Effect of physical activity on free fatty acids, insulin resistance, and blood pressure in obese older women

Woo-Hyeon Son; Min-Seong Ha; Tae-Jin Park

doi:10.20463/pan.2024.0009

Phys Act Nutr > Volume 28(2); 2024 > Article

Son, Ha, and Park: Effect of physical activity on free fatty acids, insulin resistance, and blood pressure in obese older women

Original Article

Physical Activity and Nutrition 2024;28(2):1-6.

Published online: June 30, 2024

DOI: https://doi.org/10.20463/pan.2024.0009

Effect of physical activity on free fatty acids, insulin resistance, and blood pressure in obese older women

Woo-Hyeon Son¹, Min-Seong Ha², Tae-Jin Park^3,^*

¹Institute of Convergence Bio-Health, Dong-A University, Busan, Republic of Korea

²Laboratory of Sports Conditioning: Nutrition Biochemistry and Neuroscience, Department of Sport, College of the Arts and Sports, University of Seoul, Seoul, Republic of Korea

³Department of Sports Healthcare, College of Humanities & Social Sciences, Inje University, Gimhae, Republic of Korea

*Corresponding author : Tae-Jin Park Department of Sports Healthcare, College of Humanities & Social Sciences, Inje University, 197 Inje-ro, Gimhae, 50834, Republic of Korea.
Tel: +82-10-4550-5635 E-mail: ptj74@inje.ac.kr

Received March 6, 2024 Revised March 23, 2024 Accepted April 3, 2024

This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/2.0/) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

[Purpose]

Obesity is characterized by a progressive increase in body fat accompanied by insulin resistance (IR) and elevated blood pressure (BP), and presents significant health risks, particularly in aged individuals. This study aimed to evaluate the effects of physical activity (PA) on free fatty acid (FFA) levels, IR, and BP in obese older women.

[Methods]

Twenty-three participants were randomly assigned to either the control group (CON, n = 11) or the physical activity group (PA, n = 12). The PA group was provided with a target of achieving >7,000 steps/day for 5 days each week. Body composition, FFA levels, IR, and BP were measured at pre- and post- of the 12-week intervention.

[Results]

The analysis revealed a statistically significant interaction between FFA (p < 0.01), IR (p < 0.01), and SBP (p < 0.001). FFA (p < 0.5), IR (p < 0.5), and systolic blood pressure (SBP) (p < 0.01) were significantly decreased in the PA group compared to those in the CON group, which showed no significant changes in FFA, IR, and SBP.

[Conclusion]

PA significantly decreased FFA, IR, and SBP in older women with obesity. Therefore, PA is an effective intervention for the prevention and management of obesity and cardiovascular diseases in obese older women.

Keywords: obesity, physical activity, free fatty acid, insulin resistance, blood pressure

INTRODUCTION

Individuals aged ≥ 65 years constitute more than 9.6% of the global population. In the Republic of Korea, the older adults represented 14.2% of the population in 2018, and by 2025, we will have a super-aged society, with older adults exceeding 20% of the total population [1]. Concurrently, the prevalence of obesity in individuals aged ≥ 65 years has escalated to 40.6%, posing a health concerns [2]. Notably, the incidence of obesity is higher in older women than in men [3]. This disparity is largely attributed to diminished physical activity (PA) levels and a postmenopausal decline in estrogen levels, which contribute to decreased skeletal muscle mass and increased fat mass [4].

Increased adipose tissue leads to a greater release of FFA, resulting in higher FFA concentrations. Elevated plasma FFA levels are associated with increased risks of hypertension, thrombosis, and atherosclerosis [5,6]. Furthermore, increased plasma FFA concentrations inhibit hepatic glucose production and induce resistance to the effect of insulin on blood glucose, thereby reducing glucose uptake in skeletal muscle [7,8]. Therefore, elevated plasma FFA contribute to metabolic dysregulation and insulin resistance (IR) [9,10]. A previous study demonstrated the relationship between elevated FFA levels and IR [11].

IR is defined as a metabolic state in which insulin sensitivity is reduced compared to the normal state [12]. IR increases blood pressure (BP) and hypertension through hyperinsulinemia, which stimulates sodium metabolism in the kidney [13,14]. Castro et al. (2023) have corroborated the relationship between IR and hypertension [15].

