Effectiveness of health-related quality-of-life measurement in clinical practice: a prospective, randomized controlled trial in patients with chronic liver disease and their physicians

Background This study assessed the effectiveness of computerized measurement and feedback of health-related quality of life (HRQoL) in daily clinical practice in patients with chronic liver disease.
Introduction
Health-related quality of life (HRQoL), or psychological, social, and physical functioning [1], has become an important outcome measure in medical care. Standardized assessment of HRQoL preceding each consultation may potentially provide physicians with valuable information. Several studies have shown that physicians vary in their ability to elicit psychosocial information or that they underestimate patients’ HRQoL [2–5]. Furthermore, various studies have shown that when communication with the physician encompasses both physical and psychosocial issues, patients have better treatment compliance, are more satisfied with the consultation, and report less symptoms [6–8].
Nevertheless, relatively few studies have assessed the value of HRQoL measurement in clinical practice. Some have shown positive results with regard to acceptance by patients and physicians or a significant increase in the identification and/or discussion of HRQoL issues [9–14]. Less consistent and favorable results have been obtained with regard to the effectiveness of standardized HRQoL measurement in actually improving HRQoL or psychosocial outcomes. Even though decreased depression [15], improved overall and emotional functioning [10], improved mental health [16], and a decrease in disease-specific debilitating symptoms of patients undergoing chemotherapy [13] have been associated with HRQoL measurement in clinical practice, several other studies found no significant improvement in HRQoL or psychosocial outcomes [9, 17–20]. A possible explanation might be that the majority of existing studies assessing the effectiveness of HRQoL measurement in clinical practice with regard to patients’ psychosocial functioning or HRQoL have included oncological patients or patients from general practice. Oncological patients can be considered a special group due to the life-threatening nature of the disease. Patients from general practice, on the other hand, may be too diverse and often present with generally minor complaints, which may hamper the discovery of beneficial effects. Both groups impede generalization of results to other chronic patient populations.
Two important studies [9, 10] used designs in which physicians were part of both the control and the experimental group, either by using a crossover design (physicians were first assigned to one group, then crossed over to the other group halfway through the study) [9] or by assigning patients rather than physicians to the different groups [10]. This may possibly have caused bias. Two systematic reviews have stressed the need for further research evaluating the effectiveness of repeated measurements of HRQoL in clinical practice [18, 20] and the need for further research to help health care professionals identify patients who would benefit most from such interventions [20].
The study reported here differs from previous studies by including a patient population with chronic liver disease (CLD) in order to study the effects of HRQoL use in clinical practice in a population that is more representative of other patients with a chronic disease. CLD is one of the most prevalent diseases in the world. The most common causes of CLD, hepatitis B virus (HBV) and hepatitis C virus (HCV), have been estimated to affect 360 million and 200 million people worldwide, respectively (http://www.epidemic.org, 4-12-2006). In addition, alcohol is another main cause of end-stage liver disease worldwide and the second most common reason for liver transplantation in the United States [21]. CLD is a serious disease that is associated with significant physical and psychological symptoms such as impaired cognition, hepatic coma, fluid in the abdomen, abdominal pain, joint pain, fatigue, depression, and anxiety [22–28]. Not surprisingly, HRQoL in patients with CLD has been shown to be impaired [29, 30]. CLD is an appropriate example of a typical chronic disease, with patients experiencing substantial comorbidity and possibly mortality, as is the case in other chronic diseases such as kidney disease and chronic obstructive pulmonary disease.
Our study also differs from previous studies by assessing the benefits of HRQoL measurement for patients with different demographic characteristics (e.g., men and women, young and old), which is essential for determining which patients are most likely to benefit from HRQoL measurement in clinical practice, a point recently reiterated in a systematic review on this topic [20]. In addition, in our study, physicians rather than patients were assigned to the control or the experimental group. This assigning of physicians to only one group prevents bias of physicians being focused on discussing HRQoL when seeing patients in the control group.
The aims of the study were twofold: the first was to assess the effectiveness of real-time computerized measurement of HRQoL in various patients with CLD and presentation of the results to physicians before the consultation in terms of improvement in patient HRQoL, patient management, and patient satisfaction with the consultation by means of a randomized trial with repeated measurements. The second aim was to assess hepatologists’ experiences with the availability of real-time HRQoL patient data and to measure the possible effect(s) it had on their consultations.
Patients and methods
Patient recruitment
This study was performed at the Department of Gastroenterology and Hepatology of the Erasmus Medical Centre, Rotterdam, where HRQoL measurement on a regular basis was implemented for the duration of 1 year. All patients older than 17 years of age with CLD visiting the department between September 2004 and January 2005 were invited to participate. Written information about the study was sent to the patients 3 days before their consultation at the outpatient department. Patients interested in participating informed their physician, who consequently directed them to the researcher for further explanation of the study and to sign informed consent. For this effectiveness study, we included all patients with two or more measurement moments. All physicians working at the Department of Hepatology participated. The protocol was in accordance with the ethical guidelines of the modified 1975 Declaration of Helsinki and approved by the Medical Ethics Committee of the Erasmus MC.
Study objectives
The primary aim of this study was to assess the effectiveness of computerized measurement of HRQoL in clinical practice. The primary outcome measures were patients’ generic HRQoL (physical and mental component score separately) and disease-specific HRQoL. Secondary outcome measures were patient satisfaction with the consultation and patient management. The secondary aim of this study was to assess hepatologists’ experiences with the availability of real-time HRQoL patient data.
Study design and intervention
Physicians
Physicians were randomly assigned to either the experimental or control group by means of a restricted randomization procedure called blocking. To ensure an equal number of physicians in each group, it was decided to include six in the experimental group and five in the control group. We used a random sequence table to assign physicians to one of the conditions. Due to the nature of the intervention, it was impossible to blind physicians to group assignment.
Physicians in the experimental group were able to obtain an instant computerized graphical output of HRQoL patient data, which also included data from previous measurement moments so that changes in patients’ HRQoL could be monitored (Fig. 1). Prior to the study, physicians received instructions from a psychologist with expertise in the field of HRQoL measurement on how to interpret this output. First, physicians were shown the questionnaires in order to familiarize them with the content. Second, they were informed that the red line in the graph was the average score of patients with CLD on the Short Form-36 (SF-36) measuring generic HRQoL and that scores under this line were to be considered low. They were also told that the average score of healthy people on this questionnaire was 50. The physicians were instructed to interpret the disease-specific Liver Disease Symptom Index 2.0 (LDSI 2.0) at item level, with scores ranging from 1 (not at all) to 5 (to a large extent). The physicians were asked to use the HRQoL data in all consultations for 1 year. No recommendations for specific responses were given. Instead, they were instructed to use their clinical experience to choose an appropriate treatment. After seeing a participating patient, physicians in both groups completed a checklist about the content of the consultation. Physicians in the control group conducted their consultations as usual.
Fig. 1Example of the graphical output of patients’ health-related quality of life as presented to physicians in the intervention group. A score of 50 is the average score of a healthy norm population. The dashed line represents the mean score for patients with chronic liver disease
Patients
Through the random assignment of physicians, patients were indirectly allocated to either group. Patients were initially blinded to the group assignment. All patients participating in the study completed a computerized generic- and disease-specific HRQoL questionnaire and the first part of a pen-and-paper questionnaire on patient satisfaction with the consultation before each consultation at the outpatient Department of Hepatology for 1 year. They also completed the second part of the satisfaction questionnaire after the consultation. More specific information on the content of the questionnaires is provided in “Study measures”. To ascertain good questionnaire completion, a researcher was always available to answer questions about the computer and/or questionnaires at the patient’s request.
Study measures
HRQoL
Disease-specific HRQoL: This was assessed by means of the LDSI 2.0, which measures severity and hindrance of nine symptoms: itch, joint pain, pain in the right upper abdomen, decreased appetite, jaundice, fatigue, depressed mood, worries about family situation, and fear of complications [24]. Because of time constraints, only items measuring symptom severity were included in this study (n = 9). The physicians were instructed to interpret the questionnaire at item level, with scores ranging from 1 (not at all) to 5 (to a large extent). For data analysis, a total score, ranging from 9 to 45, was computed by summing the scores of each item. The reliability of the LDSI 2.0 is good (internal consistency α > 0.79), as is the construct validity [30].
