Blood-based bioenergetic profiling has promising applications as a minimally invasive biomarker of systemic bioenergetic capacity. In the present study, we examined peripheral blood mononuclear cell (PBMC) mitochondrial function and brain morphology in a cohort of African Americans with long-standing Type 2 diabetes. Key parameters of PBMC respiration were correlated with white matter, gray matter, and total intracranial volumes. Our analyses indicate that these relationships are primarily driven by the relationship of systemic bioenergetic capacity with total intracranial volume, suggesting that systemic differences in mitochondrial function may play a role in overall brain morphology.

Introduction

Type 2 diabetes mellitus (T2DM) is one of the most common diseases in older adults [1]. Those with T2DM are susceptible to diseases like neuropathy, retinopathy, nephropathy, stroke, and ischemic heart disease, and are more likely to suffer from cognitive impairment and experience a higher risk of dementia [2–4], cerebral infarctions and loss of total gray matter, white matter, and hippocampal volumes [5,6].

Studies of brain tissues and neurons show that mitochondrial bioenergetics regulate brain energy homeostasis and metabolism during development [7] and affect brain function and cognition [8]. Indeed, the brain has an exceptionally high metabolic demand rendering it highly sensitive to changes in systemic bioenergetic capacity [9,10]. There is growing evidence linking central and peripheral metabolic alterations in the pathophysiology of neurodegenerative diseases. Such relationships may be due to intercellular signaling mediated by non-cellular, blood-borne circulating factors such as inflammatory cytokines, redox stress, mitokines, and exosomes [11–15]. These factors can have systemic effects on the bioenergetic capacity across multiple organs, as well as circulating cells that are continuously exposed to them. Previous publications from our group support this premise and provide direct evidence that the assessment of mitochondrial function in circulating cells is associated with the bioenergetic capacity of different highly metabolically active organs such as the brain, heart, and skeletal muscle [23,24]. To date, the bioenergetic profiles of peripheral blood mononuclear cells (PBMCs) and platelets have been associated with several age-related disorders, including diabetes, atherosclerosis, and neurodegeneration [16–22].

The present study aimed to examine the relationships between the bioenergetic capacity of PBMCs and key features of brain morphology: total gray matter volume (TGM), total white matter volume (TWM), and total intracranial volume (TICV). We focused on African American individuals with T2DM who participated in African American-Diabetes Heart Study MIND (AA-DHS MIND). African Americans have a higher risk of T2DM [25], leading to an increased risk for cognitive impairment [26,27]. We tested the hypothesis that PBMC bioenergetic capacity correlates with brain structure and cognitive performance by examining the relationships between PBMC respiration and total and regional brain volumes measured by MRI and Montreal Cognitive Assessment (MoCA) scores. To our knowledge, this is the first study to specifically examine these relationships in an African American population.

Experimental methods

Participants

AA-DHS MIND is a cross-sectional genetic and epidemiologic study designed to evaluate and improve the understanding of risk factors for impaired cognitive performance and to assess cerebral architecture using magnetic resonance imaging (MRI) in African Americans with T2D. It builds on the AA-DHS study, which is an extension of the Diabetes Heart Study (DHS) [28] designed to assess the relationship between cognitive impairment and cerebrovascular disease in an African American cohort. The study was approved by the Wake Forest School of Medicine (WFSM) Institutional Review Board and all participants provided written, informed consent. We recruited 16 unrelated African Americans. Participants (9 women and 7 men) were older (51.7–81.8 years), overweight or obese (body mass index [BMI] > 25.5–50.6 kg/m2), and sedentary. Examinations were performed in the WFSM Clinical Research Unit.

Cerebral magnetic resonance imaging (MRI)

Detailed methods for MRI scans and analyses in AA-DHS MIND have been reported previously [29–31]. As previously described, all MRI scans were obtained on a 3T MRI scanner [32]. T1-weighted images were analyzed for structural analysis to obtain total intracranial volume, total gray matter, and total white matter volumes using the SPM8 segmentation procedure implemented in the VBM8 toolbox [33,34]. Brain MRI was performed at the first visit of the participants.

Body weight, blood draw, and MoCA examinations

Body weight and fasting blood glucose were assessed at the day of visit for the present study. Fasting measures of HbA1c were acquired at the first visit of the participants. The MoCA, a screening test that assesses cognitive impairment [35], was administered after the participants had breakfast.

Respirometry of blood cells

Mitochondrial oxidative phosphorylation can be measured by evaluating the rate of oxygen consumption in cells and tissues of interest [36–38]. Blood cell respirometry was performed using two complementary approaches. Intact PBMCs were assessed with a Seahorse XF24-3 extracellular flux analyzer (Seahorse Bioscience, Billerica, MA). A total of 250,000 PBMCs were loaded into each well and assessed in quadruplicate using previously described methods [39]. Briefly, basal oxygen consumption rate (OCR) was monitored prior to chemical additions, followed by OCR measurements after sequential injections of oligomycin (0.75 μM), carbonyl cyanide-4- (trifluoromethoxy) phenylhydrazone (FCCP; 1 μM), and antimycin A + rotenone (A/R; 1 μM each). All chemicals were obtained from SigmaAldrich. PBMC respiration was reported as pmol·min−1.

High-resolution respirometry of permeabilized PBMCs was performed in parallel to provide key measures of fatty acid oxidation and respiration driven by individual complexes. For these studies, 4 million PBMCs were loaded into each of two chambers of an Oroboros Oxygraph-2K (Oroboros, Innsbruck, Austria). Respirometry was performed following a substrate-uncoupler-inhibitor-titration reference protocol in which multiple substrates and inhibitors are sequentially added to measure oxygen flux due to fatty acid oxidation, followed by oxidative phosphorylation. PBMCs were placed into a chamber with 2 ml mitochondrial respiration medium, mitochondrial respirometry solution constituting of 0.5 mM EGTA, 3 mM MgCl2, 60 mM lactobionic acid, 20 mM taurine, 10 mM KH2PO4, 20 mM HEPES, 110 mM D-Sucrose, and 1 g/l fatty acid free BSA, pH 7.1. Chambers were equilibrated at room oxygen concentration at 37°C for at least 30 min and routine endogenous respiration was measured, followed by addition of 7.5 mM ADP. Cells were then permeabilized with 0.04 mg/ml digitonin, followed by addition of 0.5 mM octanoylcarnitine to evaluate fatty acid oxidation capacity and 0.05 mM malate to kinetically saturate the fatty acid oxidation pathway. This was followed by sequential addition of 2 mM malate, 10 µM cytochrome c to assess outer mitochondrial membrane integrity, 5 mM pyruvate, 10 mM glutamate, 50 mM succinate, and 10 mM glycerophosphate. These additions target complexes I, II, and ubiquinone or coenzyme Q of the electron transport chain, resulting in detailed measurements of mitochondrial function. The electron transport chain was uncoupled with FCCP (by titrating 0.5 µM FCCP in each step), and then inhibited by the addition of complex I and III inhibitors, 0.5 µM rotenone, and 2.5 µM antimycin A, measuring residual oxygen consumption. PBMC respiration was reported as fmol·s−1·cell−1.

Statistical analyses

Shapiro–Wilk tests were performed to check for normal distribution of all variables. Log transformations were performed for parameters with non-normal distribution. Pearson correlation coefficients were assessed between all variables, both raw and normalized values, and partial correlations adjusted for age and sex were also assessed. Significance was set at an α-level of 0.05. Analyses were performed using SPSS software (SPSS v22; Armonk, NY).

Results

Demographic and bioenergetic parameters of the human participants

Demographic parameters (age, BMI, duration of T2DM, fasting blood sugar, HbA1c, and MoCA scores), bioenergetic parameters, and brain morphology parameters analyzed are summarized in Table 1. Representative bioenergetic profiles from a participant are shown in Figure 1A,B.

Representative graphs of the two different techniques used to measure PBMC respiration

Figure 1
Representative graphs of the two different techniques used to measure PBMC respiration

Bioenergetic profiles of PBMCs isolated from one participant. Respiration is measured as oxygen consumption rate. (A) Representative graph generated by the Seahorse XF24-3 extracellular flux analyzer. (B) Representative graph generated by the Oroboros O2K respirometer. As shown in (A), injections were as follows: O = oligomycin, U = uncoupler (FCCP), R = rotenone, A = antimycin A. As shown in (B), multiple substrates and inhibitors were sequentially added to permeabilize cells and measure oxygen flux due to fatty acid oxidation, followed by oxidative phosphorylation. D = adenosine diphosphate, Dig = Digitonin, Oct = Octanoylcarnitine, M1 = 0.05 mM malate, M2 = 2 mM malate, c = cytochrome c, P = pyruvate, G = glutamate, S = succinate, Gp = glycerophosphate, U = uncoupler (FCCP), R = rotenone, A = antimycin A.

Figure 1
Representative graphs of the two different techniques used to measure PBMC respiration

Bioenergetic profiles of PBMCs isolated from one participant. Respiration is measured as oxygen consumption rate. (A) Representative graph generated by the Seahorse XF24-3 extracellular flux analyzer. (B) Representative graph generated by the Oroboros O2K respirometer. As shown in (A), injections were as follows: O = oligomycin, U = uncoupler (FCCP), R = rotenone, A = antimycin A. As shown in (B), multiple substrates and inhibitors were sequentially added to permeabilize cells and measure oxygen flux due to fatty acid oxidation, followed by oxidative phosphorylation. D = adenosine diphosphate, Dig = Digitonin, Oct = Octanoylcarnitine, M1 = 0.05 mM malate, M2 = 2 mM malate, c = cytochrome c, P = pyruvate, G = glutamate, S = succinate, Gp = glycerophosphate, U = uncoupler (FCCP), R = rotenone, A = antimycin A.

Table 1
Demographics, bioenergetics, and brain morphology parameters
N=16 Mean SD Range 
Age (years) 64.42 7.71 51.65–81.76 
BMI (kg/m234.11 7.92 25.51–50.64 
Duration of T2D (years) 12.97 8.86 3.66–39.12 
Fasting blood sugar (mg/dl) 150.67 52.53 79–283 
HbA1c (%) 7.82 1.66 5.8–12.6 
MoCA score 21.5 4.7 13.00–29.00 
Bioenergetic parameters    
Basal (pmol·min−1113.13 51.15 42.05–169.35 
Maximal uncoupled respiration (pmol·min−1245.53 139.12 91.30–657.92 
Spare respiratory capacity (pmol·min−1132.40 94.20 14.30–412.14 
ATP-linked respiration (pmol·min−163.81 45.33 (−19.54)–111.4 
FAO (fmol·s−1·cell−12.66 × 10−3 1.02 × 10−3 1.19 × 10−3–5.35 × 10−3 
FAO+ComplexI (fmol·s−1·cell−13.66 × 10−3 1.86 × 10−3 1.175 × 10−3–8.29 × 10−3 
FAO+ComplexI+ComplexII (fmol·s−1·cell−15.05 × 10−3 2.605 × 10−3 1.475 × 10−3–13.09 × 10−3 
Max ETS (fmol·s−1·cell−17.55 × 10−3 4.59 × 10−3 2.30 × 10−3–11.70 × 10−3 
Brain anatomy parameters    
Total gray matter volume (TGM) (cm3570.85 49.27 492.21–648.74 
Total white matter volume (TWM) (cm3478.73 45.21 422.24–553.91 
Total intracranial volume (TICV) (cm31294.10 117.59 1134.77–1560.82 
N=16 Mean SD Range 
Age (years) 64.42 7.71 51.65–81.76 
BMI (kg/m234.11 7.92 25.51–50.64 
Duration of T2D (years) 12.97 8.86 3.66–39.12 
Fasting blood sugar (mg/dl) 150.67 52.53 79–283 
HbA1c (%) 7.82 1.66 5.8–12.6 
MoCA score 21.5 4.7 13.00–29.00 
Bioenergetic parameters    
Basal (pmol·min−1113.13 51.15 42.05–169.35 
Maximal uncoupled respiration (pmol·min−1245.53 139.12 91.30–657.92 
Spare respiratory capacity (pmol·min−1132.40 94.20 14.30–412.14 
ATP-linked respiration (pmol·min−163.81 45.33 (−19.54)–111.4 
FAO (fmol·s−1·cell−12.66 × 10−3 1.02 × 10−3 1.19 × 10−3–5.35 × 10−3 
FAO+ComplexI (fmol·s−1·cell−13.66 × 10−3 1.86 × 10−3 1.175 × 10−3–8.29 × 10−3 
FAO+ComplexI+ComplexII (fmol·s−1·cell−15.05 × 10−3 2.605 × 10−3 1.475 × 10−3–13.09 × 10−3 
Max ETS (fmol·s−1·cell−17.55 × 10−3 4.59 × 10−3 2.30 × 10−3–11.70 × 10−3 
Brain anatomy parameters    
Total gray matter volume (TGM) (cm3570.85 49.27 492.21–648.74 
Total white matter volume (TWM) (cm3478.73 45.21 422.24–553.91 
Total intracranial volume (TICV) (cm31294.10 117.59 1134.77–1560.82 

Note: PBMC bioenergetic parameters recorded by Seahorse XF24-3 extracellular flux analyzer are reported as oxygen consumption rate (pmol·min−1) per 250,000 cells.

Associations between PBMC bioenergetics and brain morphology

Pearson correlation coefficients were used to compare PBMC bioenergetic parameters with brain morphology (Table 2). Representative scatter plots are shown in Supplementary Figure S1A–D. Basal, maximal FCCP-induced respiration, and ATP-linked respiration of PBMCs significantly positively correlated with TWM volume (R = 0.666, 0.547, and 0.563), while basal and maximal FCCP-induced respiration correlated significantly with TICV (R = 0.588 and 0.550). Fatty acid oxidation-mediated oxygen flux (respiration of cells after addition of malate to kinetically saturate the fatty acid oxidation [FAO] pathway) significantly correlated with TWM volume and TICV (R = 0.591 and 0.684). Similar relationships were observed between maximal electron transport (measured by FCCP titration) and TWM volume and TICV. Significant positive association were seen between FAO + complex I activity and TWM volume and TICV. Significant associations were observed between combined FAO + complex I + complex II activity and TWM volume and TICV.

Table 2
Relationship between PBMC respiration and brain morphology parameters measured by Pearson correlation
Respirometry parameters TGM TWM TICV 
Basal respiration R = 0.338 R = 0.666 R = 0.588 
 P = 0.218 P = 0.007 P = 0.021 
Maximal respiration R = 0.375 R = 0.547 R = 0.550 
 P = 0.169 P = 0.035 P = 0.034 
Spare respiratory capacity R = 0.367 R = 0.408 R = 0.477 
 P = 0.178 P = 0.131 P = 0.072 
ATP-linked respiration R = 0.253 R = 0.563 R = 0.490 
 P = 0.364 P = 0.029 P = 0.064 
FAO R = 0.477 R = 0.591 R = 0.684 
 P = 0.062 P = 0.016 P = 0.003 
FAO+ComplexI R = 0.467 R = 0.519 R = 0.564 
 P = 0.068 P = 0.040 P = 0.023 
FAO+ComplexI+ComplexII R = 0.375 R = 0.502 R = 0.528 
 P = 0.152 P = 0.047 P = 0.035 
Max ETS R = 0.349 R = 0.503 R = 0.503 
 P = 0.199 P = 0.047 P = 0.047 
Respirometry parameters TGM TWM TICV 
Basal respiration R = 0.338 R = 0.666 R = 0.588 
 P = 0.218 P = 0.007 P = 0.021 
Maximal respiration R = 0.375 R = 0.547 R = 0.550 
 P = 0.169 P = 0.035 P = 0.034 
Spare respiratory capacity R = 0.367 R = 0.408 R = 0.477 
 P = 0.178 P = 0.131 P = 0.072 
ATP-linked respiration R = 0.253 R = 0.563 R = 0.490 
 P = 0.364 P = 0.029 P = 0.064 
FAO R = 0.477 R = 0.591 R = 0.684 
 P = 0.062 P = 0.016 P = 0.003 
FAO+ComplexI R = 0.467 R = 0.519 R = 0.564 
 P = 0.068 P = 0.040 P = 0.023 
FAO+ComplexI+ComplexII R = 0.375 R = 0.502 R = 0.528 
 P = 0.152 P = 0.047 P = 0.035 
Max ETS R = 0.349 R = 0.503 R = 0.503 
 P = 0.199 P = 0.047 P = 0.047 

Pearson correlation coefficients and P-values for each association are shown. FAO = fatty acid oxidation, ETS = maximal ETC mediated respiratory system activity. Bold type = P-value ≤ 0.05. Spare respiratory capacity is calculated as the difference between maximal and basal Respiration.

As shown in Table 3, after adjusting for age and sex, basal, maximal FCCP-linked, and ATP-linked bioenergetic capacity of PBMC remained significantly positively correlated with TWM volume and TICV. Spare respiratory capacity showed significant positive correlation with TICV in both cases, and with TGM volume when specifically controlling for sex. Fatty acid oxidation-mediated respiration and FAO + complex I-mediated respiration were significantly positively correlated with TGM volume, TWM volume, and TICV. Similar significant correlations were observed between FAO + complex I + complex II and maximal FCCP-linked respiration and TWM volume and TICV.

Table 3
Relationship between PBMC respiration and brain morphologic parameters statistically adjusted for age and sex
 Adjusted for age Adjusted for sex 
Respirometry parameters TGM TWM TICV TGM TWM TICV 
Basal respiration R = 0.346 R = 0.668 R = 0.607 R = 0.570 R = 0.705 R = 0.709 
 P = 0.225 P = 0.009 P = 0.021 P = 0.033 P = 0.005 P = 0.005 
Maximal respiration R = 0.408 R = 0.566 R = 0.610 R = 0.627 R = 0.586 R = 0.676 
 P = 0.148 P = 0.035 P = 0.021 P = 0.016 P = 0.028 P = 0.008 
Spare respiratory capacity R = 0.412 R = 0.430 R = 0.552 R = 0.489 R = 0.416 R = 0.525 
 P = 0.143 P = 0.125 P = 0.041 P = 0.076 P= 0.139 P = 0.054 
ATP-linked respiration R = 0.258 R = 0.564 R = 0.504 R = 0.532 R = 0.620 R = 0.644 
 P = 0.372 P = 0.036 P = 0.066 P = 0.050 P = 0.018 P = 0.013 
FAO R = 0.474 R = 0.589 R = 0.683 R = 0.460 R = 0.603 R = 0.674 
 P = 0.074 P = 0.021 P = 0.005 P = 0.084 P = 0.017 P = 0.006 
FAO+ComplexI R = 0.483 R = 0.529 R = 0.594 R = 0.436 R = 0.534 R = 0.547 
 P = 0.068 P = 0.043 P = 0.020 P= 0.105 P = 0.040 P = 0.035 
FAO+ComplexI+ComplexII R = 0.411 R = 0.531 R = 0.593 R = 0.443 R = 0.502 R = 0.548 
 P = 0.128 P = 0.042 P = 0.020 P = 0.099 P = 0.057 P = 0.034 
Max ETS R = 0.379 R = 0.527 R = 0.556 R = 0.393 R = 0.503 R = 0.513 
 P = 0.164 P = 0.044 P = 0.031 P = 0.147 P = 0.056 P = 0.050 
 Adjusted for age Adjusted for sex 
Respirometry parameters TGM TWM TICV TGM TWM TICV 
Basal respiration R = 0.346 R = 0.668 R = 0.607 R = 0.570 R = 0.705 R = 0.709 
 P = 0.225 P = 0.009 P = 0.021 P = 0.033 P = 0.005 P = 0.005 
Maximal respiration R = 0.408 R = 0.566 R = 0.610 R = 0.627 R = 0.586 R = 0.676 
 P = 0.148 P = 0.035 P = 0.021 P = 0.016 P = 0.028 P = 0.008 
Spare respiratory capacity R = 0.412 R = 0.430 R = 0.552 R = 0.489 R = 0.416 R = 0.525 
 P = 0.143 P = 0.125 P = 0.041 P = 0.076 P= 0.139 P = 0.054 
ATP-linked respiration R = 0.258 R = 0.564 R = 0.504 R = 0.532 R = 0.620 R = 0.644 
 P = 0.372 P = 0.036 P = 0.066 P = 0.050 P = 0.018 P = 0.013 
FAO R = 0.474 R = 0.589 R = 0.683 R = 0.460 R = 0.603 R = 0.674 
 P = 0.074 P = 0.021 P = 0.005 P = 0.084 P = 0.017 P = 0.006 
FAO+ComplexI R = 0.483 R = 0.529 R = 0.594 R = 0.436 R = 0.534 R = 0.547 
 P = 0.068 P = 0.043 P = 0.020 P= 0.105 P = 0.040 P = 0.035 
FAO+ComplexI+ComplexII R = 0.411 R = 0.531 R = 0.593 R = 0.443 R = 0.502 R = 0.548 
 P = 0.128 P = 0.042 P = 0.020 P = 0.099 P = 0.057 P = 0.034 
Max ETS R = 0.379 R = 0.527 R = 0.556 R = 0.393 R = 0.503 R = 0.513 
 P = 0.164 P = 0.044 P = 0.031 P = 0.147 P = 0.056 P = 0.050 

Correlation coefficients and P-values for each association are shown. Bold type = P-value ≤ 0.05.

As shown in Supplementary Tables S1–S4, adjustment for duration of T2DM, BMI and T2DM severity (HbA1c) did not affect these relationships between brain morphology and PBMC bioenergetic capacity.

Associations between PBMC bioenergetics and normalized brain morphology parameters

Table 4 shows the relationships between PBMC bioenergetics and brain morphologic parameters statistically adjusted for TICV. This adjustment caused all correlations to become less significant, indicating that TICV was the main driver of the associations with TWM volume and TGM volume.

Table 4
Relationships between PBMC respiration and brain morphologic parameters statistically adjusted for TICV measured by partial correlation
 Adjusted for TICV 
Respirometry parameters TGM TWM 
Basal respiration R = −0.105 R = 0.510 
 P = 0.734 P = 0.062 
Maximal respiration R = 0.051 R = 0.313 
 P = 0.870 P = 0.277 
Spare respiratory capacity R = 0.165 R = 0.143 
 P = 0.589 P = 0.627 
ATP-linked respiration R = −0.144 R = 0.445 
 P = 0.638 P = 0.110 
FAO R = 0.089 R = 0.251 
 P = 0.761 P = 0.366 
FAO+ComplexI R = 0.166 R = 0.282 
 P = 0.571 P = 0.308 
FAO+ComplexI+ComplexII R = −0.003 R = 0.322 
 P = 0.991 P = 0.242 
Max ETS R = 0.030 R = 0.386 
 P = 0.918 P = 0.156 
 Adjusted for TICV 
Respirometry parameters TGM TWM 
Basal respiration R = −0.105 R = 0.510 
 P = 0.734 P = 0.062 
Maximal respiration R = 0.051 R = 0.313 
 P = 0.870 P = 0.277 
Spare respiratory capacity R = 0.165 R = 0.143 
 P = 0.589 P = 0.627 
ATP-linked respiration R = −0.144 R = 0.445 
 P = 0.638 P = 0.110 
FAO R = 0.089 R = 0.251 
 P = 0.761 P = 0.366 
FAO+ComplexI R = 0.166 R = 0.282 
 P = 0.571 P = 0.308 
FAO+ComplexI+ComplexII R = −0.003 R = 0.322 
 P = 0.991 P = 0.242 
Max ETS R = 0.030 R = 0.386 
 P = 0.918 P = 0.156 

Correlation coefficients and P-values for each association are shown.

Associations between PBMC bioenergetic capacity and MoCA test scores

Pearson and partial correlation coefficients were calculated to compare PBMC bioenergetic parameters with MoCA test scores before and after adjusting for age of the participants. The associations are summarized in Table 5. Basal respiration was significantly positively associated with both raw values as well as age adjusted MoCA scores; similar trends were observed for the other bioenergetic parameters.

Table 5
Relationship between PBMC respiration and MoCA scores (raw values and values adjusted for age) measured by Pearson correlation (raw values) (first panel) and partial correlation (adjusted for age) (second panel)
 Raw values Adjusted for age 
Respirometry parameters MoCA MoCA 
Basal respiration R = 0.571 R = 0.579 
 P = 0.026 P = 0.030 
Maximal respiration R = 0.301 R = 0.328 
 P = 0.276 P = 0.252 
Spare respiratory capacity R = −0.067 R = −0.050 
 P = 0.812 P = 0.866 
ATP-linked respiration R = 0.479 R = 0.484 
 P = 0.071 P = 0.079 
FAO R = 0.196 R = 0.191 
 P = 0.468 P = 0.495 
FAO+ComplexI R = −0.002 R = 0.006 
 P = 0.995 P = 0.982 
FAO+ComplexI+ComplexII R = 0.131 R = 0.153 
 P = 0.630 P = 0.586 
Max ETS R = 0.155 R = 0.175 
 P = 0.566 P = 0.534 
 Raw values Adjusted for age 
Respirometry parameters MoCA MoCA 
Basal respiration R = 0.571 R = 0.579 
 P = 0.026 P = 0.030 
Maximal respiration R = 0.301 R = 0.328 
 P = 0.276 P = 0.252 
Spare respiratory capacity R = −0.067 R = −0.050 
 P = 0.812 P = 0.866 
ATP-linked respiration R = 0.479 R = 0.484 
 P = 0.071 P = 0.079 
FAO R = 0.196 R = 0.191 
 P = 0.468 P = 0.495 
FAO+ComplexI R = −0.002 R = 0.006 
 P = 0.995 P = 0.982 
FAO+ComplexI+ComplexII R = 0.131 R = 0.153 
 P = 0.630 P = 0.586 
Max ETS R = 0.155 R = 0.175 
 P = 0.566 P = 0.534 

Pearson correlation coefficients and P-values for each association are shown. Bold type = P-value ≤ 0.05.

Discussion

Mitochondrial bioenergetics plays a key role in the effects of aging on neuronal function [40]. Mitochondrial dysfunction is related to numerous age-related diseases, including T2DM, obesity, Parkinson’s, and Alzheimer’s disease [41–44]. Recent work from our laboratory and others indicate that measures of mitochondrial function performed in circulating cells can report on systemic bioenergetic capacity and are related to various age-related conditions [45–52]. The present study provides the first report that systemic bioenergetic capacity is related to key measures of brain morphology.

Our results indicate that systemic bioenergetic capacity, assessed by PBMC respirometry, is significantly positively related to TICV, a parameter estimating maximum pre-morbid brain volume [53]. This finding suggests that differences in systemic bioenergetic capacity may be related to the overall development and atrophy of the brain. Our results also indicate that basal respiration of intact PBMCs is significantly positively related to cognitive function, measured using the MoCA assessment. ATP-linked respiration shows a strong trend while other measures in intact PBMCs are not significant. Our results also show that statistically adjusting for age, sex, BMI, and T2DM severity (HbA1c) does not affect the relationships observed in the present study. It is notable that while BMI and T2DM have been associated with alterations in mitochondrial function in previous studies, the relationship of systemic bioenergetic capacity with brain morphology is independent of these variables.

TICV is currently the most accepted and widely used measure of brain reserve and is associated with higher cognitive performance after adjusting for the amount of pathology in Alzheimer’s disease [15]. Moreover, it has been previously reported that greater premorbid brain volume results in better clinical and cognitive outcomes [54,55]. BMI, hyperglycemia, and T2DM are associated with brain atrophy, cognitive impairment and dementia, with duration of T2DM strongly associated with brain volume loss [54]. This is possibly a result of direct neurologic insult from altered glucose and mitochondrial metabolism, leading to mitochondrial dyshomeostasis and loss of synaptic integrity, affecting brain functions and morphology. Future studies will address whether these central metabolic alterations are relayed to PBMC mitochondria via non-cellular, blood-borne circulating factors that are potentially released from the brain, altering PBMC mitochondrial bioenergetics.

To our knowledge, the assay protocols utilized in the present study are the most comprehensive assessment of PBMC bioenergetics to date. We examined the respiration of intact and permeabilized PBMCs in parallel to enable in depth analysis of electron transport chain activity. It should be considered that the measures presented here are interrelated and focused on contributors to overall mitochondrial function. For example, the individual activities of complexes 1 and 2, as well as fatty acid oxidation all contribute to the bioenergetic capacity of a cell. It should be noted that even if we were to choose P = 0.01 as the level of significance, key relationships remain significant; particularly the relationships between the basal respiration and the FAO-mediated respiration with TWM and TICV. Moreover, these relationships remain when adjusting for age, sex, duration and severity of T2DM, BMI and blood glucose levels.

The composition of the cohort utilized for the present study is also unique and representative of a group that is at a greater risk of developing cognitive decline [55]. Future studies should be designed to determine if the results can be extended to other cohorts. It is also important to remember that bioenergetic profiling was performed at a single time point. Therefore, longitudinal studies should be designed to more definitely assess the role of PBMC bioenergetic capacity in brain development or degeneration. Blood-based bioenergetic profiling is rapidly emerging as a reliable measure of systemic bioenergetic capacity. To date, studies have focused on mixed PBMCs, as performed here, but also other cell types such as platelets and monocytes [52,56–59]. The design of future studies will continue to improve as we continue to advance our understanding of how various circulating cell types reflect bioenergetic changes associated with various conditions and disorders.

