kindling hypothesis psychiatry

• Open access
• Published: 25 February 2015

The influence of life events on first and recurrent admissions in bipolar disorder

• Sanne M Kemner 1 ,
• Neeltje EM van Haren 1 ,
• Florian Bootsman 1 ,
• Marinus JC Eijkemans 2 ,
• Ronald Vonk 3 ,
• Astrid C van der Schot 4 ,
• Willem A Nolen 5 &
• Manon HJ Hillegers 1  

International Journal of Bipolar Disorders volume  3 , Article number:  6 ( 2015 ) Cite this article

10k Accesses

14 Citations

7 Altmetric

Metrics details

Life events play an important role in the onset and course of bipolar disorder. We will test the influence of life events on first and recurrent admissions in bipolar disorder and their interaction to test the kindling hypothesis.

We collected information about life events and admissions across the life span in 51 bipolar patients. We constructed four models to explore the decay of life event effects on admissions. To test their interaction, we used the Andersen-Gill model.

The relationship between life events and admissions was best described with a model in which the effects of life events gradually decayed by 25% per year. Both life event load and recurrent admissions significantly increased the risk of both first and subsequent admissions. No significant interaction between life event load and number of admissions was found.

Conclusions

Life events increase the risk of both first and recurrent admissions in bipolar disorder. We found no significant interaction between life events and admissions, but the effect of life events on admissions decreases after the first admission which is in line with the kindling hypothesis.

The presence of psychopathology is often explained on the basis of stress-diathesis interactions (Monroe and Simons 1991 ). The diathesis-stress model serves to explore how non-biological or genetic traits (diatheses) interact with environmental influences (stressors) to trigger the onset of psychiatric disorders (Moffitt et al. 2005 ; Harris 2001 ). The environmental factor most frequently studied in this context is stress, often operationalized as life events. Numerous studies have demonstrated that life events play a role in the onset and course of both unipolar depression and bipolar disorder (Bender and Alloy 2011 ; Brown and Harris 1989 ; Hillegers et al. 2004 ; Hlastala et al. 2000 ; Malkoff-Schwartz et al. 1998 ).

Methodological limitations are a major issue when interpreting and comparing studies regarding the influence of life events on the onset and course of mood disorders (Johnson 2005 ). In many of these studies, data were obtained retrospectively, which complicates the reliable reporting of both life events and mood episodes due to recall bias. Moreover, regardless of the number of questions in an interview, people gradually forget life events (Paykel 1997 ; Brown and Harris 1982 ; Harris 2001 ). Furthermore, most studies so far used the number of episodes to define the course of illness whereas especially episodes longer ago are difficult to be remembered reliable, while it might be more reliable to report episodes which were associated with psychiatric admissions, as these are likely to reflect the most severe mood episodes and can often be confirmed with medical records.

The type of life event measures varies greatly between studies and poses another major obstacle in life event research. In particular, self-administered measures of stressful life events appear unreliable (Johnson 2005 ). Bender and Alloy ( 2011 ) confirmed that the gold standard of life stress measurements is the Life Events and Difficulties Schedule (LEDS) (Brown and Harris 1978 ). The LEDS provides the opportunity to categorize, date and rate both positive and negative life events. Furthermore, in contrast to life event questionnaires, the LEDS interview includes both major and minor types of stress, making it more suitable for testing the kindling hypothesis where life events play a greater role in the onset of initial episodes than in subsequent later episodes, which can even occur more or less spontaneously (Post 1992 ). The kindling model was originally described as the electrical kindling in relation to epilepsy where after many repetitions of kindled seizures ‘spontaneity’ occurs, i.e. seizures develop in the absence of external stimulation (Pinel 1981 ; Wada et al. 1974 ). Interestingly, several studies have demonstrated that a history of episodes is a significant risk factor for future recurrences in mood disorders (Judd et al. 2008 ; Keller et al. 1983 ; Perlis et al. 2006 ). In bipolar disorder, several studies report that after the first admission, 50% to 75% of patients have a recurrence within 4 to 5 years (Bromet et al. 2005 ; Leverich et al. 2001 ).

So far, research on the kindling hypothesis has mainly focused on unipolar depression and a majority of studies indeed found supportive evidence (Bender and Alloy 2011 ). However, studies in bipolar disorder are limited and findings are inconsistent. Bender and Alloy ( 2011 ) integrated the current literature and showed that about half the studies failed to find evidence for the kindling hypothesis in bipolar disorder. Crucially, LEDS interview-based studies all failed to find such evidence (Dienes et al. 2006 ; Hammen and Gitlin 1997 ; Hlastala et al. 2000 ; Swendsen et al. 1995 ). However, they did establish significant associations between the onset and course of bipolar disorder and life events.

In an ongoing naturalistic longitudinal twin study on bipolar disorder, we obtained detailed life event information throughout the life span by using the LEDS and were able to look for possible associations with first and recurrent admissions. Our aims are (1) to assess the influence of the effect of life events on first and recurrent admissions; (2) to assess the influence of prior admissions on the risk of subsequent admissions; and (3) to test the interaction between life event load and number of admissions (i.e. as indication for a kindling effect) in those twins with bipolar disorder.

