Confidential: For Review Only - bmj.com

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The impact of communicating genetic risks of disease on

risk-reducing health behaviour: systematic review with meta-analysis

Journal: BMJ

Manuscript ID BMJ.2015.030916

Article Type: Research

BMJ Journal: BMJ

Date Submitted by the Author: 14-Dec-2015

Complete List of Authors: Hollands, Gareth; University of Cambridge, Behaviour and Health Research Unit French, David; University of Manchester, School of Psychological Sciences Griffin, S; University of Cambridge, Public Health and Primary Care; MRC, Epidemiology Unit Prevost, Andrew; Kings College London, Primary Care and Public Health Sciences Sutton, Stephen; University of Cambridge, Department of Public Health and Primary Care King, Sarah; University of Cambridge, Behaviour and Health Research Unit Marteau, Theresa; University of Cambridge, Behaviour and Health Research Unit

Keywords: genetic testing; risk communication; personalised medicine; systematic review; health behaviour

https://mc.manuscriptcentral.com/bmj

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TITLE PAGE

The impact of communicating genetic risks of disease on risk-reducing health behaviour:

systematic review with meta-analysis

Gareth J Hollands, Senior Research Associate, Behaviour and Health Research Unit, University of

Cambridge, Cambridge, UK

Email: [email protected]

David P French, Professor of Health Psychology, School of Psychological Sciences, University of

Manchester, Manchester, UK


Simon J Griffin, Professor of General Practice, Department of Public Health and Primary Care,

University of Cambridge, Cambridge, UK


A Toby Prevost, Professor of Medical Statistics, Department of Primary Care and Public Health

Sciences, King’s College London, London, UK


Stephen Sutton, Professor of Behavioural Science, Department of Public Health and Primary Care,

University of Cambridge, Cambridge, UK


Sarah King, Visiting Fellow, Behaviour and Health Research Unit, University of Cambridge,

Cambridge, UK


*Theresa M Marteau, Director and Professor of Behaviour and Health, Behaviour and Health

Research Unit, University of Cambridge, Cambridge, UK

*Corresponding author. Email: [email protected]

The Corresponding Author has the right to grant on behalf of all authors and does grant on behalf of

all authors, an exclusive licence (or non exclusive for government employees) on a worldwide basis

to the BMJ Publishing Group Ltd to permit this article (if accepted) to be published in BMJ editions

and any other BMJPGL products and sublicences such use and exploit all subsidiary rights, as set

out in our licence.

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The impact of communicating genetic risks of disease on risk-reducing health behaviour:

systematic review with meta-analysis

ABSTRACT

Background

DNA-based disease risk information is becoming widely available. Expectations are high that

communicating this information motivates behaviour change to reduce risk.

Objective

To assess the impact of communicating DNA-based disease risk estimates on risk-reducing health

behaviours and motivation to engage in such behaviours.

Design

Systematic review with meta-analysis, using Cochrane methods. We assessed risk of bias for each

included study and quality of evidence for each behavioural outcome.

Data sources

We searched the following databases up to 25th February 2015: MEDLINE, Embase, PsycINFO,

CINAHL, Cochrane Central Register of Controlled Trials. We also conducted backward and

forward citation searches.

Eligibility criteria for selecting studies

Randomised and quasi-randomised controlled trials involving adults in which one group received

personalised DNA-based disease risk estimates for diseases for which the risk could be reduced by

behaviour change. Eligible studies included a measure of risk-reducing behaviour.

Results

We examined 10,515 abstracts and included 18 studies, which reported on seven behavioural

outcomes, including diet (seven studies; n = 1784), smoking cessation (six studies; n = 2663), and

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physical activity (six studies; n = 1704). Meta-analysis revealed a marginally non-significant effect

of communicating DNA-based risk estimates on diet (standardised mean difference 0.12, 95%

confidence interval -0.00 to 0.24, p = 0.05) and no statistically significant effects on smoking

cessation (odds ratio 0.92, 95% confidence interval 0.63 to 1.35, p = 0.67) or physical activity

(standardised mean difference -0.03, 95% confidence interval -0.13 to 0.08, p=0.62). There were

also no effects on any other behaviours (alcohol use, medication use, sun protection behaviours, and

attendance at screening or behavioural support programmes), on motivation to change behaviour,

and no adverse effects, including depression and anxiety. Subgroup analyses provided no clear

evidence that communication of a risk-conferring genotype impacted on behaviour more than did

communication of the absence of such a genotype. However, studies were predominantly at high or

unclear risk of bias and evidence was typically of low quality.