Clinical perspectives

  • African American individuals represent a cohort experiencing a higher risk of T2DM, consequently exhibiting an increased risk of cognitive decline.

  • PBMC bioenergetic capacity is directly related to overall brain morphology and cognitive function.

  • Blood-based bioenergetic profiling is a promising minimally invasive biomarker of systemic bioenergetic capacity.

We acknowledge the editorial assistance of Karen Klein, MA, in the Wake Forest Clinical and Translational Science Institute (UL1 TR001420; PI: McClain).

Competing Interests

The authors declare that there are no competing interests associated with the manuscript.

Funding

National Institutes of Health/National Institute on Aging [R01 AG054523 (to A.J.A.M.)]; National Institutes of Health/National Institute on Aging [R21 AG051077 (to A.J.A.M)]; American Heart Association [15MCPRP25680019 (to A.J.A.M)]; National Iinstitutes of Health/National Institute of Neurological Disorders and Stroke [NS075107 (to B.I.F.)]; Wake Forest School of Medicine Pepper Center [P30 AG21332]; and Wake Forest School of Medicine Alzheimer's Disease Research Center [P30 AG049638].

Author Contribution

G.M. played a key role in the conceptualization and development of this ancillary study, isolated PBMCs from the blood samples, performed all respirometric analyses, and played a lead role in data analyses and manuscript preparation. S.C.S. supervised the blood draw and performed MoCA analyses. T.M.H. played a key role in the data analyses and manuscript preparation. B.W. performed the MRI scans and the analyses to generate the data for the brain volumetric measurements. J.A.M provided oversight for all the MRI analyses. B.I.F. was the PI of the parent study and played a key role in the development of this ancillary study. A.J.A.M. was responsible for the development of the present study and provided oversight for all mitochondrial assessments, worked directly to coordinate the experimental plan, and supervised data analyses and manuscript preparation. All authors reviewed the manuscript.

Abbreviations

     
  • 3T MRI

    3 Tesla Magnetic Resonance Imaging

  •  
  • AA-DHS Mind

    African American–Diabetic Heart Study Mind

  •  
  • BMI

    basal metabolic rate

  •  
  • ETS

    maximal ETC mediated respiratory system activity

  •  
  • FAO

    fatty acid oxidation

  •  
  • FCCP

    carbonyl cyanide-4- (trifluoromethoxy) phenylhydrazone

  •  
  • MRI

    magnetic resonance imaging

  •  
  • MoCA

    Montreal Cognitive Assessment

  •  
  • OCR

    oxygen consumption rate

  •  
  • PBMC

    peripheral blood mononuclear cell

  •  
  • T2DM

    Type 2 diabetes mellitus

  •  
  • WFSM

    Wake Forest School of Medicine

References

References
1
Zheng
Y.
,
Ley
S.H.
and
Hu
F.B.
(
2018
)
Global aetiology and epidemiology of type 2 diabetes mellitus and its complications
.
Nat. Rev. Endocrinol.
14
,
88
98
[PubMed]
2
Luchsinger
J.A.
,
Reitz
C.
,
Patel
B.
,
Tang
M.X.
,
Manly
J.J.
and
Mayeux
R.
(
2007
)
Relation of diabetes to mild cognitive impairment
.
Arch. Neurol.
64
,
570
575
[PubMed]
3
Stewart
R.
and
Liolitsa
D.
(
1999
)
Type 2 diabetes mellitus, cognitive impairment and dementia
.
Diabet. Med.
16
,
93
112
[PubMed]
4
Strachan
M.W.
,
Deary
I.J.
,
Ewing
F.M.
and
Frier
B.M.
(
1997
)
Is type II diabetes associated with an increased risk of cognitive dysfunction? A critical review of published studies
Diabetes Care
20
,
438
445
[PubMed]
5
Moran
C.
,
Phan
T.G.
,
Chen
J.
,
Blizzard
L.
,
Beare
R.
,
Venn
A.
et al.  (
2013
)
Brain atrophy in type 2 diabetes: regional distribution and influence on cognition
.
Diabetes Care
36
,
4036
4042
[PubMed]
6
Reitz
C.
,
Guzman
V.A.
,
Narkhede
A.
,
DeCarli
C.
,
Brickman
A.M.
and
Luchsinger
J.A.
(
2017
)
Relation of dysglycemia to structural brain changes in a multiethnic elderly cohort
.
J. Am. Geriatr. Soc.
65
,
277
285
[PubMed]
7
Flippo
K.H.
and
Strack
S.
(
2017
)
Mitochondrial dynamics in neuronal injury, development and plasticity
.
J. Cell Sci.
,
671
681
,
8
Picard
M.
and
McEwen
B.S.
(
2014
)
Mitochondria impact brain function and cognition
.
Proc. Natl Acad. Sci. U.S.A.
,
7
8
9
Clarke
D.D.
and
Sokoloff
L.
(
1999
)
Circulation and energy metabolism of the brain
. In
Basic Neurochemistry: Molecular, Cellular and Medical Aspects
,
6th edn
(
Siegel
G.J.
,
Agranoff
B.W.
,
Albers
R.W.
et al. 
Lippincott-Raven
,
Philadelphia
10
Berg
J.M.
,
Tymoczko
J.L.
and
Stryer
L.
(
2002
)
Section 30.2. Each Organ Has a Unique Metabolic Profile
.
Biochemistry
,
5th edn
,
W H Freeman
,
New York
11
Conte
M.
(
2018
)
Human aging and longevity are characterized by high levels of mitokines
.
J. Gerontol. A Biol. Sci. Med Sci.
,
12
Woo
D.K.
(
2010
)
Mitochondrial stress signals revise an old aging theory
.
Cell
,
11
12
,
13
Durieux
J.
(
2010
)
The cell-non-autonomous nature of electron transport chain-mediated longevity author links open overlay panel
.
Cell
,
79
91
,
14
Pan
W.
(
2011
)
Cytokine signaling modulates blood-brain barrier function
.
Curr Pharm Des.
,
3729
3740
[PubMed]
15
van Loenhoud
A.C.
(
2018
)
Is intracranial volume a suitable proxy for brain reserve?
Alzheimer’s Res.Ther.
,
91
,
16
Avila
C.
,
Huang
R.J.
,
Stevens
M.V.
,
Aponte
A.M.
,
Tripodi
D.
,
Kim
K.Y.
et al.  (
2012
)
Platelet mitochondrial dysfunction is evident in type 2 diabetes in association with modifications of mitochondrial anti-oxidant stress proteins
.
Exp. Clin. Endocrinol. Diabetes
120
,
248
251
[PubMed]
17
Japiassu
A.M.
,
Santiago
A.P.
,
d'Avila
J.C.
,
Garcia-Souza
L.F.
,
Galina
A.
,
Castro Faria-Neto
H.C.
et al.  (
2011
)
Bioenergetic failure of human peripheral blood monocytes in patients with septic shock is mediated by reduced F1Fo adenosine-5'-triphosphate synthase activity
.
Crit. Care Med.
39
,
1056
1063
[PubMed]
18
Hartman
M.L.
,
Shirihai
O.S.
,
Holbrook
M.
,
Xu
G.
,
Kocherla
M.
,
Shah
A.
et al.  (
2014
)
Relation of mitochondrial oxygen consumption in peripheral blood mononuclear cells to vascular function in type 2 diabetes mellitus
.
Vasc. Med.
19
,
67
74
[PubMed]
19
Widlansky
M.E.
,
Wang
J.
,
Shenouda
S.M.
,
Hagen
T.M.
,
Smith
A.R.
,
Kizhakekuttu
T.J.
et al.  (
2010
)
Altered mitochondrial membrane potential, mass, and morphology in the mononuclear cells of humans with type 2 diabetes
.
Transl Res.
156
,
15
25
[PubMed]
20
Tyrrell
D.J.
,
Bharadwaj
M.S.
,
Van Horn
C.G.
,
Marsh
A.P.
,
Nicklas
B.J.
and
Molina
A.J.
(
2015
)
Blood-cell bioenergetics are associated with physical function and inflammation in overweight/obese older adults
.
Exp. Gerontol.
70
,
84
91
[PubMed]
21
Fisar
Z.
,
Hroudova
J.
,
Hansikova
H.
,
Spacilova
J.
,
Lelkova
P.
,
Wenchich
L.
et al.  (
2016
)
Mitochondrial respiration in the platelets of patients with Alzheimer’s disease
.
Curr Alzheimer Res.
13
,
930
941
[PubMed]
22
Chen
X.
,
Stern
D.
and
Yan
S.D.
(
2006
)
Mitochondrial dysfunction and Alzheimer’s disease
.
Curr. Alzheimer. Res.
3
,
515
520
[PubMed]
23
Tyrrell
D.J.
,
Bharadwaj
M.S.
,
Jorgensen
M.J.
,
Register
T.C.
and
Molina
A.J.
(
2016
)
Blood cell respirometry is associated with skeletal and cardiac muscle bioenergetics: Implications for a minimally invasive biomarker of mitochondrial health
.
Redox. Biol.
10
,
65
77
[PubMed]
24
Tyrrell
D.J.
,
Bharadwaj
M.S.
,
Jorgensen
M.J.
,
Register
T.C.
,
Shively
C.
,
Andrews
R.N.
et al.  (
2017
)
Blood-based bioenergetic profiling reflects differences in brain bioenergetics and metabolism
.
Oxid. Med. Cell Longev.
2017
,
7317251
[PubMed]
25
Marshall
M.
(
2005
)
Diabetes in African Americans
.
Postgrad. Med. J.
,
734
740
26
Noble
J.M.
,
Manly
J.J.
,
Schupf
N.
,
Tang
M.X.
and
Luchsinger
J.A.
(
2012
)
Type 2 diabetes and ethnic disparities in cognitive impairment
.
Ethn. Dis.
22
,
38
44
[PubMed]
27
Mayer-Davis
E.J.
,
Beyer
J.
,
Bell
R.A.
,
Dabelea
D.
,
D'Agostino
R.
Jr
,
Imperatore
G.
et al.  (
2009
)
