544
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Aim To identify physical, cognitive, and metabolic factors 
affecting gait speed in patients with type-2 diabetes mel-
litus (T2DM) without neuropathy.
Methods This cross-sectional study enrolled 71 diabetic 
patients without neuropathy (mean age 55.87 ± 7.74 years, 
85.9% women). Neuropathy status was assessed with 
Douleur Neuropathique 4. We used a cut-off point for gait 
speed of 1 m/s to classify the participants into two groups: 
slow walkers (SW) and average and brisk walkers (ABW). 
The groups were compared in terms of age, sex, body mass 
index (BMI), hemoglobin A1c (HbA1c), fasting glucose, sys-
tolic blood pressure, maximal aerobic capacity (VO2 max), 
percentage of muscle mass, percentage of lower extrem-
ity muscle mass, Mini-Mental State Examination (MMSE) 
score, and years of education.
Results Compared with the ABW group, the SW group 
had significantly lower VO2max (14.49 ± 2.95 vs 16.25 ± 2.94 
mL/kg/min) and MMSE score (25.01 ± 3.21 vs 27.35 ± 1.97), 
fewer years of education, and these patients were more 
frequently women (P < 0.05). In the multivariate regression 
models, the combination of VO2 max, sex, and MMSE score 
explained only 23.5% of gait speed (P < 0.001). MMSE score 
and VO2 max independently determined gait speed after 
adjustment for age, BMI, HbA1c, fasting glucose, systolic 
blood pressure, percent of muscle mass, percent of lower 
extremity muscle mass, and years of education.
Conclusion In diabetic patients without neuropathy, phys-
ical impairment and disability could be prevented by an im-
provement in aerobic capacity and cognitive function.
ClinicalTrials.gov number: NCT04758364
Received: October 4, 2021
Accepted: November 30, 2022
Correspondence to: 
Gulin Findikoglu 
Pamukkale University, Faculty of 
Medicine 
Department of Physical Medicine 
and Rehabilitation 
Pamukkale-Denizli, Turkey 
gulin_dr@yahoo.com
Gulin Findikoglu1, 
Abdurrahim Altinkapak1, 
Hakan Alkan1, Necmettin 
Yildiz1, Hande Senol2, 
Fusun Ardic1
1Department of Physical Medicine 
and Rehabilitation, Pamukkale 
University, Denizli, Turkey
2Department of Biostatistics, 
Pamukkale University, Denizli, 
Turkey
Cognitive function and 
cardiorespiratory fitness affect 
gait speed in type-2 diabetic 
patients without neuropathy
RESEARCH ARTICLE 
 
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https://doi.org/10.3325/cmj.2022.63.544
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Findikoglu et al: Gait speed in type-2 diabetic patients without neuropathy
www.cmj.hr
In elderly and middle-aged adults, gait performance indi-
cates health and functional status. Gait speed at the usu-
al pace is a strong predictor for a range of adverse out-
comes and denotes the multisystemic well-being of an 
individual (1).
Patients with diabetes mellitus (DM) with neuropathy com-
pared with individuals without DM have a slower walking 
speed, shorter step length, increased step width, prolonged 
stance phase, increased gait variability, and improper dis-
tribution of foot pressure (2,3). These alterations have been 
attributed to an impairment of sensory or motor nerves or 
the central nervous system and to a decreased strength 
of lower extremity muscles (2,4). However, impaired gait, 
physical capacity (5), and functional mobility tests were 
also found in diabetic patients without neuropathy com-
pared with individuals without DM (3).
DM was also associated with an increased risk of cognitive 
deficits and dementia (6). Reduced cognitive function was 
identified even in early stages of DM. DM and hyperten-
sion were also separate risk factors for dementia due to the 
development of cerebrovascular pathologies (7).
There is a lack of studies on factors associated with gait 
speed in diabetic individuals without neuropathy (8). 
Therefore, the aim of this study was to compare possible 
factors affecting gait speed between slow walkers (SW) 
and average or brisk walkers (ABW) with DM without neu-
ropathy. The second aim was to investigate the effect of 
age, sex, muscle mass, aerobic capacity, cognitive func-
tion, blood pressure, metabolic measures, and years of 
education on gait speed in diabetic individuals without 
neuropathy.
