Type 2 diabetes mellitus (T2DM), the most significant public health concern and prevalent form of diabetes in adults, is characterized by a metabolic disorder with sustained hyperglycemia and a chronic endocrine abnormality (World Health Organization, 2020). In Thailand, a recent study found that cognitive impairments were present in about half of older adults with T2DM. The authors emphasized the absolute necessity for including cognitive evaluation in the established medical evaluation practice for T2DM, with particular attention on those with advanced age, limited education, or not independently managing their medications (Panyawattanakit et al., 2022). Another recent study demonstrated a relationship between high systemic blood pressure (systolic and/or diastolic) and cognitive dysfunction, which was also true concerning elevated pulse pressure values (Mizuhara et al., 2022). Participants with diabetes showed poorer performance in episodic and working memories and executive function compared with healthy individuals (Palta et al., 2014). In contrast, current literature indicated that memory was nearly unaffected or preserved in individuals with diabetes (van Harten et al., 2007). Prediabetes was associated with significantly poorer working memory, as measured by the digit span test, compared with those with normal glucose tolerance. Higher 2-hr postload glucose levels were also associated with worse working memory (Ennis et al., 2020). The digit span forward (DSF) and backward tests are widely used to assess working memory, while the Trail Making Test Part B is used for the evaluation of executive function in diabetes studies (Palta et al., 2014). The present study primarily focuses on working memory and executive function using commonly utilized and easy-to-administer tools, such as the digit span test and the trail making test.
Physical activity is one of the fundamental aspects in the management programs that are recommended for T2DM patients. Both aerobic and resistance exercises elicit benefits through immediate and long-term improvements of glucose homeostasis (Kirwan et al., 2017). Normally, exercise may improve cognitive performance by mitigating the neurodegenerative processes, promoting neurotrophic factors, or reducing vascular risk factors, including inflammatory markers (Ahlskog et al., 2011). Current literature findings suggest that exercise training interventions over at least 8 weeks without incorporating any treatment may induce improvement of global cognitive function in older adults with T2DM (Cai et al., 2022). Moreover, combined exercise training (strength and aerobic exercise) for 8 weeks also elicited improved cognitive performance (inhibitory, working memory, and cognitive flexibility) in middle-aged and older adults with T2DM (Silveira-Rodrigues et al., 2021). Generally, 8-week exercise training programs are commonly used to enhance cognitive function and neuroplasticity across different age groups. For instance, an 8-week exercise intervention was found to enhance neural efficiency in both sedentary adults and patients suffering from major depressive disorder, as evidenced by changes in hippocampal function assessed through functional magnetic resonance imaging (Gourgouvelis et al., 2017). In addition, an 8-week Tai Chi Chuan exercise training program found more beneficial effects on brain plasticity compared with general exercise and is associated with increased gray matter volume and enhanced functional connectivity in the brain (Cui et al., 2019). A previous study observed that 8 weeks of qigong training increased aerobic exercise capacity and tended to enhance working memory in sedentary young females, together with a trend of decreasing neutrophils. The authors speculated that reduced neutrophils adhering to cortical capillaries might promote improvement in cognitive function (Klarod et al., 2023). Thus, we expected that qigong will enhance cognitive performance, and would be associated with meaningful changes in neurotrophic factors, and blood biomarkers.
Reduced levels of brain-derived neurotrophic factor (BDNF) were associated with learning and cognitive impairment, depression, and advanced neurodegeneration status (Nagahara & Tuszynski, 2011). Current literature suggests that BDNF, as a metabotrophin, plays a crucial role in regulating metabolism, particularly concerning energy balance and insulin resistance (Gomez-Pinilla et al., 2008). Maintaining energy homeostasis is essential for the survival of both individual cells and the entire organism. Recent evidence demonstrates that energy metabolism may play an important role in influencing higher order cognitive functions (Gomez-Pinilla et al., 2008). However, the clinical relevance of BDNF levels is still uncertain, especially in individuals with T2DM. Previous study indicated that levels of serum and plasma BDNF (pBDNF)are decreased in T2DM when compared with nondiabetic people, questioning the association between BDNF levels and cognitive function in individuals with T2DM (Fujinami et al., 2008). On the other hand, Boyuk et al. (2014) reported increased serum BDNF levels in T2DM compared with healthy controls. These authors suggested that BDNF plays a role in regulating glucose, and lipid metabolism, as well as inflammation. Additionally, BDNF levels were found to be positively related to fat mass, as well as energy metabolism, and, thus associated with cardiometabolic risk factors (Suwa et al., 2006). It has been theorized that the level of BDNF in T2DM might be increased for a compensatory mechanism to boost the neuroprotective system in response to chronic hyperglycemia and other cardiometabolic risk factors (Arentoft et al., 2009; Suwa et al., 2006). In T2DM individuals of both sexes, exercise training did not result in BDNF level changes (Jamali et al., 2020). Gender may additionally impact BDNF levels, with women showing higher pBDNF than men. These differences may be disturbed by the individual body mass (Arentoft et al., 2009). This is consistent with well-established gender differences in vascular health biomarkers regarding cognition, such as HDL and C-reactive protein (CRP; Lakoski et al., 2006). Thus, the meaning of the impact of exercise training on BDNF levels in T2DM remains controversial as outlined by a recent meta-analysis (Jamali et al., 2020). Due to the clinical relevance and cost-effectiveness of qigong exercise, the availability of commonly used and easy-to-administer tools for measuring cognitive functions, the rapid increase in elderly patients with Type 2 diabetes (T2DM), and gender-specific differences in pBDNF levels, this study was aimed at evaluating those effects and potential interrelationships and accompanied changes in systemic blood pressure values, antioxidant, and blood cell indices following qigong training in middle-aged and older women with T2DM. We hypothesized that a minimum of 8 weeks of qigong training in this population would result in pBDNF changes and related improvements in cognitive performances.
Materials and Methods
Design
As a result of the COVID-19 pandemic we were obliged to apply a quasi-experimental design, placebo-controlled study, with a blinded outcome assessor. We had to decrease the sample size to 40 sedentary middle-aged, and elderly women with T2DM, from the planned sample size of 50 patients. The participants were recruited from communities around Burapha University in Bangsean, Chonburi, Thailand. Criterion for inclusion were female, with a medical diagnosis of T2DM of more than 10 years, age range between 45 and 70 years, being sedentary or lacking regular physical activity, and abstaining from exercise for a minimum of 3 months. Exclusion criteria included smoking, alcohol consumption, the use of antioxidant supplements, and any condition that would impede safely performing qigong exercises. Participants were instructed to maintain their regular dietary routines. The exercise group practiced qigong under the guidance of an instructor, who ensured that participants performed the movements and breathing correctly during each exercise session. Participants in both the qigong (QG) and control (CG) groups willingly participated, with those in the QG group showing particular enthusiasm for attending every session. To ensure comparability, participants of similar age ranges were matched between groups. The Thai version of Montreal Cognitive Assessment Basic test was used to assess PRE- and POST-intervention (with copyright permission) for screening the cognitive function level of the T2DM participants (Julayanont et al., 2015). The Physical Activity Readiness Questionnaire (PAR-Q) was employed to assess potential restrictions on qigong exercise. The sedentary lifestyle was evaluated through the utilization of records of physical activity (Tremblay et al., 2017). The study was reviewed and received approval by the Burapha University Human Institutional Review Board for Protection of Human Subjects in Research (041/2563) and the Thai Clinical Trials Registry (TCTR20221003001). The baseline characteristics of the patient cohort are the same as those reported in the previous publication (Klarod et al., 2024); the current study involves different, and extended data analyses. This work was completed following the appropriate institutional review body standards, and carried out in accordance with the ethical standards outlined in the Declaration of Helsinki, 1975.
The Qigong Training Intervention
Participants were assigned to qigong exercise (QG, n = 20) or to the control groups (CG, n = 20). The qigong routine practice contained gentle exercises focusing on both movement and breathing, promoting relaxation, and mindfulness through 18 foundational gestures adjusted following Ladawan et al. (2017). Ladawan et al. (2018) suggested that the intensity of 18 postures of qigong exercise was light, approximately 49% of maximal heart rate (Ladawan et al., 2018). The practice commenced with a 2-min stretching warm-up session, followed by a 60-min execution of the 18 movements, and, concluding with a 2-min stretch cooldown. Qigong exercise instruction was guided by a skilled instructor. The training was thrice a week for a duration of 8 weeks. Participants in the CG were instructed to sustain their regular behaviors and dietary habits throughout the 8 weeks.
Measurements
Body composition were assessed for baseline characteristics such as body weight, body mass index, height, waist to hip ratio, adipose tissue mass, and muscle mass through bioelectrical impedance analysis (In Body 270, Body composition Analyzer). Fasting plasma glucose was measured by the NHealth system (ARCHITECT ci4100, integrated system, Abbott Diagnostics). Cognitive performance tests and blood samples were performed/collected before and after completing qigong exercise training. Blood samples were taken around 7 a.m. at baseline (PRE), and in the morning after completing 8 weeks of qigong exercise training (POST for QG) or 8 weeks of observation (POST for CG). Blood samples were transferred to ethylenediaminetetraacetic acid tubes for blood cell indices and kept at −80 °C for peroxidase enzyme activity (POX) and pBDNF. The samples were transferred to clot activator tubes for ferritin and high-sensitivity C-reactive protein (hs-CRP).
Measurement of Cognitive Functions
The DSF was used to evaluate short-term memory, and the digit span backward assessed working memory. The digital span test was assessed using a computerized system that delivered randomized digit lists, adjusting adaptively by both increasing and decreasing difficulty to repeated samples. The longest DSF (LDSF) or digit span backward were the highest length completely repeated before failing to recall two consecutive lists of the same length, with further details in the study of Woods et al. (2011). The Trail Making Test Part B relative to Trail Making Test Part A was measured, indicating an index of executive function (Arbuthnott & Frank, 2000).
Measurement of pBDNF
All pBDNF levels were determined by the enzyme-linked immunosorbent assay method from Sigma–Aldrich commercial kit (Catalog number RAB0026). The method was applied following the manufacturer’s instructions. The pBDNF was demonstrated as nanograms per milliliter. The measurement method for additional details is described in Fraga et al. (2021).
Measurement of Mean Arterial Pressure and Pulse Pressure
Blood pressure was measured in the morning around 7 a.m. at PRE and POST. The participants were requested to fast overnight before measurement. Systolic and diastolic blood pressure values were assessed using an automatic blood pressure device on the right arm after a minimum of 10 min of rest in a seated position. The mean arterial pressure (MAP) was calculated as: MAP = [(2 × diastolic) + systolic]/3. The pulse pressure was calculated as systolic minus diastolic blood pressure (Ladawan et al., 2017).
Measurement of POX
POX was measured by using a colorimetric assay of 3,30-diaminobenzidine tetrahydrochloride (Herzog & Fahimi, 1973). Eight hundred microliters of 3,30-diaminobenzidine tetrahydrochloride-gelatin solution and 100 ml serum were mixed in a 1 ml glass cuvette. The reaction was started by adding 100 ml of 30% hydrogen peroxide (H2O2) solution. The change absorbance at 465 nm at 25 °C was recorded at various times. Horseradish peroxidase enzyme was used as standard. The POX activities are expressed as kilo units per liter (kU/L).
Measurement of Blood Cell Indices, Ferritin, and hs-CRP
The samples were directly analyzed by the NHealth system (the National Healthcare System by Samitivej Chonburi Hospital). Complete blood counts contained hemoglobin, hematocrit, and platelet count, and red blood cells were analyzed using an automated hematology analyzer (Stromatolyser-4DS; Sysmex XS1000i Asia Pacific Pte Ltd.). Serum ferritin level was measured using the electrochemiluminescence immunoassay (ECLIA), designed for use with Elecsys and cobas e immunoassay analyzers (Cobas Elecsys 2010, Modular analytics E170 Cobas e 601, Roche Diagnostics GmbH). Acute phase protein in inflammatory reactions as hs-CRP assessment based on the principle of particle-enhanced immunological agglutination (Cobas c 311/501 analyzers, Roche Diagnostics GmbH).
Data Analysis
Data analyses were performed by using SPSS (version 17.0). The Kolmogorov–Smirnov test was applied to test normality. Normally distributed data were expressed as mean ± SD. Nonnormally distributed data are presented as median (interquartile range). For the evaluation of nonparametric data (hs-CRP and serum ferritin levels), the changes from PRE to POST within each group Wilcoxon signed-rank tests were used. Mann–Whitney U tests were executed to compare mean differences POST minus PRE between groups. The main outcomes (cognitive performance and pBDNF) and secondary outcomes (MAP, pulse pressure, POX, and blood cell indices) were analyzed using a repeated measures analysis of variance with two factors: the fixed factor being the group (QG or CG) and the repeated factor being the training time points (PRE and POST). If the main effect (training time) or the interaction (Group × Time) were significant, paired t tests were applied for within group or unpaired t test for across groups analyses. Pearson’s correlation was used to determine the relationship between cognitive performances and other parameters (pBDNF, heart rate, blood cell indices, and antioxidants). Statistical significance was set at a p < .05.
Power and sample size estimations were based on a prior investigation by Ladawan et al. (2017). This study compared executive function before and after qigong training for 8 weeks in middle-aged individuals (Ladawan et al., 2017). Fifteen subjects per group were needed to reach an effect size of 0.91 with an alpha (alpha error probability) of .05 and a power (1− beta) of 0.90 (G*power 3.1 software).
Results
A total of 19 female participants with T2DM completed the 8-week qigong exercise training program, while 16 females with T2DM served as controls. One participant had discontinued because she did not maintain the practice. Three people in CG ceased because they were anxious to capture COVID-19. One participant of CG dropped out because she could not complete the cognitive performance assessments. Baseline characteristics are shown in Table 1; there were no significant differences between the QG and the CG. The baseline levels of cognitive function by using Thai version of Montreal Cognitive Assessment Basic at PRE-intervention was normal (MoCA scores ≥ 25; Julayanont et al., 2015) in both groups (mean score = 25.8 for QG and 25.9 for CG; Table 1). The levels of cognitive performance of the participants in both groups at POST-intervention were normal without significant differences across groups (mean score ± SD = 26.1 ± 2.7 for QG and 25.7 ± 4.8 for CG, p = .78).
Baseline Characteristics of the QG and CG
| Parameters | QG (n = 19) | CG (n = 16) | p |
|---|---|---|---|
| Age (years) | 62.6 ± 4.5 | 61.6 ± 7.0 | .628 |
| Weight (kg) | 69.7 ± 11.1 | 62.9 ± 15.5 | .139 |
| Height (m) | 157.2 ± 6.1 | 155.8 ± 6.6 | .535 |
| BMI (kg/m2) | 28.2 ± 4.4 | 25.8 ± 5.8 | .166 |
| WHR | 1.0 ± 0.1 | 0.9 ± 0.1 | .372 |
| Adipose tissue mass (kg) | 27.8 ± 8.0 | 24.0 ± 11.2 | .250 |
| Muscle mass (kg) | 22.7 ± 3.2 | 20.8 ± 3.4 | .100 |
| FPG (mg/dl) | 144.7 ± 27.9 | 151.5 ± 59.0 | .657 |
| MoCA-B-Thai | 25.8 ± 2.6 | 25.9 ± 3.2 | .923 |
Note. Data are presented as mean ± SD. BMI = body mass index; WHR = waist to hip ratio; FPG = fasting plasma glucose; MoCA-B-Thai = Thai version of Montreal Cognitive Assessment Basic; QG = qigong group; CG = control group.
Cognitive Performances
There was no significant main effect of training time or interaction (Group × Training time) on cognitive performances (Table 2).
Cognitive Functions at PRE and POST in the QG and CG
| Parameters (mean ± SD) | PRE | POST | Main effect training time | Interaction training time × Group | ||
|---|---|---|---|---|---|---|
| p | Effect size ( | p | Effect size ( | |||
| DSF (score) | .171 | .056 | .261 | .038 | ||
| QG (n = 19) | 9.6 ± 4.5 | 9.8 ± 3.9 | ||||
| CG (n = 16) | 7.8 ± 5.4 | 9.3 ± 3.7 | ||||
| DSB (score) | .410 | .021 | .276 | .036 | ||
| QG (n = 19) | 5.3 ± 4.1 | 5.5 ± 2.7 | ||||
| CG (n = 16) | 5.4 ± 3.5 | 4.3 ± 3.2 | ||||
| LDSF (span length) | .507 | .013 | .507 | .013 | ||
| QG (n = 19) | 4.4 ± 1.7 | 4.4 ± 1.4 | ||||
| CG (n = 16) | 4.0 ± 2.2 | 4.4 ± 1.5 | ||||
| LDSB (span length) | .767 | .003 | .238 | .042 | ||
| QG (n = 19) | 2.7 ± 1.8 | 3.0 ± 1.0 | ||||
| CG (n = 16) | 2.9 ± 1.6 | 2.4 ± 2.0 | ||||
| TMT-B/TMT-A (s) | .712 | .004 | .070 | .096 | ||
| QG (n = 19) | 2.2 ± 0.9 | 2.6 ± 1.3 | ||||
| CG (n = 16) | 2.4 ± 0.8 | 2.2 ± 0.8 | ||||
Note. Data are presented as mean ± SD. DSF = digital span forward; DSB = digit span backward; LDSF = longest digit span forward; LDSB = longest digit span backward; TMT-A = Trail Making Test Part A; TMT-B = Trail Making Test Part B; QG = qigong group; CG = control group; PRE = baseline; POST = after the 8-week intervention period.
pBDNF, hs-CRP, and Serum Ferritin
The results of two-way repeated analysis of variance reveal no significant main effect of training time, F(1, 28) = 0.38, p = .946,
Antioxidant Level, Peripheral Blood pBDNF, and Blood Cell Indices at PRE Baseline and POST in the QG and CG
| Parameters (mean ± SD) | PRE | POST | Main effect training time | Interaction training time × Group | ||
|---|---|---|---|---|---|---|
| p | Effect size ( | p | Effect size ( | |||
| POX (kU/L) | <.001* | 0.414 | .103 | 0.079 | ||
| QG (n = 19) | 0.7 ± 0.1 | 0.6 ± 0.2a | ||||
| CG (n = 16) | 0.6 ± 0.3 | 0.4 ± 0.2a,b | ||||
| pBDNF (ng/ml) | .946 | 0.000 | .007* | 0.234 | ||
| QG (n = 17) | 301.6 ± 179.1 | 216.1 ± 114.6a | ||||
| CG (n = 13) | 213.3 ± 179.7 | 302.8 ± 165.3 | ||||
| Hb (g/dl) | .165 | 0.058 | .735 | 0.004 | ||
| QG (n = 19) | 12.8 ± 1.4 | 12.7 ± 1.2 | ||||
| CG (n = 16) | 12.6 ± 1.4 | 12.5 ± 1.6 | ||||
| HCT (%) | .279 | 0.035 | .357 | 0.026 | ||
| QG (n = 19) | 38.1 ± 4.0 | 37.6 ± 3.3 | ||||
| CG (n = 16) | 37.9 ± 4.2 | 37.8 ± 4.3 | ||||
| PLT (103/mm3) | .887 | 0.001 | .767 | 0.003 | ||
| QG (n = 19) | 278.1 ± 65.4 | 277.3 ± 72.7 | ||||
| CG (n = 16) | 275.1 ± 62.5 | 277.5 ± 63.7 | ||||
| WBC (103/mm3) | .500 | 0.014 | .249 | 0.040 | ||
| QG (n = 19) | 6.6 ± 1.7 | 6.2 ± 1.6 | ||||
| CG (n = 16) | 6.3 ± 1.7 | 6.4 ± 1.4 | ||||
| Neutrophils (%) | .135 | 0.066 | .575 | 0.010 | ||
| QG (n = 19) | 57.5 ± 5.8 | 55.2 ± 7.5 | ||||
| CG (n = 16) | 54.9 ± 9.3 | 53.9 ± 8.3 | ||||
| Lymphocyte (%) | .166 | 0.057 | .369 | 0.025 | ||
| QG (n = 19) | 34.1 ± 4.6 | 36.2 ± 6.1 | ||||
| CG (n = 16) | 36.1 ± 8.1 | 36.5 ± 7.2 | ||||
| Red blood cell (106/mm3) | .352 | 0.026 | .692 | 0.005 | ||
| QG (n = 19) | 4.4 ± 0.5 | 4.3 ± 0.4 | ||||
| CG (n = 16) | 4.7 ± 0.7 | 4.7 ± 0.6 | ||||
Note. Data are presented as mean ± SD. POX = peroxidase enzyme activity; pBDNF = peripheral blood brain-derived neurotrophic factor in plasma; Hb = hemoglobin; HCT = hematocrit; PLT = platelet count; WBC = white blood cell count; QG = qigong group; CG = control group; PRE = baseline; POST = after the 8-week intervention period.
aSignificant difference within group versus PRE. bSignificant difference between groups.
*Significant difference (p < .05).
Acute Phase Protein in Inflammatory Reactions at PRE and POST in the QG and CG
| Parameters as Median (IQR) | PRE | POST | pa | Effect size (Cohen’s d) | Mean differences (POST − PRE) ± SD | pb | Effect size (Cohen’s d) |
|---|---|---|---|---|---|---|---|
| hs-CRP level (mg/dl) | .857 | 0.034 | |||||
| QG (n = 19) | 2.2 (1.2–5.2) | 1.8 (1.2–4.4) | .304 | 0.167 | −0.3 (−0.5 to 0.4) | ||
| CG (n = 16) | 2.7 (1.0–4.8) | 1.6 (0.9–4.7) | .245 | 0.204 | −0.1 (−1.1 to 0.4) | ||
| Serum ferritin level (ng/ml) | .707 | 0.067 | |||||
| QG (n = 19) | 99.4 (59.4–235.8) | 107.9 (54.2–175.4) | .936 | 0.013 | 4.3 (−16.8 to 10.6) | ||
| CG (n = 16) | 83.8 (58.1–152.8) | 89.9 (64.6–154.9) | .778 | 0.049 | 2.7 (−21.5 to 18.2) |
Note. Data are presented as median (IQR). hs-CRP = high-sensitivity C-reactive protein; IQR = interquartile range; QG = qigong group; CG = control group; PRE = baseline; POST = after the 8-week intervention period.
aUsing Wilcoxon signed-rank test. bUsing Mann–Whitney U test.
*Significant difference (p < .05).
MAP and Pulse Pressure
The MAP, main effect of training time: F(1, 33) = 14.41, p = .001,
Pulse Pressure and Mean Arterial Pressure, Peripheral Antioxidant at PRE and POST in the QG and CG
| Parameters (mean ± SD) | PRE | POST | Main effect training time | Interaction training time × Group | ||
|---|---|---|---|---|---|---|
| p | Effect size ( | p | Effect size ( | |||
| Mean arterial pressure (mmHg) | .001* | .304 | .396 | .022 | ||
| QG (n = 19) | 97.0 ± 11.6 | 89.3 ± 9.4a | ||||
| CG (n = 16) | 90.9 ± 11.1 | 86.0 ± 10.1 | ||||
| Pulse pressure (mmHg) | .010* | .185 | .756 | .003 | ||
| QG (n = 19) | 63.7 ± 9.2 | 56.7 ± 10.4a | ||||
| CG (n = 16) | 59.1 ± 17.3 | 53.6 ± 8.9 | ||||
Note. Data are presented as mean ± SD. QG = qigong group; CG = control group; PRE = baseline; POST = after the 8-week intervention period.
aSignificant difference within group versus PRE.
*Significant difference (p < .05).
Antioxidant Levels and Blood Cell Indices
The POX level, main effect of training time: F(1, 33) = 23.29, p < .001,
Correlations
Changes in pulse pressure from PRE to POST were negatively correlated with changes in the LDSF (r = −.34, p = .049).
Discussion
The main findings of this study only partly support our hypothesis, as 8 weeks of qigong training resulted in significant changes (decrease) in pBDNF levels, but cognitive performance did not improve. Both MAP and pulse pressure values decreased after qigong training but no between-group changes were found. The pulse pressure reduction after qigong training was significantly correlated with the increase in LDSF. Antioxidant levels decreased from PRE to POST within both groups (but slightly more pronounced in the CG).
Significant improvements of cognitive performance would probably need a longer time to occur, as findings from a recent meta-analysis suggest mindfulness exercise of moderate intensity for 45–60 min, three times per week over 6 months is needed to improve working memory (Zhidong et al., 2021). In this regard, our findings are in line with those from a previous study demonstrating no difference in digit span score after a 15-week Tai Chi intervention (including mostly elderly females; Chang et al., 2011). Similarly, Ladawan et al. (2017) also did not detect any changes in digit span score but found increased motor and visual control and speed after 8 weeks qigong training (in healthy middle-aged people). Gourgouvelis et al. (2017) observed a development of neural network during the memory encoding process but no change in memory performance after 8 weeks of moderate to vigorous intensity aerobic and strengthening exercises. Moreover, a recent study reported no significant change in the memory test in the elderly who were regularly engaged in either mind–body or cardiovascular exercise training, which is likely because their memory performance was superior to those who did not participate in regular exercise (Chan et al., 2005). A previous study suggested that poor working memory is associated with poorly controlled glycemic levels (Ennis et al., 2020). Gomez-Pinilla et al. (2008) proposed that the effects of exercise on synaptic plasticity and cognitive function are linked to energy metabolism, with BDNF acting as a metabotrophin at the intersection of these processes. In our study, it is likely that the diabetic participants had better glycemic control, which may help to explain why they had good working memory without the need to be improved by exercise.
Data on BDNF levels in individuals with T2DM (with or without performing exercise training) are controversial. Baker et al. (2010) demonstrated a tendency of reduced pBDNF levels after 6 months period of aerobic exercise and improved cognitive function in prediabetic elderly adults. Others reported no changes in serum BDNF after 12 weeks of combined exercise training in women with T2DM (Ghodrati et al., 2023) and after 9 months of several types of exercise in middle-aged and older individuals with T2DM (Swift et al., 2012). Damirchi et al. (2014) demonstrated that 6 weeks of aerobic exercise decreased serum BDNF levels in metabolic syndrome patients, which returned (increased) to baseline during detraining. Our result also confirms decreasing pBDNF levels in the QG but a trend to increase in the CG. The reduction of pBDNF may be attributed to its role as a metabotrophin, as decreased energy intake and increased energy expenditure through exercise may modulate BDNF levels in patients with Type 2 diabetes (Jamali et al., 2020). The increase of these levels observed in the CG may therefore indicate disturbed metabolism. This is consistent with the elevated serum BDNF levels observed in females newly diagnosed with Type 2 diabetes compared with healthy females (Suwa et al., 2006). Furthermore, BDNF levels could increase in feedback to damage due to the discharge of BDNF from platelets into plasma (Fujimura et al., 2002). Arentoft et al. (2009) found that elevated pBDNF levels were associated with decreased memory performance tasks related to insulin resistance in females with T2DM. Moreover, BDNF levels may rise in women experiencing severe insulin resistance as a compensating mechanism for endothelial dysfunction, which is commonly seen in insulin resistance (Arentoft et al., 2009). These authors proposed that the enhanced BDNF levels represent a compensatory mechanism when organs attempt to respond to injury and may not serve as an indicator of well-being as generally speculated (Arentoft et al., 2009). In an animal model, the elevation of BDNF and mRNA expression in the hippocampus occurred with the development of diabetes; however, a decrease in BDNF protein and gene expression was seen in response to exercise in diabetic rats compared with controls (Salehi et al., 2010). The reduction of antioxidant protection observed in particular diseases may exacerbate neuronal injury and apoptosis induced by reactive oxygen species (ROS; Salehi et al., 2010). ROS activates the upregulation of BDNF expression, whereas antioxidants inhibit the augmentation of BDNF (Wang et al., 2006). These observations might help to interpret the decreased pBDNF levels observed within the qigong group, and the increased pBDNF levels in controls with T2DM. The increase of BDNF in controls (no exercise intervention) with T2DM might be a compensatory response to an injury process associated with the more pronounced increase of ROS, which is indicated by a larger decrease of antioxidants compared with the QG. Thus, because of the likely lower ROS in QG, there is no need to increase BDNF to mitigate the potentially detrimental effects of oxidative stress (Salehi et al., 2010).