Generic HRQoL: This was assessed by means of the Short Form-12 version 1 (SF-12). The SF-12 produces a Physical Component Summary (PCS) and Mental Component Summary (MCS), representing physical and emotional functioning, respectively. The mean score of the PCS and MCS in the general population is 50 [standard deviation (SD) 10] with higher scores representing better HRQoL. Mean scores and SD of the PCS and MCS of CLD patients was calculated from a large database (n = 1,175) [29, 31] (PCS: mean 43.2, SD 10.7; MCS: mean 44.4, SD 12.8). These means were used as a reference point (red line) in the graphical representation for physicians so they could easily identify patients scoring below average within the CLD group. The SF-12 has been shown to be reliable between test and retest (MCS r = 0.76, PCS r = 0.89), and median relative validity estimates of 0.67–0.97 for the PCS and MCS, respectively, have been found [32].
Patient satisfaction with the consultation
Patients’ satisfaction with the consultation was measured with the QUOTE-Liver, a newly developed questionnaire consisting of 20 items that assesses the discrepancy between patients’ needs/expectations (importance: measured before the consultation) and the actual care that they receive (performance: measured after the consultation). The internal consistency of the overall QUOTE-Liver was excellent (α = 0.90), as was the face validity: all patients (n = 152) in the validation study and three psychologists and a hepatologist agreed that the items of the QUOTE-Liver adequately reflected the most important aspects of care for CLD patients. Construct validity, as measured by the correlation between a visual analog scale (VAS) measuring overall satisfaction and the total score on the QUOTE-Liver was good (r = 0.69; P < 0.01). Content validity was also good: none of the 152 patients in the validation study suggested new items to be included (Gutteling et al. 2006, unpublished). A reduced version consisting of the nine items ranked by patients as most important and the two liver-disease-specific items was used in our study. Using a formula applied for all QUOTE-Liver instruments (10-importance × performance), a total satisfaction score can be computed ranging from 0 to 10, with 0 meaning not satisfied at all and 10 meaning completely satisfied [33].
Patient management
The effect of the intervention on patient management was measured by means of a checklist that physicians completed after each consultation with a study participant, including the question: “Have you changed your treatment in any way?” and a subquestion: “If so, what have you done?” followed by several options: “Prescription of antidepressants,” “Referral to psychosocial care,” “Altering the frequency of consultations,” and “Other.”
Physicians’ experiences
Experiences of physicians with the experimental condition were assessed through the checklists that they completed after each consultation with a study participant, asking the question: “Did you find the HRQoL information useful? Why?” with the answering options: “Yes, it provided new information,” “Yes, it saved time,” “Yes...,” “No, the patient is doing well,” “No, I know this patient well enough,” “No, the patient tells me a lot,” “No...”. Also, a semistructured interview was conducted 6 months into the study and at the end of the study. The interview included questions about the effort to request HRQoL information, the usefulness of the information, whether the availability of HRQoL information increased the duration of the consultation, and whether participating patients addressed HRQoL issues more often than patients who did not participate. Physicians were also asked whether there were certain subgroups of patients whose HRQoL information they found particularly useful. Opinions of physicians in the control group toward possible future availability of HRQoL information during the consultation were assessed by means of the same semistructured interview at 6 months only.
Statistical analysis
Sample size
A nonclustered power analysis based on a medium effect size (Cohen’s D = 0.50) with a 5% significance level and 80% power indicated that at least 64 patients were needed in each group to detect a statistically significant difference.
Data selection
For patients who were included in both groups because they had consultations with physicians from the control group as well as physicians from the experimental group during the year of the study, data from the condition in which they had most often been was included (n = 33). For patients who had been in both conditions equally (n = 19), all data were excluded. The first measurement moment of all patients (T1) was considered a baseline measure, as no HRQoL data had yet been presented to the physicians.
Data analysis
Differences on the variables gender, diagnosis, disease severity, and age between participants and nonparticipants were assessed by means of χ2 tests or t tests. The same was done for assessing differences between patients in the control group and the intervention group. Scores of participating patients on measurement moments (T2−Ti) were summarized into one overall score per variable in the study. Univariate analyses of variance were performed in SPSS 11.0. Fixed factors were age, gender, disease severity, presentation of HRQoL data to the clinicians (feedback), and interactions between these variables. Differences in diagnoses between patients in both groups were controlled for by entering one propensity score of the variable diagnosis as a covariate in the analyses. Propensity scores were especially designed for situations in which study participants could not be randomly assigned to groups, and their characteristics were therefore not balanced among the groups. A propensity score was defined as the conditional probability of assignment to a certain treatment group given a set of observed pretreatment characteristics and was usually estimated by means of a logistic regression analysis [34]. Thereby, the background characteristic(s), in this case diagnosis, was reduced to one single score, the propensity score. We calculated the propensity score by entering the different diagnoses (HBV, HCV, cholestatic liver disease, pretransplantation, posttransplantation, autoimmune hepatitis, and other) as dummy variables (M-1) in a logistic regression analysis. The unstandardized logistic regression weights were then multiplied by the relevant dummy variable and summed, together with the constant. This score was used in the univariate analysis to adjust for baseline confounding.
Univariate analyses of variance were performed for each outcome variable (disease-specific HRQoL and generic HRQoL MCS and PCS) separately. A forward technique was used in which the main effects of the fixed factors were assessed in the first block, and the interactions between feedback of HRQoL data and each of the other fixed factors (age, gender, severity of the disease) were explored in the second block. Differences between the two groups on patient management variables and satisfaction with the consultation were assessed by means of Mann–Whitney tests.
Hepatologists’ experiences with the availability of real-time patient HRQoL data was assessed by means of semistructured interviews and checklists. These data were of a descriptive nature and are presented as such.
Results
Characteristics of patients and physicians in the study
Of the 587 patients that agreed to participate in the study, 181 completed the questionnaires more than once. Of these, 19 were included in the experimental and control conditions equally often and were therefore excluded from the analyses. One hundred and sixty-two patients (control group n = 80, experimental group n = 82) were included (Fig. 2). Differences in age, gender, diagnosis, and disease severity between patients in the study and nonrespondents are presented in Table 1. Demographic characteristics of the 162 patients are presented in Table 2. Patients in the control and experimental groups were comparable, except for the variables diagnosis and disease severity (Table 2). In the analyses, these differences between conditions were controlled for. All physicians working at the Department of Hepatology (n = 11, ten men) agreed to participate. Their mean age was 39 (range 27–55) years, and their average working experience was 8.7 (range 0–27) years.
Fig. 2Patients in the studyTable 1Differences in age, gender, diagnosis, and disease severity between patients in the study and nonrespondentsPatients in the analyses (n = 162)Patients excluded from the analyses (n = 165)P valuePatients excluded from the study (n = 260)P valueAge, mean (range)47.5 (20–75)48.6 (20–81)0.5247.6 (18–80)0.92Gender, n (%)    Male96 (59)87 (53)0.24136 (52)0.21    Female66 (41)78 (47)124 (48)Diagnosis, n (%)    Hepatitis B22 (13)25 (15)0.0449 (19)0.00    Hepatitis C23 (14)24 (15)56 (22)    Cholestatic liver disease11 (7)22 (13)32 (12)    Pretransplantation11 (7)7 (4)1 (0)    Posttransplantation62 (38)48 (29)55 (21)     Autoimmune hepatitis12 (8)11 (7)18 (7)     Other21 (13)28 (17)49 (19)Disease severity, n (%)    No cirrhosis101 (62)105 (64)0.43159 (61)0.96    Compensated cirrhosis42 (26)45 (27)69 (27)     Decompensated cirrhosis19 (12)15 (9)32 (12)Differences were assessed by means of χ2 tests (except for age: t test). Reference group for comparison of both P values is the group of patients in the analysesTable 2Characteristics of patients included in the data analysisControl group (n = 80)Experimental group (n = 82)P valueGender, n (%)    Women38 (48)28 (34)0.08    Men42 (52)54 (66)Age, mean (range)47.5 (21–74)47.6 (20–74)0.98Diagnosis, n (%)    Hepatitis B1 (1)20 (25)0.00    Hepatitis C7 (9)16 (19)    Cholestatic liver disease4 (5)6 (7)    Pretransplantation5 (6)3 (4)    Posttransplantation43 (54)23 (28)    Autoimmune hepatitis6 (7)6 (7)    Other14 (18)8(10)Disease severity, n (%)    No cirrhosis44 (55)56 (68)0.01    Compensated cirrhosis16 (20)22 (27)    Decompensated cirrhosis20 (25)4 (5)Differences were assessed by means of χ2 tests (except for age: t test)
Descriptives
The number of times that patients in the control and experimental groups completed the questionnaires varied between two and 11 (Table 3). Mean scores of patients at T1 and T2−Ti on the outcome variables generic HRQoL and disease-specific HRQoL are presented in Table 4.
Table 3Questionnaire completion rate of patients in the control and experimental groupsNumber of times questionnaires were completedTotal (n)234568911Control (n)2229117712180Experimental (n)451895221082Table 4Patients’ adjusted means and 95% confidence intervals at T1 and T2−TiT1P valueT2−TiP valueControl ExperimentalControlExperimentalOverallSF-12 PCS41.5 (39.0–43.9)45.6 (42.0–49.3)0.0642.0 (39.6–44.4)44.8 (41.4–48.3)0.19SF-12 MCS43.4 (40.3–46.5)46.0 (41.4–50.6)0.3543.8 (41.0–46.5)44.8 (40.8–48.8)0.69LDSI 2.021.2 (19.0–23.4)18.9 (15.7–22.2)0.2720.4 (18.6–22.2)18.8 (16.1–21.4) 0.31Male patientsSF-12 PCS10.2 (37.1–43.3)47.0 (42.9–51.2)0.1041.3 (38.2–44.2)45.7 (41.7–49.7)0.29SF-12 MCS41.6 (37.7–45.4)45.6 (40.4–50.8)0.4941.2 (37.8–44.6)46.7 (42.1–51.2)0.02LDSI 2.022.8 (20.0–25.5)18.1 (14.4–21.8)0.1021.4 (19.2–23.6)18.0 (15.0–21.0)0.14Female patientsSF-12 PCS42.7 (39.2–46.3)44.2 (39.8–48.7)42.8 (39.4–46.2)44.0 (39.7–48.2)SF-12 MCS45.2 (40.7–49.6)46.4 (40.8–52.0)46.3 (42.4–50.2)42.9 (37.9–47.8)LDSI 2.019.6 (16.4–22.8)19.8 (15.8–23.8)19.4 (16.9–22.0)19.5 (16.3–22.7)Older patientsSF-12 PCS41.5 (38.4–44.6)44.6 (40.7–48.6)0.4940.4 (37.4–43.3)43.4 (39.9–47.5)0.72SF-12 MCS41.5 (37.6–45.4)46.3 (41.4–51.3)0.2641.2 (37.8–44.7)45.9 (41.6–50.3)0.03LDSI 2.022.8 (20.0–25.5)19.1 (15.6–22.7)0.3122.1 (19.9–24.3)18.1 (15.3–21.0)0.04Younger patientsSF-12 PCS41.4 (37.9–44.9)46.7 (42.2–48.6)43.6 (40.3–47.0)45.9 (41.6–50.3)SF-12 MCS45.3 (40.9–49.7)45.7 (40.0–51.3)46.3 (42.5–50.2)43.6 (38.7–48.6)LDSI 2.019.6 (16.5–22.7)18.7 (14.7–22.8)18.8 (16.2–21.3)19.4 (16.1–22.6)The means at T1 and T2−Ti were obtained from the univariate analyses of variance with fixed factors: age, gender, severity of the disease, study group (control or experimental), and interactions between these variables. Differences in diagnoses between patients in both groups were controlled for. The significance level reflects the group for which the largest difference on the variable was foundSF-12 Short Form-12, PCS Physical Component Summary, MCS Mental Component Summary, LDSI 2.0 Liver Disease Symptom Index 2.0
Effects of the experimental condition on patients’ HRQoL and satisfaction with the consultation
Disease-specific HRQoL
There was no main effect for the experimental condition on disease-specific HRQoL. There was a statistically significant interaction effect for the variables age and feedback of HRQoL data on the outcome variable disease-specific HRQoL (Table 5): older patients (>48 years of age, as determined by the median split) in the experimental group had significantly lower total scores on the LDSI 2.0 (meanAdj = 18.1, 95% CI: 15.3–21.0) (F = 4.18; P < 0.05), indicating better disease-specific HRQoL, than other patients, especially older patients in the control group (meanAdj = 22.1, 95% CI: 19.9–24.3). This difference between older patients in the experimental group and the control group on disease-specific HRQoL is equivalent to a Cohen’s D of 0.51, reflecting a medium difference [35].
Table 5Interaction effects between age, gender, disease severity, and feedback on the outcome variable disease-specific HRQoL, controlled for diagnosisSourceF valuedfP valueR2Corrected model2.11100.03Intercept599.8310.00Diagnosis (propensity score)1.8010.180.08Gender0.0410.85Disease severity3.3920.04Age0.8410.36Feedback1.0510.31Gender * Feedback2.1710.140.12Severity * Feedback0.1520.86Age * Feedback4.1810.04Dependent variable: mean total score of the Liver Disease Symptom Index   2.0 [disease-specific health-related quality of life (HRQoL)] for the measurement moments T2...Ti
Generic HRQoL
Mental Component Summary score
No main effect for the experimental condition on mental HRQoL was found. However, a significant interaction effect for the variables age and feedback of HRQoL data was found. Older patients in the experimental group had higher scores on the SF-12 MCS (meanAdj = 45.9, 95% CI: 41.6–50.3) (F = 4.62; P < 0.05), reflecting better HRQoL, than other patients, especially older patients in the control group (meanAdj = 41.3, 95% CI: 37.8–44.7) (Table 6). Furthermore, a significant interaction effect was found for the variables gender and feedback of HRQoL data, with male patients in the experimental group showing higher scores on the SF-12 MCS (meanAdj = 46.7, 95% CI: 42.1–51.2) (F = 6.10; P < 0.05) than other patients, especially male patients in the control group (meanAdj = 41.2, 95% CI: 37.8–44.6) (Table 6).
Table 6Univariate analysis of variance with the variables age, gender, disease severity, and feedback on the outcome variable mental generic HRQoL, controlled for diagnosisSourceF valuedfP valueR2Corrected model1.65100.10Intercept1337.0510.00Diagnosis (propensity score)1.3410.250.03Gender0.1410.71Disease severity0.4020.67Age0.6510.42Feedback0.1610.69Gender * Feedback6.1010.020.10Severity * Feedback0.1320.88Age * Feedback4.6210.03Dependent variable: mean total score of Short Form-12 Mental Component Summary (SF-12 MCS) [generic mental-health-related quality of life (HRQoL)] for the measurement moments T2...Ti
Physical Component Summary score
No significant main effect or interaction effects were found for the variables feedback of HRQoL data and age, gender, and disease severity on the SF-12 PCS.
Patients’ satisfaction with the consultation
The scores on patient satisfaction did not differ significantly between the experimental and control groups (z = −1.20, P = 0.23). Also, no interaction effects of age, gender, and/or disease severity were found on this outcome variable.
Effects of the experimental condition on the consultation and on patient management
Physicians in the experimental group requested information of their patients in 92% of consultations, and they discussed it with their patients in 58% of consultations. They indicated finding the HRQoL information useful in 45% of consultations, which is generally in accordance with the percentage of patients in the experimental group scoring below average on the MCS (39%) and PCS (42%). They mostly found the HRQoL useless when a patient was doing well. Physicians in the experimental group indicated significantly more often than physicians in the control group that they spent more time than usual discussing psychosocial issues (30.7% vs. 6.6% of consultations, z = −6.65; P < 0.001). Treatment policy was altered significantly more often in the experimental group (11% of consultations vs. 1% of consultations in the control group; z = −3.73, P < 0.001). Most commonly, frequency of consultations was increased (n = 5). Other alterations concerned prescription of medication [3], increased attention for physical complaints [4], referral to psychosocial care [1] or occupational health physician [1], and increased attention to explanations/reassurance [2].
Physicians’ experiences with the availability of HRQoL information in clinical practice
Experiences of physicians in the experimental group at 6 months and at the end of the study did not differ. All physicians in the experimental condition found the HRQoL information useful, except for one older physician who claimed to know his patients very well. They indicated being better able to understand some of their patients through the extra information that was provided by the questionnaires. These physicians did not perceive requesting the information as an extra effort on their part. Furthermore, they did not think that using the information lengthened their consultations. All physicians in the experimental group indicated that they wanted to continue using the HRQoL information in the future. Physicians in the control group were similarly positive toward the possible availability of HRQoL information during their consultations in the future, on the condition that it would not be time consuming. This specifically concerned patients awaiting liver transplantation, patients with hepatitis C, and nonnative speakers (mostly patients with hepatitis B).
Discussion
Computerized, real-time measurement of HRQoL at our busy outpatient Department of Hepatology and presentation of the results to physicians before each consultation did not show a main effect on patients’ overall HRQoL. However, secondary analyses showed that the HRQoL measurements positively affected disease-specific HRQoL and generic mental HRQoL of older patients (>48 years of age) with CLD and also generic mental HRQoL of male CLD patients. The results of our study are among the first to show a beneficial effect of presenting HRQoL data to physicians in clinical practice. Most other studies have failed to show evidence for the actual improvement in HRQoL or psychosocial outcomes [9, 17–20]. Of the studies that did find a beneficial effect, one showed a decrease in disease-specific debilitating symptoms [13], and another showed improved emotional functioning [10], which is in line with findings of our study. It should be noted that due to the cross-sectional data analyses, a causal relationship between intervention and HRQoL could not be demonstrated. Future studies should address this in further detail.
Our study found no differences between patients in the experimental and control groups with regard to satisfaction with the consultation, which is in line with findings from previous studies [9, 36, 37]. The lack of observed differences between the study groups may have been due to high levels of satisfaction, resulting in a ceiling effect.
This study was among the first to show a significant difference in patient management between experimental and control groups, with physicians in the experimental group mostly reporting a significant increase in the frequency of consultations. Our findings were statistically significant and in accordance with the findings of a systematic review [20] and subscribe to the increasingly acknowledged importance of using HRQoL information for the improvement of physician consultations [38]. However, it should be noted that even though the differences in patient management between control experimental groups were statistically significant, the absolute numbers were small. Therefore, the results should be interpreted cautiously, and further studies using more elaborate methods of data collection—for instance, monitoring patients’ medical records or administering more detailed checklists—are recommended.
Physicians’ experiences with using HRQoL information during the consultation were generally positive; requesting the information was not considered an extra effort on their part, and they found the information especially useful for certain groups of patients, such as those awaiting liver transplantation, those with hepatitis C, and nonnative speakers. All physicians but one found the information useful for at least some (45%) of their patients. Physicians indicated finding the information least useful when patients were doing well in terms of HRQoL or when they knew the patient well. These generally positive experiences are in accordance with findings from previous studies [9–14], which assessed oncologists’ attitudes toward using HRQoL information in clinical practice. The confirmation of these results in hepatologists suggests that HRQoL information may also be well accepted by physicians treating patients with other chronic conditions. Another result of our study was that when HRQoL information was available, more time was spent discussing psychosocial issues and more treatments were altered. Interview and checklist data were contradictory regarding the duration of consultations when HRQoL information was available. In a previous study in which the duration of consultations was timed, no increase in consultation time was found [14]. Future studies should shed more light on whether the availability of HRQoL information increases the length of consultations in hepatology.
The strength of our study lies in the analyses performed, where benefits for specific groups of liver patients were explored by entering interactions between gender, age, disease severity, and feedback of HRQoL data, rather than solely investigating main effects between the intervention and control groups. Also, this study included patients with CLD rather than patients with cancer or patients from general practice, making it especially relevant to a more general population of patients with a chronic illness.
We are aware of several limitations of this study. First, physicians rather than patients were randomly assigned to either the intervention or control group. Randomization is a complicated issue in these kinds of implementation studies, and both methods are subject to limitations. An important advantage of the randomization of physicians is that the control group was not biased toward mentioning HRQoL topics more often than usual. Future studies using the same design but including more physicians are needed to further explore possible main effects of HRQoL measurement on patients’ overall HRQoL. A second limitation was the high number of nonparticipants. Part of the explanation may lie in the fact that patients were responsible for contacting their physician if they were interested in participating in the study. In addition, the number of non-Dutch-speaking patients visiting the department is relatively large (hepatitis B, for example, is most common among people from North Africa). These patients were also invited to participate but were less likely to respond. The relatively large number of patients who completed the questionnaires only once may be explained by the small window of opportunity to complete the questionnaires before each consultation. In addition, for such implementation endeavors, cooperation of all staff members is essential, and future research should explore this further. A last limitation of this study was that the checklists used to assess consultation content were not very detailed. This was done on purpose, as longer inventories would have compromised physician participation. However, considering the positive outcomes of this study, it is advisable that future studies consider ways to obtain a more detailed view of how the HRQoL information affects consultation content, for example, by recording consultations.
In conclusion, although a main effect of the intervention was not found, this study showed a beneficial effect of implementation of HRQoL measurement in clinical practice on the HRQoL of older and male patients with CLD and on patient management. Nevertheless, the study had several shortcomings, and further studies are needed to substantiate these findings. Physicians’ experiences with the availability of HRQoL information were positive, especially for patients awaiting liver transplantation, patients with hepatitis C, and nonnative speakers. They expressed an interest in continued use of HRQoL information. These results advocate the continued use of measuring HRQoL in a clinical practice of hepatology. Including older patients and male patients, who have been shown to benefit most from such a procedure, should be aimed for.

liver
quality of life
hepatology
implementation


Adenosine A1 receptor: Functional receptor-receptor interactions in the brain

Over the past decade, many lines of investigation have shown that receptor-mediated signaling exhibits greater diversity than previously appreciated. Signal diversity arises from numerous factors, which include the formation of receptor dimers and interplay between different receptors. Using adenosine A1 receptors as a paradigm of G protein-coupled receptors, this review focuses on how receptor-receptor interactions may contribute to regulation of the synaptic transmission within the central nervous system. The interactions with metabotropic dopamine, adenosine A2A, A3, neuropeptide Y, and purinergic P2Y1 receptors will be described in the first part. The second part deals with interactions between A1Rs and ionotropic receptors, especially GABAA, NMDA, and P2X receptors as well as ATP-sensitive K+ channels. Finally, the review will discuss new approaches towards treating neurological disorders.
Introduction
The vertebrate central nervous system (CNS) is characterized by a dynamic interplay between signal transduction molecules and their cellular targets. Modulation of synaptic transmission by metabotropic or ionotropic receptors is an important source of control and dynamical adjustment in synaptic activity.
Recent studies have provided new insights into the role of ligand-gated ion channels in modifying synaptic transmission. Along with a growing list of different types of pre- and postsynaptic ionotropic receptors and the cell types that express them, there have also been advances in characterizing the modulatory mechanisms of the receptors that link to receptor activation. This is important due to the convergence of data from biochemical, molecular, and electrophysiological studies, implicating ionotropic receptors in the effects of psychoactive and addictive drugs.
G protein-coupled receptors (GPCRs) make up the largest and most diverse family of membrane receptors in the human genome, relaying information on the presence of diverse extracellular stimuli to the cell interior. An estimated 1% of the mammalian genome encodes for GPCRs, and about 450 of the approximately 950 predicted human GPCRs are thought to be receptors for endogenous ligands [1]. The manipulation of transmembrane signaling by GPCRs may constitute the most important therapeutic target in medicine. Nearly 40% of all current therapeutic drugs target GPCRs [2].
All known GPCRs share a common architecture of seven membrane-spanning helices connected by intracellular and extracellular loops. Drugs acting on GPCRs have been classified as agonists, partial agonists, or antagonists based on a “two-state model of receptor function.” Since experimental evidence pointed out the impossibility of explaining the operation of GPCRs without considering dimers as the minimum structure for many GPCRs the “two-state dimer receptor model” was developed based on the communication between the two subunits of the receptor dimmer [1, 3, 4]. This model is an extension of the “two-state model of receptor function” but considers dimeric structures able to bind one molecule to the orthosteric center in each monomer.
GPCR signaling is subject to extensive negative regulation through receptor desensitization, sequestration, and downregulation, termination of G protein activation by GTPase-activating proteins, and enzymatic degradation of second messengers. Additional protein-protein interactions positively modulate GPCR signaling by influencing ligand binding and specificity.
Multiprotein complexes mediate most cellular functions. In neurons, these complexes are directly involved in the neuronal transmission, which is responsible for learning, memory, and developments. The first publication in this direction came from Hökfelt’s group in 1983. The publication describes how substance P may modulate the high-affinity serotonin (5-HT) binding site in a spinal cord membrane preparation [5]. Over the past decade, the number and outcomes of interactions between receptors have increased continuously [6]. Recent studies have demonstrated close physical interactions where activation of one receptor affects the function of the other.
Adenosine is an endogenous purine nucleoside that has evolved to modulate many physiological processes. Extracellular adenosine mostly originates from release of intracellular adenosine and from release and extracellular breakdown of cAMP and ATP by ecto-5′-nucleotidase and phosphodiesterase [7]. Cellular signaling by adenosine occurs through four known adenosine receptor subtypes (A1Rs, A2ARs, A2BRs, and A3Rs), all of which are seven-transmembrane-spanning GPCRs. Of the four known adenosine receptors, A1Rs and A2ARs are primarily responsible for the central effects of adenosine, especially in modulating synaptic transmission [8]. Adenosine can act on A1Rs to depress transmitter release and neuronal sensitivity to the transmitter [9, 10]. As a result, the A1Rs are important in the regulation of synaptic plasticity, playing a role in determining the amplitude of long-term potentiation or long-term depression [11].
There are numerous reviews that describe regulation of brain adenosine levels, adenosine receptors, their cellular and subcellular localization, signaling pathways, and function in the brain under physiological and pathophysiological conditions as well as selective receptor agonists and antagonists. Using A1Rs as a paradigm of GPCRs, this review focuses on how receptor-receptor interactions contribute to regulatory processes within the central nervous system.
Considering the various types of receptors, one may expect to find three principle paths of receptor interaction: (1) interactions between ionotropic receptors, (2) interactions between a metabotropic receptor and an ionotropic receptor, and (3) interactions between metabotropic receptors. The examples mentioned below stem from the second and third type of interaction. Interactions with metabotropic dopamine receptors as well as A2A, A3, NPY, and P2Y1 receptors will be described in the first part. The second part deals with interactions between A1Rs and ionotropic receptors, especially the GABAA, NMDA, and P2X receptors as well as ATP-sensitive K+ channels. Finally, new approaches for neurological disorders will be discussed.
Functional interactions with metabotropic receptors
Two forms of GPCR classification exist. There is the historical division into three main families: (1) rhodopsin-like family which includes adenosine receptors, (2) secretin-like family, and (3) metabotropic glutamate receptor-like family. The families share some basic similarities—the seven-transmembrane-spanning domains, intracellularly located C terminus, and extracellularly residing N terminus. Differences between the families arise in the length of the intracellular and extracellular termini and amino acid sequences, disulfide bridge linking, and conserved domains. Five different groups can be classified by applying phylogenetic analyses. The GRAFS system distinguishes between glutamate, rhodopsin, adhesion, fizzled/taste, and secretin-like GPCRs [12].
The agonist binding on the receptor results in coupling to heterotrimeric G proteins and regulates a variety of cell responses. In brief, an exchange of G protein-bound GDP to GTP occurs, and the heterotrimer dissociates into the α subunit and the βχ dimer. The resulting products activate or inhibit effectors independently from each other. Currently, 16 different genes encode G protein α subunits, five genes encode β subunits, while 14 genes encode χ subunits [13]. The α subunits can be categorized into four basic groups: the stimulatory  family couples to adenylate cyclase and increases cAMP levels, whereas the inhibitory  family acts in the opposite way. Moreover, the  family activates the phospholipase  (), and lastly, the  family which regulates Rho proteins.  dimers are capable of triggering effects on inward rectifier K+ channels (GIRK1–4), voltage-dependent Ca2+ channels (VDCC), and phospholipase A2 (PLA2), , and the Na+/H+ exchanger (NHE1). Thus, it is not surprising that GPCRs are such interesting candidates in current drug research with their amazing potential in affecting signaling events. A single GPCR possesses the potential to activate more than just one signaling pathway [12]; for example, activation of A1Rs includes coupling to  and increasing of IP3 level [14, 15]. Furthermore, homodimerization and heterodimerization are common paths of interaction and have been described in A1Rs and the A2Rs several times [16–18]. In addition, functional interactions on the A1R without receptor assembling have already been revealed [19, 20], or are currently being elucidated. The next paragraphs will deal with a few selected examples of this limitlessly wide topic.
Relationship between A1Rs and A3Rs
The A3R was the latest receptor subtype of the adenosine receptor family to be identified [21], and its functional role is still controversially discussed. Several findings indicate neuroprotective as well as neurotoxic action depending on experimental approach [22–29]. A3Rs couple to inhibition of adenylyl cyclase as well as to activation of PLC, and to elevation of inositol triphosphate levels [30, 31]. Furthermore, an increase in intracellular Ca2+ levels due to release from intracellular stores and Ca2+ influx has been described [32, 33]. One interesting example of A3Rs’ functional role is their involvement in acute neurotoxic situations and interplay with A1Rs. Dunwiddie et al. [34] reported on the potential of A3Rs to modify responses via A1Rs in the hippocampus. The activation of hippocampal A3Rs induced a desensitization of A1Rs on combined superfusion of Cl-IB-MECA and adenosine. This phenomenon was thought to reduce the protective effects of endogenous adenosine caused by the lack of sensitivity of A1Rs. Further investigations on pyramidal cells of the rat cingulate cortex did not confirm Dunwiddie et al.’s assumption [35]. In this brain area, A1Rs and A3Rs did not show any interaction. The receptor subtypes were unable to affect each other. The discrepancy was taken to be a genetic phenomenon, such as alternative splicing of the rat A3R transcript causing distinguished pharmacological and functional properties in the brain. Furthermore, Hentschel et al. [36] demonstrated the involvement of A3Rs in inhibition of excitatory neurotransmission during hypoxic conditions, indicating a neuroprotective action of endogenously released adenosine on A3Rs in addition to A1Rs. Lastly, Lopes et al. [37] attempted to define the possible role of A3Rs in the rat hippocampus using experiments similar to those of Dunwiddie et al. in non-stressful and stressful situations, with particular attention to whether A3Rs control A1Rs. These data suggested that no interaction between the two receptor subtypes exist, but confirm that A3Rs do not affect synaptic transmission on superfusion with A3R agonist Cl-IB-MECA or A3R antagonist MRS 1191. The authors pointed out that Cl-IB-MECA binds to A1Rs even at low nanomolar concentrations. Thus, the existence of an interaction between A1Rs and A3Rs has to wait for reliable ligands.
Antagonistic interaction between A1Rs and A2ARs
A2ARs are widely distributed in the CNS, but local and subcellular differences in allocation exist. They show high levels in all subregions of the striatum and in the globus pallidus. A2ARs are also expressed in neurons in the neocortex and limbic cortex, but at a density a twentieth of that found in basal ganglia [38]. Colocalization of A1Rs and A2ARs was approved for glutamatergic nerve terminals in the hippocampus [39]. In the striatum, A1R/A2AR heteromers were found on synapses with spines of medium spiny neurons and integrated in the presynaptic membrane of glutamatergic terminals that represent the cortical-limbic-thalamic input [18]. A1Rs and A2ARs modulate excitatory synaptic transmission, albeit in an opposite manner. A1R activation inhibited glutamatergic synaptic transmission mainly through presynaptic inhibition of glutamate release, while A2ARs have been shown to facilitate glutamatergic synaptic transmission [40–42]. At first sight, stimulating A1Rs and inhibiting A2ARs may have a neuroprotective influence on the mature CNS. However, problems arise due to long-term desensitization of A1Rs. A2ARs do not upregulate after antagonist administration, but have a low abundance in hippocampal and cortical areas compared with A1Rs [40, 43, 44]. A1Rs and A2ARs cannot be regarded in isolation from one another since cross talk between the subtypes has been described several times [16, 17, 45–47]. A2AR activation by agonists caused A1R desensitization resulting in decreased binding affinity for CPA in the hippocampus in young adult rats. Controlling A1Rs by A2ARs was mediated by protein kinase C in a cAMP-independent manner. A2AR activation was seen to play a role in fine-tuning A1Rs by attenuating the tonic effect of presynaptic A1Rs located on glutamatergic nerve terminals [46, 47]. In the striatal system, A1R/A2AR heteromers became prominent to show an antagonistic reciprocal interaction [18]. As in the hippocampus, A2AR stimulation decreased the affinity of A1Rs for agonists. The A1R/A2AR heteromer allows adenosine to perform a detailed modulation of glutamate release [16, 48]. Regarding A1Rs and A2ARs, basal conditions generate a low tone of endogenous adenosine and cause A1R activation, in contrast to situations of increased adenosine where A2AR activation becomes dominant. When adenosine concentrations rise, as during anoxia, likely also time appears to be important in regulating A2A receptor activity, which means A2A receptors are “active” under prolonged stimulation [49]. Finally, activation of the A1R/A2AR heteromer contributes to A2AR signaling when adenosine levels are elevated and may provide a mechanism to facilitate plastic changes in the excitatory synapse [18].
Interactions between adenosine and dopaminergic system
Dopamine is an important transmitter in basal ganglia and is noted for influencing motor activity, playing an important role in Parkinson’s disease. Adenosine-dopamine interactions are complex and cannot be limited on functional considerations of A1Rs. Intramembrane heteromeric receptor-receptor interactions and the involvement of A2ARs in influencing dopaminergic signaling have to be mentioned due to the implications in the treatment of Parkinson’s disease. Ginés et al. [50] described the formation of functionally interacting heteromeric complexes between dopamine D1 receptors (D1Rs) and A1Rs in mouse fibroblast Ltk− cells cotransfected with respective cDNAs. Coaggregation occurred when cells were pretreated with R-PIA as A1R agonist, but was decreased by combined pretreatment with R-PIA and SKF-38393, a D1R agonist. Furthermore, the D1R agonist-induced cAMP accumulation was reduced by combined pretreatment of D1R- and A1R agonist, but remained unaffected when given alone, respectively. The results confirmed an antagonistic interaction between A1Rs and D1Rs that had already been observed by Ferré et al. [51] in behavioral studies using reserpinized mice and rabbits. In vivo and in vitro data on adenosine-dopamine interactions were mostly obtained from investigations in the basal ganglia and limbic regions [52, 53] due to the high abundance of A1Rs, A2ARs, D1Rs, and D2Rs in these areas and their involvement in the pathology of Parkinson’s disease. The antagonistic interaction of combined receptor activation seems to distinguish between adenosine and dopamine receptor subtypes. While A1Rs communicate mainly with the D1R subtype in strionigral-strioentopenduncular neurons, A2AR and D2R interaction occurs in striopallidal neurons. Studies on mice and monkeys pretreated with MPTP suggest that some degree of dopaminergic activity is needed to obtain adenosine antagonistic-induced motor activity. Furthermore, blockade of dopaminergic neurotransmission counteracts the antagonistic effect induced by adenosine [54]. Sufficient endogenous adenosine is present interstitially in the substantia nigra pars reticulata to control dopaminergic effects. The effects of adenosine are absent when dopaminergic influence is suppressed [53]. Thus, it seems that monotherapy with A2AR antagonists may only be useful in the early stages of Parkinson’s disease, but could support a therapeutic treatment with dopamine agonists in advanced stages. A promising approach using these therapeutic strategies can be seen in istradefylline, an A2AR antagonist that has since successfully passed clinical trials [55]. However, A1R blockade may also contribute to an increased dopamine release but this effect seems without clinical relevance.
Relationship between A1Rs and NPY
Neuropeptide Y (NPY) is one of the most abundant neuropeptides and exerts various functions on at least six GPCR subtypes (Y1Rs-Y5Rs, y6Rs). Immunohistochemical investigations revealed the appearance of the Y1R and Y5R subtypes in the rat frontal cortex [56–58]. Activation of NPY receptors results in an inhibition of excitatory synaptic transmission, while a presynaptic influence on cortical neurons has been postulated [59]. NPY receptors affect pertussis toxin-sensitive G proteins, which inhibit adenylyl cyclase and decrease cAMP levels. Inhibitory and facilitating effects on K+ and Ca2+ mobilization have also been observed [60]. Receptor-receptor interactions between Y1Rs have already been described, such as the antagonistic interaction with galanin receptors in the hypothalamus of the rat and their functional relevance for food intake. In contrast, a facilitatory interaction between the two receptors exists in the amygdala which may be of relevance for fear-related behavior [61].
In the CNS, A1Rs and NPY receptors share some similarities in distribution. Both A1Rs and Y1Rs are located on neurons of the prefrontal cortex, and their activation inhibits glutamatergic neurotransmission [62]. This is evidence for potential interaction between A1Rs and Y1Rs that may modulate long-term desensitization of A1Rs during pathophysiological situations. To investigate possible functional interactions, postsynaptic potentials (PSPs) were generated by electrical field stimulation on pyramidal neurons of layer V in the rat cingulate cortex as described by Brand et al. [35] and Hentschel et al. [36]. The Y1R agonist [F7,P34]pNPY inhibited the amplitude of PSPs. The inhibitory effect was reversible and reproducible, indicating that no desensitization appeared (Fig. 1a). An additional decrease in PSP amplitude was observed when NPY was superfused in combination with the A1R agonist CPA (Fig. 1b).
Fig. 1Effect of the selective Y1 agonist [F7,P34]pNPY as well as neuropeptide Y (NPY) alone and in combination with selective A1R agonist N6-cyclopentyladenosine (CPA) on the amplitude of postsynaptic potentials (PSPs) evoked by electrical field stimulation (0.2 Hz, 2 ms) in layer I of the rat cingulate cortex (Sichardt et al., unpublished results). Intracellular recordings were performed in rat brain slices using glass microelectrodes placed in pyramidal cells of layer V. a [F7,P34]pNPY superfused for 5 min inhibits reversibly the PSPs by 34.6 ± 8%. Y1 antagonist BIBP3226 itself reduces the PSP by 23 ± 10%, whereas [F7,P34]pNPY has no inhibitory effect in the presence of the antagonist. b NPY depresses PSPs by 28.6 ± 0.7%. The combined superfusion of NPY and CPA resulted in an additional depression of the PSPs by 48.1 ± 5%. The depressant effects of the two agonists were reversible during washout. Data are expressed as mean ± SEM from n = 3 independent experiments. *p < 0.05 significant vs control; #p < 0.05 significant vs NPY alone
The additional inhibition induced by CPA was in the same range as that found with CPA alone (48.1 ± 5% vs 55 ± 3%). NPY inhibited PSPs after blockading CPA-mediated inhibitory effects by DPCPX. No significant changes existed before and after blockading A1Rs (Fig. 2). The results suggest that no interaction between A1Rs and Y1Rs exist. Each neuromodulator contributes to inhibitory regulation of excitatory neurotransmission. Regarding the desensitization of A1Rs but not of Y1Rs, this may be important under pathophysiological conditions with increased adenosine concentration in the synaptic cleft.
Fig. 2Effect of neuropeptide Y (NPY) on the PSPs alone and in combination with the selective A1R agonist N6-cyclopentyladenosine (CPA) after preincubation with the selective A1R antagonist 1,3-dipropyl-8-cyclopentylxantine (DPCPX) (Sichardt et al., unpublished results). The experimental procedure was similar to that shown in Fig. 1. In the presence of NPY, PSPs were decreased by 35.4 ± 7%. Combined superfusion of NPY and CPA after preincubation with DPCPX decreased PSPs by 32.7 ± 8%. The depression was reversible during washout. Data are expressed as mean ± SEM from n = 5 independent experiments. *p < 0.05 significant vs control; #p < 0.05 significant vs superfusion of CPA and DPCPX
Interaction of A1Rs and P2Y1Rs
P2Y1Rs have been cloned and characterized in several species including human and rat, whereas mRNA was detected in various regions of the brain. The receptor subtype can be activated by ATP, but ADP as a degradation product of ATP is a more potent endogenous agonist. Cellular signaling differs between A1Rs and P2Y1Rs since A1Rs couple to Gi/0 and P2Y1Rs to Gq/G11. In fact, P2Y1Rs can be assumed to exert stimulatory effects in cells. P2Y1R signaling occurs in non-neuronal and non-muscular cell types, as well as on neurons in the CNS [63] where a colocalization of A1Rs and P2Y1Rs was demonstrated immunohistochemically in rat brain cortex, hippocampus, and cerebellum [64]. In 1996, Ikeuchi et al. reported the activation of an undefined P2YR by adenosine in patch clamp and calcium imaging experiments on hippocampal neurons [65]. Furthermore, extensive heteromerization experiments have been conducted on cotransfected HEK293 cells using immunoprecipitation, Western blotting, and bioluminescence resonance energy transfer (BRET). Receptor binding experiments in combination with cAMP assays have also been described [64, 66–70]. These respective studies confirmed heteromerization associated with changes in the agonist binding and signaling compared to monomer properties. The binding for selective A1R agonists was decreased while the A1R antagonist binding remained unaffected. Interestingly, ADP binding was blocked by DPCPX but not by the P2Y1R antagonist, suggesting an altered binding pocket on the A1R/P2Y1R complex. The G protein-coupling was sensitive to pertussis toxin and revealed a Gi/0 status for the heteromer. Although colocalization of A1Rs and P2Y1Rs in several brain areas has been demonstrated, there is still a lack of functional investigations. Nevertheless, the physiological relevance of this interaction has been postulated as follows: costorage and release of ATP with neurotransmitters, such as glutamate or noradrenaline, occurs in the CNS [71–73]. ADP, the degradation product of ATP, acts as an A1R agonist due to the activation of the A1R/P2Y1R complex and contributes to the inhibitory modulation of excitatory synaptic transmission via adenosine acting on A1Rs. This interaction can be assumed as an additional mechanism for influencing and fine-tuning synaptic neurotransmission.
Functional interaction with ionotropic receptors
Neuronal excitability is regulated by voltage and ligand-gated ion channels. Ionotropic receptors also referred to as ligand-gated ion channels (LGICs) are a group of intrinsic transmembrane ion channels that open and close in response to binding of a chemical messenger, as opposed to voltage-gated ion channels or stretch-activated ion channels. Ion channels are regulated by a ligand and are usually very selective to one or more ions such as Na+, K+, Ca2+, or Cl−. These receptors located at synapses convert the chemical signal of presynaptically released neurotransmitter directly and very quickly into a postsynaptic electrical signal. Many LGICs are additionally modulated by allosteric ligands by channel blockers, ions, or membrane potential.
