Short-term interval training alters brain glucose metabolism in subjects with insulin resistance

Abstract

Brain insulin-stimulated glucose uptake (GU) is increased in obese and insulin resistant subjects but normalizes after weight loss along with improved whole-body insulin sensitivity. Our aim was to study whether short-term exercise training (moderate intensity continuous training (MICT) or sprint interval training (SIT)) alters substrates for brain energy metabolism in insulin resistance. Sedentary subjects (n = 21, BMI 23.7–34.3 kg/m², age 43–55 y) with insulin resistance were randomized into MICT (n = 11, intensity≥60% of VO_2peak) or SIT (n = 10, all-out) groups for a two-week training intervention. Brain GU during insulin stimulation and fasting brain free fatty acid uptake (FAU) was measured using PET. At baseline, brain GU was positively associated with the fasting insulin level and negatively with the whole-body insulin sensitivity. The whole-body insulin sensitivity improved with both training modes (20%, p = 0.007), while only SIT led to an increase in aerobic capacity (5%, p = 0.03). SIT also reduced insulin-stimulated brain GU both in global cortical grey matter uptake (12%, p = 0.03) and in specific regions (p < 0.05, all areas except the occipital cortex), whereas no changes were observed after MICT. Brain FAU remained unchanged after the training in both groups. These findings show that short-term SIT effectively decreases insulin-stimulated brain GU in sedentary subjects with insulin resistance.

Introduction

Although the brain represents only a small proportion of the entire body mass, the brain’s energy consumption is much higher than the energy consumption of other organs at rest.¹ The brain has an extremely limited capacity to store energy in the form of ATP and glycogen and therefore maintaining an uninterrupted supply of ATP is pivotal for the survival of brain tissue and thus the entire organism.¹ Glucose has long been considered to be the sole substrate for energy production in the brain, and ATP arising from aerobic metabolism of glucose is essential for maintaining ion gradients across the plasma membrane of neurons, as well as subserving general metabolism. During strenuous exercise or prolonged starvation, glucose can be supplemented, for example with lactate or ketone bodies,^2,3 and it is now appreciated that fatty acids (FAs) can contribute significantly to brain energy metabolism in the developing brain.^4,5

Disturbed glucose sensing in the central nervous system (CNS), insulin signaling, and cerebral hypoperfusion has been linked to the pathophysiology of obesity and type 2 diabetes.^6–9 Compared to healthy subjects, subjects with insulin resistance have increased insulin-stimulated brain glucose uptake (GU), while insulin-stimulated GU is decreased in peripheral tissues (e.g. skeletal muscle),^10–13 thus brain GU in response to insulin challenge behaves in a contrary manner to GU uptake in peripheral tissues such as skeletal muscle of individuals with insulin resistance or type 2 diabetes. The molecular mechanisms of the increased brain GU are currently unclear and need to be studied in detail. However, increased metabolic sensitivity of the brain to insulin could be a sign of brain insulin resistance, which could be caused by a disturbance in the insulin transported through the blood–brain barrier (BBB) or a weakened neuronal responses to insulin.^14,15 It is well known that weight loss with or without concomitant exercise training improves skeletal muscle and whole-body insulin sensitivity.^16,17 In our recent study with morbidly obese subjects, we further showed that bariatric surgery, which caused marked weight loss and improved whole-body insulin sensitivity, led to decreased insulin-stimulated brain GU.¹³ Furthermore, free fatty acid uptake (FAU) in the brain of subjects with metabolic syndrome is similarly increased as in peripheral tissues and decreases substantially after rapid weight loss.¹⁸

Exercise training lessens insulin resistance and especially increases skeletal muscle insulin sensitivity; however, there have only been a few studies on the effects of exercise on brain glucose metabolism in humans. The possible positive effects of exercise training on the brain may be of particular interest given the increasing awareness of the increased risk for neurodegenerative disease among patients with diabetes. It is known that increasing acute exercise intensity decreases brain GU, most probably because of the increased use of lactate as an energy source.¹⁹ We and others have shown that two weeks of extremely demanding sprint interval training (SIT), consisting only of 15 min of total working time, can already improve aerobic capacity^21,21 and peripheral insulin sensitivity;^21–23 this improvement is similar or superior to moderate intensity continuous training (MICT) in healthy men and in subjects with insulin resistance. However, the effects of exercise training and different training intensities on brain metabolism are unclear.

