2012; 33:2307–2316. doi: 10.1093/eurheartj/ehs020CrossrefMedlineGoogle Scholar
  • 39. Jassal B, Matthews L, Viteri G, Gong C, Lorente P, Fabregat A, Sidiropoulos K, Cook J, Gillespie M, Haw R, et al. The reactome pathway knowledgebase.Nucleic Acids Res. 2020; 48(D1):D498–D503. doi: 10.1093/nar/gkz1031CrossrefMedlineGoogle Scholar
  • 40. Jones DP, Mason HS. Metabolic hypoxia: accumulation of tyrosine metabolites in hepatocytes at low pO2.Biochem Biophys Res Commun. 1978; 80:477–483. doi: 10.1016/0006-291x(78)91593-0CrossrefMedlineGoogle Scholar
  • 41. Martinez Sosa S, Smith KJ. Understanding a role for hypoxia in lesion formation and location in the deep and periventricular white matter in small vessel disease and multiple sclerosis.Clin Sci (Lond). 2017; 131:2503–2524. doi: 10.1042/CS20170981CrossrefMedlineGoogle Scholar
  • 42. Cox SR, Lyall DM, Ritchie SJ, Bastin ME, Harris MA, Buchanan CR, Fawns-Ritchie C, Barbu MC, de Nooij L, Reus LM, et al. Associations between vascular risk factors and brain MRI indices in UK Biobank.Eur Heart J. 2019; 40:2290–2300. doi: 10.1093/eurheartj/ehz100CrossrefMedlineGoogle Scholar
  • 43. Ouzzine M, Gulberti S, Ramalanjaona N, Magdalou J, Fournel-Gigleux S. The UDP-glucuronosyltransferases of the blood-brain barrier: their role in drug metabolism and detoxication.Front Cell Neurosci. 2014; 8:349. doi: 10.3389/fncel.2014.00349CrossrefMedlineGoogle Scholar
  • 44. Eelen G, de Zeeuw P, Treps L, Harjes U, Wong BW, Carmeliet P. Endothelial cell metabolism.Physiol Rev. 2018; 98:3–58. doi: 10.1152/physrev.00001.2017CrossrefMedlineGoogle Scholar
  • 45. Jeerakathil T, Wolf PA, Beiser A, Massaro J, Seshadri S, D’Agostino RB, DeCarli C. Stroke risk profile predicts white matter hyperintensity volume: the Framingham Study.Stroke. 2004; 35:1857–1861. doi: 10.1161/01.STR.0000135226.53499.85LinkGoogle Scholar
  • 46. Scharf EL, Graff-Radford J, Przybelski SA, Lesnick TG, Mielke MM, Knopman DS, Preboske GM, Schwarz CG, Senjem ML, Gunter JL, et al. Cardiometabolic health and longitudinal progression of white matter hyperintensity: the Mayo Clinic Study of Aging.Stroke. 2019; 50:3037–3044. doi: 10.1161/STROKEAHA.119.025822LinkGoogle Scholar
  • 47. Marathe GK, Pandit C, Lakshmikanth CL, Chaithra VH, Jacob SP, D’Souza CJ. To hydrolyze or not to hydrolyze: the dilemma of platelet-activating factor acetylhydrolase.J Lipid Res. 2014; 55:1847–1854. doi: 10.1194/jlr.R045492CrossrefMedlineGoogle Scholar
  • 48. Yan JJ, Jung JS, Lee JE, Lee J, Huh SO, Kim HS, Jung KC, Cho JY, Nam JS, Suh HW, et al. Therapeutic effects of lysophosphatidylcholine in experimental sepsis.Nat Med. 2004; 10:161–167. doi: 10.1038/nm989CrossrefMedlineGoogle Scholar
  • 49. Sevastou I, Kaffe E, Mouratis MA, Aidinis V. Lysoglycerophospholipids in chronic inflammatory disorders: the PLA(2)/LPC and ATX/LPA axes.Biochim Biophys Acta. 2013; 1831:42–60. doi: 10.1016/j.bbalip.2012.07.019CrossrefMedlineGoogle Scholar
  • 50. Ousman SS, David S. Lysophosphatidylcholine induces rapid recruitment and activation of macrophages in the adult mouse spinal cord.Glia. 2000; 30:92–104.CrossrefMedlineGoogle Scholar
  • 51. Ganna A, Salihovic S, Sundström J, Broeckling CD, Hedman AK, Magnusson PK, Pedersen NL, Larsson A, Siegbahn A, Zilmer M, et al. Large-scale metabolomic profiling identifies novel biomarkers for incident coronary heart disease.PLoS Genet. 2014; 10:e1004801. doi: 10.1371/journal.pgen.1004801CrossrefMedlineGoogle Scholar
  • 52. Mapstone M, Cheema AK, Fiandaca MS, Zhong X, Mhyre TR, MacArthur LH, Hall WJ, Fisher SG, Peterson DR, Haley JM, et al. Plasma phospholipids identify antecedent memory impairment in older adults.Nat Med. 2014; 20:415–418. doi: 10.1038/nm.3466CrossrefMedlineGoogle Scholar
