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. Migraine comorbidities
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
Other Pain Disorders MI
Angina
Stroke
Hypertension/ hypotension
Raynaud’s phenomenon
PFO
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
Depression
Migraine
Shared genetic risk factors E G
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 ( ) ( )
Panic disorder ( ) ( )
Agoraphobia ( ) ( )
Any phobia ( ) ( )
* 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 25-55
§ AOR, Sex- and age-adjusted odds ratios

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

11. Do comorbidities predict migraine progression?
In the general migraine population:
2–3% per year CM EM
1. Bigal ME et al. Headache. 2008;48:1157.; 2. Lipton RB et al. Neurology. 2015;84:688–695.

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, 2005–2007
2.6*; 95%CI (1.5–4.2)
2.3*; 95%CI (1.5–3.6)
1.8*; 95%CI (1.3–2.5)
None/Mild
Moderate
Moderately–
Severe
*P<0.05
Odds ratio for CM Onset
Ashina S et al. J Headache Pain. 2012;13:615–624.

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

14. Modifiable risk factors for onset of chronic migraine: practical suggestions
Comorbidities
Depression
Anxiety
Other pain disorders
Exogenous factors
Stressful life events
Head/neck injury
Caffeine
Treatment/intervention: Weight loss, exercise, behavioral interventions
Treatment/intervention: Assess, treat/refer with pharmacologic and behavioral therapies
Obesity
Asthma
Snoring
Headache features
Attack frequency (headache days)
Persistent, frequent nausea
Allodynia
Treatment-related
Poor treatment efficacy
Medication overuse
Treatment/intervention: Diagnose and treat sleep apnea, weight loss
Prevalence estimates of allodynia in migraine patients range from 50–80% (Lovati et al., 2009).
Bigal ME et al. Headache. 2008;48:1157–1168. Scher AI et al. Pain. 2003;106:81–89. Buse DC et al. J Neurol Neurosurg Psychiatry. 2010;81:428–432. Scher AI et al. Cephalalgia. 2008;28:868– Couch JR et al. Neurology. 2007;69:1169–1177. Scher AI et al. Neurology. 2003;60:1366–1368. Scher AI et al. Neurology. 2004;63:2022–2027. Ashina S et al. J Headache Pain. 2012;13:615– Lipton RB et al. Ann Neurol. 2008;63(2):148–158. Scher AI et al. Headache. 2006;46:1416–1423. Smitherman TA, et al. Curr Pain Headache Rep. 2009;13:326–331. Bigal ME, Lipton RB. Headache. 2006;46:1334– Reed ML, et al. Headache. 2015;55:76–87. Louter MA, et al. Brain. 2013;136:3489–3496. Lipton RB, et al. Neurology. 2015;84:688–695

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
Highest on Respiratory and Psychiatric (sinusitis & anxiety)
1,332 (11.3)
Class 3: Respiratory/Pain
Highest on Respiratory and Joint pain (sinusitis & neck pain)
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)
Natural Migraine Subgroups
From
LCA abbreviated summary outline
Lines 5 & 6

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
Implications of Research
From
NY Headache Club Talk
Slide 50

17. 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.
Conclusions

18. 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.
Closing thoughts

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