Conclusions

Expectations that communicating DNA-based risk estimates changes behaviour are not supported

by existing evidence. These results do not support use of genetic testing or the search for risk-

conferring gene variants for common complex diseases on the basis that they motivate risk-reducing

behaviour.

Systematic review registration

This is a revised and updated version of a Cochrane review from 2010 1 adding 11 studies to the

seven previously identified.

Keywords: genetic testing; risk communication; personalised medicine; systematic review; health

behaviour

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INTRODUCTION

Considerable attention continues to be given to searching for gene variants associated with risks of

common complex conditions including diabetes and various cancers 2 3. While the main target of

such research is more effective treatments, more precise prediction of disease has also been

anticipated. Less attention has so far been given to evaluating whether health benefits, in particular,

risk-reducing changes in behaviour, can be realised through communicating the results of such

predictions. For example, does communicating to smokers that they have an increased genetic risk

of developing lung cancer motivate smoking cessation, or telling middle aged adults that they have

an increased genetic risk of developing diabetes motivate increased physical activity to reduce this

risk? These are particularly timely questions given high levels of interest in personalised medicine,

and in direct-to-consumer testing. Following an initial rush to market over ten years ago, with

direct-to-consumer tests being introduced for a range of common complex disorders, these continue

to be sold in the UK and Canada (e.g. https://www.23andme.com/en-gb/health/;

https://www.23andme.com/en-ca/). In the USA, this was tempered in 2013 by the Food and Drug

Administration ordering the company 23andme to stop selling its testing kits due to concerns about

the accuracy and usefulness of the tests, although as of October 2015, the company has resumed

selling some health-related services. Regulatory systems in the US are now being developed to

ensure public protection in anticipation of rapid developments in this field including increased

commercial interests in direct-to-consumer genomic testing 4.

As the science develops, it is increasingly possible to provide information about multiple single

genes each relating to different disease risks, and also to aggregate multiple risk loci and identify

patterns of characteristics across multiple genes that in combination confer increased risks of one or

more diseases. However, DNA-based disease risk estimates will translate into health benefits only

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insofar as (a) acting on them actually modifies disease outcomes, and (b) those informed of these

disease risk estimates undertake the relevant actions. The focus of this review is upon this second

point: the behavioural impact of communicating DNA-based disease risk.

Three competing predictions regarding such impact are evident in the literature. The first is that

communicating DNA-based risk estimates motivates behaviour change more strongly than do

disease risk estimates based on other types of risk information, particularly if risk estimates are

based on the detection of risk-conferring mutations5-7. Such expectations are consistent with

theories of attitude change which suggest that the greater the personal salience of information, such

as information regarding one’s own DNA, the greater the impact8. The second prediction is that

communicating DNA-based disease risk estimates de-motivates behaviour change9. This is based on

the observation that diseases considered to have a genetic basis are perceived as less controllable10

and using DNA to estimate disease risks may induce a sense of lack of control or fatalism over the

ability to improve outcomes11. The third prediction is that communicating such information is likely

to have, at best, only a small effect on behaviour. This is based on review evidence showing that

perceptions of disease risk exert, at most, only a small influence on behaviour12, and that

communicating the results of a wide range of biomarker tests has no consistent effect on

behaviour13 14

.

Several narrative reviews have been conducted assessing the emotional and behavioural outcomes

of communicating DNA-based disease risk estimates15-18

and the outcomes of genetic health

services for common adult onset conditions19. However, these reviews identified few clinical

studies using randomised designs to assess effects on behaviour and did not include quantitative

syntheses of effects. Although systematic reviews have been conducted more recently, these have

focused on single behaviours such as smoking cessation20 21

. We aimed to assess the impact of

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communicating DNA-based disease risk estimates on risk-reducing behaviours and motivation to

undertake such behaviours. We also aimed to examine whether communicating the presence of a

risk-conferring genotype would elicit a stronger (and potentially counteractive) motivational

response than would communicating its absence22.