We conducted a secondary analysis with three a priori questions within an ongoing study among twins (affected twin pairs, n  = 51; healthy control twin pairs, n  = 35) with bipolar disorder at the University Medical Center Utrecht (UMCU), The Netherlands. Of this cohort, all 51 twins with bipolar disorder (bipolar I disorder, n  = 37; bipolar II disorder, n  = 14) were included in the current study. The design of the study and the recruitment of the bipolar twin pairs have been described in detail elsewhere (Van der Schot et al. 2009 ; Vonk et al. 2007 ). All participants were enrolled between 2001 and 2006. There were no restrictions on duration or stage of illness for inclusion in the study, and all patients were treated naturalistically.

Demographic information is displayed in Table  1 . All diagnoses were confirmed with the Structured Clinical Interview for DSM-IV (First et al. 1996 ) and the Structured Interview for DSM-IV Personality (Pfohl et al. 1997 ). Hospitalizations were confirmed through available medical records. Current mood state was assessed using the Young Mania Rating Scale (YMRS; (Young et al. 1978 )) and the Inventory for Depressive Symptomatology (IDS; (Rush et al. 1996 )). At the time of the study, all patients were euthymic with a YMRS score of 4 or less and an IDS score of 12 or less.

The study was approved by the medical ethics review board of the University Medical Center Utrecht, and all participants gave written informed consent after full explanation of the study aims and procedures.

Life event measures

All subjects included in the current study were interviewed with the investigator-based Bedford College LEDS (Brown and Harris 1978 , 1989 ). The LEDS is a semi-structured interview for assessing life events and long-term difficulties in adults. It collects detailed information about the event itself, the timing of its occurrence (date) and relevant contextual information for each event. Each event is categorized into one of ten domains, consisting of education, work, reproduction, housing, money/possessions, crime/legal, health, marital/partner, other relationships and miscellaneous/death. Based on the contextual information, the threat for each event is rated via standardized rating procedures. The threat score represents the severity of the event, ranging from mild (1) to severe (4), hereby differentiating between mild life events and more stressful life events. The contextual threat is conceptualized as: ‘What most people would be expected to feel about an event in a particular set of circumstances and biography, taking no account of what the respondent says either about his or her reaction or about any psychiatric or physical symptoms that followed it’ (Brown and Harris 1989 ). Several studies have supported the reliability (e.g. interrater) and validity (e.g. multiple informant) of the LEDS in adults exhibiting a variety of psychiatric symptoms (Brown and Harris 1978 , 1989 ; Ormel et al. 2001 ).

Only events occurring from the age of 5 years were included. All severe events were defined by the extent they were related to the bipolar disorder and to what extent they were dependent on the respondents’ own behaviour. To determine relatedness to the disorder, each severe event was rated on a three-point scale: 1) not related to psychopathology; 2) possibly related to psychopathology; or 3) clearly related to psychopathology. Only events with score 1 were included for further analyses. To determine if life events occurred independent of will or influence of the respondents’ own behaviour, each severe event was rated on a seven-point scale: 1) completely independent; 2) nearly independent; 3) possible influence, however, very unlikely; 4) physical illness; 5) cooperation or agreement with external situation; 6) likely neglect or carelessness; and 7) intentional choice. Events rating 1 to 5 were included for further analyses. Each life event was dated per year. Age was then calculated for each event.

All interviewers and raters were trained by MH, who was trained by G.W. Brown and T.O. Harris, who developed the LEDS. The interviews were conducted at the participant’s home or at the UMCU. Events were rated by two independent raters who had not been involved in the interviews. A panel consisting of the four raters (including SK and MH) reached consensus on the events that raised rating problems.

Statistical analysis

Life event load.

Life event load represents the sum of the threat scores of the life events occurring in each year.

We calculated three different life event load measures: (1) cumulative load (CL), i.e. the life event load at a particular point in time (year Y ) calculated as the sum of the life event load in year Y and all preceding years; (2) cumulative load excluding events possibly or clearly related to the bipolar disorder (CL-NoBP); and (3) cumulative load including only independent events, thus excluding events possibly or clearly dependent on the respondents’ own behaviour (CL-I).

Next, the life event load before the first or since the last admission was calculated. After each admission, life event load was reset to zero and was calculated as described above. The cumulative life event load in the year preceding the admission was used for analysis.

Decay model

Previous studies showed a decay effect, implying that the presumed effect of life events diminishes over time, e.g. the death of a close relative that occurred 3 or 4 years before admission has less impact compared to the same event 1 year before admission (Hillegers et al. 2004 ). We will investigate which decay model statistically fits the data best. To explore the degree to which the effect of life events diminishes over time, a time-specific life event load variable was calculated for every year and subjected to an exponential decay function. We tested four models; in model I, we tested the purely cumulative effect, and in models II to IV, the decay function implied a 25%, 50% and 75% loss of effect per year, respectively. The decay function yielding the best model fit (−2× log-likelihood) will be used for all further analysis.

Andersen-Gill model

The Andersen-Gill model (A-G model), an extension of the standard Cox proportional hazard model for recurrent events, accommodates censored data and time-dependent covariates (Fleming and Harrington 1991 ; Therneau and Grambsch 2000 ).

