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. Author manuscript; available in PMC: 2025 Mar 27.
Published in final edited form as: J Aging Phys Act. 2020 Apr 21;28(5):723–730. doi: 10.1123/japa.2019-0221

Hemodynamic Function of Forearm Muscle in Postmenopausal Women With Type 2 Diabetes

Luca Pollonini 1, Lauren Gulley Cox 2, Stacey L Gorniak 3
PMCID: PMC11948345  NIHMSID: NIHMS2053680  PMID: 32315982

Abstract

Changes in the hemodynamic function of muscle are speculated as a causal mechanism for reduced motor capabilities with aging in Type 2 diabetes mellitus (DM). The focus of this study was to evaluate changes in muscle oxygenation during sustained force production in postmenopausal women with DM compared with controls. Near-infrared spectroscopy was used to monitor deoxyhemoglobin and oxyhemoglobin in the flexor digitorum superficialis. Sensorimotor function and health state covariates were also assessed. Increased deoxyhemoglobin was found during force production, whereas oxyhemoglobin remained constant. Changes were found in the time structure of the hemodynamic data during force production. No between-group differences were found; instead, measures covaried with the health state. Sex-based differences in the manifestation of DM-related sensorimotor dysfunction are likely. These data indicate that basic cardiovascular health measures may be more beneficial to monitoring hyperemic status and muscle function in postmenopausal women with DM, compared with DM diagnosis.

Keywords: health state markers, hyperemic response, near-infrared spectroscopy, sex-based differences

Almost 24% of the 40 million individuals in the United States aged 60 years and older are currently living with Type 2 diabetes mellitus (DM; Centers for Disease Control and Prevention, 2012). Persons with DM experience declines in hand/finger sensorimotor function compared with healthy individuals (Christman, Vannorsdall, Pearlson, Hill-Briggs, & Schretlen, 2009; van den Berg et al., 2008); however, self-awareness of these changes is low (Gorniak, Khan, Ochoa, Sharma, & Phan, 2014). Reduced functional hand use has been associated with a loss of independent living and reduced quality of life (Anderson, 2004; Snoek, IJzerman, Hermens, Maxwell, & Biering-Sorensen, 2004). Tactile dysfunction due to peripheral neuropathy (PN) has been implicated as the primary cause of motor deficits in persons with DM (Allen, Kimpinski, Doherty, & Rice, 2014; Casanova, Casanova, & Young, 1991; Cederlund et al., 2009; Sayer et al., 2005); however, our recent work has demonstrated that motor changes in persons with DM occur independent of tactile impairment, unrelated to disease duration and severity (Ochoa, Gogola, & Gorniak, 2016). Our data point toward other factors, such as proprioceptive changes, alterations in motor unit structure and function, and reduced hemodynamic function, as underlying mechanisms for these motor changes. Due to the confluence of multiple systemic changes in persons with DM, the contribution of impaired systems, beyond tactile dysfunction, to motor dysfunction prior to PN diagnosis is fully plausible.

Altered hemodynamic responses due to both microvascular and macrovascular changes have been implicated as a potential source of global motor changes in persons with DM (Allen et al., 2014; Zochodne, 2007). Specifically, endothelial dysfunction within dermal and muscle tissues in adults with DM has been related to altered motor behaviors, including abnormal force production (Petrofsky, 2011; Petrofsky et al., 2005). Reduced blood flow to the extremities during isometric actions likely directly relates to reduced grip force production and abnormal kinetic output exhibited in persons with DM (Ochoa et al., 2016; Petrofsky, 2011; Petrofsky et al., 2005). Correspondingly, our hypothesis is that altered hemodynamic function during isometric actions directly relates to poor motor performance in persons with DM. In particular, postmenopausal women may experience significant deterioration of both hemodynamic function and resultant motor behaviors, given the disproportionate risk of cardiovascular complications in women with DM compared with men with DM and persons without DM (Kautzky-Willer, Harreiter, & Pacini, 2016; Raparelli, Morano, Franconi, Lenzi, & Basili, 2017).