PATieNTS ANd meTHodS
Patients
This cross-sectional study was conducted at the Physical 
Medicine and Rehabilitation Clinic of Pamukkale University 
in February and March 2021. A total of 109 individuals with 
type-2 DM selected with computer-based randomization 
were interviewed and assessed for the presence of neu-
ropathy. Participants self-reported a physician’s diagnosis 
of DM and time of onset of DM. All participants were under 
medical supervision and were taking anti-diabetic and/or 
antihypertensive agents. All could ambulate independent-
ly. We also inquired about the presence of depression and 
hypothyroidism, factors that also affect gait speed.
As DM duration longer than 10 years is strongly associat-
ed with the development of diabetic neuropathy, we en-
rolled patients with T2DM duration shorter than 10 years 
but longer than 1 year (5). These patients were assessed for 
the presence of neuropathic symptoms with the Douleur 
Neuropathique 4 (DN4) questionnaire. DN4 is used to as-
sess neuropathic pain (9) and was validated for diabetic 
neuropathy (10). Its validity and reliability were confirmed 
for Turkish patients (11). Diabetic patients with scores less 
than 4 out of 10 points were included in the study.
Exclusion criteria were insulin therapy, poor glycemic con-
trol, manifesting cardiovascular disease, retinopathy or 
other visual problems, diabetic neuropathy, nephropathy, 
cerebrovascular disease, prominent cognitive impairment, 
alcohol dependence, cancer, chemo/radiotherapy, foot ul-
cer, orthopedic or surgical problems interfering with gait, 
wheelchair or any assistive devices for ambulation, or knee 
or hip arthritis. Eighty participants met the inclusion and 
exclusion criteria, and 71 accepted to participate.
Gait speed was assessed with G-walk (BTS Bioengineering, 
Quincy, MA, USA), a system with demonstrated validity and 
reliability (12) consisting of inertial sensors: a triaxial accel-
erometer, magnetometer, and gyroscope. It was positioned 
on the S1 vertebra with a semi-elastic band. The participants 
walked on a smooth surface for 7 m at their usual pace and 
returned. The cut-off point for SW was 1.0 m/s (8,13).
The factors related to both gait and DM were considered 
potential explanatory variables. These included age, sex, 
BMI, HbA1c level, fasting glucose, systolic blood pressure, 
maximal oxygen consumption (VO2max), percentage of 
muscle mass, percentage of lower extremity muscle mass, 
MMSE score, and years of education.
Height was measured without shoes on a stadiometer. 
Body composition was evaluated with Tanita MC580 (Tan-
ita, Arlington Heights, IL, USA), a valid and reliable bioelec-
tric impedance analyzer (14). Weight, body mass index, 
percentage of muscle mass, and percentage of lower ex-
tremity muscle mass were assessed. Muscle mass percent-
age was expressed with respect to body weight. Before the 
analyses, participants did not eat or drink for more than 
three hours but were prompted to urinate.
Blood glucose levels and HbA1c were detected in blood 
samples after overnight fasting. Blood pressure was mea-
sured with a sphygmomanometer on the left arm in 
the sitting position after rest.
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VO2 max was measured with the cardiopulmonary exercise 
test on a bicycle (Bike-med, Technogym, Cesena, Italy) by 
an ergometer (CareFusion 234 Gmb 2011, Hoechberg Ger-
many) using breath-by-breath technique. Exercise testing 
was made by a ramp protocol starting with 30 W and in-
creasing 15 W per minute until respiratory exchange ratio 
≥1.10, when VO2max was measured. Blood pressure, heart 
rate, and ECG were monitored during resting, exercise test-
ing, and the recovery period. Exercise tests ended without 
any complications.
MMSE, a questionnaire evaluating orientation, attention, 
calculation, memory recall, language, and visual-spatial 
skills (15), has been widely used for cognitive function as-
sessment. The scores between 24 and 30 denote a normal 
cognitive function, scores between 18 and 23 indicate mild 
dementia, and scores below 17 indicate severe dementia. 
Its validity and reliability were confirmed for Turkish patients 
(16). Due to a close relationship of cognitive functions and 
education, education level was expressed in years.
The study was approved by the Non-invasive Clinical Re-
search Ethics Committee of Pamukkale University. This 
study conformed to the Declaration of Helsinki. All partici-
pants provided written informed consent.