Data for the A-G model are structured such that for each individual, intervals at risk are defined by variables describing the start and end times of each year of age. An event variable is coded as ‘1’ for admission and ‘0’ for no admission.

The A-G approach follows the usual assumption of the Cox model that the hazard or risk ratio is proportional over time and more specifically that the risk of being admitted is unaffected by earlier admissions. Time-dependent covariates, such as the cumulative load of life events or the number of previous admissions, may be used to relax the latter assumption. The hazard ratio represents the proportionate change in the ‘admission’ rate due to a unit change in the respective covariate, in this case the cumulative life event load.

Andersen-Gill model: interaction effect

The presence of an interaction effect will be tested by integrating an interaction function in the A-G model, testing the effect of the interaction between the number of admissions and the cumulative load between the admissions in the best-fitted decay model, also known as a kindling effect (Post 1992 ).

The general characteristics of our sample are shown in Table  1 . At least one admission had occurred for 35 of the 51 bipolar patients, with a maximum of 11 admissions in two patients. Figure  1 and Table  2 display the number and polarity for all admissions.

Number and polarity of admissions.

Influence of life event effect on first and recurrent admissions

The relationship between life event load and admission (irrespective of the number of admissions) is depicted in Table  3 . The exponentiated linear coefficients from the A-G model are interpreted as risk ratios relating the magnitude of a covariate (or multiple covariates) to admission. Positive coefficients indicate increased hazard for admission (‘1’ vs ‘0’). Figure  2 illustrates the cumulative life event load over time and the cumulative life event load between the admissions.

Course of cumulative load.

Independent of the model employed (cumulative, 25%, 50% or 75% decay), the life event load was significantly associated with an increased risk of hospitalization per unit life event load. Adjustment for age and gender did not change the life event risk ratios. According to the log-likelihood, indicating the quality of fit, the decay model in which the life event load accumulates and at the same time decreases with a function of 25% with every subsequent year (model II) was most in agreement with the observed data. Therefore, all further analyses will be done under model II.

Table  4 displays the results of the A-G model with the three different types of load between the admissions: CL, CL-NoBP and CL-I.

All coefficients for both life event load and number of admissions are positive and significant. Positive effect of all types of life event load indicates that the risk of getting admitted grows with an increasing life event load. This effect is independent of the type of life event load.

The A-G model with lifetime cumulative load and number of admissions shows a positive and significant risk ratio for both the lifetime load (coef = .0985, SE = .0166, p  < .001) and number of admissions (coef = .4597, SE = .0892, p  < .001), indicating that in addition to the cumulative load between the admissions, the lifetime cumulative load also contributes to the risk of getting admitted.

Effect of number of previous admissions on admissions

The positive and significant coefficient of the number of admissions on the risk of getting admitted implies an increase in the chance of getting admitted with each subsequent admission.

Interaction between life event load and number of admissions

The interaction effect between the cumulative load between admissions in model II (with 25% decay) and number of admissions did not reach significance, indicating that the effect of cumulative load on the risk of admission does not change with subsequent admissions. However, the influence of life events on first admissions is higher compared with the influence of life events after on readmissions, suggesting a shift in the effect of life events between the first and subsequent admissions.

Results did not change when excluding the concordant co-twins from the sample. Also, neither age, age of onset of the first bipolar episode, age of first admission nor gender affected any of the above findings.

Our main finding is that an increased life event load, taking into account the number and threat of life events, impacts both first and recurrent admissions in bipolar patients. This has also been found in previous studies (Bender and Alloy 2011 ; Hunt et al. 1992 ; Kessing et al. 1998 , 2004 ), but it was hypothesized that this might be due to life events occurring as a consequence of the disease (Kessing et al. 2004 ). We now extended these previous findings by showing that the effect of life events on admissions did not change when events related to the disorder were excluded from the analyses. This suggests that the effect of life events is independent of life events occurring in relation to the disorder. We consider this robust influence of life events on first and recurrent admissions an important finding, as exposure and responses to life events are potentially modifiable. A better understanding of how they impact the risk of being admitted may yield specific strategies for prevention and early intervention.

Our next finding was that the effect of the number of prior admissions on the risk of getting admitted was positive and significant, demonstrating that the risk increases with each admission. Several studies reported that after the first admission for bipolar disorder, 50% to 75% of patients relapse within 4 to 5 years (Bromet et al. 2005 ; Leverich et al. 2001 ). Our findings indicate that the risk of readmission increases as a function of the number of previous admissions. Given our finding that the risk of getting admitted is independent of events that are related to the disorder, such as admissions, the association between the number of previous admissions and increased risk of readmission might be interpreted as an indicator for illness severity. Moreover, this finding also suggests a possible kindling effect; previous admissions could trigger the next admission.

