Sherzad Abdulrahman Muhammad, College of Dentistry, Erbil 60th Street, near Shorsh overpass.
© 2024 Sherzad Abdulrahman Muhammad, this is an open-access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
saliva, common beverages, light exercise, salivary flow rate, salivary buffering capacity, salivary albumin
Background: Salivary characteristics play a crucial role in maintaining oral health and can be affected by various lifestyle factors. There is very little research to cover the influence of individuals lifestyle behavior on the salivary biomarkers. This research paper aims to explore the influence of popular drinks, light exercise, and sleeping hours on salivary flow rate, buffering capacity, and salivary albumin level in young adults.
Materials and methods: The study involved 92 participants aged 18-25 years who were completed a detailed questionnaire on their dietary habits, physical activity, and sleep patterns. Saliva samples were collected and analyzed to determine the salivary flow rate, buffering capacity, and albumin level. Unstimulated whole saliva was collected from participants, and flow rate was noted down during collection of the sample. pH was assessed with a digital pH meter and buffering capacity was estimated using Ericsson method. Salivary albumin was estimated using the Bromocresol green method. Kolmogorov-Smirnov, Pearson correlation, Spearman’s rank correlation, Mann-Whitney U and t-test were used for statistical analysis.
Results: The results showed nonsignificant correlations between salivary parameters and the lifestyle factors studied. However, significant correlations were observed between body mass index and light exercise, and consumption of water and water-milk. Also, a significant correlation was obtained between gender and salivary buffering capacity.
Conclusion: Combination of lifestyle factors could influence salivary characteristics in young adults. Further research with larger number of participants is needed to elucidate the complex interplay between lifestyle behaviors and oral health biomarkers.
Among factors that play a role in reducing the development of the most common oral condition in the world, which is tooth decay, salivary flow rate, buffering capacity, and salivary albumin (Veiga et al., 2016). Saliva plays a decisive role in maintaining oral health; it uses a variety of physical and biochemical mechanisms to carry out its mechanical cleaning and protecting functions, including the integrity of the enamel, because of its ability to act as a buffer, as well as regulating the equilibrium between demineralization and remineralization at the enamel's surface (Polyakova et al., 2024). An adequate flow rate and buffering capacity of saliva are essential for maintaining a balanced oral environment (Birkhed & Heintze, 2021). A study has found that soft drink consumption was linked to a salivary pH fall of several degrees followed by a gradual recovery to standard pH points due to salivary buffering power and clearance (Polyakova et al., 2024).
In addition to potential protection from the pellicle on the surface, another study suggests that the inhibitory effects of albumin absorbed into the pores are the primary means by which human dental enamel is shielded against demineralization (M. N. Hegde, Raksha Bhat, Ashwitha Punja, & Chitharanjan Shetty, 2014). Furthermore, albumin protein plays many physiological roles, including colloidal osmotic of blood pressure, neutralizing free radicals, carrying of endogenic and exogenic substrates, including minerals required for mineralization of teeth, and maintaining of oral soft tissues such as calcium, zinc, and copper (Mahmood et al., 2024), (Hassan & Kaddam, 2021), (Reddy et al., 2021). Proteins have antioxidant abilities due to their free thiol groups, which make albumin the most abundant and powerful extracellular antioxidant and one of the salivary antioxidant proteins that serves as the body's first line of defense against oxidative stresses (Metgud & Patel, 2014).
Diet, including different beverages, may affect locally the oral health such as the integrity of teeth, pH, plaque formation, and salivary composition, which may in turn influence salivary flow rate, buffering capacity, and albumin level (Uma et al., 2018). Since water is a major constituent of saliva (97–99.5%) that enters saliva from plasma across acinar cells, decreased saliva flow rate has been perceived during times of water depletion (Walsh, Montague, Callow, & Rowlands, 2004). On the other hand, a relationship between exercise and body hydration status has been established, which causes changes in salivary composition and flow rate and may alter the concentrations of key electrolytes and proteins, including albumin (Rutherfurd-Markwick, Starck, Dulson, & Ali, 2017), (Proctor & Shaalan, 2021).
Physical exercise is an intense activator of the sympathetic neural system that controls the secretion of protein-rich saliva, which may affect saliva composition, flow rate, viscosity, and elevated concentrations of proteins. This may be due to evaporation during exercise and systemic dehydration after prolonged exercise. Furthermore, the exercise may increase the secretion of salivary mucins, although it is controlled by the parasympathetic neural system (Ligtenberg, Brand, van den Keijbus, & Veerman, 2015).
Since the epidemiological records show concerns about the impact of dietary changes on oral health, particularly in relation to dental caries, and young adults are often known for consuming various beverages throughout the day (Attila & Çakir, 2011). On the other hand, a large majority of young adults do not meet international recommendations on exercise, and the proportion of obesity is increasing in both genders and across all age groups (Grasdalsmoen, Eriksen, Lønning, & Sivertsen, 2019).
Another factor that may affect the salivary secretion and composition is sleeping. It is important to investigate the effects of different beverage consumption and light exercise on salivary flow rate, buffering capacity, and albumin level in young adults. Understanding the impacts of lifestyle on the salivary properties can provide valuable insights into oral health maintenance. In this study, we aim to explore the influence of these common drinks, light exercise activity and sleeping hours on salivary flow rate, buffering capacity, and albumin level in young adults.
The approval for this study protocol was obtained from the ethics committee at the College of Dentistry-HMU, Iraq-Erbil, after the agreement from all participants. Subjects included in the study were 92 young adults of one age group (18–25 years of age). The elimination criteria involved systematic sicknesses, smoking, pregnancy, and the use of chronic condition medications (Karnik, Pagare, Krishnamurthy, Vahanwala, & Waghmare, 2015). A questionnaire was filled for each participant regarding the daily and weekly consumption of beverages, light exercise and sleeping hours.
Collection of saliva
The same researcher collected rest saliva samples from the participants in the morning while they were physiologically resting. The participants were instructed not to eat or drink anything for at least one hour before the collection of saliva; samples were collected before meals or at least 2 hours after meals. Before collection of saliva and to minimize the residue of food remains, participants were requested to rinse out their mouths with water for 5 minutes. Saliva was collected by the spitting method in the plastic sterile test tubes for 5 minutes (Kumar et al., 2020).
Evaluation of the salivary flow rate
The flow rate of saliva was evaluated by asking the participants to spit into plastic test tubes for 5 minutes, then the collected amount of saliva was divided with the time required for collection and recorded in milliliters per minute (ml/min) to determine the flow rate (Rajendra et al., 2023).
Estimation of buffering capacity of saliva
Calibration of the digital pH meter (Eutech pH 700-meter, pH electrode ECFC7252101B, ATC Probe PH5TEMB01P) was first carried out with a pH 7 buffer, followed by a pH 4 buffer (if an acidic sample is anticipated) and a pH 10 buffer (if a basic sample is anticipated). Following calibration of the pH electrode, it is immersed in the saliva sample for estimation, and the reading is recorded. The submerged part of the electrode washed off with distilled water before the subsequent sample.
Buffering capacity of saliva was estimated using the Ericsson method. Buffer capacity evaluates a buffer's capability in maintaining pH constancy. Traditionally, buffer capacity (β) is defined as the quantity of strong acid or base, specified in gram-equivalents, needed to alter the pH of 1 liter of solution by one unit. The initial pH of the saliva sample is measured and the value is recorded. Subsequently, 1.5 ml of 0.005 mol/L hydrochloric acid is mixed with 0.5 ml of saliva in a centrifuge tube. The mixture is blended well, centrifuged for one minute, and left to stand for ten minutes, after which the final pH is measured using a digital pH meter. In a quantitative manner, buffering capacity is the ratio of the amount of acid dispensed to the resultant change in pH (e.g., mEq./pH for a specific volume). (Ansari et al., 2022).