Nicotinic acetylcholine receptor serves as the prototypical LGIC [74] and consists of a pentamer of protein subunits with two binding sites, which, when bound, alter the receptor configuration and cause an internal pore to open. This pore, permeable to Na+ ions, allows them to flow down their electrochemical gradient into the cell. With a sufficient number of channels opening at once, the intracellular Na+ concentration rises to the point at which the positive charge within the cell is sufficient to depolarize the membrane, and an action potential is initiated [75]. Many important ion channels are ligand-gated, and they show a great degree of homology at the genetic level. The LGICs are classified into three superfamilies; the first—the Cys-loop receptor family—is subdivided into the anionic GABAA and glycine receptors on the one hand, and cationic 5-HT3 serotonin and nicotinic acetylcholine receptors on the other. The second group—ionotropic glutamate receptors—consists of NMDA, kainate, and AMPA receptors. The third group covers the ATP-gated channels—the P2X receptors [76].
Adenosine is known to inhibit glutamatergic neurotransmission by activation of presynaptic A1Rs [35]. This is probably due to reduction of the calcium influx, possibly by modulating both P/Q- and N-type presynaptic voltage-dependent calcium channels, which in turn controls transmitter release [77]. Furthermore, A1Rs have long been known to mediate neuroprotection by reduction of excitatory effects at the postsynaptic level [10, 78, 79]. In addition to its direct presynaptic and postsynaptic actions on neurons, A1R interaction with NMDA [80–82, 84], GABAA [85–88], and P2X receptors [80, 89–91] contributes to fine-tuning neuromodulation via adenosine.
Interaction between A1Rs and NMDA receptors
Glutamate is the major excitatory neurotransmitter in the mammalian central nervous system. In most brain areas, glutamate mediates fast synaptic transmission by activating ionotropic receptors of the AMPA, kainate, and NMDA type. Additionally, NMDA receptors play a critical role in synaptic plasticity, synaptic development, and neurotoxicity. Recent studies suggest that some NMDA-mediated actions are altered or mediated by adenosine. Synaptic currents mediated by glutamate in rat substantia nigra pas reticulata neurons were reduced by adenosine acting via A1Rs. The inhibitory action was not mediated by a postsynaptic site since adenosine did not block currents evoked by local application of glutamate [86].
NMDA is known to increase the extracellular level of adenosine via bidirectional adenosine transporters or from released adenine nucleotides degraded by a chain of ectonucleotidases [92, 93]. On the other hand, endogenous adenosine present in the extracellular fluid of hippocampal slices tonically inhibits NMDA receptor-mediated dendritic spikes as well as AMPA/kainate receptor-mediated synchronized EPSPs by activation of A1Rs in CA1 pyramidal cells [81]. In line with these results, it has been shown that the tonic activation of A1Rs by ambient adenosine depressed field potentials in the striatum. The effect of adenosine in the striatum [84] or hippocampus [94] has not been found in A1R knockout mice and clearly demonstrates the involvement of A1Rs. The involvement of A1Rs was also supported by experiments using the selective receptor ligand 2-CA. In isolated rat hippocampal pyramidal cells [95] and in bipolar cells of the retina [96], 2-CA decreased inward currents induced by iontophoretic application of NMDA.
Another interesting interaction concerns NMDA preconditioning to protect against glutamate neurotoxicity. The A1R antagonist 8-CPT has been shown to prevent neuroprotection evoked by NMDA preconditioning against glutamate-induced cellular damage in cerebellar granule cells [83]. In this study, the functionality of A1Rs was not affected by NMDA preconditioning, but this treatment promoted A2AR desensitization in concert with A1R activation [83]. These results are in line with other studies indicating that adenosine downregulates excitatory and inhibitory synaptic transmission in several brain areas through activation of A1Rs and A2AR [97, 98]. Furthermore, activation of A1Rs mediates reversal of long-term potentiation (LTP) produced by brief application of NMDA in hippocampal CA1 neurons [99].
Taken together, there are several ways in which adenosine may interact with NMDA-induced cellular events. Adenosine can affect glutamatergic transmission via both presynaptic and postsynaptic mechanisms by activating A1Rs [78]. NMDA receptors and A1Rs interact to downregulate glutamate release presynaptically in pyramidal cells of the cingulate cortex [35], neurons of the hippocampus [100], and striatal neurons [84]. Another putative mechanism is related to postsynaptic A1Rs; adenosine elevates the threshold to open NMDA receptor-operated channels by antagonizing membrane depolarization [101].
Interaction between A1Rs and GABAA receptors
Fast synaptic inhibition in the brain and spinal cord is largely mediated by GABAA receptors that are also targeted by drugs such as benzodiazepines, barbiturates, neurosteroids, and some anesthetics. The modulation of their function will have important consequences for neuronal excitation [102]. One accepted means of modifying the efficacy is a functional interaction with adenosine. Adenosine may have an effect on either presynaptic GABA release in interneurons and/or on postsynaptic GABAA receptors in projection neurons. The site of action may be studied electrophysiologically by inducing fast inhibitory postsynaptic potentials (IPSPs) or application of GABA directly onto the cell. Adenosine and selective A1R agonist CHA reduced the amplitude of the fast IPSP in lateral amygdala slice preparations. The effect of CHA was blocked by DPCPX, indicating the involvement of A1Rs. Additionally, adenosine did not block currents evoked by local application of GABA [85]. Thus, the modulatory effect of adenosine on the GABAergic neurotransmission appears to take place on a presynaptic site by inhibiting GABA release from nerve terminals [85, 86]. The assumption that the activation of A1Rs can presynaptically modulate inhibitory postsynaptic responses agrees with findings in several brain areas, such as the thalamus [87], suprachiasmatic and arcuate nucleus [88], and substantia nigra pars compacta [86].
There is some evidence that activation of A1Rs is also involved in GABAA receptor downregulation, implying a facilitation of the neurotransmission on a postsynaptic site. GABA but not adenosine evoked an inward current in rat sacral dorsal commissural neurons (SDCN). The GABA-induced current was significantly reduced be adenosine. CHA and DPCPX, but not selective ligands for A2ARs, mimicked or blocked the inhibitory effect of adenosine, respectively [103]. Adenosine and muscimol induced a concentration-dependent reduction in the amplitude of population potentials in hippocampal slices. Additionally, adenosine potentiated the ability of muscimol to inhibit evoked potentials, which were blocked by the A1R-selective antagonist 8-CPT. The effects of adenosine as well as muscimol were reduced by the chloride channel blocker DIDS, indicating the ability of adenosine to regulate the GABAA chloride channel by activation of A1Rs [104].
Sebastiao’s group studied the mechanisms by which GABA modulates adenosine-mediated effects and found that endogenous GABA exerts an inhibitory effect through GABAA receptors via a predominant adenosine-mediated action in the hippocampus. The authors concluded that there is an A1R-mediated ability to inhibit synaptic transmission [19]. Further, this study showed that the blockade of GABAergic inhibition induced the release of NO, which was able to potentiate the inhibitory action of adenosine. They therefore suggested that the modulation of the A1R-mediated response by activation of GABAA receptors occurs indirectly via NO [19].
Activation of GABAA receptors is effective in limiting neuronal ischemic damage [105] and endogenous adenosine that arises during hypoxia, and acts neuroprotectively partly by activating A1Rs [36]. Therefore, the contribution and potential interactions of GABA and adenosine as modulators of synaptic transmission during hypoxia has been investigated. Activation of A1Rs inhibits the release of GABA from the ischemic cerebral cortex in vivo [106]. In contrast, the administration of an A1R agonist in the hippocampus failed to affect the release of GABA during ischemia [107]. In the light of these controversial results, the role of the two neuromodulators during hypoxia was investigated in the CA1 area of rat hippocampal slices using selective A1R antagonists [108]. Indeed, activation of A1R and GABAA receptors is partly involved in the inhibition of synaptic transmission during hypoxia. The action of GABA becomes evident when A1Rs are blocked. Regarding the desensitization of A1Rs during hypoxia [109, 110], it may be assumed that GABAA-mediated inhibition of the synaptic transmission is evident when the A1R is desensitized or downregulated [108].
