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| ORIGINAL ARTICLE |
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| Year : 2022 | Volume
: 8
| Issue : 2 | Page : 146-151 |
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Comparison of forearm muscle fatigue among apparently healthy young adults with and without diabetogenic genes
Leander Pradeep1, U Karthika Jyothish2, Rajesh Jeniton Fernando2, Kandasamy Ravichandran3, Subhasis Das2
1 MBBS Student, Pondicherry Institute of Medical Sciences, Pondicherry, India
2 Department of Physiology, Pondicherry Institute of Medical Sciences, Pondicherry, India
3 Department of Biostatistics, Pondicherry Institute of Medical Sciences, Pondicherry, India
| Date of Submission | 02-Aug-2022 |
| Date of Decision | 01-Sep-2022 |
| Date of Acceptance | 02-Sep-2022 |
| Date of Web Publication | 23-Dec-2022 |
Correspondence Address:
U Karthika Jyothish
Department of Physiology, Pondicherry Institute of Medical Sciences, Pondicherry
India
 Source of Support: None, Conflict of Interest: None  | Check |
DOI: 10.4103/jcrsm.jcrsm_67_22
Background: Diabetes is primarily a genetic disorder. Whether the forearm muscle fatigue, handgrip strength (HGS), and phase angle difference between young adults with diabetic genes and their counterparts are not known. We designed a cross-sectional study to compare these variables among young healthy adults with diabetic parents in one group and nondiabetic parents in the other.
Methodology: Forearm muscle fatigue, HGS, and phase angle were measured in 60 young healthy adults aged 18 to 23 years with body mass index between 18 and 23.4. Among them, 30 had at least one parent who had been a diabetic for more than 3 years and the other thirty had nondiabetic parents (both parents having fasting blood sugar <100 mg/dl).
Results: The continuous variables between the groups, which were normally distributed, were analyzed using the independent sample t-test. Data that were nonnormally distributed were analyzed using the Mann–Whitney U test. Forearm muscle fatigue of young adults with diabetic parents increased significantly compared to their counterparts (P = 0.005). HGS was greater among adults with nondiabetic parents compared to adults with diabetic parents, although this was not statistically significant. Phase angle did not show any significant difference between the two groups.
Conclusion: A simple noninvasive measurement like forearm muscle fatigue is found to be increased among young healthy adults with diabetogenic genes when compared to their counterparts without diabetogenic genes. Prospective studies need to be performed to show increased muscle fatigue as a predictor of future incidence of diabetes.
Keywords: Bioelectrical impedance analysis, diabetes, handgrip strength, muscle fatigue, phase angle
How to cite this article:
Pradeep L, Jyothish U K, Fernando RJ, Ravichandran K, Das S. Comparison of forearm muscle fatigue among apparently healthy young adults with and without diabetogenic genes. J Curr Res Sci Med 2022;8:146-51 |
How to cite this URL:
Pradeep L, Jyothish U K, Fernando RJ, Ravichandran K, Das S. Comparison of forearm muscle fatigue among apparently healthy young adults with and without diabetogenic genes. J Curr Res Sci Med [serial online] 2022 [cited 2023 Mar 20];8:146-51. Available from: https://www.jcrsmed.org/text.asp?2022/8/2/146/364504 |
| Introduction | |  |
Type 2 diabetes mellitus is a metabolic disorder characterized by hyperglycemia associated with derangement in fat and protein metabolism.[1],[2] The pathogenesis of type 2 diabetes mellitus is predominately genetic[3] with epigenetic factors such as reduced physical activity and nutrition contributing to it.[1] The global prevalence of diabetes is half a billion and is estimated to reach 783 million in 2045.[2] Therefore, the global financial burden of diabetes, which was 966 billion USD in 2021, is also estimated to rise by over 1054 billion USD in 2045.[2] Low- and middle-income countries are hit hardest by the financial burden since three out of four diabetics live in these countries.[2]
Skeletal muscle fatigue is defined as a predictable, reversible, and temporary decline in the force and power of voluntary muscle contraction due to exercise.[4] Skeletal muscle is the largest insulin-sensitive tissue in the body, and there is an accelerated reduction in muscle mass (sarcopenia) among diabetics due to poor glucose and amino acids uptake, reduced physical activity, increased inflammatory mediators, psychological factors (depression), oxidative stress, reduced growth hormone, reduced Vitamin D levels,[5] and impairment in microcirculation and intracellular calcium regulation,[6] leading to ease muscle fatigue. Since sarcopenia[7] and reduced handgrip strength (HGS)[8] are associated with future diabetes, it is plausible that young healthy adults with diabetogenic genes may have sarcopenia, and therefore, increased skeletal muscle fatigue and reduced HGS.
