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Impact of genetic risk assessment on nutrition-related lifestyle behaviours

Published online by Cambridge University Press:  25 October 2012

Jacqueline A. Vernarelli*
Affiliation:
Department of Nutritional Sciences, The Pennsylvania State University, University Park, PA 16802, USA
*
Corresponding author: Dr Jacqueline Vernarelli, fax+1 814 865 5870, email: jvern@psu.edu
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Abstract

Genetic susceptibility testing for common complex disease is a practice that is currently in clinical use. There are two types of gene mutations, and therefore, two varieties of genotype testing: deterministic and susceptibility. As the term suggests, deterministic genes determine whether or not a person will develop a given trait in Mendelian fashion, such as Huntington's disease. Genotype screening for such deterministic mutations has existed for decades, and is commonly used in routine medical practice. In recent years, the sequencing of the human genome has identified several ‘susceptibility genes’ or genes with incomplete penetrance. Mutations in these genes may increase disease susceptibility, but are not causative for disease. Genetic susceptibility testing allows unaffected individuals to obtain risk information for a variety of common complex diseases and health conditions including Alzheimer's disease (AD), CVD, cancer and diabetes. The availability of genetic susceptibility testing has increased over the past decade, and several studies are now focusing on the impact that genetic testing has on health and other lifestyle behaviours related to nutrition. The aim of this paper is to review the literature and evaluate what, if any, impact genetic risk assessment has on behaviours related to nutrition and physical activity. This paper summarises seven clinical studies that evaluated the impact of disclosing genetic risk information for disease on nutrition-related health behaviour changes. Of these seven studies, only three studies reported that health behaviour change was influenced by genotype disclosure.

Type
Conference on ‘Translating nutrition: integrating research, practice and policy’
Copyright
Copyright © The Author 2012

Abbreviations:
AD

Alzheimer's disease

REVEAL

Risk Evaluation and Education for Alzheimer's disease

Genetic susceptibility testing for common complex disease is a practice that is currently in clinical use. There are two types of gene mutations, and therefore, two varieties of genotype testing: deterministic and susceptibility. As the term suggests, deterministic genes determine whether or not a person will develop a given trait in Mendelian fashion, such as Huntington's disease. Genotype screening for such deterministic mutations has existed for decades, and is commonly used in routine medical practice. In recent years, the sequencing of the human genome has identified several ‘susceptibility genes’ or genes with incomplete penetrance. Mutations in these genes may increase disease susceptibility, but are not causative for disease. Genetic susceptibility testing allows unaffected individuals to obtain risk information for a variety of common complex diseases and health conditions including Alzheimer's disease (AD), CVD, cancer and diabetes. The availability of genetic susceptibility testing has increased over the past decade, and individuals are now able to purchase direct-to-consumer genetic tests to obtain risk estimates for a variety of diseases and conditions. As access to genetic information increases, it is important to understand the potential consequences of genetic susceptibility testing on lifestyle, particularly those changes related to nutrition or exercise behaviours, some of which have been summarised previously( Reference Vernarelli 1 ). It is important to note that the studies reviewed in this paper evaluating health behaviour change following genetic risk assessment for disease are not association studies, and do not summarise how the general population will react to genetic risk assessment. Instead, the focus of this paper provides insight as to how at-risk individuals behave in response to genetic susceptibility testing for disease. In the growing field of personalised nutrition, it is important to evaluate the behaviour of at-risk individuals, as they may provide insight as to what types of health behaviour changes may be expected in individuals who learn of their risk for a disease. Examining nutritional behaviours in particular is of interest given the lack of currently published information in the context of the influence of nutrition behaviour on disease prevention. By understanding the impact of genetic testing on nutrition behaviours, the scientific community may be better able to determine the most effective methods of promoting disease prevention.

