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Urinary catecholamines and plasma hormones predict mood state in rapid cycling bipolar affective disorder
JOURNAL
ELSEVIER
OF
AFFECTIVE DISORDERS
Journal of Affective Disorders 33 (1995) 233-243
Urinary catecholamines and plasma hormones predict mood state in rapid cycling bipolar affective disorder Peter R. Joyce a,d,*, David M. Fergusson a, Gerald Woollard b, Robyn M. Abbott d, L. John Honvood a, Jeff Upton ’ a University Department of Psychological Medicine, Christchurch School of Medicine, Christchurch, New Zealand b Department of Clinical Chemistry, Auckland Hospital, Auckland, New Zealand ’ Department of Clinical Biochemistry, Christchurch Hospital, Christchurch, New Zealand d Clinical Research Unit, Sunnyside Hospital, Sunnyside, New Zealand
Received 24 January 1994; revised 24 October
1994; accepted
26 October
1994
Abstract Over the course of 1 year, a patient with a rapid cycling bipolar affective disorder was followed at weekly intervals to examine whether plasma hormones and urinary catecholamines could predict current or future mood. Higher cortisol levels were found to predict depressed mood 3 days after blood sampling, higher urinary dopamine predicted
a manic mood 3 days after blood sampling, urinary norepinephrine was associated with severity of current mood and prolactin was lower with concurrent depressed mood. In multivariate analyses of mood against cortisol, prolactin and three urinary catecholamines, > 50% of the variance in mood state in 3 days was explained by combinations of these biologic measures, especially cortisol and urinary dopamine, while all five biologic variables contributed to explaining 50% of the variance in current mood state. Based on the interrelationships between urinary dopamine, norepinephrine and mood, we postulate the existence of an overcompensating mechanism which is reflected in opposing correlations between urinary dopamine and norepinephrine with mood, despite the two urinary catecholamines being positively correlated.
NE (Bunney
1. Introduction
and Davis,
1965; Schildkraut,
1965).
Over the past 25 years, there has been increasing The early amine hypothesis of affective disorders suggested that depression was associated with a deficiency of norepinephrine (NE) and that mania may be associated with an excess of
* Corresponding author. University Department of Psychological Medicine, Christchurch School of Medicine, PO Box 4345, Christchurch, New Zealand. 0165-0327/95/$09.50 0 1995 Elsevier SSDI 0165-0327(94)00094-8
Science
research into the biochemical correlates of affective disorders. Although a variety of other neurotransmitters and neuropeptides, such as serotonin, acetylcholine and y-amino butyric acid, have been implicated in the pathophysiology of affective disorders, NE remains a key transmitter in depression and dopamine may be of importance in bipolar affective disorder (Silverstone and Cookson, 1982; Post, 1980). Attempts to confirm biochemical theories of
B.V. All rights reserved
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affective disorders have had to face many methodological issues. One major issue has been the feasibility of assessing changes in CNS transmitter systems by studying cerebrospinal, plasma and/or urinary levels of catecholamines and their metabolites (Potter et al., 1987). Many studies have focused on the NE metabolite of 3-methoxy4-hydroxy-phenylglycol (MHPG) and examined whether subtypes of depression and response to treatment can be predicted on the basis of urinary MHPG levels (Maas, 1978; Kelwala et al., 1983; Joyce and Paykel, 1989). A recent review of cross-sectional studies on groups of depressed patients concluded that bipolar depressed patients have lower urinary MHPG and NE than controls and unipolar depressed patients (Potter et al., 1987). Furthermore, in unipolar melancholic patients, increasing levels of urinary and plasma catecholamines are correlated with indices of cortisol hypersecretion although this correlation does not apply in bipolar patients (Joyce et al., 1987). Recent studies are increasingly measuring more than one catecholamine or metabolite and it has been found that, from combinations of measures, it may be possible to separate unipolar and bipolar depressed patients (Maas et al., 1987; Schatzberg et al., 1989). A complementary approach to finding experimental confirmation of the amine hypothesis of affective disorders has been to utilize neuroendocrine measures, on the assumption that brain neurotransmitters control neuroendocrine secretion, and that changes in measurable endocrine output reflect changes in brain amines (Checkley, 1980). Many neuroendocrine abnormalities have now been reported in depressed patients, with the most consistent finding being the cortisol hypersecretion shown by many patients (Joyce, 1985). A number of studies have suggested that changes in the hypothalamic pituitary adrenal axis may occur before a change in mood state (Carroll, 1982; Holsboer et al., 1983) and, in an earlier study, we produced evidence that mean plasma cortisol levels could increase 2-3 days before a patient entered a depressed state (Joyce et al., 1987). Neuroendocrine findings in mania have been fewer and are less consistent (Cookson, 1985).
Presumably, many of these documented biologic changes are not completely independent of one another but are reflections of an underlying pathophysiological process. Although many early studies tended to concentrate on one neurotransmitter or biologic variable, there is increasing evidence that neurotransmitters do not work in isolation and that changes in one transmitter may lead to changes in another transmitter system L4ntelman and Caggiula, 1977). While the majority of studies on biologic correlates of mood have utilized groups of subjects who are compared in a static cross-sectional manner, there are some studies utilizing individual patients who have been followed through prospective mood cycles (Jones et al., 1973; Bunney et al., 1965). The advantages of the single-case design are that it can potentially allow for the examination of how biologic variables may change with mood state and, by eliminating interindividual variability, may detect biologic changes with mood, even if mood disorders in general, are biologically heterogeneous. We, thus, set out to examine in detail a single subject with a rapid cycling bipolar affective disorder during repeated episodes of mania and depression. During these recurrent episodes, it was planned to measure urinary NE, epinephrine and dopamine, and the plasma hormones cortisol and prolactin which may, in part, be under dopaminergic and adrenergic control. From the combination of measures, it was planned to examine whether any of these variables could be used to predict the patient’s mood state. Furthermore, and of greatest potential interest, was whether the patient’s future mood state could be predicted from combinations of these biologic measures.
2. Methods 2.1. Patient
Mrs. X is a had been under > 6 years. Her age of 17 when
47-year-old divorced woman who the clinical care of P.R. Joyce for psychiatric history began at the she was hospitalized for a major
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depressive episode. After recovery from this episode, she remained well until the birth of her second child when she was 27 years old. From the birth of this child to the time of commencement of this study, she had never had a prolonged period of euthymia, had been hospitalized for her bipolar disorder on > 30 occasions and in the 5 years before the commencement of this study, while under the care of P.R. Joyce, had ‘cycled’ from mania to depression and back with only brief spells of euthymia which were never > 1 month in duration. Over the years, she had been tried on a variety of treatments. In recent years, carbamazepine had been added to her lithium carbonate; this decreased the severity of her mood swings but had not decreased the frequency of her affective episodes or changed her cycle. = 6 months before the commencement of this study, sodium valproate had been added to her lithium-carbamazepine combination; this had resulted in a marked shortening of her depressive episodes from typically > 4 to l-2 weeks and had decreased further the severity of the mood changes. Over the course of this l-year study, she was treated as an outpatient and, for the first time in 20 years, was never hospitalized throughout the course of a year. During this time, she was maintained on lithium carbonate 750 mg at night, carbamazepine 400 mg morning and night and sodium valproate 500 mg morning and night. At earlier stages, she had been tried on higher doses of all these drugs but side-effects and/or no further therapeutic benefits had been obtained during these trials of treatment. Towards the end of this study, her sodium valproate was increased to 1500 mg daily and, on this second attempt, at a higher dosage, she did not develop side-effects and she has so far remained totally euthymic for 3 years for the first time in > 20 years. Her remaining well brought the study to an end. 2.2. Assessment of mood Mrs. X was seen on 51 occasions over the course of 1 year. On each occasion, she would arrive for her outpatient appointment at 13:00
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and she would remain for = 2 h. On the basis of interview and observation made at each visit, Mrs. X’s mood was rated by the same research nurse (R.M. Abbott) on a 7-point scale for mania and a 7-point scale for depression which were based on the global clinical evaluation. As she never experienced a mixed mood state, these two scales were subsequently collapsed into a single mood scale which potentially ranged from + 6 for extreme mania, to 0 for euthymia, to - 6 for extreme depression. During these 51 weekly assessments, her mood was rated as euthymic on 25 occasions as depressed on 14 occasions and as manic on 12 occasions. This represented 10 episodes of mania and nine episodes of depression. During the year, the longest sequences of similar moods on consecutive visits was 3 weeks; this happened on three occasions, twice when she was well for 3 weeks and once when she was depressed for 3 weeks. Although it had been possible to rate her mood from +6 to -6, the actual range was from + 2 to -3. The limited range of ratings are reflected in the fact that she did not require hospitalization during this year and that her medication decreased the severity of her mood swings. After the research nurse had rated the patient’s current mood state, she retrospectively rated her mood for each day of the past week using a global rating of depressed, euthymic or manic. This seldom presented any difficulties in that the onset of depressive episodes in this patient was always clear-cut and could easily be dated and the onset of manic episodes was usually clear from changes in activities, energy and mood. As biologic assays were not completed until the end of the study, the research nurse was always blind to the results of the biologic assessment. During each visit, the patient rated herself on four bipolar visual analogue scales which rated her mood (very sad to very happy), energy (lacking energy to full of energy), nervousness (very relaxed to tense/on edge) and confidence (feels good about self to feels bad about self). The clinician rating and the patient self-ratings were well-correlated (0.63-0.72, P < 0.001). In subsequent analyses, only the clinician rating (CLMOOD) was used.
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2.3. Biologic assessment
After arrival at the research clinic, Mrs. X would rest quietly on a bed and an indwelling i.v. catheter was inserted into an antecubital vein from which blood samples would be drawn at intervals during her visit. From an initial sample, we obtained a plasma lithium level, plasma electrolytes and a full blood count. Three resting samples would be drawn at 20-min intervals for measurement of mean afternoon cortisol and prolactin levels. During this time, the patient would also collect a resting l-h urine sample for urinary catecholamines. Lista (1989) has provided data to support the utility of short time urine sampling, provided the patient is at rest.
3 ml/min), a WISP 720 autosampler, a M460 electrochemical detector at 0.6 V and a 840 system controller. The column was a 100 x 8-mm Waters Novapak Cl8 cartridge, 4-pm particle size, radially compressed in a RCM 8 X 10 compression module. The mobile phase consisted of 6.8 g sodium acetate (adjusted to pH 4.81, 1 g sodium octylsulphonate and 70 ml acetonitrile/l. Results for urinary catecholamines are expressed as ~mol/mmol creatinine. 2.5. Statistical analysis Data from the patient, including the clinical and biologic variables, were entered and statistical analyses were carried out using SYSTAT.
2.4. Biologic assays 3. Results Plasma cortisol (nmol/l) was measured by ELISA (Lewis and Elder, 1985) and prolactin (mIU/l) by RIA (Livesey and Donald, 1981) (interassay coefficient of variation of < 11 and 13%, respectively). Urinary catecholamines were measured using high-performance liquid chromatography with electrochemical detection. Samples were prepared using cationic exchange on Biorex 70 following the method described by Bio-Rad Laboratories (1985) with minor modification to the ammonium borate elution procedure. The chromatographic separation was similar to that of Goldstein et al. (1981). The instrument used was a Waters Associates (Milford, MA) chromatographic system consisting of a 510 pump (flowrate
3.1. Associations of biologic variables with current mood From Table 1, which shows the levels of cortisol (nmol/l), prolactin (mIU/l), urinary epinephrine, NE and dopamine, depending upon whether the patient was clinically rated as depressed, euthymic or manic on the day that the samples were drawn, it can be seen that during depression this patient had lower prolactin levels. Cortisol levels vary with current mood state and, while there is a trend for cortisol levels to be higher in depression, this did not reach significance on posthoc tests although it was significant that cortisol levels are lower in mania.
Table 1 Levels of hormones and urinary catecholamines during different mood states within this patient Depression Cortisol Prolactin
247 153
(49) (31)
Euthymia n = 25 230 199
Epinephrine NE Dopamine
0.007 0.016 0.088
(0.003) (0.012) (0.087)
0.006 0.009 0.089
n = 14
P
(47) (59)
Mania n = 12 198 19.5
(41) (51)
(0.004) (0.006) (0.077)
0.006 0.012 0.146
(0.006) (0.009) (0.139)
Results are expressed as a mean f SD. ’ F = 3.74; df = 2, 48; P = 0.031. ’ F = 3.93; df = 2, 48; p = 0.026.
I 2
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Table 2 Pearson correlations between biologic variables and clinician rating of mood (CLMOOD) (range: - 3 to + 2), and concurrent mood state (MSOO) (range: - 1 to + 1)
Cortisol Prolactin Epinephrine NE Dopamine
CLMOOD
MS00
-0.29 * 0.31 * -0.14 -0.37 * 0.09
-0.36 ** 0.30 * -0.12 -0.16 0.20
lactin and that severity of current depression does not explain increases of cortisol. 3.2. Biologic variables as predictors of past, current and future moods
* P < 0.05. * * P < 0.01
Table 2 presents the Pearson correlations of each biologic variable with the clinician assessment of mood (CLMOOD; range: -3 to + 2) and with current mood state (MSOO; range: - 1 to + 1). Inspection of this table shows that cortisol and NE are significantly negatively correlated with mood (i.e., higher levels in depression) and that prolactin is positively correlated with mood (i.e., lower levels in depression). For NE and prolactin but not cortisol, the correlations are less with MS00 than when severity of current mood state is included; indeed, for NE, the correlation failed to reach a P < 0.05 level of significance when the mood is just summarized as manic (+ 11, euthymic (0) or depressed (-- 1). This suggests that severity of current depression contributes to the association between NE and mood, is perhaps of little significance as regards pro-
To examine the question as to whether the biologic variables best predicted past, current or future mood states, each biologic variable was correlated with mood state at -7, -3, 0, +3 and +7 days. Inspection of Table 3 shows that none of the biologic variables predicted mood state either a week before or a week later. The strongest correlations which were found were between cortisol and dopamine and mood state in 3 days time. The only significant correlate of cortisol was a negative one with urinary dopamine (r = - 0.28, P < 0.05). However, increasing cortisol predicts depression and increasing dopamine predicts mania; thus, it is possible that the negative association reflects the opposing relationships to mood state in 3 days. Prolactin levels were not significantly correlated with any other biologic variable. The urinary catecholamines were all interrelated as dopamine correlated with NE (r = 0.59, P < O.OOl),with epinephrine (r = 0.53, P < 0.001) while the correlation of NE and epinephrine approached significance (r = 0.31).
Table 3 Pearson correlation matrix of mood states (7 days before biologic assessment days after assessment (MS + 3) and 7 days following (MS + 7)) and biologic
MS-3 MS00 MS+3 MS+7 Cortisol Prolactin Epinephrine NE Dopamine For correlations p < 0.001. For correlations
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(MS-7), 3 days before variables
(MS-3), concurrently
(MSOO), 3
MS-7
MS-3
MS00
MS+3
MS+7
CORT
PROL
EPI
NEPI
0.39 0.11 -0.14 - 0.24 - 0.00 0.06 -0.13 0.01 - 0.02
0.69 0.21 0.03 - 0.36 0.26 - 0.08 - 0.08 0.22
0.41 0.15 - 0.36 0.30 -0.12 -0.16 0.20
0.72 -0.51 0.01 0.04 0.04 0.44
- 0.25 0.05 0.03 - 0.04 0.27
0.09 -0.11 -0.14 - 0.28
0.11 -0.23 - 0.15
0.31 0.53
0.59
of mood states, cortisol involving
catecholamines,
and prolactin,
n = 51. If r > 0.27, then P < 0.05; if r > 0.35, then p < 0.01; if r > 0.44, then
n = 36. If r > 0.32, then P < 0.05; if r > 0.41, then P < 0.01; if r > 0.51, then P < 0.001.
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3.3. Multivariate prediction of mood from biologic variables
As the neuroendocrine and urinary catecholamine changes in this patient are peripheral measures, which may reflect an underlying central pathophysiologic process, we then examined using multiple regression analysis how combinations of biologic variables may be able to predict clinician rating of mood and mood state currently and in 3 days. As data for cortisol and prolactin were available for all 51 assessments but the urinary catecholamine data were only available on 36 assessments, an initial analysis was completed using just cortisol and prolactin and then a second analysis was completed adding the urinary catecholamines. Table 4 shows the results of multiple regression analyses in which cortisol and prolactin were used to predict the clinician assessment of mood (CLMOOD), current mood state (MSOO) and mood state in 3 days (MS + 3). The table shows that all three mood assessments were able to be moderately predicted from knowledge of cortisol and prolactin levels, with 20-26% of the variance in patient mood being predicted from these neuroendocrine measures. Cortisol levels are related to both current and future mood states and the negative regression coefficients indicate that higher cortisol levels predict a tendency for the
Table 4 Results of multiple regression analyses of cortisol and prolactin as predictors of mood states (CLMOOD is current mood with ratings from -3 to +2; MS00 and MS+3 are mood states concurrently and in 3 days on a scale of - 1 to +l)(df=2,48)
Multiple r r2 F P
Cortisol
t P
Prolactin
t P
CLMOOD
MS00
MS+3
0.45 0.20 6.07 0.004 - 2.50 0.016 2.64 0.011
0.49 0.24 7.50 0.001 -3.07 0.003 2.63 0.011
0.51 0.26 8.48 0.001 -4.12 0.000 0.49 0.624
Table 5 Results of multiple regression analyses of cortisol prolactin and urinary catecholamines as predictors of mood states (CLMOOD is current mood with ratings from -3 to + 2; MS00 and MS + 3 are mood state concurrently and in 3 days on a scale of - 1 to + 1.1 (df = 5, 30)
Multiple r r2 F P
Cortisol
t P
Prolactin
t P
Dopamine
t
NE
t
P P
Epinephrine
t P
CLMOOD
MS00
MS+3
0.71 0.50 5.96 0.001 - 1.94 0.062 2.68 0.012 3.18 0.003 - 3.343 0.002 - 2.42 0.022
0.66 0.44 4.74 0.003 - 2.84 0.033 2.84 0.008 2.94 0.006 - 1.84 0.075 - 2.47 0.020
0.73 0.54 7.02 0.000 -3.82 0.001 0.99 0.330 3.50 0.001 - 1.76 0.088 - 1.84 0.076
mood to become more depressed. Prolactin levels were associated with current but not future mood and the positive regression coefficients indicate that higher prolactin levels are predictive of a less depressed mood. In Table 5, the results of expanded multiple regression analyses are shown; in these analyses, the urinary catecholamines are added to the plasma hormones as predictors of current and future mood states. This table shows that, when all five biologic measures are considered simultaneously, that quite strong predictions of current and future mood states were possible. Indeed, these measures explain in the region of 50% of the variance in the patient’s mood. In general, the results in Table 5 suggest that: (1) Cortisol levels show a strong negative association with mood state in 3 days, a weaker association with current mood state and a nonsignificant association with current mood. This implies that higher cortisol levels are not related to severity of current depression but are strongly predictive of a future depressed mood state. (2) In agreement with the results of Table 4,
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higher prolactin levels are associated with a less depressed current mood. (3) In the multiple regression, urinary dopamine was positively associated with both current mood and mood states in 3 days, i.e., higher levels tend to be associated with current or future hypomania. In the bivariate correlations (Tables 1,2), dopamine was not significantly associated with current mood although it was with future mood state (Table 3). This apparent contradiction between the bivariate and multiple regression analyses may be explained as follows. In Table 3, it can be seen that dopamine levels are positively correlated with NE and epinephrine levels but these are negatively correlated with mood. The effect of this correlation between dopamine and NE or epinephrine is to mask the effects of dopamine on mood. When this correlation is taken into account in the multiple regression model, it becomes apparent that higher dopamine levels not only predict future mood state but also contribute to the prediction of current mood. (4) Both NE and epinephrine are associated with current mood but are only weakly predictive of future mood state. For NE, the association is considerably stronger when severity of current mood, as opposed to current mood state, is being predicted which suggests that higher levels of NE are associated with more severe levels of current depression. (5) The results of Tables 4 and 5 suggest that rising urinary dopamine and falling cortisol levels predict the onset of hypomania and that this is accompanied by a rise in urinary NE and epinephrine. Higher levels of NE and epinephrine are in turn associated weakly with a future depressed mood; the onset of depression will also be predicted by a rise in plasma cortisol levels. When the patient becomes depressed, cortisol levels will remain elevated, prolactin levels will decrease and higher urinary NE levels will predict a more severe current depression. Decreasing cortisol levels will then predict moving from a depressed to a euthymic state. As the patient moves into the euthymic phase, prolactin lev-
els will rise and the urinary epinephrine will decrease.
NE
and
4. Discussion In this one patient, who was assessed on > 50 occasions while on the same medication, it was possible to demonstrate that a number of biologic measures could predict her mood. The significance of these findings needs to be interpreted in the light of the strengths and weaknesses of the measures, the fact that she was on medication during the course of the study and that it was a single-case design. The following of a single patient over repeated episodes of mania and depression has the advantage of eliminating interindividual differences and, thus, may increase the likelihood of finding associations with mood change which could be obscured in group designs. It would be desirable to collect a series a singlecase studies but patients, such as the one described, are not readily available. She was suitable for this study because so many treatments had failed to control her mood swings and, thus, no reasonable treatment options were being withheld during the study period. Although the medication had not prevented her mood swings, they had been dampened by her medication and there was no need to introduce extra medication, such as neuroleptics during manic episodes for further symptom control. Apart from occasional patients, such as this one, another group of suitable patients could be those with cyclothymia and family histories of bipolar disorder; such patients could be studied drug-free. Although this patient was on three drugs throughout the course of this study, the dosages were held constant and she was very compliant with medication. It is difficult to see how constant dosages of medication could produce variations in biologic measures with mood. A limitation in all studies are the measures of mood state and the biologic variables. The patient’s long history, the long-term relationship with two of the authors (P.R. Joyce and R.M. Abbott), the fact that her mood swings were mild to moderate and the strong correlations between
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the subject’s self-report and the clinician assessment suggests that the assessment of her mood was not problematic. Even assessing mood changes between weekly visits was seldom difficult. The onset of depressive episodes was clearly dated by the patient and usually took the form of the patient waking early in a depressed state. The onset of hypomanic episodes between visits was usually easy to date based on a detailed review of the previous week during each weekly assessment. The decision to code mood state 3 days before and 3 days after each visit was to a degree arbitrary but was meant to be approximately half way between visits. In an earlier study (Joyce et al., 1987), we had noted increased cortisol levels 48 h before a change of mood into depression. There was insufficient data to examine in detail whether biologic variables best predicted mood state in 1, 2, 3,4 or 5 days as mood state over any short period of time will be highly correlated and a detailed examination of this issue would best be examined using a different research design. Thus, predictions of mood state in 3 days should be interpreted in the light of these considerations. No attempt was made to assess the severity of mood states between visits; thus, mood is just rated as depressed (- 11, euthymic (0) or manic (+ 1). A criticism could be that the mood swings in this patient were relatively mild during the course of the study; this is true but, if relevant, should have prevented biologic predictors of mood being found, rather than producing multivariate predictions of mood which explained > 50% of the variance in current or future mood state. Cortisol and prolactin have been widely used as peripheral neuroendocrine measures in affective disorder research although with neither is there sufficient understanding of the central and other mechanisms regulating their release. Urinary catecholamines are perhaps more problematic (Esler et al., 1990). NE levels in plasma and urine are correlated and probably reflect sympathetic nervous system activity. Epinephrine is predominantly released from the adrenal medulla, presumably in part by neural control. Least is known about dopamine but it appears that the majority of urinary dopamine has its origin in the
kidney and, thus, is perhaps the most problematic, and least used, catecholamine measure in psychiatric research. However, urinary catecholamines will continue to be utilized in psychiatric research until there are more direct ways of assessing brain function. Although there may be problems in interpreting the significance of variations in peripheral biologic variables, these measurement problems are likely to obscure rather than reveal actual biologic changes with mood. Within the methodological constraints of this study, there are a number of provocative findings which have emerged because of the single-case design and perhaps because of the nature of this patient’s mood cycles. In the first instance, the data show that prolactin levels were lower when the patient was depressed and cortisol levels were lower when the patient was manic. Neither of these findings is new. Lower prolactin levels in depression have been reported (Mendlewicz et al., 1980), especially as regards to bipolar depression (Joyce et al., 1988). Prolactin levels were significantly correlated with current mood state, regardless as to whether severity of mood state was considered and was not correlated with past or future mood state. This is consistent with a view that prolactin levels decreased when this patient became depressed. The findings as regards cortisol are of more interest in that current mood state appears not to be the explanation for altered cortisol levels with mood. In the initial analysis (Table l), mania was associated with lower cortisol levels; such a finding has previously been reported in longitudinal studies of cortisol with mood (Rubinow et al., 1984; Joyce et al., 1987) although cross-sectional studies of acutely admitted manics usually report elevated cortisol levels (e.g., Cookson, 1985; Christie et al., 1986). These findings suggest that severity, psychosis and/or dysphoria in mania are associated with elevated cortisol while less severe, euphoric mania may be associated with lower cortisol levels. When cortisol levels are correlated with mood state at varying times from sampling (Table 31, the strongest correlation is between cortisol levels and mood state in 3 days. In a multiple regression of cortisol levels as a function of mood state before, at and after blood sam-
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pling, current mood state does not explain cortisol levels once the impact of future mood state (in 3 days) is included in the regression. As expected, it is especially depression in 3 days which is predicted by the elevated cortisol levels. We have previously reported that cortisol levels increase in individual patients a few days before the onset of depression (Joyce et al., 1987) and that cortisol hypersecretion after recovery from depression with ECT treatment predicts early depressive relapse (Cosgriff et al., 1990). Others have also reported on DST nonsuppression being a predictor of depressive relapse (Carroll, 1982; Holsboer et al., 1983; Charles et al., 1989). These findings are strongly suggestive of the fact that increased cortisol secretion is, at least in part, not a correlate of current mood state but is a marker of an underlying neurotransmitter change which may be occurring before the clinical expression of a pathophysiologic change. In this single-case study, alternative explanations for the association of cortisol secretion with future mood state, such as adverse life events, appear implausible. The results with the urinary catecholamines are also of interest. At the first level of analysis when levels of catecholamines were examined by current mood state, there were no significant differences in levels by mood. However, when severity of mood state was incorporated into the analysis, then higher urinary NE levels were associated with a more depressed mood. This suggests that urinary NE reflects on severity of current depression. Of greater interest are the results with urinary dopamine which was that higher dopamine is predictive of a brighter mood state in 3 days time. We, thus, again have a finding which suggests that there are detectable biologic changes before changes in mood state, in this instance increased dopamine before the onset of hypomania. We are not aware of any prior report comparable to this although there remains the dilemma with urinary dopamine as to its physiologic significance. It is also of interest that, when cortisol, prolactin and the urinary catecholamines are related to the concurrent clinical rating of mood in a multiple regression (Table 51, that dopamine becomes a significant positive correlate and NE a negative correlate of mood, despite the
241
fact that at a bivariate level dopamine is not associated with current mood. Furthermore, urinary dopamine and NE are positively correlated, as has been reported by others (Linnoila et al., 1988), despite their opposite correlations with current mood severity. The urinary catecholamine findings are that urinary dopamine increases before the onset of mania, increased urinary NE is associated with a more severe current depressed state; in a multiple regression, dopamine and NE are both strongly associated with current mood severity (but in opposite directions) and urinary dopamine and NE are positively correlated. Collectively, these findings suggest the existence of what may be described as a ‘push/pull’ set of relationships between dopamine, NE and mood. On the one hand, rising dopamine was predictive of the onset of mania but, on the other hand, rising dopamine was associated with rising NE levels which in turn were associated with a tendency towards more severe depression. This configuration of results strongly hints at the presence of a regulatory mechanism by which the effects of one physiologic change (which is reflected by a rising dopamine), which was associated with mania, are in part, offset by concomitant increase in NE which correlated with increasing dysphoria. We propose that the rapid cycling bipolar disorder seen in this patient may be a reflection of malfunctions in the regulatory mechanism which result in changes in dopamine and noradrenaline ‘overcompensating’ for each other. Such a malfunction might have the effect of moving the patient towards mania (as dopamine levels increased) and then moving the patient towards depression as the compensating effects of increased NE exerted a dysphoric effect on the patient’s mood. The net result of such a process would be to produce cyclic changes in mood reflecting the changing balance of dopamine and NE levels on the patient. It is possible that it is not the direct effects of an interaction between dopamine and NE but the physiological mechanisms which are reflected by measurable changes in urinary catecholamines. While this account is clearly speculative, the presence of a positive correlation between two substances (dopamine
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and NE) which appear to have antagonistic associations with mood levels is thought provoking and suggests a possible mechanism which could explain the rapid cycling mood changes experienced by this patient. Although the dopamine-NE ‘overcompensating’ hypothesis of mood swings developed on the basis of this single case is speculative, it is an attractive model for explaining rapid cycling in bipolar patients. Even in the broader area of other mood disorders, it is becoming increasingly clear that transmitter systems do not work in isolation from one another and that it may be the interactions between such systems which predict treatment outcomes (Hsiao et al., 1987). To date, the major interactive hypothesis of affective disorders is the cholinergic-adrenergic imbalance hypothesis of Janowsky et al. (1972). A dopamine-adrenergic imbalance hypothesis would be more consistent with the evidence for the role of dopamine in mania, than the cholinergic-adrenergic imbalance. Furthermore, in this overcompensating model and consistent with the evidence that dopamine and NE are positively correlated, it is likely to be the relative balance of dopamine and NE rather than absolute levels which is associated with mood. However, there are some inconsistencies between the usual findings for urinary catecholamines in bipolar depression and that reported for this patient. In general, bipolar depression is associated with lower levels of urinary NE and of the dopamine metabolite homovanillic acid (HVA) while mania is associated with higher levels of CSF, plasma and urinary NE and MHPG and of HVA (Potter et al., 1987). Other studies, as well as the present study, usually report that urinary dopamine and NE are positively correlated (Linnoila et al., 1982;,1988). This suggests that in the usual crosssectional studies that in mania there exists increased dopaminergic and noradrenergic activities while depression is associated with lower levels of dopaminergic and noradrenergic activities. The discrepant finding in this study was the increased NE with severity of current depression. However, one of the differences between the patient reported in this study and the bipolar patients recruited for most cross-sectional studies
is that this patient has short duration depressions from which she improves spontaneously. Thus, it is possible that high urinary NE could be found in bipolar depression, if the patient has only just developed depression or if it is a depression from which they will spontaneously improve or in the depressions of rapid cycling patients. This hypothesis could be tested in future studies. Thus, in the broadest situation, we suggest that changes in dopaminergic and noradrenargic are linked, that a relative dopamine excess will precipitate mania but that compensatory mechanisms will increase noradrenergic function which will be associated with the move into depression, if the depression persists it will be associated with low levels of both dopaminergic and noradrenergic activities but with a balance towards NE. Finally, we would comment that the single subject design in this study may provide information that cannot be obtained from cross-sectional studies on groups of patients. This is because the biochemistry of mood disorders may be heterogeneous and interindividual differences may obscure intraindividual differences and because biochemical changes may not just be associated with mood state but may be associated with changes in mood state and/or with the longitudinal course of affective disorders (Post et al., 1986).
Acknowledgements
This study was funded by a grant from the Medical Research Council of New Zealand (to P.R. Joyce).
References Antelman, SM. and Caggiula, A.R. (1977) Norepinephrinedopamine interactions and behaviour. Science 195, 646653. Bio-Rad Laboratories (198.5) Urinary Free Catecholamines by HPLC. Cat. No. 195-6005. Bunney, W.E. and Davis, J.M. (1965) Norepinephrine in depressive reactions: a review. Arch. Gen. Psychiatry 13, 483-494. Bunney, E., Haretman, E.L. and Mason, J.W. (1965) Study of a patient with 4%hour manic-depressive cycles. Arch. Gen. Psychiatry 12, 619-625.
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Carroll, B.J. (1982) The dexamethasone suppression test for melancholia. Br. J. Psychiatry 140, 292-304. Charles, G.A., Schittecatte, M., Rush, A.J., Panzer, M. and Wilmotte, J. (1989) Persistent cortisol non-suppression after clinical recovery predicts symptomatic relapse in unipolar depression. J. Affect. Disord. 17, 271-278. Checkley, S.A. (1980) Neuroendocrine tests of monoamine function in man: a review of basic theory and its application to the study of depressive illness. Psycho]. Med. 10, 35-53. Christie, J.E., Whalley, L.J., Dick, H., Blackwood, D.H.R., Blackburn, I.M. and Fink, G. (1986) Raised plasma cortisol concentrations a feature of drug-free psychotics and not specific for depression. Br. J. Psychiatry 148, 58-65. Cookson, J.C. (1985) The neuroendocrinology of mania. J. Affect. Disord. 8, 233-241. Cosgriff, J.P., Abbott, R.M., Oakley-Browne, M.A. and Joyce, P.R. (1990) Cortisol hypersecretion predicts early depressive relapse after recovery with electroconvulsive therapy. Biol. Psychiatry 28, 1007-1010. Esler, M., Jennings, G., Lambert, G., Meredith, I., Horne, M. and Eisenhofer, G. (1990) Overflow of catecholamine neurotransmitters to the circulation: source, fate, and functions. Physiol. Rev. 70, 963-985. Goldstein, D.S., Feuerstein, G., Izzo, J.L., Kopin, I.J. and Keiser, H.R. (1981) Validity and reliability of liquid chromatography with electrochemical detection for measuring plasma levels of norepinephrine and epinephrine. Life Sci. 28, 467-475. Holsboer, F., Steiger, A. and Maier, W. (1983) Four cases of reversion to abnormal dexamethasone suppression test response as indicator of clinical relapse: a preliminary report. Biol. Psychiatry 18, 911-916. Hsiao, J.K., Agren, H., Bartko, J.J., Rudorfer, M.V., Linnoila, M. and Potter, W.Z. (1987) Monoamine neurotransmitter interactions and the prediction of antidepressant response. Arch. Gen. Psychiatry 44, 1078-1083. Janowsky, D.S., El-Yousef, M.K., Davis, J.M. and Sekerke, H.J. (1972) A cholinergic-adrenergic hypothesis of mania and depression. Lancet 2, 632-635. Jones, R.D., Maas, J.W., Dekirmenjian, H. and Fawcett, J.A. (1973) Urinary catecholamine metabolites during behavioural changes in a patient with manic-depressive cycles. Science 179, 300-302. Joyce, P.R. (1985) Neuroendocrine changes in depression. Aust. N.Z. J. Psychiatry 19, 120-127. Joyce, P.R. and Paykel, E.S. (1989) Predictors of drug response in depression. Arch. Gen. Psychiatry 46, 89-99. Joyce, P.R., Donald, R.A. and Elder, P.A. (1987) Individual differences in plasma cortisol changes during mania and depression. J. Affect. Disord. 12, l-5. Joyce, P.R., Sellman, J.D., Donald, R.A., Livesey, J.H. and Elder, P.A. (1988) The unipolar-bipolar depressive dichotomy and the relationship between afternoon prolactin and cortisol levels. J. Affect. Disord. 14, 189-193. Kelwala, S., Jones, D. and Sitaram, N. (1983) Monoamine metabolites as predictors of antidepressant response: a
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critque. Prog. Neuropsychopharmacol. Biol. Psychiatry 7, 229-240. Lewis, J.G. and Elder, P.A. (1985) An enzyme-linked immunosorbent assay (ELISAl for plasma cortisol. J. Steroid Biochem. 22, 673-676. Linnoila, M., Karoum, F. and Potter, W.Z. (1982) High correlation of norepinephrine and its major metabolite excretion rates. Arch. Gen. Psychiatry 39, 521-523. Linnoila, M., Oliver, J., Adinoff, B. and Potter, W.Z. (1988) High correlations of norepinephrine, dopamine, and epinephrine and their major metabolite excretion rates. Arch. Gen. Psychiatry 45, 701-704. Lista, A.L. (1989) Differential rates of urinary noradrenalin excretion in affective disorders: utility of a short time sampling procedure. Psychiatry Res. 30, 253-258. Livesey, J.H. and Donald, R.A. (1981) Prevention of absorption losses during radioimmunoassay of polypeptide hormones. Effectiveness of albumins, gelatins, caesins, Tween 20 and plasma. Clin. Chim. Acta 123, 193-198. Maas, J.W. (1978) Clinical and biochemical heterogeneity of depressive disorders. Ann. Intern. Med. 88, 556-563. Maas, J.W., Koslow, S.H., Davis, J., Katz, M., Frazer, A., Bowden, C.L., Berman N., Gibbons, R.. Stokes, P. and Landis, D.H. (1987) Catecholamine metabolism and disposition in healthy and depressed subjects. Arch. Gen. Psychiatry 44, 337-344. Mendlewicz, J., Van Cauter, E., Linkowski, F., L’Hermite, M. and Robyn, C. (1980) The 24 hour profile of prolactin in depression. Life Sci. 27, 2015-2024. Post, R.M. (1980) Biochemical theories of mania. In R.H. Belmaker and H.M. van Praag (Eds.), Mania: An Evolving Concept. MTP, Lancaster, UK, pp. 217-265. Post, R.M., Rubinow, D.R. and Ballenger, J.C. (1986) Conditioning and sensitisation in the longitudinal course of affective illness. Br. J. Psychiatry 149, 191-201. Potter, W.Z., Rudorfer, M.V. and Goodwin, F.K. (1987) Biological findings in bipolar disorders. Am. Psychiatric Assoc. Annu. Rev. 6, 32-60. Rubinow, D.R., Post, R.M., Gold, P.W., Ballenger, J.C. and Wolff, E.A. (1984) The relationship between cortisol and clinical phenomenology of affective disorders. In R.M. Post and J.C. Ballenger (Eds.), Neurobiology of Mood Disorders. Williams and Wilkins, Baltimore, MD. pp. 271289. Schatzberg, A.F., Samson, J.A., Bloomingdale, K.L., Orsulak, P.J., Gerson, B., Kizuka, P.P., Cole, J.O. and Schildkraut, J.J. (1989) Towards a biochemical classification of depressive disorders. X Urinary catecholamines, their metabolites, and D-type scores in subgroups of depressive disorders. Arch. Gen. Psychiatry 46, 260-268. Schildkraut, J.J. (1965) The catecholamine hypothesis of affective disorders: a review of supporting evidence. Am. J. Psychiatry 122, 509-522. Silverstone, J.T. and Cookson, J. (1982) The biology of mania. In K. Granville-Grossman (Ed.), Recent Advances in Clinical Psychiatry, No 4. Churchill Livingstone, New York, NY, pp. 201-241.
ELSEVIER
OF
AFFECTIVE DISORDERS
Journal of Affective Disorders 33 (1995) 233-243
Urinary catecholamines and plasma hormones predict mood state in rapid cycling bipolar affective disorder Peter R. Joyce a,d,*, David M. Fergusson a, Gerald Woollard b, Robyn M. Abbott d, L. John Honvood a, Jeff Upton ’ a University Department of Psychological Medicine, Christchurch School of Medicine, Christchurch, New Zealand b Department of Clinical Chemistry, Auckland Hospital, Auckland, New Zealand ’ Department of Clinical Biochemistry, Christchurch Hospital, Christchurch, New Zealand d Clinical Research Unit, Sunnyside Hospital, Sunnyside, New Zealand
Received 24 January 1994; revised 24 October
1994; accepted
26 October
1994
Abstract Over the course of 1 year, a patient with a rapid cycling bipolar affective disorder was followed at weekly intervals to examine whether plasma hormones and urinary catecholamines could predict current or future mood. Higher cortisol levels were found to predict depressed mood 3 days after blood sampling, higher urinary dopamine predicted
a manic mood 3 days after blood sampling, urinary norepinephrine was associated with severity of current mood and prolactin was lower with concurrent depressed mood. In multivariate analyses of mood against cortisol, prolactin and three urinary catecholamines, > 50% of the variance in mood state in 3 days was explained by combinations of these biologic measures, especially cortisol and urinary dopamine, while all five biologic variables contributed to explaining 50% of the variance in current mood state. Based on the interrelationships between urinary dopamine, norepinephrine and mood, we postulate the existence of an overcompensating mechanism which is reflected in opposing correlations between urinary dopamine and norepinephrine with mood, despite the two urinary catecholamines being positively correlated.
NE (Bunney
1. Introduction
and Davis,
1965; Schildkraut,
1965).
Over the past 25 years, there has been increasing The early amine hypothesis of affective disorders suggested that depression was associated with a deficiency of norepinephrine (NE) and that mania may be associated with an excess of
* Corresponding author. University Department of Psychological Medicine, Christchurch School of Medicine, PO Box 4345, Christchurch, New Zealand. 0165-0327/95/$09.50 0 1995 Elsevier SSDI 0165-0327(94)00094-8
Science
research into the biochemical correlates of affective disorders. Although a variety of other neurotransmitters and neuropeptides, such as serotonin, acetylcholine and y-amino butyric acid, have been implicated in the pathophysiology of affective disorders, NE remains a key transmitter in depression and dopamine may be of importance in bipolar affective disorder (Silverstone and Cookson, 1982; Post, 1980). Attempts to confirm biochemical theories of
B.V. All rights reserved
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affective disorders have had to face many methodological issues. One major issue has been the feasibility of assessing changes in CNS transmitter systems by studying cerebrospinal, plasma and/or urinary levels of catecholamines and their metabolites (Potter et al., 1987). Many studies have focused on the NE metabolite of 3-methoxy4-hydroxy-phenylglycol (MHPG) and examined whether subtypes of depression and response to treatment can be predicted on the basis of urinary MHPG levels (Maas, 1978; Kelwala et al., 1983; Joyce and Paykel, 1989). A recent review of cross-sectional studies on groups of depressed patients concluded that bipolar depressed patients have lower urinary MHPG and NE than controls and unipolar depressed patients (Potter et al., 1987). Furthermore, in unipolar melancholic patients, increasing levels of urinary and plasma catecholamines are correlated with indices of cortisol hypersecretion although this correlation does not apply in bipolar patients (Joyce et al., 1987). Recent studies are increasingly measuring more than one catecholamine or metabolite and it has been found that, from combinations of measures, it may be possible to separate unipolar and bipolar depressed patients (Maas et al., 1987; Schatzberg et al., 1989). A complementary approach to finding experimental confirmation of the amine hypothesis of affective disorders has been to utilize neuroendocrine measures, on the assumption that brain neurotransmitters control neuroendocrine secretion, and that changes in measurable endocrine output reflect changes in brain amines (Checkley, 1980). Many neuroendocrine abnormalities have now been reported in depressed patients, with the most consistent finding being the cortisol hypersecretion shown by many patients (Joyce, 1985). A number of studies have suggested that changes in the hypothalamic pituitary adrenal axis may occur before a change in mood state (Carroll, 1982; Holsboer et al., 1983) and, in an earlier study, we produced evidence that mean plasma cortisol levels could increase 2-3 days before a patient entered a depressed state (Joyce et al., 1987). Neuroendocrine findings in mania have been fewer and are less consistent (Cookson, 1985).
