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1.
Cephalalgia ; 42(11-12): 1255-1264, 2022 10.
Article in English | MEDLINE | ID: mdl-35642092

ABSTRACT

BACKGROUND: The heterogeneity of migraine has been reported extensively, with identified subgroups usually based on symptoms. Grouping individuals with migraine and similar comorbidity profiles has been suggested, however such segmentation methods have not been tested using real-world clinical data. OBJECTIVE: To gain insights into natural groupings of patients with migraine using latent class analysis based on electronic health record-determined comorbidities. METHODS: Retrospective electronic health record data analysis of primary-care patients at Sutter Health, a large open healthcare system in Northern California, USA. We identified migraine patients over a five-year time period (2015-2019) and extracted 29 comorbidities. We then applied latent class analysis to identify comorbidity-based natural subgroups. RESULTS: We identified 95,563 patients with migraine and found seven latent classes, summarized by their predominant comorbidities and population share: fewest comorbidities (61.8%), psychiatric (18.3%), some comorbidities (10.0%), most comorbidities - no cardiovascular (3.6%), vascular (3.1%), autoimmune/joint/pain (2.2%), and most comorbidities (1.0%). We found minimal demographic differences across classes. CONCLUSION: Our study found groupings of migraine patients based on comorbidity that have the potential to be used to guide targeted treatment strategies and the development of new therapies.


Subject(s)
Migraine Disorders , Mindfulness , Cohort Studies , Comorbidity , Humans , Migraine Disorders/diagnosis , Retrospective Studies
2.
Commun Biol ; 4(1): 864, 2021 07 22.
Article in English | MEDLINE | ID: mdl-34294844

ABSTRACT

Migraine is a common disabling primary headache disorder that is ranked as the most common neurological cause of disability worldwide. Women present with migraine much more frequently than men, but the reasons for this difference are unknown. Migraine heritability is estimated to up to 57%, yet much of the genetic risk remains unaccounted for, especially in non-European ancestry populations. To elucidate the etiology of this common disorder, we conduct a multiethnic genome-wide association meta-analysis of migraine, combining results from the GERA and UK Biobank cohorts, followed by a European-ancestry meta-analysis using public summary statistics. We report 79 loci associated with migraine, of which 45 were novel. Sex-stratified analyses identify three additional novel loci (CPS1, PBRM1, and SLC25A21) specific to women. This large multiethnic migraine study provides important information that may substantially improve our understanding of the etiology of migraine susceptibility.


Subject(s)
Genetic Loci/genetics , Genetic Predisposition to Disease/genetics , Genome-Wide Association Study/methods , Meta-Analysis as Topic , Migraine Disorders/genetics , Adult , Black or African American/genetics , Aged , Asian/genetics , Chromosome Mapping , Cohort Studies , Female , Genetic Association Studies/methods , Genetic Association Studies/statistics & numerical data , Genetic Predisposition to Disease/ethnology , Genome-Wide Association Study/statistics & numerical data , Hispanic or Latino/genetics , Humans , Male , Middle Aged , Migraine Disorders/ethnology , Polymorphism, Single Nucleotide , Sex Factors , White People/genetics
3.
Headache ; 61(3): 462-484, 2021 Mar.
Article in English | MEDLINE | ID: mdl-33368248

ABSTRACT

OBJECTIVE: To characterize patients who utilize services for migraine in a large integrated health care network, and describe patterns of care and utilization. BACKGROUND: Within health care systems, migraine is a common reason for seeking primary and neurology care, but relatively little is documented about who seeks care and the factors that explain variation in utilization. METHODS: We conducted a retrospective cohort study using electronic health record (EHR) data from Sutter Health primary care (PC) patients who had at least one office visit to a PC clinic between 2013 and 2017. Migraine status was ascertained from diagnosis codes and medication orders. Control status was assigned to those with no evidence of care for any type of headache. We divided the primary care migraine cohort into two groups: those who received all their care for migraine from PC (denoted PC-M) and those who had ≥1 encounter with a neurologist for migraine (denoted N-M). Migraine cases were also designated as having preexisting migraine if they had an encounter with a migraine diagnosis within (±) 6 months of their first study period PC visit and, otherwise, designated as first migraine consult. Two levels of contrasts included: patients with migraine and controls; and within the group of patients with migraine, PC-M and N-M groups. Comorbid conditions were determined from EHR encounter diagnosis codes. RESULTS: We identified 94,149 patients with migraine (including 21,525 N-M and 72,624 PC-M) and 1,248,763 controls. Comorbidities: Proportions of psychiatric [29.8% (n = 28,054) vs. 11.8% (n = 147,043)], autoimmune [(4.4% (n = 4162) vs. 2.6% (n = 31,981)], pain [13.2% (n = 12,439) vs. 5.8% (n = 72,049)], respiratory [24.6% (n = 23,186) vs. 12.3% (n = 153,692)], neurologic [2.9% (n = 2688) vs. 0.9% (n = 11,321)], and cerebrovascular [1.0% (n = 945) vs. 0.6% (n = 7500)] conditions were higher in the migraine group compared to controls, all p < 0.001. Among patients with migraine, the N-M group was similar to the PC-M group in sex, age, ethnicity, and marital status, but were more likely to have preexisting migraine (49.9% (n = 10,734) vs. 36.2% (n = 26,317), p < 0.001). Proportions of comorbid conditions were higher among the N-M group than the PC-M group {psychiatric [38.5% (n = 8291) vs. 27.2% (n = 19,763)], autoimmune [6.3% (n = 1365) vs. 3.9% (n = 2797)], pain [19.6% (n = 4218) vs. 11.3% (n = 8211)], respiratory [30.3% (n = 6516) vs. 23.0% (n = 16,670)], neurologic [6.0% (n = 1288) vs. 1.9% (n = 1400)], cardiovascular [9.7% (n = 2091) vs. 7.0% (n = 5076)], and cerebrovascular [2.3% (n = 500) vs. 0.6% (n = 445)], all p < 0.001}. Medications: During the study period, 82.6% (n = 77,762) of patients with migraine received ≥1 prescription order for an acute migraine medication [89.4% (n = 19,250) of N-M vs. 80.6% (n = 58,512) of PC]. Opioids were prescribed to 52.9% (n = 49,837) of patients with migraine [63.5% (n = 13,669) for N-M and 49.8% (n = 36,168) for PC-M patients). During the study period, 61.4% (n = 57,810) of patients received ≥1 prescription for a migraine preventive medication [81.4% (n = 17,521) of N-M and 55.5% (n = 40,289) of PC-M patients]. The most commonly prescribed classes of preventive medications were antidepressants. CONCLUSIONS: Among patients with migraine in a large health system, those who were also cared for in neurology were more likely to receive both acute and preventive medication migraine orders than those patients who did not see a neurologist, with triptans and antidepressants the most commonly prescribed classes of acute and preventive pharmacotherapies, respectively. Opioids were prescribed to approximately half of the total sample and more common in the N-M group. Adjusting for demographics, patients with migraine had higher rates of nearly every comorbidity we assessed and were more likely to utilize services compared to those without migraine. Overall, patients with migraine also cared for in neurology practices used more of all health care resource types under consideration and had more medical issues, which may be due in some part to a more severe, frequent and disabling disease state compared to those who sought care exclusively from PC practices.


Subject(s)
Facilities and Services Utilization/statistics & numerical data , Migraine Disorders/drug therapy , Neurologists/statistics & numerical data , Primary Health Care/statistics & numerical data , Adolescent , Adult , Aged , Aged, 80 and over , California/epidemiology , Comorbidity , Delivery of Health Care, Integrated/statistics & numerical data , Electronic Health Records/statistics & numerical data , Female , Humans , Male , Middle Aged , Migraine Disorders/epidemiology , Retrospective Studies , Young Adult
4.
Circulation ; 107(1): 43-8, 2003 Jan 07.
Article in English | MEDLINE | ID: mdl-12515741

ABSTRACT

BACKGROUND: Little is known about hormone replacement therapy (HRT) and risk for myocardial infarction (MI) in diabetic women. We examined associations of current HRT, estrogen dosage, and time since HRT initiation with risk of acute MI in diabetic women. METHODS AND RESULTS: Cox proportional hazards models, with current HRT modeled as a time-dependent covariate, were used to assess the 3-year risk of MI. Among 24 420 women without a recent MI (mean age 64.9 years), 1110 incident MIs were identified. After adjustment for cardiovascular risk factors, current HRT was associated with reduced MI risk (relative hazard [RH] 0.84, 95% CI 0.72 to 0.98). The RH for MI associated with current estrogen plus progestin use was 0.77 (95% CI 0.61 to 0.97), and the RH for MI associated with current unopposed estrogen use was 0.88 (95% CI 0.73 to 1.05). Women were at reduced MI risk if they were taking a low or medium dose of estrogen (equivalent to <0.625 or 0.625 mg of conjugated estrogen, respectively) but not a high dose (>0.625 mg of conjugated estrogen or its equivalent). Among those whose current use of HRT was <1 year, the RH for MI was 1.03 (95% CI 0.74 to 1.44), whereas among users for > or =1 year, the RH was 0.81 (95% CI 0.66 to 1.00). Among 580 women with a recent MI (mean age 69.2 years), 89 recurrent MIs were identified. An increased risk of recurrent MI was observed among current HRT users (RH 1.78, 95% CI 1.06 to 2.98), which was higher among those with current use for <1 year (RH 3.84, 95% CI 1.60 to 9.20). CONCLUSIONS: In women without a recent MI, use of estrogen plus progestin was associated with decreased risk of MI. However, HRT was associated with increased risk of MI in women with history of a recent MI. Data from clinical trials in diabetic women are needed.


Subject(s)
Diabetes Complications , Estrogen Replacement Therapy/adverse effects , Estrogens/adverse effects , Myocardial Infarction/epidemiology , Progestins/adverse effects , Aged , Dose-Response Relationship, Drug , Drug Therapy, Combination , Estrogens/therapeutic use , Female , Follow-Up Studies , Humans , Middle Aged , Myocardial Infarction/etiology , Myocardial Infarction/mortality , Progestins/therapeutic use , Proportional Hazards Models , Registries , Risk Factors
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