Table 3 shows the statistically significant associations of metabolic markers in nondiabetic participants with a positive or negative family history of diabetes. We found strong positive correlations among triglycerides and BMI, insulin and BMI, triglycerides and insulin, and triglycerides and C-reactive protein. We found strong negative correlations among HDL cholesterol and BMI, triglycerides and HDL cholesterol, and insulin and HDL cholesterol. Glycated hemoglobin (A1c) was not included in the table because it was not significantly correlated with the other biomarkers. A strong negative correlation between HDL cholesterol and C-reactive protein was noted, but only in the participants with a negative family history of diabetes. It is not known why this association was not seen in participants with a positive family history of diabetes.
Spearman Correlations between Common Metabolic Biomarkers in Nondiabetic Participants with Negative or Positive Family Histories of Diabetes
||Negative Family History of Diabetes||
||Positive Family History of Diabetes
Abbreviations: BMI: body mass index; TRG: triglycerides; HDL: high density lipoprotein; CRP: C-reactive protein.
aCorrelation is significant at p < .05 (two-tailed).
bFisher r-to-z transformation.
The creation of a secure online survey permitted a more rapid means of populating family history information into our database compared to waiting for participants to visit the research center on an annual basis. Importantly, using a well-recognized web-based survey instrument allowed for easier participation and data collection. Having family history data in a digital format resulted in easy importation into our database and into a statistical software package for analysis.
Digital family histories can be used for a variety of purposes as demonstrated by the examples provided in this article. Table 1 compared the prevalence of common medical conditions with a genetic component in our cohort with national prevalence rates for these conditions. The results revealed that, on average, our cohort of military veterans did not clearly match a typical cross-section of the US population, most likely because they represented a higher socioeconomic status, as discussed above. For example, the prevalence of type 2 diabetes and hypertension in our participants and their families was much lower than the national average. We were particularly interested in whether nondiabetic participants with a positive family history of diabetes were different from nondiabetic participants with no family history of diabetes. In our cohort, participants with a family history of diabetes had higher insulin levels and BMIs than those with a negative family history. As a result of these preliminary data, several associations will be analyzed in more detail in future research. Our study did not show any differences in the anticipated correlations among common metabolic markers, with the exception that we found no statistically significant relationship between HDL cholesterol and C-reactive protein in participants with a positive family history of diabetes, for unclear reasons.
Digital family histories have the potential to enhance population health and become part of future clinical decision support in EHR systems. One study that looked at the effect of adding a systematic family history to a cardiovascular risk assessment showed a significant increase in high-risk patients identified. 23 Unique populations, like ours, can be analyzed to see if the proband or first-degree relatives have an increased or decreased prevalence of medical disorders compared with local, state, or national statistics. Integrating family histories into EHRs could alert clinicians if a patient is at increased risk of a medical condition with a genetic component. Algorithms embedded in the EHR might generate an alert to clinicians that the patient is at increased risk of a future condition based on a strong family history. In addition, new risk score calculators that combine family history data with biomarkers to calculate risk scores for multiple medical conditions are likely to be developed. An existing risk score calculator for type 2 diabetes that includes family history could be easily be part of clinical decision support in EHR systems.24,25
The transition to Meaningful Use Stage 2 measures projected for 2014 will change how clinicians and hospitals must address family histories. If the family history measure is adopted, clinicians and hospitals must record family histories for first-degree relatives as structured data for more than 20 percent of all unique patients seen. To accomplish this, appropriate vocabularies and standards such as SNOMED-CT and HL7 Version 3 Standard: Clinical Genomics; Pedigree must be used.26 Patient portals that are integrated with EHRs will likely provide an option for patients to upload and edit their family histories.
For common diseases, self-reported family histories correlate reasonably well with review of actual medical records. According to a review by Yoon et al, the accuracy of reported family histories is high, with excellent sensitivities and specificities.27 Family histories also correlate moderately well with formal pedigree studies.28
Several limitations of the family history should be pointed out, however. A systematic review of family history was sponsored by the Agency for Healthcare Research and Quality in 2009 and was the subject of a National Institutes of Health conference that year. The review evaluated five major research questions and drew the following conclusions: (1) family history definitions demonstrated suboptimal accuracy in predicting disease risk in individuals; (2) reports of relatives without a disease tend to be more accurate than reports of relatives with a disease; (3) it is not known if risk assessment based on family history will affect preventive behaviors, such as smoking cessation; (4) there is limited evidence to show that personalized risk assessment causes adverse outcomes; and (5) there is inadequate evidence on the optimal means to collect and report family history in primary care.29
Limitations of our study should also be mentioned. Our patient population consisted of primarily elderly white men with a high socioeconomic status; therefore, results may not be generalizable to other populations consisting of younger participants, both genders, multiple ethnicities, and other socioeconomic statuses. Furthermore, because our cohort comprised only male participants, whereas the national prevalence statistics for diabetes, hypertension, stroke, and heart attack included both genders, they are not entirely comparable.
More research is needed to determine how family history data can be optimally used and integrated with EHRs. The creation and adoption of a standardized FHQ to be used by primary care clinicians is essential because they are the most likely healthcare providers to inquire about family history. Existing standards for family history data need to be thoroughly tested and validated. Ultimately, computable family history data will need to be integrated with personal risk factors, laboratory tests, and genetic profiles in the EHR so that clinical decision support tools can be designed and tested. Lastly, clinicians will need to decide whether to input family histories at each visit or to use a FHQ such as the one we designed to upload results in batches.
Family history is an important part of any medical record and is a potentially valuable tool for disease prediction, prevention, and research. We are moving toward genetic information being part of all medical record systems, but obstacles remain, such as cost, incomplete data standards, and the fact that we have only begun to include family histories in EHRs. Family history information should be readily available in a computable format so that clinical decision support tools can remind clinicians of important testing and risk assessment needs. Unfortunately, no standard, simple generic FHQ is available for common use in primary care, with or without an EHR system.
A web-based FHQ was developed as part of this research study to help evaluate our unique cohort. Further work is needed to determine and validate the optimal family history core questions, the best methods to collect this information, how to integrate computable family history information into EHRs, interoperable data standards, and future clinical decision support tools.
Support was provided by the Office of Naval Research (ONR) under the Force Health Protection Future Naval Capabilities program. (N0001411AF00002).
We would like to thank Justice Mbizo, DrPH; Jeffrey Moore, PhD; Georgina Palombo, MBA; and Alison Fields, BA, for their contributions to this manuscript.
A copy of the FHQ is available upon request to the corresponding author, Dr. Hoyt, at email@example.com.
Robert Hoyt, MD, FACP, is the Director of the Medical Informatics Program in the School of Allied Health and Life Sciences at the University of West Florida in Pensacola, FL. He is also a clinical researcher at the Robert E. Mitchell Center for Prisoner of War Studies in Pensacola, FL.
Steven Linnville, PhD, is a researcher at the Robert E. Mitchell Center for Prisoner of War Studies in Pensacola, FL
Hui-Min Chung, PhD, is a geneticist in the School of Allied Health and Life Sciences at the University of West Florida in Pensacola, FL.
Brent Hutfless, MS, is an information technology security manager at Austal USA, LLC, in Mobile, AL.
Courtney Rice, MS, CGC, is a genetic counselor in the TriHealth Cancer Institute in Cincinnati, OH.
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Hoyt, Robert; Linnville, Steven; Chung, Hui-Min; Hutfless, Brent; Rice, Courtney.
"Digital Family Histories for Data Mining"
Perspectives in Health Information Management
(Fall, October 2013).