  • 53. Bazinet RP, Layé S. Polyunsaturated fatty acids and their metabolites in brain function and disease.Nat Rev Neurosci. 2014; 15:771–785. doi: 10.1038/nrn3820CrossrefMedlineGoogle Scholar
  • 54. Chen CT, Kitson AP, Hopperton KE, Domenichiello AF, Trépanier MO, Lin LE, Ermini L, Post M, Thies F, Bazinet RP. Plasma non-esterified docosahexaenoic acid is the major pool supplying the brain.Sci Rep. 2015; 5:15791. doi: 10.1038/srep15791CrossrefMedlineGoogle Scholar
  • 55. Sugasini D, Thomas R, Yalagala PCR, Tai LM, Subbaiah PV. Dietary docosahexaenoic acid (DHA) as lysophosphatidylcholine, but not as free acid, enriches brain DHA and improves memory in adult mice.Sci Rep. 2017; 7:11263. doi: 10.1038/s41598-017-11766-0CrossrefMedlineGoogle Scholar
  • 56. Zöller I, Meixner M, Hartmann D, Büssow H, Meyer R, Gieselmann V, Eckhardt M. Absence of 2-hydroxylated sphingolipids is compatible with normal neural development but causes late-onset axon and myelin sheath degeneration.J Neurosci. 2008; 28:9741–9754. doi: 10.1523/JNEUROSCI.0458-08.2008CrossrefMedlineGoogle Scholar
  • 57. Stadelmann C, Timmler S, Barrantes-Freer A, Simons M. Myelin in the central nervous system: structure, function, and pathology.Physiol Rev. 2019; 99:1381–1431. doi: 10.1152/physrev.00031.2018CrossrefMedlineGoogle Scholar
  • 58. Mielke MM, Bandaru VV, Haughey NJ, Rabins PV, Lyketsos CG, Carlson MC. Serum sphingomyelins and ceramides are early predictors of memory impairment.Neurobiol Aging. 2010; 31:17–24. doi: 10.1016/j.neurobiolaging.2008.03.011CrossrefMedlineGoogle Scholar
  • 59. Beisiegel U, Heeren J. Lipoprotein lipase (EC targeting of lipoproteins to receptors.Proc Nutr Soc. 1997; 56:731–737. doi: 10.1079/pns19970073CrossrefMedlineGoogle Scholar
  • 60. Lillis AP, Mikhailenko I, Strickland DK. Beyond endocytosis: LRP function in cell migration, proliferation and vascular permeability.J Thromb Haemost. 2005; 3:1884–1893. doi: 10.1111/j.1538-7836.2005.01371.xCrossrefMedlineGoogle Scholar
  • 61. Wardlaw JM, Makin SJ, Valdés Hernández MC, Armitage PA, Heye AK, Chappell FM, Muñoz-Maniega S, Sakka E, Shuler K, Dennis MS, et al. Blood-brain barrier failure as a core mechanism in cerebral small vessel disease and dementia: evidence from a cohort study.Alzheimers Dement. 2017; 13:634–643. doi: 10.1016/j.jalz.2016.09.006CrossrefGoogle Scholar
  • 62. Muilwijk M, Callender N, Goorden S, Vaz FM, van Valkengoed IGM. Sex differences in the association of sphingolipids with age in Dutch and South-Asian Surinamese living in Amsterdam, the Netherlands.Biol Sex Differ. 2021; 12:13. doi: 10.1186/s13293-020-00353-0CrossrefMedlineGoogle Scholar
  • 63. Netto CA, Sanches E, Odorcyk FK, Duran-Carabali LE, Weis SN. Sex-dependent consequences of neonatal brain hypoxia-ischemia in the rat.J Neurosci Res. 2017; 95:409–421. doi: 10.1002/jnr.23828CrossrefMedlineGoogle Scholar
  • 64. Demarest TG, Schuh RA, Waddell J, McKenna MC, Fiskum G. Sex-dependent mitochondrial respiratory impairment and oxidative stress in a rat model of neonatal hypoxic-ischemic encephalopathy.J Neurochem. 2016; 137:714–729. doi: 10.1111/jnc.13590CrossrefMedlineGoogle Scholar
  • 65. van den Heuvel DM, Admiraal-Behloul F, ten Dam VH, Olofsen H, Bollen EL, Murray HM, Blauw GJ, Westendorp RG, de Craen AJ, van Buchem MA; PROSPER Study Group. Different progression rates for deep white matter hyperintensities in elderly men and women.Neurology. 2004; 63:1699–1701. doi: 10.1212/01.wnl.0000143058.40388.44CrossrefMedlineGoogle Scholar
