Perceptions of Genetic Testing for Personalized ...

J Nutrigenet Nutrigenomics 2014;7:94–104 DOI: 10.1159/000365508 Published online: August 22, 2014

© 2014 S. Karger AG, Basel 1661–6499/14/0072–0094$39.50/0 www.karger.com/jnn

Original Paper

Perceptions of Genetic Testing for Personalized Nutrition: A Randomized Trial of DNA-Based Dietary Advice Daiva E. Nielsen

Sarah Shih

Ahmed El-Sohemy

Department of Nutritional Sciences, University of Toronto, Toronto, Ont., Canada

Key Words Nutrigenomics · Nutrigenetics · Personalized nutrition · Genetic testing · Perceptions

A. El-Sohemy Department of Nutritional Sciences, Room 350 University of Toronto, 150 College St Toronto, ON M5S 3E2 (Canada) E-Mail a.el.sohemy @ utoronto.ca

Downloaded by: University Toronto Libr. 128.100.208.76 - 8/22/2014 4:15:41 PM

Abstract Background/Aims: Direct-to-consumer (DTC) genetic tests have facilitated easy access to personal genetic information related to health and nutrition; however, consumer perceptions of the nutritional information provided by these tests have not been evaluated. The objectives of this study were to assess individual perceptions of personalized nutrition and genetic testing and to determine whether a personalized nutrition intervention modifies perceptions. Methods: A double-blind, parallel-group, randomized controlled trial was conducted among healthy men and women aged 20–35 years (n = 138). Participants in the intervention group (n = 92) were given a report of DNA-based dietary advice and those in the control group (n = 46) were given a general dietary advice report. A survey was completed at baseline and 3 and 12 months after distributing the reports to assess perceptions between the two groups. Results: No significant differences in perceptions of personalized nutrition and genetic testing were observed between the intervention and control group, so responses of both groups were combined. As compared to baseline, participant responses increased significantly toward the positive end of a Likert scale at 3 months for the statement ‘I am interested in the relationship between diet and genetics’ (mean change ± SD: 0.28 ± 0.99, p = 0.0002). The majority of participants indicated that a university research lab (47%) or health care professional (41%) were the best sources for obtaining accurate personal genetic information, while a DTC genetic testing company received the fewest selections (12%). Most participants (56%) considered dietitians to be the best source of personalized nutrition followed by medical doctors (27%), naturopaths (8%) and nurses (6%). Conclusions: These results suggest that perceptions of personalized nutrition changed over the course of the intervention. Individuals view a research lab or health care professional as better providers of genetic information than a DTC genetic testing company, and registered dietitians are considered to be the best provid© 2014 S. Karger AG, Basel ers of personalized nutrition advice.

95

J Nutrigenet Nutrigenomics 2014;7:94–104 DOI: 10.1159/000365508

© 2014 S. Karger AG, Basel www.karger.com/jnn

Nielsen et al.: Perceptions of Genetic Testing for Personalized Nutrition: A Randomized Trial of DNA-Based Dietary Advice

Introduction

The direct-to-consumer (DTC) genetic testing industry has provided individuals with easy access to their own personal genetic information. Many of these tests give information on susceptibility to different diseases and some provide nutrition information [1]. As a result, proponents of this technology claim that the information may have a positive impact on health behaviors such as diet, smoking and exercise in an effort to prevent the development of chronic diseases. However, controversies surrounding DTC genetic testing continue to arise, in part due to the unregulated nature of the industry. The US Food and Drug Administration has recently ordered 23andMe, one of the largest DTC genetic testing companies, to stop the sales and marketing of their genetic test [2], and a class action lawsuit was filed against the company shortly after claiming that the company’s advertising misled consumers [3]. Concerns over the analytical validity and clinical utility of the tests, along with little information about what consumers do with the genetic test results have sparked a debate on how to properly regulate this emerging technology. Government bodies around the world are working to create standards and guidelines to ensure an appropriate distribution of these tests to consumers [4–6]; however, these efforts require a comprehensive understanding of the public’s perceptions of these technologies, as well as evidence of the impact of test results on consumer actions and behaviors. Previous qualitative studies have shown substantial public interest in genetic testing and personal genomics [7–9], including interest in the field of nutrigenomics [10–13], the study of how human genetic variations modify an individual’s response to diet on various health outcomes. The benefits of the application of nutrigenomics include the creation of gene-based dietary interventions to prevent chronic diseases, personalized dietary recommendations to optimize an individual’s dietary response and more precise public health advice for dietary intake and supplement use [14]. A European survey reported that out of 5,967 respondents, 66% were willing to undergo nutrigenomics testing and 27% would follow a personalized diet based on their test results [10]. In addition, DNA-based dietary advice was considered to be more useful and understandable than general population-based dietary recommendations in another study, and individuals in that study also reported that they would be more motivated to change their diet if provided with gene-based personalized nutrition information [15]. However, individual perceptions of sources and providers of personal genetic information and personalized nutrition advice, as well as the utility of DNA-based dietary advice compared with general dietary recommendations, have not been explored. Therefore, the objective of the present study was to assess and compare perceptions of genetic testing and personalized nutrition in a population of young adults who are given DNA-based dietary advice or general dietary recommendations using a randomized controlled trial.

Study Design and Participants Materials for the present study were distributed and completed by mail or electronically, and no in-person contact was made with participants, since the study was intended to mimic the nature of a DTC genetic test. Ethics approval was obtained from the University of Toronto Institutional Review Board, and the study is registered with http://clinicaltrials.gov (NCT 01353014). Recruitment took place at the University of Toronto, which does not offer a dietetics program. Details on the study design have been published elsewhere [15]. Briefly, eligible participants (n = 354) who had previously participated in a nutrigenomics research study and had provided a blood sample were invited to take part. The dietary recommendations provided in this study were based on caffeine, vitamin C, sugar, and sodium; therefore, participants were excluded if they consumed less than 100 mg of caffeine per day, less than 10% of energy from total


Methods

96




354 eligible 354 e-mailed; 141 responded with interest 194 phoned; 30 interested 164 unreachable 14 did not provide mailing address

16 declined 3 ineligible

157 sent the baseline survey 149 completed the baseline survey 138 randomized 92 I; 46 C 138 sent the 3-month survey

135 completed the 3-month survey (90 I; 45 C)

128 sent the 12-month survey

125 completed the 12-month survey (83 I; 42 C)

7 not interested in the 12-month follow-up (6 I; 1 C)

Fig. 1. Flow of participants throughout the trial. I = Intervention; C = control. Of the 138 randomized participants, 11 did not complete an additional study questionnaire required for randomization.

Dietary Advice Reports Participants were given a dietary advice report that provided recommendations for daily intakes of caffeine, vitamin C, added sugars and sodium approximately 1 week after they completed the baseline survey. Monthly reminders of the report were e-mailed to the participants over the course of 12 months. The report for the intervention group included the participants’ personal genotypes for genes that affect response to each of the four dietary components and the corresponding dietary recommendation was based on the participants’ genotype. The report for the control group provided general dietary recommendations for caffeine [16], vitamin C [17], added sugars [18], and sodium [17] from recognized health institutes with no genetic information. The intervention group reports were developed in collaboration with Nutrigenomix Inc., a personalized nutrition genetic testing company in Toronto, Canada. The genetic information provided included the following genes and their associated dietary response: CYP1A2 and caffeine metabolism [19, 20], GSTT1 and


sugars per day, less than 1,500 mg of sodium per day and if they used vitamin C-containing supplements. Women who were pregnant or breast-feeding at the time of recruitment were also excluded. A total of 157 participants expressed interest in participating in the study and were sent a baseline survey to assess perceptions of genetic testing and personalized nutrition. Those who completed the baseline survey were randomized to an intervention or control group using Random Allocation Software (fig. 1). A 2:1 ratio of participants in the intervention group compared to the control group was used to account for the proportion of participants in the intervention group who would carry either the ‘risk’ or ‘non-risk’ genotype for each of the genes that were tested in the study (table 1). Participant blinding was achieved by informing participants that they would receive DNA-based dietary advice at some point during the study. Participants who were randomized to the control group were given the DNA-based advice after the 12-month follow-up assessment was completed. Investigators were blinded to study outcomes, as the dietary intakes were self-reported by participants and nutrient analyses were made without knowledge of study group assignment.

97

J Nutrigenet Nutrigenomics 2014;7:94–104 © 2014 S. Karger AG, Basel www.karger.com/jnn

DOI: 10.1159/000365508


Table 1. Prevalence of risk alleles in the intervention group and associated risk

Gene

Risk allele, n (%)

Nonrisk allele, n (%)

Associated risk

CYP1A2

48 (52)

44 (48)

GSTM1 + 52 (57) GSTT1

40 (43)

TAS1R2

41 (45)

51 (55)

ACE

64 (70)

28 (30)

increased risk of myocardial infarction when consuming >200 mg of caffeine/day increased risk of serum ascorbic acid deficiency when consuming below the RDA for vitamin C increased risk of overconsuming sugars increased risk of sodium-sensitive hypertension when consuming above the AI for sodium

RDA = Recommended daily amounts; AI = adequate intake.

GSTM1 and vitamin C utilization [21], TAS1R2 and sweet taste perception, and ACE and sodium sensitivity [22, 23] (table 1). A ‘targeted’ dietary recommendation was provided in cases where a participant carried the version of the gene that has been associated with increased risk of a health outcome when consuming above or below a certain daily amount of the corresponding dietary component. The targeted recommendation was more stringent than the current general recommendation for intake. Participants who possessed the version of the gene that has not been associated with increased risk of a health outcome were given the current general recommendation for daily intake.

Statistical Analysis Statistical analyses were conducted using the Statistical Analysis Software version 9.2 (SAS Institute Inc., Cary, N.C., USA). Participant characteristics between the intervention and control group were compared using a χ2 test for categorical variables and a Student t test for continuous variables. For the multiple-choice and yes/no/no opinion questions, survey responses between the intervention and control group were compared using the χ2 test. Fisher’s exact test was used instead if a response category contained fewer than 5 counts. Responses of the intervention and control group were combined if no significant differences were present between the groups. To examine changes in responses to the repeated Likert-type survey items,


Surveys Four surveys were collected from the intervention and control group over the course of the present study: at baseline, immediately after the advice reports were distributed, and at the 3- and 12-month followup assessments. The surveys were administered online and assessed participants’ perceptions toward genetic testing and personalized nutrition. The baseline survey also collected demographic information. Specifically, items in the surveys assessed participants’ interests in personalized nutrition, motivations for seeking out genetic testing, and perceptions of (a) sources for obtaining personal genetic information, (b) providers of personalized nutrition advice and (c) the utility of DNA-based dietary advice. Whether or not the information in the dietary reports was shared with others was also assessed. The surveys consisted of 5-point Likert-type scales where participants were asked to indicate the extent to which they agreed with various statements on a scale that contained the following response options: strongly disagree, somewhat disagree, neither agree nor disagree, somewhat agree and strongly agree; multiple-choice questions where the participants were instructed to select all response options that applied, and one ‘yes’, ‘no’ or ‘no opinion’ question where the participants could only select one response. A number of items included in the surveys were adapted from questions used in a previous UK survey of public interest in personal genome testing [7], and 4 of these items that were present in the baseline survey were repeated at the follow-up assessments to determine if responses to these items changed after the intervention.

98


DOI: 10.1159/000365508


Table 2. Participant characteristics

Intervention (n = 92)

Control (n = 46)

p value

26 ± 4 113 (76)

27 ± 3 69 (75)

26 ± 3 37 (80)

0.82 0.48 0.57

92 (62) 31 (21) 16 (11) 10 (7)

59 (64) 19 (21) 9 (10) 5 (5)

24 (52) 12 (26) 6 (13) 4 (9)

20 (13) 76 (51) 53 (36)

9 (10) 50 (54) 33 (36)

8 (17) 22 (48) 16 (35)

Baseline (n = 149) Age, mean ± SD, years Female Ethnicity Caucasian East Asian South Asian Other Education Some college or undergraduate training College or undergraduate degree Graduate degree

0.43

Figures are numbers with percentages in parentheses, unless indicated otherwise. p value compares characteristics between the intervention and control group.

responses were coded from 1 to 5, with 1 denoting the negative end of the scale (strongly disagree) and 5 denoting the positive end (strongly agree). Baseline responses were subtracted from follow-up responses and the nonparametric Kruskal-Wallis test was used to compare changes of mean responses between the intervention and control group. Responses were combined if no significant differences were present between groups. The nonparametric Wilcoxon signed-rank test was used to identify significant changes in the responses between baseline and the 3- and 12-month follow-up surveys. Since multiple tests were conducted to examine changes in perceptions, the Benjamini and Yekutieli (B-Y) false discovery rate method was applied, which is similar to the Bonferroni correction for multiple comparisons but is less conservative [24]. The B-Y critical value (B-Ycrit) is calculated by α/Σ(1/i) where ‘α’ is 0.05 and ‘i’ varies between 1 and 8 for the number of tests (n = 8) that were conducted when changes in responses to repeated survey items were assessed. The B-Ycrit value in the present study is 0.018.

Results

Opinions of Sources for Personal Genetic Information and Personalized Nutrition Advice In the 3-month survey, participants were asked to indicate which source they felt would provide them with the most accurate personal genetic information. The response options were ‘university research lab’, ‘health care professional’, and ‘direct-to-consumer genetic testing company’ with participants being instructed to select all that applied. Since no significant differences were observed between the responses of the intervention group when compared


Participant Characteristics Of the 157 participants who were sent the baseline survey, 125 completed the 12-month follow-up assessment giving an overall retention rate of 80%. There were no significant differences between the characteristics of participants in the intervention group when compared to the control group (table 2). The mean age of the participants was 26.5 ± 3.0 years and 78% were female. The study population was multiethnic with Caucasian, East Asian, and South Asian groups representing the majority of ethnic backgrounds. Over half of the population possessed at least an undergraduate degree. Some survey results from the baseline and intervention time point surveys have been published previously [15].

99


DOI: 10.1159/000365508


Table 3. Survey responses at baseline, 3 months and 12 months

Survey statement

Strongly disagree

Numerical value 1

Somewhat Neither Somewhat Strongly disagree agree nor agree agree disagree 2

3

4

5

I am interested in the relationship between diet and genetics Baseline 3 (2) 6 (5) 4 (3) 58 (46) 54 (43) 3 months 3 (2) 2 (2) 3 (2) 37 (30) 80 (64) 12 months 3 (2) 0 (0) 5 (4) 49 (39) 68 (54) I would take a genetic test to learn more about myself Baseline 2 (2) 4 (3) 13 (10) 45 (36) 61 (49) 3 months 4 (3) 2 (2) 9 (7) 44 (35) 66 (53) 12 months 5 (4) 5 (4) 14 (11) 40 (32) 61 (49) I would take a genetic test to encourage myself to adopt a healthier lifestyle Baseline 4 (3) 7 (6) 9 (7) 51 (41) 54 (43) 3 months 4 (3) 3 (2) 11 (9) 51 (41) 56 (45) 12 months 4 (3) 10 (8) 16 (13) 49 (39) 46 (37) I would take a genetic test to have my health care professional monitor my health more closely Baseline 2 (2) 8 (6) 19 (15) 48 (38) 48 (38) 3 months 3 (2) 4 (3) 10 (8) 49 (39) 59 (47) 12 months 3 (2) 8 (6) 19 (15) 48 (38) 47 (38) Figures are numbers with percentages in parentheses. Percentages may not total to 100% due to rounding.

Changes in Perceptions of Personalized Nutrition and Genetic Testing Four statements that were included in the baseline survey to assess individual interest in personalized nutrition and motivations for seeking out genetic testing were repeated in the 3- and 12-month survey in order to determine whether perceptions of these statements changed over the course of the study (table 3). Participants who completed the entire 12-month study were included in these analyses (n = 125). No significant differences in the mean response changes were observed between the intervention and control group for any of the four statements (data not shown) and, therefore, responses of the two groups were combined. At 3 months, participant responses increased significantly toward the positive end


to the control group (data not shown), the responses of both groups were combined. The most commonly selected response among the 135 participants was ‘university research lab’ (47%), followed by ‘health care professional’ (41%) and ‘direct-to-consumer genetic testing company’ (12%). Participants were also asked to indicate which source they felt would provide them with the best personalized nutrition advice. The response options were ‘registered dietitian’, ‘medical doctor’, ‘registered nurse’, ‘naturopath’, or ‘other’, and participants were instructed to select all that applied. The option ‘other’ required participants to specify an open-ended response. Similar to the previous finding, no significant differences were observed between the responses of the intervention group when compared to the control group (data not shown), so the responses of both groups were combined. The most commonly selected response was ‘registered dietitian’ (56%), followed by ‘medical doctor’ (27%), ‘naturopath’ (8%), ‘registered nurse’ (6%), and ‘other’ (3%). Four participants selected ‘other’ and the open-ended responses were either ‘myself’ (n = 3: 2 participants from the intervention group, 1 from the control group) or ‘a researcher’ (n = 1: participant from the intervention group).

100


DOI: 10.1159/000365508


Table 4. Changes in perceptions of personalized nutrition and genetic testing

Survey statement

Mean response ± SD

Mean change from baseline ± SD

p value compared to baseline

I am interested in the relationship between diet and genetics Baseline 4.23 ± 0.91 3 months 4.51 ± 0.83 0.0002 0.28 ± 0.99 0.02 12 months 4.43 ± 0.79 0.20 ± 1.04 I would take a genetic test to learn more about myself Baseline 4.27 ± 0.91 0.32 3 months 4.33 ± 0.92 0.06 ± 0.86 12 months 4.17 ± 1.05 –0.10 ± 1.03 0.42 I would take a genetic test to encourage myself to adopt a healthier lifestyle Baseline 4.15 ± 1.00 3 months 4.21 ± 0.94 0.35 0.06 ± 0.95 –0.17 ± 1.05 0.08 12 months 3.98 ± 1.05 I would take a genetic test to have my health care professional monitor my health more closely Baseline 4.06 ± 0.97 0.02 3 months 4.26 ± 0.91 0.20 ± 1.00 12 months 4.01 ± 1.00 –0.03 ± 1.08 0.89 B-Ycrit correction level of statistical significance is p < 0.018.

Sharing of Information and Utility of Personalized Nutrition In the 12-month survey, participants were asked if they shared the information in the report with anyone. The available choices were ‘family member’, ‘significant other’, ‘friend’, ‘health care professional’, and ‘I did not share the information with anyone’, and participants were instructed to select all that applied. A significantly greater proportion of participants in the intervention group shared the information with a family member (31 vs. 9%, p = 0.005) or a friend (33 vs. 7%, p = 0.001). A significantly greater proportion of participants in the control group selected ‘I did not share the information with anyone’ when compared to the intervention group (64 vs. 10%, p < 0.0001). Only 1 participant in the study, who was in the intervention group, shared the information with a health care professional. At 12 months, participants were also asked if they thought DNA-based dietary advice is more useful than general population-based dietary advice and the possible responses were ‘yes’, ‘no’, or ‘no opinion’. Since no significant differences were observed between the responses of the intervention group when compared to the control group (data not shown), the responses of both groups were combined. The majority of the 125 participants (73%) selected ‘yes’, 14% selected ‘no’ and 14% selected ‘no opinion’.


of the scale for the statement ‘I am interested in the relationship between diet and genetics’ (mean change ± SD: 0.28 ± 0.99, p = 0.0002; table 4). Trends in increases toward the positive end of the scale were observed for the statement ‘I would take a genetic test to have my health care professional monitor my health more closely’ (mean change ± SD: 0.20 ± 1.00, p = 0.02) at 3 months, and ‘I am interested in the relationship between diet and genetics’ (mean change ± SD: 0.20 ± 1.04, p = 0.02) at 12 months, as compared to baseline.

101




The present study is the first randomized controlled trial to compare attitudes and perceptions of genetic testing and personalized nutrition between those who receive DNAbased dietary advice and those who receive general dietary recommendations. The findings demonstrated that certain perceptions of personalized nutrition and genetic testing changed over the course of the present study, although these changes do not appear to be the result of providing DNA-based dietary advice. However, participants in the control group were aware that they would be given DNA-based dietary advice at some point in the study and this knowledge may have influenced their responses to certain survey statements. Since proponents of DTC genetic tests claim that the genetic information can have a positive impact on health behavior, evaluating individuals’ perceptions of genetic testing has clinically relevant behavioral implications. Theories of health behavior change, such as the Health Belief Model, assert that an individual’s beliefs about a health risk and the effectiveness of a particular behavior are indicative of one’s likelihood of engaging in that behavior [25]. Therefore, the reported changes in perceptions in the present study may have an influence on individual health behaviors, such as changing one’s diet. Indeed, previous surveys of individuals who have undergone consumer genetic testing have reported that the genetic information had an impact on their dietary behaviors, although the information was not specifically related to dietary response [26, 27]. Consequently, genetic information for personalized nutrition may have an even greater impact on dietary behavior. A university research lab and health care professional were viewed more favorably than a DTC genetic testing company as providers of accurate personal genetic information, and a registered dietitian was selected as the best provider of personalized nutrition advice. The low proportion of participants who selected DTC genetic testing company may reflect a lack of awareness of such companies, which we previously reported [15]. Nevertheless, these findings are in line with other reports of consumer preferences for health care professional involvement in genetic testing [28, 29] and highlight an important consideration. While proper communication of genetic test results is needed to prevent individuals from misinterpreting the information and to guide them toward making appropriate lifestyle changes where necessary [30], health care professionals in general, including dietitians, have expressed low genetic self-efficacy to be able to interpret and explain genetic test results to patients [31–33]. Some argue that genetic counselors should communicate genetic test results to consumers [34], but it is important to note that genetic counselors are not educated to counsel on genetic variants that confer risk to chronic diseases, which are polyetiological in nature, and do not possess the required training to provide nutrition and lifestyle advice. Increasing genomics education in health care professional training programs is, therefore, an important priority that has been acknowledged by a number of health centers [35]. In fact, a recent study demonstrated that incorporating personal genome testing in a course on personalized medicine improved both self-reported and assessed genomics knowledge among medical students who underwent testing [35]. Since registered dietitians were viewed as the best providers of personalized nutrition advice in the present study, the dietetics curricula may benefit from increasing education on genomics and genetic testing. Although health care professional involvement in personal genome testing is preferred by the general public, there is concern that the use of this technology could increase the burden on health care systems [36]. A previous study reported low levels of physician involvement and no difference in the use of medical screening tests 3 months after undergoing DTC testing [37], but at 12 months it was reported that nearly 40% of participants had shared their genetic test results with their health care professional and undergone a greater number of screening tests than those who did not share their test results [38]. However, that


Discussion

102




study did not include a control group, so the interpretation of those findings is limited. In the present study, only 1 participant in the intervention group shared the genetic information with a health care professional, which suggests that genetic testing for personalized nutrition might not have an impact on health care resources in the manner that genetic testing for disease susceptibility may. Nevertheless, participants in the intervention group were more likely to share the information from their dietary report with a family member or friend compared with participants in the control group, who mostly reported that they did not share their dietary information with anyone. This finding may be reflective of individuals’ interests and enthusiasm for personalized nutrition information. Indeed, the majority (73%) of participants from both the intervention and control group in the present study indicated that DNAbased dietary advice is more useful than general recommendations. As a result, personalized dietary advice may be more effective at motivating adherence to dietary recommendations than general population-based dietary advice. Overall, the application of nutrigenomics is not considered to bear substantial risk such as potentially medicalizing food, hindering individual autonomy for food choices, or obliging individuals to comply with DNA-based dietary recommendations [39]. Limitations of the present study include the small sample size and selective group of participants that may not be representative of other populations. However, previous studies that have examined individual responses following disclosure of personal genetic information have consisted of comparable sample sizes [40–42]. Moreover, while the participants in the present study were highly educated and recruited from a previous nutrigenomics study, they are representative of the early adopters of consumer genetic testing who are more likely to be highly educated, Caucasian and between the ages of 18 and 49 years [27, 37, 43]. One previous study reported that a larger proportion of consumers were female [43]. Nevertheless, the survey findings reported in the present study may not be generalizable to populations that are less educated, older, non-Caucasian or managing a chronic disease. In addition to these limitations, the survey items used in the present study were not validated, though a number of items were adapted from questions used in a previous large-scale survey of public interest in personal genome testing [7]. Strengths of the present study are the inclusion of a control group, which provided a method of comparing the perceptions of DNA-based dietary advice to general populationbased recommendations, as well as the randomized design, which minimizes the potential for confounding effects. In addition, conducting the study in a manner that closely resembled a DTC genetic test increases the validity of the findings to reflect the perceptions among DTC genetic test users. The present study was the first to examine and compare perceptions of genetic testing and personalized nutrition among individuals who are given DNA-based advice or general dietary recommendations. The findings provide novel information on individual perceptions toward personalized nutrition and genetic testing, as well as perceptions of sources of personal genetic information, providers of personalized nutrition advice and the utility of DNA-based advice compared with general dietary recommendations.

This study was supported by a grant from the Advanced Foods and Materials Network (AFMNet) and the Canadian Institutes of Health Research (CIHR; MOP-89829). D.E.N. is the recipient of an Ontario Graduate Scholarship and a Banting & Best Diabetes Centre Graduate Studentship. The authors thank Alyssa Katzikowski and Maria Tassone for their assistance with subject recruitment and data collection.


Acknowledgements

103




Disclosure Statement A.E.-S. holds shares in Nutrigenomix Inc., a genetic testing company for personalized nutrition.

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25

Sterling R: The on-line promotion and sale of nutrigenomic services. Genet Med 2008;10:784–796. Coghlan A: 23andMe ordered to stop selling $99 genetic test. New Scientist, 2013. http://www.newscientist. com/article/dn24656–23andme-ordered-to-stop-selling-99-genetic-test.html (accessed April 23, 2014). O’Connor L: 23andMe faces $5 million lawsuit days after being banned by FDA. The Huffington Post, December 4, 2013. http://www.huffingtonpost.com/2013/12/04/23andme-lawsuit_n_4387699.html (accessed April 23, 2014). Human Genetics Commission: A common framework of principles for direct-to-consumer genetic testing services. 2010. http://www.cellmark.co.uk/pdfs/HGCprinciples.pdf (accessed April 23, 2014). Wagner JK: Understanding FDA regulation of DTC genetic tests within the context of administrative law. Am J Hum Genet 2010;87:451–456. Borry P, van Hellemondt RE, Sprumont D, Jales CF, Rial-Sebbag E, Spranger TM, Curren L, Kaye J, Nys H, Howard H: Legislation on direct-to-consumer genetic testing in seven European countries. Eur J Hum Genet 2012;20:715–721. Cherkas LF, Harris JM, Levinson E, Spector TD, Prainsack B: A survey of UK public interest in internet-based personal genome testing. PLoS One 2010;5:e13473. Goddard KA, Duquette D, Zlot A, Johnson J, Annis-Emeott A, Lee PW, Bland MP, Edwards KL, Oehlke K, Giles RT, Rafferty A, Cook ML, Khoury MJ: Public awareness and use of direct-to-consumer genetic tests: results from 3 state population-based surveys, 2006. Am J Public Health 2009;99:442–445. Kolor K, Duquette D, Zlot A, Foland J, Anderson B, Giles R, Wrathall J, Khoury MJ: Public awareness and use of direct-to-consumer personal genomic tests from four state population-based surveys, and implications for clinical and public health practice. Genet Med 2012;14:860–867. Stewart-Knox BJ, Bunting BP, Gilpin S, Parr HJ, Pinhao S, Strain JJ, de Almeida MD, Gibney M: Attitudes toward genetic testing and personalised nutrition in a representative sample of European consumers. Br J Nutr 2009; 101:982–989. Ronteltap A, van Trijp JC, Renes RJ: Consumer acceptance of nutrigenomics-based personalised nutrition. Br J Nutr 2009;101:132–144. Roosen J, Bruhn M, Mecking RA, Drescher LS: Consumer demand for personalized nutrition and functional food. Int J Vitam Nutr Res 2008;78:269–274. Goddard KA, Moore C, Ottman D, Szegda KL, Bradley L, Khoury MJ: Awareness and use of direct-to-consumer nutrigenomic tests, United States, 2006. Genet Med 2007;9:510–517. Stenne R, Hurlimann T, Godard B: Benefits associated with nutrigenomics research and their reporting in the scientific literature: researchers’ perspectives. Account Res 2013;20:167–183. Nielsen DE, El-Sohemy A: A randomized trial of genetic information for personalized nutrition. Genes Nutr 2012;7:559–566. Nawrot P, Jordan S, Eastwood J, Rotstein J, Hugenholtz A, Feeley M: Effects of caffeine on human health. Food Addit Contam 2003;20:1–30. Otten JJ, Hellwig JP, Meyers LD (eds): Dietary Reference Intakes: The Essential Guide to Nutrient Requirements. Washington, National Academies Press, 2006. Nishida C, Uauy R, Kumanyika S, Shetty P: The joint WHO/FAO expert consultation on diet, nutrition and the prevention of chronic diseases: process, product and policy implications. Public Health Nutr 2004;7:245–250. Cornelis MC, El-Sohemy A, Kabagambe EK, Campos H: Coffee, cyp1a2 genotype, and risk of myocardial infarction. JAMA 2006;295:1135–1141. Palatini P, Ceolotto G, Ragazzo F, Dorigatti F, Saladini F, Papparella I, Mos L, Zanata G, Santonastaso M: CYP1A2 genotype modifies the association between coffee intake and the risk of hypertension. J Hypertens 2009;27: 1594–1601. Cahill LE, Fontaine-Bisson B, El-Sohemy A: Functional genetic variants of glutathione S-transferase protect against serum ascorbic acid deficiency. Am J Clin Nutr 2009;90:1411–1417. Giner V, Poch E, Bragulat E, Oriola J, Gonzalez D, Coca A, De La Sierra A: Renin-angiotensin system genetic polymorphisms and salt sensitivity in essential hypertension. Hypertension 2000;35:512–517. Poch E, Gonzalez D, Giner V, Bragulat E, Coca A, de La Sierra A: Molecular basis of salt sensitivity in human hypertension. Evaluation of renin-angiotensin-aldosterone system gene polymorphisms. Hypertension 2001; 38:1204–1209. Narum SR: Beyond Bonferroni. Less conservative analyses for conservation genetics. Conserv Genet 2006;7: 783–787. Rosenstock IM, Strecher VJ, Becker MH: Social learning theory and the health belief model. Health Educ Q 1988;15:175–183.


References

104




28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43

Maher B: Nature readers flirt with personal genomics. Nature 2011;478:19. Kaufman DJ, Bollinger JM, Dvoskin RL, Scott JA: Risky business: risk perception and the use of medical services among customers of DTC personal genetic testing. J Genet Couns 2012;21:413–422. Almeling R, Gadarian SK: Public opinion on policy issues in genetics and genomics. Genet Med 2014;16:491– 494. Morin K: Knowledge and attitudes of Canadian consumers and health care professionals regarding nutritional genomics. OMICS 2009;13:37–41. Ferguson LR, Barnett MPG: Research in nutrigenomics and potential applications to practice. Nutr Diet 2012; 69:198–202. Weir M, Morin K, Ries N, Castle D: Canadian health care professionals’ knowledge, attitudes and perceptions of nutritional genomics. Br J Nutr 2010;104:1112–1119. Birmingham WC, Agarwal N, Kohlmann W, Aspinwall LG, Wang M, Bishoff J, Dechet C, Kinney AY: Patient and provider attitudes toward genomic testing for prostate cancer susceptibility: a mixed method study. BMC Health Serv Res 2013;13:279. Cormier H, Tremblay BL, Paradis AM, Garneau V, Desroches S, Robitaille J, Vohl MC: Nutrigenomics – perspectives from registered dietitians: a report from the Quebec-wide e-consultation on nutrigenomics among registered dietitians. J Hum Nutr Diet 2014;27:391–400. Harris A, Kelly SE, Wyatt S: Counseling customers: emerging roles for genetic counselors in the direct-toconsumer genetic testing market. J Genet Couns 2013;22:277–288. Salari K, Karczewski KJ, Hudgins L, Ormond KE: Evidence that personal genome testing enhances student learning in a course on genomics and personalized medicine. PLoS One 2013;8:e68853. McBride CM, Bryan AD, Bray MS, Swan GE, Green ED: Health behavior change: can genomics improve behavioral adherence? Am J Public Health 2012;102:401–405. Bloss CS, Schork NJ, Topol EJ: Effect of direct-to-consumer genomewide profiling to assess disease risk. N Engl J Med 2011;364:524–534. Bloss CS, Wineinger NE, Darst BF, Schork NJ, Topol EJ: Impact of direct-to-consumer genomic testing at long term follow-up. J Med Genet 2013;50:393–400. Hurlimann T, Menuz V, Graham J, Robitaille J, Vohl MC, Godard B: Risks of nutrigenomics and nutrigenetics? What the scientists say. Genes Nutr 2014;9:370. Grant RW, O’Brien KE, Waxler JL, Vassy JL, Delahanty LM, Bissett LG, Green RC, Stember KG, Guiducci C, Park ER, Florez JC, Meigs JB: Personalized genetic risk counseling to motivate diabetes prevention: a randomized trial. Diabetes Care 2013;36:13–19. Arkadianos I, Valdes AM, Marinos E, Florou A, Gill RD, Grimaldi KA: Improved weight management using genetic information to personalize a calorie controlled diet. Nutr J 2007;6:29. Chao S, Roberts JS, Marteau TM, Silliman R, Cupples LA, Green RC: Health behavior changes after genetic risk assessment for Alzheimer disease: the reveal study. Alzheimer Dis Assoc Disord 2008;22:94–97. Gollust SE, Gordon ES, Zayac C, Griffin G, Christman MF, Pyeritz RE, Wawak L, Bernhardt BA: Motivations and perceptions of early adopters of personalized genomics: perspectives from research participants. Public Health Genomics 2012;15:22–30.


26 27

Perceptions of Genetic Testing for Personalized ...

Perceptions of Genetic Testing for Personalized ...

Suggest Documents

Personalized Medicine Molecular Genetic Laboratory Testing Legal ...

Genetic testing and personalized ovarian cancer ... - Springer Link

Consumers' perceptions about personalized advertising

Young women perceptions towards genetic testing of breast cancer ...

Probing personalized genetic platforms for novel ...

Genetic Testing for Beryllium - CPWR

Genetic testing for Emberger syndrome

Genetic Testing for Beryllium - CPWR

Pharmacogenetics driving personalized medicine: analysis of genetic ...

Ethical Considerations of Genetic Presymptomatic Testing for ...

Psychological consequences of predictive genetic testing for ...

Genetic Testing For Sale: Implications of ...

Genetic testing of children and adolescents for

health technology assessment of genetic testing for

Testing the Impact of Personalized Feedback - National Center for ...

frequency of genetic subtypes and guidelines for genetic testing

Personalized Oxycodone Dosing: Using Pharmacogenetic Testing ...

Personalized Learning Environment: Alpha Testing, Beta ... - CORE

genetic testing - UCL Discovery

Genetic Testing - Coriell Institute

genetic testing - UCL Discovery

Cross-Border Genetic Testing

genetic testing

Genetic Testing - Google Sites