Finally, we found no significant interaction between life event load and the number of prior admissions on the risk to be readmitted, suggesting that the effect of life event load does not decrease as a function of subsequent admissions. However, we did find a stronger effect of life events on the first compared to subsequent admissions which does suggest a possible kindling effect. In this respect, it should however be realized that the kindling effect has mostly been found after the occurrence of five to seven episodes (Kendler et al. 2000 ; Kendler and Gardner 2001 ; Slavich et al. 2011 ) while we only looked at admissions and the average number of admissions lies between three and four in our sample. Previous studies using the LEDS in bipolar patients (Dienes et al. 2006 ; Swendsen et al. 1995 ; Hammen and Gitlin 1997 ) looking at episodes rather than admissions did not find evidence for either presence or absence of a kindling effect. This is in contrast with findings in unipolar depression, which might be explained by the more complex course of contrasting mood episodes (i.e. mania and depression) in bipolar disorder as compared with unipolar depression (only depression). Bipolar episodes can be manic, hypomanic, depressive or mixed, and it is possible that the influence of life events differs across these various episodes.

The effect of life events on admissions was best described by model II in which the influence of life events steadily accumulates (as one gets older, more life events occur) but at the same time gradually decays with 25% per year as time goes by (an event that has occurred years ago will no longer have the same impact as when it just happened). This decay model is in accordance with previous findings from our group in a sample of offspring of parents with bipolar disorder (Hillegers et al. 2004 ). The decay model of 25% (Figure  3 ) best explained the influence of stressful life events on the onset of mood disorders when compared to the purely cumulative model or models with 50% or 75% decay per year. The underlying mechanisms that cause this decay are not known; a possible explanation lies in the interaction of life stress with coping strategies and temperament. Coping responses influence the association between stress and the onset of mood episodes. Temperamental traits influence individual coping styles and modify the impact of stressful life events on mood episode onset (Compas et al. 2004 ).

Course of cumulative load under the 25% decay model.

There are several limitations that need to be taken into account when interpreting our findings. Firstly, methodological limitations are a major issue when interpreting and comparing studies regarding the influence of life events on the onset and course of mood disorders (Johnson 2005 ). In many of these studies, information on life events was obtained retrospectively with queries or (semi-) structured interviews, which complicates the reliable reporting due to recall bias. Regardless of the number of queries in an interview, people gradually forget life events (Paykel 1997 ; Brown and Harris 1982 ; Harris 2001 ). The average participant in our sample had to report life events over a time span of 35 years. One could question the reliability of the LEDS when it is used retrospectively to collect lifetime life event data. Most studies restrict the reporting of life events to a 12-month period. However, the LEDS is probably more reliable compared to (retrospective) checklist inventories (Hillegers et al. 2004 ; Ormel et al. 2001 ), as the LEDS minimizes recall bias; information is actively obtained in a very structured interview by detailed questions in ten domains. Furthermore, there is evidence that recall bias is more pronounced for minor events, suggesting that major life changes are under less influence of recall bias (Funch and Marshall 1984 ).

Secondly, more than one admission could occur per year. It is not clear to what extent this influenced our results, since admissions occurring within 3 to 6 months after the first admission are associated with more subclinical affective symptoms and therefore could be due to the same bipolar episode (Bromet et al. 2005 ). Unfortunately, the data on life events was dated per year and did not allow us to conduct the analysis in more detail.

We made no distinction between admissions due to mania, depression or psychosis. However, as can be seen in Figure  1 , the polarity of the admissions is equally divided across the number of admissions for manic and depressive episodes.

Our sample is drawn from a longitudinal twin study. Having participants in the sample that share their genes and environment to a large extent might influence the study results. However, excluding the bipolar co-twins ( n  = 8) resulting in only one twin per pair in the analysis ( n  = 43) did not change our findings.

Finally, although most analyses yielded significant results, we have a small sample size consisting of patients with bipolar I as well as bipolar II disorders. So far, most studies limit their sample to bipolar type I (Bender and Alloy 2011 ). The small sample size did not allow us to compare the two subtypes.

Life events, taking into account the number and threat of life events, appeared to have an impact on both first and recurrent admissions in bipolar patients, and this effect appeared not be dependent on events related to the illness. In addition, the number of prior admissions was positively related to the risk of getting readmitted. Finally, we did not find an interaction between life events and admissions on the risk for readmission, although the effect of life events was stronger on first admissions compared to readmissions, which suggests a possible kindling effect.

Abbreviations

cumulative load

cumulative load including only events occurring independent of the respondents’ own behaviour

cumulative load excluding all events occurring as a consequence of bipolar disorder

Life Events and Difficulties Schedule

Bender RE, Alloy LB. Life stress and kindling in bipolar disorder: review of the evidence and integration with emerging biopsychosocial theories. Clin Psychol Rev. 2011;31:383–98.

Article   PubMed Central   PubMed   Google Scholar  

Bromet EJ, Finch SJ, Carlson GA, Fochtmann L, Mojtabai R, Craig TJ, et al. Time to remission and relapse after the first hospital admission in severe bipolar disorder. Soc Psychiatry Psychiatr Epidemiol. 2005;40:106–13.

Article   PubMed   Google Scholar  

Brown GH, Harris TO. Social origins of depression: a study of psychiatric disorder in woman. London: Tavistock Publications; 1978.

Google Scholar  

Brown GH, Harris TO. Fall-off in the reporting of life events. Soc Psychiatr. 1982;17:23–8.

Article   Google Scholar  

Brown GH, Harris TO. Life events and illness. New York: Guilford Press; 1989.

Compas BE, Connor-Smith J, Jaser SS. Temperament, stress reactivity, and coping: implications for depression in childhood and adolescence. J Clin Child Adolesc Psychol. 2004;33:21–31.