Nonpharmacological interventions, such as regular PA, are advocated for managing obesity and metabolic disorders [16]. Regular PA, which results in weight loss and body fat reduction, is effective in ameliorating metabolic disorders [17]. Walking is a form of exercise that is highly manageable and requires no specialized equipment or training; therefore, it is a feasible option for the elderly who face constraints such as time and location [18]. Specifically, for older individuals, walking >7,000 steps/day reduces mortality and metabolic disorders [19,20].

Therefore, this study hypothesized that engaging in PA by walking >7,000 steps/day would have a positive effect on FFA levels, insulin resistance, and blood pressure in obese older women.

METHODS

Participants

This study included obese older women from B metropolitan city, all of whom met the criteria for obesity set by the Korean Society for the Study of Obesity (KSSO) [body mass index (BMI) ≥ 25 kg/m²] [21], no engagement in regular exercise within the preceding six months, and a willingness to participate in the intervention program. The participants were fully briefed on the study aims and procedures and provided written informed consent prior to inclusion. A total of 23 participants were randomly allocated to the physical activity group (PA; n = 12) or the control group (CON; n = 11). This randomization process was designed to impartially allocate participants to either group for the 12-week study period.

Measurement

Body composition, BP, and blood sampling were conducted at baseline and after completion of the 12-week intervention period. All assessments were performed under identical conditions to guarantee the consistency and control of potential confounding variables. Participants were instructed to fast for 12 h prior to evaluation to standardize their metabolic status, with all measurements scheduled between 8:00 and 10:00 AM to minimize the effects of diurnal variations. This protocol strictly followed for the pre-and post-intervention assessments to ensure accuracy and comparability of the data.

Body composition

Body composition was assessed with the participants wearing light clothing to ensure measurement accuracy. Height was accurately measured using a portable extensometer (InLabS50; Inbody, Republic of Korea), which facilitated precise acquisition of height for subsequent analyses. Weight and body fat percentage were quantified using an Inbody S10 (Inbody), a bioelectrical device for impedance analysis renowned for its reliability and accuracy in assessing body composition metrics. This method leverages the principle of bioelectrical impedance, providing a non-invasive and efficient means of determining body fat percentage and body weight, thereby contributing valuable data for the evaluation of physical health parameters.

BP

BP was measured by using an automatic blood pressure monitor (HEM-1022; Omron, Tokyo, Japan). Prior to measurement, the participants were seated comfortably with back support for a minimum of 5 min to stabilize their cardiovascular parameters and ensure a resting state. This preparatory step is crucial for obtaining accurate and representative blood pressure readings. Following this rest period, two consecutive blood pressure measurements were obtained from each participant. The average of these two readings was calculated and used for the analysis to enhance the precision and mitigate the influence of transient fluctuations. This approach ensured that the data reflected a reliable and consistent measure of each participant’s blood pressure, thus contributing to the overall integrity of the study.

PA

Participants allocated to the PA group were advised to achieve a minimum walking goal of 7,000 steps/day, for five days each week. All participants were equipped with a Fitbit Charge 4 activity tracker (Fitbit, San Francisco, CA, USA) to facilitate the precise tracking of PA levels. This advanced wearable technology enabled continuous monitoring of step count, providing a detailed and accurate account of each participant’s daily activity. The participants were instructed to wear the activity tracker throughout the day, removing it only for sleep and water-based activities, such as bathing, to maintain integrity and consistency of the activity data. Weekly monitoring sessions were conducted to review the participants’ activity records, verify their adherence to the walking regimen, and provide feedback and encouragement. This protocol was designed to support participants in achieving the prescribed PA goals and gather robust data on their activity levels during the study period. The participants in the CON group were asked to maintain their usual lifestyle habits.