Statistical analysis
Continuous variables are expressed as means ± standard 
deviation (SD), and categorical data are expressed as fre-
quencies and percentages. The normality of distributio was 
tested with the Shapiro-Wilk test. Independent sample t 
test or Mann-Whitney U test were used for comparison be-
tween two groups. The differences in categorical variables 
were assessed with the χ2 test or Fisher exact test. The pow-
er of the study was 90%, and beta was 0.10 with respect 
to VO2 max for the comparison between the groups. Multi-
variate linear regression models were performed to deter-
mine factors effecting gait speed. A multivariate regression 
models with backward elimination method was performed 
by entering all of the independent variables into the equa-
tion first, then deleting one variable at a time if it did not 
contribute to the regression. P < 0.05 was considered signif-
icant. The analysis was performed with SPSS 17.0 software 
(SPSS Inc., Chicago, IL, USA).
ReSULTS
The study enrolled 71 patients (61 women) with a mean 
age of 55.87 ± 7.74 years (min 38- max 74). The patients’ 
characteristics are presented in Table 1.
The factors associated with gait speed in SW and ABW 
are shown in Table 2. Compared with the ABW group, the 
SW group had significantly lower VO 2max (14.49 ± 2.95 vs 
16.25 ± 2.94 mL/kg/min) and MMSE score (25.01 ± 3.21 vs 
27.35 ± 1.97), had fewer years of education, and these pa-
tients were more frequently women (P < 0.05). The number 
of patients with depression and hypothyroidism did not 
significantly differ between the groups. Gait speed was re-
lated to sex, VO2 max, muscle mass, MMSE score, and years 
of education (P < 0.05) (Figure 1).
TAbLe 1. Characteristics of patients with type-2 diabetes mellitus (N = 71)
mean ± standard deviation 
or number (%)
min-max
Age (years)
 55.87 ± 7.74
38-74
Sex (male/female)
 10/61 (14.1/85.9)
 -
Body mass index (kg/cm2)
 31.75 ± 4.63
Gait speed (m/s)
  1.09 ± 0.18
 0.76-1.55
Hemoglobin A1c (%)
  6.91 ± 0.88
 5.20-10.20
Fasting glucose (mg/dL)
114.93 ± 24.20
81-212
Systolic blood pressure (mmHg)
125.86 ± 10.14
90-140
VO2 max (kg/mL/min)
 15.86 ± 3.09
Percent of muscle mass
 61.35 ± 6.45
Percent of lower extremity muscle mass
 33.69 ± 17.83
10.9-109.0
Hypertension
 32 (39.5)
 -
Hypothyroidism
 13 (16)
 -
Depression
  6 (7.4)
 -
Mini Mental State Examination Score
 26.62 ± 2.71
18-30
Douleur Neuropathique 4 score
  0.36 ± 0.12
 0-1
Years of education
  8.37 ± 3.72
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Findikoglu et al: Gait speed in type-2 diabetic patients without neuropathy
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A series of multifactorial linear regression models was per-
formed to examine the relationship between multiple fac-
tors and gait speed (R) and assess how these factors po-
tentially explained gait speed (R2). Sex, age, and years of 
education were included in the models as confounding 
factors (Table 3). Adjusted R2 was used to eliminate the ef-
fect of several variables on R2. Model 1 included VO2max, 
sex, MMSE score, age, body mass index, fasting glucose, 
HbA1c, systolic blood pressure, percentage of muscle 
mass, percentage of muscle mass of lower extremities, and 
years of education (P < 0.05). All the models had a signifi-
cant effect on gait speed. Significance progressively in-
creased with each model, and Model 10, which included 
VO2 max, sex, and MMSE score, attained the lowest P value. 
VO2 max and MMSE score significantly positively correlated 
with gait speed after adjustment for age, BMI, HbA1c, fast-
ing glucose, systolic blood pressure, percentage of mus-
cle mass, percentage of lower extremity muscle mass, and 
years of education (Table 4).
diSCUSSioN
In this study, the SW group had significantly lower VO2 
max and MMSE score, fewer years of education, and the 
patients were more frequently women. However, the com-
bination of VO2 max, sex, and MMSE score explained only 
23.5% of gait speed. VO2 max, and MMSE scores were mu-
tually positively correlated and significantly contributed to 
gait speed.
Older adults are known to have a slower gait speed (4,17). 
Older adults with T2DM have decreased stride length and 
FiGURe 1. Correlation matrix for the involved parameters. Lighter tones indicate negativa correlation and darker tones indicate 
positiva correlation.