A literature search of the PubMed database of the United States National Library of Medicine was conducted to find studies of adult, human subjects that evaluated impact of genetic risk assessment for common complex diseases on lifestyle behaviours specifically related to nutrition. For the purpose of this paper, qualifying lifestyle behaviours included: changes in diet, use of nutritional supplements or changes in physical activity. Only full-text articles written in English were included in this review. Studies were excluded if (a) study participants did not receive the results of genetic testing; (b) prenatal or childhood genetic testing was conducted; (c) studies assessed single-gene disorders (i.e., Huntington's disease). On the basis of these search criteria, seven clinical studies that included evaluation of health behaviour changes following genetic risk assessment were identified. The seven identified studies, along with related references, are summarised in Table 1 and later in the text.

Table 1. Summary of studies evaluating nutrition-related changes following genetic risk assessment

FH, familial hypercholesterolemia; AD, Alzheimer's disease; BC, breast cancer.

* For each study, ‘baseline’ refers to a time point pre-genotype disclosure.

Genetic susceptibility for common complex diseases

Following the sequencing of the human genome, genetic susceptibility testing for several complex common diseases has been identified. Genetic variants that increase the risk for various cancers (including breast, ovarian, oesophageal and colorectal), obesity, diabetes and AD are all known. Mutations resulting in a predisposition for various types of cancer development were among the first to be identified. One of the first genetic susceptibility tests to be widely used in both research and clinical practice is screening for mutations in the BRCA1/2 tumour suppressor genes to confer increased risk of breast and ovarian cancer. Mutations in the BRCA1 or BRCA2 gene confer a 10–87% lifetime risk for development of breast or ovarian cancer( Reference Howlader, Noone and Krapcho 9 ). Hereditary non-polyposis colorectal carcinoma is associated with multiple gene mutations, some of which confer a lifetime risk of disease development over 80%( Reference Terdiman, Conrad and Sleisenger 10 , Reference Solomon, Pho and Burt 11 ). Mutations in the mitochondrial aldehyde dehydrogenise enzyme 2 gene are associated with risk for development of alcohol-related cancers. In particular, individuals who are heterozygous for the ALDH2*2 allele have an increased risk for development of oesophageal cancer( Reference Chen, Chen and Wu 12 , Reference Yokoyama and Omori 13 ). ApoE is an apoprotein that has an essential role in lipid transport. Mutations in the apoE gene have been implicated in the causal pathway for several diseases, including AD, metabolic syndrome, cognitive disorders, immunoregulation and atherosclerosis( Reference Farrer, Cupples and van Duijn 14 ). The presence of the ε4 allele increases the risk for developing AD 5–15-fold, depending on the number of copies present( Reference Cupples, Farrer and Sadovnick 15 ). Extensive research conducted at Boston University has led to the development of risk curves for development of AD based on mutations of the apoE gene. The risk curves allow for individualised lifetime risk estimates for AD that are based on not only genotype but also age, sex and race( Reference Cupples, Farrer and Sadovnick 15 ). Genetic risk assessment for AD is currently available only for research use, and currently not available for clinical practice. More recent research has identified genetic variations that result in disease risk for other complex syndromes, including obesity. Recent studies have focused on variation in the fat mass and an obesity-associated gene (FTO) as a genetic determinant of obesity( Reference Scuteri, Sanna and Chen 16 , Reference Loos and Bouchard 17 ), although risk varies greatly based on ethnicity( Reference Peng, Zhu and Xu 18 ).

Genetic risk assessment and behaviour change

The influence of genetic risk assessment and genotype disclosure on behaviour change has been extensively reviewed by Marteau and co-workers( Reference Shaw, Abrams and Marteau 19 , Reference Marteau, French and Griffin 20 ). The most recent systematic review provides substantial information regarding the influence of genetic tests on a wide variety of health behaviour changes, and provides useful information for further reading. Of the studies that have evaluated behaviour change following genetic risk assessment, very few studies have evaluated the impact of genetic risk assessment on diet and exercise behaviours. Instead, the majority of studies have focused on other lifestyle behaviours, such as smoking cessation or cancer screening uptake. A summary table of studies included in this review is presented in Table 1.