Presumably, many of these documented biologic changes are not completely independent of one another but are reflections of an underlying pathophysiological process. Although many early studies tended to concentrate on one neurotransmitter or biologic variable, there is increasing evidence that neurotransmitters do not work in isolation and that changes in one transmitter may lead to changes in another transmitter system L4ntelman and Caggiula, 1977). While the majority of studies on biologic correlates of mood have utilized groups of subjects who are compared in a static cross-sectional manner, there are some studies utilizing individual patients who have been followed through prospective mood cycles (Jones et al., 1973; Bunney et al., 1965). The advantages of the single-case design are that it can potentially allow for the examination of how biologic variables may change with mood state and, by eliminating interindividual variability, may detect biologic changes with mood, even if mood disorders in general, are biologically heterogeneous. We, thus, set out to examine in detail a single subject with a rapid cycling bipolar affective disorder during repeated episodes of mania and depression. During these recurrent episodes, it was planned to measure urinary NE, epinephrine and dopamine, and the plasma hormones cortisol and prolactin which may, in part, be under dopaminergic and adrenergic control. From the combination of measures, it was planned to examine whether any of these variables could be used to predict the patient’s mood state. Furthermore, and of greatest potential interest, was whether the patient’s future mood state could be predicted from combinations of these biologic measures.
2. Methods 2.1. Patient
Mrs. X is a had been under > 6 years. Her age of 17 when
47-year-old divorced woman who the clinical care of P.R. Joyce for psychiatric history began at the she was hospitalized for a major
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depressive episode. After recovery from this episode, she remained well until the birth of her second child when she was 27 years old. From the birth of this child to the time of commencement of this study, she had never had a prolonged period of euthymia, had been hospitalized for her bipolar disorder on > 30 occasions and in the 5 years before the commencement of this study, while under the care of P.R. Joyce, had ‘cycled’ from mania to depression and back with only brief spells of euthymia which were never > 1 month in duration. Over the years, she had been tried on a variety of treatments. In recent years, carbamazepine had been added to her lithium carbonate; this decreased the severity of her mood swings but had not decreased the frequency of her affective episodes or changed her cycle. = 6 months before the commencement of this study, sodium valproate had been added to her lithium-carbamazepine combination; this had resulted in a marked shortening of her depressive episodes from typically > 4 to l-2 weeks and had decreased further the severity of the mood changes. Over the course of this l-year study, she was treated as an outpatient and, for the first time in 20 years, was never hospitalized throughout the course of a year. During this time, she was maintained on lithium carbonate 750 mg at night, carbamazepine 400 mg morning and night and sodium valproate 500 mg morning and night. At earlier stages, she had been tried on higher doses of all these drugs but side-effects and/or no further therapeutic benefits had been obtained during these trials of treatment. Towards the end of this study, her sodium valproate was increased to 1500 mg daily and, on this second attempt, at a higher dosage, she did not develop side-effects and she has so far remained totally euthymic for 3 years for the first time in > 20 years. Her remaining well brought the study to an end. 2.2. Assessment of mood Mrs. X was seen on 51 occasions over the course of 1 year. On each occasion, she would arrive for her outpatient appointment at 13:00
235
and she would remain for = 2 h. On the basis of interview and observation made at each visit, Mrs. X’s mood was rated by the same research nurse (R.M. Abbott) on a 7-point scale for mania and a 7-point scale for depression which were based on the global clinical evaluation. As she never experienced a mixed mood state, these two scales were subsequently collapsed into a single mood scale which potentially ranged from + 6 for extreme mania, to 0 for euthymia, to - 6 for extreme depression. During these 51 weekly assessments, her mood was rated as euthymic on 25 occasions as depressed on 14 occasions and as manic on 12 occasions. This represented 10 episodes of mania and nine episodes of depression. During the year, the longest sequences of similar moods on consecutive visits was 3 weeks; this happened on three occasions, twice when she was well for 3 weeks and once when she was depressed for 3 weeks. Although it had been possible to rate her mood from +6 to -6, the actual range was from + 2 to -3. The limited range of ratings are reflected in the fact that she did not require hospitalization during this year and that her medication decreased the severity of her mood swings. After the research nurse had rated the patient’s current mood state, she retrospectively rated her mood for each day of the past week using a global rating of depressed, euthymic or manic. This seldom presented any difficulties in that the onset of depressive episodes in this patient was always clear-cut and could easily be dated and the onset of manic episodes was usually clear from changes in activities, energy and mood. As biologic assays were not completed until the end of the study, the research nurse was always blind to the results of the biologic assessment. During each visit, the patient rated herself on four bipolar visual analogue scales which rated her mood (very sad to very happy), energy (lacking energy to full of energy), nervousness (very relaxed to tense/on edge) and confidence (feels good about self to feels bad about self). The clinician rating and the patient self-ratings were well-correlated (0.63-0.72, P < 0.001). In subsequent analyses, only the clinician rating (CLMOOD) was used.
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2.3. Biologic assessment
After arrival at the research clinic, Mrs. X would rest quietly on a bed and an indwelling i.v. catheter was inserted into an antecubital vein from which blood samples would be drawn at intervals during her visit. From an initial sample, we obtained a plasma lithium level, plasma electrolytes and a full blood count. Three resting samples would be drawn at 20-min intervals for measurement of mean afternoon cortisol and prolactin levels. During this time, the patient would also collect a resting l-h urine sample for urinary catecholamines. Lista (1989) has provided data to support the utility of short time urine sampling, provided the patient is at rest.
3 ml/min), a WISP 720 autosampler, a M460 electrochemical detector at 0.6 V and a 840 system controller. The column was a 100 x 8-mm Waters Novapak Cl8 cartridge, 4-pm particle size, radially compressed in a RCM 8 X 10 compression module. The mobile phase consisted of 6.8 g sodium acetate (adjusted to pH 4.81, 1 g sodium octylsulphonate and 70 ml acetonitrile/l. Results for urinary catecholamines are expressed as ~mol/mmol creatinine. 2.5. Statistical analysis Data from the patient, including the clinical and biologic variables, were entered and statistical analyses were carried out using SYSTAT.
2.4. Biologic assays 3. Results Plasma cortisol (nmol/l) was measured by ELISA (Lewis and Elder, 1985) and prolactin (mIU/l) by RIA (Livesey and Donald, 1981) (interassay coefficient of variation of < 11 and 13%, respectively). Urinary catecholamines were measured using high-performance liquid chromatography with electrochemical detection. Samples were prepared using cationic exchange on Biorex 70 following the method described by Bio-Rad Laboratories (1985) with minor modification to the ammonium borate elution procedure. The chromatographic separation was similar to that of Goldstein et al. (1981). The instrument used was a Waters Associates (Milford, MA) chromatographic system consisting of a 510 pump (flowrate
3.1. Associations of biologic variables with current mood From Table 1, which shows the levels of cortisol (nmol/l), prolactin (mIU/l), urinary epinephrine, NE and dopamine, depending upon whether the patient was clinically rated as depressed, euthymic or manic on the day that the samples were drawn, it can be seen that during depression this patient had lower prolactin levels. Cortisol levels vary with current mood state and, while there is a trend for cortisol levels to be higher in depression, this did not reach significance on posthoc tests although it was significant that cortisol levels are lower in mania.
Table 1 Levels of hormones and urinary catecholamines during different mood states within this patient Depression Cortisol Prolactin
247 153
(49) (31)
Euthymia n = 25 230 199
Epinephrine NE Dopamine
0.007 0.016 0.088
(0.003) (0.012) (0.087)
0.006 0.009 0.089
n = 14
P
(47) (59)
Mania n = 12 198 19.5
(41) (51)
(0.004) (0.006) (0.077)
0.006 0.012 0.146
(0.006) (0.009) (0.139)
Results are expressed as a mean f SD. ’ F = 3.74; df = 2, 48; P = 0.031. ’ F = 3.93; df = 2, 48; p = 0.026.
I 2
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Table 2 Pearson correlations between biologic variables and clinician rating of mood (CLMOOD) (range: - 3 to + 2), and concurrent mood state (MSOO) (range: - 1 to + 1)
Cortisol Prolactin Epinephrine NE Dopamine
CLMOOD
MS00
-0.29 * 0.31 * -0.14 -0.37 * 0.09
-0.36 ** 0.30 * -0.12 -0.16 0.20
lactin and that severity of current depression does not explain increases of cortisol. 3.2. Biologic variables as predictors of past, current and future moods
* P < 0.05. * * P < 0.01
Table 2 presents the Pearson correlations of each biologic variable with the clinician assessment of mood (CLMOOD; range: -3 to + 2) and with current mood state (MSOO; range: - 1 to + 1). Inspection of this table shows that cortisol and NE are significantly negatively correlated with mood (i.e., higher levels in depression) and that prolactin is positively correlated with mood (i.e., lower levels in depression). For NE and prolactin but not cortisol, the correlations are less with MS00 than when severity of current mood state is included; indeed, for NE, the correlation failed to reach a P < 0.05 level of significance when the mood is just summarized as manic (+ 11, euthymic (0) or depressed (-- 1). This suggests that severity of current depression contributes to the association between NE and mood, is perhaps of little significance as regards pro-
To examine the question as to whether the biologic variables best predicted past, current or future mood states, each biologic variable was correlated with mood state at -7, -3, 0, +3 and +7 days. Inspection of Table 3 shows that none of the biologic variables predicted mood state either a week before or a week later. The strongest correlations which were found were between cortisol and dopamine and mood state in 3 days time. The only significant correlate of cortisol was a negative one with urinary dopamine (r = - 0.28, P < 0.05). However, increasing cortisol predicts depression and increasing dopamine predicts mania; thus, it is possible that the negative association reflects the opposing relationships to mood state in 3 days. Prolactin levels were not significantly correlated with any other biologic variable. The urinary catecholamines were all interrelated as dopamine correlated with NE (r = 0.59, P < O.OOl),with epinephrine (r = 0.53, P < 0.001) while the correlation of NE and epinephrine approached significance (r = 0.31).
Table 3 Pearson correlation matrix of mood states (7 days before biologic assessment days after assessment (MS + 3) and 7 days following (MS + 7)) and biologic
MS-3 MS00 MS+3 MS+7 Cortisol Prolactin Epinephrine NE Dopamine For correlations p < 0.001. For correlations
237
(MS-7), 3 days before variables
(MS-3), concurrently
(MSOO), 3
MS-7
MS-3
MS00
MS+3
MS+7
CORT
PROL
EPI
NEPI
0.39 0.11 -0.14 - 0.24 - 0.00 0.06 -0.13 0.01 - 0.02
0.69 0.21 0.03 - 0.36 0.26 - 0.08 - 0.08 0.22
0.41 0.15 - 0.36 0.30 -0.12 -0.16 0.20
0.72 -0.51 0.01 0.04 0.04 0.44
- 0.25 0.05 0.03 - 0.04 0.27
0.09 -0.11 -0.14 - 0.28
0.11 -0.23 - 0.15
0.31 0.53
0.59
of mood states, cortisol involving
catecholamines,
and prolactin,
n = 51. If r > 0.27, then P < 0.05; if r > 0.35, then p < 0.01; if r > 0.44, then
n = 36. If r > 0.32, then P < 0.05; if r > 0.41, then P < 0.01; if r > 0.51, then P < 0.001.
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ofAffective Disorders 33 (1995) 233-243
3.3. Multivariate prediction of mood from biologic variables
As the neuroendocrine and urinary catecholamine changes in this patient are peripheral measures, which may reflect an underlying central pathophysiologic process, we then examined using multiple regression analysis how combinations of biologic variables may be able to predict clinician rating of mood and mood state currently and in 3 days. As data for cortisol and prolactin were available for all 51 assessments but the urinary catecholamine data were only available on 36 assessments, an initial analysis was completed using just cortisol and prolactin and then a second analysis was completed adding the urinary catecholamines. Table 4 shows the results of multiple regression analyses in which cortisol and prolactin were used to predict the clinician assessment of mood (CLMOOD), current mood state (MSOO) and mood state in 3 days (MS + 3). The table shows that all three mood assessments were able to be moderately predicted from knowledge of cortisol and prolactin levels, with 20-26% of the variance in patient mood being predicted from these neuroendocrine measures. Cortisol levels are related to both current and future mood states and the negative regression coefficients indicate that higher cortisol levels predict a tendency for the
Table 4 Results of multiple regression analyses of cortisol and prolactin as predictors of mood states (CLMOOD is current mood with ratings from -3 to +2; MS00 and MS+3 are mood states concurrently and in 3 days on a scale of - 1 to +l)(df=2,48)
Multiple r r2 F P
Cortisol
t P
Prolactin
t P
CLMOOD
MS00
MS+3
0.45 0.20 6.07 0.004 - 2.50 0.016 2.64 0.011
0.49 0.24 7.50 0.001 -3.07 0.003 2.63 0.011
0.51 0.26 8.48 0.001 -4.12 0.000 0.49 0.624
Table 5 Results of multiple regression analyses of cortisol prolactin and urinary catecholamines as predictors of mood states (CLMOOD is current mood with ratings from -3 to + 2; MS00 and MS + 3 are mood state concurrently and in 3 days on a scale of - 1 to + 1.1 (df = 5, 30)
Multiple r r2 F P
Cortisol
t P
Prolactin
t P
Dopamine
t
NE
t
P P
Epinephrine
t P
CLMOOD
MS00
MS+3
0.71 0.50 5.96 0.001 - 1.94 0.062 2.68 0.012 3.18 0.003 - 3.343 0.002 - 2.42 0.022
0.66 0.44 4.74 0.003 - 2.84 0.033 2.84 0.008 2.94 0.006 - 1.84 0.075 - 2.47 0.020
0.73 0.54 7.02 0.000 -3.82 0.001 0.99 0.330 3.50 0.001 - 1.76 0.088 - 1.84 0.076
mood to become more depressed. Prolactin levels were associated with current but not future mood and the positive regression coefficients indicate that higher prolactin levels are predictive of a less depressed mood. In Table 5, the results of expanded multiple regression analyses are shown; in these analyses, the urinary catecholamines are added to the plasma hormones as predictors of current and future mood states. This table shows that, when all five biologic measures are considered simultaneously, that quite strong predictions of current and future mood states were possible. Indeed, these measures explain in the region of 50% of the variance in the patient’s mood. In general, the results in Table 5 suggest that: (1) Cortisol levels show a strong negative association with mood state in 3 days, a weaker association with current mood state and a nonsignificant association with current mood. This implies that higher cortisol levels are not related to severity of current depression but are strongly predictive of a future depressed mood state. (2) In agreement with the results of Table 4,
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higher prolactin levels are associated with a less depressed current mood. (3) In the multiple regression, urinary dopamine was positively associated with both current mood and mood states in 3 days, i.e., higher levels tend to be associated with current or future hypomania. In the bivariate correlations (Tables 1,2), dopamine was not significantly associated with current mood although it was with future mood state (Table 3). This apparent contradiction between the bivariate and multiple regression analyses may be explained as follows. In Table 3, it can be seen that dopamine levels are positively correlated with NE and epinephrine levels but these are negatively correlated with mood. The effect of this correlation between dopamine and NE or epinephrine is to mask the effects of dopamine on mood. When this correlation is taken into account in the multiple regression model, it becomes apparent that higher dopamine levels not only predict future mood state but also contribute to the prediction of current mood. (4) Both NE and epinephrine are associated with current mood but are only weakly predictive of future mood state. For NE, the association is considerably stronger when severity of current mood, as opposed to current mood state, is being predicted which suggests that higher levels of NE are associated with more severe levels of current depression. (5) The results of Tables 4 and 5 suggest that rising urinary dopamine and falling cortisol levels predict the onset of hypomania and that this is accompanied by a rise in urinary NE and epinephrine. Higher levels of NE and epinephrine are in turn associated weakly with a future depressed mood; the onset of depression will also be predicted by a rise in plasma cortisol levels. When the patient becomes depressed, cortisol levels will remain elevated, prolactin levels will decrease and higher urinary NE levels will predict a more severe current depression. Decreasing cortisol levels will then predict moving from a depressed to a euthymic state. As the patient moves into the euthymic phase, prolactin lev-
els will rise and the urinary epinephrine will decrease.
NE
and
4. Discussion In this one patient, who was assessed on > 50 occasions while on the same medication, it was possible to demonstrate that a number of biologic measures could predict her mood. The significance of these findings needs to be interpreted in the light of the strengths and weaknesses of the measures, the fact that she was on medication during the course of the study and that it was a single-case design. The following of a single patient over repeated episodes of mania and depression has the advantage of eliminating interindividual differences and, thus, may increase the likelihood of finding associations with mood change which could be obscured in group designs. It would be desirable to collect a series a singlecase studies but patients, such as the one described, are not readily available. She was suitable for this study because so many treatments had failed to control her mood swings and, thus, no reasonable treatment options were being withheld during the study period. Although the medication had not prevented her mood swings, they had been dampened by her medication and there was no need to introduce extra medication, such as neuroleptics during manic episodes for further symptom control. Apart from occasional patients, such as this one, another group of suitable patients could be those with cyclothymia and family histories of bipolar disorder; such patients could be studied drug-free. Although this patient was on three drugs throughout the course of this study, the dosages were held constant and she was very compliant with medication. It is difficult to see how constant dosages of medication could produce variations in biologic measures with mood. A limitation in all studies are the measures of mood state and the biologic variables. The patient’s long history, the long-term relationship with two of the authors (P.R. Joyce and R.M. Abbott), the fact that her mood swings were mild to moderate and the strong correlations between
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the subject’s self-report and the clinician assessment suggests that the assessment of her mood was not problematic. Even assessing mood changes between weekly visits was seldom difficult. The onset of depressive episodes was clearly dated by the patient and usually took the form of the patient waking early in a depressed state. The onset of hypomanic episodes between visits was usually easy to date based on a detailed review of the previous week during each weekly assessment. The decision to code mood state 3 days before and 3 days after each visit was to a degree arbitrary but was meant to be approximately half way between visits. In an earlier study (Joyce et al., 1987), we had noted increased cortisol levels 48 h before a change of mood into depression. There was insufficient data to examine in detail whether biologic variables best predicted mood state in 1, 2, 3,4 or 5 days as mood state over any short period of time will be highly correlated and a detailed examination of this issue would best be examined using a different research design. Thus, predictions of mood state in 3 days should be interpreted in the light of these considerations. No attempt was made to assess the severity of mood states between visits; thus, mood is just rated as depressed (- 11, euthymic (0) or manic (+ 1). A criticism could be that the mood swings in this patient were relatively mild during the course of the study; this is true but, if relevant, should have prevented biologic predictors of mood being found, rather than producing multivariate predictions of mood which explained > 50% of the variance in current or future mood state. Cortisol and prolactin have been widely used as peripheral neuroendocrine measures in affective disorder research although with neither is there sufficient understanding of the central and other mechanisms regulating their release. Urinary catecholamines are perhaps more problematic (Esler et al., 1990). NE levels in plasma and urine are correlated and probably reflect sympathetic nervous system activity. Epinephrine is predominantly released from the adrenal medulla, presumably in part by neural control. Least is known about dopamine but it appears that the majority of urinary dopamine has its origin in the
kidney and, thus, is perhaps the most problematic, and least used, catecholamine measure in psychiatric research. However, urinary catecholamines will continue to be utilized in psychiatric research until there are more direct ways of assessing brain function. Although there may be problems in interpreting the significance of variations in peripheral biologic variables, these measurement problems are likely to obscure rather than reveal actual biologic changes with mood. Within the methodological constraints of this study, there are a number of provocative findings which have emerged because of the single-case design and perhaps because of the nature of this patient’s mood cycles. In the first instance, the data show that prolactin levels were lower when the patient was depressed and cortisol levels were lower when the patient was manic. Neither of these findings is new. Lower prolactin levels in depression have been reported (Mendlewicz et al., 1980), especially as regards to bipolar depression (Joyce et al., 1988). Prolactin levels were significantly correlated with current mood state, regardless as to whether severity of mood state was considered and was not correlated with past or future mood state. This is consistent with a view that prolactin levels decreased when this patient became depressed. The findings as regards cortisol are of more interest in that current mood state appears not to be the explanation for altered cortisol levels with mood. In the initial analysis (Table l), mania was associated with lower cortisol levels; such a finding has previously been reported in longitudinal studies of cortisol with mood (Rubinow et al., 1984; Joyce et al., 1987) although cross-sectional studies of acutely admitted manics usually report elevated cortisol levels (e.g., Cookson, 1985; Christie et al., 1986). These findings suggest that severity, psychosis and/or dysphoria in mania are associated with elevated cortisol while less severe, euphoric mania may be associated with lower cortisol levels. When cortisol levels are correlated with mood state at varying times from sampling (Table 31, the strongest correlation is between cortisol levels and mood state in 3 days. In a multiple regression of cortisol levels as a function of mood state before, at and after blood sam-
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pling, current mood state does not explain cortisol levels once the impact of future mood state (in 3 days) is included in the regression. As expected, it is especially depression in 3 days which is predicted by the elevated cortisol levels. We have previously reported that cortisol levels increase in individual patients a few days before the onset of depression (Joyce et al., 1987) and that cortisol hypersecretion after recovery from depression with ECT treatment predicts early depressive relapse (Cosgriff et al., 1990). Others have also reported on DST nonsuppression being a predictor of depressive relapse (Carroll, 1982; Holsboer et al., 1983; Charles et al., 1989). These findings are strongly suggestive of the fact that increased cortisol secretion is, at least in part, not a correlate of current mood state but is a marker of an underlying neurotransmitter change which may be occurring before the clinical expression of a pathophysiologic change. In this single-case study, alternative explanations for the association of cortisol secretion with future mood state, such as adverse life events, appear implausible. The results with the urinary catecholamines are also of interest. At the first level of analysis when levels of catecholamines were examined by current mood state, there were no significant differences in levels by mood. However, when severity of mood state was incorporated into the analysis, then higher urinary NE levels were associated with a more depressed mood. This suggests that urinary NE reflects on severity of current depression. Of greater interest are the results with urinary dopamine which was that higher dopamine is predictive of a brighter mood state in 3 days time. We, thus, again have a finding which suggests that there are detectable biologic changes before changes in mood state, in this instance increased dopamine before the onset of hypomania. We are not aware of any prior report comparable to this although there remains the dilemma with urinary dopamine as to its physiologic significance. It is also of interest that, when cortisol, prolactin and the urinary catecholamines are related to the concurrent clinical rating of mood in a multiple regression (Table 51, that dopamine becomes a significant positive correlate and NE a negative correlate of mood, despite the
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fact that at a bivariate level dopamine is not associated with current mood. Furthermore, urinary dopamine and NE are positively correlated, as has been reported by others (Linnoila et al., 1988), despite their opposite correlations with current mood severity. The urinary catecholamine findings are that urinary dopamine increases before the onset of mania, increased urinary NE is associated with a more severe current depressed state; in a multiple regression, dopamine and NE are both strongly associated with current mood severity (but in opposite directions) and urinary dopamine and NE are positively correlated. Collectively, these findings suggest the existence of what may be described as a ‘push/pull’ set of relationships between dopamine, NE and mood. On the one hand, rising dopamine was predictive of the onset of mania but, on the other hand, rising dopamine was associated with rising NE levels which in turn were associated with a tendency towards more severe depression. This configuration of results strongly hints at the presence of a regulatory mechanism by which the effects of one physiologic change (which is reflected by a rising dopamine), which was associated with mania, are in part, offset by concomitant increase in NE which correlated with increasing dysphoria. We propose that the rapid cycling bipolar disorder seen in this patient may be a reflection of malfunctions in the regulatory mechanism which result in changes in dopamine and noradrenaline ‘overcompensating’ for each other. Such a malfunction might have the effect of moving the patient towards mania (as dopamine levels increased) and then moving the patient towards depression as the compensating effects of increased NE exerted a dysphoric effect on the patient’s mood. The net result of such a process would be to produce cyclic changes in mood reflecting the changing balance of dopamine and NE levels on the patient. It is possible that it is not the direct effects of an interaction between dopamine and NE but the physiological mechanisms which are reflected by measurable changes in urinary catecholamines. While this account is clearly speculative, the presence of a positive correlation between two substances (dopamine
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and NE) which appear to have antagonistic associations with mood levels is thought provoking and suggests a possible mechanism which could explain the rapid cycling mood changes experienced by this patient. Although the dopamine-NE ‘overcompensating’ hypothesis of mood swings developed on the basis of this single case is speculative, it is an attractive model for explaining rapid cycling in bipolar patients. Even in the broader area of other mood disorders, it is becoming increasingly clear that transmitter systems do not work in isolation from one another and that it may be the interactions between such systems which predict treatment outcomes (Hsiao et al., 1987). To date, the major interactive hypothesis of affective disorders is the cholinergic-adrenergic imbalance hypothesis of Janowsky et al. (1972). A dopamine-adrenergic imbalance hypothesis would be more consistent with the evidence for the role of dopamine in mania, than the cholinergic-adrenergic imbalance. Furthermore, in this overcompensating model and consistent with the evidence that dopamine and NE are positively correlated, it is likely to be the relative balance of dopamine and NE rather than absolute levels which is associated with mood. However, there are some inconsistencies between the usual findings for urinary catecholamines in bipolar depression and that reported for this patient. In general, bipolar depression is associated with lower levels of urinary NE and of the dopamine metabolite homovanillic acid (HVA) while mania is associated with higher levels of CSF, plasma and urinary NE and MHPG and of HVA (Potter et al., 1987). Other studies, as well as the present study, usually report that urinary dopamine and NE are positively correlated (Linnoila et al., 1982;,1988). This suggests that in the usual crosssectional studies that in mania there exists increased dopaminergic and noradrenergic activities while depression is associated with lower levels of dopaminergic and noradrenergic activities. The discrepant finding in this study was the increased NE with severity of current depression. However, one of the differences between the patient reported in this study and the bipolar patients recruited for most cross-sectional studies
is that this patient has short duration depressions from which she improves spontaneously. Thus, it is possible that high urinary NE could be found in bipolar depression, if the patient has only just developed depression or if it is a depression from which they will spontaneously improve or in the depressions of rapid cycling patients. This hypothesis could be tested in future studies. Thus, in the broadest situation, we suggest that changes in dopaminergic and noradrenargic are linked, that a relative dopamine excess will precipitate mania but that compensatory mechanisms will increase noradrenergic function which will be associated with the move into depression, if the depression persists it will be associated with low levels of both dopaminergic and noradrenergic activities but with a balance towards NE. Finally, we would comment that the single subject design in this study may provide information that cannot be obtained from cross-sectional studies on groups of patients. This is because the biochemistry of mood disorders may be heterogeneous and interindividual differences may obscure intraindividual differences and because biochemical changes may not just be associated with mood state but may be associated with changes in mood state and/or with the longitudinal course of affective disorders (Post et al., 1986).
Acknowledgements
This study was funded by a grant from the Medical Research Council of New Zealand (to P.R. Joyce).
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