Blood sampling

For biochemical analysis, 5 mL blood was collected from the brachial vein of each participant under aseptic conditions. Blood samples were immediately placed in plain tubes and centrifuged (Multifuge X1 R Pro; Thermo Fisher Scientific) at 3,000 rpm for 10 min to efficiently separate plasma from the cellular components of the blood. Plasma was carefully collected and stored at -70 °C to preserve the integrity of biomolecules until analysis. FFAs were quantified using an autoanalyzer (Hitachi 7150; Hitachi, Tokyo, Japan) to ensure the high precision and reliability of the measurements. Blood glucose level was determined using a Hitachi 7600 analyzer (Hitachi), and insulin concentration was measured using an Elecsys 2010 analyzer (Roche Diagnostics, Indianapolis, IN, USA), both of which are recognized for accuracy and consistency in clinical biochemistry. Homeostatic model assessment of insulin resistance was performed to evaluate the extent of IR using the following formula:

IR = Fasting insulin (μU/mL) × fasting glucose (mg/dL)/405 [22].

This calculation provides a reliable estimate of IR, which is a key factor in the metabolic profiles of participants.

Statistical analysis

Statistical analyses of the collected data were conducted utilizing SPSS/PC v.27.0 (IBM Corp., Armonk, NY, USA). Descriptive statistics were calculated for each variable, and the data are presented as mean ± standard deviation. Repeated-measures analysis of variance (ANOVA) was used to assess the effects of the 12-week PA intervention, including the main effects of time (pre- vs. post-intervention), group (PA vs. control), and interaction between time and group, thereby elucidating the differential impact of the intervention. Post hoc analyses were performed using paired t-tests to assess within-subject differences between the pre- and post-intervention measurements, which allowed for a detailed investigation of the changes within individuals, contributing to a deeper understanding of the efficacy of the intervention. Statistical significance was set at p< 0.05. This criterion was applied to ensure that the findings were identified with a reasonable degree of confidence, minimizing the risk of Type I errors, while appropriately recognizing statistically meaningful differences and interactions.

RESULTS

Change in FFA content

A significant effect of time (F, 8.553; p<0.01) and group-by-time interaction (F, 11.075; p<0.01) for FFA levels were noticed. Specifically, FFA levels significantly decreased (p<0.05) in the PA group post-intervention, in contrast to that in the con group that showed no significant changes in FFA levels.

Change in IR

Significant changes in glucose levels over time (F, 8.553; p<0.01) with a notable group-by-time interaction (F, 11.075; p<0.01) was observed, indicating a significant reduction in glucose levels (p<0.01) in the PA group post-intervention. In contrast, the CON group exhibited no significant changes in glucose levels. Furthermore, a significant group-by-time interaction was observed for insulin levels (F, 7.114; p<0.05), with a marked decrease observed in the PA group (p<0.05), whereas the CON group showed no significant changes. The IR data also displayed a significant time effect (F, 4.712; p<0.05) and group-by-time interaction (F, 8.544; p<0.01). Unlike the CON group, the PA group exhibited a significant reduction in IR (p<0.01).

Change in BP

A significant effect of time (F, 10.384; p<0.01) and a substantial group-by-time interaction (F, 20.100; p<0.001) on SBP were noticed. The results indicated a significant decrease in SBP levels (p<0.01) in the PA group following intervention, in contrast to the stable SBP levels in the CON group that showed no significant changes. The analysis did not demonstrate a significant difference in diastolic BP (DBP), indicating that the intervention specifically influenced SBP.

DISCUSSION

This study evaluated the impact of regular PA on key health indicators, including FFA, IR, and SBP, in obese older women. Consistent with our hypothesis, the PA group demonstrated significant improvements in these parameters compared with the control group, suggesting that PA serves as a potent modulator of metabolic health and BP in this population.

FFAs play crucial physiological roles as major energy substrates in the liver and heart [23]. An increase in body fat due to obesity leads to an increased release of fatty acids, resulting in elevated plasma FFA levels, which potentially trigger metabolic syndrome. An increase in FFA levels inhibits glucose metabolism in skeletal muscle and directly impacts insulin secretion, leading to IR [24]. Additionally, it is a significant contributor to hypertension and cardiovascular diseases (CVD) [6]. Elevated fasting plasma FFA levels increase the risk of CVD [25].

However, regular PA participation decreased the plasma FFA levels [26]. A previous study reported a significant reduction in plasma FFA levels following increased PA in adolescent obese girls [27]. The decrease in FFA levels is attributed to the reduction in body fat through increased PA and increased utilization of fatty acids as the primary energy source in muscles during PA [28,29]. In our study, plasma FFA levels in the PA group significantly decreased, indicating that the reduction in BMI after PA contributed to the decrease in FFA levels. Therefore, participation in regular PA may improve plasma FFA levels associated with obesity.