Dienes KA, Hammen C, Henry RM, Cohen AN, Daley SE. The stress sensitization hypothesis: understanding the course of bipolar disorder. J Affect Disord. 2006;95:43–9.

First MB, Spitzer RL, Gibbon M, Williams J. Structured clinical interview for DSM-IV axis I disorders- patient. 20th ed. New York: New York State Psychiatric Institute; 1996.

Fleming T, Harrington D. Counting processes and survival analysis. New York, NY, USA: Wiley; 1991.

Funch DP, Marshall JR. Measuring life stress: factors affecting fall-off in the reporting of life events. J Health Soc Behav. 1984;25:453–64.

Article   CAS   PubMed   Google Scholar  

Hammen C, Gitlin M. Stress reactivity in bipolar patients and its relation to prior history of disorder. Am J Psychiatry. 1997;154:856–7.

Harris T. Recent developments in understanding the psychosocial aspects of depression. Br Med Bull. 2001;57:17–32.

Hillegers MH, Burger H, Wals M, Reichart CG, Verhulst FC, Nolen WA, et al. Impact of stressful life events, familial loading and their interaction on the onset of mood disorders: study in a high-risk cohort of adolescent offspring of parents with bipolar disorder. Br J Psychiatry. 2004;185:97–101.

Hlastala SA, Frank E, Kowalski J, Sherrill JT, Tu XM, Anderson B, et al. Stressful life events, bipolar disorder, and the ‘kindling model’. J Abnorm Psychol. 2000;109:777–86.

Hunt N, Bruce-Jones W, Silverstone T. Life events and relapse in bipolar affective disorder. J Affect Disord. 1992;25:13–20.

Johnson SL. Life events in bipolar disorder: towards more specific models. Clin Psychol Rev. 2005;25:1008–27.

Judd LL, Schettler PJ, Akiskal HS, Coryell W, Leon AC, Maser JD, et al. Residual symptom recovery from major affective episodes in bipolar disorders and rapid episode relapse/recurrence. Arch Gen Psychiatry. 2008;65:386–94.

Keller MB, Lavori PW, Lewis CE, Klerman GL. Predictors of relapse in major depressive disorder. JAMA. 1983;250:3299–304.

Kendler KS, Gardner CO. Monozygotic twins discordant for major depression: a preliminary exploration of the role of environmental experiences in the aetiology and course of illness. Psychol Med. 2001;31:411–23.

CAS   PubMed   Google Scholar  

Kendler KS, Thornton LM, Gardner CO. Stressful life events and previous episodes in the etiology of major depression in women: an evaluation of the ‘kindling’ hypothesis. Am J Psychiatry. 2000;157:1243–51.

Kessing LV, Andersen PK, Mortensen PB. Predictors of recurrence in affective disorder. A case register study. J Affect Disord. 1998;49:101–8.

Kessing LV, Agerbo E, Mortensen PB. Major stressful life events and other risk factors for first admission with mania. Bipolar Disord. 2004;6:122–9.

Leverich GS, Nolen WA, Rush AJ, McElroy SL, Keck PE, Denicoff KD, et al. The Stanley Foundation Bipolar Treatment Outcome Network. I. Longitudinal methodology. J Affect Disord. 2001;67:33–44.

Malkoff-Schwartz S, Frank E, Anderson B, Sherrill JT, Siegel L, Patterson D, et al. Stressful life events and social rhythm disruption in the onset of manic and depressive bipolar episodes: a preliminary investigation. Arch Gen Psychiatry. 1998;55:702–7.

Moffitt TE, Caspi A, Rutter M. Strategy for investigating interactions between measured genes and measured environments. Arch Gen Psychiatry. 2005;62:473–81.

Monroe SM, Simons AD. Diathesis-stress theories in the context of life stress research: implications for the depressive disorders. Psychol Bull. 1991;110:406–25.

Ormel J, Oldehinkel AJ, Brilman EI. The interplay and etiological continuity of neuroticism, difficulties, and life events in the etiology of major and subsyndromal, first and recurrent depressive episodes in later life. Am J Psychiatry. 2001;158:885–91.

Paykel ES. The interview for recent life events. Psychol Med. 1997;27:301–10.

Perlis RH, Ostacher MJ, Patel JK, Marangell LB, Zhang H, Wisniewski SR, et al. Predictors of recurrence in bipolar disorder: primary outcomes from the Systematic Treatment Enhancement Program for Bipolar Disorder (STEP-BD). Am J Psychiatry. 2006;163:217–24.

Pfohl B, Blum N, Zimmerman M. Structured interview for DSM-IV personality; SIDP-IV. Iowa: American Psychiatric Press; 1997.

Pinel JP. Kindling-induced experimental epilepsy in rats: cortical stimulation. Exp Neurol. 1981;72:559–69.

Post RM. Transduction of psychosocial stress into the neurobiology of recurrent affective disorder. Am J Psychiatry. 1992;149:999–1010.

Rush AJ, Gullion CM, Basco MR, Jarrett RB, Trivedi MH. The Inventory of Depressive Symptomatology (IDS): psychometric properties. Psychol Med. 1996;26:477–86.

