Type 1 diabetes mellitus is an autoimmune disease resulting from the self-destruction of pancreatic β cells and subsequent insulin deficiency, which favours chronic hyperglycaemia. It is an important public health problem. 1 The incidence of type 1 diabetes has increased globally.Reference Maahs, West, Lawrence and Mayer-Davis 2 In addition, it is appearing increasingly in younger individuals.Reference Fradkin and Rodgers 3
Individuals with type 1 diabetes may present complications arising from microvascular and macrovascular alterations that lead to dysfunction and insufficiency in different organs. 1 The incidence of complications in type 1 diabetes is directly related to glycaemic control.Reference Menke, Orchard, Imperatore, Bullard, Mayer-Davis and Cowie 4 It is increased by the presence of cardiovascular risk factors, such as a sedentary lifestyle, smoking, obesity, hypertension, and hyperlipidaemia.Reference Daneman 5
Among the complications induced by type 1 diabetes, cardiac autonomic neuropathy stands out, as it is present in 25% of those with type 1 diabetesReference Vinik and Ziegler 6 and is closely related to the increased mortality rate observed in these individuals.Reference Maser, Mitchell, Vinik and Freeman 7
This type of neuropathy is a result of damage to the autonomic nerve fibres that innervate the heart and blood vessels, causing abnormalities in the control of heart rate and cardiovascular dynamicsReference Khatoon, Kumar and Abdul 8 and subsequent problems in circulation and blood distribution.
Heart rate variability is a simple, non-invasive method that describes the oscillations in the intervals between consecutive heart beats (RR intervals), related to the influences of the autonomic nervous system on the sinus node.Reference Vanderlei, Pastre, Hoshi, Carvalho and Godoy 9 It has been recommended by the American Diabetes AssociationReference Boulton, Vinik and Arezzo 10 for assessing the presence of autonomic alterations in patients with diabetes.
Individuals with type 1 diabetes present reductions in global heart rate variability when compared with healthy individuals in various age groups.Reference Guzik, Piskorski, Contreras and Migliaro 11 – Reference Kardelen, Akçurin, Ertuğ, Akcurin and Bircan 14 Reduction in both autonomic componentsReference Chessa, Butera and Lanza 13 , Reference Kardelen, Akçurin, Ertuğ, Akcurin and Bircan 14 or reduction in the parasympathetic component with increases in the sympathetic component,Reference Jaiswal, Urbina and Wadwa 15 observed through heart rate variability indices, have also been related to type 1 diabetes.
By correlating cardiovascular risk factors with linear indices of heart rate variability in type 1 diabetic patients, Jaiswall et alReference Jaiswal, Urbina and Wadwa 15 observed that older age, female gender, and high levels of low-density lipoprotein cholesterol and triglycerides were associated with reduced heart rate variability. Reduction in heart rate variability was also observed in individuals with type 1 diabetes who presented high blood pressure and body mass index values and low levels of physical activity.Reference Colhoun, Francis, Rubens, Underwood and Fuller 16 In addition, studies have revealed that both individuals with altered glycaemia and diabetics present a reduction in the parasympathetic componentReference Singh, Larson, O’Donnell, Wilson, Tsuji, Lloyd-Jones and Levy 17 , Reference Schroeder, Chambless and Liao 18 and that in type 2 diabetics some factors such as systolic blood pressure and at-rest heart rate are associated with reduced heart rate variability.Reference Cambri, Oliveira and Gevaerd 19
As discussed, several factors may be related to alterations in the autonomic modulation of individuals with diabetes. Nevertheless, the majority of studiesReference Colhoun, Francis, Rubens, Underwood and Fuller 16 – Reference Schroeder, Chambless and Liao 18 have focussed on patients above 30 years of age. Consequently, as the occurrence of type 1 diabetes is becoming increasingly frequent in individuals below 30 years of age and variables related to body composition and cardiovascular parameters may be associated with autonomic alterations in diabetics, studies are required that aim to evaluate the influence of these factors on the autonomic modulation of young patients with type 1 diabetes. Such studies are vital as they allow identification of possible physical and clinical characteristics that may influence the dynamics of the autonomic nervous system in these individuals, and consequently the development of more appropriate health interventions.