Cancer

A multitude of studies have been conducted to assess health behaviour changes following risk assessment for breast cancer, with the majority of studies addressing behaviour changes related to update of prophylactic surgery, medication or screening( Reference O'Neill, Valdimarsdottir and Demarco 21 Reference Phillips, Jenkins and Lindeman 28 ). In the case of colorectal cancer, the study of Ceballos et al. ( Reference Ceballos, Newcomb and Beasley 29 ) evaluated the motivation of participants to increase screening uptake following genetic risk assessment. Other studies evaluating changes in lifestyle behaviours following risk assessment for cancer evaluated changes in smoking behaviours. McBride et al. ( Reference McBride, Bepler and Lipkus 30 ) conducted several studies to evaluate the influence of genetic risk assessment for cancer predisposition on smoking behaviours. In a study of 557 African-American smokers attending an urban health clinic in the United States, smokers who were provided with genetic risk information were more likely to report smoking cessation attempts at 6 months post genetic disclosure than those who were not provided with genetic risk information. However, by 12 months, no differences between groups were observed. In the study by Carpenter and co-workers, the study personnel provided α1 antitrypsin deficiency genetic testing to healthy smokers; where positive α1 antitrypsin gene mutation confers increased risk of emphysema following cigarette exposure( Reference Carpenter, Strange and Jones 31 , Reference Strange, Dickson and Carter 32 ). The study found that smokers, who were told that they had a mutation associated with severe α1 antitrypsin deficiency, were significantly more likely to report smoking quit attempts immediately after disclosure than non-carriers or unaffected carriers in the short term. Three months post-genotype disclosure, however, there were no differences in quit attempts between groups. This may reflect in part the difficult nature of making a health behaviour change in regards to smoking cessation( Reference Carpenter, Alberg and Gray 33 ).

Only two studies focused on changes in diet and physical activity behaviours following BRCA1/2 screening and genotype disclosure. In the results reported by O'Neill et al. ( Reference O'Neill, Kaufman and DeMarco 2 ), 115 women with a family or personal history of breast cancer were screened for BRCA1/2 mutations and subsequently evaluated for post-genotype disclosure behaviour changes in diet and exercise; see Table 1. Of the women screened, 40% were positive for the mutation; 40% had an ambiguous test result (owing to the lack of comparison with an affected relative) and 20% were negative. Lifestyle habits pertaining to diet (including fat intake) and exercise behaviours were surveyed at baseline, 3 and 6 months post-genotype disclosure. Repeated measures analysis was used to evaluate differences across time. The authors did not find any differences in diet or exercise behaviours at any time point among the genotype groups. Similarly, Quach et al. ( Reference Quach, Porter and Leventhal 3 ) evaluated the nutrition behaviours of 120 Ashkenazi Jewish individuals with a family or personal history for breast/ovarian cancer following genotype testing for BRCA1/2 mutations, presented in Table 1. Participants were evaluated at baseline and 6 months post-genotype disclosure. The authors did not observe any differences in diet or exercise behaviours related to BRCA1/2 testing during the follow-up visit.

A third study, although not a clinical trial, evaluated changes in dietary behaviours following risk assessment for ‘alcohol-related cancers (e.g. oesophageal cancer)( Reference Hendershot, Otto and Collins 7 ). This study randomised 200 Asian college students to receive information regarding cancer risk with or without genotype disclosure. Data on dietary behaviours (specifically alcohol consumption) were evaluated at baseline and at 1-month post-risk disclosure. The authors found that participants who learned that they were positive for a genotype that confers increased cancer risk reported a significant decrease in alcohol consumption between baseline and the 30 d follow-up period.

CVD

Marteau et al. ( Reference Marteau, Senior and Humphries 4 ) conducted a study involving adults with a family history of familial hypercholesterolaemia were randomised to receive risk assessment by clinical diagnosis alone or clinical diagnosis plus genetic testing. During subsequent follow-up visits, participants provided information about risk perceptions as well as lifestyle behaviours. The study found that there are no differences between the disclosure groups in terms of changes in diet (assessed as fat and saturated fat intake) or exercise behaviours 6 months after receiving the information. In another study of ninety-eight African–American women randomised to receive genetic information about hypertension risk, Taylor & Wu( Reference Taylor and Wu 8 ) evaluated post-disclosure changes in diet and physical activity. The authors found that women who learned that they were at an increased risk for hypertension reported increased physical activity between baseline and the 6-month follow-up period; however, the difference in physical activity were not statistically significant.

Diabetes

Presently, very little evidence has been published regarding the relationship between genetic risk assessment for diabetes and subsequent health behaviour changes. One study evaluates the potential impact of such testing by surveying twenty-two adults at a risk for development of Type II diabetes (risk defined by the presence of one or more of the following: abnormal fasting blood glucose levels, history of hypertension or dyslipidaemia, elevated body weight status) on the potential impact of additional genetic risk assessment. The authors found that subjects responded that disclosure of genetic risk information would motivate them to make risk-reducing health behaviours. However, given the hypothetical nature of this study, it is impossible to speculate whether or not genetic risk assessment would have resulted in any lifestyle behaviour changes( Reference Markowitz, Park and Delahanty 34 ).

Alzheimer's disease

The Risk Evaluation and Education for Alzheimer's disease (REVEAL) study is an ongoing series of multi-centre randomised clinical trials that examine the impact of genetic risk assessment (including apoE genotype disclosure) on cognitively normal adults( Reference Roberts and Tersegno 35 , Reference Green, Roberts and Cupples 36 ). The REVEAL trial was the first to evaluate the disclosure of genetic risk information for a complex, progressive disease with no known method of treatment or cure. Several data collection methods were used, including validated survey tools to collect information about health behaviours, nutritional choices and exercise habits. Additional psychological data were collected using various validated tools to collect information about cognitive status, stress and depression( Reference Roberts, Cupples and Relkin 37 ). High response rates and detailed follow-up visits allowed for excellent data collection and provided researchers with a robust dataset. Details of the overall REVEAL study, including the clinical trial rationale, study design, survey tools and other results have been previously described elsewhere( Reference Vernarelli, Roberts and Hiraki 6 , Reference Green, Roberts and Cupples 36 ). The REVEAL study provides an instructive context to examine health behaviour changes following genetic risk assessment. In the first REVEAL trial, Chao et al. ( Reference Chao, Roberts and Marteau 5 ) determined that participants who learned that they were at an increased genetic risk for AD were approximately three times as likely to report making an AD-related health behaviour change than those who are apoE ε4 negative; see Table 1. In this study, health behaviour change was assessed using a composite variable that encompassed changes in diet, exercise, supplements and medications, and was derived from a relatively small, homogeneous study sample. Subsequent results from the second REVEAL trial( Reference Vernarelli, Roberts and Hiraki 6 ) involving 253 cognitively normal adults with a family history of AD were provided with apoE genotype disclosure. The study indicated that adults who were ε4+ were over twice as likely to report making an AD-related change in nutrition behaviour following genotype disclosure than those who were ε4 − . Subsequent analysis determined that this change was driven by a change in supplement use among ε4+ participants, where those testing positive for the risk-increasing allele had 4·75 times the odds of reporting a change in dietary supplement use (95% CI 2·2, 10·1; P < 0·0001). Interestingly, there were no significant differences in reported changes in overall dietary habits or exercise behaviours between ε4− and ε4+ participants. The authors speculated that part of the behaviour changes observed in this trial may have been due to the popularity of supplements marketed for ‘brain health’ at the time of data collection, despite the lack of scientifically valid studies indicating that supplements were useful in the prevention of AD. Data from both REVEAL trials have been the first to demonstrate a robust change in nutrition-related behaviours following genetic risk assessment. Currently, the fourth REVEAL trial is underway (NCT01434667) and will evaluate the impact of genotype disclosure on additional health and lifestyle behaviours.

Ongoing and future studies

A search of the Clinical Trials Registry http://www.clinicaltrials.gov), a multi-national registry of over 128 000 clinical trials from over 180 countries (at the time of publication) was conducted to identify current trials evaluating the impact of genetic risk assessment on health behaviour changes. Several trials were identified, and involved a variety of complex conditions, such as AD (NCT01434667), obesity (NCT01355224), heart disease (NCT01577719), several types of cancer (NCT00782366, NCT00150917 and NCT00287898) and diabetes (NCT01186354, NCT01060540 and NCT01034319). Complete descriptions of two trials (22013171 and 3280160), in which researchers are evaluating whether genetic risk disclosure for Type 2 diabetes leads to changes in dietary intake, diet patterns or exercise behaviours, have recently been published. At the time of this publication, all of the aforementioned studies were ongoing, and no results have been published.

Summary

Genetic risk assessment is a relatively new development in the medical world. The influence that test results have on health behaviour changes is relatively unknown. Health behaviour outcomes are of particular importance to public health research, as communication of disease risk may prompt specific health behaviour changes. In this review of the literature, it was observed that the influence of genetic risk assessment on health behaviour changes differed by disease model and disclosure type. The majority of studies reviewed for this paper indicate that genetic risk assessment for disease is unlikely to lead to health behaviour change, a finding similar to those in a review of health behaviour changes following direct-to-consumer genetic test results( Reference Bloss, Schork and Topol 38 ). There are several reasons for these negative findings, some of which may be explained in part by various theories of health behaviour, including the health belief model( Reference Rosenstock 39 ) and Leventhal's common sense model( Reference Leventhal, Meyer, Nerenz and Rachman 40 ). Making changes to dietary behaviours, physical activity or smoking practices generally require changes to behavioural capacity and self-efficacy. Using the health belief model theories, one could suggest that the perceived susceptibility and/or severity for disease development were not enough to overcome the perceived barriers to behaviour change in disease models with known mechanisms for treatment.

Only one group of studies summarised in this paper demonstrated a robust response in nutrition-related lifestyle behaviour changes related to nutrition following genetic risk assessment( Reference Chao, Roberts and Marteau 5 , Reference Vernarelli, Roberts and Hiraki 6 ). The response of adults who learned that they were genetically susceptible to develop AD differed from the response of adults in any other study of any other disease model. Unlike other disease models, the presence of the ε4 allele of apoE gene confers a clearly documented susceptibility for AD, a severe disease with no known treatment or cure. It is possible that the perceived severity of AD is greater than that of other diseases, such as familial hypercholesterolemia or even various cancers, because there are no known treatments for AD. The four perceptions of the health belief model (perceived severity, susceptibility, barriers and benefits) that influence health behaviour changes are readily identifiable in the context of these studies. When looking at the perceived severity of AD, it is not unreasonable to extrapolate that for first-degree relatives of patients with AD, the disease itself seems exceptionally severe for participants in the REVEAL II study. The magnitude of perceived severity may be even greater among first-degree relatives who also served as primary caregivers for the AD patient. It is also possible that there is an age-related effect pertaining to nutrition-related behaviour changes; middle-aged relatives of AD sufferers may be more likely to report behaviour changes as an attempt to prevent the onset of disease in later life. It is important to note that there is no scientific evidence that supports the use of dietary supplements as a method of treating or preventing AD. Surprisingly, in the findings published by Alamian involving women who underwent genetic testing for breast cancer, it was women who received inconclusive results, as opposed to those testing positive or negative for the BRCA mutation who were more likely to report supplement use, potentially suggesting that women with perceived susceptibility for disease development based on family history alone may make health behaviour changes regardless of genotype status( Reference Alamian, Rouleau and Simard 41 ). Nicher and Thompson( Reference Nichter and Thompson 42 ) observed this same concept in a study conducted to survey supplement users. The authors found that one participant reported the use of over five dietary supplements as a method for preventing breast cancer; her risk perception was based on family history alone, without ever having genetic testing performed. The authors point out that current societal beliefs suggest that supplementing the diet may help aid in the promotion of wellness as well as the prevention of illness, especially since surveys have demonstrated that adults generally perceive dietary supplements and medical foods to be safe( Reference Reichenbach and Juni 43 ). Additionally, changes to dietary supplement use require fewer psychological and behavioural barriers than making changes to dietary intake or smoking behaviours, and, therefore, it may be more likely that changes in supplement use would be observed in any studies conferring increased risk for disease, not just studies that disclose genotype information.

Conclusions

Despite the recent surge in popularity of personalised medicine, research regarding the impact of genetic testing on health behaviours is very limited. Application of the health belief model to predicting health behaviour changes in these types of situations is difficult: little is known how the public (let alone individuals) perceive genetic test results. The majority of studies evaluating health behaviour changes have had negative results, with very few studies evaluating health behaviour changes related to nutrition. The current state of the literature demonstrates a clear need for future research to evaluate health behaviour changes following genetic risk assessment, particularly as the availability of direct-to-consumer genetic testing continues to increase.

Acknowledgements

The author has no conflicts of interest to disclose. The author is a faculty member at the Pennsylvania State University. Some research described in this manuscript was supported in part by grants HG/AG02213 (the Risk Evaluation and Education for Alzheimer's Disease Study), AG13846 (the Boston University Alzheimer's Disease Center), AG09029 (the Multi-Institutional Research in Alzheimer's Genetic Epidemiology Study) and M01RR00533 (the Boston University General Clinical Research Center) from the National Institutes of Health.

References

1. Vernarelli, JA (2010) Genetic Risk Assessment and Nutrition Behaviors: Influence and Impact. Boston, MA: Boston University.Google Scholar
2. O'Neill, SC, Kaufman, E, DeMarco, T et al. (2008) Changes in diet and physical activity following brca1/2 testing. J Psychosoc Oncol 26, 6380.Google Scholar
3. Quach, J, Porter, K, Leventhal, H et al. (2009) Health behaviors among Ashkenazi Jewish individuals receiving counseling for brca1 and brca2 mutations. Fam Cancer 8, 241250.CrossRefGoogle ScholarPubMed
4. Marteau, T, Senior, V, Humphries, SE. et al. (2004) Psychological impact of genetic testing for familial hypercholesterolemia within a previously aware population: a randomized controlled trial. Am J Med Genet A 128, 285293.CrossRefGoogle Scholar
5. Chao, S, Roberts, JS, Marteau, TM et al. (2008) Health behavior changes after genetic risk assessment for Alzheimer disease: the reveal study. Alzheimer Dis Assoc Disord 22, 9497.CrossRefGoogle ScholarPubMed
6. Vernarelli, JA, Roberts, JS, Hiraki, S et al. (2010) Effect of Alzheimer disease genetic risk disclosure on dietary supplement use. Am J Clin Nutr 91, 14021407.Google Scholar
7. Hendershot, CS, Otto, JM, Collins, SE et al. (2010) Evaluation of a brief web-based genetic feedback intervention for reducing alcohol-related health risks associated with aldh2. Ann Behav Med 40, 7788.Google Scholar
8. Taylor, JY & Wu, CY (2009) Effects of genetic counseling for hypertension on changes in lifestyle behaviors among African–American women. J Natl Black Nurses Assoc 20, 110.Google Scholar
9. Howlader, N, Noone, AM, Krapcho, M et al. (2009) Seer Cancer Statistics Review, 1975–2009 (Vintage 2009 Populations). Bethesda, MD: National Cancer Institute.Google Scholar
10. Terdiman, JP, Conrad, PG & Sleisenger, MH (1999) Genetic testing in hereditary colorectal cancer: indications and procedures. Am J Gastroenterol 94, 23442356.CrossRefGoogle ScholarPubMed
11. Solomon, CH, Pho, LN & Burt, RW (2002) Current status of genetic testing for colorectal cancer susceptibility. Oncology 16, 161171;discussion 76, 79–80.Google Scholar
12. Chen, YJ, Chen, C, Wu, DC et al. (2006) Interactive effects of lifetime alcohol consumption and alcohol and aldehyde dehydrogenase polymorphisms on esophageal cancer risks. Int J Cancer 119, 28272831.Google Scholar
13. Yokoyama, A & Omori, T (2003) Genetic polymorphisms of alcohol and aldehyde dehydrogenases and risk for esophageal and head and neck cancers. Jpn J Clin Oncol 33, 111121.Google Scholar
14. Farrer, LA, Cupples, LA, van Duijn, CM et al. (1995) Apolipoprotein e genotype in patients with Alzheimer's disease: implications for the risk of dementia among relatives. Ann Neurol 38, 797808.Google Scholar
15. Cupples, LA, Farrer, LA, Sadovnick, AD et al. (2004) Estimating risk curves for first-degree relatives of patients with Alzheimer's disease: the reveal study. Genet Med 6, 192196.CrossRefGoogle ScholarPubMed
16. Scuteri, A, Sanna, S, Chen, WM. et al. (2007) Genome-wide association scan shows genetic variants in the fto gene are associated with obesity-related traits. PLoS Genet 3, e115.Google Scholar
17. Loos, RJ & Bouchard, C (2008) Fto: the first gene contributing to common forms of human obesity. Obes Rev 9, 246250.CrossRefGoogle ScholarPubMed
18. Peng, S, Zhu, Y, Xu, F. et al. (2011) Fto gene polymorphisms and obesity risk: a meta-analysis. BMC Med 9, 71.CrossRefGoogle ScholarPubMed
19. Shaw, C, Abrams, K & Marteau, TM (1999) Psychological impact of predicting individuals’ risks of illness: a systematic review. Soc Sci Med 49, 15711598.Google Scholar
20. Marteau, TM, French, DP, Griffin, SJ. et al. (2010) Effects of communicating DNA-based disease risk estimates on risk-reducing behaviours. Cochrane Database Syst Rev CD007275.Google ScholarPubMed
21. O'Neill, SC, Valdimarsdottir, HB, Demarco, TA et al. (2010) Brca1/2 test results impact risk management attitudes, intentions, and uptake. Breast Cancer Res Treat 124, 755764.Google Scholar
22. Schwartz, MD, Isaacs, C, Graves, KD et al. (2011) Long-term outcomes of brca1/brca2 testing: risk reduction and surveillance. Cancer 118, 510517.CrossRefGoogle ScholarPubMed
23. Calderon-Margalit, R & Paltiel, O (2004) Prevention of breast cancer in women who carry brca1 or brca2 mutations: a critical review of the literature. Int J Cancer 112, 357364.Google Scholar
24. Metcalfe, KA, Birenbaum-Carmeli, D, Lubinski, J et al. (2008) International variation in rates of uptake of preventive options in brca1 and brca2 mutation carriers. Int J Cancer 122, 20172022.Google Scholar
25. Meijers-Heijboer, H, Brekelmans, CT, Menke-Pluymers, M et al. (2003) Use of genetic testing and prophylactic mastectomy and oophorectomy in women with breast or ovarian cancer from families with a brca1 or brca2 mutation. J Clin Oncol 21, 16751681.Google Scholar
26. Metcalfe, KA, Lubinski, J, Ghadirian, P et al. (2008) Predictors of contralateral prophylactic mastectomy in women with a brca1 or brca2 mutation: the hereditary breast cancer clinical study group. J Clin Oncol 26, 10931097.Google Scholar
27. Metcalfe, KA, Foulkes, WD, Kim-Sing, C et al. (2008) Family history as a predictor of uptake of cancer preventive procedures by women with a brca1 or brca2 mutation. Clin Genet 73, 474479.Google Scholar
28. Phillips, KA, Jenkins, MA, Lindeman, GJ et al. (2006) Risk-reducing surgery, screening and chemoprevention practices of brca1 and brca2 mutation carriers: a prospective cohort study. Clin Genet 70, 198206.Google Scholar
29. Ceballos, RM, Newcomb, PA, Beasley, JM et al. (2008) Colorectal cancer cases and relatives of cases indicate similar willingness to receive and disclose genetic information. Genet Test 12, 415420.Google Scholar
30. McBride, CM, Bepler, G, Lipkus, IM et al. (2002) Incorporating genetic susceptibility feedback into a smoking cessation program for African-American smokers with low income. Cancer Epidemiol Biomarkers Prev 11, 521528.Google Scholar
31. Carpenter, MJ, Strange, C, Jones, Y et al. (2007) Does genetic testing result in behavioral health change? Changes in smoking behavior following testing for alpha-1 antitrypsin deficiency. Ann Behav Med 33, 2228.CrossRefGoogle ScholarPubMed
32. Strange, C, Dickson, R, Carter, C et al. (2004) Genetic testing for alpha1-antitrypsin deficiency. Genet Med 6, 204210.CrossRefGoogle ScholarPubMed
33. Carpenter, MJ, Alberg, AJ, Gray, KM et al. (2010) Motivating the unmotivated for health behavior change: a randomized trial of cessation induction for smokers. Clin Trials 7, 157166.Google Scholar
34. Markowitz, SM, Park, ER, Delahanty, LM et al. (2011) Perceived impact of diabetes genetic risk testing among patients at high phenotypic risk for type 2 diabetes. Diabetes Care 34, 568573.Google Scholar
35. Roberts, JS & Tersegno, SM (2010) Estimating and disclosing the risk of developing Alzheimer's disease: challenges, controversies and future directions. Future Neurol 5, 501517.Google Scholar
36. Green, RC, Roberts, JS, Cupples, LA et al. (2009) Disclosure of apoE genotype for risk of Alzheimer's disease. N Engl J Med 361, 245254.Google Scholar
37. Roberts, JS, Cupples, LA, Relkin, N et al. (2005) Genetic risk assessment for adult children of people with Alzheimer's disease: the Risk Evaluation and Education for Alzheimer's Disease (REVEAL) study. J Geriatr Psychiatr Neurol 18, 250255.CrossRefGoogle ScholarPubMed
38. Bloss, CS, Schork, NJ & Topol, EJ (2011) Effect of direct-to-consumer genomewide profiling to assess disease risk. N Engl J Med 364, 524534.Google Scholar
39. Rosenstock, IM (1966) Why people use health services. Milbank Mem Fund Q 44, Suppl., 94127.Google Scholar
40. Leventhal, H, Meyer, D & Nerenz, D (1980) The common sense representation of illness danger. In Contributions to Medical Psychology, pp. 730 [Rachman, S, editor] New York: Pergamon Press.Google Scholar
41. Alamian, A, Rouleau, I, Simard, J et al. (2006) Use of dietary supplements among women at high risk of hereditary breast and ovarian cancer (HBOC) tested for cancer susceptibility. Nutr Cancer 54, 157165.Google Scholar
42. Nichter, M & Thompson, JJ (2006) For my wellness, not just my illness: North Americans’ use of dietary supplements. Cult Med Psychiatry 30, 175222.CrossRefGoogle Scholar
43. Reichenbach, S & Juni, P (2012) Medical food and food supplements: not always as safe as generally assumed. Ann Intern Med 156, 894895.Google Scholar
Figure 0

Table 1. Summary of studies evaluating nutrition-related changes following genetic risk assessment