The World Journal of Biological Psychiatry, 2015; Early Online: 1–10

ORIGINAL INVESTIGATION

Distinguishing bipolar II depression from unipolar major depressive disorder: Differences in heart rate variability

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Hsin-An Chang1, Chuan-Chia Chang1, Terry B. J. Kuo2 & San-Yuan Huang1 1Department 2Institute

of Psychiatry, Tri-Service General Hospital, National Defense Medical Center, Taipei, Taiwan, and of Brain Science, National Yang-Ming University, Taipei, Taiwan

Abstract Objectives. Bipolar II (BPII) depression is commonly misdiagnosed as unipolar depression (UD); however, an objective and reliable tool to differentiate between these disorders is lacking. Whether cardiac autonomic function can be used as a biomarker to distinguish BPII from UD is unknown. Methods. We recruited 116 and 591 physically healthy patients with BPII depression and UD, respectively, and 421 healthy volunteers aged 20–65 years. Interviewer and self-reported measures of depression/anxiety severity were obtained. Cardiac autonomic function was evaluated by heart rate variability (HRV) and frequency-domain indices of HRV. Results. Patients with BPII depression exhibited significantly lower mean R–R intervals, variance (total HRV), low frequency (LF)-HRV, and high frequency (HF)-HRV but higher LF/HF ratio compared to those with UD. The significant differences remained after adjusting for age. Compared to the controls, the patients with BPII depression showed cardiac sympathetic excitation with reciprocal vagal impairment, whereas the UD patients showed only vagal impairment. Depression severity independently contributed to decreased HRV and vagal tone in both the patients with BPII depression and UD, but increased sympathetic tone only in those with BPII depression. Conclusions. HRV may aid in the differential diagnosis of BPII depression and UD as an adjunct to diagnostic interviews. Key words: bipolar II depression; unipolar depression; differentiation; heart rate variability; cardiac autonomic function

Introduction Bipolar II disorder (BPII) is commonly misdiagnosed as unipolar major depressive disorder (UD), especially on initial presentation, and a misdiagnosis can delay the correct diagnosis for up to 10 years (Ghaemi et al. 2000b). As most BPII patients present for treatment when depressed rather than when hypomanic, differentiating cross-sectionally between BPII and UD is difficult (Ghaemi et  al. 2000a). This issue has important clinical implications, as the treatments used for UD may exacerbate the course of illness in bipolar disorder, elevating the risks of switch or cycling (Bowden 2005; Perlis et al. 2010). The lack of a reliable means to differentiate BPII depression from UD prompted the present investigation into whether objective psychophysiological assessments could lead to a clinically useful biomarker to differentiate between the two disorders.

Angst et al. (2002) reported that BPII patients face a greater risk of cardiovascular mortality than UD patients, with the relative cardiovascular standardised mortality rates being 1.67 for BPII and 1.36 for UD. Despite the absence of a possible explanation for this difference, this finding was the rationale behind our psychophysiological approach to distinguish BPII from UD. The higher risk of cardiovascular mortality associated with both disorders (Osby et  al. 2001) is probably multifactorial (for review, see Weiner et  al. 2011; Huffman et  al. 2013), and in both disorders it may be partially related to an increased prevalence of traditional cardiovascular risk factors such as smoking, inadequate exercise, obesity, hypertension, hyperlipidemia, and diabetes. Other factors for the higher risk of cardiovascular mortality in both disorders may include an unidentified increased prevalence of nontraditional cardiovascular risk factors such as inflammation and cardiac autonomic

Correspondence (equal contributions): San-Yuan Huang, MD, PhD (Professor and Attending Psychiatrist) and Chuan-Chia Chang, MD (Assistant professor and Attending Psychiatrist), Department of Psychiatry, Tri-Service General Hospital, No. 325, Cheng-Kung Road, Sec. 2, Nei-Hu District, Taipei, 114, Taiwan, ROC. Tel:  886-2-8792-7220. Fax:  886-2-8792-7221. E-mail: [email protected] (Received 15 November 2014; accepted 6 February 2015) ISSN 1562-2975 print/ISSN 1814-1412 online © 2015 Informa Healthcare DOI: 10.3109/15622975.2015.1017606

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2 H.-A. Chang et al. dysregulation. Increasing focus has been placed on the cardiac autonomic dysregulation in bipolar disorder (Voss et al. 2006) and UD (Kemp et al. 2010) to explain the elevated risk of cardiovascular mortality, which has raised the important question of whether BPII and UD have differential inherent effects on cardiac autonomic regulation. If this is the case, then whether the differential effects contribute to differences in the risk for cardiovascular mortality between the two disorders also needs to be investigated. To date, no study has directly compared the cardiac autonomic regulation of BPII patients to that of individuals with UD. Heart rate variability (HRV) refers to the complex beat-to-beat variation in heart rate produced by the interplay of sympathetic and parasympathetic (vagal) neural activity at the sinus node of the heart. Lower HRV is an indicator of dysregulation of cardiac autonomic function and a predictor of poor health status (Dekker et  al. 2000). Short-term power spectral analysis of HRV, standardized since 1996 (Task Force of the European Society of Cardiology and the North American Society of Pacing and Electrophysiology 1996), has been developed as a reliable and non-invasive tool to probe the autonomic regulation of the heart. As highlighted by the largest meta-analysis published so far, HRV is clearly reduced in UD patients relative to controls (Kemp et  al. 2010). Compared to the volume of research relating to the HRV in UD, a smaller body of research has examined HRV in bipolar I disorder (Cohen et  al. 2003; Henry et  al. 2010; Lee et al. 2012). However, no report has yet examined the resting HRV in BPII depression. Unlike most previous HRV studies that did not avoid confounding variables affecting autonomic tone such as physical health, psychiatric co-morbidities, and medications (Voss et  al. 2006; Kemp et  al. 2010), the present study investigated physically healthy, non-comorbid, and unmedicated patients with BPII depression to better examine the true effects of this disorder on HRV. The aim of this study was to investigate whether there was a difference in cardiac autonomic regulation between patients with BPII depression and UD. Power spectral analyses of HRV among physically healthy, non-comorbid, and unmedicated patients with the two disorders and healthy controls were carried out to make comparisons.

Tri-Service General Hospital, a medical teaching hospital of the National Defense Medical Center in Taipei, Taiwan. All participants were aged from 20 to 65 years, and all provided written informed consent. After detailed questionnaire screening, chart review, clinical examination, electrocardiography, and relevant laboratory investigations, subjects who were pregnant, had cancer, cardiovascular, respiratory, neurological, or endocrinological disorders that affect HRV, or those engaged in regular and strenuous physical training were excluded. Current or past smokers were also excluded. All participants were drug-naïve or had not used psychotropic medications or any medications that have been reported to affect autonomic functioning (e.g., anti-psychotics, anti-cholinergics, anti-depressants, oral contraceptives, anti-convulsants, anxiolytics, cerebral metabolic activators, or cerebral vasodilators) for at least 1 month prior to the beginning of the study. Drawn from a pool of 3897 psychiatric inpatients evaluated with the Chinese Version of the Modified Schedule of Affective Disorder and SchizophreniaLifetime (Endicott and Spitzer 1978), the patient population consisted of 707 individuals meeting the criteria for a major depressive episode (MDE) at the time of presentation, 591 of whom were diagnosed with UD and 116 with BPII depression. Patients meeting the criteria for MDE who had never had a hypomanic or manic episode were classified as having UD. Those meeting the criteria for MDE who had a history of hypomania were classified as having BPII depression. Consensus diagnoses by two research psychiatrists were made according to the DSM-IV criteria for UD or BPII depression. Our previous studies have reported diagnostic data with satisfactory inter-rater reliability (Chang et al. 2007a, 2007b). BPII depression and UD patients with the 17-item version of the Hamilton Depression Rating Scale (HAM-D) score greater than 16 were enrolled in the study. The patients with BPII depression also had Young Mania Rating Scale scores lower than 10 (Young et al. 1978). None of the patients with BPII depression or UD had any current psychiatric comorbidity or a history of substance dependence. The control group consisted of 421 physically healthy volunteers with no lifetime history of mental disorders who were recruited from the community as described previously (Chang et al. 2014a, 2014b). Control variables

Methods Participants This study was approved by the Institutional Review Board for the Protection of Human Subjects of the

Based on our previous studies (Kuo et  al. 1999; Chen et  al. 2014), factors significantly affecting the autonomic control of heart rate include gender, age, body mass index, physical activity, and alcohol use. These factors were thus selected as the control

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Heart rate variability in bipolar II and unipolar depression        3 variables. The participants’ self-reported weekly habitual physical activity was calculated by the formula: average frequency of physical exercise hours spent on a typical exercise day. The average frequency of physical exercise was rated with a five-point scale according to the frequency of exercise with hard breathing and sweating as “never”, “seldom”, “once a week”, “twice a week”, and “more than twice a week” (Henje Blom et al. 2009). Alcohol use, assessed with two items of the Alcohol Use Disorder Identification Test questionnaire (Babor et al. 1992), was defined by the average frequency of drinking and the amount of drinks on a typical drinking day in the past year. From these items, we derived the average amount of alcoholic drinks per day, with one drink referring to one glass of a drink containing alcohol, and a standard drink being equivalent to 10 grams of alcohol (Li et al. 2011). Assessment of depression/anxiety severity All subjects were assessed using self-reported measures of depression and anxiety with the Beck Depression Inventory and the Beck Anxiety Inventory, respectively. They were also assessed by attending psychiatrists using clinician-rated scales, i.e., the HAM-D and the Hamilton Anxiety Rating Scale (HAM-A). Both the self-reported inventories and clinician-rated scales provided global indices of depression/anxiety severity. To avoid multiple testing of the same hypothesis, the analysis of the relationship between HRV parameters and global depression severity was based on the HAM-D. The results were unchanged whether the outcome was derived from the interviewer or self-reported measures. Measurements of heart rate variability The detailed procedures for the analysis of HRV were as reported in our previous studies (Kuo et al. 1999; Liu et  al. 2003). Briefly, the subject first sat quietly for 20 minutes, then a lead I electrocardiogram was recorded for 5 min while lying quietly in the supine position. To prevent respiratory interference of HRV indices (Hayano et  al. 1994), we ensured that all of the subjects had a respiratory rate of 12–15 breaths/min by recording their respiratory movements during the HRV measurement. An HRV analyser (SSIC, Enjoy Research Inc., Taiwan) acquired, stored, and processed the electrocardiography signals. With a sampling rate of 512 Hz, the signals were recorded using an 8-bit analogue-todigital converter. Stationary R–R interval values were re-sampled and interpolated at a rate of 7.11 Hz to produce continuity in the time domain. Power

spectral analysis was performed using a nonparametric method of fast Fourier transformation. The direct current component was deleted, and a Hamming window was used to attenuate the leakage effect (Kuo et  al. 1999). The power spectrum was subsequently quantified into the standard frequency-domain measurements defined previously (Task Force of the European Society of Cardiology and the North American Society of Pacing and Electrophysiology 1996), namely variance (variance of R–Rinterval values), very low-frequency power (VLF, 0.003–0.04 Hz), low-frequency power (LF, 0.04– 0.15 Hz), high-frequency power (HF, 0.15–0.40 Hz), and the ratio of LF to HF power (LF/HF). All of the measurements were logarithmically transformed to correct for a skewed distribution (Kuo et al. 1999). Vagal control of HRV was represented by HF, whereas both vagal and sympathetic control of HRV were jointly represented by LF. The LF/HF ratio could mirror sympatho-vagal balance or sympathetic modulation, with a larger LF/HF ratio indicating a greater predominance of sympathetic activity over cardiac vagal control. The VLF component has been attributed variously to thermoregulatory processes, peripheral vasomotor activity, and the renin-angiotensin system; however, its definite physiological meaning is under debate (Lombardi 2002). Statistical analysis SPSS (version 13.0) statistical software was used for all analyses. Multivariate analysis of variance (MANOVA) with the Hotelling-Lawtrace multivariate test of significance was used to compare cardiac measures between groups. Analysis of covariance adjusted for any significant differences in baseline characteristics known to affect cardiac measures. Single factor ANOVA compared clinical ratings and other characteristics between the groups. Relationships between the HRV indices and control variables were analysed using Spearman correlation coefficients. Hierarchical regression analysis was used to explore the effect of depression on HRV after adjusting for the control variables. The control variables relating to HRV in univariate analysis (P  0.05) were entered into step 1 of the hierarchical regression analysis, when mean R–R intervals and HRV indices were the dependent variables. Data including R2, R2-changes, F, standardization regression coefficient (b) and P value in the regression model are presented. In addition, tolerance and variance inflation factors were used to check for multicollinearity. A binary logistic regression analysis was used in

4 H.-A. Chang et al. patients with BPII depression and age- and sexmatched UD patients to determine the optimal model for the prediction of BPII in depressive patients. Nagelkerke’s R2 was used to approximate the percent of variance explained by the model. The area under the receiver-operating characteristic (ROC) curve (AUC) was also used to determine the predictive power of the logistic model. The predicted probability with the highest Youden index was selected as the optimal cut-off point. The a-level was set at 0.05 per comparison.

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Results Sample characteristics The patients with BPII depression and UD had equally higher levels of depression and anxiety than the controls (Table I). Compared to the patients with UD, those with BPII depression had a significantly lower age, age at onset, more major depressive episodes, atypical features, and family history of bipolar disorder. The patients with BPII depression were also significantly younger than the controls. There were no significant differences in other demographic data or clinical characteristics among the three groups.

Heart rate variability parameters The MANOVA comparing the various HRV measures between groups showed significant results [F(12)  3.57, P  0.001, h2  0.02], which did not significantly alter after adjusting for age [F(12)  4.77, P  0.001, h2  0.03]. Post-hoc testing revealed reduced mean R–R intervals (P  0.001), variance (P  0.001), VLF (P  0.001), LF (P  0.001), HF (P  0.001), and increased LF/HF ratio (P  0.001) in the patients with BPII depression compared to the controls. Variance (P  0.008), LF (P  0.001), and HF (P  0.001) were reduced in the patients with UD compared to the controls. The patients with BPII depression had significantly lower mean R–R intervals, variance, VLF, LF, HF, and higher LF/HF ratio compared to the patients with UD (Figure 1).

Factors associated with resting HRV Analysis of associations between HRV indices and potential moderators (Table II) revealed that men had significantly lower variance, LF, and HF than women. The older patients had lower variance, VLF, LF, and HF. The patients who were more habitually

Table I. Sample characteristics. BPII depression

UD

Healthy controls

Omnibus P value

116 47 (40.52) 30.86  9.22

591 296 (50.08) 38.55  13.96

421 216 (51.30) 39.03  12.18

0.11  0.001

Age of onset first MDE (years) – 20.79  4.19 27.43  7.89 Duration of illness (years) – 10.07  8.34 11.12  11.05 34 (29.31) 83 (14.04) –  4 MDEs (%) Psychotic features (%) 9 (7.76) 63 (10.66) – Melancholic features (%) 18 (15.52) 130 (22.00) – Atypical features (%) 52 (44.83) 81 (13.70) – Family history of bipolar disorder (%) 48 (41.38) 53 (8.97) – BMI, mean SD, kg/m2 22.34  3.56 23.11  3.93 23.05  3.56 Weekly regular exercise, hours 1.08  1.34 0.99  1.48 1.22  1.85 Alcohol use, drinks/day 0.02  0.04 0.01  0.03 0.01  0.03 SBP, mean SD, mmHg 115.38  11.41 117.79  13.48 118.28  13.34 DBP, mean SD, mmHg 72.59  8.89 73.26  9.50 73.38  7.03 – – YMRS scores, mean SD 1.16  1.40 HAM-D scores, mean SD 28.79  8.24 29.62  9.51 5.03  2.50

 0.001 0.33  0.001 0.35 0.12  0.001  0.001 0.12 0.09 0.26 0.11 0.67 –  0.001

BDI scores, mean SD

34.91  10.49

36.21  11.65

5.86  3.65

 0.001

HAM-A scores, mean SD

13.41  4.57

13.88  5.18

6.00  2.09

 0.001

BAI scores, mean SD

16.02  4.64

16.14  5.44

8.08  2.62

 0.001

Clinical and demographic data Number of participants Female sex (%) Age, mean SD, years

Significant comparisons

BPII depression vs. UD; BPII depression vs. Healthy control BPII depression vs. UD BPII depression vs. UD

BPII depression vs. UD BPII depression vs. UD

BPII depression vs. Healthy control UD vs. Healthy control BPII depression vs. Healthy control UD vs. Healthy control BPII depression vs. Healthy control UD vs. Healthy control BPII depression vs. Healthy control UD vs. Healthy control

­SD, standard deviation; MDE, major depressive episode; BMI, body mass index (calculated as weight in kilograms divided by height in meters squared); SBP, systolic blood pressure; DBP, diastolic blood pressure; YMRS, Young Mania Rating Scale; HAM-D, Hamilton Depression Rating Scale; BDI, Beck Depression Inventory; HAM-A, Hamilton Anxiety Rating Scale; BAI, Beck Anxiety Inventory.

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Heart rate variability in bipolar II and unipolar depression        5

Figure 1. Mean R–R intervals and all measures of HRV for the patients with bipolar II depression, unipolar depression and controls. Values are adjusted for age. Asterisks indicate significant differences between the patients and controls. Error bars are standard error of the mean.

physically active had significantly longer mean R–R intervals, higher variance, and higher HF.

(Table IV). Tolerance (range: 0.62–0.99) and variance inflation (range: 1.01–1.61) did not indicate multicollinearity.

Effects of depression severity on resting HRV Overall, the HAM-D scores accounted for the additional variance in the prediction of the dependent variables in step 2, with the test of R2-change being significant for all dependent variables (data not shown but available on request). When the two patient groups were analysed separately, the HAM-D scores contributed independently of the covariates to reduced variance, VLF, LF, and HF, but increased LF/HF ratio for the patients with BPII depression (Table III), and to reduced mean R–R intervals, variance, VLF, LF, and HF for the patients with UD

Mode of differentiation between BPII depression and UD patients Earlier onset of MDE ( 30 years of age at onset) has been reported associated with significantly elevated risk of diagnostic conversion from UD into bipolar disorder (Dudek et al. 2013). It is therefore possible that some of our younger participants with apparent UD actually had been suffering from BPII. To control for this possibility, the prediction model was carried out in depressed patients aged more than 30 years but not in the sample as a whole. The HRV

Table II. Factors associated with resting HRV indices among all participants.

Gender (women/men)† Age‡ BMI‡ Physical activity‡ Alcohol use‡

RR interval

variance (total HRV)

VLF

LF

HF

LF/HF

20.06 0.03 20.00 0.41* 20.04

20.12* 20.40* 20.06 0.14* 20.08

20.09 20.34* 20.04 0.10 20.07

20.16* 20.41* 20.06 0.09 20.06

20.11* 20.40* 20.10 0.15* 20.07

20.06 0.03 0.08 20.10 0.04

BMI, body mass index; VLF, very low-frequency power [ln(ms2)]; LF, low frequency power [ln(ms2)]; HF, high frequency power [ln(ms2)]; LF/HF, ratio of LF to HF [ln(ratio)]. †Point-biserial correlations; first category in parenthesis is the reference group. ‡Product-moment correlations. *P  0.001; with a Bonferroni correction, only correlations with P  0.001 were considered significant.

6 H.-A. Chang et al. Table III. Hierarchical regression analyses of mean RR intervals and all HRV indices for patients with bipolar II depression. Mean RR intervals

Variance

VLF

LF

HF

Ratio

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Standardized regression coefficient and P value

Univariate analyses Gender Age BMI Physical activity Alcohol use Step 1 Gender Age BMI Physical activity Alcohol use R2 Step 2 Gender Age BMI Physical activity Alcohol use HAM-D R2 changes F P value

b

P

b

P

b

P

b

P

b

P

b

P

20.33 20.03 0.06 0.36 20.00

 0.001 0.75 0.59  0.001 0.99

20.22 20.29 20.06 0.14 20.10

0.02 0.002 0.49 0.14 0.29

20.11 20.17 20.05 20.08 20.11

0.24 0.07 0.60 0.39 0.25

20.16 20.25 20.01 20.10 20.13

0.08 0.008 0.91 0.30 0.16

20.24 20.26 20.11 0.10 20.12

0.01 0.005 0.22 0.27 0.22

0.14 0.06 0.15 20.21 0.05

0.13 0.54 0.12 0.02 0.58

– – – – –

– – – – –

0.001 20.30 – – – – 0.33  0.001 – – 21.6%

20.19 0.046 20.26 0.005 – – – – – – 11.4%

20.30 – – 0.32 – 20.13

20.19 0.046 20.26 0.005 – – – – – – 20.24 0.007 5.5% 7.44 P  0.007

 0.001 – –  0.001 – 0.13 1.6% 2.28 P  0.13

0.0%

– – 20.25 0.008 – – – – – – 6.0%

20.20 0.03 20.23 0.01 – – – – – – 10.7%

– – – – – – 0.02 20.21 – – 4.6%

– – – – – – – – – – 20.26 0.006 6.5% 7.96 P  0.006

– – 20.20 0.03 – – – – – – 20.20 0.03 3.8% 4.71 P  0.03

20.23 0.01 20.15 0.09 – – – – – – 20.29 0.001 7.9% 10.87 P  0.001

– – – – – – 0.04 20.19 – – 0.18 0.04 3.5% 4.26 P  0.04

of the variance is explained by the models of different cardiac measures. The total AUC was significantly greater than 0.5 in models of mean RR intervals and

control variables in BPII (n  44) and UD (n  220) patients were comparable. Results are shown in Table V. The Nagelkerke R2 values show that 5–19%

Table IV. Hierarchical regression analyses of mean RR intervals and all HRV indices for patients with unipolar depression. Mean RR intervals

Variance

VLF

LF

HF

Ratio

Standardized regression coefficient and P-value Univariate analyses Gender Age BMI Physical activity Alcohol use Step 1 Gender Age BMI Physical activity Alcohol use R2 Step 2 Gender Age BMI Physical activity Alcohol use HAM-D R2 changes F P value

b

P

20.03 0.01 0.02 0.27 20.01

0.50 0.91 0.61  0.001 0.82

– – – – – – 0.27  0.001 – – 7.1% – – – 0.26 – 20.09

– – –  0.001 – 0.018 0.9% 5.66 P  0.018

b

P

b

P

b

0.001 20.11 0.01 20.13 20.18 20.44  0.001 20.38  0.001 20.46 0.08 20.06 0.13 20.07 20.07 0.05 0.24 0.03 0.42 0.03 0.27 20.05 0.19 20.05 20.02

P

b

 0.001  0.001 0.08 0.53 0.59

20.13 20.43 20.10 0.08 20.03

P

b

0.001 20.05 0.01  0.001 0.02 0.05 0.07 20.09 0.48 0.01

P 0.19 0.83 0.20 0.03 0.73

0.13 0.74 0.02 0.59 0.45 0.01 0.84 20.03 20.45  0.001 20.39  0.001 20.46  0.001 20.43  0.001 – – – – – – 0.49 20.03 – – – – – – – – – – – – – – – – 19.6% 14.5% 20.7% 18.5%

– – – – – – –0.09 0.03 – – 0.8%

0.00 0.06 20.11 0.01 0.34 20.04 20.45  0.001 20.38  0.001 20.45  0.001 – – – – – – – – – – – – – – – – – – 0.002 20.10 0.006 20.14  0.001 20.12 1.9% 1.4% 1.0% 14.37 9.50 7.67 P  0.001 P  0.002 P  0.006

– – – – – – –0.09 0.03 – – 0.06 0.12 0.4% 2.43 P  0.12

0.96  0.001 0.43 – –  0.001 1.8% 12.88 P  0.001

20.00 20.43 20.03 – – 20.13

Heart rate variability in bipolar II and unipolar depression        7 Table V. Binary logistic regression analyses in patients with bipolar II depression and those with unipolar depression. Analysis

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Variables All subjects Mean RR intervals Constant variance Constant VLF Constant LF Constant HF Constant ratio Constant

ROC

Beta

SE

Wald

P-value

OR

95% CI

20.01 3.36 20.85 4.26 20.51 1.44 20.47 0.93 20.96 3.15 0.73 21.92

0.00 1.23 0.22 1.48 0.18 1.08 0.17 0.88 0.20 0.95 0.22 0.21

15.57 7.47 15.01 8.23 7.79 1.79 8.04 1.10 23.27 10.96 11.21 83.66

 0.001 0.006  0.001 0.004 0.005 0.18 0.005 0.29  0.001 0.001 0.001  0.001

0.99

0.99–0.99

0.43

Nagelkerke

R2

AUC

95% CI

0.12

0.73

0.64–0.82

0.28–0.66

0.11

0.68

0.59–0.76

0.60

0.42–0.86

0.05

0.63

0.54–0.72

0.63

0.45–0.87

0.06

0.61

0.52–0.69

0.38

0.26–0.57

0.19

0.74

0.67–0.81

2.06

1.35–3.16

0.08

0.35

0.26–0.43

­SE, standard error; Wald, Wald statistic; OR, odds ratio; ROC, receiver-operating characteristic; AUC, area under the curve; CI, confidence interval.

all HRV indices except for LF/HF ratio. These results indicate that the model of HF power can best predict BPII depression and UD. The following prediction model was derived: Predicted Probability  1/[1  exp(3.150–0.957   HF value)] At the optimal cut-off point of 4.995 determined by the Youden index, the sensitivity and the specificity of differentiating BPII depression from UD were 75 and 65%, respectively. Discussion To the best of our knowledge, this is the first study to directly compare the resting HRV of patients with UD to that of patients with BPII depression. In addition, this is the first study to examine the resting HRV in unmedicated patients with BPII depression without any co-morbidities. A major finding of this study is that the mean R–R intervals and all HRV indices differentiated BPII depression from UD. Specifically, the patients with BPII depression had a faster heart rate, lower HRV and parasympathetic tone, and higher sympathetic tone than those with UD. These differences were not due to the severity of symptoms (both patient groups rated similarly on levels of anxiety and depression) or other potential confounding variables such as gender, body mass index, alcohol use, or physical activity. Although the patients with BPII depression were younger than those with UD, the significant differences remained after adjusting for age. A high degree

of HRV aids healthy cardiac activity and provides a protective effect against myocardial infarction and heart failure (Bigger et al. 1988), whereas decreased parasympathetic tone (Thayer and Lane 2007) and/ or increased sympathetic tone (La Rovere et  al. 2003) are associated with an increased risk of cardiovascular disease and mortality. In the present cross-sectional study, the finding of more adverse cardiac autonomic regulation in the patients with BPII depression than in those with UD suggests a possible mechanism behind the greater risk of cardiovascular mortality in BPII compared to UD. To verify this mechanism, future longitudinal studies are needed to establish whether the differential cardiac autonomic regulation in the two disorders predicts different rates of the development of cardiovascular disease or cardiovascular mortality. Importantly, our results imply that BPII depression and UD may be two distinct disorders. The current diagnostic systems separate mood disorders categorically into bipolar and depressive disorders (Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition, Text-Revised, American Psychiatric Association 2000). Several features of the two disorders support such a categorical distinction (e.g., bipolar depression being more likely to be associated with atypical features, early onset, and a family history of bipolar disorder compared to unipolar depression) (for review, see Bowden 2005). However, recent studies have leaned towards supporting a continuity/ spectrum of mood disorders that includes overlapping and dimensional disorders ranging from bipolar I and bipolar II disorders to UD (Akiskal and Benazzi 2003; Angst et al. 2003; Cassano et al. 2004). Mixed states and especially mixed depression (i.e., depression

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8 H.-A. Chang et al. plus co-occurring noneuphoric and hypomanic symptoms) is a well-known feature that may support continuity between BPII depression and UD. Overall, aside from replicating previous findings on the clinical differences between BPII depression and UD, the present study provides novel evidence that HRV may be a psychophysiological feature supporting a categorical distinction between BPII depression and UD. If replicated, our results could aid in developing objective psychophysiological methods for differentiating between the two conditions. Compared to the controls, the patients with BPII depression had an increased sympathetic tone with a reciprocal decrease in parasympathetic tone, and thus an overall reduction in total HRV. The alterations in cardiac autonomic regulation were dependent on severity and possibly contributed to the reduced mean R–R intervals (Figure 1), i.e., elevated resting heart rate, which is influenced by both the sympathetic and parasympathetic nervous systems simultaneously. Interestingly, such a pattern of autonomic imbalance has also been observed in bipolar patients in the manic phase (Henry et  al. 2010; Chang et  al. 2014). An earlier report described reduced HRV but increased parasympathetic tone in patients with euthymic bipolar disorder (Cohen et al. 2003). In addition, a recent study reported that bipolar patients in the subsyndromal depressive phase have reduced HRV and parasympathetic tone compared to controls (Lee et  al. 2012). Taken together, it is possible that reduced HRV could represent a consistent trait marker of bipolar disorder. In contrast, it is also conceivable that cardiac sympathetic excitation with reciprocal vagal impairment, observed primarily in the syndromal (either depressive or manic) phase of the illness, could be statedependent. The increase in sympathetic tone may reflect the evidence that both bipolar depression and mania are associated with increased plasma cortisol (Cervantes et  al. 2001) and catecholamine levels (Lake et al. 1982). However, in bipolar patients during the depressed state, most studies have reported lower levels of urinary 3-methoxy-4-hydroxylphenyglycol, a major norepinephrine metabolite used as an index of sympathetic activity (Schildkraut et  al. 1978; Muscettola et al. 1984; Schatzberg et al. 1989). To make a definitive conclusion, additional studies using cardiac noradrenaline spillover are recommended to adequately assess the sympathetic tone specific to BPII depression. On the other hand, consistent with previous studies (van der Kooy et  al. 2006; Udupa et al. 2007), our results showed reduced HRV and parasympathetic tone in the patients with UD compared to the controls. The reliability of our results is strengthened by this study being the largest case–control study to date using frequency-domain