This paper presents a study of the effects of two weeks SIT and MICT on brain GU and FAU using positron emission tomography (PET) in sedentary middle-aged men and women with insulin resistance. Based on the result from the previous studies,^13,18,21 our hypothesis was that SIT would decrease brain GU and FAU more than MICT due to its superior effects on whole-body insulin sensitivity in the short-term.

Materials and methods

Study subjects

The study was a part of a larger randomized controlled clinical HITPET trial comparing the effects of short-term SIT on tissue specific glucose and fat metabolism (https://clinicaltrials.gov/ct2/show/NCT01344928). The study was approved by the ethical committee of the Hospital District of Southwest Finland (Turku, Finland, decision 95/180/2010 §228) and was carried out according to the Declaration of Helsinki. All the participants gave their written informed consent. The study was performed at Turku PET Centre, University of Turku and Turku University Hospital (Turku, Finland) and the Paavo Nurmi Centre (Turku, Finland) between February 2013 and October 2015 and all the subjects were from the Southwest region of Finland.

In the current study, 21 of the 26 sedentary, non-smoking, middle-aged subjects were included. These subjects had been brain scanned and had pre-diabetes (impaired glucose tolerance/impaired fasting glucose, n = 8, females n = 5) or type 2 diabetes (n = 13, average duration 4.4 years, females n = 4). The inclusion criteria were as follows: an age of 40–55years, a BMI of 18.5–35 kg/m2, a VO_2peak < 40 ml/kg/min and no insulin treatment. The exclusion criteria were as follows: the use of insulin treatment in the case of T2DM, other chronic diseases or defects which might hinder daily life, smoking or the use of narcotics, a history of anorexia nervosa or bulimia, a history of asthma, current, regular, and systematic exercise training or a history of such training, any other condition that in the opinion of the investigator could create a hazard to the participant’s safety, endanger the study procedures, or interfere with the interpretation of the study results. The screening and physical examination of the study subjects were performed by the physicians JJE and KAV.

Subjects were randomized into two different training modes, either SIT or MICT, with a ratio of 1:1 in blocks of four subjects. Given the nature of the interventions, no blinding was used. Nine of the subjects in the SIT group and four in the MICT group were treated by oral hypoglycemic agents (metformin/sitagliptin/glimepiride) (Table 1). The subjects were instructed not to alter their eating habits or daily activities during the intervention. During the intervention, one participant left the study due to migraine and four for personal reasons. Due to technical problems with the PET-scanner, the radiotracer production, and the drop-outs, the brain scans were performed before the intervention for 21 subjects and after the intervention for 15 subjects. (Figure 1)

Table 1.

Basic characteristics of the subjects at before and after training intervention and comparison of training response between SIT and MICT groups (time × training).

