Jennifer K. Pai

Haptoglobin Genotype Is a Consistent Marker of Coronary Heart Disease Risk Among Individuals With Elevated Glycosylated Hemoglobin

OBJECTIVESnThis study sought to investigate into the biologically plausible interaction between the common haptoglobin (Hp) polymorphism rs#72294371 and glycosylated hemoglobin (HbA(1c)) on risk of coronary heart disease (CHD).nnnBACKGROUNDnStudies of the association between the Hp polymorphism and CHD report inconsistent results. Individuals with the Hp2-2 genotype produce Hp proteins with an impaired ability to prevent oxidative injury caused by elevated HbA(1c).nnnMETHODSnHbA(1c) concentration and Hp genotype were determined for 407 CHD cases matched 1:1 to controls (from the NHS [Nurses Health Study]) and in a replication cohort of 2,070 individuals who served as the nontreatment group in the ICARE (Prevention of Cardiovascular Complications in Diabetic Patients With Vitamin E Treatment) study, with 29 CHD events during follow-up. Multivariate models were adjusted for lifestyle and CHD risk factors as appropriate. A pooled analysis was conducted of NHS, ICARE, and the 1 previously published analysis (a cardiovascular disease case-control sample from the Strong Heart Study).nnnRESULTSnIn the NHS, Hp2-2 genotype (39% frequency) was strongly related to CHD risk only among individuals with elevated HbA(1c) (≥ 6.5%), an association that was similar in the ICARE trial and the Strong Heart Study. In a pooled analysis, participants with both the Hp2-2 genotype and elevated HbA(1c) had a relative risk of 7.90 (95% confidence interval: 4.43 to 14.10) for CHD compared with participants with both an Hp1 allele and HbA(1c) <6.5% (p for interaction = 0.004), whereas the Hp2-2 genotype with HbA(1c) <6.5% was not associated with risk (relative risk: 1.34 [95% confidence interval: 0.73 to 2.46]).nnnCONCLUSIONSnHp genotype was a significant predictor of CHD among individuals with elevated HbA(1c).

Collaborative pooled analysis of data on C-reactive protein gene variants and coronary disease: judging causality by Mendelian randomisation

Many prospective studies have reported associations between circulating C-reactive protein (CRP) levels and risk of coronary heart disease (CHD), but causality remains uncertain. Studies of CHD are being conducted that involve measurement of common polymorphisms of the CRP gene known to be associated with circulating concentrations, thereby utilising these variants as proxies for circulating CRP levels. By analysing data from several studies examining the association between relevant CRP polymorphisms and CHD risk, the present collaboration will undertake a Mendelian randomisation analysis to help assess the likelihood of any causal relevance of CRP levels to CHD risk. A central database is being established containing individual data on CRP polymorphisms, circulating CRP levels, and major coronary outcomes as well as age, sex and other relevant characteristics. Associations between CRP polymorphisms or haplotypes and CHD will be evaluated under different circumstances. This collaboration comprises, at present, about 37,000 CHD outcomes and about 120,000 controls, which should yield suitably precise findings to help judge causality. This work should advance understanding of the relevance of low-grade inflammation to CHD and indicate whether or not CRP itself is involved in long-term pathogenesis.

Dietary fiber intake and mortality among survivors of myocardial infarction: prospective cohort study

Objective To evaluate the associations of dietary fiber after myocardial infarction (MI) and changes in dietary fiber intake from before to after MI with all cause and cardiovascular mortality. Design Prospective cohort study. Setting Two large prospective cohort studies of US women and men with repeated dietary measurements: the Nurses’ Health Study and the Health Professionals Follow-Up Study. Participants 2258 women and 1840 men who were free of cardiovascular disease, stroke, or cancer at enrollment, survived a first MI during follow-up, were free of stroke at the time of initial onset of MI, and provided food frequency questionnaires pre-MI and at least one post-MI. Main outcome measures Associations of dietary fiber post-MI and changes from before to after MI with all cause and cardiovascular mortality using Cox proportional hazards models, adjusting for drug use, medical history, and lifestyle factors. Results Higher post-MI fiber intake was significantly associated with lower all cause mortality (comparing extreme fifths, pooled hazard ratio 0.75, 95% confidence interval 0.58 to 0.97). Greater intake of cereal fiber was more strongly associated with all cause mortality (pooled hazard ratio 0.73, 0.58 to 0.91) than were other sources of dietary fiber. Increased fiber intake from before to after MI was significantly associated with lower all cause mortality (pooled hazard ratio 0.69, 0.55 to 0.87). Conclusions In this prospective study of patients who survived MI, a greater intake of dietary fiber after MI, especially cereal fiber, was inversely associated with all cause mortality. In addition, increasing consumption of fiber from before to after MI was significantly associated with lower all cause and cardiovascular mortality.

Predictive Modeling of Physician-Patient Dynamics That Influence Sleep Medication Prescriptions and Clinical Decision-Making

Insomnia remains under-diagnosed and poorly treated despite its high economic and social costs. Though previous work has examined how patient characteristics affect sleep medication prescriptions, the role of physician characteristics that influence this clinical decision remains unclear. We sought to understand patient and physician factors that influence sleep medication prescribing patterns by analyzing Electronic Medical Records (EMRs) including the narrative clinical notes as well as codified data. Zolpidem and trazodone were the most widely prescribed initial sleep medication in a cohort of 1,105 patients. Some providers showed a historical preference for one medication, which was highly predictive of their future prescribing behavior. Using a predictive model (AUCu2009=u20090.77), physician preference largely determined which medication a patient received (ORu2009=u20093.13; pu2009=u20093u2009×u200910−37). In addition to the dominant effect of empirically determined physician preference, discussion of depression in a patient’s note was found to have a statistically significant association with receiving a prescription for trazodone (ORu2009=u20091.38, pu2009=u20090.04). EMR data can yield insights into physician prescribing behavior based on real-world physician-patient interactions.

Low Carbohydrate Diet From Plant or Animal Sources and Mortality Among Myocardial Infarction Survivors

Background The healthiest dietary pattern for myocardial infarction (MI) survivors is not known. Specific long‐term benefits of a low‐carbohydrate diet (LCD) are unknown, whether from animal or vegetable sources. There is a need to examine the associations between post‐MI adherence to an LCD and all‐cause and cardiovascular mortality. Methods and Results We included 2258 women from the Nurses Health Study and 1840 men from the Health Professional Follow‐Up Study who had survived a first MI during follow‐up and provided a pre‐MI and at least 1 post‐MI food frequency questionnaire. Adherence to an LCD high in animal sources of protein and fat was associated with higher all‐cause and cardiovascular mortality (hazard ratios of 1.33 [95% CI: 1.06 to 1.65] for all‐cause mortality and 1.51 [95% CI: 1.09 to 2.07] for cardiovascular mortality comparing extreme quintiles). An increase in adherence to an animal‐based LCD prospectively assessed from the pre‐ to post‐MI period was associated with higher all‐cause mortality and cardiovascular mortality (hazard ratios of 1.30 [95% CI: 1.03 to 1.65] for all‐cause mortality and 1.53 [95% CI: 1.10 to 2.13] for cardiovascular mortality comparing extreme quintiles). An increase in adherence to a plant‐based LCD was not associated with lower all‐cause or cardiovascular mortality. Conclusions Greater adherence to an LCD high in animal sources of fat and protein was associated with higher all‐cause and cardiovascular mortality post‐MI. We did not find a health benefit from greater adherence to an LCD overall after MI.

Development of an Algorithm to Identify Patients with Physician-Documented Insomnia

We developed an insomnia classification algorithm by interrogating an electronic medical records (EMR) database of 314,292 patients. The patients received care at Massachusetts General Hospital (MGH), Brigham and Women’s Hospital (BWH), or both, between 1992 and 2010. Our algorithm combined structured variables (such as International Classification of Diseases 9th Revision [ICD-9] codes, prescriptions, laboratory observations) and unstructured variables (such as text mentions of sleep and psychiatric disorders in clinical narrative notes). The highest classification performance of our algorithm was achieved when it included a combination of structured variables (billing codes for insomnia, common psychiatric conditions, and joint disorders) and unstructured variables (sleep disorders and psychiatric disorders). Our algorithm had superior performance in identifying insomnia patients compared to billing codes alone (area under the receiver operating characteristic curve [AUROC]u2009=u20090.83 vs. 0.55 with 95% confidence intervals [CI] of 0.76–0.90 and 0.51–0.58, respectively). When applied to the 314,292-patient population, our algorithm classified 36,810 of the patients with insomnia, of which less than 17% had a billing code for insomnia. In conclusion, an insomnia classification algorithm that incorporates clinical notes is superior to one based solely on billing codes. Compared to traditional methods, our study demonstrates that a classification algorithm that incorporates physician notes can more accurately, comprehensively, and quickly identify large cohorts of insomnia patients.

The Effect of Limited Health Literacy on How Internet Users Learn About Diabetes

The Internet continues to be an important supplemental health information resource for an increasing number of U.S. adults, especially for those with a new or existing chronic condition. Here we examine how people use the Internet to learn about Type 2 diabetes and how health literacy (HL) influences this information-seeking behavior. We analyzed the searches of approximately 2 million people who queried for diabetes-related information on Microsoft’s Bing search engine. The HL of searchers was imputed through a community-based HL score. Topics searched were categorized and subsequent websites were assessed for readability. Overall, diabetes information–seeking strategies via the Internet are similar among adults with limited and adequate HL skills. However, people with limited HL take a longer time to read pages that are quickly read by people with adequate HL and vice versa. Information seeking among the former is terminated prematurely, as is evident from a Hidden Markov Model of the search process. Our findings indicate that the reading level required to understand the majority of diabetes-related information is high. Especially on government websites, more than 80% of information requires a reading level corresponding to 7th grade or higher. Our results indicate that individuals with lower HL may disproportionately struggle with Internet searches and fail to get an equivalent benefit from this information resource compared to users with greater HL. Future interventions should target the quality and ease of navigation of health care websites and find ways to leverage other relevant professionals to encourage and promote successful information access on the Web.