Obesity impairs the cellular responses to insulin, which is a fundamental cause of IR [30,31]. IR is a state in which the response to insulin for glucose in tissues, such as the muscle and liver, is lower than normal [32]. It reduces energy expenditure and impairs glucose disposal, thereby increasing the risk of metabolic syndrome and CVD [33].

However, modifications in lifestyle and PA can improve IR [34], and elderly individuals engaged in regular PA exhibit higher glucose tolerance and lower IR than sedentary elderly individuals [35].

The reduction in plasma insulin concentrations through PA mobilizes fatty acids and glucose from adipose tissue and the liver, thereby improving insulin response and potentially ameliorating IR [36,37]. Fujieda (2023) have reported a significant reduction in IR after starting PA with a target of 10,000 steps/day in sedentary males [38]. Yokoyama et al. (2001) reported a decrease in IR following increased PA (average of 8,829±3,801 steps/day) in a study targeting patients with type 2 diabetes [39]. Increased PA promotes glucose uptake in muscles, enhances insulin responsiveness, and corrects the mismatch between fatty acid oxidation and uptake in skeletal muscles, thereby ameliorating IR [40-42].

In this study, IR was significantly decreased in the PA group, suggesting that the reduction in FFAs due to PA is a mechanism for ameliorating IR. Furthermore, the decrease in IR suggests the potential of PA to prevent the onset of type 2 diabetes mellitus. Increased activity of the sympathetic nervous system and endothelial dysfunction due to obesity contribute to elevated BP [43]. This elevated BP leads to the development of hypertension, which is a major cause of CVD [44]. In contrast, lowering BP significantly reduces the risk of CVD. A reduction in SBP by 5 mmHg decreases the occurrence of cardiovascular events [45].

Changes in lifestyle, particularly regular PA and medication, can improve BP [46]. Regular PA lowers BP and reduces cardiovascular risk [47]. Several studies have reported a decrease in SBP following an increase in PA (>10,000 steps/ day) [48,49]. A decrease in IR and improvement of the autonomic nervous system [50] owing to PA lead to an improvement in BP. Our study found that a decrease in SBP in the PA group. Therefore, PA is beneficial for improving BP in obese older women.

In conclusion, improvements in FFA level, IR, and SBP through increased PA may aid in addressing metabolic disorders and CVD in elderly obese population.

Acknowledgments

We are grateful to the participants in this study.

Table 1.

Physical characteristics of the subjects.

Variables	PA (n=12)		CON (n=11)
Variables	Pre	Post	Pre	Post
Age (yrs)	69.80±1.23		69.90±0.94
Height (cm)	153.74±3.90		155.70±2.45
Weight (kg)	62.73±4.42	61.49±4.25	63.04±2.15	63.36±2.27
BMI (kg/m²)	26.22±1.03	26.02±1.23^***	26.04±1.14	26.15±1.17

Values are means and SD,

^*** p <.001

PA: physical activity group, CON: control group, BMI: body mass index

Table 2.

Number of steps taken by the participants.

Variables	PA (n=12)	CON (n=11)
Walking step	9380.18±959.64	1982.11±71.76
Sedentary time (min)	807.82±73.32	1218.22±126.74

Values are means and SD

PA: physical activity group, CON: control group

Table 3.

Change in FFA pre and post in control and physical activity group.

Variable	PA (n=12)		CON (n=11)		Two-way repeated ANOVA
Variable	Pre	Post	Pre	Post	Time	Group	T×G
FFA (μEq/L)	512.20±72.18	460.40±61.92^*	553.56±71.73	582.90±79.02	0.978	8.796^**	10.922^**

Values are means and SD,

^* p <.05,

^** p <.01

PA: physical activity group, CON: control group

Table 4.

Change in insulin resistance pre and post in control and physical activity group.

