Comorbidities of Migraine

Transcription

1 Comorbidities of Migraine Richard B. Lipton, MD Edwin S Lowe Professor and Vice Chair of Neurology Director, Montefiore Headache Center Albert Einstein College of Medicine

2 Overview What is comorbidity? Why study comorbidity? Explanations for comorbidity Thinking about comorbidities one at a time: Depression as an example Using comorbidity to identify natural subgroups Implications for practice and research

3 What is Comorbidity? The greater than chance association between two conditions in the same individual. Concomitant conditions occur together in the same individual with a frequency that would be predicted by chance Feinstein J, Chronic Disease, 1970

4 Why Study Comorbidity? Comorbidity may complicate diagnosis Diagnostic parsimony may lead to under-diagnosis Comorbidity informs and limits treatment Therapeutic two-fers Therapeutic limitations Some comorbidities are risk factors for disease progression Comorbidities can be used to Identify homogeneous patient groups Comorbidity contributes to disease burden HRQoL (Lipton et al, Neurology, 2000) Economic impact (Lafata et al, AGIM, 2003)

5 5 Migraine comorbidities Other Pain Disorders MI Angina Stroke Hypertension/ hypotension Raynaud s phenomenon PFO Epilepsy Essential tumor Restless leg syndrome Vestibular disorders Bell s palsy Depression Anxiety Panic disorder Bipolar disorder Snoring/sleep apnea Asthma/allergy Non-headache chronic pain IBS IBS, irritable bowel syndrome; MI, myocardial infarction; PFO, patent foramen ovale.

6 Explanations for Comorbidity Unidirectional causal Migraine Depression Depression Migraine Shared environmental risk factors E Depression Migraine Shared genetic risk factors G Depression Migraine Lipton and Silberstein, Neurology, 1994

7 Consider Comorbidities One at a Time Cross-sectional association in population studies. Longitudinal association: Unidirectional or bidirectional; Dose-response; Specificity Risk factor for progression Mechanistic implications Therapeutic implications

8 Lifetime Association of Migraine with Specific Psychiatric Disorders Baltimore ECA Study* DASH AOR (95% CI) AOR (95% CI) Major depression 2.2 ( ) 3.6 ( ) Panic disorder 3.4 ( ) 4.9 ( ) Agoraphobia 1.9 ( ) 2.4 ( ) Any phobia 1.4 ( ) 2.2 ( ) * Swartz et al s (2000) Baltimore ECA study presents DSM-III disorders. Age 30+ The Detroit Area Study of Headache (1997) DSM-IV disorders (WHO-CIDI). Age AOR, Sex- and age-adjusted odds ratios

9 Is the Relationship Between Migraine and Depression Bidirectional? First Onset of Depression Given Migraine First Onset of Migraine Given Depression Detroit Study ( ) ( ) Breslau et al, 1991 (n = 1007) DASH 5.8 ( ) ( ) 2 Breslau et al, 2003 (n = 1696) 1 Sex and education adjusted HR 2 Sex and education adjusted OR

10 Is the Relationship Between Migraine and Depression Specific or does it Occur with Other Severe Headache? First Onset of Depression Given Severe Headache First Onset of Severe Headache Given Depression DASH 2.7 ( ) ( ) 1 Breslau et al, Sex and education adjusted OR

11 Do comorbidities predict migraine progression? In the general migraine population: EM 2 3% per year CM 1. Bigal ME et al. Headache. 2008;48:1157.; 2. Lipton RB et al. Neurology. 2015;84:

12 Depression is a severity dependent risk factor for the new onset of CM in person with EM depression in persons with EM one-year predicts new onset CM the next: Dose-response Data from AMPP Study, Severe 2.6*; 95%CI ( ) Moderately Severe 2.3*; 95%CI ( ) Moderate 1.8*; 95%CI ( ) None/Mild *P< Odds ratio for CM Onset Ashina S et al. J Headache Pain. 2012;13:

13 Comorbidity Considerations for Migraine and Depression Cross-sectional association in clinic-based studies-yes Cross-sectional association in population studies-yes Longitudinal association: Risk factor for progression-yes Mechanistic implications Therapeutic implications Unidirectional or bidirectional Dose-response-Yes Specificity-Yes (depression does not predict severe headache)