Slavich GM, Monroe SM, Gotlib IH. Early parental loss and depression history: associations with recent life stress in major depressive disorder. J Psychiatr Res. 2011;45:1146–52.

Swendsen J, Hammen C, Heller T, Gitlin M. Correlates of stress reactivity in patients with bipolar disorder. Am J Psychiatry. 1995;152:795–7.

Therneau TM, Grambsch PM. Modeling survival data: extending the Cox model. New York, NY, USA: Springer-Verlag; 2000.

Book   Google Scholar  

van der Schot AC, Vonk R, Brans RG, van Haren NE, Koolschijn PC, Nuboer V, et al. Influence of genes and environment on brain volumes in twin pairs concordant and discordant for bipolar disorder. Arch Gen Psychiatry. 2009;66:142–51.

Vonk R, van der Schot AC, Kahn RS, Nolen WA, Drexhage HA. Is autoimmune thyroiditis part of the genetic vulnerability (or an endophenotype) for bipolar disorder? Biol Psychiatry. 2007;62:135–40.

Wada JA, Sato M, Corcoran ME. Persistent seizure susceptibility and recurrent spontaneous seizures in kindled cats. Epilepsia. 1974;15:465–78.

Young RC, Biggs JT, Ziegler VE, Meyer DA. A rating scale for mania: reliability, validity and sensitivity. Br J Psychiatry. 1978;133:429–35.

Download references

Acknowledgements

The authors wish to thank Annemiek van den Brand, Mirre Hubers, Guusje Havenaar, Nanja Hibbel and Afanaisa Lazo for assistance in conducting and rating the LEDS interviews.

Financial support

This study was supported by grant 22963 from the 7FP of the European Commission, grant 9120818 from the Netherlands Organisation for Scientific Research (NWO) and Stanley Medical Research Institute.

Author information

Authors and affiliations.

Brain Center Rudolf Magnus, Department of Psychiatry, University Medical Center Utrecht, A00.241, PO Box 85500, 3508 GA, Utrecht, The Netherlands

Sanne M Kemner, Neeltje EM van Haren, Florian Bootsman & Manon HJ Hillegers

Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht, The Netherlands

Marinus JC Eijkemans

Reinier van Arkel Group, ’s-Hertogenbosch, The Netherlands

Ronald Vonk

Alzheimer Nederland, Amersfoort, The Netherlands

Astrid C van der Schot

Department of Psychiatry, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands

Willem A Nolen

You can also search for this author in PubMed   Google Scholar

Corresponding author

Correspondence to Sanne M Kemner .

Additional information

Competing interests.

MH has received grants from the Netherlands Organisation for Health Research and Development and the Brain and Behavior Research Foundation and has received speaker’s fees from Astra Zeneca, Benecke, Shire and Lundbeck. WN has received grants from the Netherlands Organisation for Health Research and Development, the European Union, Astra Zeneca, GlaxoSmithKline and Wyeth, and has received honoraria/speaker’s fees from Astra Zeneca and Lundbeck. The other authors report no competing interests.

Authors’ contributions

SK carried out the data acquisition, data preparation and analyses, and drafted and edited the manuscript. NH participated in the design of the study and drafted the manuscript. FB carried out the data acquisition and data preparation and reviewed the manuscript. ME designed and carried out and assisted with statistical analysis. RV participated in the study design and data acquisition. AS participated in the study design and data acquisition. WA conceived the study and participated in its design and coordination. MHJH participated in the study design and drafted the manuscript. All authors read and approved the final manuscript.

Rights and permissions

Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License ( https://creativecommons.org/licenses/by/4.0 ), which permits use, duplication, adaptation, distribution, and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.

Reprints and permissions

About this article

Cite this article.

Kemner, S.M., van Haren, N.E., Bootsman, F. et al. The influence of life events on first and recurrent admissions in bipolar disorder. Int J Bipolar Disord 3 , 6 (2015). https://doi.org/10.1186/s40345-015-0022-4

Download citation

Received : 26 October 2014

Accepted : 26 January 2015

Published : 25 February 2015

DOI : https://doi.org/10.1186/s40345-015-0022-4

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

• Bipolar disorder
• Mood disorder
• Life events

Log in using your username and password

• Search More Search for this keyword Advanced search
• Latest content
• Current issue
• BMJ Journals More You are viewing from: Google Indexer

You are here

• Volume 69, Issue 1
• The psychoses of epilepsy
• Article Text
• Article info
• Citation Tools
• Rapid Responses
• Article metrics

• King's College Hospital, Department of Psychological Medicine, King's College Hospital, Denmark Hill, London SE5 9RS, UK
• Dr B K Toone, King's College Hospital, Department of Psychological Medicine, King's College Hospital, Denmark Hill, London SE5 9RS, UK brian.toone& kcl.ac.uk

https://doi.org/10.1136/jnnp.69.1.1

Statistics from Altmetric.com

Request permissions.

If you wish to reuse any or all of this article please use the link below which will take you to the Copyright Clearance Center’s RightsLink service. You will be able to get a quick price and instant permission to reuse the content in many different ways.