	SIT		MICT		Results p-values
	Pre n = 10	Post n = 6	Pre n = 11	Post n = 9	Baseline	Time	Time × Training
Age (y)	50 (46–54)		48 (44–52)		–	–	–
Gender (M/F)	6/4	3/3	5/6	4/5	–	–	–
Diabetes (pre-T2D/T2D)	2/8	1/5	6/5	5/4	–	–	–
Diabetes medication (yes/no)	7/3	5/1	3/11	2/7	–	–
VO2peak (ml/kg/min)	26.6 (24.2–29.0)	27.8 (25.4–30.3)	26.7 (24.4–28.9)	26.7 (24.3–29.0)	0.97	0.13	0.13
Body mass (kg)	88.3 (80.3–97.1)	87.6 (79.6–96.3)	89.9 (82.2–98.3)	89.0 (81.4–97.3)	0.81	0.032	0.69
BMI (kg/m2)	29.9 (27.9–32.0)	29.8 (27.7–31.8)	30.9 (29.0–32.8)	30.6 (28.7–32.5)	0.46	0.025	0.55
Whole body fat (%)	33.9 (30.8–37.2)	33.1 (30.0–36.3)	34.2 (31.3–37.3)	33.1 (30.2–36.2)	0.87	0.007	0.75
M-value (mmol/mL/kg)	21.6 (14.5–28.8)	25.2 (17.9–32.4)	15.8 (9.1–22.4)	19.6 (12.4–26.4)	0.23	0.007	0.89
HbA1c (%)	5.7 (5.5–6.0)	5.6 (5.4–5.9)	5.7 (5.5–6.0)	5.5 (5.3–5.8)	0.99	0.010	0.53
HbA1c (mmol/mol)	39 (37–42)	38 (35–41)	39 (37–42)	37 (34–40)	0.99	0.010	0.53
fP Glucose (mmol/L)	7.0 (6.3–7.6)	6.9 (6.3–7.6)	6.6 (6.0–7.1)	6.3 (5.7–6.9)	0.36	0.26	0.33
P-Glucose (mmol/L)(clamp)	4.8 (4.6–5.0)	4.9 (4.7–5.0)	5.0 (4.8–5.1)	5.0 (4.8–5.2)	0.18	0.63	0.88
fS Insulin (mmol/L)a	10.6 (7.2–15.6)	9.4 (6.4–14.0)	10.7 (7.5–15.4)	10.4 (7.2–15.1)	0.96	0.34	0.54
S-Insulin (mmol/L)(clamp)	88.1 (80.0–96.3)	89.5 (80.6–98.5)	85.3 (77.7–92.9)	83.6 (75.4–91.9)	0.62	0.97	0.62
fS FFA (mmol/L)	0.82 (0.69–0.95)	0.79 (0.66–0.93)	0.84 (0.73–0.95)	0.80 (0.68–0.92)	0.77	0.31	0.74
Lactate 48h (mmol/l)a	1.6 (1.3–2.0)	1.6 (1.3–2.0)	1.4 (1.1–1.7)	1.2 (0.9–1.5)	0.41	0.30	0.32
Lactate 72h (mmol/L)a	1.1 (0.9–1.2)	1.1 (1.0–1.3)	1.1 (0.9–1.2)	1.0 (0.9–1.2)	0.98	0.66	0.48
fS Acetone 48h (mmol/L)a	0.043 (0.038–0.048)	0.039 (0.034–0.044)	0.038 (0.034–0.043)	0.039 (0.034–0.044)	0.95	0.28	0.18
fS Acetoacetate 48h (mmol/L)a	0.038 (0.027–0.054)	0.040 (0.029–0.057)	0.036 (0.026–0.049)	0.036 (0.025–0.051)	0.85	0.69	0.79
fS D-β-Hydroxybutyrate 48h (mmol/L)a	0.24 (0.15–0.35)	0.26 (0.17–0.40)	0.23 (0.15–0.48)	0.23 (0.15–0.34)	0.90	0.52	0.50

Note: The P value for baseline describes the difference between SIT and MICT groups before training, and ‘time’ describes training effect in the whole group (n = 21). ‘Training × time’ compares all SIT trained (n = 10) to all MICT trained (n = 11). All the data are presented as model based means (95% confidence interval, CI). The values are LSmeans transformed into original unit.

SIT: sprint interval training; MICT: moderate intensity continuous training; T2D: type 2 diabetes; fS: fasting serum value; S: serum value; fP: fasting plasma value; P: plasma value; FFA: free fatty acid; HbA1c; glycated hemoglobin.

^aVariables with logarithmic transformation to achieve the normal distribution. Boldfaced values are significantly different after intervention, P<0.05.

Figure 1.

Consort flow of the study subjects.

Study design

Initial screening included a physical examination, a bioimpedance measurement, an oral glucose tolerance test (OGTT), and a cycling VO_2peak test to assess the participant’s health, glycemic status, body composition and aerobic capacity (day 1, Figure 2). The participants then underwent two PET imaging sessions on two different days. On the first study day, the fasting brain FAU was studied and on the second day the brain GU was studied during an euglycemic hyperinsulinemic clamp using PET and radiotracers 14(R,S)-[¹⁸F]fluoro-6-thia-heptadecanoic acid ([¹⁸F]FTHA) and FDG,2-[¹⁸F]fluoro-2-deoxy-D-glucose ([¹⁸F]FDG), respectively, at resting conditions (Figure 2). All PET studies were conducted after an overnight fast. Subjects were instructed to refrain from caffeine-containing nutrients and strenuous physical activity for 12 and 24 h prior to the studies and anti-diabetic medication for 48 h.