For that reason, this study aimed to evaluate the associations between heart rate variability indices and physical activity practise, body composition, and metabolic and cardiovascular parameters in young individuals with type 1 diabetes. The hypothesis proposed that in individuals with type 1 diabetes, associations would be found between clinical and physical characteristics and heart rate variability indices.
Method
Casuistry
Volunteers for this study were recruited from the data bank of Basic Units of Health and through direct contact with physicians specialising in the area of endocrinology, in the city of Presidente Prudente, Sao Paulo, Brazil. The final cohort consisted of 43 young individuals aged 18–30 years, of both genders (20 men and 23 women; mean age 21.82±5.07 years), with a clinical diagnosis of type 1 diabetes (age since diagnosis 11.20±6.01 years).
Young individuals who satisfied the following conditions were included in the study: clinical diagnosis of type 1 diabetes mellitus, confirmed by blood test and medical diagnosis (type 1 diabetes mellitus group); not having cardiorespiratory diseases; and not being a smoker and/or alcoholic. Volunteers were disqualified if they were found to have <95% sinus beats on prolonged RR interval recordings.Reference Godoy, Takakura and Correa 20
All individuals were informed about the objectives and procedures of the study. After agreeing to participate, they signed an informed consent form. All study procedures followed the Declaration of Helsinki and were approved by the Research Ethics Committee of the Faculty of Science and Technology – Universidade Estadual Paulista, Presidente Prudente (CAAE: 22530813.9.0000.5402/Protocol: 417.031).
Data collection
Data collection was undertaken in a room with temperature between 21 and 23°C and humidity of 40–60%, between 13:00 and 18:00 to minimise the influences of circadian rhythm.Reference Ewing, Neilson, Shapiro, Stewart and Reid 21 The assessments were performed individually. The volunteers were instructed not to consume alcoholic beverages and/or autonomic nervous system stimulants such as coffee, tea or chocolate in the 24 hours preceding the assessment.
Initially, data were collected on age, gender, time since first diagnosis, and use of medications. Next, physical, clinical, and autonomic evaluations were executed, as described below. After completing these procedures, the volunteers were free to leave.
Physical and clinical assessments
Following this, cardiovascular parameters, body composition, physical activity practise, and random blood glucose levels of the volunteers were evaluated.
Cardiovascular parameters
Blood pressure
Verification of systolic and diastolic blood pressure was undertaken indirectly, using a stethoscope (Littman, Saint Paul, Minnesota, United States of America) and an aneroid sphygmomanometer (Welch Allyn – Tycos, New York, United States of America) on the left arm of the patient while sitting, using the criteria established by the VI Brazilian Guidelines on Hypertension. 22
Heart rate
The volunteers were told to remain at rest for 5 minutes in the sitting position, and after this period the value of heart rate was recorded using a Polar S810i (Polar Electro, Kempele, Finland) heart rate monitor.
Body composition
For estimation of body composition, measurements of mass, height, waist circumference, and hip circumference were performed, followed by percentage fat measurement using bioimpedance.
For anthropometric analysis, the volunteers were instructed to remain barefoot and wear light clothing (pants and shirt). Height was measured using a stadiometer (Sanny, São Paulo, Brazil), in the standing position, with the feet parallel, and body mass distributed evenly between them throughout the measurement. Measurement of body mass was performed using a digital scale (Welmy R/I 200; Welmy, Santa Bárbara D’Oeste/SP, Brazil). From the data, the body mass index=mass/height2 (kg/m2) was calculated according to the Brazilian Guidelines for Obesity. 23
The measurements of waist circumference – that is, the smallest abdominal perimeter situated between the last rib and the higher iliac crest – and hip circumference – that is, the greatest gluteal portion at the height of the greater trochanter – were obtained with the help of an inelastic tape measure (Sanny) with the volunteers standing, arms abducted. The waist–hip ratio was calculated from these measurements. 24
The body fat percentage was calculated using Bioimpedance, Maltron BF 906, Body Fat Analyzer (Maltron, United Kingdom) equipment with the individual in the supine position on a non-conductive surface, with no contact with metallic objects, the lower limbs abducted at 45°, and the upper limbs at 30° to avoid contact between the limbs and the trunk. Before this assessment, the volunteers were instructed not to practise intense physical activity for 8 hours, suspend the use of diuretics for 24 hours, and not to ingest liquids, alcohol, or consume foods for 4 hours. 25
To complete the analysis, the electrodes were placed, with a minimum distance of 5 cm between them. Sterilisation with alcohol was undertaken at the following locations: the base of the third finger, just above the wrist joint, near the styloid process of the right hand, at the base of the third toe on the right foot, and just above the ankle joint, between the medial and lateral malleolus.Reference Lukaski 26
Physical activity practise
Physical activity practise was assessed by applying the International Physical Activity Questionnaire, short version.Reference Matsudo, Timoteo and Matsudo 27 This questionnaire consists of questions that investigate physical activity performed during a normal week and is divided into four different domains: physical activity as a means of transport and activities at work, home, and during relaxation. The International Physical Activity Questionnaire allows classification of individuals into the following subgroups:very active, active, insufficiently active, and sedentary.
Random blood glycaemia
Random blood glycaemia was calculated through a fingertip puncture examination, where the sample obtained was placed on a One-touch ultra dipstick (Johnson & Johnson Medical, Brazil) and analysed using a One-touch ultra glucometer (Johnson & Johnson Medical). The volunteers were not constrained by an eating or fasting period.
Autonomic assessment
For autonomic evaluation, after the initial instructions, a recording strap was positioned on the chest of the volunteers in the sternum region, and the Polar S810i (Polar Electro, Finland) heart rate receiver was placed on the wrist. This equipment had been previously validated to capture beat-to-beat heart rate, and the resulting data were used for the analysis of heart rate variability.Reference Vanderlei, Silva, Pastre, Azevedo and Godoy 28 After insertion of the strap and the monitor, the volunteers were placed in the supine position on a stretcher where they remained at rest for 30 minutes. The volunteers were instructed to remain at rest, awake, spontaneously breathe, and to avoid conversations during data collection. After data collection of the autonomic modulation, the volunteers were free to leave.
For analysis of heart rate variability indices, heart rate was recorded beat-to-beat throughout the experimental protocol; 1000 consecutive RR intervals were chosen from the period with the greatest signal stability after digital filtering by Polar Precision Performance SW software (version 4.01.029), complemented by manual filtering to eliminate premature ectopic beats and artefacts; only series with >95% sinus beats were included in the studyReference Godoy, Takakura and Correa 20
For heart rate variability analysis, linear indices in the time and frequency domains and non-linear indices were applied.Reference Vanderlei, Pastre, Hoshi, Carvalho and Godoy 9 The heart rate variability analysis in the time domain was undertaken through standard deviation of all normal RR intervals, root-mean square of differences between adjacent normal RR intervals in a time interval, and percentage of adjacent RR intervals with a difference of duration >50 ms indices. As for heart rate variability analysis in the frequency domain, the high frequency spectral components (0.15–0.40 Hz) were used, in milliseconds squared and normalised units. The spectral analysis was calculated using Fast Fourier Transform.
The non-linear indices used were extracted from the Poincaré plot. The Poincaré plot is a graphical representation of a time series and allows each RR interval to be plotted versus the previous interval defining a point on the plot.Reference Vanderlei, Pastre, Júnior and Godoy 29 For assessment of the plot, the following indices were calculated: standard deviation of the instantaneous variability beat-to-beat and standard deviation of the long-term variability.Reference Vanderlei, Pastre, Hoshi, Carvalho and Godoy 9
All indices were evaluated using heart rate variability analysis software – Kubios HRV version 2.0Reference Niskanen, Tarvainen, Ranta-aho and Karjalainen 30 (Biosignal Analysis and Medical Image Group, Department of Physics, University of Kuopio, Finland).
Data analysis
Normality of data was verified by the Kolmogorov–Smirnov test. The variables that supported the Gaussian distribution model are presented as mean and standard deviation. The non-normally distributed variables are shown as median and interquartile range.
The correlations among the heart rate variability indices and independent variables – resting heart rate, systolic and diastolic blood pressures, percentage body fat, body mass index, waist-to-hip ratio, random blood glycaemia, and physical activity practise – were verified through Linear Regression in unadjusted and adjusted models, considering the gender and age of the patients. The statistical significance was set at 5% and the confidence interval at 95%. The programme applied for statistical analysis was Statistical Package for Social Sciences – version 15.0 (SPSS Inc., Chicago, Illinois, United States of America).
Results
While undertaking this study, data sets of 43 volunteers with type 1 diabetes were analysed. From this group, four volunteers (9.3%) were disqualified for having an error in the series of RR intervals >5%. Thus, the final sample consisted of 39 young people with type 1 diabetes (19 men and 20 women; p=0.384).
In Table 1, the general characteristics of the volunteers are presented. Among them, three were obese – two individuals with obesity degree II and one with obesity degree III – and the time since diagnosis of diabetes varied between 3 to 26 years. All the volunteers were insulin dependent and maintained their normal treatment on the assessment day, in addition to which 15 of them (38.46%) were taking other drugs besides insulin. From this group, five (12.82%) used medications to control blood pressure, eight (20.51%) for thyroid disorders, three (7.69%) for cholesterol control, five (12.82%) for contraceptive use, and eight (20.51%) for various reasons such as rhinitis, peripheral neuropathy, and epilepsy.
Table 1 Characteristics of volunteers with type 1 diabetes mellitus.
* Mean (standard deviation)
** Median (interquartile range)
Table 2 illustrates the association of the heart rate variability indices with the variables: heart rate (at rest), systolic blood pressure, and diastolic blood pressure, with and without adjustment for gender and age. It can be seen that independent of age or gender, the heart rate (at rest) variable was significantly associated with the following six calculated variables: standard deviation of all normal RR intervals recorded in a time interval expressed in milliseconds, root-mean square of differences between adjacent normal RR intervals in a time interval expressed in milliseconds, the high-frequency component in milliseconds squared, the percentage of adjacent RR intervals with a difference of duration >50 ms, the standard deviation of the instantaneous variability beat-to-beat, and the standard deviation of the long-term variability (p⩽0.005).
Table 2 Correlation of heart rate variability indices with the variables heart rate at rest, systolic blood pressure, and diastolic blood pressure with and without adjustment for age and sex in young people with type 1 diabetes mellitus.
HF (ms2)=high-frequency component in milliseconds squared; HF (nu)=high-frequency component in normalised units; pNN50=percentage of adjacent RR intervals with a difference of duration >50 ms; rMSSD=root-mean square of differences between adjacent normal RR intervals in a time interval expressed in milliseconds; SD1=standard deviation of the instantaneous variability beat-to-beat; SD2=standard deviation of the long-term variability; SDNN=standard deviation of all normal RR intervals recorded in a time interval expressed in milliseconds Bold values are significant at p <0.05
Table 3 demonstrates the analysis of the association between the heart rate variability indices and the following variables: body fat, body mass index, and waist-to-hip ratio of the volunteers analysed, with and without adjustment for gender and age. It is significant that the waist–hip ratio was the only variable associated with heart rate variability and only for the high-frequency component in normalised units index, independent of adjustment for gender or age (p=0.040).
Table 3 Correlation of heart rate variability indices with the variables body fat, body mass index and waist-to-hip ratio with and without adjustment for age and sex in young people with type 1 diabetes mellitus.
HF (ms2)=high-frequency component in milliseconds squared; HF (nu)=high-frequency component in normalised units; pNN50=percentage of adjacent RR intervals with a difference of duration >50 ms; rMSSD=root-mean square of differences between adjacent normal RR intervals in a time interval expressed in milliseconds; SD1=standard deviation of the instantaneous variability beat-to-beat; SD2=standard deviation of the long-term variability; SDNN=standard deviation of all normal RR intervals recorded in a time interval expressed in milliseconds
Bold values are significant at p < 0.05
Table 4 illustrates the connection of heart rate variability indices with the following variables: random blood glycaemia and physical activity, with and without adjustment for gender and age. No significant associations were observed between the variables and evaluated indices even after the adjustments.
Table 4 Correlation of heart rate variability indices with the variables random blood glycaemia and physical activity with and without adjustment for age and sex in young people with type 1 diabetes mellitus.
HF (ms2)=high-frequency component in milliseconds squared; HF (nu)=high-frequency component in normalised units; pNN50=percentage of adjacent RR intervals with a difference of duration >50 ms; rMSSD=root-mean square of differences between adjacent normal RR intervals in a time interval expressed in milliseconds; SD1=standard deviation of the instantaneous variability beat-to-beat; SD2=standard deviation of the long-term variability; SDNN=standard deviation of all normal RR intervals recorded in a time interval expressed in milliseconds
Discussion
In the present study, it was revealed that in young people with type 1 diabetes, high values of at rest heart rate are associated with reduced parasympathetic activity – root-mean square of differences between adjacent normal RR intervals in a time interval expressed in milliseconds, high frequency component in milliseconds squared, percentage of adjacent RR intervals with a difference of duration >50 ms, and standard deviation of the instantaneous variability beat-to-beat – and global heart rate variability – standard deviation of all normal RR intervals recorded in a time interval expressed in milliseconds and standard deviation of the long-term variability – and that higher values of waist-to-hip ratio are related with lower parasympathetic activity – high-frequency component in normalised units – independent of age or gender. The variables systolic blood pressure, diastolic blood pressure, body mass index, body fat, random blood glycaemia, and physical activity practise demonstrated no significant associations with the evaluated heart rate variability indices, before or after the adjustments.
Young patients with type 1 diabetes, independent of age or gender, were found to have higher heart rate at rest values associated with parasympathetic activity and global variability. Similar results were observed by Cambri et al,Reference Cambri, Oliveira and Gevaerd 19 who demonstrated that a reduction in the standard deviation of the instantaneous variability beat-to-beat index was correlated negatively (r=−0.73) with at-rest heart rate values in middle-aged adults with type 2 diabetes, indicating that the lower the parasympathetic tone the greater the at-rest heart rate values. Similar results were found by Fronchetti et al,Reference Fronchetti, Nakamura, Aguiar and De-Oliveira 31 wherein the standard deviation of the instantaneous variability beat-to-beat index was negatively related to heart rate at rest in healthy young individuals.
Heart rate is influenced by vagal activity, which maintains low values at rest,Reference Lima, Oliveira and Ferreira-Júnior 32 whereas its increase is the result of increased action of the sympathetic pathway and/or reduced parasympathetic activity – that is, vagal inhibition.Reference Acharya, Paul Joseph, Kannathal, Lim and Suri 33 The autonomic modifications found in this study, which manifest as reduced heart rate variability, are an indicator of abnormal and insufficient adaptation of the autonomic nervous system,Reference Vanderlei, Pastre, Hoshi, Carvalho and Godoy 9 which, in patients already at risk, such as those with diabetes, can be particularly hazardous.Reference Schroeder, Chambless and Liao 18 Several studies have revealed that high heart rate at rest increases the risk of cardiovascular mortalityReference Menown, Davies, Gupta, Kalra, Lang, Morley and Padmanabhan 34 , Reference Palatini 35 both in healthy individualsReference Cooney, Vartiainen, Laatikainen, Juolevi, Dudina and Graham 36 and in type 2 diabetics.Reference Stettler, Bearth and Allemann 37 Recent findings have also shown that increased heart rate at rest is associated with an increased risk of developing type 2 diabetes in both men and women.Reference Nagaya, Yoshida, Takahashi and Kawai 38
According to Tarvainen et al,Reference Tarvainen, Laitinen, Lipponen, Cornforth and Jelinek 39 prolonged hyperglycaemia can be allied with a moderate increase in heart rate at rest and decreased heart rate variability – root-mean square of differences between adjacent normal RR intervals in a time interval expressed in milliseconds and the high-frequency component. Another aspect stated by the authors to be strongly associated with reduced heart rate variability is the duration of the diabetes – the most significant association being present in the first 5–10 years of the disease, pointing to a decrease in parasympathetic activity.Reference Tarvainen, Laitinen, Lipponen, Cornforth and Jelinek 39
Another factor observed to be associated with heart rate variability was the waist-to-hip ratio, which is considered an important measure in predicting cardiovascular risk.Reference Mushtaq, Gull, Abdullah, Shahid, Shad and Akram 40 In patients with type 2 diabetes, the waist-to-hip ratio has been shown to be slightly more accurate for predicting cardiovascular risk than body mass index.Reference Qiao and Nyamdorj 41 An increase in visceral adipose tissue, measured by the waist-to-hip ratio, is associated with higher metabolic alterations, including decreased glucose tolerance and reduced insulin levels, both of which are risk factors for type 2 diabetes and cardiovascular disease.Reference Mushtaq, Gull, Abdullah, Shahid, Shad and Akram 40 Another factor is the release of inflammatory substances by adipose tissue that could also contribute to alterations in autonomic modulation.Reference Kotsis, Stabouli, Papakatsika, Rizos and Parati 42
In addition, independent of age or gender, an increase in the waist-to-hip ratio is associated with reduced parasympathetic activity – high-frequency component in normalised units – in young patients with type 1 diabetes, suggesting that higher abdominal adiposity is related to a reduction in vagal modulation, and therefore a higher risk of mortality and cardiovascular and metabolic complications in individuals with type 1 diabetes.
As the waist-to-hip ratio and at-rest heart rate are related to parasympathetic autonomic derangement and possibly to the prognosis of individuals with type 1 diabetes, measures such as an appropriate diet,Reference Strychar, Cohn and Renier 43 performing physical activity,Reference Quirk, Blake, Tennyson, Randell and Glazebrook 44 and glycaemic controlReference Plank, Köhler and Rakovac 45 should all be encouraged to reduce and prevent the metabolic consequences of this disease state.Reference Piłaciński and Zozulińska-Ziółkiewicz 46
The regular practise of physical activity as an addition to treatment for type 1 diabetes can assist in adequate glycaemic control,Reference Salem, Aboelasrar, Elbarbary, Elhilaly and Refaat 47 reducing the daily dose of insulin, decreasing the risk of complications associated with diabetes,Reference Rachmiel, Buccino and Daneman 48 and controlling cardiovascular risk factors.Reference Salem, Aboelasrar, Elbarbary, Elhilaly and Refaat 47 Besides, physical exercise in adolescents with type 1 diabetes can contribute to increased vagal control and slight sympathetic reduction.Reference Lucini, Zuccotti, Scaramuzza, Malacarne, Gervasi and Pagani 49 Yet, in this study, no association was observed between higher levels of physical activity practise and increases in autonomic modulation. A possible reason for this may have been the method used for measuring physical activity. In this study, physical activity was assessed using a questionnaire, which can incur potential memory bias in self-reporting some activities.
In addition, the present investigation evaluated the association between heart rate variability indices and the following variables: diastolic blood pressure, systolic blood pressure, body mass index, body fat, random blood glycaemia, and physical activity. Interestingly, no association was found between these variables and the heart rate variability indices used in the study, even after adjustments. Conversely, Colhoun et alReference Colhoun, Francis, Rubens, Underwood and Fuller 16 found links between heart rate variability indices and variables such as blood pressure, high body mass index, and low levels of physical activity when contrasting men and women with type 1 diabetes.
Generally, the participants in this study presented characteristics within the normal standards (Table 1), which could explain the non-existence of associations with heart rate variability indices. This is similar to the findings of Jaiswal et alReference Jaiswal, Urbina and Wadwa 15 where no association was found between variables such as age, gender, body mass index, and diastolic blood pressure with the standard deviation of all normal RR intervals recorded in a time interval expressed in milliseconds, the root-mean square of differences between adjacent normal RR intervals in a time interval expressed in milliseconds, the high-frequency component in normalised units, low frequency in normalised units, and the low-frequency component and high-frequency component ratio in young patients with type 1 diabetes.
Individual diabetics may be predisposed to various complications that could be exacerbated in the presence of cardiovascular risk factors.Reference Daneman 5 According to the literature, these factors are associated with modifications in autonomic nervous system behaviour.Reference Jaiswal, Urbina and Wadwa 15 , Reference Colhoun, Francis, Rubens, Underwood and Fuller 16 , Reference Cambri, Oliveira and Gevaerd 19 , Reference Fronchetti, Nakamura, Aguiar and De-Oliveira 31 Nonetheless, the literature still lacks the necessary studies that focus on the assessment of the association of risk factors to heart rate variability in young patients with type 1 diabetes.
There are limitations to the present study. For example, the presence of obesity in three of the volunteers and the variation of the time interval between the diagnosis of type 1 diabetes and the study period of between 3 and 26 years may have prejudiced the results. Moreover, some volunteers administered medications that may influence autonomic behaviour, such as those to combat epilepsy and rhinitis; however, the tracings obtained from these volunteers were carefully evaluated and demonstrated good quality, with values of heart rate variability indices very similar to those of the volunteers who did not use any medications other than insulin. Another weakness concerns the assessment of physical activity through a questionnaire. It is suggested that future studies use more accurate evaluation methods such as tri-axial accelerometry. Moreover, the cross-sectional nature of the study only permitted the association of the data; the dose response relationship between autonomic modulation and the risk factors analysed could not be determined.
A novel aspect of this research was the exploration of these associations in patients with type 1 diabetes, as the majority of studies have evaluated type 2 diabetics.Reference Cambri, Oliveira and Gevaerd 19 , Reference Tarvainen, Laitinen, Lipponen, Cornforth and Jelinek 39 , Reference Qiao and Nyamdorj 41 Furthermore, using heart rate variability assessment is more sensitive than only using the relationship between at-rest heart rate and cardiovascular risk factors, noting that in this study the at-rest heart rate was also treated as an independent variable.
In summary, from the results obtained in this study we can conclude that in young patients with type 1 diabetes, increases in at rest heart rate values are associated with reduced parasympathetic activity and global heart rate variability, whilst higher waist-to-hip ratio values are related to lower parasympathetic activity, both independent of age and gender. These results support professionals that get involved with treatment of young patients with type 1 diabetes. In this sense, it addresses the importance of indirect monitoring of cardiovascular health state, through periodic measurements of at rest heart rate and waist-to-hip ratio. Also, it allows drawing a profile of these variables during treatment, to identify changes related to the autonomic behavior and to motive changes in the lifestyle to complement the drug treatment, such as physical activity and healthy eating habits. These changes could reduce the negative impact of the disease in the quality of life of this population.
Acknowledgements
The authors thank FAPESP (Support Foundation for Research of São Paulo State) for financial support for this study (Case Number: 2013/19055-0).
Authors’contribution: A.K.F.S. and M.P.C.R.B. participated in the acquisition of data. A.K.F.S., M.P.C.R.B., F.M.V., D.G.D.C., D.M.G., and L.C.M.V. participated in the revision of the manuscript. A.K.F.S., M.P.C.R.B., F.M.V., D.G.D.C., and L.C.M.V. determined the design, interpreted the data, and drafted the manuscript. All the authors read and gave final approval to the version submitted for publication.
Financial Support
The study was funded by the Support Foundation for Research of São Paulo State.
Conflicts of Interest
None.
Ethical Standards
The authors assert that all study procedures followed the Declaration of Helsinki and were approved by the Research Ethics Committee of the Faculty of Science and Technology – FCT/UNESP, Presidente Prudente (CAAE: 22530813.9.0000.5402/Protocol: 417.031).