People who have epilepsy seem particularly liable to certain major psychiatric disorders: a chronic interictal psychosis that closely resembles schizophrenia; and episodic psychotic states, some of which may arise in close temporal relation with seizure activity. These disorders are conventionally referred to as the psychoses of epilepsy although some of the episodic forms would be more accurately described as acute confusional states. These conditions have for long puzzled and intrigued psychiatrists and neurologists, but in recent years this interest has quickened especially among biologically minded psychiatrists in search of a neurological model for schizophrenia. In the psychoses of epilepsy and in schizophrenia converging lines of enquiry, in neuroimaging and in neuropathology in particular, have implicated the mesial temporal structures, the more so in the dominant hemisphere, and comparable abnormalities have been reported. 1 This review will seek to identify areas of progress, areas of difficulty, and persisting dilemmas.

Epilepsy and schizophrenia

The precise nature of this relation has taxed clinical observers since before the turn of the century. Over the past four decades a consensus has begun to take shape—namely, that certain forms of epilepsy may act as risk factors for the subsequent development of a chronic interictal psychosis, a syndrome sometimes referred to as the schizophrenia-like psychoses of epilepsy (SLPE). This psychosis does resemble schizophrenia in its phenomenological manifestations, 2 3 pursues a similar course, is as responsive to antipsychotic medication, and is largely uninfluenced by concurrent seizure activity. Given these similarities it is reasonable to question whether this comorbidity could not have arisen as a result of a chance association between two relatively common disorders. The answer should lie in community based comparisons of unbiased samples of epileptic and non-epileptic subjects with respect to prevalence of psychosis. Attempts have been made to do this, yielding a prevalence of schizophrenia within an epileptic population that varies between 3% and 7% (prevalence in general population 1%). However, a failure to use strictly defined and internationally recognised diagnostic criteria for schizophrenia and to distinguish between SLPE and episodic psychoses may have resulted in a spurious inflation in the prevalence figures. In the past year two studies that have to some extent redressed this difficulty have been published. In Iceland a case controlled study 4 found that although there was no excess of psychiatric illness in epileptic subjects, among those who were psychiatrically ill a disproportionate number were psychotic. In Denmark Bredkjaer et al 5 used two national inpatient registers for epilepsy and for psychosis respectively to compare the subsequent incidence of schizophrenia in patients who had at some point undergone admission to hospital for epilepsy with that in the general population. A standardised incidence ratio of 1.48 for all epilepsy and 2.35 for psychomotor epilepsy argues strongly for epilepsy as a risk factor for schizophrenia, but an epilepsy sample defined by need for inpatient care may be considered unrepresentative. Although vulnerable to similar criticism, the neurology clinic based study of Mendes et al 6 report convincing data. The prevalence of schizophrenia, diagnosed according to DSM3R criteria, was nine times greater among clinic attenders with epilepsy than those with migraine. Taken together these studies provide strong, though far from conclusive evidence, that the SLPE is a unique disorder and not an artefact of random association. The definitive epidemiological study is still awaited. Meanwhile other approaches could be explored: if the SLPE is indeed a secondary or symptomatic psychosis the prevalence of schizophrenia among family members should not greatly exceed the prevalence in the general population. It should certainly fall short of that found in the families of schizophrenic probands. Early studies—for example, that of Slater and Glithero, 7 suggest that this is the case, but as yet no family history study using state of the art methodology has been published.

The episodic psychoses

The most common by far are the postictal psychoses. Others include some drug induced psychoses and the alternating psychoses that arise during periods of improved seizure control. The salient features of postictal psychoses have been consistently reported (for example, see Logsdail and Toone 14 ). They usually follow exacerbations, especially clusters, of complex partial seizures, sometimes without but more commonly with generalisation. Characteristically, cessation of seizure activity is followed by a brief interlude—a “lucid interval”—of 12-72 hours before the mental state deteriorates. The psychosis, which comprises affective, schizophrenic, and confusional elements, may last for up to a week. The EEG may exhibit increased epileptic discharge activity or a slowed dominant rhythm. The episodes resolve spontaneously, but often recur, usually displaying similar phenomenology.

The same question that was asked of the SLPE may also be asked of the postictal psychoses: what factors predispose the minority of epileptic patients to develop postictal psychoses? Although a far from uncommon disorder, postictal psychoses have received considerably less attention than SLPE and many of the accounts that have been published describe an iatrogenic psychosis that occurs for the first time after an increase in seizure activity due to anticonvulsant withdrawal during presurgical evaluation. Although such studies may be informative, particularly as they are prospective and accompanied by telemetric information, the cases they describe are likely to be unrepresentative of the spontaneously occurring postictal psychoses. Some centres report an association between complex partial seizures and postictal psychoses 14 15 ; others fail to do so. 16 Postictal psychotic subjects report more seizure clustering 17 and ictal fear (complex partial seizures containing fear as a major component). 18 19 Bilateral EEG discharges are seen more often in subjects with postictal psychoses than non-psychotic controls. 16-18 Compared with SLPE, both age of epilepsy onset 17 and psychosis onset 19 are delayed.

