Introduction
Unipolar and bipolar disorders differ with regard to several clinical and course of illness variables (Serretti et al. Reference Serretti, Mandelli, Lattuada, Cusin and Smeraldi2002). The validity of the bipolar versus unipolar distinction for affective disorders is supported by a general diagnostic stability (Rice et al. Reference Rice, McDonald-Scott, Endicott, Coryell, Grove, Keller and Altis1986, Reference Rice, Rochberg, Endicott, Lavori and Miller1992; Coryell et al. Reference Coryell, Keller, Endicott, Andreasen, Clayton and Hirschfeld1989), familial aggregation (Coryell et al. Reference Coryell, Endicott, Reich, Andreasen and Keller1984; Endicott et al. Reference Endicott, Nee, Andreasen, Clayton, Keller and Coryell1985; Rice et al. Reference Rice, Reich, Andreasen, Endicott, Van Eerdewegh, Fishman, Hirschfeld and Klerman1987) and course of illness (Coryell et al. Reference Coryell, Keller, Endicott, Andreasen, Clayton and Hirschfeld1989; Goldberg & Harrow, Reference Goldberg and Harrow2004). One important sequela of affective disorders is suicide. Suicide was the fifth leading cause of death when ranked by years of potential life lost before age 65 in the USA from 1999 to 2002 (CDC, 2005), and the majority of suicide victims have a unipolar or bipolar depressive disorder at the time of suicide (Rihmer, Reference Rihmer1996; Mann et al. Reference Mann, Apter, Bertolote, Beautrais, Currier, Haas, Hegerl, Lonnqvist, Malone, Marusic, Mehlum, Patton, Phillips, Rutz, Rihmer, Schmidtke, Shaffer, Silverman, Takahashi, Varnik, Wasserman, Yip and Hendin2005).
Several (Dunner et al. Reference Dunner, Gershon and Goodwin1976; Kupfer et al. Reference Kupfer, Carpenter and Frank1988; Tondo et al. Reference Tondo, Baldessarini, Hennen, Minnai, Salis, Scamonatti, Masia, Ghiani and Mannu1999) but not all (Cassano et al. Reference Cassano, Akiskal, Savino, Musetti and Perugi1992) retrospective studies have suggested higher rates of suicide attempts among individuals with bipolar, particularly type II, versus unipolar disorders. Bipolar disorder was associated with greater risk of suicide attempts and completions in one prospective study of 2826 Italian out-patients with affective disorders followed for a mean of 3.6 years, during which 27 completed suicides and 363 suicide attempts were recorded (Tondo et al. Reference Tondo, Lepri and Baldessarini2007). However, bipolar disorder has not been consistently associated with greater risk of suicidality prospectively (Coryell et al. Reference Coryell, Andreasen, Endicott and Keller1987). For bipolar patients, suicide attempts are most likely to occur in the setting of depressed states (Tondo et al. Reference Tondo, Baldessarini, Hennen, Minnai, Salis, Scamonatti, Masia, Ghiani and Mannu1999; Oquendo et al. Reference Oquendo, Waternaux, Brodsky, Parsons, Haas, Malone and Mann2000; Valtonen et al. Reference Valtonen, Suominen, Mantere, Leppamaki, Arvilommi and Isometsa2005), although mixed states and cycling have also been associated (Tondo et al. Reference Tondo, Baldessarini, Hennen, Minnai, Salis, Scamonatti, Masia, Ghiani and Mannu1999; Dalton et al. Reference Dalton, Cate-Carter, Mundo, Parikh and Kennedy2003; Valtonen et al. Reference Valtonen, Suominen, Mantere, Leppamaki, Arvilommi and Isometsa2005). To our knowledge, there are no studies that directly test for differential effects of well-established risk factors for suicide by affective disorder polarity.