Accordingly, the focus of this study was to evaluate changes in muscle oxygenation and features of kinetic performance during sustained maximal grip and pinch force production in postmenopausal women both with and without DM. We expected to see between-group changes in muscle oxygenation indices such as oxygenated hemoglobin (HbO) and deoxygenated or reduced hemoglobin (HbR) in superficial hand/finger extrinsic muscles during sustained maximal force production tasks (Hypothesis 1). We also expected to see declines in force produced between the onset and end of each trial, with between-group differences emerging (Hypothesis 2). In addition, based on our previous work, we expected to see between-group differences in the temporal structure of force variability and hemodynamic variability (Hypothesis 3). No hypotheses regarding changes in sensorimotor function with disease state were developed a priori, as an investigation of sensorimotor deterioration with disease state was an exploratory aim of this study.

Materials and Methods

Participants

Twenty-one postmenopausal women with DM and 21 age- and sex-matched healthy controls volunteered to participate in the study; the demographics, including age, body mass index (BMI), glycated hemoglobin (A1c), total cholesterol, blood pressure, and duration of DM (if applicable), are presented in Table 1. Handedness was assessed by the Edinburgh Inventory (Oldfield, 1971), ranging from a laterality quotient of −100 (strong left-handedness) to +100 (strong right-handedness). The participants had a laterality quotient average of +88 and had no previous history of trauma to the upper limbs. Study participants were excluded if they reported a history of neurological and/or musculoskeletal disorders (Parkinson disease, Huntington’s disease, polio, multiple sclerosis, stroke, traumatic brain injury, carpal tunnel syndrome, rheumatoid arthritis, monoclonal gammopathy of undetermined significance, paraproteinaemic demyelinating neuropathy, Myasthenia Gravis, amputation, a history of major surgical intervention of the upper extremity, or hereditary or compression neuropathies). We did not exclude individuals based on history of occupation, experience in housekeeping, typing, or other upper-extremity-intensive tasks. In accordance with the Declaration of Helsinki, the participants provided informed consent according to the regulations established by the Institutional Review Board at the University of Houston (protocol no. 15615-01). The data collection processes failed for five participants (e.g., a reliable signal was not detected) due to device malfunction. The data from those participants have been excluded from these analyses.

Table 1.

Demographic and Clinical Characteristics of Participants With DM

Participant Age
(years)		 Menopausal
age (years)		 BMI
(kg/m2)		 DM duration
(months)		 A1c
(%)		 Total cholesterol
(mg/dL)		 Systole
(mmHg)		 Diastole
(mmHg)
1 63 50 27.4 60 6.7 – 151 81
2 79 45 28.3 144 7.9 – 155 75
3a 65 50 40.7 120 7.1 – 145 97
4b 66 50 29.3 186 8.7 199 111 62
5a 64 40 44.1 60 6.2 109 180 91
6 60 50 37.5 387 10.4 224 161 78
7a 60 55 33.7 245 8 143 130 70
8 57 49 36.9 41 8.6 176 130 88
9 73 60 25.3 201 6.8 125 167 78
10 68 23 31.8 168 5.7 219 164 89
11 70 45 26.9 200 6.1 266 130 71
12 62 38 32.4 36 6.2 189 124 70
13 67 45 30.2 1 8 144 158 97
14a,b 66 45 31.4 262 6.3 175 142 75
15a 69 55 42.3 298 8.4 185 139 63
16 58 51 32.8 95 7.4 143 153 89
17a 55 27 38.6 385 7.4 126 133 68
18 67 25 30.5 1 7.7 183 148 73
19a 71 52 42.9 196 8.5 173 105 60
20 69 27 36.3 149 8.7 187 202 100
21 60 37 30.1 1 6.7 183 179 111
Mean 65 43 33.8 154 7.5 175 148 80
SD 6 11 5.6 117 1.2 39 23 14
Controls 67 ± 6 50 ± 7 24.1 ± 4.5 – 5.3 ± 0.3 200 ± 43 147 ± 21 86 ± 14
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Note. DM = diabetes mellitus; A1c = glycated hemoglobin; BMI = body mass index; –, data collection failure.

a

A clinical diagnosis of diabetic peripheral neuropathy.

b

A history of Prempro Rx (in addition to three control participants).

