Prakriti, the constitution of an individual according to Ayurveda, is the sum total of physical, psychological and physiological traits expressed in that individual. According to the principles of Ayurveda, Doshas determine one’s Prakriti (Tripathi and Sin gh, 1994). There are three Doshas: Vata, Pitta and Kapha. Doshas are defined as the fundamental, mutually antagonistic - yet reciprocal - mechanisms responsible for maintaining the homeostasis, and thus, health. When these mechanisms deviate from their state of equilibrium, the result is often ill-health and disease (Dubey et al., 2015; Patwardhan, 2013). Ayurveda constructs all its principles governing the physiological, nutritional, pathological, and pharmacological understandings around the axial framework of this theory (Jayasundar, 2010). Each of these Doshas has been ascribed with specific mutually opposite attributes (Gunas). Ayurveda proposes that a specific ‘attribute’ of a Dosha has a causal relationship with the specific trait expressed in an individual (Dubey et al., 2015). For instance, Pitta possesses the ‘Ushna’ (heat) attribute which determines the enhanced digestive and metabolic abilities of an individual, whereas, Kapha possesses ‘Shita’ (cold) attribute that causes sluggishness in digestive and metabolic abilities. In the same manner, Vata possesses Cala (mobility) attribute that makes the individual active, whereas, Kapha possesses Stimita (rigid) attribute that makes the individual less active (Dubey et al., 2015; Tripathi et al., 2011).
The Prakriti of an individual is determined by the dominance of one, two or three Doshas expressed in that individual. Though it is determined genetically, several environmental factors too contribute in its manifestation. Therefore, on the basis of one, two or three dominant Doshas expressed in an individual, one can have any one of the seven possible types of Prakriti: Vata, Pitta, Kapha, Vata-Pitta, Pitta-Kapha, Vata-Kapha, and Sama Doshaja (balanced state of all the three Doshas).
In the recent years, the individualized approach to therapeutics has received impetus with the growing understandings in the field of genetics. Several workers have investigated the possible association of constitutional types with the individual genetic make-up, metabolic abilities and chronic diseases. There have been several efforts to see whether certain physiological, haematological or biochemical tools can be used to establish a link between constitution types and other health-related parameters. (Aggarwal et al., 2010; Bhalerao and Deshpande, 2012; Dey and Pahwa, 2014; Ghodke et al., 2011; Patwardhan et al., 2005; Prasher et al., 2008).
A few workers in the past have hypothesised that certain autonomic responses might vary in accordance with the constitutional types as defined in Ayurveda (Thompson, 2005). One of the early studies in this regard has reported that the healthy individuals with Vata, Pitta and Kapha Prakriti exhibited a relative preponderance of blood Cholinesterase, Monoamine oxidase and Histaminase activity, respectively (Udupa et al., 1975). We, in one of our previous studies have reported that individuals having Pitta as a contributing component to their Prakriti, tend to show a significant rise in diastolic blood pressure immediately after isotonic exercise in comparison to others indicating a possible higher sympathetic activity (Tripathi et al., 2011). Considering the fact that certain autonomic responses might vary in accordance to age and gender of an individual, it was logical for us to hypothesise that these responses might also vary according to Prakriti (Manor et al., 1981; Moodithaya and Avadhany, 2009; Moodithaya and Avadhany, 2012). However, there are no studies in this area reporting a possible link between Prakriti and autonomic responses. If this kind of a relationship is confirmed, it would, apart from being helpful in predicting the susceptibility of an individual to certain h ealth conditions, also prove beneficial in determining one’s constitution by providing some objective and easy-to-conduct laboratory tests. This may be of value given the fact that the process of determining one’s Prkariti by itself is a challenging task (Kurande et al., 2013; Kurande et al., 2013; Tripathi et al., 2011).
With this background, we planned the present study to investigate a possible relationship between certain autonomic responses and Prakriti. We hypothesized that people belonging to Kapha-dominant Prakriti might have a higher parasympathetic activity because of the specific attributes of Kapha such as Manda (slow), Guru (heavy), Snigdha (fatty/oily), Sandra (dense) and Stimita (rigid). Similarly, we hypothesized that individuals with Vata dominant Prakriti may have a higher sympathetic activity because of the attributes such as Laghu (light), Sukshma (minuscule), Cala (mobile), and Shighra (swift) (Dubey et al., 2015).
The approval from the institutional ethics committee was obtained before starting the study.
The population for the present study was defined in terms of students who were aged between 17 - 35 years and registered under various courses of study at our institution.
The students were informed about the study in their classrooms through verbal announcements. The details of the study were explained to them and their voluntary participation in the work was solicited. After obtaining the written consent from those who responded to our request, a thorough clinical examination was carried out to confirm that they were clinically healthy. A detailed pro-forma was used to record the findings of the interview that included history taking and physical examination. Those who gave no history of any acute /chronic illnesses, or did not complain of any physical / psychological symptoms, and those who were found to be ‘within the normal limits’ on all parameters of systemic physical examination, were defined as ‘clinically healthy’ and were included in the study. Blood biochemistry or hematology parameters were not assessed. Students with obesity were excluded (one volunteer).
There are quite a few difficulties that have been reportedly encountered in determining one’s Ayurveda constitution (Prakriti). The age, physical and psychological status of the individual along with the season prevailing while assessing one’s constitution are the major factors that tend to distort the outcome of this exercise (Tripathi et al., 2011). For instance, elderly people are likely to exhibit dominant features indicative of Vata such as dry and wrinkled skin. These features of Vata are likely to be exhibited dominantly during extreme winters as well. Differences in the subjective perceptions of the physicians also can make the assessment ambiguous resulting in high inter-rater variability (Kurande et al., 2013). The absence of definite criteria for designating one’s constitution to be either due to a single Dosha (Eka-doshaja) or due to two Doshas (Dvandvaja) is another problem (Tripathi et al., 2011). Scarcity of standardised and validated tools for assessing Prakriti makes the situation even worse. Most of the tools available today are either based on too many textbooks or require physician-participant interaction in the form of a personalized interview and a detailed physical examination. This situation has led to the problems such as: lengthy and time-consuming questionnaires, inclusion of contradictory statements, and unnecessary divulgence of personal details by the participants (Tripathi et al., 2011).
To avoid these problems, the latest version of the ‘Self assessment questionnaire for determining Prakriti’ designed by our team was used to assess Prakriti of the volunteers (Tripathi et al., 2010). This tool has been already validated and is available in the public domain. This was the tool that originally provided us with some insig hts related to cardiovascular reactivity in relation to Prakriti (Tripathi et al., 2011).
This tool uses simple questions or statements that reflect each trait / feature as described in Charaka Samhita alongside the specific attribute of a given Dosha. The respondents were asked to record their agreement or disagreement with the statement/question in the form of “yes” or “no.” After having completed the questionnaire, the volunteers were asked to calculate the percentage of each Dosha and report it. Th ey were not asked to submit the filled-in questionnaire for reasons that have been discussed in our earlier paper (Tripathi et al., 2011).
We determined the Prakriti of each individual based on traditional approach of interview too. The first author of this report did this exercise. He is an institutionally trained graduate and certified physician in Ayurveda and was undergoing his postgraduate training during this study. He did not have access to the scores reported by the volunteers in response to the tool that was administered. Only the corresponding author had access to this information. We did not consider the question of inter-rater variability as the two methods were of different nature and hence, were not comparable. Only when the most dominant Dosha contributing to one’s Prakriti assessed through both these methods matched, the volunteer was included in the study. 17 volunteers (11 males and 6 females) were excluded because of this criterion. One hundred and six volunteers (69 male and 37 female) fulfilled these requirements and were registered for the study.
All the registered volunteers (n = 106) underwent the following tests to record cardiovascular and pupillary responses. The tests related to pupillary reactions were carried out in the noon hours (11 am to 1 pm) whereas those related to cardiovascular responses were carried out in the evening hours (4 pm to 7 pm). However, due to some practical difficulties, we could not record the pupil cycle time in 5 enrolled volunteers, rendering the total sample size in this case to 101.
The test is conducted in following steps:
1) The test is explained to the subject and he/she is made to sit on a chair comfortably. The baseline blood pressure is recorded. 2) The subject is asked to immerse one hand up to the level of wrist in cold water maintained at 4 to 5°C for 2 min. The Blood Pressure (BP) is recorded from the other arm at 30 sec intervals. 3) The maximum increases in systolic and diastolic pressures are noted and compared with the baseline readings.
The Systolic Blood Pressure (SBP) normally increases by 16 - 20 mmHg, while the Diastolic Blood Pressure (DBP) normally increases by 12 - 15 mmHg on an average. Reduced Sympathetic activity is indicated by a lesser than 16 mm Hg rise in SBP and lesser than 12 mm Hg rise in DBP (Ghai, 2007; Noronha et al., 1981).
When a normal person lies down from a standing position, there is at first a rise in Heart Rate (HR) which then is slowed down. This rise and fall of HR is due to changes in the vagal tone. The test is performed in following steps:
1) The procedure is explained to the subject. ECG leads are connected for recording lead II. The subject is asked to stand in the upright position quietly for two minutes and without taking any support is then asked to lie down supine. 2) ECG is recorded for 20 beats before and for 60 beats after lying down. The point of change of position on the ECG paper is noted. 3) Calculation of S/L ratio: The average of R-R interval during 5 beats before lying down is noted and the shortest R-R interval during 10 beats after lying down is also noted down. Any abnormally low ratio indicates parasympathetic insufficiency, the normal ratio being > 1 (Ghai, 2007).
Valsalva manoeuvre is defined as the forced expiration against a closed glottis. This straining, associated with changes in HR, is a simple test for baroreceptor activity.
1) The subject is seated on a stool and procedure is explained to him / her. ECG leads and BP cuff are connected to him / her, and the nostrils are closed with a nose clip. 2) The cuff is disconnected from another BP apparatus and the subject is asked to take a deep breath, blow into the manometer and maintain the pressure at 40 mm Hg for 15 s. 3) ECG (lead II) is recorded for 1 minute before the straining, and for 45 s after the release of strain. 4) Calculation of Valsalva Raio: The Valsalva ratio is calculated as the ratio of the longest RR interval after manoeuvre to shortest R-R interval during manoeuvre. A value > 1.21 is taken as normal and a value less than that is indicative of parasympathetic insufficiency (Neumann and Schmid, 1997).
During the straining there is decrease in venous return, fall in cardiac output, and vasoconstriction. The HR increases throughout straining due to vagal inhibition initially and sympathetic activation later. After this, the HR slowly decreases. A failure of HR to increase during straining suggests sympathetic insufficiency, while failure of HR to slow down after the effort suggests a parasympathetic insufficiency (Ghai, 2007).
Besides the cardiovascular autonomic activity evaluation tests, the pupil has been recognized to be a useful parameter for the study of the physiology of autonomic nervous system. It has exclusively autonomic innervations, and is accessible in vivo to direct influences of physical and chemical agents (Cahill et al., 2001)
Stimulation of the parasympathetic division leads to the contraction of the constrictor muscles of the pupil resulting in miosis. On the other hand, stimulation of sympathetic nerves causes contraction of dilator pupillae resulting in mydriasis (Patel , 1999). Various methods have been used to measure pupillary functions, however, the pupil cycle time and the pupil diameter measurement in light and dark are the useful ones.
We captured the photographs of right and left eyes of the subjects from a uniform distance of 1.5 feet with the Nikon coolpix 6500 camera mounted on a tripod. We also managed to set each subject’s image with uniform 4× optical zoom. For the measurement of light-adapted pupil size, we captured the eye image in the 40 watt tube-light illumination from a fixed direction. For the measurement of dark-adapted pupil size, we captured the image in darkroom by switching the lights off. We waited for 30 sec after switching off the lights and thereafter, we captured the image of the eye using an inbuilt flash light with camera set at shutter speed of 1/125. After capturing the image we transferred this image on to the computer and measured the photographic corneal size and photographic pupil size with the Vernier scale. We then calculated the pupil size using the following algebraic formula:
Where, actual corneal size was directly measured by placing the Vernier scale in front of the subject’s eye.
The pupil cycle time was measured in both the eyes using a slit lamp and a connected computer with video recording facility. This is a modification of the method described by Miller and Thompson (Miller and Thomson, 1978). Intensity of illumination was kep t fixed for the entire study. The volunteer was comfortably seated at the slit lamp in a dimly lit room. He/she removed his/her spectacles if he/she wore one. A 0.5 mm thick slit beam of light was focussed on to the pupillary margin. The beam was then adjusted in a manner so that half of the slit fell on iris and half entered into the pupil. Pupil contracted due to retinal stimulation and prevented further entry of light in the eye. With the retina now in darkness, the pupil dilates to allow the entry of light into the eye, thus setting up persistent oscillations. 30 s of time was fixed for capturing the pupillary oscillation video in the computer. We then counted the number of oscillations of pupil per 30 s and calculated the average time taken for each cycle. The average time taken was then expressed in terms of milliseconds.
For comparing the various mean readings of different tests among different Prakriti groups, One-way ANOVA was applied, followed by the post hoc test, namely, the Least Significant Difference (LSD) test, for pair-wise group comparison. Wherever the data did not follow the normal distribution, (as a general rule of thumb, whenever the standard deviation exceeded half of the mean) Kruskal Wallis test was applied. The Mann-Whitney test was applied to test the significance of difference between two groups whenever Kruskal Wallis test gave significant results. We had to go for these non -parametric tests in the case of maximum increase in systolic and diastolic BP during cold pressor test. p < 0.05 was considered as statistically significant. As the sample was a homogenous group of healthy volunteers, we did not go for descriptive statistics.
After administering the self-assessment tool to determine Prakriti among the volunteers, we observed that no volunteer had scored zero for any Dosha. In other words, each volunteer had scored at least some points for each Dosha. Therefore, we could not name any class of volunteers to be strictly 'Ekadoshaja' (Prakriti with single Dosha) or 'Dvandvaja' (Prakriti with two Doshas). Similarly, we found no volunteer who scored equal scores for all the three Doshas and, hence, there was no 'Samadoshaja' individua l in our sample either. Therefore, we decided to classify the sample into three groups based on the primary Dosha (most dominant Dosha) that contributed to one’s Prakriti. Table 1 shows that out of 106 volunteers, the maximum number (50%) of volunteers had Kapha as the primary Dosha while minimum number (22%) of volunteers had Pitta as the primary Dosha. Table 1 also shows that the mean percentage scores for primary Dosha in one group were significantly greater (p < 0.001) than the mean percentage scores for the same Dosha in other two groups. Table 2 gives the details of distribution of the whole sample (n = 106) according to the score range for Vata, Pitta and Kapha separately.
The consolidated Table 3 shows the results of all the tests, except for pupil cycle time which are shown in Table 4. From the Table 3 it can be seen that the maximum increase in systolic and diastolic blood pressure during the cold exposure varied significantly as per primary Dosha, the increase being significantly higher in Vata group in comparison to Kapha group in both the cases (p = 0.000 and p = 0.021 respectively). However, the maximum increase in diastolic BP was significantly greater in Vata group in comparison to Pitta group too (p = 0.046). The S/L ratio significantly varied as per primary Dosha, the ratio being significantly greater in Kapha group in comparison to Vata (p = 0.024) group. The Valsalva ratio significantly varied as per primary Dosha, the ratio being significantly lower in Pitta group in comparison to Vata and Kapha groups (p = 0.035 and p = 0.000 respectively). The right pupil diameter in light varied significantly as per primary Dosha, the diameter being lower in Kapha group in comparison to Pitta and Vata groups (p = 0.002 and p = 0.036 respectively). Similarly, the left pupil diameter in light too varied significantly as per primary Dosha, the diameter being lower in Kapha group in comparison to Pitta and Vata groups (p = 0.007 and p = 0.035 respectively).
Table 4 suggests that the pupil cycle time varied significantly in right eye as per primary Dosha group, the mean cycle time being greater in Kapha group in comparison to Pitta group (p = 0.026). Similarly, the pupil cycle time varied significantly in the left eye too as per primary Dosha group. In this case too, the mean cycle time was greater in Kapha group in comparison to Pitta group (p = 0.036).
The tool that we have used in the present study assumes that each Dosha can express itself in a person to its fullest extent (100%) and calculates the percentage expression of that Dosha on ‘absolute’ basis unlike some other popular tools available for assessing Prakriti such as AyuSoft, which calculate the percentage contribution of each Dosha on a ‘relative’ basis (AyuSoft, a decision support software developed by C-DAC, 2005).
In this kind of relative calculation, if the score-wise contributions of Vata, Pitta and Kapha in an individual are, say, 5, 10 and 5 respectively, the final result displayed will be: Vata-25%, Pitta-50% and Kapha-25%. Therefore, when this kind of a tool expresses the contribution of a Dosha to be 50%, it need not necessarily mean that the concerned Dosha expresses 50% of the total traits ascribed to it in the classical Ayurveda textbooks. This is because, the denominator used in such a calculation is not the maximum ‘attainable’ scores for that Dosha, rather, it is the sum of the total scores ‘attained’ for all the three Doshas by that individual. This calculation ignores the total number of traits (i.e., the maximum attainable scores) ‘ascribed’ to a Dosha, but only considers the total number of scores ‘attained’ by an individual for each Dosha for final analysis of the results.
In the present tool that we have used in our study, however, the results are derived in terms of absolute percentage values, where, the calculation of contribution of one Dosha does not depend on the contribution of other Doshas. Therefore, if this tool expresses the contribution of a Dosha to be 50%, it definitely means that the concerned Dosha expresses 50% of the total traits ascribed to it.
Though, ‘which kind of a tool is ideal and suitable for research’ is a matter of debate, the absolute expression has certain edge over the relative expression because it gives a precise idea about the extent of expression of a particular Dosha. Further, the results obtained by this method can always be converted into ‘relative’ expression, if a researcher desires so.
From a point of scientific curiosity, we also tried classifying the sample on the basis of ‘two most dominant Doshas’. However, while doing so, we encountered a question as to how should we group the individuals with same Dosha composition but with varying dominance? For instance, if we group people with ‘Vata-Kapha’ and ‘Kapha-Vata’ into a single group, the significance of dominance would be lost, compromised and even may get nullified. Similarly, if we classify ‘Vata-Kapha’ and ‘Kapha-Vata’ into two different groups, there would be a total of ten groups, which is again, against the recommendation of the classical textbooks of Ayurveda. Therefore, we concluded that the classification of the sample by most dominant Dosha (primary Dosha) was the most rational one.
The autonomic nervous system has two components: a. Sympathetic, and, b. Parasympathetic. The sympathetic activity is dominant during emergency “fight-or-flight” situations and during exercise. The effect of sympathetic stimulation under such circumstances is to prepare the body for vigorous physical activity by increasing the blood flow to the skeletal muscles. The parasympathetic system, on the other hand, is dominant during quiet, resting conditions. The effect of the parasympathetic system in such situations is to conserve energy and to regulate basic body functions such as digestion, and, an optimal, moderate heart rate (McCorry, 2007).
According to the concept of constitution in Ayurveda, Kapha people are usually less dynamic, more stable, more relaxed and are more lethargic; whereas, the Vata and Pitta people are more excitable, anxious, aggressive, and are often volatile. These traits prompted us to propose a hypothesis that, Vata and Pitta individuals may be sympathetically dominant while the Kapha individuals may be parasympathetically dominant (Low et al., 2013).
As the results of Cold Pressor Test indicate, the sympathetic activity in Vata group is higher than that in Kapha group as far as Cold Pressor Test is concerned. Similarly, the results of S/L ratio indicate that the corresponding sympathetic activity in Vata group is higher than that in Kapha group. The results of Valsalva ratio suggest that the corresponding parasympathetic activity in Pitta group is lower than that in Vata and Kapha groups. Further, the Pupil Diameter in light was relatively smaller in Kapha group in comparison to Pitta and Vata groups suggesting that the corresponding sympathetic activity may be relatively higher in Vata and Pitta groups in comparison to Kapha group. However, since the differences in the mean pupil diameter were too small to have any clinical significance, a similar study in a large sample is recommended. The results of Pupil Cycle Time suggest that the corresponding ocular parasympathetic activity is relatively lower in Kapha group in comparison to Pitta group. This is in accordance with the observation recorded in an earlier study that cardiac parasympathetic activity may be negatively correlated with pupillary parasympathetic activity (Moodithaya and Avadhany, 2009). It is to be noted however, that all the results mentioned above were within the physiological limits in all the volunteers.
The overall impression that we could gather from this study is that Vata and Pitta individuals may have a relatively dominant sympathetic activity whereas Kapha individuals may have a relatively dominant parasympathetic activity (within the physiological limits) when it comes to cardiovascular responses. However, when the pupil cycle time is considered, the opposite seems to hold true, i.e., Kapha individuals may have a relative dominance of sympathetic activity than Pitta individuals.
A relatively small sample size of the study limits the generalizability of the results to a larger population. Volunteers belonging to both the genders were included in the study; hence, it ignores any possible gender differences with respect to autonomic responses.
The study suggests that people with Kapha as the most dominant Dosha tend to have eith er a higher parasympathetic activity or a lower sympathetic activity in comparison to other groups in the context of cardiovascular reactivity. The study also suggests a possibility that autonomic function tests, especially the ones related with cardiovascular reactivity and pupillary responses may serve as indicators to identify the primary Dosha contributing to the Prakriti in an individual. Further, the present model of grouping people depending on their ‘primary Dosha’ seems to be a useful and practical approach that researchers may like to explore, while investigating various aspects of Prakriti.