Variable	PA (n=12)		CON (n=11)		Two-way repeated ANOVA
Variable	Pre	Post	Pre	Post	Time	Group	T×G
Glucose (mg/dLL)	104.30±6.45	101.20±5.92^**	105.60±8.60	105.80±5.51	8.553^**	0.798	11.075^**
Insulin (μU/mLg)	7.31±2.94	5.93±2.20^*	7.75±2.43	7.99±2.45	3.523	1.329	7.114^*
HOMA-IR	1.90±0.80	1.49±0.57^*	2.03±0.72	2.09±0.69	4.712^*	1.478	8.544^**

Values are means and SD,

^* p <.05,

^** p <.01

PA: physical activity group, CON: control group

Table 5.

Change in blood pressure pre and post in control and physical activity group.

Variable	PA (n=12)		CON (n=12)		Two-way repeated ANOVA
Variable	Pre	Post	Pre	Post	Time	Group	T×G
SBP (mmHg)	142.50±5.91	137.80±7.42^**	143.20±8.83	143.60±10.49	10.384^**	1.451	20.100^***
DBP (mmHg)	83.80±7.73	82.40±7.66	84.03±5.08	84.50±5.42	1.580	0.200	2.810

Values are means and SD,

^** p <.01,

^*** p <.001

PA: physical activity group, CON: control group

REFERENCES

1. Kim K, Hwnag N, Jin H, You J. Diversity and sociopolicy response of the elderly in post-aged society. Sejong: KIHASA. 2020.

2. Lee YS, Lee Y. Comparison of the nutrient intake and health status of elderly Koreans according to their BMI status: focus on the underweight elderly population. KJCN 2022;27:422-34.

3. Lee YH, Jung KS, Kim SU, Yoon HJ, Yun YJ, Lee BW, Kang ES, Han KH, Lee HC, Cha BS. Sarcopaenia is associated with NAFLD independently of obesity and insulin resistance: nationwide surveys (KNHANES 2008-2011). J Hepatol 2015;63:486-93.

4. Du Y, Wang X, Xie H, Zheng S, Wu X, Zhu X, Zhang X, Xue S, Li H, Hong W, Tang W, Chen M, Cheng Q, Sun J. Sex differences in the prevalence and adverse outcomes of sarcopenia and sarcopenic obesity in community dwelling elderly in East China using the AWGS criteria. BMC Endocr Disord 2019;19:109.

5. Breitling LP, Rothenbacher D, Grandi NC, Marz W, Brenner H. Prognostic usefulness of free fatty acids in patients with stable coronary heart disease. Am J Cardiol 2011;108:508-13.

6. Pilz S, Scharnagl H, Tiran B, Wellnitz B, Seelhorst U, Boehm BO, Marz W. Elevated plasma free fatty acids predict sudden cardiac death: a 6.85-year follow-up of 3315 patients after coronary angiography. Eur Heart J 2007;28:2763-9.

7. Boden G, Chen X, Ruiz J, White JV, Rossetti L. Mechanisms of fatty acid-induced inhibition of glucose uptake. J Clin Invest 1994;93:2438-46.

8. Boden G. Fatty acid-induced inflammation and insulin resistance in skeletal muscle and liver. Curr Diab Rep 2006;6:177-81.

9. Arner P. Insulin resistance in type 2 diabetes: role of fatty acids. Diabetes Metab Res Rev 2002;18:S5-9.

10. Boden G. Effects of free fatty acids (FFA) on glucose metabolism: significance for insulin resistance and type 2 diabetes. Exp Clin Endocrinol Diabetes 2003;111:121-4.

11. Xin Y, Wang Y, Chi J, Zhu X, Zhao H, Zhao S, Wang Y. Elevated free fatty acid level is associated with insulin-resistant state in nondiabetic Chinese people. Diabetes Metab Syndr Obes 2019;12:139-47.

12. Ivy JL. Muscle insulin resistance amended with exercise training: role of GLUT4 expression. Med Sci Sports Exerc 2004;36:1207-11.

13. DeFronzo RA. The effect of insulin on renal sodium metabolism. A review with clinical implications. Diabetologia 1981;21:165-71.

14. Skott P, Hother-Nielsen O, Bruun NE, Giese J, Nielsen MD, Beck-Nielsen H, Parving HH. Effects of insulin on kidney function and sodium excretion in healthy subjects. Diabetologia 1989;32:694-9.

15. Castro L, Brant L, Diniz MdF, Lotufo P, Bensenor IJ, Chor D, Griep R, Barreto SM, Ribeiro AL. Association of hypertension and insulin resistance in individuals free of diabetes in the ELSA-Brasil cohort. Sci Rep 2023;13:9456.