Health Status Information

Blood pressure and A1c values were assessed for all study participants on site. A1c values were assessed using a commercially available point of care evaluation kit (A1c Now+; PTS Diagnostics, Indianapolis, IN). The DM sample group contained a crosssection of individuals with various A1c levels, as indicated in Table 1. A1c levels below 7.0% are considered well-managed DM. The normal, healthy A1c range is 4.7–5.7% in persons without DM. Blood pressure was measured using a commercially available device (Omron Intellisense 10 series blood pressure monitor, model BP785; Omron Healthcare, Inc., Bannockburn, IL). The presence of PN (PN status) was determined by abnormalities on either clinical examination or Electromyography/Nerve Conduction Velocity testing (EMG/NCV) (per physician). A brief menopause questionnaire was also administered regarding several aspects of menopausal characteristics (e.g., age at onset of menopause, hormone replacement therapy [HRT] history, etc.). All study participants declared themselves to be postmenopausal; 11 participants claimed a history of HRT (five with a history of Prempro use).

Sensory Evaluations

The Semmes–Weinstein monofilament test was used to evaluate tactile sensation of the dominant hand (Feng, Schlosser, & Sumpio, 2009). The monofilament testing sites included the tip of the thumb/Digit 1 (median nerve), the tip of hypothenar eminence/ Digit 5 (ulnar nerve), and the dorsal aspect of the thumb (radial nerve). During the test, the participants kept their eyes closed and verbally indicated if and where they perceived the monofilament touch. The monofilament size was increased until the subject was able to detect its touch a minimum of two times at the same location.

Muscle Hemodynamics Measurements

To measure hemodynamic function, we used a near-infrared spectroscopy (NIRS) sensor, measuring concentrations of HbO and HbR of the flexor digitorum superficialis (FDS) muscle (Pollonini, 2018; Pollonini, Re, Simpson, & Dacso, 2012; Pollonini, Younes, & Gorniak, 2017). NIRS is an optical technique in which light in the near-infrared wavelength range (650–1,000 nm) is shined into tissues using low-power emitters (lasers or light emitting diodes). After the light is partially absorbed by HbO and HbR, it is then partially back scattered to the skin surface by the tissue structure. This back scatter is detected at a certain distance using a photodetector. Different absorption spectra of HbO and HbR as a function of wavelength permits determination of the overall contribution of each chromophore to the light absorption, which in turn, depends on their individual concentration changes according to the modified Beer–Lambert Law (Kocsis, Herman, & Eke, 2006).

The custom-designed NIRS apparatus we used was previously described (Pollonini, 2018; Pollonini et al., 2012, 2017). In brief, the NIRS probe consisted of three laser diodes emitting at 685, 830, and 980 nm, and a photodetector located 30 mm away from the diodes. The optical layout is estimated to probe tissues at an average of 13–15 mm (Patil, Safaie, Abrishami Moghaddam, Wallois, & Grebe, 2011), sufficient to investigate the FDS during force production tasks. The FDS is within this tissue distance within the forearm. Given the measurement depth limitations of the device, we were not able to measure the hemodynamics of deeper hand/finger flexors, such as the flexor digitorum profundus. Previous publications have indicated similar devices (e.g., 30 mm separation distances) as appropriate for hemodynamic analysis of the FDS (Pollonini et al., 2017; Soller et al., 2006; van Beekvelt, van Engelen, Wevers, & Colier, 2002). The three-wavelength scheme, including the peak optical absorption of water in the near-infrared spectrum (i.e., 980 nm), enabled an estimation of the stoichiometric concentration of HbO and HbR (Pollonini, 2018; Pollonini et al., 2012; Rovati, Bandera, Donini, Salvatori, & Pollonini, 2004). Muscle oxygenation measurements were acquired at 50 Hz before, during, and after the force production tasks. Muscle oxygenation measurements are reported in micromolar (μM).