14 Modifiable risk factors for onset of chronic migraine: practical suggestions 14 Comorbidities Depression Anxiety Other pain disorders Obesity Asthma Snoring Exogenous factors Treatment/intervention: Treatment/intervention: Assess, Weight Stressful treat/refer loss, life exercise, events with pharmacologic behavioral interventions and behavioral Head/neck therapies injury Caffeine Treatment/intervention: Headache features Diagnose and treat sleep apnea, weight loss Attack frequency (headache days) Persistent, frequent nausea Allodynia Treatment-related Poor treatment efficacy Medication overuse Bigal ME et al. Headache. 2008;48: Scher AI et al. Pain. 2003;106: Buse DC et al. J Neurol Neurosurg Psychiatry. 2010;81: Scher AI et al. Cephalalgia. 2008;28: Couch JR et al. Neurology. 2007;69: Scher AI et al. Neurology. 2003;60: Scher AI et al. Neurology. 2004;63: Ashina S et al. J Headache Pain. 2012;13: Lipton RB et al. Ann Neurol. 2008;63(2): Scher AI et al. Headache. 2006;46: Smitherman TA, et al. Curr Pain Headache Rep. 2009;13: Bigal ME, Lipton RB. Headache. 2006;46: Reed ML, et al. Headache. 2015;55: Louter MA, et al. Brain. 2013;136: Lipton RB, et al. Neurology. 2015;84:

15 Natural Migraine Subgroups Class Overview Each class had a distinct comorbidity profile. Distribution of CaMEO respondents across classes was variable. <10% of respondents in Class 1 (Many Comorbidities). One-third of respondents in Class 8 (Fewest Comorbidities). Class Characteristics N (%) Class 1: Most Comorbidities High on many comorbidities 676 (5.7) Class 2: Respiratory/Psychiatric Class 3: Respiratory/Pain Highest on Respiratory and Psychiatric (sinusitis & anxiety) Highest on Respiratory and Joint pain (sinusitis & neck pain) 1,332 (11.3) 913 (7.7) Class 4: Respiratory Highest on Respiratory (sinusitis) 2,355 (19.9) Class 5: Psychiatric Highest Psychiatric (anxiety and depression) 898 (7.6) Class 6: Cardiovascular Highest Cardiovascular (hypertension and high cholesterol) 917 (7.7) Class 7: Pain Highest on Joint/Pain (neck) 720 (6.1) Class 8: Fewest Comorbidities Low on many comorbidities 4,026 (34.0)

16 Implications of Research Why are LCA-Derived Comorbid Classes Important? Migraine is a heterogeneous disease People with migraine have: Different symptom profiles Different comorbidities Different responses to treatment Different prognoses Different clinical trajectories A number of genes have been associated with migraine yet these only account for a small proportion of the variation observed. It is important to parse that heterogeneity so that biologically homogenous groups may be identified. Identification of biologically homogeneous groups May help inform the classification of headache. May help predict prognosis. May help identify people who respond to specific treatment classes (rather than using a trial and error approach). May lead to more powerful clinical trials. Trials could include only those individuals with the subgroup of migraine expected to respond to the specific treatment. Our results are a step in a much larger process

17 Conclusions Comorbidities are more common in people with CM than in those with EM. In the first stage of our research we identified 8 naturally occurring comorbid classes of migraine. We have shown that the LCA-derived comorbid class of migraine differ in various sociodemographic and headache characteristics not used to identify the subgroups. For example, we found CM was more common in members of the Most Comorbidity class. The identified LCA-derived comorbid classes of migraine may differ in underlying migraine-related mechanisms. In the second stage of our research we have found that the risk of migraine disease progression from EM to CM is associated with the LCA-derived comorbid classes. Even when potential confounders (e.g. MIDAS, MSSS, allodynia and medication overuse) were added to the model to explain some of the differences we observed, comorbid classes still differed in their risk of progression to CM. This suggests that there are underlying biologic or genetic similarities linking members of each class.

18 Closing thoughts Assess other external validators: Imaging, genes, biomarkers, treatment response to evaluate biologic substrates Develop LCA models using other indicators: Clinical features, Latent trajectory models Replicate findings in a distinct sample: Exploratory and confirmatory LCA Remember that we can go from LCA Biology and back Other statistical approaches for identifying natural subgroups Approaches are applicable to many other disorders: Cluster headache, Epilepsy, Cognitive Aging and Dementia, M.S.

19 Thank you to my colleagues Dr. Vincent Martin Dr. Michael Reed Dr. Kristina Fanning Dr. Aubrey Manack Adams Dr. Dawn Buse Dr. Peter Goadsby

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