Figure 2.

The study design. VO_2peak: aerobic capacity; PET: positron emission tomography; FDG: 2-[¹⁸F]fluoro-2-deoxy-D-glucose; FTHA: 14(R,S)-[¹⁸F]fluoro-6-thia-heptadecanoic acid; FFA: free fatty acid; SIT: high-intensity interval training; MICT: moderate-intensity continuous training.

In both training modes, the training intervention took two weeks and included six supervised training sessions. After the two-week training intervention, either SIT or MICT, all the studies were repeated after the last exercise session starting with an [¹⁸F]-FTHA PET after 48 h, an [¹⁸F]-FDG PET during an euglycemic hyperinsulinemic clamp after 72 h, and finally after 96 h OGTT and VO_2peak tests were conducted.

Exercise intervention

The SIT sessions consisted of 4–6 × 30 s supra-maximal all-out cycling bouts (Wingate protocol) (Monark Ergomedic 828E, MONARK, Vansbro, Sweden) with 4 min recovery time between each. The number of bouts increased progressively starting with four bouts and increasing by one every other session up to six bouts. The total duration of training was only 15 min including all six training sessions. The training load was individually determined (10 % of lean body mass in kg). The SIT protocol was based on a previous study by Burgomaster et al.²⁰ All the participants were familiarized with the SIT training protocol (2 × 30 s bouts) before they were randomized into the training groups. The MICT sessions consisted of 40–60 min of cycling at moderate intensity, which was ≥60 % of the maximal output calculated from the individual VO_2peak (Tunturi E85, Tunturi Fitness, Almere, Netherlands). The duration of MICT-training increased progressively starting with 40 min and increasing by 10 min every other session up to 60 min. The total duration of training was 300 min including all six training sessions.

PET imaging and analysis

PET imaging was conducted using a Ge Discovery VCT PET/CT scanner (General Electric Medical Systems, Milwaukee, WI, USA) as previously described.²³ PET scanning of the brain was started ∼80 min after the [¹⁸F]FDG /[¹⁸F]FTHA injection and during the [¹⁸F]FDG PET study ∼170 min after the start of the euglycemic clamp. Eskelinen et al.²¹ have previously described in detail the technical aspects of the PET imaging procedures. The euglycemic hyperinsulinemic clamp was performed based on the original protocol by DeFronzo et al.²⁴ The insulin-stimulated whole-body GU (M-value) was calculated from the glucose infusion rate and the measured glucose values collected during the PET scan. Arterialized venous plasma glucose was determined in duplicate by the glucose oxidase method and the mean of these values was taken to represent the plasma glucose. (Analox GM9 Analyzer; Analox Instruments LTD, London, United Kingdom).

The production of radiotracers [¹⁸F]FDG and [¹⁸F]FTHA has been previously described.^21,25 [¹⁸F]FTHA is a palmitate analogue for fatty acid metabolism. [¹⁸F]FTHA is injected into the circulation from the point where it crosses the BBB and enters the brain.²⁶ In the brain, it either subsequently infiltrates the mitochondria or is incorporated into complex lipids,²⁵ mostly triglycerides. In mitochondria, [¹⁸F]FTHA undergoes the initial steps of β-oxidation and is thereafter trapped as further β-oxidation is blocked by its sulfur heteroatom.²⁵ The half-life of [¹⁸F]FTHA is 109 min.

All PET imaging data were preprocessed using SPM8 (Wellcome institute, London, UK) and manual volumes-of-interest were delineated using Carimas (version 2.9, Turku PET Centre). Fractional uptake rates (FUR) were calculated regionally relative to the concentration of the tracer in the plasma, and subsequently the metabolic rate of the glucose, or GU, was calculated relative to individual glucose concentration in the blood.