16. Myers J, Kokkinos P, Nyelin E. Physical activity, cardiorespiratory fitness, and the metabolic syndrome. Nutrients 2019;11:1652.

17. Ostman C, Smart NA, Morcos D, Duller A, Ridley W, Jewiss D. The effect of exercise training on clinical outcomes in patients with the metabolic syndrome: a systematic review and meta-analysis. Cardiovasc Diabetol 2017;16:110.

18. Hanson S, Jones A. Is there evidence that walking groups have health benefits? A systematic review and meta-analysis. Br J Sports Med 2015;49:710-5.

19. Tudor-Locke C, Craig CL, Aoyagi Y, Bell RC, Croteau KA, De Bourdeaudhuij I, Ewald B, Gardner AW, Hatano Y, Lutes LD, Matsudo SM, Ramirez-Marrero FA, Rogers LQ, Rowe DA, Schmidt MD, Tully MA, Blair SN. How many steps/day are enough? For older adults and special populations. Int J Behav Nutr Phys Act 2011;8:80.

20. Park S, Park H, Togo F, Watanabe E, Yasunaga A, Yoshiuchi K, Shephard RJ, Aoyagi Y. Year-long physical activity and metabolic syndrome in older Japanese adults: cross-sectional data from the Nakanojo Study. J Gerontol A Biol Sci Med Sci 2008;63:1119-23.

21. Kim BY, Kang SM, Kang JH, Kang SY, Kim KK, Kim KB, Kim B, Kim SJ, Kim YH, Kim JH, Kim JH, Kim EM, Nam GE, Park JY, Son JW, Shin YA, Shin HJ, Oh TJ, Lee H, Jeon EJ, Chung S, Hong YH, Kim CH; Committee of Clinical Practice Guidelines; Korean Society for the Study of Obesity(KSSO). 2020 Korean society for the study of obesity guidelines for the management of obesity in Korea. J Obes Metab Syndr 2021;30:81-92.

22. Matthews DR, Hosker JP, Rudenski AS, Naylor BA, Treacher DF, Turner RC. Homeostasis model assessment: insulin resistance and beta-cell function from fasting plasma glucose and insulin concentrations in man. Diabetologia 1985;28:412-9.

23. Boden G, Shulman GI. Free fatty acids in obesity and type 2 diabetes: defining their role in the development of insulin resistance and β-cell dysfunction. Eur J Clin Invest 2002;32:14-23.

24. Birnbaum MJ. Lipolysis: more than just a lipase. J Cell Biol 2003;161:1011-2.

25. Pilz S, Scharnagl H, Tiran B, Seelhorst U, Wellnitz B, Boehm BO, Schaefer JR, Marz W. Free fatty acids are independently associated with all-cause and cardiovascular mortality in subjects with coronary artery disease. J Clin Endocrinol Metab 2006;91:2542-7.

26. Kim DY, Jung SY, Seo BD. Effect of exercise intervention on changes in free fatty acid levels and metabolic risk factors in stroke patients. J Phys Ther Sci 2014;26:275-9.

27. Park C, Jung J, Yang J. Effects of physical activity level on blood concentration of free fatty acid, ceramide and insulin resistance in obesity middle school girls. The Korean Jounal of Physical Education 2013;52:641-52.

28. Kourtoglou GI. Insulin therapy and exercise. Diabetes Res Clin Pract 2011;93:S73-7.

29. Yagura C, Takamura N, Goto Y, Sugihara H, Sota T, Oka S, Shimoda T, Yoshizumi K. Cardiorespiratory fitness and metabolic markers in healthy young adult men. JPTS 2011;23:845-9.

30. Kahn SE, Hull RL, Utzschneider KM. Mechanisms linking obesity to insulin resistance and type 2 diabetes. Nature 2006;444:840-6.

31. Mu L, Zhao Y, Lai Y, Li R, Qiao J. Insulin resistance and β-cell dysfunction and the relationship with cardio-metabolic disorders among women with polycystic ovary syndrome. Clin Endocrinol 2018;89:779-88.