Other forms of episodic psychosis are distinctly uncommon. The phenomenon of forced normalisation, whereby improved seizure control, usually as a result of change of anticonvulsant medication, is associated with a “normalisation” of the EEG and the emergence of psychotic features, may underlie some episodes of psychotic behaviour, particularly those associated with the introduction of novel antiepileptic drugs. As first described by Landolt, forced normalisation occurred in the context of partial epilepsy, but more recent accounts implicate the succinimide group in the treatment of primary generalised epilepsy. 20 The use of vigabatrin 21 and zonisamide 22 may also carry an increased risk of psychotic disturbance.

Psychosis and temporal lobectomy

In the early days of temporal lobe surgery for treatment of epilepsy the psychiatrically unwell made up a significant proportion, sometimes most of those who went forward for surgery. One in six was psychotic. There was then a hope, even an expectation, that surgery might benefit psychosis as it had epilepsy. This was not borne out and the proportion of epileptic patients with psychosis has gradually fallen. But as one author has pointed out, it might still be considered better to be psychotic without seizures than to be psychotic with them. 23 Chronic schizophrenia need not be a contraindication and carefully selected patients may benefit from surgery. 24 A history of postictal psychoses should be considered a positive indication for surgery. Psychosis may present for the first time— “de novo psychosis”—after surgery. The condition was noted in early surgical series, but has recently received greater attention. It is difficult to know whether this represents a true increase in incidence, better recognition, or a fall in the age of surgery. The de novo psychoses are a mixed bag: some are depressive in character, some schizophrenic; some pursue a chronic course, some are episodic, the de novo postictal psychoses forming a definite subgroup. 25 Their aetiology may be similarly diverse. In some the development of psychosis may be predetermined by earlier events and the surgical procedure an irrelevant artefact. In a few cases seizure control may lead to forced normalisation and an alternating psychosis. Only one feature stands out clearly: preoperatively the presence of SLPE is associated with a left temporal focus; 85% of de novo psychoses follow right temporal surgery. The reasons for this are unclear. There is some indication, 26 hardly yet substantiated, that depression is commoner after right sided lobectomy. The amount of resected tissue is also more generous.

Aetiology of the epileptic psychosis

Epilepsy and psychosis may each arise out of some form of cerebral dysfunction common to both; or psychosis may be a consequence of seizure activity. The first seems more likely. Most forms of epileptic psychoses occur more commonly in the partial epilepsies, especially complex partial seizures. Within the surgical series patients with developmental lesions may be at particular risk. Parallels have been drawn between anomalies of cerebral architecture in schizophrenia and the epileptogenic cortical dysplasias 27 but in clinical practice such associations have yet to be reported. Distinctive patterns of hypometabolism/hypoperfusion have been reported 28 29 but with no consistency. Dominant hemispheric temporal lobe involvement may be associated with a more pure form of SLPE. 30 The role of seizure activity finds its theoretical justification in the kindling hypothesis but in humans even the kindling of epileptic seizures remains debatable. There is no precedent for the kindling of behavioural change. Liability to SLPE is not related to seizure frequency or severity; indeed it often occurs at a time when seizure frequency is declining. However, the extent of mesial temporal and, particularly, extratemporal damage may be a risk factor. Recent neuroimaging data suggest that mesial temporal lesions may be associated with loss of tissue volume and with neuronal damage in areas well beyond the temporal lobes including the striatum, thalamus, and frontoparietal grey matter. 31 32 Reductions in hippocampal volume, tissue damage, and neuronal loss may be progressive and may reflect the duration of epilepsy. 33 34 Function may also deteriorate with chronicity. 35 Extratemporal neuronal damage may also occur 36 although it may be reversible. Bruton et al 37 reported ventricular enlargement, periventricular gliosis, and white matter abnormalities in epileptic patients with psychosis compared with those without. Subjects with SLPE have smaller whole brain and bilateral hippocampal volumes compared with matched non-psychotic epileptic controls (Mellers et al , personal communication). The association between degree of damage and chronicity may explain the time interval between onset of seizures and onset of psychosis in both SLPE and postictal psychoses. In conclusion, an accumulation of admittedly less than perfect evidence strongly suggests an environmental rather than a genetically determined form of psychosis. The aetiology, like that of schizophrenia itself, will in all probability prove to be multifactorial with no one factor predominating.

• Ashtari M ,
• Bilder RM ,
• Garralda ME ,
• Stefansson SB ,
• Olafsson E ,
• Bredkjaer SR ,
• Mortensen PB ,
• Mendez MF ,
• Flor-Henry P
• Heathfield KGG ,
• Henson RA ,
• Andermann LF ,
• Meencke HJ ,
• Roberts GW ,
• Bruton CJ ,
• Logsdail SJ ,
• Lancman ME ,
• Craven WJ ,
• Asconape JJ ,
• Devinsky O ,
• Abramson H ,
• Umbricht D ,
• Degreef G ,
• Andermann F ,
• Olivier A ,
• Kanemoto K ,
• Kawasaki JJ ,
• Sander JWAS ,
• Trimble MR ,
• Matsuura M ,
• Reutens DC ,
• Manchanda R ,
• McLachlin RS
• Gallhofer B ,
• Frackowiac R ,
• Mellers JDC ,
• Morrell MJ ,
• De Carli D ,
• Fazilat S ,
• Kälviäinen R ,
• Salmenperä T ,
• Partanen K ,
• Stevens JR ,

Read the full text or download the PDF: