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. Author manuscript; available in PMC: 2024 Dec 1.
Published in final edited form as: Arch Oral Biol. 2023 Oct 13;156:105824. doi: 10.1016/j.archoralbio.2023.105824

Anticholinergic burden of medications is associated with dry mouth and reflected in minor labial gland secretion

Abdallah Michail 1, Mohammed Almirza 1, Furqan Alwaely 1,2, Szilvia Arany 2,*
PMCID: PMC10841478  NIHMSID: NIHMS1939543  PMID: 37865013

Abstract

(1). Objective:

Medications with anticholinergic potential inhibit saliva secretion. Polypharmacy potentiates anticholinergic burden, causing dry mouth symptoms and chronic deterioration of oral health. Patients of any age can be affected by anticholinergic medication-triggered hyposalivation (the objective measure of dry mouth); therefore, seeking predictions of hyposalivation to screen dry mouth is needed.

(2). Design:

In our prospective, cross-sectional clinical study, 55 middle-aged adult patients participated. We examined whether the anticholinergic burden calculated from anticholinergic medications (anticholinergic drug score; ADS) and blood serum anticholinergic activity (SAA; the gold standard measure of anticholinergic burden) is associated with hyposalivation. As no prior studies measured minor salivary glands regarding the quantifiable anticholinergic burden, we assessed hyposalivation by the minor saliva flow (MSF) and unstimulated whole saliva (UWS) secretion.

(3). Results:

Our data showed a negative linear relationship between SAA and UWS (p < 0.05); when SAA increases by one pmol/ml unit, the saliva flow decreases by 0.058 ml/min. MSF showed a linear correlation (p < 0.005) with UWS. In a multivariate logistic regression model (including age, gender, race, smoking status, xerostomia severity, ADS, and BMI), we identified SAA and age as predictors of hyposalivation (p < 0.05).

(4). Conclusions:

We provide evidence for the significant relationship between measurable anticholinergic burden and saliva flow. The correlation between UWS and MSF suggests that both saliva flow rate measurement methods could reflect anticholinergics-induced changes in salivary health.

Keywords: Hyposalivation, Anticholinergic, Minor salivary gland, Unstimulated, Secretion, Medication

Introduction

Dry mouth is clinically apparent as reduced saliva flow, aka. hyposalivation. Hyposalivation is most frequently presented or preceded by the subjective feeling of oral dryness (xerostomia). The amount of unstimulated whole saliva (UWS) is reduced in hyposalivation, while stimulated saliva flow might be unaffected (L. M. Sreebny, Valdini, & Yu, 1989). UWS is independent of stimuli (food, chewing, speaking, etc.) and has been proposed as the test of choice to measure changes in saliva secretion. Approximately 75% of unstimulated saliva is produced by the submandibular and sublingual glands, 15% to 20% by the parotid glands, and 5% to 8% by the minor salivary glands (Wang, Shen, Liu, Si, & Yu, 2015). Minor salivary gland secretion rates have been suggested to be correlated with the UWS flow rates (Lee, Lee, Chung, Kim, & Kho, 2002). As the minor salivary glands secrete saliva continuously, their output is driven under an exclusive parasympathetic, cholinergic signaling; thus, their secretion rates might be directly affected by anticholinergic drugs, but no studies included the minor salivary glands in the investigation of the effect of the anticholinergic burden.

Hundreds of medications (antidepressants, antipsychotics, antiparkinson’s, antispasmodics, cold and allergy agents) can block saliva secretion due to their cholinergic interference with salivary function. The cholinergic (parasympathetic) receptors in the salivary glands are most sensitive to anticholinergic medications, affecting neural stimulation by cholinergic signaling (Arany, Kopycka-Kedzierawski, Caprio, & Watson, 2021), which results in dry mouth with 20-30% frequency in the US (Van Kerrebroeck et al., 2001). Individuals with dry mouth have an increased risk for severe or recurrent caries, mucosal infections, candidiasis, glossitis, orofacial pain, and susceptibility to intraoral traumas and lesions, often associated with impaired use of dentures. Anticholinergics can directly inhibit acetylcholine neurotransmission in the salivary glands or secondarily inhibit acetylcholine in the central nervous system (Villa et al., 2016). Anticholinergic medication exposure has increased significantly in the past few decades (Kachru, Carnahan, Johnson, & Aparasu, 2015). The combination of medications potentiates anticholinergic effects (Singh, Loke, Enright, & Furberg, 2014), posing an increased risk for dry mouth (Salahudeen, Duffull, & Nishtala, 2015).

Various scales were developed to measure the anticholinergic burden from medications and quantify anticholinergic exposure of patients (Salahudeen et al., 2015; Villalba-Moreno, Alfaro-Lara, Pérez-Guerrero, Nieto-Martín, & Santos-Ramos, 2016). The Anticholinergic Drug Scale (ADS) score is proposed to be correlated to the anticholinergic activity measured in the blood by the Serum Anticholinergic Activity (SAA) (Carnahan, Lund, Perry, Pollock, & Culp, 2006). SAA is considered the gold standard in measuring anticholinergic burden (Thienhaus, Allen, Bennett, Chopra, & Zemlan, 1990; L. Tune, Carr, Hoag, & Cooper, 1992; L. E. Tune & Egeli, 1999). The association between SAA and salivary secretion has been suggested (L. E. Tune et al., 1981), but SAA was not evaluated in predicting the risk of dry mouth.

We designed this study to explore the potential correlation between anticholinergic burden and saliva secretion by measuring the flow rate of major and minor salivary glands. We tested the assumption that the anticholinergic burden, calculated from the individual’s anticholinergic medications (ADS), correlates with the blood serum anticholinergic activity (SAA). We investigated whether flow rate measurements in minor saliva secretion could be useful in screening dry mouth. Covariables with potential influence on saliva secretion (age, gender, xerostomia severity, smoking status, and BMI) were used in the analysis.

Materials and Methods

The research protocol was reviewed by the University of Rochester Medical Center institutional review board (RSRB STUDY00005666, approved on June 15, 2021) by Federal regulation 45 CFR 46 under the University’s Federal-wide Assurance (FWA00009386). The study was designed as a prospective, cross-sectional clinical investigation. We recruited middle-aged adult patients (45-64 years of age) to avoid potential confounding age-related changes in drug metabolism. Individuals (n=55) were enrolled from adult patients at the Specialty Care Clinic, General Dentistry (GD), Eastman Institute for Oral Health. Information to determine eligibility was collected by patient self-reporting. When potential subjects met the inclusion criteria and wished to participate in the study, the informed consent process was initiated.

Inclusion criteria: 1) age between 45 and 64 years; 2) self-reported oral dryness, aka. xerostomia; 3) continuous usage of at least one anticholinergic medication, taken for at least the past 30 days before the study visit (dry mouth develops within four weeks after AC drug therapy is initiated) (Nagler et al., 2001) and verified from an up-to-date list of medication. Exclusion criteria: 1) Sjögren’s syndrome or other known diseases affecting the SGs; 2) past or current head and neck radiation therapy or radioiodine treatment; 3) known liver damage or liver injury; 4) current treatment with a cholinergic agonist.

Sample Size Estimation

Calculations were based on published data on the primary response variable (UWS), using reported means of healthy individuals (Bardow, Nyvad, & Nauntofte, 2001). To achieve 80% power, significance set at α = 0.05, we established an accrual goal of 52 patients.

Dry mouth measurements

All participants signed an eConsent document using the University of Rochester REDCap platform at the study visit. Dry mouth was assessed by saliva flow rate measurements, Xerostomia Inventory (XI), and Oral Health Impact Profile (OHIP-14) questionnaires. Saliva flow was assessed between 9:00 a.m. and 12:00 p.m. to avoid circadian fluctuations (Dawes & Ong, 1973). Participants did not have anything to eat or drink, did not smoke, brush their teeth, use mouthwash, chew gum, or rinse out their mouths for at least one hour before the visit. UWS determination was preceded by a 15-20-minute period of rest. We employed the “spitting” method (Flink, Bergdahl, Tegelberg, Rosenblad, & Lagerlof, 2008; Navazesh & Christensen, 1982) to measure UWS into preweighed plastic tubes for 10 minutes in an upright sitting position. Saliva volume (ml) was determined gravimetrically, using a calibrated analytical balance and the weight of saliva, assuming a specific gravity of 1.0. Flow rates were expressed in ml/min. Minor SG flow rate (MSF) from the upper lip was measured by the absorption method. We used cotton rolls to cover the orifices of parotid ducts on both sides and placed a standardized 5x35mm paper strip (modified Schirmer’s test by (Falcao et al., 2014) under the lip for one minute. Patients were asked to avoid mouth movements as mechanical stimulation can increase flow rates (Boros, Keszler, & Zelles, 1999). Flow rates were expressed by the pre-and post-weight difference of the strip, divided by total area and time (μl/cm2/min).

We collected information on various variables (dry mouth variables) such as ADS, SAA, minor saliva flow, xerostomia severity, smoking status, age, gender, and BMI influencing saliva flow and examined their association with saliva secretion in patients taking anticholinergic medications. We compared those dry mouth variables between patients with medication-induced hyposalivation and those with normosalivation. Subjective perception of dry mouth was determined by the xerostomia inventory (XI) questionnaire, which registers oral dryness on a continuous scale with 11 questions each on a 5-point Likert scale (Jager, Bots, Forouzanfar, & Brand, 2018). The total score ranges from 11 to 55; higher scores indicate more severe xerostomia. XI is a validated survey for xerostomia severity, which correlates with UWS rates. Other covariates, such as age, smoking, BMI, gender, and medications, were collected during the visit.

The anticholinergic burden of each individual was calculated by the anticholinergic drug scale and from the serum levels of anticholinergic activity. ADS was calculated from electronic health records or medical provider-verified medication lists as the cumulative effect of AC drugs. ADS developed by Carnahan et al. (Carnahan et al., 2006) classifies medications based on their anticholinergic activity. ADS is an ordinal scale of between) and 3: 0, no anticholinergic activity; 1, potential anticholinergic activity; 2, anticholinergic adverse events are sometimes noted; 3, marked anticholinergic activity (Chew et al., 2008). A single blood draw provided serum for the SAA assay (1x 8.5ml SST-serum separator tube or non-heparinized tube). Blood samples for SAA analysis were collected at the Clinical Research Center, Clinical Translational Science Institute of the University of Rochester Medical Center. Serum was separated and freeze-stored at the Clinical Trials Processing Laboratory (URMC). SAA analysis of deidentified serum samples was performed at the Centre for Addiction and Mental Health (CAMH) at the University of Toronto. SAA was measured by an in vitro CHO cell-based radio-receptor assay (Nobrega, Raymond, & Pollock, 2017) and expressed in pmol/mL atropine equivalents.

Statistical approaches

All tests are performed at α = 0.05 significance level. Descriptive statistics were performed to summarize demographics (age, sex, and race), saliva secretion rates (UWS and MSF), oral health outcomes (XI and OHIP-14), anticholinergic burden assessments (ADS and SAA), as well as covariates such as BMI and smoking status that might affect saliva rates. Validity tests of minor SG are completed using convergent validity: we assessed convergent validity by computing and testing the correlation coefficient between UWS and minor SG with accompanying scatterplots. Wilcoxon rank-sum test was used for assumptions between the normosalivation and hyposalivation groups. The association of the proportions of smoking status between the two groups was examined using the χ2 test. In examining the correlation between ADS and SAA (gold standard), we used inference analysis by the Spearman’s Rank test. A multiple logistic regression model estimated 95% confidence intervals (CI) between SAA, flow rates, and ADS.

Results

The descriptive statistics of the categorical variables are summarized in Table 1. The mean age of enrolled individuals was 55.25 ± 5.96 years. Continuous study variables are described in Table 2. The mean value ± SD of UWS from the entire study sample was 0.21 ml/min ± 0.20, and MSF was 5.76 μl/min/cm2 ± 2.88. The mean serum anticholinergic burden level was 1.60 ±1.34 (Figure 1.), and the mean of the calculated ADS was 3.60 ± 3.18. The mean XI score of xerostomia perception was 36.73 ± 7.62 among all patients. The mean value of BMI was 31.66 ± 7.59 (underweight, two patients; normal weight, ten patients; overweight, 12 patients; obese, 31 patients).

Table 1.

Summary of nominal variables of 55 patients with self-reported medication-induced xerostomia.

Frequency
Gender n Percent (%) Cumulative (%)
 Female 39 75.0 75.0
 Male 13 25.0 100.0
Race
 White 41 78.78 78.8
 Black 7 13.5 92.3
 Other 4 7.7 100
Smoking
 Never 25 48.1 48.1
 Former 18 34.6 82.7
 Current 9 17.3 100.0
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Table 2.

Comparison of dry mouth-related variables in normo-and hyposalivation groups.

Hyposalivation (UWS≤0.1) Normosalivation (UWS>0.1) Wilcoxon Rank Sum Test
Mean (SD) Median (Q1;Q3) Mean (SD) Median (Q1;Q3) P
Age (years) 57.11 (4.90) 57.00 (55.00;61.00) 54.18 (6.31) 55.00 (48.00;60.00) 1
BMI (kg/m2) 31.70 (7.04) 32.10 (25.39;37.88) 31.63 (8.00) 30.18 (26.00;36.58) 0.42
XI (score) 37.68 (5.72) 36.00 (34.00;40.50) 36.18 (8.55) 37.00 (33.00;42.00) 0.37
SAA (pmol/ml) 1.24 (1.31) 1.02 (0.54;2.03) 1.67 (1.37) 1.13 (0.71;2.40) 0.69
ADS (score) 3.68 (3.53) 2.00 (1.00;5.00) 3.54 (3.02) 3.00 (1.00;5.00) 0.51
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Abbreviations: UWS, unstimulated whole saliva; SD, standard deviation; BMI, body mass index; XI, xerostomia inventory; SAA, serum anticholinergic assay; ADS, anticholinergic drug scale. Wilcoxon rank-sum test.

Figure 1.

Figure 1.

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Box-plot visualization of study variables of n=55 middle-aged adults includes minimum, maximum, median, lower (Q1) and upper Q3) quartile, and outlier values. Abbreviations: UWS, unstimulated whole saliva; MSF, minor saliva flow; BMI, body mass index; XI, xerostomia inventory; SAA, serum anticholinergic assay; ADS, anticholinergic drug scale

Historically, hyposalivation is defined by UWS as ≤ 0.1ml/min (L. Sreebny, 1992). Accordingly, we used this cut-off value (Figure 2.) to create the normo- (n = 33; median UWS = 0.24 ml/min) and hyposalivation groups (n = 19; median UWS = 0.05 ml/min) for comparisons. We detected differences between the median values of MSF normosalivation (6.50 μl/cm2/min) and hyposalivation groups (4.00 μl/cm2/min) without statistical significance. We examined the association between saliva secretion and various dry mouth variables in both groups of patients. Accordingly, the proportions of smoking status (yes, current; no, former, or never) were examined using Pearson’s χ2 test, and the proportions of individuals who smoke in both groups were identical. No statistically significant differences were found between hyposalivation and normosalivation corresponding to age, gender, SAA, ADS, XI, and BMI (Table 2.).

Figure 2.

Figure 2.

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The cut-off value of 0.1 ml/min UWS flow rate was used to establish the two study groups of (1) hyposalivation and (2) normosalivation (box plot representations of the minimum, maximum, median, lower quartile, upper quartile, and outlier values). Abbreviations: UWS, unstimulated whole saliva; MSF, minor saliva flow.

Regarding salivary flow rate measurements from the major and minor glands, we detected collinearity between UWS and MSF (p < 0.005, rho = 0.491). We examined the correlation between ADS and SAA. The Spearman’s Rank Correlation Coefficient suggested a weak, linear relationship (p = 0.074, rho = 0.249), which did not reach statistical significance. To quantify the relationship between saliva flow (UWS) and SAA (gold standard), we excluded outliers (due to measurement methods in SAA and UWS analysis); thus, our further analysis included 43 samples. Our data indicated a negative linear relationship between SAA and UWS (p < 0.05). Accordingly, UWS tends to decrease by - 0.06 (ml/min) as SAA increases by one unit (pmol/ml).

The independent variables were tested in the multivariate logistic regression model to determine if they were significantly associated with the SAA. In the forward selection method, we introduced predictors (XI, ADS, and BMI) and indicators (age, gender, race, and smoking) into the model. The correlation between UWS and two predictors, SAA (p < 0.05) and age (p < 0.05), showed a significant correlation.

Discussion

We explored a statistically significant, moderate linear correlation between UWS and MSF, suggesting the potential that both measurement methods could reflect medication-induced changes in saliva flow rate. Our study confirms previous observations that the labial SG secretion rate (Eliasson, Birkhed, & Carlen, 2009) is correlated to UWS flow rates. In contrast to buccal and palatal minor salivary glands, the labial glands reportedly affect the subjective feeling of oral dryness regardless of the amount of whole saliva secretion. Those results were based on measurements with a calibrated moisture detection device, resulting in significant variability of minor mucosal gland secretion rates. We adopted the modified Schirmer’s saliva test (Falcao et al., 2014; Gaubenstock, 1995), a simple, cost-effective method that allows a rapid dental chair-side assay. Studies that collected saliva from minor mucosal glands should be cautiously interpreted due to methodological inconsistencies and varying locations in the oral cavity (Lee et al., 2002; Speirs, 1984). Around 800 to 1,000 minor salivary glands are found throughout the oral cavity in the buccal, labial, and lingual mucosa, the soft palate, the lateral parts of the hard palate, and the floor of the mouth, or between muscle fibers of the tongue. Their secretions are predominantly mucous and serve various functions, including a protective coating of the oral cavity or allowing denture retention to the palate. Affoo et al. (Affoo, Foley, Garrick, Siqueira, & Martin, 2015) concluded in their systematic review that minor saliva secretion does not appear to be significantly decreased by age, even though the volume of secretory cells in labial minor glands is lower in older individuals.

Our investigation included potential predictor or indicator variables of dry mouth, and we found that SAA and age influence UWS as weak predictors. The determination of SAA from patients’ circulating blood is the “gold standard” measurement of the cumulative antimuscarinic burden from all sources (Carnahan, Lund, Perry, & Pollock, 2002). We utilized SAA as a complementary method to ADS to assess the impact of anticholinergic medications on salivary health. The assumption that the SAA, blood serum anticholinergic activity is correlated with ADS score calculated from the individual’s anticholinergic medications, could not be assured in our study. SAA reflects the cumulative anticholinergic activity in the peripheral blood (Mulsant et al., 2004; Pollock et al., 1998). Serum levels are affected by the intake of anticholinergic medications as well as endogenous sources such as plasma proteins and hormones. These endogenous factors, not included in the study measurements, have contributed to discrepancies between the SAA and ADS scores.

Only a few studies (Kersten, Molden, Willumsen, Engedal, & Bruun Wyller, 2013; Tiisanoja et al., 2018) determined anticholinergic burden by ADS. Accordingly, ADS>3 poses a relative risk for decreased unstimulated whole saliva (UWS) secretion in white female patients (mean age=80.3), and high ADS (>6) results in 0.7-fold lower saliva production compared with moderate ADS (=3) in subjects aged above 73 years (Kersten, Molden, Willumsen, Engedal, & Bruun Wyller, 2013). The latest study (Tiisanoja, Syrjala, Kullaa, & Ylostalo, 2019) in a 46-year-old adult cohort found that participants with anticholinergic exposure are more likely to have low (<0.1 ml/min) UWS rates. ADS showed a prognostic value on the impact of anticholinergic medications on saliva flow rates (Tiisanoja et al., 2018). Although our study provided evidence for the proposed SAA - UWS correlation, UWS was not significantly associated with the measured ADS levels. It might be due to the fact that ADS scores are arbitrary estimates and potentiation effects of medications, or their active metabolites are not considered in calculations (L. E. Tune, 2016). Moreover, our SAA assay tests peripheral blood, which does not necessarily reflect detected salivary gland direct impacts.

We compared several dry mouth-related variables, including smoking status, perceived oral dryness, and BMI, between normo-and hyposalivation study groups. We employed the XI questionnaire as a validated survey to assess xerostomia severity, which showed a correlation with UWS rates by Jager et al. in 2018 (Jager et al., 2018). Xerostomia among normo- and hyposalivation patients did not differ significantly, similar to the study by Nederfors et al. (Nederfors, Holmstrom, Paulsson, & Sahlberg, 2002) shows that individuals with preserved saliva secretion can report xerostomia using the XI assay. Moreover, a systematic review by (Affoo et al., 2015) of 129 articles on medication-induced xerostomia showed the variability among xerostomia prevalence was due to the lack of “unequivocal” methodology for recording the perception of oral dryness in most previous studies (Billings, Proskin, & Moss, 1996; Fox, Busch, & Baum, 1987; Nederfors, Isaksson, Mornstad, & Dahlof, 1997).

The connection between BMI and salivary secretion has been evaluated in several studies. Modeer et al. (Modeer, Blomberg, Wondimu, Julihn, & Marcus, 2010) found that BMI was associated with stimulated whole saliva secretion; obese adolescents had lower saliva flow rates. A recent meta-analysis (Hatipoglu, Maras, Hatipoglu, & Saygin, 2022) confirmed a lower stimulated saliva flow rate in individuals with obesity. Similar to our findings, they did not obtain significance for UWS. Additionally, although smoking is a known relevant risk factor for symptoms of dry mouth, we could not conclude evidence regarding the smoking effect due to the fact that only a small fraction of patients were active smokers. The study by Rad et al. (Rad, Kakoie, Niliye Brojeni, & Pourdamghan, 2010) focused on the impact of long-term smoking, as some reports demonstrated only short-term effects of smoking on UWS. Our findings confirm previous observations on the higher prevalence of hyposalivation in middle-aged women (Flink et al., 2008) and that age and the intake of xerogenic drugs increase the risk of very low UWS (<0.1ml/min)

Our study is the first report on anticholinergic medication-triggered changes in MSF secretion regarding anticholinergic burden. We found that the mean values of MSF secretion were lower in hyposalivation patients than those with preserved saliva rates. However, this difference was not statistically significant. Besides, data evaluation of MSF rates showed a tendency to be impacted by a higher anticholinergic burden without evidence of a statistically significant association with SAA and ADS. The lack of a statistically significant relationship might be due to our insufficient sample size in the hyposalivation group (power analysis was based on UWS) and yet unrevealed limitation in our MSF estimation method. The list of covariables potentially affecting saliva secretion is not exhaustive and thus cannot address the entirety of the dry mouth problem (e.g., anxiety effect, comorbidities such as diabetes).

This study is exploratory and remains within the frames of a cross-sectional design. Our investigation revealed a measurement bias due to the SAA analytical method, which requires samples to be processed in batches. Standard curves need to be established for each batch, and then samples are fitted on the curves, with decreasing sensitivity when the measurements fall on the ends of the curve (extremely low or high dilutions). Also, the analytical balance reaches a detection limit when measuring the collection tubes with only a trace amount of saliva. Increasing the sample size within the detection range of SAA and UWS measurements would be needed to confirm the results related to the previously suggested linear relationship. Although all medications were assessed in the ADS calculation, it should be added that ADS is an estimate and does not account for variation in drug utilization as well as dosage. Medication lists were verified by electronic providers’ orders through EHR, but recollection bias on whether the medication was taken might alter the findings.

We conclude that MSF is a potential clinical marker of reduced salivary function. As UWS output is rarely assayed in general and medical offices due to time and feasibility constraints, testing the MSF could be a more feasible option to assess dry mouth by the suggested rapid method. Our study confirmed that dental patients taking anticholinergic medications require identifying potential predictors of hyposalivation, such as anticholinergic burden assay and age. Screening patients for medication-induced dry mouth is crucial in initiating preventive care since the intraoral damage due to hyposalivation is progressive and often permanent.

Figure 3.

Figure 3.

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UWS (unstimulated whole saliva) is associated with MSF (minor saliva flow): moderately strong (rs = 0.5) linear relationship by Spearman’s Rank Correlation test.

Figure 4.

Figure 4.

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An inverse relationship exists between UWS (unstimulated whole saliva) and SAA (serum anticholinergic activity). Spearman’s Rank Correlation Coefficient rs = − 0.35 suggest a low-strength linear relationship.

Highlights.

  • Increased serum anticholinergic levels from medications related to hyposalivation

  • Decreased unstimulated whole saliva relates to lower minor gland secretion

  • Screening minor saliva flow is useful in predicting medication-induced dry mouth

Acknowledgments

This work was supported by the National Institute of Dental & Craniofacial Research of the National Institutes of Health under Award Number K23DE031021. The content is solely the authors’ responsibility and does not necessarily represent the official views of the National Institutes of Health.

Footnotes

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Declaration of interests

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

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