Diabetes in African American youth: prevalence, incidence, and clinical characteristics: the SEARCH for Diabetes in Youth Study
.
Diabetes Care
32
,
S112
S122
[PubMed]
28
Bowden
D.W.
,
Cox
A.J.
,
Freedman
B.I.
,
Hugenschimdt
C.E.
,
Wagenknecht
L.E.
,
Herrington
D.
et al.  (
2010
)
Review of the Diabetes Heart Study (DHS) family of studies: a comprehensively examined sample for genetic and epidemiological studies of type 2 diabetes and its complications
.
Rev. Diabet. Stud.
7
,
188
201
[PubMed]
29
Whitlow
C.T.
,
Sink
K.M.
,
Divers
J.
,
Smith
S.C.
,
Xu
J.
,
Palmer
N.D.
et al.  (
2015
)
Effects of Type 2 diabetes on brain structure and cognitive function: African American-Diabetes Heart Study MIND
.
Am. J. Neuroradiol.
36
,
1648
1653
[PubMed]
30
Ashburner
J.
and
Friston
K.J.
(
2000
)
Voxel-based morphometry–the methods
.
Neuroimage
11
,
805
821
[PubMed]
31
Maldjian
J.A.
,
Whitlow
C.T.
,
Saha
B.N.
,
Kota
G.
,
Vandergriff
C.
,
Davenport
E.M.
et al.  (
2013
)
Automated white matter total lesion volume segmentation in diabetes
.
Am. J. Neuroradiol.
34
,
2265
2270
[PubMed]
32
Whitlow
C.T.
(
2015
)
Effects of type 2 diabetes on brain structure and cognitive function: African American-Diabetes Heart Study MIND
.
Am. J. Neuroradiol.
,
1648
1653
33
Freedman
B.I.
(
2015
)
APOL1 associations with nephropathy, atherosclerosis, and all-cause mortality in African Americans with type 2 diabetes
.
Kidney Inc.
,
176
181
34
Gaser
C.
VBM: Structural Brain Mapping Group
,
35
Nasreddine
Z.S.
,
Phillips
N.A.
,
Bedirian
V.
,
Charbonneau
S.
,
Whitehead
V.
,
Collin
I.
et al. ,
The Montreal Cognitive Assessment, MoCA: a brief screening tool for mild cognitive impairment
.
J Am Geriatr Soc.
532005
,
695
699
36
Dai
D.F.
,
Chiao
Y.A.
,
Marcinek
D.J.
,
Szeto
H.H.
and
Rabinovitch
P.S.
(
2014
)
Mitochondrial oxidative stress in aging and healthspan
.
Longev. Healthspan
3
,
6
[PubMed]
37
Onyango
I.G.
,
Dennis
J.
and
Khan
S.M.
(
2016
)
Mitochondrial dysfunction in alzheimer's disease and the rationale for bioenergetics based therapies
.
Aging Dis.
7
,
201
214
[PubMed]
38
Maruszak
A.
and
Zekanowski
C.
(
2011
)
Mitochondrial dysfunction and Alzheimer's disease
.
Prog. Neuropsychopharmacol. Biol. Psychiatry
35
,
320
330
[PubMed]
39
Wu
M.
,
Neilson
A.
,
Swift
A.L.
,
Moran
R.
,
Tamagnine
J.
,
Parslow
D.
et al.  (
2007
)
Multiparameter metabolic analysis reveals a close link between attenuated mitochondrial bioenergetic function and enhanced glycolysis dependency in human tumor cells
.
Am. J. Physiol. Cell Physiol.
292
,
C125
C136
[PubMed]
40
Pandya
J.
(
2016
)
Age- and brain region-specific differences in mitochondrial bioenergetics in Brown Norway rats
.
Neurobiol. Aging
,
25
34
[PubMed]
41
Chen
X.
(
2006
)
Mitochondrial dysfunction and Alzheimer’s disease
.
Curr Alzheimer Res.
,
515
520
42
Jha
S.K.
(
2017
)
Linking mitochondrial dysfunction, metabolic syndrome and stress signaling in neurodegeneration
.
Biochim. Biophys. Acta
,
1132
1146
43
Moreira
P.I.
(
2010
)
Mitochondrial dysfunction is a trigger of Alzheimer’s disease pathophysiology
.
Biochim. Biophys. Acta
,
2
10
44
Mounsey
R.B.
(
2011
)
Mitochondrial dysfunction in Parkinson’s disease: pathogenesis and neuroprotection
.
Parkinson’s Disease
,
45
Tyrrell
D.J.
(
2016
)
Blood cell respirometry is associated with skeletal and cardiac muscle bioenergetics: Implications for a minimally invasive biomarker of mitochondrial health
.
Redox Biol.
,
65
77
[PubMed]
46
Tyrrell
D.J.
(
2015
)
Blood-cell bioenergetics are associated with physical function and inflammation in overweight/obese older adults
.
Exp. Gerontol.
,
84
91
[PubMed]
47
Tyrrell
D.J.
(
2017
)
Blood-based bioenergetic profiling reflects differences in brain bioenergetics and metabolism
.
Oxid. Med. Cell. Longev.
,
48
Kramer
P.A.
(
2014
)
A review of the mitochondrial and glycolytic metabolism in human platelets and leukocytes: Implications for their use as bioenergetic biomarkers
.
Redox Biol.
,
206
210
49
Kramer
P.
(
2015
)
Decreased Bioenergetic Health Index in monocytes isolated from the pericardial fluid and blood of post-operative cardiac surgery patients
.
Biosci. Rep.
,
e00237
[PubMed]
50
Willig
A.
(
2017
)
Monocyte bioenergetic function is associated with body composition in virologically suppressed HIV-infected women
.
Redox Biol.
,
648
656
[PubMed]
51
Sjövall
F.
(
2013
)
Patients with sepsis exhibit increased mitochondrial respiratory capacity in peripheral blood immune cells
.
Critical Care
,
R152
52
Ehinger
J.K.
(
2015
)
Mitochondrial dysfunction in blood cells from amyotrophic lateral sclerosis patients
.
J. Neurol.
,
1493
1503
[PubMed]
53
Malone
I.B.
(
2015
)
Accurate automatic estimation of total intracranial volume: A nuisance variable with less nuisance
.
Neuroimage
,
366
372
54
Weinstein
G.
(
2015
)
Glucose indices are associated with cognitive and structural brain measures in young adults
.
Neurology
,
2329
2337
[PubMed]
55
Barness
L.L.
and
Bennett
D.A.
(
2014
)
Alzheimer's disease in African Americans: risk factors and challenges for the future
.
Health Aff. (Millwood)
,
580
586
,
56
Miró
Ò
(
2003
)
Mitochondrial DNA depletion and respiratory chain enzyme deficiencies are present in peripheral blood mononuclear cells of HIV-infected patients with HAART-related lipodystrophy
.
Antivir. Ther.
,
333
338
57
Kramer
P.A.
(
2014
)
A review of the mitochondrial and glycolytic metabolism in human platelets and leukocytes: Implications for their use as bioenergetic biomarkers
.
Redox Biol.
,
206
210
58
Aburawi
E.H.
(
2012
)
Lymphocyte respiration in children with Trisomy 21
.
BMC Pediatr.
,
193
[PubMed]
59
Molina
A.J.A.
(
2017
)
Blood-based bioenergetic profiling: a readout of systemic bioenergetic capacity that is related to differences in body composition
.
Redox Biol.
,
410
420