The Use of an Isometric Handgrip Test to Show Autonomic Damage in People with Diabetes

Introduction Muscle strength may play a role in cardiometabolic disease. We examined the relationship between hand grip strength and diabetes and hypertension in a sample of healthy weight adults. Methods In 2015, we analyzed the National Health and Nutrition Examination Survey 2011–2012 for adults aged ≥20 years with healthy BMIs (between 18.5 and <25 kg/m2) and no history of cardiovascular disease (unweighted n=1,469; weighted n=61,672,082). Hand grip strength was assessed with a dynamometer. Diabetes was based on hemoglobin A1c level and reported diabetes diagnosis. Hypertension was based on measured blood pressure and reported hypertension diagnosis. Results Individuals with undiagnosed diabetes compared with individuals without diabetes had lower grip strength (51.9 vs 69.8, p=0.0001), as well as among individuals with diagnosed diabetes compared with individuals without diabetes (61.7 vs 69.8, p=0.008). Mean grip strength was lower among individuals with undiagnosed hypertension compared with individuals without hypertension (63.5 vs 71.5, p=0.008) as well as among individuals with diagnosed hypertension compared with those without hypertension (60.8 vs 71.5, p<0.0001). In adjusted analyses controlling for age, sex, race, smoking status, and first-degree relative with disease, mean grip strength was lower for undiagnosed diabetes (β= −10.02, p<0.0001) and diagnosed diabetes (β= −8.21, p=0.03) compared with individuals without diabetes. In adjusted analyses, grip strength was lower among individuals with undiagnosed hypertension (β= −6.6, p=0.004) and diagnosed hypertension (β= −4.27, p=0.04) compared with individuals without hypertension. Conclusions Among healthy weight adults, combined grip strength is lower in individuals with diagnosed and undiagnosed diabetes and hypertension.

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“…81 Handgrip strength has been shown to be an indicator of autonomic damage in individuals with diabetes. 82 …”

Section: Discussionmentioning

confidence: 99%

Grip Strength as a Marker of Hypertension and Diabetes in Healthy Weight Adults

Mainous

Tanner

Anton

et al. 2015

American Journal of Preventive Medicine

130

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“…The reported study interventions consisted of cognitive exercises (k = 21) (Cacioppo et al, 2000;Steptoe et al, 2002Wood et al, 2002;Kunz-Ebrecht et al, 2003;Marmot, 2005, 2006;Hamer and Steptoe, 2007;Capuana et al, 2012;Christensen Stephanie and Wright Heather, 2014;Collste et al, 2014;Lin et al, 2014Lin et al, , 2017aCrowley Olga et al, 2016;Norcliffe-Kaufmann et al, 2016;Wawrzyniak Andrew et al, 2016;Beer Noa et al, 2017;Betz Linda et al, 2017;Kuraoka et al, 2019;Perpetuini et al, 2019), cardiorespiratory exercises (k = 18) (Perini et al, 2000;Bartels Matthew et al, 2003;Takahashi et al, 2003;Davrath Linda et al, 2006;Virtanen et al, 2007;Mayumi et al, 2008;Karavirta et al, 2009;Dourado et al, 2010;Alves Naiane Ferraz et al, 2011;Sales et al, 2011;Wang Norman et al, 2011;Corrêa et al, 2013;Ahmadian and Dabidi Roshan, 2015;Junior Adalberto et al, 2019;Rodrigues Jhennyfer et al, 2019;Wittstein et al, 2019;Kaltsatou et al, 2020), resistance exercises (k = 6) (Piepoli et al, 1996;Petrofsky et al, 2009;Millar et al, 2011;<...>…”

Section: Study Characteristicsmentioning

confidence: 99%

“…Bartels Matthew et al, 2003;Alves Naiane Ferraz et al, 2011;Kaltsatou et al, 2020 Alves Naiane Ferraz et al, 2011;Ahmadian and Dabidi Roshan, 2015;Rodrigues Jhennyfer et al, 2019;Kaltsatou et al, 2020 Takahashi et al, 2003;Davrath Linda et al, 2006;Virtanen et al, 2007;Mayumi et al, 2008;Karavirta et al, 2009;Wang Norman et al, 2011;Corrêa et al, 2013;Ahmadian and Dabidi Roshan, 2015;Beer et al, 2017 (Continued) Frontiers in Physiology | www.frontiersin.org Piepoli et al, 1996;Petrofsky et al, 2009;Millar et al, 2011;Beer Noa et al, 2017 ց 1 Beer Noa et al, 2017 Millar et al, 2011;Machado-Vidotti et al, 2014;Beer Noa et al, 2017 ↑ n = significant (p < 0.05) increase from resting HRV, ր n = increasing trend (not significant or not statistically tested) → n = no significant change from resting HRV, ց n = decreasing trend (not significant or not statistically tested), ↓ n = significant (p < 0.05) decrease from resting HRV, n = number of studies reporting this effect, NR = not reported.…”