The optical probe was placed on the dominant forearm of each subject, directly over the radial aspect of the FDS. The probe emitter and detector were aligned with the estimated line of action of the FDS, as determined by palpation of the muscle during repeated flexion of the fingers (Figure 1a-1c). The probe was lightly, yet securely, attached to the forearm of the subject using a self-adherent wrap (Coban; 3M, St. Paul, MN; Figure 1c). The measurement site was then covered with a black cloth to prevent light contamination from the testing environment. Prior to the application of the probe, the depth of the subcutaneous fat layer was measured over the radial aspect of the FDS belly using a commercial skinfold caliper (Slim Guide; Creative Health Products, Ann Arbor, MI). The average forearm fat layer in participants was 3.0 ± 0.5 mm. This particular oximeter device used a software algorithm to adjust the HbO and HbR readings to account for the thickness of the fat layer (Niwayama, Lin, Shao, Kudo, & Yamamoto, 2000; Pollonini, 2018).

Figure 1 —

Figure 1 —

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Picture of the NIRS device placement. The light-blocking fabric overlay for the device was removed in all photos in this series to illustrate the location of the NIRS device on the forearm. (a) Palpation of the FDS. (b) Orientation of the NIRS probe directly on top of the palpated FDS. (c) The NIRS device secured to the forearm using a self-adherent wrap (Coban; 3M). NIRS = near-infrared spectroscopy; FDS = flexor digitorum superficialis.

To prevent compression of the NIRS probe into the forearm, the participants sat with their dominant forearms completely supported in the supine position by a custom padded support rig. All force production tasks were completed with the arm extended and supinated. Given this configuration, the subjects were not asked to bear the weight of the object; instead, all kinetic testing devices were suspended so that the participants could easily touch and exert force on the devices without movement of the hand/fingers/forearm (SnakeClamp Products, Riner, VA).

Force Production Tasks and Evaluation

We asked the participants to perform a series of force production tasks consisting of hand gripping at maximal strength and pinching at maximal strength using the dominant hand. The isometric force production task is consistent with our use of isometric contractions utilized in our previous hand/finger force tracking investigations in persons with DM (Gorniak et al., 2014; Gorniak, Ray, Lee, & Wang, 2019; Ochoa et al., 2016; Pollonini et al., 2017). All 30 s force production trials were preceded by a 5 s baseline NIRS evaluation of FDS hemodynamic function and immediately followed by a 30 s NIRS evaluation of hemodynamic recovery. Maximal hand grip strength was evaluated using a biometrics hand dynamometer and wireless DataLOG system (Dynamometer Model G200, DataLOG model MWX8; Biometrics Ltd., Newport, United Kingdom). Once the word “begin” was announced, the participants were to exert maximal grip using a five-fingered grasp for 30 s before relaxing. Force production during the 30 s interval was monitored visually by study personnel to ensure the participants exerted force for the entire duration. Verbal encouragement was provided throughout each trial. After each trial, a break was permitted for at least two full minutes. Three maximum grip force production trials were collected. Maximal pinch evaluation was conducted using a biometrics pinch dynamometer and wireless DataLOG system (Precision Pinchmeter model P200, DataLOG model MWX8; Biometrics Ltd.). At the instruction of the examiner, the participant was to pinch the device using Digits 1 and 2 only for approximately 30 s before relaxing. We collected three maximum pinch force production trials, allowing for a minimum of a 2 min resting period between trials, with more rest given when requested by the participant.

The structure of force output variability and hemodynamic function variability was quantified via approximate entropy (ApEn) and detrended fluctuation analysis. Further analysis of the correlation among force production and hemodynamic responses during the 30 s force production interval was evaluated via simple correlation and cross-correlations (taking time lags between the two data types into account).

Statistical Analysis

The data are presented as mean ± SE. The data were compared between groups using mixed model analyses of covariance (ANCOVA) via SPSS 25 (IBM Corp., Armonk, NY). Between-subject factors were group (two levels: DM vs. controls). For the monofilament data, the main factors included group and nerve (three levels: one level each for the median, radial, and ulnar nerves). An evaluation of health state covariates was done to control for health state variability both within and across the two sample groups. Analyses of covariance included health state covariates of A1c, systolic and diastolic blood pressure, low-density lipoprotein (LDL) cholesterol, high-density lipoprotein (HDL) cholesterol, disease duration, menopausal age, BMI, PN status (via indicator variable), history of prediabetes diagnosis (via indicator variable), and history of treatment with Prempro (via indicator variable). The covariates were selected via automatic linear modeling using forward stepwise selection functions in SPSS. In the event of significant covariates determined via automatic linear modeling and analysis of covariance, follow-up correlation analyses were performed between the health state covariate and the measured behavior. The monofilament data were log transformed due to nonlinearity. Nontransformed values are shown in the figures to avoid reader confusion.

Results

Tactile Evaluation

A significant difference in tactile detection thresholds was not specifically confirmed between groups (p = .159); however, the addition of covariates with the statistical model led to a between- group difference that approached significance (p = .098). Tactile detection threshold values did differ among all tested sites, nerve: F (2,195) = 4.52, p < .05 (Figure 2), with a significant difference between the median and radial nerve tactile detection thresholds. No differences were detected between hands (p ~ .3). Tactile function was worsened in persons with higher BMI, higher A1c, and in those diagnosed with PN (see Table 2 for covariate results).

Figure 2 —

Figure 2 —

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Tactile sensory function data from the Semmes–Weinstein monofilament exam. Group mean and SE values are shown for each of the three nerves of the hand.

Table 2.

Significant ANCOVA Covariate Output and Regression Results

Measure Covariate F p r p
Tactile evaluation BMI 11.68 <.001 .29 <.001
A1c 4.49 <.05 .15 <.05
PN status 25.05 <.001 .27 <.001
Duration 49.56 <.001 – –
Pinch force ApEn Age at menopause 7.66 <.05 – –
Pinch force DFA Prediabetes 16.39 <.05 −.49 <.005
HbO drop pinch force Diastole 6.88 <.05 – –
PN status 8.78 <.05 −.39 <.05
ApEn HbO (pinch) A1c 9.77 <.05 – –
Prediabetes 8.42 <.01 .47 <.005
DFA HbO (pinch) Duration 9.21 <.05 −.39 <.05
ApEn HbR (pinch) HDL cholesterol 9.52 <.05 −.49 <.005
r (HbO and grip force) LDL cholesterol 15.76 <.005 – –
rmax (HbO and grip force) LDL cholesterol 24.33 <.005 – –
Diastole 14.34 <.01 −.41 <.05
r (HbR and grip force) Diastole 7.50 <.05 −.35 <.05
r (HbR and pinch force) BMI 5.32 <.05 – –
Systole 6.26 <.05 – –
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Note. A1c = glycated hemoglobin; ANCOVA = analysis of covariance; ApEn = approximate entropy; BMI = body mass index; DFA = detrended fluctuation analysis; DM= diabetes mellitus; HbO = oxygenated hemoglobin; HbR = deoxygenated hemoglobin; HDL cholesterol = high-density lipoprotein cholesterol; LDL cholesterol = low-density lipoprotein cholesterol; PN status = diabetic peripheral neuropathy diagnosis.

Maximal Force Production

The average force produced at the onset of the 30 s interval for grip and pinch testing was 190.6 ± 54.5 N and 39.86 ± 6.1 N, respectively, across all participants (Figure 3a). During the maximal force production tasks lasting 30 s, the forces produced in both tasks dropped by 60.3 ± 2.33% in the grip trials and 60.0 ± 2.43% in the pinch trials between the maximal force produced and the end of the force production period across all participants (Figure 3b). Exemplar data for maximal grip and maximal pinch force production of a control subject in the 30 s interval can be found in Figure 3c and 3d. Neither between-group differences nor covariates emerged for any maximal force production values.

Figure 3 —

Figure 3 —

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Force production data. (a) Mean and SE of maximal forces produced in maximal grip and pinch trials. (b) Mean and SE of force decay exhibited. (c) Exemplar data illustrating the decline of force production in a grip force production trial. (d) Exemplar data illustrating the decline of force production in a pinch force production trial. (e) Exemplar HbO (left axis) and HbR (right axis) during grip force production. (f) Exemplar HbO (left axis) and HbR (right axis) during pinch force production. DM = diabetes mellitus; HbO = oxygenated hemoglobin; HbR = deoxygenated hemoglobin.

