Annals of Indian Academy of Neurology
: 2022  |  Volume : 25  |  Issue : 3  |  Page : 457--463

Establishment of normative data for autonomic function tests in Indian population

Sheena Singh1, Vineeth Jaison2, Himani Khatter2, Silky Adya2, Bharat Singh2, Jeyaraj D Pandian2,  
1 Department of Physiology, Christian Medical College and Hospital, Ludhiana, Punjab, India
2 Department of Neurology, Christian Medical College and Hospital, Ludhiana, Punjab, India

Correspondence Address:
Jeyaraj D Pandian
Professor and Principal, Department of Neurology, Christian Medical College and Hospital, Ludhiana, Punjab 141008


Background: Normative data for autonomic function tests (AFT) is not available for Indian population. Objective: The aim of the study was to establish normative data in AFT and its correlation with age, gender, and body mass index. Material and Methods: The study was done on 254 healthy subjects of age ≥18 years. All AFTs were done in autonomic laboratory at the Department of Neurology, Christian Medical College and Hospital, Ludhiana. Cardiovascular tests (heart rate response to deep breathing, HR changes in Valsalva maneuver and head-up tilt test (HUT)) and quantitative sudomotor axon reflex testing (QSART) were performed in all the subjects. Fifty subjects underwent thermoregulatory sweat test (TST). Results: The mean age (SD) of study participants was 43 (16.0) years (range 20–84), and 129 (50.8%) were men. The normative value range (2.5–97.5 percentile) for HR difference, E: I ratio, and Valsalva ratio (VR) was 3.5–47.0, 1.05–1.93, and 1.11–2.64, respectively, for all the subjects. HR difference and E: I ratio showed an significant inverse relation with age (r = -0.623 and r = -0.584, respectively). VR also showed an inverse relation with age (r = -0.575, P =< 0.001), and female had a lower value than male (1.63 vs 1.78, P =< 0.001). In QSART, mean (SD) sweat volume was higher in males 0.630 (0.230) compared to females 0.513 (0.132) for all sites, P < 0.001, and similar trend was noticed for sweat area in TST. Discussion and Conclusion: Normative AFT data has been established for Indian population for the first time. The values are comparable to previously published studies.

How to cite this article:
Singh S, Jaison V, Khatter H, Adya S, Singh B, Pandian JD. Establishment of normative data for autonomic function tests in Indian population.Ann Indian Acad Neurol 2022;25:457-463

How to cite this URL:
Singh S, Jaison V, Khatter H, Adya S, Singh B, Pandian JD. Establishment of normative data for autonomic function tests in Indian population. Ann Indian Acad Neurol [serial online] 2022 [cited 2022 Oct 2 ];25:457-463
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The autonomic function test (AFT) is a battery of tests devised to study the sympathetic and parasympathetic branches of the autonomic nervous system (ANS). The effect of specific provocative maneuvers on cardiovascular reflexes forms the basis of these tests. Sympathetic activity can be studied by the blood pressure (BP) response to orthostatic testing and Valsalva maneuver (VM). Parasympathetic function can be evaluated by studying the changes in heart rate (HR) during orthostatic testing, VM and deep breathing (DB).[1],[2],[3],[4],[5]

There are age and gender differences in the values of autonomic tests. The largest study for normative data in individuals between 10 and 83 years was done in USA, which showed a decrease in cardiovagal function with age. The same study demonstrated gender differences in quantitative sudomotor axon reflex testing (QSART).[6] There is lack of similar data for the Indian subcontinent. Availability of normative data is important to diagnose patients with autonomic disorders. Hence, we aim to establish normative data for the Indian subcontinent and its correlation with age, gender, and body mass index (BMI).[7],[8]

 Materials and Methods

A total of 254 healthy subjects of aged ≥20 years were recruited in the study during the period from 04-01-2017 till 09-31-2019. Cardiovascular tests (heart rate response to deep breathing (HRDB), HR changes in VM and HUT) and QSART were performed in all the subjects. Fifty subjects underwent thermoregulatory sweat test (TST). They were evenly distributed by age and gender. Participants with any systemic diseases like diabetes mellitus, hypertension, cardiac diseases, or taking medication with effects on the ANS were excluded from the study. The study was approved by institutional ethics committee and written informed consent was taken from each subject. The age and gender distribution of the 254 normal subjects by tests are shown below:

Age-group (males, females): 20–30 (32, 30), 31–40 (41, 39), 41–50 (16, 16), 51–60 (15, 15), 61–70 (15, 15), and ≥71 (10, 10).