Expectations are high that advances in genetics will usher in a new era of personalised medicine,

and that because communicating genetic risks will motivate risk-reducing behaviour changes, they

have a role in risk reduction strategies aimed at improving population health23. The results of this

review will inform debates regarding the role of genetic testing in public health policies. They will

also contribute to the evidence base concerning the impact on risk-reducing behaviours of

communicating disease risk based on a wide range of biological markers of disease, of which DNA

is but one13 14 24

.

METHODS

The methods are described in greater detail elsewhere1.

Data sources

We searched the following databases up to 25th February 2015: MEDLINE, Embase, PsycINFO,

CINAHL and Cochrane Central Register of Controlled Trials. We also conducted backward and

forward citation searches from included studies. The MEDLINE search strategy is presented as

Appendix 1.

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Inclusion/exclusion criteria

Eligible studies had the following characteristics:

(a) were randomised controlled trials or quasi-randomised controlled trials (controlled trials that

employ a non-random method of allocation to arm such as alternation or by date of birth);

(b) recruited adult populations (18 years and over);

(c) included one group which received personalised DNA-based risk estimates for diseases for

which behaviour change could reduce risk (including heart disease, cancers and Alzheimer’s

disease).

Ineligible studies comprised those that evaluated communication of DNA-based risk estimates of

diseases for which there is no known intervention to reduce that risk, such as Huntington’s disease.

Effects of the intervention were assessed relative to the effects of communicating non-DNA based

disease risk estimates (assessment based on family history, biological markers of disease,

demographic characteristics, or a combination thereof) or of communicating no disease risk

estimates. Included studies therefore formed three main groups, defined by differences in the

intervention and comparison groups: i) disease risk estimates based on DNA, versus non-DNA

based disease risk estimates; ii) disease risk estimates based on DNA plus non-DNA based disease

risk estimates, versus only non-DNA based disease risk estimates; or iii) disease risk estimates

based on DNA, versus no disease risk estimates.

The primary outcome was performance of a behaviour that could reduce the risk of disease.

Behaviours included smoking, alcohol consumption, diet and physical activity. We only included

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studies that measured at least one of the primary outcomes. Secondary outcomes were motivation to

change behaviour, and levels of depression and anxiety.

Data extraction and synthesis

Two review authors pre-screened all search results (titles and abstracts) against the inclusion criteria

and those selected by either or both reviewers were subjected to a full-text assessment. Two authors

independently assessed the selected full-text articles for inclusion. Two authors independently

extracted data concerning study participants, study design, interventions, outcome measures, results

and risk of bias characteristics. Extracted data were entered into Review Manager software by one

author and checked by a second author. We contacted study authors for additional information

about included studies as required.

Studies were analysed by type of behaviour, combining data across diseases and interventions. We

summarised study effect sizes for each outcome using forest plots. Effect sizes for dichotomous

data were odds ratios, with values greater than one favouring the intervention group. Effect sizes for

continuous outcomes were standardised mean differences, centred on zero, with values greater than

zero favouring the intervention group and those less than zero favouring the comparison group.

When different studies reported either dichotomous or continuous data for the same outcome, these

data were combined using the generic inverse variance method25 and effect sizes reported as

standardised mean differences. We obtained pooled effect sizes with 95% confidence intervals

using a random effects model applied on the scale of standardised mean differences and log odds

ratios. We tested for heterogeneity using the Chi2 test and quantified it using the I

2 statistic, with a

value of 50% or greater being considered as representing substantial heterogeneity 25.

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If multiple indices of a given behavioural outcome were reported, we used the most stringent and

valid measure of behaviour available (e.g. an objective measure such as biochemically validated

smoking cessation). When a study had more than one follow-up time point, we used data from the

longest follow-up available. Final values were always used rather than changes from baseline.

When there were multiple intervention and control arms, we chose as the comparison that which

allowed the purest isolation of the effect of the DNA risk communication component.

Subgroup analysis

When data were available, we examined the effect of genetic test result within those participants

receiving DNA-based disease risk estimates, comparing the effect of communicating the presence

versus the absence of a risk-conferring genotype. A risk-conferring genotype in this context is a

variant associated with an increased likelihood of disease.