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increased gait variability, particularly during dual-task con-
ditions irrespective of the neuropathy status (18). Similar 
results were reported in middle-aged patients with diabe-
tes (19). Our study involved mostly middle-aged patients, 
while other studies involved elderly or frail people, which 
might have obscured the effect of age on gait speed. In 
TAbLe 2. Comparison of factors associated with gait speed between slow and average/brisk walkers in patients with type-2 diabetes*
Walkers
slow
(n = 21)
average or brisk
(n = 50)
P
Gait speed (m/s)
  0.89 ± 0.65
  1.18 ± 0.14
<0.001
Age (years)
 56.45 ± 8.92
 55.89 ± 6.74
0.723
Sex (male/female)(%)
  0/21 (0/100)
 10/40 (20/80)
0.027
body mass index (kg/cm2)
 31.90 ± 4.46
 31.46 ± 4.87
0.743
Hemoglobin A1c (%)
  6.88 ± 0.79
  6.93 ± 0.93
0.629
Fasting glucose (mg/dL)
118.45 ± 31.61
112.98 ± 20.28
0.643
Systolic blood pressure (mmHg)
127.0 ± 8.01
124.56 ± 10
0.556
maximal aerobic capacity (kg/mL/min)
 14.49 ± 2.95
 16.25 ± 2.94
0.029
Percentage of muscle mass
 60.71 ± 5.28
 61.75 ± 6.99
0.845
Percentage of lower extremity muscle mass
 31.37 ± 0.74
 34.67 ± 21.23
0.629
mini mental State examination Score
 25.01 ± 3.21
 27.35 ± 1.97
0.040
Year of education (years)
  5.39 ± 3.73
  9.54 ± 3.14
0.010
Comorbid diseases
hypertension
  9 (42.9)
 23 (46)
0.808
hypothyroidism
  5 (23.8)
  8 (16.0)
0.437
depression
  1 (4.8)
  5 (10)
0.469
Pharmacological therapies
(user/non-user)
(user/non-user)
metformin
 20/1 (95.2/4.8)
 46/4 (92/8)
0.999
dipeptidyl peptidase-4 inhibitors
  6/15 (28.6/71.4)
 18/32 (36/64)
0.546
sulphonylureas
  3/18 (14.3/85.7)
  8/42 (16.0/84)
0.855
SGLT2 inhibitors
  2/19 (9.5/90.5)
  7 /43(14.0/86)
0.716
angiotensin receptor blockers
  6/15 (28.6/71.4)
 11/39 (22.0/78)
0.554
calcium channel blockers
  1/20 (4.8/95.2)
  9/41 (18/82)
0.262
β blockers
  4/17 (19/81)
  3/47 (6/94)
0.184
diuretics
  1/20 (4.8/95.2)
  3/47 (6/94)
0.999
angiotensin converting enzyme inhibitors
  2/19 (9.5/90.5)
  5/45 (10/90)
0.999
*data are presented as mean ± standard deviation or number (%).
TAbLe 3. multivariate linear regression models of each factor associated with gait speed corrected for age, sex, and education years 
for patients with type-2 diabetes mellitus
Factors
b  
(Standard error)
Standardized 
beta
p
factor
R
Adjusted
R2
p
model
95% confidence 
interval
Variance 
inflation factor
Body mass index (kg/cm2)
-0.002 (0.005)
-0.048
0.671
0.519
0.269
0.01
-0.11 -0.007
1.080
Hemoglobin A1c
-0.005 (0.024)
-0.022
0.761
0.489
0.189
0.02
-0.53-0.43
1.010
Fasting glucose
-0.001(0.001)
-0.084
0.459
0.522
0.224
0.01
-0.002- 0.001
1.047
Systolic blood pressure
-0.003(0.002)
-0.136
0.281
0.533
0.284
0.01
-0.007- 0.002
1.334
Maximal aerobic capacity
  0.012 (0.008)
  0.192
0.158
0.542
0.294
0.01
-0.005-0.028
1.559
Percent of muscle mass
  0.002 (0.004)
  0.061
0.642
0.519
0.269
0.01
-0.006-0.009
1.429
Hypothyroidism
-0.010 (0.054)
-0.021
0.855
0.418
0.124
0.01
-0.117-0.098
1.034
Depression
-0.023 (0.075)
-0.034
0.736
0.419
0.125
0.01
-0.171- 0.126
1.026
Mini Mental State 
Examination score
  0.007 (0.009)
  0.099
0.451
0.523
0.227
0.01
-0.011- 0.24
1.446
Percent of lower 
extremity muscle mass
  0 (0.001)
-0.001
0.995
0.517
0.220
0.01
-0.002- 0.002
1.120
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Findikoglu et al: Gait speed in type-2 diabetic patients without neuropathy
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our study, age did not differ between SW and ABW, but it 
was included in regression models due to its importance 
in the literature.