The benefits of 8 weeks of qigong exercise training on decreased pulse pressure and MAP are compatible with findings reported by Ladawan et al. (2017) after 8 weeks of qigong training in healthy middle-aged subjects. Possible explanations may be related to the controlled breathing coordinated with movements during qigong exercise which likely influences autonomic nervous system activity, supported by current literature findings indicating reduced sympathetic, and elevated parasympathetic activity (Chow & Tsang, 2007). The observed reduction in pulse pressure was correlated with an increase in LDSF, indicating improved simple attention. This is in agreement with current studies in nondemented middle-age and elderly Japanese people, which reported increased pulse pressure related to decreased cognitive performance (Mizuhara et al., 2022). Similarly, Ang et al. (2022) observed that T2DM associated with cognitive dysfunction is influenced by reduced vascular compliance, as evidenced by increased pulse pressure index. Vascular dysfunction causes injuries and harm to the brain, which have been linked to cognitive dysfunction associated with diabetes (Biessels & Despa, 2018). An elevation in pulse pressure may occur due to an enhancement of central arterial stiffness and resistance in peripheral vessels as well as, contributing to impaired integrity of the blood–brain barrier, resulting in elevated microbleeds, increased oxidative stress, and inflammatory cytokines aggregation (Biessels & Despa, 2018).
Several limitations need to be addressed. First, according to the COVID-19 outbreak period, the long monitoring of exercise training, and time restrictions, participants were not randomly assigned to the QG or CG. However, participants of similar age ranges were matched, and based their fundamental characteristic, no differences were observed between the QG and CG. Second, as blood samples were obtained at baseline and after exercise, we were unable to assess the sequential progression of alteration throughout the exercise intervention. Third, preestablished power calculations reveal that the study may lack sufficient power for specific outcomes; however, the observed changes within the QG could hold significance and will encourage future investigations with larger cohorts. Fourth, we did not measure the (individual) intensity of qigong training, but the previous study of the 18 basic movements of qigong indicate that the intensity is light to light–moderate-intensity exercise. The main strengths of the study encompass the homogenous sedentary cohort including middle-aged and elderly women with T2DM. Moreover, qigong training was supervised by experienced staff and controlled for movements in the exercise group during each exercise session. Last, we assessed cognitive functions along with physiological responses and chemical biomarkers that enabled us to discuss potential relationships.
Conclusions
This study demonstrated reduced pBDNF levels following 8 weeks of qigong training in sedentary middle-aged and elderly women with T2DM, which were not related to improvements in cognitive function, but may be interpreted as a favorable response to exercise in T2DM. In addition, qigong training resulted in reduced blood and pulse pressure related to indices of improved cognitive function, likely a consequence of favorably impacting the autonomic nervous system function by qigong training. Future studies should evaluate the effects of more extended qigong training on cognitive performance, pBDNF levels, and metabolic markers in individuals with T2DM. Additionally, it would be helpful to include more comprehensive measurements of different cognitive domains.
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