Nonlinear analysis (ApEn and detrended fluctuation analysis) of maximal force production profiles did not yield any significant differences in either group status or covariate values with respect to grip force profiles across all subjects. Similarly, group status did not significantly impact pinch force production; however, effects of the covariates in the pinch force production profiles were found. Age at menopause and previous diagnosis of prediabetes impacted nonlinear features of pinch force production (see Table 2 for details).

Hemodynamic Responses

At baseline, the average HbO and HbR values were 61.5 ± 2.9 μM and 14.4 ± 2.2 μM, respectively, across all tasks and all participants. In the grip force production tasks, the average HbO and HbR values were 61.6 ± 2.8 μM and 15.8 ± 2.1 μM. The average HbO and HbR values were 59.9 ± 3.1 μM and 13.8 ± 2.0 μM during maximal pinch force production. Exemplar data for HbO and HbR during both tasks produced by the same control subject in Figure 3c and 3d can be found in Figure 3e and 3f.

During maximal force production tasks lasting 30 s, HbO stayed the same (no overall change between the onset of force production and the end of the 30 s force production), whereas HbR increased overall, confirmed via one sample t test (p < .05), shown in Figure 4a. HbR increased by 11.7 ± 5.0% and 9.4 ± 3.6% in the grip and pinch tasks, respectively. A significant group difference in HbR increase emerged only in the pinch tasks, such that the control group had a net positive increase in HbR, whereas the DM group did not (p < .05; Figure 4b).

Figure 4 —

Figure 4 —

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Hemodynamic change data. (a) Mean and SE of average changes in HbO and HbR in grip force production trials. (b) Mean and SE of average changes in HbO and HbR in pinch force production trials. DM = diabetes mellitus; HbO = oxygenated hemoglobin; HbR = deoxygenated hemoglobin. *Statistical significance.

With respect to group-based or covariate influences on hemodynamic function, no significant effects were found during grip force production; however, a number of significant effects emerged during pinch force production. HbO drop during force production was found to covary with both diastole and PN status (see Table 2 for covariate results). ApEn of HbO covaried with both A1c and a previous diagnosis of prediabetes. Detrended fluctuation analysis of HbO negatively covaried with the duration of the disease, and ApEn of HbR negatively covaried with HDL cholesterol. None of the presented correlations significantly changed when group status was accounted for in the models.

Correlations

An assessment of the bivariate and maximal correlation (via crosscorrelation) values did indicate some effects of covariates on measures of interest, but no significant group effects were indicated.

With respect to grip force production, LDL cholesterol emerged as a significant covariate for the bivariate correlation between HbO and grip force, as well as the maximal correlation (rmax) between HbO and grip force (see Table 2). Diastole emerged as a significant covariate for the time occurrence of maximal correlation between HbO and grip force, as well as HbR and grip force. None of the presented correlations significantly changed when group status was accounted for in the models.

With respect to pinch force production, BMI and systole emerged as significant covariates for the bivariate correlation between HbR and pinch force. No significant correlations emerged.

Discussion

The purpose of the current study was to evaluate changes in muscle oxygenation and features of kinetic performance during sustained maximal force production of the hand/fingers in postmenopausal women both with and without DM. In terms of our hypotheses, the data support (at least in part) each hypothesis. With respect to Hypothesis 1, the HbR values differed between groups during pinch force production tasks, whereas the HbO values remained mostly constant. In support of Hypothesis 2, declines in the maximal force production output were observed in both tasks across all subjects. Regarding Hypothesis 3, some changes were found in the temporal structure of the hemodynamic data during the pinch force tasks. These changes did not appear to differ between groups; however, they instead covaried with health state markers. With respect to our exploratory hypothesis, we did find evidence of a relationship between altered hemodynamic responses and health state measures. In the following paragraphs, we discuss the results of this study in regard to the published literature as it relates to possible sex-based differences in hyperemic responses and the impact of health state markers in the assessment of hemodynamic function.

Contrary to our previous findings (Gorniak et al., 2014; Ochoa et al., 2016; Ochoa & Gorniak, 2014), differences in tactile function, maximal force profiles, and force production variability measures between postmenopausal women with DM and controls were not found. On the surface, this lack of result is surprising; however, this particular study focused on postmenopausal women, whereas our previous work included a crosssection of participant ages, including both males and females. It is possible that sex-based differences in sensorimotor function exist in the DM population, particularly as age increases. Sex-based differences in the presentation of DM complications is an emerging area of interest in metabolic research (Norhammar & Schenck-Gustafsson, 2013). We are currently exploring the potential confound of sex as a factor in our previous work with respect to sensorimotor function, as well as preparing for the investigation of sex-based differences in our future work.