Section: Phasic Hrv Responses To Cognitive Exercisesmentioning

confidence: 99%

Can Reactivity of Heart Rate Variability Be a Potential Biomarker and Monitoring Tool to Promote Healthy Aging? A Systematic Review With Meta-Analyses

et al. 2021

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Background: Monitoring phasic responses of heart rate variability (HRV) in terms of HRV reactivity [i. e., the absolute change from resting state to on-task (i.e., absolute values of HRV measured during exercise)] might provide useful insights into the individual psychophysiological responses of healthy middle-aged to older adults (HOA) to cognitive and physical exercises.Objectives: To summarize the evidence of phasic HRV responses to cognitive and physical exercises, and to evaluate key moderating factors influencing these responses.Methods: A systematic review with meta-analyses was performed. Publications up to May 2020 of the databases Medline (EBSCO), Embase, Cochrane Library, CINAHL, Psycinfo, Web of Science, Scopus, and Pedro were considered. Controlled clinical trials and observational studies measuring phasic HRV responses to cognitive and/or physical exercises in HOA (≥50 years) were included.Results: The initial search identified 6,828 articles, of which 43 were included into the systematic review. Compared to resting state, vagally-mediated HRV indices were significantly reduced during all types of exercises [Hedge's g = −0.608, 95 % CI (−0.999 to −0.218), p = 0.002] indicating a significant parasympathetic withdrawal compared to rest. The key moderating variables of these responses identified included exercise intensity for physical exercises, and participant characteristics (i.e., level of cognitive functioning, physical fitness), task demands (i.e., task complexity and modality) and the individual responses to these cognitive challenges for cognitive exercises. In particular, higher task demands (task complexity and physical exercise intensity) were related to larger HRV reactivities. Better physical fitness and cognition were associated with lower HRV reactivities. Additionally, HRV reactivity appeared to be sensitive to training-induced cognitive and neural changes.Conclusion: HRV reactivity seems to be a promising biomarker for monitoring internal training load and evaluating neurobiological effects of training interventions. Further research is warranted to evaluate the potential of HRV reactivity as a monitoring parameter to guide cognitive-motor training interventions and/or as a biomarker for cognitive impairment. This may facilitate the early detection of cognitive impairment as well as allow individualized training adaptations that, in turn, support the healthy aging process by optimizing individual exercise dose and progression of cognitive-motor training.

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“…Interestingly, a recent cross‐sectional study has shown that reduced heart rate variability is associated with both early and late measures of DPN, particularly sural sensory nerve dysfunction . In addition, diabetes, and to a lesser extent age‐induced reduction in handgrip strength was associated with significantly impaired autonomic function and forearm and skin blood flow during and after isometric exercise . The autonomic nervous system, together with the intrinsic vascular smooth muscle cell myogenic response and endothelial cell autoregulation, plays a major role in capillary recruitment, a central mechanism favouring muscle metabolism during contraction.…”

Section: Mechanisms Responsible For the Decline In Neuromuscular Funcmentioning

confidence: 99%

Neuromuscular dysfunction in type 2 diabetes: underlying mechanisms and effect of resistance training

Orlando

Balducci

Bazzucchi

et al. 2015

Diabetes Metabolism Res

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Diabetic patients are at higher risk of developing physical disabilities than non-diabetic subjects. Physical disability appears to be related, at least in part, to muscle dysfunction. Several studies have reported reduced muscle strength and power under dynamic and static conditions in both the upper and lower limbs of patients with type 2 diabetes. Additional effects of diabetes include a reduction in muscle mass, quality, endurance and an alteration in muscle fibre composition, though the available data on these parameters are conflicting. The impact of diabetes on neuromuscular function has been related to the co-existence of long-term complications. Peripheral neuropathy has been shown to affect muscle by impairing motor nerve conduction. Also, vascular complications may contribute to the decline in muscle strength. However, muscle dysfunction occurs early in the course of diabetes and affects also the upper limbs, thus suggesting that it may develop independently of micro and macrovascular disease. A growing body of evidence indicates that hyperglycaemia may cause an alteration of the intrinsic properties of the muscle to generate force, via several mechanisms. Recently, resistance exercise has been shown to be an effective strategy to counteract the deterioration of muscular performance. High-intensity exercise seems to provide greater benefits than moderate-intensity training, whereas the effect of a power training is yet unknown. This article reviews the available literature on the impairment of muscle function induced by diabetes, the underlying mechanisms, and the effect of resistance training on this defect. Copyright © 2015 John Wiley & Sons, Ltd.

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The Use of an Isometric Handgrip Test to Show Autonomic Damage in People with Diabetes

Abstract: Diabetes is associated with a reduction in handgrip strength and significantly impaired autonomic function during and after isometric exercise.

Cited by 18 publications

References 22 publications

Grip Strength as a Marker of Hypertension and Diabetes in Healthy Weight Adults

Grip Strength as a Marker of Hypertension and Diabetes in Healthy Weight Adults

Can Reactivity of Heart Rate Variability Be a Potential Biomarker and Monitoring Tool to Promote Healthy Aging? A Systematic Review With Meta-Analyses

Neuromuscular dysfunction in type 2 diabetes: underlying mechanisms and effect of resistance training

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