methods to analyse HRV in unmedicated UD patients. Overall, the patients with BPII depression and UD shared a common feature of autonomic imbalance, severity-dependent cardiac vagal impairment. This finding may explain the link between depressive symptom burden and elevated cardiovascular risk in the two conditions (Fiedorowicz et al. 2009; Murray et al. 2009). This is also supported by models of neurovisceral integration (Thayer and Lane 2000), which propose that decreased parasympathetic tone may be the final common pathway linking negative affective states and conditions to ill health. In addition, the inverse correlation between parasympathetic tone and depressive symptom expression is not simply an epiphenomenon, but also provides an alternative putative mechanism of action for vagus nerve stimulation in treating bipolar and unipolar patients with chronic or recurrent depression (George and Aston-Jones 2010), presumably through correction of the decreased parasympathetic tone. The current study has several strengths. First, only unmedicated participants were recruited to eliminate the effect of medication on cardiac autonomic function and to show more clearly the direct relationship between HRV and the psychiatric disorders investigated. Second, none of the study subjects were current or past smokers. Smoking clearly depresses HRV, even among those who have recently quit, and HRV remains lower compared to that of normal non-smokers (Stein et  al. 1996). Smoking is an important factor in any study evaluating the effect of either BPII depression or UD on HRV, because a substantial proportion of patients with the two disorders smoke or have a history of smoking (Carney et al. 1987; Diaz et al. 2009). Third, all participants underwent physical examinations, blood and urine screening in addition to self-reported data of physical health. Our recent studies have emphasized this objective procedure for excluding subjects with physical co-morbidities, due to the subjects underestimating their biological risk factors (e.g., elevated glucose and atherogenic lipid profile) for cardiac autonomic dysregulation when these factors were self-reported (Chang et  al. 2014a, 2014b). Finally, since anxiety (Friedman and Thayer 1998) and substance use disorders (Henry et al. 2012; Chen et al. 2014) can profoundly influence HRV measures and are comorbidities often found in bipolar disorder (Kessler et al. 1999; El-Mallakh and Hollifield 2008) and UD (Compton et al. 2006; Hsieh et al. 2012), both patients and controls were evaluated with a structured diagnostic interview to rule out psychiatric co-morbidities and psychiatric disorders, respectively.

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Heart rate variability in bipolar II and unipolar depression        9 The present study has several significant limitations. First, the menstrual cycle of the female participants was not recorded, even though HRV is known to fluctuate during different phases of the female menstrual cycle (Sato et  al. 1995). Second, with all of the sampled cases being inpatients, illness acuity may be affected by selection bias in the clinical sample. Thus, conservative interpretation of our findings is warranted. In order to make a definitive conclusion, unmedicated outpatients with less severe symptoms should also be enrolled to study their resting HRV upon first arrival. Finally, the present study creates a logistic regression model to predict BPII in depressed patients aged  30 years. To accurately apply this model to those aged  30 years, a longterm follow-up study on our younger UD patients is needed to analyse diagnostic conversions into bipolar disorder. Previously, Perlis et  al. (2006) made a logistic regression prediction model accurately distinguishing bipolar and unipolar depression by including age at onset, number of previous depressive episodes, family history and depression severity. Their model predicted bipolarity in depressed patients with a sensitivity of 69.0% and a specificity of 94.9%. Combining their model with the present one may result in a more accurate prediction model with a wide clinical application. Conclusions Limitations notwithstanding, the present study provides new insights into the phenomenon of different mortality rates due to cardiovascular disease reported in BPII depression and UD. The results of the current study indicate that psychophysiological assessment may, in the future, aid in the differential diagnosis of BPII depression and UD as an adjunct to diagnostic interviews. In cases of ambiguous interview information, a 5-min HRV analysis may provide supplemental data without much burden on the patients.­­­­­ Acknowledgments This study was supported in part by the grants from the Ministry of Science and Technology of Taiwanese Government (MOST-103-2314-B-016-021), the Tri-Service General Hospital (TSGH-C103-135) and the National Defense Medical Research (MAB99-I-16). Statement of Interest  None to declare.

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