Yavuzer et al showed that diabetic individuals without neu-
ropathy and non-diabetic individuals significantly differed 
in gait speed of and step length, indicating that gait altera-
tion can be encountered even in diabetic patients without 
neuropathy (3). Most of the population-based studies also 
did not take into account the neuropathy status of diabet-
ic patients. One study showed that older women with DM 
duration of more than 10 years had a slower gait speed and 
smaller step length compared with women with DM dura-
tion of less than 10 years (8). To exclude the effects of dia-
betic neuropathy, our study involved participants who had 
DM for less than 10 years, and 29.6% of them were SW.
Slow gait speed independently predicted MMSE score 
decline during seven years of follow-up (20). It also pre-
dicted the onset of dementia, Alzheimer’s disease, or an 
increased cognitive decline (1). Although our participants 
had mild cognitive impairments, SW had significantly low-
er MMSE scores. Additionally, MMSE score was one of the 
independent determinants of gait speed. In another study, 
gait speed was the only independent determinant of mild 
cognitive impairment in patients with DM (13). DM impairs 
psychomotor speed and processing, visual-spatial abilities, 
learning, memory, executive functioning, and attention 
(18). In diabetic individuals with and without neuropathy, 
dual-task conditions during gait reduced gait performance 
(18). In another study, derangements in cognition and gait 
were interrelated and common in individuals with DM 
and/or hypertension (21). Furthermore, non-demented 
older adults with hypertension (22) and non-demented 
older adults with DM (21) had a decreased cognitive per-
formance. In our study, the MMSE scores were adjusted for 
several confounding factors including systolic blood pres-
sure and metabolic factors.
The mean resting systolic blood pressure in this study was 
125.86 ± 10.14 mm Hg while the participants were on anti-
hypertensive agents. Systolic blood pressure values did not 
differ between SW and ABW, and systolic blood pressure 
contributed non-significantly to all models except Model 
10. This might be explained by a close-to-normal range of 
blood pressure in our patients. In other studies, hyperten-
sive older adults had a slower gait speed than normoten-
sive older patients (23,24).
In our study, SW and ABW did not differ in either HbA1c 
or fasting glucose levels. These parameters also did not 
contribute significantly to the models. The literature re-
sults on the relationship between gait and HbA1c are in-
conclusive. Lower HbA1c and blood glucose levels were 
related to brisk walking pace (25,26). However, the Rot-
terdam study found no relation between impaired fasting 
glucose and continuous glucose levels during gait (19). 
In another study, HbA1c level was not related to knee ex-
tensor strength and gait speed after adjustment for body 
weight (17). In contrast, higher HbA1c levels were related 
to a worse physical, but not cognitive function, after ad-
justment for several factors (27). A population-based study 
TAbLe 4. Highly significant multivariate linear regression models with predictive factors for gait speed in patients with type-2 diabe-
tes mellitus
Factors
b 
(Standard error)
Standardized 
beta
p
factor
R
Adjusted
R2
p
model
95% confidence 
interval
Variance 
inflation factor
model 8
0.551
0.241
0.01
Maximal aerobic capacity
 0.015 (0.008)
  0.242
0.069
-0.01-0.031
1.368
Sex
-0.005 (0.024)
-0.022
0.027
 0.18-0.297
1.382
Mini Mental State Examination score
-0.001(0.001)
-0.084
0.059
-0.001-0.032
1.025
Fasting glucose
-0.003(0.002)
-0.136
0.326
-0.003-0.001
1.018
Systolic blood pressure
  0.012 (0.008)
  0.192
0.221
-0.007- 0.020
1.119
model 9
0.539
0.241
0.001
Maximal aerobic capacity
 0.014 (0.008)
  0.233
0.079
-0.002-0.030
1.360
Sex
 0.154 (0.069)
  0.291
0.030
-0.015-0.294
1.379
Mini Mental State Examination score
 0.016 (0.008)
  0.223
0.053
 0-0.032
1.022
Systolic blood pressure
-0.010 (0.054)
-0.021
0.855
-0.07-0.002
1.117
model 10
0.523
0.235
0.0003
Maximal aerobic capacity
 0.017 (0.008)
  0.272
0.036
  0.001-0.032
1.274
Sex
 0.131 (0.067)
  0.248
0.055
-0.03-0.266
1.274
Mini Mental State Examination score
 0.017 (0.008)
  0.239
0.038
  0.001-0.033
1.008
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found that HbA1c levels of >8% were related to a slower 
gait (28). Tight glucose control regimes might cause hypo-
glycemic episodes leading to impaired cognition. Higher 
blood glucose levels, on the other hand, could also cause 
neuropathy or impaired cognition by leading to structur-
al changes in the brain (28). In this study, neither HbA1c 
nor fasting blood glucose were correlated with any other 
factor. Fasting blood glucose was not below 70 mg/dL in 
any of the participants, thus hypoglycemia could not have 
been the factor affecting gait.
VO2 max is a measure of cardiac, pulmonary, and muscu-
lar functioning. Despite its well-known relation with gait 
speed, it has not been included in most of the population-
based studies. Therefore, direct measurement of aerobic 
capacity is a strength of this study. In the present study, 
VO2 max strongly predicted gait speed and was related to 
the percentage of muscle mass and lower extremity mus-
cle mass. In other studies, VO2max was associated with 
most of the self-selected walking speed options when 
corrected for age, weight, height, and fatness (29). Oth-
er studies showed that individuals with T2DM had lower 
aerobic exercise capacity compared with healthy controls 
(30). Another regression model that included leg strength, 
VO2 max, weight, heigh t, and muscle strength predicted 
26% of gait speed (31).
We used the percentage of muscle mass as the muscle mass 
of the body was corrected by weight. The percentage of 
muscle mass and the percentage of lower extremity mus-
cle mass did not significantly differ between SW and ABW. 
Although the percentage of muscle mass was associated 
with gait speed, BMI, sex, and VO2max, after adjustment it 
did not significantly contribute to gait speed. In some clini-
cal and population-based studies, T2DM was related to loss 
in muscle mass and strength (17,22). Diabetic neuropathy 
might cause a loss of motor neurons and thus muscle mass. 
Diabetic patients over 65 years had lower muscle density, 
knee and ankle muscle strength, muscle power and qual-
ity, and slower gait compared with non-diabetic individu-
als (32). They also had decreased quadriceps muscle power, 
strength, and gait speed. Muscular strength loss was faster 
in people who had diabetes for over 3 years (17). Longer dis-
ease duration (>6 years) and poor glycemic control (HbA1c 
>8.0%) were related to a low muscle quality (33). Muscle 
quality was significantly lower in the arms or legs of diabetic 
patients compared with non-diabetic people (17).
This study suffers from several limitations. The fitness 
level and gait speed follow a nonlinear relation (31), 
which cannot be sufficiently explained by linear regression 
models. Second, due to a limited number of participants, 
men and women were not equally distributed across SW 
and ABW groups. This might have affected the significant 
contribution of sex in the models.
In conclusion, gait is a highly integrated function of multi-
ple coordinated physiological systems, all of which are pro-
gressively impaired by DM. This study provides important 
information about alterations in gait in diabetic patients 
without neuropathy. In these patients, physical impair-
ment and disability could be prevented by an improve-
ment in aerobic capacity and cognitive function.
Funding This study was funded by the Scientific Research Committee of 
Pamukkale University (2019TIPF003).
ethical approval granted by the Ethics Committee of Pamukkale University 
(60116787-020/75097).
declaration of authorship AA conceived and designed the study; AA, HA, 
NY acquired the data; GF, AA, HS, FA analyzed and interpreted the data; GF 
and FA drafted the manuscript; AA, HA, NY, HS, FA critically revised the man-
uscript for important intellectual content; all authors gave approval of the 
version to be submitted; all authors agree to be accountable for all aspects 
of the work.
Competing interests All authors have completed the Unified Competing 
Interest form at www.icmje.org/coi_disclosure.pdf (available on request 
from the corresponding author) and declare: no support from any organi-
zation for the submitted work; no financial relationships with any organiza-
tions that might have an interest in the submitted work in the previous 3 
years; no other relationships or activities that could appear to have influ-
enced the submitted work.
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Cognitive function and cardiorespiratory fitness affect gait speed in type-2 diabetic patients without neuropathy.
Findikoglu, Gulin,Altinkapak, Abdurrahim,Alkan, Hakan,Yildiz, Necmettin,Senol, Hande,Ardic, Fusun