For the calculation of FAU, an [¹⁸F]FTHA metabolite correction was performed for the radioactivity curves.²¹ Plasma and tissue time-radioactivity curves were analyzed graphically by linear graphical analysis of brain uptake relative to the metabolite-corrected arterial input function.²⁷ To obtain brain GU/FAU, the fractional uptake rate values were multiplied by the serum glucose/FFA concentration during the [¹⁸F]FDG/[¹⁸F]FTHA PET scanning and corrected for brain density (1.04 g/mL). A Lumped constant 0.65 was used for the brain GU.²⁸

All imaging data were preprocessed using SPM8 (Wellcome institute, London, UK). Firstly, all DICOM data were converted into Nifti-format using SPM-DICOMimport. Secondly, within each PET session, the frame-to-frame misalignments were compensated for by using a mutual information-(MI) based rigid registration. A visual inspection was deemed adequate for the alignment of the PET and CT data, and thus no re-alignment was performed. Thirdly, the CT image was aligned with a CT-template in MNI coordinates using rigid registration, and the mapping was subsequently written to PET data as well. CT-based normalization (non-rigid registration) was conducted with a Clinical Toolbox²⁹ and the PET imaging data were subsequently warped into the MNI space using the result deformation. The quality of the non-rigid registration was visually inspected and a small number of failures were detected. Final normalization of the PET data was obtained using the ligand-specific template as the target and individual PET image as the source in SPM-Normalize. Both the target and source images were smoothed with a 3D Gaussian filter using an 8 mm kernel (FWHM).

Volume-of-interest (VOI) analysis was conducted using common manually delineated VOIs in MNI space. The VOIs were delineated on an MRI-template using Carimas (version 2.9, Turku PET Centre). Similar to Kemppainen et al.,¹⁹ the VOIs were placed on the anterior cingulate cortex (ACC), the medial frontal cortex (MFC), the dorsal superior frontal gyrus (SFC), the temporal cortex (TC), the occipital cortex (OC), the thalamus (THA), the cerebellum (CER), and pons. Whole brain uptake was measured using VOI covering the frontal, temporal, occipital, and parietal lobes. The brain networks were visualized with the BrainNet Viewer (http://www.nitrc.org/projects/bnv/).³⁰

Other measurements

The VO_2peak was determined via a maximal bicycle ergometer test.²¹ The test was performed at the Paavo Nurmi Center (Turku, Finland) about one week before the first training session and 96 h after the last training session. D-β-hydroxybutyrate was quantified from serum using high-throughput proton NMR metabolomics (Brainshake Ltd, Helsinki, Finland). Details of the experimentation and applications of the NMR metabolomics platform have been described previously.³¹ Blood lactate concentration was measured from capillary samples before each training session and within 1 min after each session using a handheld lactate analyzer (Lactate Pro, Arkray KDK, Kyoto, Japan); it was also measured in fasting conditions from blood samples during the [¹⁸F]FDG and [¹⁸F]FTHA days. Fat percentage was determined using the bioimpedance method (InBody 720, Mega Electronics Ltd, Kuopio, Finland).

Statistical analysis

Statistical analyses were performed using SAS (version 9.3 for Windows, SAS institute Inc., Cary, NC, USA). The normal distribution of the variables was tested with the Shapiro–Wilk test. Logarithmic transformations were performed for the variables fasting insulin, lactate, acetone, acetoacetate and D-β-hydroxybutyrate in order to achieve normal distribution. The baseline characteristics of the training groups were compared by a one-way ANOVA, which included the main effect of the training mode (SIT and MICT). The mean value changes between pre and post measurements were analyzed using a hierarchical linear mixed model. In the model, the training mode and time effects were included as well as all interactions. In addition, linear contrasts were programmed within the model to estimate the overall mean value change within group (Table 1). Missing data points were accounted for by restricted maximum likelihood estimation within the linear mixed models. Hence, we report model-based mean (SAS least square means) values (95% CI) from all the parameters measured before and after the training. Correlation analyses were carried out using Pearson’s Correlation. A p-value of less than 0.05 was considered statistically significant. The sample size was calculated for the whole study (NCT01344928) based on its primary outcome and has been described previously.²³

Results

Aerobic capacity improved 5% with SIT (time p = 0.03) but stayed unchanged after MICT (Table 1). Overall, in the whole study population, training increased the whole-body insulin sensitivity (M-value) by 20 % (time p = 0.007) and decreased slightly, but significantly, the whole-body adiposity and the HbA1c; however, no differences were observed between the training modes (Table 1).