Salivary buffer capacity (β) = Δ B/ΔΔ pH
Δ B = required gram equivalent of strong acid to change the pH of one liter of buffer solution
Δ pH = The change in pH caused by the addition of strong acid.
Estimation of the salivary albumin protein
Centrifuging the saliva samples at 4000 rpm for 10 minutes will remove cellular debris following the assessment of flow rate and buffering capacity. Then, the concentration of salivary albumin was determined using the Bromocresol green method, albumin colorimetric test, in a buffer solution at pH 4.2. Bromocresol binds to albumin to form a colored compound, whose absorbance of light can be measured at 630 nm wave length (Khandelwal & Palanivelu, 2019). The addition of bromocresol green to the saliva samples results in a change in color that is proportional to the albumin protein concentration. The reagent-sample mixtures for individual subjects were incubated for 1 minute. Then the absorbance of samples and standard were measured against a reagent blank.
Result = (Absorbance of Sample/Absorbance of Standard) X Standard concentration
Statistical analysis
The Statistical Package for Social Sciences software version 22.0 for Windows (SPSS Inc., Chicago, IL) was used; the descriptive statistics included mean, standard deviation, minimum, and maximum values of the physiological and behavioral variables. The Kolmogorov-Smirnov test was applied to assess distributions of variables for normality. Pearson correlation and Spearman’s rank correlation tests were used with the normal and non-normally distributed variables, respectively. Mann-Whitney the U test was used as well when the requirement of normal distribution for the t-test was not met. A p-value of <0.05 was considered statistically significant.
It was found that salivary flow rate, buffering capacity, and salivary albumin concentration were all normally distributed by the Kolmogorov-Smirnov test. The other variables included in the statistical analysis were not normally distributed.
The descriptive statistics reveal that the mean values of salivary pH, salivary flow rate, sleeping hours, and body mass index (BMI) are around the normal levels, but with big differences between the minimum and maximum values. Also, big differences can be observed between the minimum and maximum values regarding the weekly drinking cups of the beverages (Table 1).
Spearman’s rank correlation test has shown a nonsignificant correlation for the weekly consumption of water, tea, and milk separately or collectively with salivary flow rate, buffering capacity, or albumin level.
The same correlation test for playing light exercise was nonsignificant with the salivary flow rate, buffering capacity, and albumin level, but was significant with the BMI and consumption of the combined beverages that contain water, noting that the relationship between light exercise and BMI was directly proportional
The body mass index (BMI) has shown nonsignificant correlations with salivary flow rate, buffering capacity, and albumin level (p-values ˃ 0.05) and showed significant correlation with playing light exercise (p-value= 0.011) and weekly consumption of water and water-milk.
Sleeping hours not had significant correlation with the salivary flow rate, buffering capacity, albumin level, or BMI. (Tables 3 and 4).
Table 1: Descriptive Statistics of Physiological and Behavioral Variables
|
Mean |
Std. Deviation |
Minimum |
Maximum |
|
pH |
6.990 |
0.425 |
6.11 |
10.12 |
|
Salivary Flow rate ml/min |
0.229 |
0.067 |
0.08 |
0.40 |
|
Age yrs |
18.70 |
0.752 |
18 |
21 |
|
Sleeping hours |
7.58 |
1.361 |
4 |
12 |
|
Light exercise hrs/week |
5.37 |
6.035 |
0 |
28 |
|
BMI Kg/m2 |
22.406 |
3.515 |
16.004 |
34.602 |
|
Buffering Capacity |
0.888 |
0.193 |
0.560 |
1.626 |
|
Albumin g/dL |
0.970 |
0.036 |
0.888 |
1.079 |
|
Water cups/week |
44.587 |
22.298 |
7.00 |
105.00 |
|
Tea cups/week |
15.141 |
13.933 |
0.00 |
84.00 |
|
Milk cups/week |
1.61 |
2.081 |
0 |
8 |
|
Water + Tea cups/week |
59.728 |
26.466 |
14.00 |
168.00 |
|
Water + Milk cups/week |
46.195 |
22.550 |
7.00 |
108.00 |
|
Tea + Milk cups/week |
16.750 |
13.778 |
0.00 |
86.00 |
|
Water + Tea + Milk cups/week |
61.337 |
26.516 |
14.00 |
169.00 |
Pearson correlation tests have shown nonsignificant correlations for salivary flow rate with the buffering capacity and salivary albumin concentration. Furthermore, the correlation between the salivary buffering capacity and albumin was nonsignificant. Note that the correlations between salivary flow rate with buffering capacity, and buffering capacity with salivary albumin were direct, but for salivary flow rate with salivary albumin, was inverse (Table 2).
Table 2: Pearson correlation of the normally distributed variables.
|
pH |
Salivary Flow rate ml/min |
Buffering Capacity |
|
Salivary Flow rate ml/min |
Pearson Correlation |
-0.029 |
1 |
0.015 |
P-Value |
0.784 |
|
0.885 |
|
Buffering Capacity |
Pearson Correlation |
-0.165 |
0.015 |
1 |
P-Value |
0.117 |
0.885 |
|
|
Albumin g/dL |
Pearson Correlation |
-0.016 |
-0.005 |
0.182 |
P-Value |
0.878 |
0.962 |
0.082 |
|
* Correlation is significant at the 0.05 level (2-tailed). |
Table 3: Spearman’s rank correlation of the non-normally distributed variables.
|
Albumin g/dL |
Weekly_water |
Weekly_tea |
Milk |
Weekly_water_tea |
Weekly_water_Milk |
Weekly_Tea_Milk |
Weekly_water_tea_milk |
|
Salivary Flow rate ml/min |
Spearman Correlation Coefficient |
-0.018 |
-0.099 |
-0.107 |
0.026 |
-0.195 |
-0.091 |
-0.103 |
-0.202 |
P-Value |
0.866 |
0.347 |
0.312 |
0.806 |
0.063 |
0.387 |
0.326 |
0.053 |
|
Sleeping hours |
Spearman Correlation Coefficient |
0.068 |
-0.028 |
-0.012 |
0.111 |
-0.032 |
-0.014 |
0.023 |
-0.018 |
P-Value |
0.519 |
0.790 |
0.909 |
0.291 |
0.763 |
0.895 |
0.829 |
0.861 |
|
Light exercise |
Spearman Correlation Coefficient |
-0.111 |
0.282** |
0.108 |
0.116 |
0.288** |
0.289** |
0.143 |
0.294** |
P-Value |
0.294 |
0.006 |
0.305 |
0.270 |
0.005 |
0.005 |
0.175 |
0.004 |
|
BMI |
Spearman Correlation Coefficient |
0.026 |
0.270** |
0.005 |
0.136 |
0.173 |
0.277** |
0.023 |
0.189 |
P-Value |
0.803 |
0.009 |
0.962 |
0.197 |
0.100 |
0.007 |
0.824 |
0.071 |
|
Buffering Capacity |
Spearman Correlation Coefficient |
0.114 |
0.060 |
0.133 |
-0.036 |
0.174 |
0.048 |
0.104 |
0.160 |
P-Value |
0.280 |
0.571 |
0.205 |
0.735 |
0.097 |
0.650 |
0.322 |
0.127 |
|
Albumin g/dL |
Spearman Correlation Coefficient |
1.000 |
-0.110 |
0.037 |
0.091 |
-0.072 |
-0.108 |
0.015 |
-0.066 |
P-Value |
|
0.297 |
0.724 |
0.390 |
0.495 |
0.305 |
0.884 |
0.533 |
|
**. Correlation is significant at the 0.01 level (2-tailed). |
|||||||||
*. Correlation is significant at the 0.05 level (2-tailed). |
Table 4: Spearman’s rank correlation of the non-normally distributed variables.
|
Salivary Flow rate ml/min |
Sleeping hours |
Light exercise |
BMI |
Buffering Capacity |
|
pH |
Spearman Correlation Coefficient |
0.000 |
-0.023 |
-0.046 |
0.010 |
-0.214* |
P-Value |
0.999 |
0.826 |
0.664 |
0.923 |
0.041 |
|
Salivary Flow rate ml/min |
Spearman Correlation Coefficient |
1.000 |
0.033 |
-0.075 |
-0.094 |
0.046 |
P-Value |
|
0.752 |
0.475 |
0.371 |
0.660 |
|
Sleeping hours |
Spearman Correlation Coefficient |
0.033 |
1.000 |
0.026 |
0.002 |
0.052 |
P-Value |
0.752 |
|
0.805 |
0.987 |
0.621 |
|
Light exercise |
Spearman Correlation Coefficient |
-0.075 |
0.026 |
1.000 |
0.265* |
0.068 |
P-Value |
0.475 |
0.805 |
|
0.011 |
0.520 |
|
BMI |
Spearman Correlation Coefficient |
-0.094 |
0.002 |
0.265* |
1.000 |
0.120 |
P-Value |
0.371 |
0.987 |
0.011 |
|
0.255 |
|
Buffering Capacity |
Spearman Correlation Coefficient |
0.046 |
0.052 |
0.068 |
0.120 |
1.000 |
P-Value |
0.660 |
0.621 |
0.520 |
0.255 |
|
|
Albumin g/dL |
Spearman Correlation Coefficient |
-0.018 |
0.068 |
-0.111 |
0.026 |
0.114 |
P-Value |
0.866 |
0.519 |
0.294 |
0.803 |
0.280 |
|
Weekly_water |
Spearman Correlation Coefficient |
-0.099 |
-0.028 |
0.282** |
0.270** |
0.060 |
P-Value |
0.347 |
0.790 |
0.006 |
0.009 |
0.571 |
|
Weekly_tea |
Spearman Correlation Coefficient |
-0.107 |
-0.012 |
0.108 |
0.005 |
0.133 |
P-Value |
0.312 |
0.909 |
0.305 |
0.962 |
0.205 |
|
Milk |
Spearman Correlation Coefficient |
0.026 |
0.111 |
0.116 |
0.136 |
-0.036 |
P-Value |
0.806 |
0.291 |
0.270 |
0.197 |
0.735 |
|
Weekly_water_tea |
Spearman Correlation Coefficient |
-0.195 |
-0.032 |
0.288** |
0.173 |
0.174 |
P-Value |
0.063 |
0.763 |
0.005 |
0.100 |
0.097 |
|
Weekly_water_Milk |
Spearman Correlation Coefficient |
-0.091 |
-0.014 |
0.289** |
0.277** |
0.048 |
P-Value |
0.387 |
0.895 |
0.005 |
0.007 |
0.650 |
|
Weekly_Tea_Milk |
Spearman Correlation Coefficient |
-0.103 |
0.023 |
0.143 |
0.023 |
0.104 |
P-Value |
0.326 |
0.829 |
0.175 |
0.824 |
0.322 |
|
Weekly_water_tea_milk |
Spearman Correlation Coefficient |
-0.202 |
-0.018 |
0.294** |
0.189 |
0.160 |
P-Value |
0.053 |
0.861 |
0.004 |
0.071 |
0.127 |
The Mann-Whitney U and t-tests for two independent variables showed that there were nonsignificant differences between males and females in salivary flow rate, albumin level, or sleeping hours. However, there was a significant difference between males and females in salivary buffering capacity, with males having a higher level of it (Table 5).
Table 5: A Comparison of Biochemical, Physiological, and Behavioral Variables Between Genders
Gender |
N |
Mean |
Std. Deviation |
Test Value |
d.f. |
P-Value |
Sig. |
|
Salivary Flow rate ml/min |
Male |
37 |
0.242 |
0.070 |
1.587⸷ |
90 |
0.116 |
NS |
Female |
55 |
0.220 |
0.065 |
|||||
Albumin g/dL |
Male |
37 |
0.973 |
0.038 |
0.648⸷ |
90 |
0.519 |
NS |
Female |
55 |
0.968 |
0.034 |
|||||
Buffering Capacity |
Male |
37 |
0.943 |
0.211 |
2.293⸷ |
90 |
0.024 |
S |
Female |
55 |
0.851 |
0.171 |
|||||
Sleeping hours |
Male |
37 |
7.49 |
1.539 |
937⸸ |
90 |
0.507 |
NS |
Female |
55 |
7.64 |
1.238 |
⸷ : t-test for two independent samples.
⸸ : Mann-Whitney U test.
There is no ideal volume of beverages, such as those included in this study, to be consumed weekly, and there is also no ideal period for light exercise and amount of sleeping to apply generally to all people as they depend on many factors and it should be individualized (Chaput, Dutil, & Sampasa-Kanyinga, 2018;(Evangelista et al., 2024). This study shows how the difference in consumption of some popular beverages, light exercise, and sleeping hours may affect salivary flow rate, buffering capacity, and albumin level. The light exercise has been subjected to investigation in this study because other studies have essentially focused on the effects of moderate/vigorous physical activities on body weight while skipping the light intensity or daily life activities (Bourdier, Simon, Bessesen, Blanc, & Bergouignan, 2023).
Despite that the mean of salivary physiological and behavioral variables such as salivary pH, salivary flow rate (SFR), and sleeping hours are approximately within the normal ranges, the oral cavity secretes saliva at an average rate of 0.1-0.5 mL/min for unstimulated saliva and 1.1-3.0 mL/min for stimulated saliva, hypo unstimulated and hypo-stimulated SFR are (< 0.2 mL/min) and (< 0.7 mL/min) respectively (Bulthuis et al., 2024; Kristanto, Putri, Sumaryono, & Kusumastuti, 2023; Sambuichi, Nishimura, Morishita, & Watanabe, 2024; Zhang, Jiang, Chen, & Wang, 2022), there were big differences between the lowest and highest values of some variables, like pH (6.11–10.12), sleeping hours (4–12), light exercise hours per week (0–28), BMI (16.004–34.602), and the number of cups of drinks drunk each week, ranging from zero to several cups. However, there were no significant correlations found between the mean salivary flow rate, buffering capacity, and albumin concentration. Different results from another study about the effect of mineral water consumption show that sufficient hydration before and after exercise prevents the decline of the SFR and buffering capacity (Tanabe, Takahashi, Shimoyama, Toyoshima, & Ueno, 2013). Regarding the effect of tea consumption, a study indicates that it causes an increase in the salivary flow rate and total salivary proteins (Chong, He, Rao, Li, & Ke, 2021). The reason for the inconsistency between our results and the mentioned researches is probably due to the fact that they estimated the effect of drinking mineral water before and immediately after exercise and estimated the salivary biomarkers before and after drinking tea for a short period of time and in minutes, while our research is based on the long-term effect. In other words, the effect of drinks on the mentioned properties of saliva is a temporary effect because the composition of saliva is renewed due to its continuous secretion, so the changes that occur to saliva will disappear within a short period that can be estimated in minutes (Polyakova et al., 2024).
After looking at the study's findings from a variety of angles, we can see that the salivary flow rate is directly proportional to the buffering capacity and inversely to the albumin level, meaning that an increase in the flow rate results in an increase in the buffering capacity. In principle and not based only on the result, our result is in line with studies declaring that people with high salivary flow rates are better at neutralizing the acidity of sour solutions than people with low salivary flow rates (Wikner & Söder, 1994; Zhang et al., 2022). The inverse relationship between the flow rate and the salivary albumin concentration in our study was observed also in a study about the salivary flow rate and the salivary total protein content (TPC) (Criado, Muñoz-González, & Pozo-Bayón, 2021).
It is noted from the spearman’s rank correlation test that the weekly consumption of the three beverages is not significantly correlated to unstimulated salivary flow rate, buffering capacity, and albumin level, which are the main goals of this study, while a study showed that post-exercise flow rate decreases significantly after drinking of water but for stimulated saliva (Horswill, Stofan, Horn, Eddy, & Murray, 2006). Also, another study about children 10–12 years old shows that milk significantly affects the salivary buffering system; however, our results are for adults (Sungkar, Chismirina, Nasution, & Imaduddin, 2020). Regarding the effect of beverages on salivary albumin, one study reveals that cold water induces a greater long-term increase in salivary total protein content than the hot water (Meng et al., 2024).
Regarding the impact of light exercise on salivary flow rate, our nonsignificant result is in line with a study on how practice of exercise can change biological salivary markers (Ventura et al., 2022). On the other hand, the nonsignificant result of the effect of light exercise on the salivary buffering capacity is justified by the results of a study indicate that salivary buffering capacity varies depending on an individual's responses to adverse effects after being exposed to physically stressful situations, which means it cannot be generalized to all adults (Nakashima, Nagata, & Oho, 2016). Physical exercise might increase the salivary flow rate and generate changes in several salivary components such as immunoglobulins, hormones, electrolytes, lactate, and proteins, of course, taking factors such as exercise intensity and duration into consideration (Chicharro, Lucía, Pérez, Vaquero, & Ureña, 1998; Ntovas, Loumprinis, Maniatakos, Margaritidi, & Rahiotis, 2022), however, our study showed nonsignificant effects of light exercise on salivary albumin; this may belong to the type of activity, which is a light exercise, and the saliva collection that was not carried out during short intervals before and after the exercise.
Not far from the study objectives, it appears that light exercise has a significant spearman correlation to the BMI (Bellicha et al., 2021; Bourdier et al., 2023), and with the weekly consumption of water and some beverages combined with water, the correlation between light physical activities and water can be illustrated in that the brain monitors changes in bodily water content and concentration; thirst and oropharyngeal sensations that follow influence drinking; and the kidneys' excretion or retention of water and electrolytes is regulated by neuroendocrine responses. These complicated relationships help keep up serum concentration and total body water volume during the regular activities. However, the proportional importance of these processes varies based on the activities of daily living (Armstrong, 2021).
Body mass index (BMI) in turn has a nonsignificant correlation to salivary flow rate (FenolI-Palomares et al., 2004), but in a study, salivary flow rate was significantly influenced by BMI (Sawair, Ryalat, Shayyab, & Saku, 2009). Also, BMI is non significantly correlated to salivary buffering capacity and albumin level (p-value>0.05) while it is significantly correlated to light exercise and the weekly consumption of water and water with milk, this significant correlation may be attributed to the awareness of the participants, especially since they are a medical university students, that physical activity helps in correcting body mass index, and physical activity in turn increases the body’s need for fluids and specially water to reach the hydration status which plays a key role during exercise such as glycogenolysis and glycogen resynthesis during recovery, this motivates the person to consume significant quantities of fluids and mainly water (López-Torres, Rodríguez-Longobardo, Escribano-Tabernero, & Fernández-Elías, 2023). Another study indicates a direct relationship between BMI and the consumption of caloric fluids, but since the main fluid consumed in this study is water, the direct relationship is most likely caused by the physical activity, which increases the body's need for fluids, especially water. However, milk and tea also play roles in increasing the BMI due to the macronutrient content of milk and the added sugar in tea (Alhareky, Goodson, Tavares, & Hartman, 2024).
Spearman’s Rank correlation test shows nonsignificant correlation for the salivary flow rate, buffering capacity and albumin level that agrees with other studies (Alkhateeb, Mancl, Presland, Rothen, & Chi, 2017; M. N. Hegde, Raksha Bhat, Ashwitha Punja, & Chitharanjan Shetty, 2014; Takeuchi et al., 2015), this result may be due that the participants have similar bed time as bed time may play more effective role in the variation of the physiological and biochemical biomarkers due to the role of deprived sleep in affecting endocrine secretions including increased evening concentrations of cortisol, decreased concentrations of the anabolic hormones such as testosterone, GH, and the insulin-like growth factor I (Chennaoui, Leger, & Gomez-Merino, 2020).