33. Majnemer A, Barr RG. Association between sleep position and early motor development. J Pediatr 2006;149:623-9.

34. McGinley SK, Armstrong MJ, Boule NG, Sigal RJ. Effects of exercise training using resistance bands on glycaemic control and strength in type 2 diabetes mellitus: a meta-analysis of randomised controlled trials. Acta Diabetol 2015;52:221-30.

35. Ivy JL. Role of exercise training in the prevention and treatment of insulin resistance and non-insulin-dependent diabetes mellitus. Sports Med 1997;24:321-36.

36. Mendelson M, Michallet AS, Monneret D, Perrin C, Estève F, Lombard P, Faure P, Lévy P, Favre-Juvin A, Pépin JL, Wuyam B, Flore P. Impact of exercise training without caloric restriction on inflammation, insulin resistance and visceral fat mass in obese adolescents. Pediatr Obes 2015;10:311-9.

37. Richter EA, Sylow L, Hargreaves M. Interactions between insulin and exercise. Biochem J 2021;478:3827-46.

38. Fujieda Y, Miura K, Nakagawa H. Walking 10,000 steps per day is effective to improve coronary risk factors among Japaneses middle-aged men. JPFSM 2006;55:S37-42.

39. Yokoyama H, Emoto M, Araki T, Fujiwara S, Motoyama K, Morioka T, Koyama H, Shoji T, Okuno Y, Nishizawa Y. Effect of aerobic exercise on plasma adiponectin levels and insulin resistance in type 2 diabetes. Diabetes care 2004;27:1756-8.

40. Teixeira-Lemos E, Nunes S, Teixeira F, Reis F. Regular physical exercise training assists in preventing type 2 diabetes development: focus on its antioxidant and anti-inflammatory properties. Cardiovasc Diabetol 2011;10:12.

41. Camacho RC, Galassetti P, Davis SN, Wasserman DH. Glucoregulation during and after exercise in health and insulin-dependent diabetes. Exerc Sport Sci Rev 2005;33:17-23.

42. Turcotte LP, Fisher JS. Skeletal muscle insulin resistance: roles of fatty acid metabolism and exercise. Phys Ther 2008;88:1279-96.

43. Sowers JR, White WB, Pitt B, Whelton A, Simon LS, Winer N, Kivitz A, Van Ingen H, Brabant T, Fort JG. The effects of cyclooxygenase-2 inhibitors and nonsteroidal anti-inflammatory therapy on 24-hour blood pressure in patients with hypertension, osteoarthritis, and type 2 diabetes mellitus. Arch Intern Med 2005;165:161-8.

44. Sowers JR, Epstein M, Frohlich ED. Diabetes, hypertension, and cardiovascular disease: an update. Hypertension 2001;37:1053-9.

45. Ettehad D, Emdin CA, Kiran A, Anderson SG, Callender T, Emberson J, Chalmers J, Rodgers A, Rahimi K. Blood pressure lowering for prevention of cardiovascular disease and death: a systematic review and meta-analysis. Lancet 2016;387:957-67.

46. Williamson JD, Wright Jr JT. 2017 ACC/AHA/AAPA/ABC/ACPM/AGS/APhA/ASH/ASPC/NMA/PCNA guideline for the prevention, detection, evaluation, and management of high blood pressure in adults: executive summary: a report of the American college of cardiology/American heart association task force on clinical practice guidelines. J Am Soc Hypertens 2018;12:579.

47. Hegde SM, Solomon SD. Influence of physical activity on hypertension and cardiac structure and function. Curr Hypertens Rep 2015;17:77.

48. Iwane M, Arita M, Tomimoto S, Satani O, Matsumoto M, Miyashita K, Nishio I. Walking 10,000 steps/day or more reduces blood pressure and sympathetic nerve activity in mild essential hypertension. Hypertens Res 2000;23:573-80.

49. Yuenyongchaiwat K, Pipatsitipong D, Sangprasert P. Increasing walking steps daily can reduce blood pressure and diabetes in overweight participants. Diabetol Int 2017;9:75-9.

50. Fossum E, Høieggen A, Reims H, Moan A, Rostrup M, Eide I, Kjeldsen SE. High screening blood pressure is related to sympathetic nervous system activity and insulin resistance in healthy young men. Blood Press 2004;13:89-94.