We measured insulin-stimulated brain GU both globally in the gray matter and when divided into discrete brain regions. SIT decreased the insulin-stimulated brain GU globally by 14% (p = 0.03) and in all regions of the cortex except the occipital cortex, whereas no changes were observed after MICT (Figures 3(a) and 4). In the whole study group, at baseline, the insulin-stimulated brain GU correlated inversely with the whole-body insulin sensitivity (M-value; r = −0.68, p = 0.001, Figure 5(a)), and positively with the serum insulin concentration in a fasted state (r = 0.46, p = 0.04) and during the clamp procedure (r = 0.47, p = 0.04), but did not reach a significant level for D-β-hydroxybutyrate (r = −0.40, p = 0.07, Figure 5(b)).

Figure 3.

Brain glucose uptake (a and b) and brain FFA uptake (c and d) globally and in different areas of the brain in SIT (a and c) and MICT (b and d) training group before and after the training intervention. GLO: global; CER: cerebellum; SFC: superior frontal gyrus; MFC: medial frontal gyrus; TC: temporal cortex; THA: thalamus; AC: cingulate gyrus; OC: occipital cortex. Values are model-based means (95% confidence interval). *P < 0.05 for the time effect within the training mode (pre vs. post comparison).

Figure 4.

Pre vs. post change in brain glucose uptake in different areas of the brain in sprint-interval training (SIT) and moderate intensity continuous training (MICT) groups. P < 0.01 voxel level uncorrected, which is equal to T = 3.365 in SIT and T = 2.896 in MICT. The bar represents T values.

Figure 5.

Correlations between insulin-stimulated brain glucose uptake (GU) and whole-body insulin sensitivity (M-value) (a), brain GU and 3-hydroxybutyrate (b), brain free fatty acid uptake (FAU) and VO_2peak (c) and brain FAU and whole-body fat content (d) before the intervention in the whole study group (n = 21).

We found no change in the brain FAU in either group after training (Figure 3(b)). Before the training, the brain FAU correlated positively with the whole-body fat percent (r = 0.59, p = 0.04, Figure 5(d)) and inversely with aerobic capacity (r = −0.47, p = 0.04, Figure 5(c)). In addition, brain FAU correlated with LDL (r = 0.51, p = 0.03), total cholesterol (r = 0.56, p = 0.02), and plasma glucose during the FTHA scan (r = −0.57, p = 0.01) and tended to correlate with the serum insulin during the FTHA scan (r = −0.44, p = 0.06) (data not shown).

Discussion

These results show that insulin-stimulated brain GU correlates negatively with whole-body insulin sensitivity. However, although both SIT and MICT enhanced whole-body insulin sensitivity, only SIT decreased the insulin-stimulated brain GU in the sedentary, middle-aged subjects with impaired glucose tolerance.

The human brain is an insulin-sensitive organ, as has been shown using the [¹⁸F]FDG-PET method with and without insulin-stimulation.^3,32 In the study by Hirvonen et al.,³³ conducted in our laboratory, it was shown that brain GU is increased in insulin resistant subjects but not in healthy controls during hyperinsulinemia. This suggests that whereas the effect of insulin on brain GU in healthy subjects is already maximal in a fasting state, a higher dose of insulin is needed to increase brain GU in insulin resistant subjects.³³ Insulin-stimulated brain GU is also increased in morbidly obese compared to healthy subjects, and it decreases after bariatric surgery induced-weight loss and improvement in whole-body insulin sensitivity.¹³ The exact mechanism explaining the increased brain insulin sensitivity after the weight loss is unclear, but may be related to the decreased ratio of insulin between cerebrospinal fluid and plasma.³⁴ This decreased ratio may be caused by (1) impaired insulin transport through the BBB, (2) declined neuronal response to insulin, or (3) increased insulin clearance due to an adaptation to chronic hyperinsulinemia in insulin resistance.^13,14

In the present study, we show that brain GU correlates positively with serum insulin in a fasting state and during hyperinsulinemia, but strongly negatively with whole-body insulin sensitivity (Figures 5(a) and 6(a)). Furthermore, we show that there is a reduction in insulin-stimulated brain GU in insulin resistant subjects after SIT but not after MICT. Training improved whole-body insulin sensitivity in both groups, but aerobic capacity improved only after SIT. We found no correlation between the improvement in brain GU and the improvement in whole-body insulin sensitivity or aerobic capacity.