Although the statistical analysis shows a nonsignificant correlation among salivary flow rate, buffering capacity, albumin level, sleeping hours, and most of the beverage types consumed, it is noted that the increase in the buffering capacity is directly proportional to the mentioned behavioral, physiological, and biochemical parameters (FenolI-Palomares et al., 2004) (Mehdipour et al., 2024). This outcome supports the role of albumin protein as a salivary buffering system component (Cheaib & Lussi, 2013).
Regarding the comparison between genders, nonsignificant differences are seen between males and females with regards to the salivary flow rate, and this result agrees with a study by (Rutherfurd-Markwick et al., 2017). However, the salivary flow rate in males is slightly higher than that in females, which is also consistent with (Criado et al., 2022). Also, nonsignificant difference between genders in salivary albumin concentration is seen, which comes in consistency with a study by (Prodan et al., 2015). Furthermore, nonsignificant difference is noted between genders with regard to the recorded sleeping hours. Although these variables show approximately similar mean values for males and females, the difference in the buffering capacity between males and females is significant (p-value=0.024), and males show higher buffering capacity than females; this outcome agrees with (FenolI-Palomares et al., 2004), This result may be due to the hormonal fluctuations in women during puberty, menstruation, and pregnancy that modifies the salivary biochemical composition and its flow rate, resulting in a more cariogenic oral environment in females than males (Lukacs & Largaespada, 2006).
Our outcome agrees with another study in that the buffering capability of the saliva is higher in the boys than in the girls, and the difference between the two genders is statistically significant (Ahmadi-Motamayel, Goodarzi, Hendi, Abdolsamadi, & Rafieian, 2013). The hormonal fluctuations throughout the female’s life include the role of estrogen, which can affect many oral tissues such as salivary glands, oral mucosa, and neural networks (Mahesh et al., 2014).
Although the exact mechanism of how hormones can modify saliva composition and secretion is still inadequately understood, an imagination about the elevated estrogen level during pregnancy declares that it increases the blood flow of salivary glands, which may result in alteration in salivary secretions. A higher salivary flow rate in turn is accompanied by increased bicarbonate concentration, a main component of the salivary buffering system, which results in an increased level of salivary pH (S. Hegde et al., 2016).
The consumption of various beverages has been shown to have a significant impact on the physiological properties of saliva, which play a crucial role in maintaining oral health. Dehydration has been consistently associated with decreased salivary flow rate, while the effects of hyperhydration have been less extensively explored. Saliva production increases during drinking, which can attenuate the need to continue drinking to relieve mouth dryness (Brunstrom et al., 2000). Furthermore, the circadian rhythm, dietary influences, and the patient's age can all affect the composition and volume of saliva (Aps & Martens, 2005). Changes in the amount of water inside cells, blood flow to the salivary glands, metabolic make-up of fluid inside and outside the cells, and factors in the stomach and mouth can all cause changes in salivary flow rate (Holmes, 1964). Patients taking certain medications or experiencing specific systemic sicknesses may also exhibit salivary alterations (Aps & Martens, 2005).
This study suggested that consumption of some common beverages, playing light exercise, and sleeping hours may affect humans’ salivary flow rate, buffering capacity, and salivary albumin concentration. However, most of our results are nonsignificant, and this might be due to the narrow number of participants. Upcoming studies with a higher number of participants might approve this mode.
While there are nonsignificant correlations between sleeping hours, light exercise, and beverage consumption on salivary flow rate, buffering capacity, and albumin concentration, significant relationships exist between body mass index, light exercise, and gender differences in salivary buffering capacity in which males have a higher level of salivary buffering capacity. Overall, the study points out the complexity of factors influencing salivary parameters and their interrelations.
Acknowledgments
This work was supported by Hawler Medical University’s College of Dentistry, and the authors would like to thank the Basic Science department laboratory.
Conflicts of interest
The author has no known conflict of interest to disclose.