All tests were done in the Autonomic laboratory at the Department of Neurology, Christian Medical College and Hospital, Ludhiana. The machines used for recording the AFTs were: iVY-Cardiac Trigger Monitor 3000, WR-Test Works™ Analog Interface (WR-Medical Electronics Co), bmeye- Nexfin Monitor Model1 (Bmeye Cardiovascular Intelligence), Tilt table (WR-Medical Electronics Co), Q-SWEAT – Quantitative Sweat Measurement System (WR-Medical Electronics Co), Nexfin HRS, and wrist unit model1. The HR and BP were monitored continuously. Autonomic function testing was done as per standard protocols as follows[9]:

HRDB: We recorded BP and HR for 3 min with subject in a resting position. The subjects were then asked to breath maximally at a rate of 6 breaths/min (inspiratory and expiratory cycles of 5 s each), establishing a smooth maximal inspiratory and expiratory rhythm. Eight cycles (deep breaths) were recorded followed by resting BP and HR for 3 min. After a resting period of 3 min, DB cycles were repeated twice and recorded (a total of three times) and the five largest consecutive responses per cycle were read from the computer by the operator, manually placing a cursor over the trace. The average HRDB difference (maximum–minimum) of the five largest consecutive responses in the three sets was derivedHR and BP changes in VM: Resting recording was done with subject lying in the recumbent position for 3 min preactivation. To proceed with activation; mouthpiece of bugle with an air leak (to ensure an open glottis) was raised towards the volunteer, who was instructed to take a deep breath in. The subject formed a good seal around the mouthpiece and blew into it to maintain a column of mercury at 40 mm Hg, for 15 s. Postactivation, resting recording was taken for another 3 min. The procedure was repeated three times. The Valsalva ratio (VR) was derived from the maximum HR divided by the lowest HR following the VM. Inclusion criteria for an acceptable recording were: (i) expiratory pressure at least 30 mm Hg and maintained for 10 s; (ii) reproducible VM BP curve; and (iii) absence of a flat-top BP curve. The baseline values of BP (systolic BP [SBP], mean arterial BP [MAP], diastolic BP [DBP]) were derived from the average of readings during the stable 30 s before the VM. The amplitude of phase 1 was measured from baseline to peak (I). The reduction of early phase 2 was measured from baseline to the trough of phase 2 (IIe). The magnitude of late phase 2 (IIl) was determined from end of early phase 2 to the beginning of phase 3 (III). The amplitude of phase 3 was measured from the end of late phase 2 to the trough of phase 3 (III). The magnitude of phase 4 was determined as its height above baseline (IV). For the responses, the largest data from a satisfactory expiratory pressure was accepted

The BP recovery time was calculated for SBP, MAP, and DBP curves as described. Time intervals were then determined for two periods of the maneuver: (i) from the lowest phase 3 amplitude to complete return of SBP, MAP, and DBP to baseline pressure recovery time (PRT 100) and (ii) from the lowest phase 3 amplitude to 50% return of SBP, MAP, and DBP to baseline (PRT 50). The average SBP, MAP, and DBP in each instance was used to determine the baselineHUT: Straps were applied over the upper chest and across knees to secure the subject to the table. Baseline trace recording was done for 10 min with the volunteer resting quietly in the horizontal reclined position. The baseline SBP, MAP, DBP, and HR were recorded before the tilt. The tilt study was performed for 10 min with a 70 degree HUT. Changes in the HR, BP, and symptoms during the tilt were recorded. Finapress was used to record BP continuously from the fingertip. The systolic fall and HR increment at 30 s and at 1, 3, 5, 8, and 10 min were documented. After the HUT, the table was again made horizontal and we again recorded the SBP, MAP, DBP, and HR for 10 minQSART – The QSART assessed postganglionic sudomotor nerve fibers and sweat glands in localized areas of the skin. A multicompartmental sweat cell was used to measure the sweat production. The cell contained an inner compartment – that was filled with 10% acetylcholine chloride dissolved in distilled water. There was also the outer compartment – that took up the humidity from the axon-induced sweat production. The volume of sweat output was calculated automatically by area under the curve method. There were four sites on the extremities:

Forearm – midway along the inner forearm.Proximal leg – 3 cm below the head of the tibia over the deep peroneal nerve.Distal leg – midway between the tibia head and the lateral malleolus (ankle bone).Foot – half distance down the third metatarsal from the toes to the tarsal joint.

(Reference electrodes were placed at a distance no more than 10 cm from recording site)

TST: The lower limbs, upper limbs, and feet of the subjects were dusted with iodine solution (2% iodine powder in 96% ethyl alcohol); then, a paste which was a mixture of 50% starch in castor oil was applied on the skin surface. The subject remained in the TST room that was used to heat the body core temperature, with a heater. The heating time was approximately 45–60 min. Digital photographs were taken after 45 min of heating time to document areas of sweating. After the testing data was expressed as TST%, which was the measured area of sweat (Total area of paste applied – Area of anhidrosis) divided by the total area of paste applied, multiplied by 100.[10] The pixels of total area and the area of paste applied were calculated using Photoshop software with histogram. The sweat area was identified by adding each subject's skin tone to the software.

The manuscript was prepared according to STROBE guidelines [Supplementary Material 1].[INLINE:1]

Sample size

The sample size was calculated based on our pilot study and previous Indian study[11] by taking mean HR difference values in the age groups 20–30 years, 31–40 years, 41–50 years, 51–60 years, 61–70 years, >=71 years, and allowable error ± 10; the sample size calculated was 254.

Statistical analysis

Normative percentiles were calculated at 2.5 and 97.5% according to age and gender. Kolmogorov–Simonov test was used to check the normality of the data. Correlation of age and BMI with the autonomic parameters was obtained using Pearson correlation or Spearman rank correlation depending upon distribution of the data. Independent t-test or Mann–Whitney U test was used to obtain association of autonomic parameters with gender. Association of autonomic parameters with different age groups was obtained using one-way ANOVA or Kruskal–Wallis. Linear regression analysis was used to find predictors for autonomic parameters using age, gender, and nonlinear interactions between age and gender when both were found to be significant in the model. The significance level was set at P < 0.05. All statistical analysis was performed using SPSS, version 26.0. The photographs for sweat area in TST were analyzed using Photoshop CC software (PHSP, ALL, MLP, DRI01, MUA, 001, N/A, 1 MO, DSP).


The mean (SD) age of the subjects was 43 (16.0) years (range 20–84 years) and 129 (50.8%) were males. The mean (SD) BMI, SBP, DBP, and random blood sugar (RBS) were as follows: BMI 23.4 (2.1) (range 18.1–26.9 kg/m2), SBP 123 (9.53) (range 100–146 mm Hg), DBP 77 (6.57) mm Hg (range 65–98 mm Hg), and RBS 98 (14.53) mg/dL (range 71–140 mg/dL). The AFT parameters were analyzed for 249 subjects after removing the outliers.


The average HR difference and E: I ratio in participants were 21.1 (9.09) (range 3.5–47.0 beats per minute) and 1.35 (0.18) (range 1.05–1.93), respectively. The normative data values for HR difference and E: I ratio were calculated at 2.5th and 97.5th percentiles according to age and gender [Table 1]. HRDB difference showed an inverse relationship with age r = -0.623, P < 0.0001 [Figure 1], but no relation was observed with gender and BMI.{Figure 1}{Table 1}


The mean (SD) VR for all the participants was 1.71 (0.30) (range 1.11–2.64). The normative data values for VR was calculated at 2.5th and 97.5th percentiles according to age and gender [Table 2]. VR had an inverse relationship with age r = -0.575, P < 0.0001 [Figure 2] and was significantly higher in males 1.79 (0.31) compared to females 1.63 (0.27), P < 0.0001.{Figure 2}{Table 2}

In different phases of VM, the SBP amplitude in early phase 2 showed an inverse relationship with age r = -0.309, P < 0.0001 and a slight positive correlation was seen with phase 4, r = 0.236, P < 0.0001 [Figure 3]a and [Figure 3]b]{Figure 3}

The normative data values for BP recovery time PRT 100 and PRT 50 were calculated at 2.5th and 97.5th percentiles according to age and gender [Supplementary Material 2]. The BP recovery time (seconds) had a small positive correlation with age at complete recovery and 50% recovery from phase 3 to baseline; r = 0.244, P =< 0.0001 and r = 0.264, P =< 0.0001, respectively. VM showed no correlation with BMI.[INLINE:2]

HUT study

In HUT, a slight SBP fall was found in male compared to female at 30 s and 3 min of tilt up (P = 0.018 and P = 0.002, respectively), but the difference was nonsignificant for other intervals of time [Supplementary Material 2]. The HR increment from pretilt to tilt up showed no significant correlation with gender. There was no relation found between HUT and age. The normative data values for SBP fall and HR increment were calculated at 2.5th and 97.5th percentiles according to age and gender [Table 3].{Table 3}


The mean (SD) sweat volume for: forearm, proximal leg, distal leg, and foot was 0.596 (0.326) μL (0.116–2.721 μL), 0.596 (0.318) μL (range 0.106–2.612 μL), 0.609 (0.281) μL (0.125–2.141 μL), and 0.509 (0.232) μL (range 0.127-1.677 μL), respectively. The normative data values for all the four sites were calculated at 2.5th and 97.5th percentiles according to age and gender [Table 4]. The association between sweat volume and gender showed males had significantly more sweat volume compared to females for all the sites [Supplementary Material 2]. In association with age, a decreasing trend was found in sweat volume which was not significant for proximal leg and forearm, but a significant difference was found for distal leg and foot [Supplement Material 2]. There was no correlation with BMI.{Table 4}


The mean age (SD) was 40 (15.4) years (range 20–84). The normative value range (2.5–97.5 percentile) of sweat area for posterior lower limb (LL), anterior LL, posterior upper limb (UL), anterior UL, and dorsal and plantar surfaces of feet was calculated for all the subjects [Table 5]. The area of sweat was found to be significantly larger in males than females for lower limb, upper limb, and feet [Supplementary Material 2]. The sweat area did not correlate with age and BMI.{Table 5}

The multiple linear regression model was obtained to measure the effect of AFT parameters with age and gender of participants [Supplementary Material 2]. In HRDB, HR difference and E:I ratio showed a significant effect with age but not gender. In VM, VR showed a significant relation with age, gender, and age by gender interaction, the effect of VR was inverse with age, and females had negative effect showing a lower value for VR compared to male. PRT 100 and PRT 50 both had a significant effect with age, but no effect was noticed with gender in a model with interaction factor of age by gender. In HUT, HR increment had a negative relationship with age at 1, 3, and 5 min of tilt up. SBP showed an effect with gender at 3 min tilt-up, but no relationship was seen with age. In QSART, sweat volume in proximal leg, distal leg, and foot had a significant inverse relation with age, and the effect of sweat volume in females was negative indicating lower values.


We documented the normative data for cardiovascular AFT, QSART, and TST among Indian subjects. In HRDB, we found an inverse linear relationship with age which persisted beyond 60 years of age, which is important to consider when examining patients especially in the older age group. A linear progressive reduction with age in HRDB has been seen in various studies.[9],[12],[13] Our study also shows that HRDB in subjects over 70 years does not approach zero or level out, in contrast to what was suggested in previous study.[12] The values for HR difference and E: I ratio are similar to what has been previously reported in the younger age group.[6] These values though low among those older than 50 years were similar to another study from India.[11] There was no relation observed with gender and BMI correlating with previous studies.

Correlation of VR with age has been variable according to different studies. Some studies have mentioned that VR has no correlation with age, while other studies have shown an inverse correlation with age.[6],[13] We found a clear inverse correlation with age. Previous studies have been varied regarding the effect of gender on VR.[6],[14] Our study found the VR to be higher in males 1.79 (0.31) compared to females 1.63 (0.27), P < 0.0001. Moreover, the pattern was inversely correlating with age in both males and females unlike previously reported.

VR is more complex and has multiple factors affecting it (blood volume, rest, cardiac sympathetic and peripheral sympathetic tone, and nor-adrenaline response) unlike HRDB, which is mainly influenced by cardiovagal reflex. Our study showed different phases of VM like the SBP amplitude in early phase 2 showed a moderate inverse relationship with age and a slight positive correlation was seen with phase 4. This finding corroborates the fact that age affects different components of Valsalva in different directions.[14] PRT 100 and PRT 50 had a small positive correlation with age, which has not yet been reported.[14],[15] There was no correlation with gender or BMI.

In the HUT study, we found that SBP fall and HR increment showed no correlation with age or gender. Previous studies mention a positive correlation with SBP fall and negative correlation with HR increment for age peaking beyond 70 years.[6],[14] This may be due to the smaller sample size.

In agreement with other studies, we noted that males had significantly more sweat volume (0.630 ± 0.230) compared to females (0.513 ± 0.132) at all sites.[6],[13] This occurs due to smaller evoked sweat volume per sweat gland, rather than reduction in number of sweat glands in females.[16] We found a decrease in sweat volume with age for distal leg and foot, while the proximal leg showed a decreasing trend not reaching statistical significance. However, the forearm showed no change with age, which is concordant with other studies. This can be explained by the fact that longer unmyelinated fibers have been shown to be affected by age preferentially.[6] We found that the range of sweat volume in our population was similar to studies done in India and Taiwan but differed from studies on Western population. There is some suggestion of ethnic differences playing a role.[17],[18],[19]

We performed TST on 50 subjects and found the area of sweat to be significantly larger in males than females for lower limb, upper limb, and feet. Studies have suggested that females have different threshold for thermoregulation, which may account for this finding.[20] In our study, the sweat area did not correlate with age and BMI.

The strengths of this study are, first all the subjects were screened for diseases and medications affecting the ANS and they were prepared uniformly for the tests which were performed under similar controlled environment by the same person using standardized equipment with software. Second, the sample size was calculated based on a previous study from the country and was further modified to enroll a larger sample using data from a pilot study.[11] Third, we also used a new method to quantify TST.

The limitations are the small study population over the age of 40. This makes it difficult to draw definite conclusions for this age group. Finding subjects fulfilling all exclusion criteria in this age group was a challenge. This challenge is represented across other studies as well.[14],[21],[22],[23] Furthermore, QSART was studied on one side of the body and TST was performed only on the extremities.


We have derived the normative data for AFTs in India. Our study demonstrates the influence of age on HRDB, VR, and QSART. Gender differences were demonstrated in VR, QSART, and TST.

Data availability statement

The data that support the findings of this study are available from the corresponding author upon reasonable request

Ethics approval

The approval for the study was obtained from institutional review board Christian Medical College and Hospital, Ludhiana.

Declaration of patient consent

The authors certify that they have obtained all appropriate patient consent forms. In the form, the patient (s) has/have given his/her/their consent for his/her/their images and other clinical information to be reported in the journal. The patients understand that their names and initials will not be published and due efforts will be made to conceal their identity but anonymity cannot be guaranteed.

Financial support and sponsorship

This work was supported by Indian Council of Medical research, New Delhi, India.

Conflicts of interest

There are no conflicts of interest.


1Ziemssen T, Siepmann T. The investigation of the cardiovascular and sudomotor autonomic nervous system-A review. Front Neurol 2019;10:53.
2Zygmunt A, Stanczyk J. Methods of evaluation of autonomic nervous system function. Arch Med Sci 2010;6:11-8.
3Mathias CJ. Autonomic diseases: Clinical features and laboratory evaluation. J Neurol Neurosurg Psychiatry 2003;74:31-41.
4Junqueira LF Jr. Teaching cardiac autonomic function dynamics employing the valsalva (valsalva-weber) maneuver. Adv Physiol Educ 2008;32:100-6.
5Jaradeh SS, Prieto TE. Evaluation of the autonomic nervous system. Phys Med Rehabil Clin N Am 2003;14:287-305.
6Low PA, Denq JC, Opfer-Gehrking TL, Dyck PJ, O'Brien PC, Slezak JM. Effect of age and gender on sudomotor and cardiovagal function and blood pressure response to tilt in normal subjects. Muscle Nerve 1997;20:1561-8.
7Sakhuja A, Goyal A, Jaryal AK, Wig N, Vajpayee M, Kumar A, et al. Heart rate variability and autonomic function tests in HIV positive individuals in India. Clin Auton Res 2007;17:193-6.
8Sucharita S, Bantwal G, Idiculla J, Ayyar V, Vaz M. Autonomic nervous system function in type 2 diabetes using conventional clinical autonomic tests, heart rate and blood pressure variability measures. Indian J Endocrinol Metab 2011;15:198–203.
9Low PA. Testing the autonomic nervous system. Semin Neurol 2003;23:407–21.
10Illigens BM, Gibbons CH. Sweat testing to evaluate autonomic function. Clin Auton Res 2009;19:79-87.
11Shankar V, Veeraiah S. Age related changes in the parasympathetic control of the heart. Int J Sci Res Publ 2012;2:1-6.
12Braune S, Auer A, Schulte-Monting J, Shwerbrock S, Lucking CH. Cardiovascular parameters: Sensitivity to detect autonomic dysfunction and influence of age and sex in normal subjects. Clin Auton Res 1996;6:3-15.
13Low PA, Opfer-Gehrking TL, Proper CJ, Zimmerman I. The effect of aging on cardiac autonomic and postganglionic sudomotor function. Muscle Nerve 1990;13:152-7.
14Pandian JD, Dalton K, Scott J, Read SJ, Henderson RD. Cardiovascular autonomic function tests to provide normative data from a healthy older population. J Clin Neurosci 2010;17:731-5.
15Vogel ER, Sandroni P, Low PA. Blood pressure recovery from valsalva maneuver in patients with autonomic failure. Neurology 2005;65:1533-7.
16Kihara M, Opfer-Gehrking TL, Low PA. Comparison of directly stimulated axon-reflex-mediated sudomotor responses in human subjects and in patients with diabetes. Muscle Nerve 1993;16:655-60.
17Mal K, Abhishek HA, Chawla MP, Raju TR, Sathyaprabha TN. Influence of age and gender on the function of postganglionic sympathetic sudomotor axons. Natl Med J India 2017;30:136-8.
18Chen SF, Chang YT, Lu CH, Huang CR, Tsai NW, Chang CC, et al. Sweat output measurement of the post-ganglion sudomotor response by Q-Sweat test: A normative database of Chinese individuals. BMC Neurosci 2012;13:62.
19Lee JB, Bae JS, Matsumoto T, Yang HM, Min YK. Tropical Malaysians and temperate Koreans exhibit significant differences in sweating sensitivity in response to iontophoretically administered acetylcholine. Int J Biometeorol 2009;53:149-57.
20Kaciuba-Uscilko H, Grucza R. Gender differences in thermoregulation. Curr Opin Clin Nutr Metab Care 2001;4:533-6.
21Laitinen T, Niskanen L, Geelen G, Lansimies E, Hartikainen J. Age dependency of cardiovascular autonomic responses to head-up tilt test. J Appl Physiol 2004;96:2333—40.
22Gelber DA, Pfeifer M, Dawson B, Schumer M. Cardiovascular autonomic nervous system tests: Determination of normative values and effect of confounding variables. J Auton Nerv Syst 1997;62:40-4.
23Storm DS, Metzger BL, Therien B. Effect of age on cardiovascular autonomic responsiveness in healthy men and women. Nurs Res 1989;38:326-30.