  • 66. Ammirati E, Moroni F, Magnoni M, Rocca MA, Anzalone N, Cacciaguerra L, Di Terlizzi S, Villa C, Sizzano F, Palini A, et al. Progression of brain white matter hyperintensities in asymptomatic patients with carotid atherosclerotic plaques and no indication for revascularization.Atherosclerosis. 2019; 287:171–178. doi: 10.1016/j.atherosclerosis.2019.04.230CrossrefMedlineGoogle Scholar
  • 67. Jiménez-Sánchez L, Hamilton OK, Backhouse EV, Clancy U, Steward CR, Wardlaw JM. Sex differences in cerebral small vessel disease: a systematic review and meta-analysis.medRxiv. Preprint posted online March 8, 2021. doi: 10.1101/2021.03.04.21252853CrossrefGoogle Scholar
  • 68. Stanhewicz AE, Wenner MM, Stachenfeld NS. Sex differences in endothelial function important to vascular health and overall cardiovascular disease risk across the lifespan.Am J Physiol Heart Circ Physiol. 2018; 315:H1569–H1588. doi: 10.1152/ajpheart.00396.2018CrossrefMedlineGoogle Scholar
  • 69. Cerghet M, Skoff RP, Swamydas M, Bessert D. Sexual dimorphism in the white matter of rodents.J Neurol Sci. 2009; 286:76–80. doi: 10.1016/j.jns.2009.06.039CrossrefMedlineGoogle Scholar
  • 70. Pesaresi M, Soon-Shiong R, French L, Kaplan DR, Miller FD, Paus T. Axon diameter and axonal transport: In vivo and in vitro effects of androgens.Neuroimage. 2015; 115:191–201. doi: 10.1016/j.neuroimage.2015.04.048CrossrefMedlineGoogle Scholar
  • 71. Howard VJ, Madsen TE, Kleindorfer DO, Judd SE, Rhodes JD, Soliman EZ, Kissela BM, Safford MM, Moy CS, McClure LA, et al. Sex and race differences in the association of incident ischemic stroke with risk factors.JAMA Neurol. 2019; 76:179–186. doi: 10.1001/jamaneurol.2018.3862CrossrefMedlineGoogle Scholar
  • 72. Peters SA, Huxley RR, Woodward M. Diabetes as a risk factor for stroke in women compared with men: a systematic review and meta-analysis of 64 cohorts, including 775,385 individuals and 12,539 strokes.Lancet. 2014; 383:1973–1980. doi: 10.1016/S0140-6736(14)60040-4CrossrefMedlineGoogle Scholar
  • 73. Madsen TE, Howard G, Kleindorfer DO, Furie KL, Oparil S, Manson JE, Liu S, Howard VJ. Sex differences in hypertension and stroke risk in the REGARDS study: a longitudinal cohort study.Hypertension. 2019; 74:749–755. doi: 10.1161/HYPERTENSIONAHA.119.12729LinkGoogle Scholar
  • 74. Madsen TE, Khoury JC, Leppert M, Alwell K, Moomaw CJ, Sucharew H, Woo D, Ferioli S, Martini S, Adeoye O, et al. Temporal trends in stroke incidence over time by sex and age in the GCNKSS.Stroke. 2020; 51:1070–1076. doi: 10.1161/STROKEAHA.120.028910LinkGoogle Scholar
  • 75. Peters SAE, Carcel C, Millett ERC, Woodward M. Sex differences in the association between major risk factors and the risk of stroke in the UK Biobank cohort study.Neurology. 2020; 95:e2715–e2726. doi: 10.1212/WNL.0000000000010982CrossrefMedlineGoogle Scholar
  • 76. Wardlaw JM, Smith EE, Biessels GJ, Cordonnier C, Fazekas F, Frayne R, Lindley RI, O’Brien JT, Barkhof F, Benavente OR, et al; Standards for Reporting Vascular Changes on Neuroimaging (STRIVE v1). Neuroimaging standards for research into small vessel disease and its contribution to ageing and neurodegeneration.Lancet Neurol. 2013; 12:822–838. doi: 10.1016/S1474-4422(13)70124-8CrossrefMedlineGoogle Scholar
  • 77. Yilmaz P, Ikram MK, Niessen WJ, Ikram MA, Vernooij MW. Practical small vessel disease score relates to stroke, dementia, and death.Stroke. 2018; 49:2857–2865. doi: 10.1161/STROKEAHA.118.022485LinkGoogle Scholar
  • 78. Wardlaw J, Bath PMW, Doubal F, Heye A, Sprigg N, Woodhouse LJ, Blair G, Appleton J, Cvoro V, England T, et al; LACI-2 Trial Investigators. Protocol: the Lacunar Intervention Trial 2 (LACI-2): a trial of two repurposed licenced drugs to prevent progression of cerebral small vessel disease.Eur Stroke J. 2020; 5:297–308. doi: 10.1177/2396987320920110CrossrefMedlineGoogle Scholar
  • 79. Vidal JS, Sigurdsson S, Jonsdottir MK, Eiriksdottir G, Thorgeirsson G, Kjartansson O, Garcia ME, van Buchem MA, Harris TB, Gudnason V, et al. Coronary artery calcium, brain function and structure: the AGES-Reykjavik Study.Stroke. 2010; 41:891–897. doi: 10.1161/STROKEAHA.110.579581LinkGoogle Scholar
  • 80. Dawber TR, Meadors GF, Moore FE. Epidemiological approaches to heart disease: the Framingham Study.Am J Public Health Nations Health. 1951; 41:279–281. doi: 10.2105/ajph.41.3.279CrossrefMedlineGoogle Scholar
  • 81. Deary IJ, Gow AJ, Pattie A, Starr JM. Cohort profile: the Lothian Birth Cohorts of 1921 and 1936.Int J Epidemiol. 2012; 41:1576–1584. doi: 10.1093/ije/dyr197CrossrefMedlineGoogle Scholar
  • 82. Deary IJ, Gow AJ, Taylor MD, Corley J, Brett C, Wilson V, Campbell H, Whalley LJ, Visscher PM, Porteous DJ, et al. The Lothian Birth Cohort 1936: a study to examine influences on cognitive ageing from age 11 to age 70 and beyond.BMC Geriatr. 2007; 7:1–12. doi: 10.1186/1471-2318-7-28CrossrefMedlineGoogle Scholar
  • 83. Tillin T, Forouhi NG, McKeigue PM, Chaturvedi N; SABRE Study Group. Southall and Brent Revisited: Cohort profile of SABRE, a UK population-based comparison of cardiovascular disease and diabetes in people of European, Indian Asian and African Caribbean origins.Int J Epidemiol. 2012; 41:33–42. doi: 10.1093/ije/dyq175CrossrefMedlineGoogle Scholar
  • 84. Sigurdsson S, Aspelund T, Forsberg L, Fredriksson J, Kjartansson O, Oskarsdottir B, Jonsson PV, Eiriksdottir G, Harris TB, Zijdenbos A, et al. Brain tissue volumes in the general population of the elderly: the AGES-Reykjavik study.Neuroimage. 2012; 59:3862–3870. doi: 10.1016/j.neuroimage.2011.11.024CrossrefMedlineGoogle Scholar
  • 85. Sudre CH, Cardoso MJ, Bouvy WH, Biessels GJ, Barnes J, Ourselin S. Bayesian model selection for pathological neuroimaging data applied to white matter lesion segmentation.IEEE Trans Med Imaging. 2015; 34:2079–2102. doi: 10.1109/TMI.2015.2419072CrossrefMedlineGoogle Scholar
  • 86. de Boer R, Vrooman HA, van der Lijn F, Vernooij MW, Ikram MA, van der Lugt A, Breteler MM, Niessen WJ. White matter lesion extension to automatic brain tissue segmentation on MRI.Neuroimage. 2009; 45:1151–1161. doi: 10.1016/j.neuroimage.2009.01.011CrossrefMedlineGoogle Scholar
  • 87. Sudre CH, Smith L, Atkinson D, Chaturvedi N, Ourselin S, Barkhof F, Hughes AD, Jäger HR, Cardoso MJ. Cardiovascular risk factors and white matter hyperintensities: difference in susceptibility in South Asians compared with Europeans.J Am Heart Assoc. 2018; 7:e010533. doi: 10.1161/JAHA.118.010533LinkGoogle Scholar
  • 88. Habes M, Erus G, Toledo JB, Zhang T, Bryan N, Launer LJ, Rosseel Y, Janowitz D, Doshi J, Van der Auwera S, et al. White matter hyperintensities and imaging patterns of brain ageing in the general population.Brain. 2016; 139(Pt 4):1164–1179. doi: 10.1093/brain/aww008CrossrefMedlineGoogle Scholar


eLetters should relate to an article recently published in the journal and are not a forum for providing unpublished data. Comments are reviewed for appropriate use of tone and language. Comments are not peer-reviewed. Acceptable comments are posted to the journal website only. Comments are not published in an issue and are not indexed in PubMed. Comments should be no longer than 500 words and will only be posted online. References are limited to 10. Authors of the article cited in the comment will be invited to reply, as appropriate.

Comments and feedback on AHA/ASA Scientific Statements and Guidelines should be directed to the AHA/ASA Manuscript Oversight Committee via its Correspondence page.