Bioelectrical impedance analysis (BIA) is an inexpensive, noninvasive, reliable, and reproducible method to assess body composition if appropriate age, gender, and population-specific equations are used.[9] Phase angle (expressed in degrees) is one of the parameters of BIA and is computed as the ratio between resistance, which is related to extracellular fluid and intracellular fluid, and reactance which is the delay of current flowing through the cell membrane.[10] The phase angle is a marker of health, cellular integrity, and nutritional status and is used as a prognostic tool in many clinical conditions.[11],[12],[13] Individuals with type 2 diabetes mellitus, being in a catabolic state with reduced cell mass, have smaller phase angle values when compared with nondiabetic individuals of the same body mass index (BMI) and age.[14] The duration of diabetes and the level of hemoglobin A1c are inversely associated with phase angle.[14] Since phase angle is an index of cellular health, it is plausible that young healthy adults with diabetogenic genes may have reduced phase angle values.
The objective of our study was to compare the forearm muscle fatigability, HGS, and phase angle of apparently healthy young adults with diabetogenic genes and the ones without diabetogenic genes.
| Materials and Methods | | |
The study was conducted after obtaining ethical clearance from the institutional ethics committee (IEC NO. RC/2020/03) and written informed consent from each participant before the study. This was a cross-sectional study performed at the residence of the participants (due to COVID-19 restrictions) from October 2020 to December 2020 in the rural and urban districts of Puducherry. Individuals between the ages of 18 and 23 years and with BMI between 18 and 23.4 kg/m2 were included. A total of 101 volunteers were screened for the study with 49 volunteers in Group A whose parents were apparently nondiabetic and Group B with 52 volunteers with at least one of the parents being apparently diabetic. A fasting plasma glucose level using a venous sample was determined for both the parents of Group A volunteers (<100 mg/dl)2 to negate undiagnosed diabetes. The venous samples were collected at the residence of the participants by a phlebotomist and analyzed on the same day at a standard laboratory. Among the 49 volunteers in Group A, one parent each of five participants had fasting blood sugar (FBS) >100 mg/dl and 14 participants had BMI either <18 or >23.4 and hence were excluded and the remaining 30 participants (20 boys and 10 girls) with nondiabetic parents were included. Among the 52 volunteers in Group B, two were excluded since one of their parents was diseased due to other noncommunicable diseases, and 20 who had BMI either <18 or >23.4 were also excluded. Group B had 30 participants (18 boys and 12 girls) with at least one parent who has been a known diabetic for at least 3 years. The participants were included in Group B after checking the existing medical records of their diabetic parents. Since there were no similar studies, a convenient sample of 30 participants in each group was included. Participants from both groups did not perform any regular resistance exercises, had no medical illness nor were they under any medications. They did not consume alcohol or smoke tobacco, were not pure vegetarians and their parents did not have any other medical conditions such as hypertension. The participants were advised to refrain from any form of severe exercise 48 h before the study. They were also advised to refrain from consuming caffeinated drinks on the day of study. The study was performed in the morning between 8.00 a.m. and 9.00 a.m. after the participants had restful sleep and overnight fasting.
Measurement of anthropometry
The participants stood barefoot against a flat wall with heels, buttocks, and shoulders touching it, head in the Frankfurt plane. The highest surface of the head was marked using a pencil and the height was measured to the nearest 0.1 cm using a metal inch tape. The weight was measured to the nearest 0.1 kg using a digital weighing scale (Ionix, India). The BMI was calculated using the Quetelet formula by dividing the weight (in kilograms) by the square of height (in meters). The participants stood comfortably with their body weight evenly distributed on their legs. Waist circumference was measured using nonstretchable tape at the level of the mid-point between the lower border of the ribcage and the iliac crest. The largest width of the buttocks was measured as hip circumference. Both hip and waist circumferences were measured to the nearest 0.1 cm.[15]
Measurement of handgrip strength and forearm muscle fatigue
The participant sat comfortably on a chair with their elbow flexed at 90° and forearm rested on the arm of the chair. They performed the tests using a handheld dynamometer (INCO, Ambala, India) with their nondominant hand by pressing it to their maximum strength three times with 2 min rest in between. The best of the three values was noted as HGS in kilograms. The participants pressed the handheld dynamometer to their maximum strength once in 2 s till they were no longer able to perform the task. Since fatigue sets in immediately after the first few contractions, we considered task failure as forearm muscle fatigue as recommended by the Ciba Foundation Symposia 1981.[16] The time taken to task failure (unable to press no more) was noted as the forearm muscle fatigue in seconds. They were allowed to practice using their dominant hand to familiarize themselves and to know their grip width.
Measurement of phase angle
Body composition was estimated using bioelecticalimpedence analysis (BIA) (Quadscan 4000, BodyStat limited, British Isles, UK) in the morning fasted state.[17] The participants removed any electronic and metal objects with them. They were made to lie down comfortably in supine posture on a nonconducting surface for 5 min, and leads were placed on their right hand and right foot. Their measured height, weight, hip, and waist circumference, and their approximate physical activity level, as reported by them, were entered into the apparatus, and the phase angle was measured at 50 kHz.
Statistical analysis
The data were analyzed after confirming normal distribution. The continuous variables between the groups (offspring of nondiabetic vs. diabetic parents), which were normally distributed, were analyzed using the independent sample t-test. The data which were nonnormally distributed were analyzed using the Mann–Whitney U test. The correlations between the variables within the group were analyzed using Pearson's method for normally distributed data and Spearman's method for nonnormally distributed data. The association of gender between the groups was analyzed using the Chi-square test. The statistical significance was considered at P < 0.05. The data analysis was performed using the IBM Corp. Released 2012. IBM SPSS Statistics for Windows, Version 21.0. (Armonk, NY: IBM Corp).
| Results | | |
The descriptive data are given in [Table 1]. Age, height, weight, BMI, basal metabolic rate (BMR), fat percentage, fat mass, lean percentage, lean mass, and body cell mass were comparable between the offspring of nondiabetics and the offspring of diabetics. The number of girls and boys was comparable between the groups using the Chi-square test (P = 0.59). The correlation between phase angle and selected variables in body composition, HGS, and forearm muscle fatigue is given in [Table 2]. Among the offspring of nondiabetics, the phase angle was significantly correlated with BMI (r = 0.381; P = 0.038), lean percentage (r = 0.787; P < 0.001), body cell mass (r = 0.890; P < 0.001), and BMR (r = 0.834; P < 0.001). Among the 30 offspring of diabetics, the phase angle was also significantly correlated with lean percentage (0.835; P < 0.001), body cell mass (r = 0.795; P < 0.001), and BMR (r = 0.680; P < 0.001). However, phase angle was not significantly correlated with BMI (r = 0.215; P = 0.254). Lean mass is positively correlated with BMI among the adults with nondiabetic parents (r = 0.386; P = 0.030) and similar correlation was not evident among the adults with diabetic parents (r = 0.336; P = 0.069). Phase angle was significantly correlated with maximum handgrip among the offspring of nondiabetics (r = 0.655; P < 0.001) and among the offspring of diabetics (r = 0.729; P < 0.001). Phase angle was positively correlated with time to task failure (inverse of forearm muscle fatigue) among the offspring of nondiabetics (r = 0.383; P = 0.037) and among the offspring of diabetics (r = 0.610; P < 0.001). The maximum HGS was greater among the individuals with nondiabetic parents compared to diabetic parents, albeit not statistically significant (P = 0.66). The forearm muscle fatigue (measured as the time for task failure – which is the inverse of muscle fatigue) was significantly greater for the group with diabetic parents compared to the group with nondiabetic parents (P = 0.005). The phase angle at 50 kHz and all other estimated body composition parameters from BIA were comparable between the study groups [Table 3]. | Table 3: Comparison of bioelectrical impedance analysis variables and skeletal muscle properties between the groups
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| Discussion | | |
We selected 30 healthy adults (age 18 to 23 years with BMI 18 to 23.4) whose parents were nondiabetic (both parents having FBS <100 mg/dl) and another 30 healthy adults who had at least one parent who had been a diabetic for more than 3 years. We measured their HGS and forearm muscle fatigability using a dynamometer and estimated their body composition and phase angle through BIA. Our study demonstrated that the forearm skeletal muscle fatigability was greater among boys and girls with diabetogenic genes compared to their counterparts without diabetogenic genes. The height and gender were comparable between the groups, and therefore, do not confound the results. Surface electromyography studies demonstrated that diabetic individuals show greater knee extensor fatigue compared to normal controls[18] and the fatigue indices were correlated to the duration of diabetes.[19] Our study elucidated a similar effect even among healthy young adults with diabetic parents compared to their counterparts. Skeletal muscle fatigue had both central and peripheral determinants.[20] In whole-body exercise, central fatigue is one of the major determining factors.[21] While using small groups of muscle, like the forearm, the fatigue is determined predominantly by peripheral factors, i.e., the muscle itself.[21] Hence, the increased forearm muscle fatigue seen among the adults with diabetic parents compared to their counterparts with nondiabetic parents in our study may be due to the property of the muscle itself. However, central fatigue is still a limiting factor in our study.
The skeletal muscle is the largest organ in a nonobese individual and is responsible for metabolizing or storing approximately 75% to 90% of glucose.[22] Relative muscle mass (muscle mass/body weight) is inversely associated with the incidence of diabetes and the relationship was found to be stronger for young men and premenopausal women.[23] Therefore, reduced muscle mass (sarcopenia) can cause the development of diabetes since it is the major organ of glucose homeostasis. Local inflammation due to intramuscular fat infiltration may also contribute to inefficient glucose uptake.[7] Animal models have demonstrated that intramuscular fat infiltration is a causative factor for reduced muscle strength, however, was not associated with time to fatigue.[24] It is also evident that improving muscle mass and grip strength can reduce the risk of diabetes.[25] In our study, the individuals with nondiabetic parents, as compared to individuals with diabetic parents, had greater HGS, although this was not found to be statistically significant. The absence of statistical significance difference between the two groups in HGS could be due to the small sample size.
The body composition parameters and phase angle were comparable between our study groups. The normal value of phase angle among healthy adults has been recorded as 6° to 7°in the Western population,[26] in contrast to that in our study, where a median value of 5° was obtained. In our study, phase angle was positively correlated with HGS among the offspring of nondiabetics (r = 0.655; P < 0.001) and among the offspring of diabetics (r = 0.729; P < 0.001). Similarly, earlier studies have demonstrated positive associations between phase angle and HGS among young athletes and nonathletes.[27],[28] In our study, we were able to demonstrate that phase angle was also positively correlated with time for task failure (inversely correlated to forearm muscle fatigue) among the offspring of nondiabetics (r = 0.383; P = 0.037) and among the offspring of diabetics (r = 0.61; P < 0.001). The phase angle is significantly correlated with BMI among the adults without diabetic parents (r = 0.381; P = 0.038) which is not elucidated among the individuals with diabetic parents (r = 0.215; P = 0.254) in our study. This is because, among adults with diabetic parents, the lean mass percentage is strongly associated with phase angle (r = 0.835, P < 0.001), whereas the BMI and lean mass are not significantly associated [Figure 1]a and [Figure 1]b. The above results show that adults with diabetic parents belong to the thin-fat phenotype which is seen in the Indian population and is known for future metabolic disease risk.[29] | Figure 1: (a) Association between lean body mass and BMI among the participants with nondiabetic parents. P value was obtained using Spearman's method. (b) Association between lean body mass and BMI among the participants with diabetic parents. P value was obtained using Pearson's method. BMI: Body mass index
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Since our study has limitations of being cross-sectional, comments on increased forearm muscle fatigability as a predictor for future incidence of diabetes cannot be made. Further well-designed prospective studies involving a larger study population are needed to elucidate it.
| Conclusion | | |
Our study demonstrated that simple noninvasive measurements like forearm muscle fatigue were increased, and HGS (albeit not significant) was reduced among healthy young adults with diabetogenic genes compared to their counterparts without diabetogenic genes.
Financial support and sponsorship
This study was financially supported by the ICMR STS Project Reference No: Reference No: 2020-02221.
Conflicts of interest
There are no conflicts of interest.
| References | | |
| 1. | Roglic G. WHO Global report on diabetes: A summary. Int J Noncommun Dis 2016;1:3-8.  |
| 2. | Sun H, Saeedi P, Karuranga S, Pinkepank M, Ogurtsova K, Duncan BB, et al. IDF diabetes atlas: Global, regional and country-level diabetes prevalence estimates for 2021 and projections for 2045. Diabetes Res Clin Pract 2022;183:109119. |
| 3. | DeForest N, Majithia AR. Genetics of type 2 diabetes: Implications from Large-Scale studies. Curr Diab Rep 2022;22:227-35. |
| 4. | Bigland-Ritchie B, Woods JJ. Changes in muscle contractile properties and neural control during human muscular fatigue. Muscle Nerve 1984;7:691-9. |
| 5. | Izzo A, Massimino E, Riccardi G, Della Pepa G. A narrative review on sarcopenia in type 2 diabetes mellitus: Prevalence and associated factors. Nutrients 2021;13:183. |
| 6. | Eshima H, Poole DC, Kano Y. In vivo calcium regulation in diabetic skeletal muscle. Cell Calcium 2014;56:381-9. |
| 7. | Mesinovic J, Zengin A, De Courten B, Ebeling PR, Scott D. Sarcopenia and type 2 diabetes mellitus: A bidirectional relationship. Diabetes Metab Syndr Obes 2019;12:1057-72. |
| 8. | Boonpor J, Parra-Soto S, Petermann-Rocha F, Ferrari G, Welsh P, Pell JP, et al. Associations between grip strength and incident type 2 diabetes: Findings from the UK Biobank prospective cohort study. BMJ Open Diabetes Res Care 2021;9:e001865. |
| 9. | Baumgartner RN, Chumlea WC, Roche AF. Estimation of body composition from bioelectric impedance of body segments. Am J Clin Nutr 1989;50:221-6. |
| 10. | Schoeller DA. Bioelectrical impedance analysis. What does it measure? Ann N Y Acad Sci 2000;904:159-62. |
| 11. | Langer RD, da Costa KG, Bortolotti H, Fernandes GA, de Jesus RS, Gonçalves EM. Phase angle is associated with cardiorespiratory fitness and body composition in children aged between 9 and 11 years. Physiol Behav 2020;215:112772. |
| 12. | Streb AR, Hansen F, Gabiatti MP, Tozetto WR, Del Duca GF. Phase angle associated with different indicators of health-related physical fitness in adults with obesity. Physiol Behav 2020;225:113104. |
| 13. | Barbosa-Silva MC, Barros AJ. Bioelectrical impedance analysis in clinical practice: A new perspective on its use beyond body composition equations. Curr Opin Clin Nutr Metab Care 2005;8:311-7. |
| 14. | Dittmar M, Reber H, Kahaly GJ. Bioimpedance phase angle indicates catabolism in type 2 diabetes. Diabet Med 2015;32:1177-85. |
| 15. | Physical status: the use and interpretation of anthropometry. Report of a WHO Expert Committee. World Health Organ Tech Rep Ser 1995;854:1-452. |
| 16. | Ciba foundation. Human muscle fatigue: Physiological mechanisms. London: Pitman medicals ltd; 1981. p. 283. |
| 17. | Kushner RF. Bioelectrical impedance analysis: A review of principles and applications. J Am Coll Nutr 1992;11:199-209. |
| 18. | Bhati P, Singla D, Masood S, Hussain ME. Type 2 diabetes mellitus patients manifest greater muscle fatigability than healthy individuals during dynamic fatigue protocol. J Manipulative Physiol Ther 2021;44:205-20. |
| 19. | Park SY, Park CH. Diagnosis of muscle fatigue using surface electromyography and analysis of associated factors in type 2 diabetic patients with neuropathy: A preliminary study. Int J Environ Res Public Health 2021;18:9635. |
| 20. | Bigland-Ritchie B, Jones DA, Hosking GP, Edwards RH. Central and peripheral fatigue in sustained maximum voluntary contractions of human quadriceps muscle. Clin Sci Mol Med 1978;54:609-14. |
| 21. | Enoka RM, Duchateau J. Muscle fatigue: What, why and how it influences muscle function. J Physiol 2008;586:11-23. |
| 22. | Kim J, Wang Z, Heymsfield SB, Baumgartner RN, Gallagher D. Total-body skeletal muscle mass: Estimation by a new dual-energy X-ray absorptiometry method. Am J Clin Nutr 2002;76:378-83. |
| 23. | Hong S, Chang Y, Jung HS, Yun KE, Shin H, Ryu S. Relative muscle mass and the risk of incident type 2 diabetes: A cohort study. PLoS One 2017;12:e0188650. |
| 24. | Biltz NK, Collins KH, Shen KC, Schwartz K, Harris CA, Meyer GA. Infiltration of intramuscular adipose tissue impairs skeletal muscle contraction. J Physiol 2020;598:2669-83. |
| 25. | Yeung CH, Au Yeung SL, Fong SS, Schooling CM. Lean mass, grip strength and risk of type 2 diabetes: A bi-directional Mendelian randomisation study. Diabetologia 2019;62:789-99. |
| 26. | Barbosa-Silva MC, Barros AJ, Wang J, Heymsfield SB, Pierson RN Jr. Bioelectrical impedance analysis: Population reference values for phase angle by age and sex. Am J Clin Nutr 2005;82:49-52. |
| 27. | Di Vincenzo O, Marra M, Sammarco R, Speranza E, Cioffi I, Scalfi L. Body composition, segmental bioimpedance phase angle and muscular strength in professional volleyball players compared to a control group. J Sports Med Phys Fitness 2020;60:870-4. |
| 28. | Ballarin G, Scalfi L, Monfrecola F, Alicante P, Bianco A, Marra M, et al. Body composition and bioelectrical-impedance-analysis-derived raw variables in pole dancers. Int J Environ Res Public Health 2021;18:12638. |
| 29. | Kurpad AV, Varadharajan KS, Aeberli I. The thin-fat phenotype and global metabolic disease risk. Curr Opin Clin Nutr Metab Care 2011;14:542-7. |
[Figure 1]
[Table 1], [Table 2], [Table 3]
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