Comodulation by A1Rs and GABAA receptors was also suggested in acute cerebellar ethanol-induced ataxia. Using GABAA and A1R agonists and antagonists, respectively, a functional similarity between GABAA receptors and A1Rs has been shown even though both receptor types are known to couple to different signaling systems [111]. This provides conclusive evidence that A1Rs and GABAA receptors both play a comodulatory role in ethanol-induced cerebellar ataxia without any direct interaction.
Functional interaction between A1Rs and P2X receptors
P2X receptors are ligand-gated ion channel receptors; seven subunits (P2X1-P2X7) have been identified [63]. The P2X receptor subunits show many differences in localization, pharmacology, kinetics, and signaling pathways [112, 113]. The P2X1 to P2X6 receptors have 379–472 amino acids, with a predicted tertiary structure of transmembrane segments, a large extracellular loop and intracellular C  and N termini. The P2X2, P2X4, and P2X4/P2X6 receptors appear to be the predominant neuronal types [91]. These subunits may occur as homooligomers or as heterooligomeric assemblies of more than one subunit. The P2X7 receptor has a similar structure, but with a much larger intracellular C terminus. This contrasts strikingly with any of the other known ligand-gated ionotropic receptors [114]. P2X7 subunits do not form heterooligomeric assemblies, but are involved in mediating apoptosis and necrosis in glial cells and possibly neurons.
Interaction between adenosine receptor-mediated and P2 receptor-mediated effects have been shown to occur in neuronal and non-neuronal cells [80]. Both adenosine and ATP induce astroglial cell proliferation and formation of reactive astrocytes [89]. In hippocampus, adenosine and ATP are released on stimulation and are potent neuronal transmission inhibitors [115, 116]. It should be pointed out that the interpretation of effects induced by both is difficult since ATP is degraded enzymatically to adenosine [38]. Adenosine is formed by extracellular catabolism of released ATP via the ectonucleotidase pathway [90, 117]. The role of the ectonucleotidases in forming adenosine is difficult to study since this system is extremely efficient, and it is difficult to block an enzyme system. The experimental paradigm used by Cunha et al. [118] demonstrates that ATP has to be converted outside the cell into adenosine to exert its inhibitory effects on hippocampal synaptic transmission. The inhibitory effect of ATP was not modified by the P2 receptor antagonist suramin, but was attenuated by the ecto-5’-nucleotidase inhibitor and was nearly prevented by the adenosine A1R antagonist DPCPX, whereas dipyridamole, an inhibitor of adenosine uptake, potentiated the inhibitory effect of ATP [118]. These results offer evidence for localized catabolism of adenine nucleotides followed by substrate channeling to A1Rs. This localized catabolism may mask the adenosine-mediated ATP effect [119]. Recently it was demonstrated that the exogenous application of ATP or ATPχS reduced the hippocampal neurotransmission. The inhibitory effect was blocked by the selective A1R antagonist DPCPX and was potentiated by different ecto-ATPase inhibitors [120]. These results suggest that the synaptic inhibition may consist of an inhibitory purinergic component of ATP itself in addition to degradation to adenosine.
Interaction with neuronal ATP-sensitive K+ channels
ATP-sensitive K+ channels (KATP) are widely expressed in the cytoplasmic membrane of neurons and couple cell metabolism to excitability [121]. These channels are regulated by the intracellular ATP/ADP ratio [122] and modulated by many endogenous mediators, including adenosine, via A1Rs. Activation of A1Rs inhibited the activity of inspiratory neurons in the brainstem by opening KATP in neonatal mice [123]. A1R stimulation promotes KATP activity in principal dopamine neurons in the substantia nigra pars compacta [124] and hippocampus [125]. In contrast, one recent study has demonstrated that adenosine induces internalization of KATP, resulting in a decrease in KATP-mediated response in the hippocampus [126]. The discrepancy might be due to the additional activation of A2ARs by adenosine located in hippocampus, but not in the substantia nigra pars compacta [127]. In addition to the inhibitory effect on the presynaptic site, the activation of A1Rs acts as an inhibitory modulator to electrical activity on the postsynaptic site, and this effect has been attributed to enhancement of KATP activity. The modulating effect on the membrane potential may differ depending on the brain regions, as neuronal KATP is heterogeneous in different neurons.
A1R interactions—new approaches for neurological disorders
By activation of its receptors, adenosine regulates many pathophysiological processes, particularly in excitable tissues of the brain (Fig. 3). Its widespread functions in the body include regulation of seizure susceptibility [128, 129], neuroprotection [40], regulation of pain perception [130], sleep induction [131], and involvement in Parkinson’s disease [132]. There is increasing evidence that the functional interaction of A1Rs with other neuronal receptors may contribute to fine tuning in synaptic transmission, and A1R agonists may represent a useful therapeutic approach for the treatment of some neurological disorders by regulating homeostasis in transmitter systems. However, the use of A1R agonists has not proved clinically useful due to mainly cardiovascular side effects as well as low brain permeability. Pioneering experimental approaches have been evaluated using focal drug delivery in epilepsy models. One experimental study has used intraventricular implantation of an adenosine-releasing synthetic polymer [128]. In a later study, Guttinger et al. [133] used encapsulated C2C12 myoblasts that were engineered to release adenosine by disruption of their adenosine kinase gene [133]. The local delivery of adenosine by implanted cells appears to be a promising strategy for the control not only for affecting seizure activity but also other neurodegenerative diseases with dysregulated synaptic neurotransmission.
Concluding remarks
As a consequence of its ubiquitous distribution and because of its linkage to the energy pool, adenosine has evolved as an important messenger in extracellular signaling. Modifications in extracellular adenosine levels with subsequent alterations in the activation of its receptors interferes with the action of other receptor systems. Figure 4 summarizes possible interactions of A1Rs with metabotropic receptors, Fig. 5 shows the interactions between A1Rs and ionotropic receptors as well as with KATP, and Fig. 3 shows the neurological disorders where A1R interactions may play a role.
Fig. 3Schematic representation of possible interactions of A1Rs with metabotropic receptors. Heteromerization between presynaptically located A1Rs and A2ARs, D1Rs, and P2Y1Rs causes changes in influencing glutamate release by adenosine. Cross talk between A1Rs and P2Y1Rs contributes mainly to triggering fast attenuation of transmitter release, whereas ADP acts as a ligand on the heteromer. During elevated adenosine levels, A2AR signaling becomes dominant in the A1R/A2AR complex, providing enhancement of glutamate release. The A1R/D1R heteromer requires both adenosine and dopamine to be activated and inhibits transmitter releaseFig. 4Schematic representation of possible interactions of A1Rs with ionotropic receptors contributing to the fine-tuning of neurotransmission. Adenosine acting via presynaptic A1Rs may attenuate the influx of Ca2+ through voltage-dependent calcium channels and thus decrease the release of glutamate and GABA, which inhibits or facilitates the activation of postsynaptically located NMDA receptors, respectively. Adenosine acting through postsynaptic A1Rs may activate KATP, which leads to hyperpolarization of postsynaptic neurons and inhibits directly the activity of NMDA and GABAA receptorsFig. 5Neurological disorders where A1R interactions may play a role
There is evidence that various regulatory mechanisms exist as well as multiple mechanisms that act independently of each other on the same cell depending on the brain region and cell type. The overaction and redundancy principle ensures transmitter homeostasis under pathophysiological conditions in a special time window. The function of adenosine receptors in the regulation of the synaptic transmission is complex.
The key receptor in regulation of the neuronal transmission may be the A2AR, whereas the interaction of the A1R with metabotropic and ionotropic receptors as well as with KATP serves as fine-tuning to inhibit synaptic transmission, as mentioned by Sebastiao and Ribeiro [80]. A1Rs may play a nonessential role in normal physiology as demonstrated in mice lacking the A1Rs [134]. However, they play an important protective role under pathophysiological conditions especially during hypoxia. The activation initiates a fast inhibition of the glutamatergic neurotransmission and the receptor interactions may contribute to its maintenance or can support the A1R-mediated effects.
Most of our knowledge on receptor-receptor interactions involving the A1R results from experiments on cell cultures, slice preparation or, to a lesser extent, from in vivo experiments where regulation can be studied in principle or new drug targets can be characterized. These findings may contribute to a better understanding of disturbances in transmitter homeostasis. As our understanding of the complexity of receptor signaling and interaction develops, we may well gain new perspectives in new drug development. The clinical relevance of the testing models has often been questioned, however. Discordance between studies on cells and animal and human studies may be due to bias or failure of models to mimic clinical disease to an adequate degree. There are new techniques such as neuroimaging, nanotechnology, siPCR, and new selective receptor ligands that will help to overcome some of these aspects in the near future.

adenosine
receptor interactions
g protein-coupled receptors
ionotropic receptors
adenosine receptors
neurotransmission