The National Institute of Mental Health (NIMH) Collaborative Depression Study (CDS) dataset provides a unique opportunity to assess differential effects of risk factors for suicide on account of its duration, size and scope. To enhance the accuracy of diagnosis and reduce the risk of misclassification from relying solely on cross-sectional diagnoses, we examined the risk of suicide outcomes based on a prospective rather than an initial diagnosis. To make use of our long-term prospective dataset effectively, we further analyzed risk factors at the beginning of a mood cycle rather than simply at study intake. Given disparate findings in previous studies regarding risk of suicide outcomes by subtype of affective disorder, we sought to explore potential differential effects of previously identified risk factors by affective disorder polarity. With limited background research on this topic, we hypothesized that risk factors for suicide would not differ significantly by polarity. Although several analyses of the entire CDS dataset have assessed suicide risk factors (Scheftner et al. Reference Scheftner, Young, Endicott, Coryell, Fogg, Clark and Fawcett1988; Fawcett et al. Reference Fawcett, Scheftner, Fogg, Clark, Young, Hedeker and Gibbons1990; Young et al. Reference Young, Fogg, Scheftner and Fawcett1994; Coryell et al. Reference Coryell, Arndt, Turvey, Endicott, Solomon, Mueller, Leon and Keller2001, Reference Coryell, Haley, Endicott, Solomon, Leon, Keller, Turvey, Maser and Mueller2002; Maser et al. Reference Maser, Akiskal, Schettler, Scheftner, Mueller, Endicott, Solomon and Clayton2002), only one has looked at affective disorder type as a risk factor for suicide (Fawcett et al. Reference Fawcett, Scheftner, Clark, Hedeker, Gibbons and Coryell1987). The Fawcett et al. (Reference Fawcett, Scheftner, Clark, Hedeker, Gibbons and Coryell1987) study compared 25 patients who committed suicide by 4-year follow-up with those who did not, over a variety of clinical variables. Diagnostic subcategories did not differentiate completers from non-completers as assessed by Fisher's exact test. No CDS analysis has used prospective diagnoses in the study of suicide risk or compared the differential effects (interaction) of risk factors by affective disorder polarity.
Method
Study design
Patients with affective disorders were recruited for participation in the NIMH Collaborative Program on the Psychobiology of Depression from five academic centers: Massachusetts General Hospital and Harvard University in Boston, Rush Presbyterian – St Luke's Medical Center in Chicago, the University of Iowa in Iowa City, New York State Psychiatric Institute and Columbia University in New York, and Washington University School of Medicine in St Louis. Participants were Caucasian, English-speaking, and knowledgeable regarding their biological parents. Participants further met Research Diagnostic Criteria (RDC; Spitzer et al. Reference Spitzer, Endicott and Robins1978) for major depressive disorder, schizo-affective disorder, or manic disorder. Intake diagnosis was used to identify 936 probands. This sample was further restricted to 909 participants with at least one follow-up assessment. Participants in this sample were followed for a mean of 15.4 (median=19, s.d.=8.8) years and for up to 25 years. Seventy-eight per cent of participants were followed for a decade or more.
Follow-up assessments were completed using various forms of the Longitudinal Interval Follow-up Evaluation (LIFE; Keller et al. Reference Keller, Lavori, Friedman, Nielsen, Endicott, McDonald-Scott and Andreasen1987), which categorizes severity of psychopathology and was administered semi-annually in the first 5 years and annually thereafter. The LIFE tracked each RDC syndrome weekly and was used to prospectively diagnose participants into one of two groups: unipolar and bipolar. An initial diagnosis of bipolar disorder was based on an intake RDC diagnosis of bipolar II, bipolar I, schizo-affective manic, or schizo-affective depressed with a history of mania or schizo-affective mania, mainly affective. The latter category was included as it is almost identical to mania as defined by the DSM-IV. An initial diagnosis of unipolar depression was based on intake RDC diagnosis of major depressive disorder or schizo-affective disorder, depressed, mainly affective. The latter category was included as it is analogous to major depression as defined by the DSM-IV. Follow-up ratings from the LIFE Psychiatric Status Rating (PSR) were used to reclassify participants diagnostically. Participants with unipolar depression on intake, who developed mania or hypomania during follow-up, were thus classified as having bipolar disorder. For consistency and to make use of the most accurate diagnoses, prospective diagnoses were used for all subsequent analyses.
Data analytic procedures
Baseline demographics and prior suicide attempts were compared between participants with unipolar and bipolar disorders. Age of onset was estimated using the earliest of age of first psychiatric treatment, onset of depressive symptoms, or onset of manic symptoms from the Schedule of Affective Disorders (SADS; Endicott & Spitzer, Reference Endicott and Spitzer1978, Reference Endicott and Spitzer1979). A retrospective analysis of suicide attempts was performed to explore differences in baseline suicidality prior to enrollment in this prospective cohort study. Separate analyses were conducted for suicide attempts and severe suicide attempts. Severity of attempt was determined from the SADS using previously used cut-offs, wherein a severe attempt was operationally defined as a SADS suicidal intent (item 249) or medical lethality (item 250) score ⩾4 (Fiedorowicz & Coryell, Reference Fiedorowicz and Coryell2007). These cut-offs require a ‘serious’ level of suicidal intent to kill oneself or at least ‘moderate’ medical lethality (i.e. ‘had brief unconsciousness’). Logistic regression was performed to control for age of onset in assessing suicide attempts prior to intake by polarity.
Descriptive statistics related to suicidal behaviors during follow-up and burden of depressive symptoms were compiled for those with prospective diagnoses of unipolar or bipolar disorder. Burden of depressive symptoms during prospective follow-up was obtained from the LIFE PSR scales, which were anchored to diagnostic thresholds for RDC disorders. Based on previously published criteria (Judd et al. Reference Judd, Akiskal, Schettler, Endicott, Maser, Solomon, Leon, Rice and Keller2002), a week of depressive symptoms was operationalized as a PSR cut-off score of ⩾3/6 (moderate symptoms or impairment) on the major depression or schizo-affective depression scale or a score of 3/3 (definite criteria) for minor or intermittent depression with a PSR of ⩽2 on the major depressive scale. Differences in percentage of time depressed were assessed non-parametrically using the Wilcoxon rank sum test.
Any suicidal act was included in the survival analysis, regardless of intent or lethality and including those acts with minimal, if any, intent or no actual threat to life. Suicidal behaviors were broadly defined and encompassed suicide attempts or suicide completions. Time to suicide attempt or completion by prospective diagnosis was illustrated using Kaplan–Meier methods. Survival time represented the number of weeks from intake into the cohort study until the outcome of interest: suicide attempt or suicide completion. Cases lost to follow-up prior to suicide attempts or completions were censored. This analysis assumed that censoring and suicide attempts or completions were independent. Death indices from vital statistics were searched methodically, minimizing the risk that suicide completions went unrecognized in censored cases. When the cause of death was unknown, the death was not included as a suicide in our analysis. The above analyses were completed using SAS version 9.1 (SAS Institute Inc., Cary, NC, USA) and SPSS version 13.0 (SPSS Inc., Chicago, IL, USA).
The strength of the association between each of several previously established and a priori selected risk factors for completed suicide were examined in a grouped-time mixed-effects survival analysis (Hedeker et al. Reference Hedeker, Siddiqui and Hu2000). Given that risk factors may change over time, the unit of analysis was changed from individual participant to mood cycle to make best use of the prospective data. The mood cycle included a mood episode and the subsequent period of recovery until next episode, if applicable. Cycle length was grouped as a grouped-time, ordinal variable into the following 10 categories: ⩽8 weeks (⩽2 months), 9–16 weeks (2–4 months), 17–26 weeks (4–6 months), 27–36 weeks (6–9 months), 37–52 weeks (9–12 months), 53–104 weeks (1–2 years), 105–156 weeks (2–3 years), 157–208 weeks (3–5 years), 209–312 weeks (4–6 years), or >312 weeks (>6 years). The beginning of a cycle was determined by study intake or the first week of any recurrence following a period of recovery. The end of a cycle was determined by the week prior to any recurrence following a period of recovery or the point at which the participant was lost to follow-up. A recovery was defined as 8 consecutive weeks with no or only residual symptoms (PSR ⩽2) on the major depressive, manic, schizo-affective depressive, or schizo-affective manic scales of the PSR with no symptoms on the hypomania, minor depression, or intermittent depression scales. A recurrence was defined as any PSR scale >2 lasting 1 or 2 weeks, for (hypo)manic and depressive symptoms respectively.
Age at cycle onset was included as a continuous covariate in the assessment of the following risk factors: gender, feelings of hopelessness, history of suicide attempt, history of multiple suicide attempts, marital status (married versus others), and substance abuse. Data on hopelessness were collected only on intake. With the exception of hopelessness and gender, the most recent assessment prior to cycle onset was used for other main effects. The model containing main effects thus contained the following variables: age, male gender, hopelessness, married status, prior suicide attempts, current substance abuse, and polarity. A differential effect of these risk factors by polarity was then assessed. For each assessed polarity by main effect interaction, the likelihood ratio test compared the full model with an interaction term to the reduced model without the interaction term (main effects only). Each statistical test used a two-tailed α of 0.05. These mixed-effects grouped-time regression models were calculated using SuperMix software (Scientific Software International, Chicago, IL, USA).
Results
Stability of diagnosis
Diagnostic categories were stable during follow-up for the majority of participants, although clinically meaningful rates of conversion from unipolar major depression to a bipolar disorder were observed during follow-up. Of the patients who entered the CDS with unipolar major depression, the prospectively observed conversion rate to a diagnosis of bipolar II and bipolar I was 10.6% and 6.9% respectively. Participants were followed for a mean of 799 (median=988, s.d.=460) weeks or 15.4 (median=19, s.d.=8.8) years of prospective follow-up. Comparisons of baseline and demographic variables between diagnostic groups are detailed in Table 1. Unipolar patients were significantly older than bipolar patients at study intake and more likely to be married.
Table 1. Baseline demographic and clinical characteristics of participants by polarity
Information obtained from participants on intake from the Schedule for Affective Disorders and Schizophrenia (SADS) and Personal History of Depressive Disorders (PHDD). Differences in suicide attempts are not significant after controlling for age of onset.
Suicide attempts prior to intake into the CDS
Participants with bipolar disorder (38.4%) were somewhat more likely than those with unipolar disorder (28.7%) to have attempted suicide prior to intake, although bipolar participants were not at significantly greater risk for any prior suicide attempt [hazard ratio (HR) 1.14, 95% confidence interval (CI) 0.95–1.38, p=0.16] or multiple prior suicide attempts (HR 1.08, 95% CI 0.91–1.39, p=0.38) when age of onset was controlled for in logistic regression. However, participants with bipolar disorder were 30% more likely than those with unipolar disorder to have had prior severe suicide attempts (HR 1.30, 95% CI 1.09–1.38, p<0.005), controlling for age of onset in logistic regression.
Suicide attempts and depressive morbidity after intake into the CDS
The frequency of suicide outcome measures by diagnosis during follow-up is detailed in Table 2. Individuals with bipolar disorder had a greater frequency of suicide attempts during prospective follow-up than those with unipolar depression (34.0% v. 27.2%). However, bipolar participants were not more likely to complete suicide than their unipolar counterparts (4.4% v. 4.2%). A total of 40 completed suicides were recorded in this study: 21 among those with unipolar depression, 19 with bipolar disorder. Consistent with previously published results (Judd et al. Reference Judd, Akiskal, Schettler, Coryell, Maser, Rice, Solomon and Keller2003), significant differences in the percentage of weeks depressed for each diagnostic category were observed (Table 2), with bipolar disorder associated with a lower cumulative depressive burden. Kaplan–Meier survival curves illustrate suicide attempts and suicide completions (Fig. 1) over the course of prospective follow-up in the CDS for individuals by prospective diagnosis.
Fig. 1. Survival by affective diagnosis. Kaplan–Meier survival estimates for participants based on prospective diagnosis for time to: (a) suicide attempt and (b) completed suicide.
Table 2. Frequency of suicidal behavior and proportion of time depressed during follow-up by polarity
The table outlines the rate of selected suicide outcomes by prospective diagnosis during a median prospective follow-up of 19 years. Severity of suicide attempt was determined by a previously used score of ⩾4 for intent or lethality on the Schedule for Affective Disorders and Schizophrenia (SADS).
Main effect of risk factors on suicidal behavior: unipolar versus bipolar
The effect of polarity on suicidal behavior, defined as suicide attempts or completions, was assessed in mixed-effects grouped-time survival analysis (Table 3). Age at cycle onset was included as a continuous covariate for analysis of main effects. A statistically significant linear protective effect of age was noted (HR 0.95, 95% CI 0.94–0.96, p<0.001). Independent statistically significant main effects were also seen for hopelessness (HR 3.00, 95% CI 1.97–4.51, p<0.001) and substance abuse (HR 2.28, 95% CI 1.64–3.15, p<0.001). The impact of male gender, married status and prior suicide attempts did not reach statistical significance. Polarity did not independently predict risk of suicidal behavior.
Table 3. Main effects and main effect by polarity interactions on suicidal behavior
Hazard ratios (HRs), 95% confidence intervals (CIs) of the hazard ratio, and p values for each main effect were modeled in a mixed-effects, grouped-time survival analysis to predict suicide attempts or completions. Age at cycle onset was included as a covariate in the model. Interactions were compared individually using the likelihood ratio test for comparing nested models, where the interaction term was removed for the reduced model.
a Statistically significant main effect. There were no statistically significant polarity by risk factor interactions on risk of suicidal behavior.
Differential effect of risk factors on suicidal behavior: unipolar versus bipolar
Polarity by main effect interaction terms were individually added to the above model to assess for differential effects of risk factors on suicidal behavior by polarity. No statistically significant polarity by risk factor interactions were noted.
Discussion
The increased rate of suicide attempts prior to study entry in participants with bipolar disorder seems to largely reflect an earlier age of onset, which has been previously noted with bipolar disorder (McMahon et al. Reference McMahon, Stine, Chase, Meyers, Simpson and DePaulo1994). Although a trend was noted in our retrospective analyses of increased prior suicide attempts with bipolar disorder compared with unipolar disorder, the differences observed were statistically significant, after controlling for age of onset, only for severe suicide attempts. These findings differ somewhat with an analysis of the Epidemiologic Catchment Area database, which revealed an excess of suicide attempts in individuals with bipolar disorder even after controlling for age of onset (Chen & Dilsaver, Reference Chen and Dilsaver1996). Although frequency data may have been suggestive of increased suicidal behaviors during follow-up in participants with bipolar disorder, polarity did not independently predict suicidal behaviors when modeled in a mixed-effects grouped-time survival analysis. Age at cycle onset, hopelessness at study intake, and active substance abuse did independently predict suicidal behavior during mood cycles. The likelihood ratio tests revealed no significant main effect by polarity interactions and therefore demonstrated no evidence of differential effects of risk factors for suicidal behavior by polarity.
Prior studies have contrasted risk of suicide between those with bipolar and unipolar disorders with mixed results. Several studies have assessed suicide risk factors in unipolar and bipolar disorders (Fawcett et al. Reference Fawcett, Scheftner, Fogg, Clark, Young, Hedeker and Gibbons1990; Krupinski et al. Reference Krupinski, Fischer, Grohmann, Engel, Hollweg and Moller1998; Schneider et al. Reference Schneider, Muller and Philipp2001; Angst et al. Reference Angst, Stassen, Clayton and Angst2002; Oquendo et al. Reference Oquendo, Bongiovi-Garcia, Galfalvy, Goldberg, Grunebaum, Burke and Mann2007; Tondo et al. Reference Tondo, Lepri and Baldessarini2007), and some have conducted separate analyses by polarity (Black et al. Reference Black, Winokur and Nasrallah1988; Kallner et al. Reference Kallner, Lindelius, Petterson, Stockman and Tham2000; Angst et al. Reference Angst, Angst, Gerber-Werder and Gamma2005a). An assumption of the former studies is that risk factors do not vary by diagnosis and the latter studies do little to validate this assumption. To date, the assumption that established risk factors do not vary by polarity has not been empirically tested in a well-defined prospective cohort. None of these studies have statistically assessed, using an interaction term, differential effects of risk factors by polarity. A community sample of registered admissions did assess for differential effects of risk factors by polarity using an interaction term, but given the limitations in available data, was not able to test for any traditionally established risk factors apart from age, instead focusing on variables related to time from hospital admission (Hoyer et al. Reference Hoyer, Olesen and Mortensen2004). Another study assessed interactions but did not assess differential affects by diagnosis or polarity (Young et al. Reference Young, Fogg, Scheftner and Fawcett1994). Our study uniquely addresses this long-maintained assumption for some established suicide risk factors and supports the now empirically validated assessment of risk factors independent of polarity in affective disorders.
Of the 4204 cycles assessed, 40 ended in suicide and 384 contained suicide attempts. To ensure adequate power to assess the differential effect of risk factors for suicidal behavior by polarity, we used suicidal behavior, representing suicide attempts or completions, as an outcome. The inclusion of suicide attempts may nonetheless limit the generalizability of our findings to the prediction of risk for completed suicide. Those who complete suicide may represent an overlapping but not identical population from those who attempt suicide (Beautrais, Reference Beautrais2001). Furthermore, our focus on polarity did not allow the assessment of a differential effect of risk factors by bipolar subtype. The lower cumulative depression burden in those with bipolar disorder reflects those with bipolar I, but not bipolar II, spending less time depressed. Comparison by bipolar subtype would further allow assessment of other risk factors, such as mixed states or cycling. Our analyses controlled for a linear effect of age on suicide, which may not adequately control for a non-linear age effect. A significant linear effect of age was nonetheless revealed and analysis of the data did not suggest a non-linear age effect. The adjustment of our diagnosis for prospective changes in the psychiatric status of participants strengthens the comparison of these groups by reducing the risk of misclassification. Length of follow-up considered, our observed rate of conversion from unipolar to bipolar disorder is generally consistent with the previous literature (Akiskal et al. Reference Akiskal, Walker, Puzantian, King, Rosenthal and Dranon1983, Reference Akiskal, Maser, Zeller, Endicott, Coryell, Keller, Warshaw, Clayton and Goodwin1995; Goldberg et al. Reference Goldberg, Harrow and Whiteside2001; Angst et al. Reference Angst, Sellaro, Stassen and Gamma2005b). Screening of vital statistics and obtaining death certificates reduces our risk of misclassification for suicide; however, it remains possible that those who died of unknown causes may in fact have died of suicide and been misclassified. The CDS uniquely provides comprehensive demographic, diagnostic and phenomenological data for a large clinical sample of individuals with affective disorders followed for an extended period of time. Other advantages of this sample include rigorous clinical evaluations and low rates of loss to follow-up.
The mixed-effects grouped-time models used in this analysis pose several advantages for the assessment of suicide risk factors in a longitudinal study of this duration. Many studies in psychiatry using survival analysis have made use of more traditional time-to-event approaches such as the log-rank test or Cox proportional hazards models (Leon et al. Reference Leon, Friedman, Sweeney, Brown and Mann1990). These approaches assume independence among observations and therefore cannot include repeated observations for each participant. To accommodate changes in risk factors over time, we changed the unit of analysis to mood cycle, allowing risk factors to be reassessed each cycle. The mixed-effects grouped-time survival method represents a modern approach to examining correlated observations, such as multiple mood cycles, in a single model (Hedeker et al. Reference Hedeker, Siddiqui and Hu2000). This allows for correlation in within-subject mood cycles and for the number of mood cycles to vary widely. All prospectively observed mood cycles were able to be analyzed together, allowing us to assess polarity as a predictor of suicidal behavior and a differential effect of risk factors by polarity within a mood cycle. Many of these risk factors vary over time and our statistical modeling was able to account for this.
The lack of any independent or differential effects of polarity on suicide risk poses several clinical and public health implications. Previous research has suggested that suicide risk factors may differ between those with alcoholism and those without (Murphy et al. Reference Murphy, Wetzel, Robins and McEvoy1992). Our data do not support any such differences by polarity in affective disorders. Although the presence of an affective disorder shapes suicide risk assessment, polarity may be of limited relevance for suicidal risk or the effect of other suicide risk factors. This information may inform current efforts to develop standardized tools to augment the clinical suicide risk assessment. In the light of growing interest in the use of standardized suicide assessment psychometrics, it will be important to clearly delineate that suicide risk factors do not vary by diagnosis. Although the current study suggests no differences in risk factors between the bipolar and unipolar disorders, these findings may not generalize outside of the affective disorders. Suicide risk remains difficult to predict (Goldstein et al. Reference Goldstein, Black, Nasrallah and Winokur1991) and public health initiatives should continue to target the identification and appropriate treatment of affective disorders as a general preventative approach (Rihmer, Reference Rihmer1996; Mann et al. Reference Mann, Apter, Bertolote, Beautrais, Currier, Haas, Hegerl, Lonnqvist, Malone, Marusic, Mehlum, Patton, Phillips, Rutz, Rihmer, Schmidtke, Shaffer, Silverman, Takahashi, Varnik, Wasserman, Yip and Hendin2005).
Acknowledgments
We thank Nancy Hsuing, Carol A. Moss and Barbara M. Robb for graciously sharing their understanding of the data collection and recording procedures involved. This study was funded by NIMH grants 5R01MH025416-33 (W. Coryell), 5R01MH023864-35 (J. Endicott), 5R01MH025478-33 (M. Keller), 5R01MH025430-33 (J. Rice) and 5R01MH029957-30 (W. A. Scheftner) and was conducted with the current participation of the following investigators: M. B. Keller (Chairperson, Providence, RI); W. Coryell (Co-Chairperson, Iowa City, IA); D. A. Solomon (Providence, RI); W. Scheftner (Chicago, IL); J. Endicott, A. C. Leon and J. Loth (New York, NY); and J. Rice (St Louis, MO).
This manuscript has been reviewed by the Publication Committee of the CDS and has its endorsement. The data for this manuscript came from the NIMH Collaborative Program on the Psychobiology of Depression – Clinical Studies. The Collaborative Program was initiated in 1975 to investigate nosologic, genetic, family, prognostic and psychosocial issues of mood disorders, and is an ongoing, long-term multi-disciplinary investigation of the course of mood and related affective disorders. The original principal and co-principal investigators were from five academic centers and included Gerald Klerman* (Co-Chairperson), Martin Keller, Robert Shapiro* (Massachusetts General Hospital, Harvard Medical School), Eli Robbins*, Paula Clayton, Theodore Reich*, Amos Wellner* (Washington University Medical School), Jean Endicott, Robert Spitzer (Columbia University), Nancy Andreasen, William Coryell, George Winokur* (University of Iowa), Jan Fawcett and William Scheftner (Rush Presbyterian –St Luke's Medical Center). The NIMH Clinical Research Branch was an active collaborator in the origin and development of the Collaborative Program with Martin M. Katz, Branch Chief, as the Co-Chairperson and Robert Hirschfeld as the Program Coordinator. Other past collaborators include J. Croughan, M. T. Shea, R. Gibbons, M. A. Young and D. C. Clark. (* Deceased.)
Declaration of Interest
Dr Fiedorowicz is supported by L30 MH075180-02, the Nellie Ball Trust Research Fund, and a NARSAD Young Investigator Award. He has further received support for participating in a colleague's investigator-initiated study with Eli Lilly. Dr Leon has served as a consultant to the National Institute of Mental Health and Food and Drug Administration and as a member of Data and Safety Monitoring Boards for Pfizer, Organon, and Dainippon Sumitomo Pharma America. Dr Keller has served as a consultant or received honoraria from Collegium, Cypress Bioscience, Cyberonics, Eli Lilly, Forest Laboratories, Janssen, Organon, Otsuka, Pfizer, Pharmastar, Sepracor, Vela Pharmaceuticals, Wyeth Pharmaceuticals. He has received grants from Eli Lilly, Pfizer, and Wyeth Pharmaceuticals. He has served on advisory boards for Abbott Laboratories, Bristol-Myers Squibb, Cyberonics, Cypress Bioscience, Eli Lilly, Forest Laboratories, GlaxoSmithKline, Janssen, Novartis, Organon, Pfizer, Sepracor, and Wyeth Pharmaceuticals. Dr Solomon has served as an investigator for research funded by Jansenn Pharmaceutica, as a consultant to Solvay Pharmaceuticals, Shire, and Novartis, and has served on the lecture bureaus of AstraZeneca, Pfizer, GlaxoSmithKline, and Shire. Dr Rice is listed as an inventor on a patent (US 20070258898) held by Perlegen Sciences, Inc., covering the use of certain single nucleotide polymorphisms in determining the diagnosis, prognosis, and treatment of addiction.