Impaired blood flow to tissue (known as decreased hyperemic response) is common in persons with DM (Vogelberg, Mayer, & König, 1990). Decreases in hyperemic responses of muscle have also been found in postmenopausal females compared with males (both without DM), indicating a baseline difference in muscle blood flow due to sex (Parker et al., 2007; Parker, Smithmyer, Pelberg, Mishkin, & Proctor, 2008; Proctor, Koch, Newcomer, Le, & Leuenberger, 2003). There is some evidence that estrogen-based HRT in postmenopausal females improves muscle-based hyperemic responses (Fadel et al., 2004; Peterson et al., 2000); however, it is not clear if this improvement may also help to overcome any hyperemic deficits due to a combination of sex and DM. The discontinued use of conjugated estrogen HRT in the early 2000s due to the emergence of cardiovascular complications of the approach (e.g., Prempro) further complicates the picture (Wells & Herrington, 1999), as a history of use introduces further potential for cardiovascular disease risk and thus heterogeneity into this population of interest. In the current data set, impaired hyperemic responses were indicated in the DM group compared with controls, via a lack of change in the HbR concentration in the DM group during the pinch force production tasks. This result was not impacted by HRT replacement status (both HRT history overall and specifically by a history of Prempro use), indicating hyperemic deficits linked to metabolic dysfunction of DM without a directional influence of HRT in postmenopausal females.

Despite this group-based difference, variability in other measures of interest appeared to be more related to cardiovascular health state and not DM group status, indicating that overall cardiovascular health may be a better indicator of hyperemic status and muscle health compared with a diagnosis of DM and A1c value. As hyperemic status is an important predictor in determining patient suitability for DM-related interventions (Wall et al., 2004), this is notable. Measures such as maximal force produced, blood (de)oxygenation, variability in blood (de) oxygenation, and correlations between blood (de)oxygenation and forces were worsened with indicators of a worsened health state (e.g., prediabetes diagnosis, low HDL cholesterol, high LDL cholesterol, duration of diabetes, increased diastole, and history of PN diagnosis). In particular, the increase in variability in blood flow features indicates poor blood flow dynamics, an indicator of vessel dysfunction driven by a combination of cardiovascular changes, indicated by a worsened health state outside of DM diagnosis and severity. These data indicate that a poor overall health state, independent of specific DM medical management, is a significant contributor to the functional use of blood oxygenation by muscle.

These blood flow dynamics are not as poor as other cardiovascular disorders (e.g., peripheral arterial disease), thereby potentially explaining why motor function may not be as significantly impacted in postmenopausal females with DM as evidenced in other cardiovascular diseases (e.g., peripheral arterial disease; Myers et al., 2009); however, more work is needed to determine if this also holds for males with DM.

Conclusions

Generally, our data indicate that basic cardiovascular health measures may be beneficial to monitoring hyperemic status in persons with DM, which may be particularly helpful when assessing patient suitability for intervention. Overall, we did not find DM-related differences in sensorimotor function in postmenopausal females with DM; however, sex-based differences in DM-related complications may have led to this result. Further work is needed to clarify the role of sex-based differences in the presentation of DM-related complications.

Acknowledgments

This work was supported by an American Heart Association Grant (16BGIA27250047) to S.L. Gorniak. None of the authors has any conflict of interest to disclose.

Contributor Information

Luca Pollonini, Department of Engineering Technology, University of Houston, Houston, TX, USA..

Lauren Gulley Cox, Center for Neuromotor and Biomechanics Research, University of Houston, Houston, TX, USA; Department of Health and Human Performance, University of Houston, Houston, TX, USA..

Stacey L. Gorniak, Center for Neuromotor and Biomechanics Research, University of Houston, Houston, TX, USA; Department of Health and Human Performance, University of Houston, Houston, TX, USA.

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