Figure 6.

Correlations between insulin-stimulated brain glucose uptake (GU) and whole-body insulin sensitivity (M-value) (a) and brain GU with 3-hydroxybutyrate (b) after the intervention.

When available, the brain prefers lactate over glucose as an energy source and during exercise, the brain takes up lactate in proportion to the arterial concentration. Thus, the decreased brain GU during acute exercise with increasing intensity has been linked to an increased uptake of lactate in the brain.^19,35–37 In the present study, the SIT consisted of extremely high intensity interval cycling, which led to markedly higher lactate concentrations after the training sessions compared to MICT sessions (SIT 14.4 ± 0.9 vs. MICT 3.8 ± 1.4 mmol/l). However, we did not find correlations between blood lactate levels measured after the training sessions or at rest on PET study days and the decrease in brain GU. On the other hand, the concentration of the ketone body D-β-hydroxybutyrate (DBHB) correlated negatively with the brain GU after the training in the SIT group (Figure 6(b)). DBHB is an energy metabolite of which level increases in the liver when glucose levels are reduced, for example, after caloric restriction, fasting, or prolonged exercise.³⁸ DBHB is believed to serve as a signaling molecule in response to metabolic changes and as an energy source. In the brain, the levels of ketone bodies can reach levels as high as 1–5 mmol/L.³⁹ Thus, it might be that the decrease in GU after SIT is partly explained by the increased utilization of other energy substrates, such as DBHB.

Interestingly, at baseline, the brain FAU correlated inversely with aerobic capacity and fasting plasma glucose but positively with whole body fat percent and total and LDL cholesterols; thus, linking the high brain FAU to known metabolic risk factors. However, we did not find any changes in brain FAU that would suggest that training induces alterations more rapidly in brain glucose than lipid metabolism.

Limitations of the study

The study subjects included both pre-diabetic and type 2 diabetic subjects. Omitting subjects with pre-diabetes (IFG, IGT) from the analysis or running the analysis using grouping according to diabetes status (pre-diabetes/type 2 diabetes) did not alter the results. Medication was used as a covariate in the SAS analysis; however, it did not explain the difference between the pre-intervention and post-intervention decrease in GU. To complete the whole study, subjects had to participate in four extensive scanning days, which led to a relatively high drop-out rate. In addition, healthy controls were not studied for a comparison.

Conclusions

This study provides the first evidence that short-term exercise training alters brain glucose metabolism in subjects with impaired glucose tolerance. Our results suggest that in addition to the well-known beneficial effects of exercise training on whole-body insulin sensitivity, SIT decreases insulin-stimulated brain GU in subjects with insulin resistance.

Funding

The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This study was conducted in the Finnish Centre of Excellence in Cardiovascular and Metabolic Research, supported by the Academy of Finland, University of Turku, Turku University Hospital and Åbo Akademi University. This study was financially supported by the European Foundation for the Study of Diabetes; the Hospital District of Southwest Finland; the Orion Research Foundation; the Finnish Diabetes Foundation; the Emil Aaltonen Foundation; the Academy of Finland (grants 251399, 251572, 256470, 281440 and 283319); the Ministry of Education of the State of Finland; the Paavo Nurmi Foundation; the Novo Nordisk Foundation; the Paulo Foundation; the Finnish Medical Foundation; the Turku University Foundation and the Finnish Cultural Foundation.

Declaration of conflicting interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Authors’ contributions

SMH analyzed and interpreted the data and wrote the manuscript. JJ and KKM analyzed the data and edited the manuscript. JE and KV collected the data and edited the manuscript. EL contributed to statistical analysis and edited the manuscript. PN and JK interpreted and edited the manuscript. KK planned the experiments and edited the manuscript. JCH planned the experiments, interpreted the data and wrote the manuscript. All authors approved the version to be published. JCH is the guarantor of this work and, as such, had full access to all the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis.