Age-related decline in cerebral oxygen consumption in multiple sclerosis

Abstract

Cerebral oxygen metabolism is altered in relapsing-remitting multiple sclerosis (RRMS), possibly a result of disease related cerebral atrophy with subsequent decreased oxygen demand. However, MS inflammation can also inhibit brain metabolism. Therefore, we measured cerebral blood flow (CBF) and cerebral metabolic rate of oxygen (CMRO2) using MRI phase contrast mapping and susceptibility-based oximetry in 44 patients with early RRMS and 36 healthy controls. Cerebral atrophy and white matter lesion load were assessed from high-resolution structural MRI. Expanded Disability Status Scale (EDSS) scores were collected from medical records. The CMRO2 was significantly lower in patients (−15%, p = 0.002) and decreased significantly with age in patients relative to the controls (−1.35 µmol/100 g/min/year, p = 0.036). The lower CMRO2 in RRMS was primarily driven by a higher venous oxygen saturation in the sagittal sinus (p = 0.007) and not a reduction in CBF (p = 0.69). There was no difference in cerebral atrophy between the groups, and no correlation between CMRO2 and MS lesion volume or EDSS score. Therefore, the progressive CMRO2 decline observed before the occurrence of significant cerebral atrophy and despite adequate CBF supports emerging evidence of dysfunctional cellular respiration as a potential pathogenic mechanism and therapeutic target in RRMS.

Keywords

Aging cerebral metabolism magnetic resonance imaging multiple sclerosis oxygen extraction fraction (OEF)

Introduction

Multiple sclerosis (MS) is a leading cause of non-traumatic neurological disability in young adults, and it is associated with severe health, financial and social costs.^1
–3 Despite major recent developments in the number of available treatments, a substantial number of patients experience sub-clinical disease progression and ongoing cerebral atrophy, even with successful disease-modifying treatment.^4,5 The main focus of multiple sclerosis disease-modifying treatment is immune modulation, but a new view of the pathophysiology may be needed because these treatments do not fully halt the disease, which may lead to novel treatment strategies.^6,7 Cerebral metabolism may be one target because glucose and oxygen metabolism are affected, and the brains of multiple sclerosis patients may suffer from virtual energy failure.^6,8–10 Therefore, treatment strategies targeting this perceived energy failure may improve this subtle ongoing subclinical disease progression, despite successful disease-modifying treatment.^11
–13

Previous research showed that MS patients had a reduced cerebral metabolic rate of oxygen consumption (CMRO₂), especially in the grey matter,^14
–16 and reduced cerebral oxygen extraction.^16
–18 These studies found evidence suggesting that impaired oxygen metabolism is a global phenomenon of the multiple sclerosis brain.

The underlying pathophysiology of the reduced oxygen consumption is not fully understood. However, possible explanations are decreased mitochondrial oxygen utilisation or impaired oxygen transportation through the vasculature into the brain tissue. Damage to mitochondria has been demonstrated in multiple sclerosis, but primarily progressive disease forms have been studied.^19,20 Reduced cerebral glucose consumption is also described in multiple sclerosis.¹⁰ The reduction is diffuse and not correlated to lesion load, cognitive dysfunction, or walking speed impairment.^21
–26 Genes involved in the astrocyte-neuron lactate shuttle and the glutamate–glutamine cycle are downregulated in the normal-appearing grey matter of multiple sclerosis patients, which further indicate abnormal energy metabolism.²⁷ Bioenergetics are disrupted in MS lesions. Specifically, new lesions exhibit hypermetabolic glucose consumption, and old lesions are hypermetabolic at the rim or hypometabolic.²⁸ Astrocytes also show increased expression of glucose transporters in lesions, and a reduced number of axonal glucose transporters is observed in the inactive centre of chronic active lesions.²⁹

However, the timing and causality of these metabolic alterations are not clear, e.g., to what extent is the reduced metabolic demand a result of an atrophic brain or whether the metabolic dysfunction itself exacerbates neuronal damage. The present study addressed these questions by measuring CMRO₂ and brain atrophy in a group of RRMS patients over a large age range. We examined whether patients with relapsing-remitting multiple sclerosis had a progressive impairment of CMRO₂ and to what extent this impairment may be explained by brain atrophy and EDSS score.

Materials and methods

Participants

We recruited healthy controls via an online advertisement, and relapsing-remitting multiple sclerosis patients were recruited from the Multiple Sclerosis Clinic, Rigshospitalet. Forty-four patients with relapsing-remitting multiple sclerosis, aged 18–52 years (mean 35.34), and 36 healthy controls, aged 18–65 years (mean 34.3), were enrolled. The Ethics Committee of Copenhagen County approved this study, which was performed according to the standards of The National Committee on Health Research Ethics, protocol number H-1–2014–132. All experiments were performed in accordance with the Declaration of Helsinki of 1975, and all subjects gave written informed consent.

MRI

Scanner

We acquired all MRI data on a 3T magnetic resonance unit (Achieva dStream; Philips, Best, the Netherlands) using a 32-element phased-array head coil.

Cerebral blood flow and oxygen consumption

Global cerebral blood flow (CBF) was measured using phase-contrast mapping (PCM) MRI. The blood velocity in the feeding cerebral arteries (carotids and basilar arteries) was measured using PCM, which allows estimation of the total blood flow to the brain.^30,31 Blood velocity weighted phase-contrast maps (example shown in Figure 1(c)) were acquired using a turbo field echo sequence (1 slice, FOV, 240 × 240 mm²; voxel size, 0.75 × 0.75 × 8 mm³ TR, 12.4 ms; TE, 7.4 ms; flip angle, 10°; velocity encoding, 100 cm/s; without cardiac gating). Two measurements were performed, one measurement with the imaging plane perpendicular to the carotid arteries and a second measurement placed perpendicular to the basilar arteries (Figure 1(a) and (b)). Blood flow was calculated by drawing regions of interest (ROIs) covering each of the feeding cerebral arteries. From these ROIs, the mean blood velocity and cross-sectional area of the arteries were found, and the flow was calculated as the product of the mean velocity and area and summed together from all feeding vessels. The total flow to the brain was normalised to brain weight to correct for different brain sizes and obtain CBF in ml/100 g/min. The brain weight was estimated from the structural images, assuming a brain density of 1.05 g/ml.

Figure 1.

The cerebral metabolic rate of oxygen consumption was determined via measurement of cerebral blood flow and venous oxygen saturation. (A and B) Angiography of internal carotid arteries showing the plane of the phase-contrast mapping slice. (C) Blood velocity map. (D and E) Venography of the superior sagittal sinus showing the plane of the susceptibility slice. (F and G) Susceptibility slice and Magnified view of the susceptibility slice showing the region of interest in the superior sagittal sinus and in the surrounding tissue.

The global cerebral metabolic rate of oxygen (CMRO₂) was determined by Fick’s principle according to the following equation (1):

CMR O_{2} = [Hgb] \cdot CBF \cdot (S a O_{2} - S v O_{2})

(1)

where CMRO₂ is the cerebral oxygen consumption in µmol/100 g/min, [Hgb] is the haemoglobin concentration of the blood in mmol/l, CBF is the cerebral blood flow in ml/100 g/min, SaO₂ is the arterial oxygen saturation, and SvO₂ is the venous oxygen saturation. Haemoglobin was set to 9 mmol/l for all subjects, and arterial oxygen saturation was set to 98% for all subjects, since previous studies have shown that these do not differ significantly between MS and healthy controls.³² We measured SvO₂ in the superior sagittal sinus using a susceptibility-based oximetry (SBO) MRI technique.³³ This technique utilises venous blood, which has a different magnetic susceptibility than brain tissue, and this difference is related to oxygen saturation. The susceptibility-weighted maps were measured using a dual-echo gradient-echo sequence (1 slice, FOV, 220 × 190 mm²; voxel size, 0.5 × 0.5 × 8 mm³; TE 1, 10.89 ms; TE 2, 24.16 ms; flip angle, 30°; 5 repeated measures, total duration 1 min 30 s; SENSE-factor, 2). The difference in susceptibility between venous blood and brain tissue was found by manually drawing two ROIs, one ROI covering the superior sagittal sinus and one ROI covering the immediately surrounding tissue. The ROIs were drawn blinded to group (patient or healthy control) (Figure 1). A more in-depth discussion of the methods and data post-processing have been published previously.³⁴

Brain volumes, N-acetyl aspartate, and lesions

We acquired high-resolution 3D T1-weighted anatomical images with a turbo field 3D echo sequence (FOV, 256 × 256 × 179.9 mm³; voxel size, 0.7 × 0.7 × 0.7 mm³; TR, 11.3 ms; TE, 5.2 ms; flip angle, 8°). From these images, the FreeSurfer image analysis suite (FreeSurfer 7.1.0 Harvard, USA^35,36) was used to estimate the CSF-free total brain volume, the brain parenchymal fraction (BPF), and segmentation of the brain images into grey and white matter. The brain parenchymal fraction was used as a measure of cerebral atrophy and was calculated as the percentage total brain volume of the estimated total intracranial volume.^37,38 Grey and white matter fractions were used as measures for grey and white matter atrophy and calculated as percentage grey or white matter volumes relative to the estimated total intracranial volume.

The total lesion volume of the multiple sclerosis patients was calculated from manually delineated lesions on a T2 2D FLAIR sequence (FOV, 230 ×134.4 × 183 mm³; voxel size, 0.65 × 0.99 × 3.5 mm³ (interpolated to 0.45 × 0.45 × 3.5 mm³); TR, 11000 ms; TE, 125 ms; flip angle, 90°). HJS, who has 30+ years of experience in manual MS lesion delineation, performed the manual delineation of lesions.

We measured the concentration of lactate and the neuronal marker N-acetyl aspartate (NAA) in all patients and a subset of healthy controls (N = 24) using magnetic resonance spectroscopy in the occipital cortex using a water-suppressed point-resolved spectroscopy pulse sequence (TR, 3000 or 5000 ms; TE, 36 ms; voxel size, 30 × 35 × 30 mm³). The water concentration acquired in the spectrum was used to quantify lactate and NAA concentrations. The water concentration in the voxel was estimated from the content of grey matter, white matter, and CSF within the voxel from the segmentation of the high-resolution anatomical images.

Statistics

Statistics were computed using R (version 4.0.3). Differences in means between patients and controls of the acquired parameters were assessed using Student’s t test. When the variances were not equal according to the F test, Welch’s unequal variances t test was used. For categorical outcomes, we used Pearson's chi-squared test with Yates' continuity correction to test for significance of differences between groups.

To test for correlations between age and the acquired brain parameters, linear regression models were used. To test whether patients and controls demonstrated different effects of ageing, general linear models with an interaction term between age and disease status (multiple sclerosis yes/no) were used. The interaction term describes whether the presence of multiple sclerosis disease alters the age-related effect on the acquired parameters.

Lesion volumes were converted to a logarithmic scale to achieve a normal distribution of the data before entering the statistical modelling.

Data availability

The data that support the findings of this study are available from the corresponding author upon reasonable request.

Results

Table 1 summarises the clinical characteristics of the participants. The acquired physiology parameters and differences between patients and controls are summarised in Tables 2 and 3. Table 3 lists interaction terms for disease state in models including all subjects, whereas Figures 2 to 4 show linear correlations of parameters for healthy controls or multiple sclerosis patients, i.e. within a fixed disease state.

Table 1.

Clinical characteristics of the participants.

Characteristic	Controls(N = 36)	Patients(N = 44)	p value
Age (mean, sd [years])	34.36, 14.94	35.69, 9.07	0.64
Sex [% women]	61.1%	66.7%	0.78
EDSS (mean, range)	NA	1.93, 0–5	NA
Lesion volume (median, IQR [ml])	NA	7.69, 3.37–15.25	NA
Disease modifying treatment	NA	22 untreated8 teriflunomide8 dimethyl fumarate3 Interferon β1a2 fingolimod1 methotrexate	NA

p value for Age: Welch Two Sample t-test. p value for sex: Pearson’s Chi-squared test with Yates’ continuity correction.

Table 2.

Comparisons of means in multiple sclerosis patients and healthy controls.

	Controls	Patients	Difference	95% CI		p value
CMRO₂, µmol/100 g/min	143.18	121.25	21.94	7.82	36.05	0.003^†,**
SvO₂, %	65.69	69.58	−3.89	−7.06	−0.71	0.018^†,*
CBF, ml/100 g/min	53.17	52.58	0.59	−3.44	4.61	0.77^†
Brain parenchymal fraction, %	75.66	74.74	0.92	−0.13	1.96	0.08^†
Grey matter fraction, %	42.58	41.99	0.59	−0.43	1.61	0.25^††
White matter fraction, %	31.75	31.24	0.51	−0.18	1.20	0.15^†
NAA, mmol/l	9.29	9.52	−0.23	−0.85	0.39	0.46^††

*p < 0.05, **p < 0.01, ^†Student’s t-test, ^††Welch test.

Table 3.

Summaries of disease effects of multiple sclerosis patients from general linear models relative to healthy controls.

Model	Additional age effect in patients	95% CI of additional effect in patients		p value
Age-patient interactions
CMRO₂, µmol/100 g/min/year	−1.35	−2.587	−0.113	0.036*
SvO₂, %/year	0.389	0.115	0.663	0.007**
CBF, ml/100 g/min/year	0.213	−0.147	0.573	0.25
BPF, %/year	0.037	−0.038	0.112	0.34
NAA, mmol/l/year	0.016	−0.018	0.051	0.36
	Additional CMRO₂ effect in patients	95% CI of additional effect in patients		p value
CMRO₂-patient interaction
BPF, %/µmol/100 g/min	0.041	0.008	0.075	0.018*
	Additional NAA effect in patients	95% CI of additional effect in patients		p value
NAA-patient interaction
CMRO2, µmol/100 g/min/mmol/l	−28.18	−54.41	−1.94	0.04*

*p < 0.05, **p < 0.01.

Attrition of data

Two measurements of CBF in the multiple sclerosis patient group and one measurement from a healthy control were excluded because the acquired image was of insufficient quality due to head movement during the scan. Therefore, 42 measurements in the multiple sclerosis patient group and 35 measurements in the healthy control group were included in the final analyses.

Measurements of oxygen saturation in the sagittal sinus and blood flow in the basilar and carotid arteries were successfully performed in 40 multiple sclerosis patients (mean age 35.69 years, SD 9.07) and 35 healthy controls (mean age 34.36 years, SD 14.94) and included in the final analyses. Four measurements from multiple sclerosis patients and one measurement from a healthy control were excluded due to excessive inhomogeneity of the acquired phase MRI images.

Thirty-nine multiple sclerosis patients and 25 healthy controls completed magnetic resonance spectroscopy measurements of NAA and lactate. Five of these measurements from patients were excluded due to poor magnetic resonance spectra. All NAA and lactate measurements from healthy controls were included in the final analyses.

The EDSS score was available in the medical records of 43 patients.

Cerebral physiology

The mean CMRO₂ was significantly (p = 0.003) higher in healthy controls compared to patients (controls: 143.2 µmol/100 g/min (SD: 29.2); patients: 121.2 µmol/100 g/min (SD: 32.1)) (Table 2). CMRO₂ increased significantly with age in the control group (mean effect: +0.7 µmol/100 g/min per year, 95% CI: 0.02 to 1.32 µmol/100 g/min/year, p = 0.04), but the patient group showed a weak trend towards a decrease with age (mean effect: −0.68 µmol/100 g/min per year, 95% CI: −1.81 to 0.45 µmol/100 g/min/year, p = 0.2). The two trajectories were significantly different, as modelled by the interaction (mean effect 1.35 µmol/100 g/min per year, 95% CI: 0.11 to 2.59 µmol/100 g/min/year, p = 0.036) (Figure 2(a) and (b)). Therefore, multiple sclerosis patients demonstrated a reduction in CMRO₂ with advancing age compared to healthy controls.

Figure 2.

Cerebral oxygen consumption declines with age in relapsing-remitting multiple sclerosis but not healthy controls. (a) The cerebral metabolic rate of oxygen consumption increased with age in healthy controls (b) and showed a trend of decline with age in relapsing-remitting multiple sclerosis patients. (c) The change in oxygen consumption was not explained by changes in cerebral blood flow because this consumption was constant across ages in healthy controls and (d) multiple sclerosis patients. (e) There was a weak trend of decline in oxygen saturation in the sagittal sinus in healthy controls and (f) and there was a significant increase in oxygen saturation in the sagittal sinus with age in MS patients. Interactions between age and disease status for the abovementioned parameters are listed in Table 3. CBF: cerebral blood flow; CMRO₂: cerebral metabolic rate of oxygen consumption; HC: healthy controls; RRMS: relapsing-remitting multiple sclerosis patients. The beta coefficients represent a linear regression with a fixed disease state (HC or RRMS).

There was no significant difference (p = 0.78) in CBF between patients and controls (controls: 53.17 ml/100 g/min (SD: 8.95; patients: 52.58 ml/100 g/min (SD: 8.66)). We did not observe any correlations between CBF and age in either group (p = 0.5 for controls and p = 0.4 for patients) (Figure 2(c) and (d)) or any difference in the age-related trajectories between the two groups (p = 0.25) (Table 3). Therefore, no change in CBF was observed in the multiple sclerosis patients.

The oxygen saturation of the blood leaving the brain in the sagittal sinus was significantly higher in patients compared to controls (patients: 69.58% (SD: 7.23); controls: 65.70% (SD: 6.57), p = 0.02). Increasing age was associated with increasing oxygen saturation in the sagittal sinus in the patient group (mean effect: 0.3% per year, 95% CI: 0.02 to 0.51%/year, p = 0.03), and this effect was significantly larger compared to the healthy controls (0.39% per year, 95% CI: 0.12 to 0.66%/year, p = 0.007). Therefore, cerebral oxygen extraction was decreased in multiple sclerosis patients with advancing age compared to healthy controls (Figure 2(e) and (f)).

Brain volumes

The mean brain parenchymal fraction (BPF) was not significantly (p = 0.08) different between the groups (controls: 75.66% (SD: 2.57); patients: 74.74% (SD: 1.98)). The BPF decreased with age (p < 0.01) and at a similar (p = 0.34) rate in patients and controls, at 0.1% per year for controls and 0.08% per year for patients (Table 3 and Figure 3(a) and (b)). There were no significant differences in grey matter fraction between patients and controls (p = 0.25) (controls: 42.58% (SD: 2.61); patients: 41.99% (SD: 1.77)), and the grey matter fraction decreased with age in both groups (controls: 0.15% per year, 95% CI: 0.18 to 0.12, p < 0.001; patients: 0.09, 95% CI: 0.14 to 0.03, p = 0.002). The white matter fraction was similar (p = 0.15) in patients and controls (controls: 31.75% (SD: 1.55); patients: 31.24% (SD: 1.53)) and did not decrease with age (controls: p = 0.9; patients: p = 0.9).

Figure 3.

Patients did not show a greater age-related atrophy than controls. (a) Brain parenchymal fraction decreased with age in healthy controls and (b) multiple sclerosis patients. (c) The brain parenchymal fraction did not correlate with the cerebral metabolic rate of oxygen consumption in healthy controls and (d) but there was a trend towards a lower brain parenchymal fraction with an increasing cerebral metabolic rate of oxygen consumption in relapsing-remitting patients. Interactions between age and disease status and between the cerebral metabolic rate of oxygen consumption and disease status for the abovementioned parameters are listed in Table 3. BPF: brain parenchymal fraction; CMRO₂: cerebral metabolic rate of oxygen consumption; HC: healthy controls; RRMS: relapsing-remitting multiple sclerosis patients. The beta coefficients represent a linear regression with a fixed disease state (HC or RRMS).

BPF tended to decrease with increasing CMRO₂ in the control group (−0.03% per µmol/100 g/min, 95% CI: −0.06 to 0.002, p = 0.07), but there was a weak change in the opposite direction in patients, although non-significant (0.01% per µmol/100 g/min, 95% CI: −0.006 to 0.032, p = 0.2), such that BPF and CMRO₂ were significantly differently correlated for patients relative to controls (p = 0.02) (Table 3) (Figure 3(c) and (e)).

Therefore, the relationship between brain atrophy and age was comparable between healthy controls and multiple sclerosis patients. Higher CMRO₂ in MS patients indicated less brain atrophy relative to controls.

Energy metabolism markers N-acetyl aspartate (NAA) and lactate

There was no difference (p = 0.46) in the concentration of NAA in patients and controls (controls: 9.89 mmol/l (SD: 0.58); patients: 9.52 mmol/l (SD: 0.59)).

The CMRO₂ in patients changed differently as a function of NAA relative to healthy controls (−28.2 µmol/100 g/min per mmol/l NAA relative to controls, 95% CI: −54.4 to −1.9, p = 0.039), see Figure 4. For healthy controls, there was a weak trend towards a CMRO₂ increase as a function of the concentration of NAA (mean effect: 14.6 µmol/100 g/min per mmol/l, 95% CI: −4.9 to 34.1, p = 0.14), but the trend was in the opposite direction in patients (−13.6 µmol/100 g/min per mmol/l, 95% CI: −30.9 to 3.8, p = 0.12).

Figure 4.

CMRO₂ differentially correlated with the neuronal marker N-acetyl aspartate (NAA). (a) There was a weak trend towards a decrease in the cerebral metabolic rate of oxygen consumption with increasing concentrations of NAA and (b) but there was an opposite trend in multiple sclerosis patients with an increase in the cerebral metabolic rate of oxygen consumption in MS patients. CMRO₂: cerebral metabolic rate of oxygen consumption; HC: healthy controls; NAA: N-acetyl aspartate; RRMS: relapsing-remitting multiple sclerosis patients. The beta coefficients represent a linear regression with a fixed disease state (HC or RRMS).

Therefore, the neuronal marker NAA was oppositely related to cerebral oxygen consumption in multiple sclerosis patients and controls despite no difference in the concentration of NAA relative to age.

Clinical and imaging markers of disease severity

Patients were minimally to moderately affected by their disease, with EDSS scores between 0 and 5 (mean 1.9) and a median T2 lesion volume of 7.69 ml (interquartile range 3.37 to 15.25 ml). The CMRO₂ was not correlated with T2 lesion volume (p = 0.97) or EDSS score (p = 0.23) (Supplementary 1). The multiple sclerosis patients did not show evidence of vascular lesions or enlarged Virchow-Robin spaces.

Discussion

We observed an age-dependent decrease in CMRO₂ in relapsing-remitting multiple sclerosis patients compared to healthy controls. The lower CMRO₂ was explained by a reduced cerebral oxygen extraction, which was evident by higher venous oxygen saturation in the sagittal sinus of the patients compared to healthy controls. The reduced CMRO₂ was not explained by atrophy because the patients did not demonstrate a lower BPF compared to the control group. However, age-related atrophy seemed associated with a higher CMRO₂ in controls but not patients, which amplified the difference in CMRO₂ relative to brain parenchymal fraction between the groups.

We observed a decrease in CMRO₂ with increasing concentrations of NAA in multiple sclerosis patients compared to healthy controls. This result was not expected. NAA is synthesized in the mitochondria of neuronal axons^39,40 and transferred to oligodendrocytes.⁴¹ It is involved in neuronal energy metabolism, and NAA has been shown to decrease with oxygen consumption and ATP production when the electron transport chain was inhibited in rat neuronal cultures.^42,43 NAA is decreased in the context of demyelination in MS, but it recovers when plaques resolve and patients receive disease-modifying treatment.^9,42 Therefore, our observation may be a spurious and random finding or a result of mitochondrial dysfunction in multiple sclerosis.

Generally, our findings support the evidence of impaired oxygen metabolism as a potential pathogenic mechanism in relapsing-remitting multiple sclerosis, and the impairment seemed to become accentuated as a function of age in multiple sclerosis patients.

The decreasing CMRO₂ we observed in multiple sclerosis patients may indicate dysfunctional mitochondrial oxygen utilisation. Alternatively, it may be related to alterations in oxygen transportation across the capillary wall. Oxygen consumption in the healthy ageing brain is relatively stable or increases slightly with age when corrected for the age related decrease in brain size, which is incorporated in the term CMRO₂.^44
–47 The observed increase in CMRO₂ in healthy individuals with age is likely a result of inefficient mitochondrial energy production due to proton and electron leaks in the electron transport chain.^48,49

Previous rodent and human studies support the hypothesis of reduced oxygen utilisation by the mitochondria in multiple sclerosis. Specifically, a reduced number and morphology of the mitochondria, oxidative damage to mitochondrial DNA in plaques, and altered transport capabilities of the mitochondria have been observed.^19,50,51 Reduced activity of mitochondrial electron transport chain complexes has been observed in rodent models of multiple sclerosis.^11,12,52,53 This reduction in the activity of electron transport chain complexes was also confirmed in human post-mortem brain tissue^52,54–57 and patients with clinically isolated syndrome, a precursor for multiple sclerosis. Genes controlling mitochondria and electron transport chain complexes are downregulated in the cerebrospinal fluid from multiple sclerosis patients.⁵⁸ Elevated biomarkers of mitophagy have been found in the cerebrospinal fluid and serum from MS patients with active disease, which suggests an increased turnover of mitochondria.^59,60 Metabolomic analysis of urine from multiple sclerosis patients have shown alterations in metabolites related to energy and fatty acid metabolism and mitochondrial activity.⁶¹ This mitochondrial dysfunction may exacerbate the axonal loss observed in multiple sclerosis patients.⁶² Mouse neurons decrease the transcription of genes related to the electron transport chain when exposed to inflammatory stimuli. Neurodegeneration was exacerbated in a mouse model of multiple sclerosis when the gene Ppargc1a, which increases the number of mitochondria, complex IV activity, and maximum respiratory capacity, was deleted and ameliorated when the same gene was overexpressed.¹² Inflammation in multiple sclerosis leads to elevated levels of nitrogen oxide (NO),^63
–65 which inhibits the utilisation of oxygen in complex IV of the electron transport chain.⁶⁶

Reduced oxygen diffusion over the capillary is another potential explanation for a lower CMRO₂. The blood-brain barrier (BBB) has increased solute permeability in relapsing-remitting multiple sclerosis.⁶⁷ Pericytes govern the integrity of the BBB and regulate perfusion at the capillary level. Dysfunction of pericytes disturbs the perfusion pattern and results in higher capillary transit time heterogeneity, which impedes oxygen transport into tissue. Local inflammation along vessels also increase diffusion distances.⁶⁸ Future studies could addressed this issue using dynamic contrast-enhanced MRI.⁶⁹ It is unlikely that the decreased CMRO₂ is due to age-dependent arteriosclerotic vascular pathology because the lesions of RRMS patients were not compatible with typical large or small vessel brain disease. We did not identify enlarged Virchow-Robin spaces, lacunar infarctions, or white matter hyperintensity lesions in the basal ganglia.⁷⁰

Differences in BPF between patients and controls did not explain the decreased CMRO₂. The brains of multiple sclerosis patients swell due to inflammation.^71,72 Because CMRO₂ by definition is normalised to brain volume, swelling with increased volume but no additional metabolically active cells would present as decreased CMRO₂. However, the oxygen saturation in the sagittal sinus, which is not normalised to BPF, followed the same trend as the CMRO₂, and swelling is not a likely explanation for the decreased CMRO₂ in the present study. Notably, NAA, which has been used as a marker of neurons but is produced in the mitochondria, showed an opposite correlation to CMRO₂ in RRMS patients compared to healthy controls. This finding supports mitochondrial defects and not a lack of mitochondria as the underlying explanation for our findings, which may be a compensatory increase in NAA.

The measured levels of CMRO₂, CBF and oxygen saturation in the sagittal sinus are consistent with previous MRI studies in healthy controls and relapsing-remitting multiple sclerosis patients, but previous studies did not report any potential relationships between these factors and age.^{14
–16,18,31,73,74} Specifically, a recent study finds similar decreases in global CMRO₂ and increases in global venous oxygen saturation for grey matter in relapsing-remitting multiple sclerosis.⁷⁵ Their modelling estimates a higher partial pressure of oxygen at the mitochondria in the patients, which suggest reduced oxygen demand as an explanation for the lower CMRO₂. In contrast to our study, this study reports lower grey matter CBF in patients compared to age/sex matched controls, and negative correlations between lesion volume and both cortical CMRO₂, and lesion volume and cortical CBF. The populations studied were of similar age, sex distribution, fraction of patients receiving disease modifying treatment and EDSS score. First, this discrepancy between findings could be a result of a different methodological approach, since Chandler et al. utilized arterial spin labeling and only reported regional CBF for grey matter, whereas we utilized PCM for the assessment of global CBF, the former method yielding overall lower CBF values.⁷⁶ Furthermore, Chandler, et al. excluded patients who had a recent change in medication, relapse or steroid treatment. We did not impose similar restrictions and therefore our results may to a higher degree reflect current inflammatory activity, which has previously been shown to coincide with higher CBF values.⁷⁷ The Chandler et al. study did not examine whether there was a relationship between age and CMRO₂, but our study covered a greater age range and included twice the number of participants, which may have made this discovery possible.⁷⁵ Previously reported correlations between EDSS and total lesion volume and the measurements of CMRO₂ and oxygen saturation in the sagittal sinus are ambiguous.^{15
–18,74,75}

If a pathological gradual decrease in CMRO₂ is present in multiple sclerosis, targeting cerebral metabolism may be a new treatment strategy. A recent in vitro study showed that supporting the mitochondrial function of neurons increased remyelination,¹¹ but currently no treatments exist for relapsing-remitting multiple sclerosis that addresses metabolic dysfunction.⁷⁸ Further studies addressing serial CMRO₂ measurements in the context of multiple sclerosis disease-modifying treatment, relapses and atrophy development over time would be highly beneficial.

Strengths and limitations

One main strength of the study is that we examined a cohort consisting of only relapsing-remitting multiple sclerosis subjects, which contrasts with previous work that, except for one study,¹⁸ focused on clinically definite multiple sclerosis^14,15 or investigated a mixed population of relapsing-remitting and progressive multiple sclerosis patients.^16,17 A wide age range was included, which increased the external validity of our findings.

There are also some limitations to the present study. For the determination of the CMRO₂, we would like to address two assumptions. The arterial oxygen saturation was assumed to be 98% in all participants, whether healthy or sick. If multiple sclerosis patients had a lower arterial oxygen saturation, this factor would be a competing explanation for the decreased cerebral oxygen consumption. However, we are not aware of any evidence of MS patients having a lower arterial oxygen saturation, and previous studies on oxygen extraction and venous oxygen saturation in multiple sclerosis assumed that patients had the same arterial oxygen saturation as healthy controls.^16,17 Future studies can measure arterial oxygen saturation using pulse-based oximetry if they are interested in ameliorating this potential difference. We also assumed that patients and controls had a haemoglobin concentration of 9 mmol/l, consistent with previous work finding no difference in haemoglobin between healthy controls and early MS.⁷⁹ Women of reproductive age have a lower haemoglobin concentration than men due to monthly blood loss in relation to their menstrual periods, and older patients are more likely to have a lower haemoglobin concentration due to undiscovered disease, such as bleeding in the gastrointestinal canal. These differences would have implications for the interpretation of the results. However, we consider these risks to be balanced between patients and controls because the age and sex of patients and controls were similar.

The present study measured the oxygen saturation in the sagittal sinus and interpreted it as the venous oxygen saturation of the entire brain. However, the sagittal sinus only drains approximately 50% of the cerebrum and the falx cerebri and pericranium. We normalised the total CMRO₂ measurement to the total brain volume. This normalisation introduces a bias if the inferior cerebrum or the cerebellum are differently affected by multiple sclerosis than the cerebrum. Normalising the CMRO₂ to total brain volume is generally used,^33,80 and previous studies used a similar approach^16,17 or found no significant regional differences between the cerebellum and cerebrum.

Interpretation of the spectroscopic measurements of NAA and lactate are primarily limited by two conditions. The first condition is age. The healthy controls with magnetic resonance spectroscopic data were an average of 8.6 years younger than the patients. Our data showed no significant effects of age on the concentration of NAA or lactate, but the concentrations of both metabolites changes with age.⁴⁶ Therefore, we only made age-adjusted comparisons between patients and controls. However, any changes in multiple sclerosis from middle age and beyond would go undetected in our study. The second condition is that we only acquired magnetic resonance spectroscopy in the occipital lope, and thus we cannot draw conclusions about NAA or lactate concentrations in other parts of the brain.

Conclusion

The present study supports the evidence of mitochondrial dysfunction as a potential pathogenic mechanism in relapsing-remitting multiple sclerosis. Whether reduced cerebral oxygen consumption predicts future atrophy and may be used as an early biomarker require further study.

Supplemental Material

sj-pdf-1-jcb-10.1177_0271678X231224502 - Supplemental material for Age-related decline in cerebral oxygen consumption in multiple sclerosis

Supplemental material, sj-pdf-1-jcb-10.1177_0271678X231224502 for Age-related decline in cerebral oxygen consumption in multiple sclerosis by Maria H Knudsen, Mark B Vestergaard, Ulrich Lindberg, Helle J Simonsen, Jette L Frederiksen, Stig P Cramer and Henrik BW Larsson in Journal of Cerebral Blood Flow & Metabolism

Footnotes

Funding

The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This study was supported by Sanofi Genzyme [Grant number GZ-2016-11629] and The Danish Multiple Sclerosis Society [Grant numbers A37989, A40212, A41682]. The funding bodies had no influence on the study design, inclusion of patients, data analysis, interpretation or writing of the final manuscript.

Acknowledgements

The authors thank the neurologists and multiple sclerosis patients from the multiple sclerosis clinic at Rigshospitalet. The authors also thank radiographers B.S. Møller, K.E. Segers and R.H. Tavangar.

Declaration of conflicting interests

The author(s) declared the following potential conflicts of interest with respect to the research, authorship, and/or publication of this article: The authors declare the following potential conflicts of interest with respect to the research, authorship and/or publication of this article. M.H. Knudsen received funding from Sanofi Genzyme and The Danish Multiple Sclerosis Society and non-financial support from Merck. H.B.W. Larsson & S.P. Cramer received funding from Sanofi Genzyme and The Danish Multiple Sclerosis Society. Sanofi Genzyme had no influence on the study design, inclusion of patients, data analysis or interpretation. U. Lindberg, M.B. Vestergaard and H.J. Simonsen have nothing to disclose. J.L. Frederiksen received no funding to support this study. She has served on scientific advisory boards for and received funding for travel related to these activities and honoraria from Biogen Idec, Merck Serono, Sanofi-Aventis, Teva, Novartis and Almirall. She has also received speaker honoraria from Biogen Idec, Teva and Novartis. She has served as an advisor on preclinical development for Takeda. J. L Frederiksen participated in advisory board meetings with Alexion and Chiesi.

Authors’ contributions

MHK, MBV, UL, SPC and HBWL contributed to the design of the study, acquisition, analysis and interpretation of the data, and the drafting of the article. HJS contributed to the acquisition and analysis of the data and the drafting of the article. JLF contributed to the acquisition and interpretation of the data and the drafting of the article. All authors critically revised the draft and approved the version to be published.

Supplementary material

Supplemental material for this article is available online.

ORCID iDs

Maria H Knudsen

Ulrich Lindberg

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