Treatment of missing data

We analysed data according to participants’ randomised groups accounting for missing data where

possible, either using data as provided by authors or, for dichotomous outcomes when data were not

provided, assuming that participants with missing outcomes were engaging in the risk-increasing

behaviour (e.g. continuing to smoke). When such analysis was not possible, either due to missing

data, or where the outcomes were reported as continuous data, due to the problematic nature of

imputation without available individual-level data, we analysed outcomes as reported.

Assessment of risk of bias and of quality of evidence

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We assessed the methodological characteristics of included studies in accordance with Cochrane

guidance25, including assessment of sequence generation; allocation concealment; blinding;

incomplete outcome data; selective outcome reporting, and other bias. For each criterion, we

determined whether this represented a low, unclear or high risk of bias and we generated a summary

risk of bias assessment based on the individual domains. If the judgement in at least one domain

was ’high risk of bias’ then summary risk of bias was determined to be high. Summary risk of bias

was only judged ’low’ if judgements in all domains were ’low risk of bias’. The summary risk of

bias contributed to the GRADE assessment of the quality of evidence, which was applied to each

primary outcome in terms of the extent of our confidence in the estimates of effects. GRADE

criteria for assessing quality of evidence encompass study limitations, inconsistency, imprecision,

indirectness, publication bias and other considerations26.

Patient involvement

Patients were not involved in the design or conduct of this systematic review.

RESULTS

We identified 10,515 references which were screened for possible inclusion. 18 studies met the

inclusion criteria. See Figure 1 for details of the search and screening process and Table 1 for

details of the included studies. The reasons for excluding studies after full-text assessment were:

ineligible study design; not including a relevant outcome measure; no study arm receiving

personalised DNA-based disease risk estimates; no eligible comparison; and, the study being

ongoing and yet to report its results.

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PLEASE INSERT FIGURE 1 AND TABLE 1 ABOUT HERE

Settings for the studies were principally outpatient or primary care clinics or various community

populations. Five studies communicated genetic risks for lung or oesophageal cancer to smokers27-31

and one study communicated risks of Crohn's disease to smokers32. Two studies communicated

risks of oesophageal and other cancers with alcohol consumption33 34

. One study communicated

melanoma risks35 and one study communicated colorectal cancer risks

36. Three studies

communicated risk of type 2 diabetes37-39

. Three studies communicated risks of heart disease,

cardiovascular disease or hypertension40-42

, one study communicated predictive genetic testing for

Alzheimer’s disease43 and one study communicated genetic risks of obesity

44. Eight studies were

conducted in the USA27 30 33 35 36 38 39 43

, five in the UK31 32 37 41 44

, three in Japan28 29 34

and one study

was conducted in each of Finland40 and Canada

42. The mean ages of participants, where reported,

ranged from 30 to 56 years and the gender mix of participants ranged between 0% and 73% female.

Primary outcome analysis

Results are shown in separate forest plots for outcome data that were only dichotomous (Figure 2),

only continuous (Figure 3) and for combined dichotomous and continuous outcome data (Figure 4).

PLEASE INSERT FIGURES 2, 3 AND 4 ABOUT HERE

Smoking cessation (Figure 2; Analysis 1.1.1)

Six studies assessed smoking cessation27-32

, all but one32 using self-report measures. Pooled analysis

(n=2663) showed no statistically significant effect of DNA risk communication on this outcome

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(odds ratio 0.92, 95% confidence interval 0.63 to 1.35, p = 0.67; I2 = 39%). Within-intervention arm

subgroup analysis, assessing the effect of the presence (versus absence) of a risk-conferring

genotype, was possible for five of the six studies28-32

. Pooling these data revealed no evidence of a

benefit from communicating the presence of a risk-conferring genotype (odds ratio 1.26, 95%

confidence interval 0.81 to 1.97, p = 0.30).

Medication use (Figure 2; Analysis 1.1.2)

One study (n = 162) assessed self-reported medication or vitamin use to reduce risk of Alzheimer's

disease, at 12-month follow-up43. The odds ratio was 1.26, 95% confidence interval 0.58 to 2.72, p

= 0.56, suggesting no effect of DNA risk communication. In subgroup analysis, the odds ratio was

2.61, 95% confidence interval 1.09 to 6.23, p=0.03, indicating a positive effect on medication use of

information concerning the presence of a risk-conferring genotype.

Alcohol use (Figure 3; Analysis 1.2.1)

Pooled data from the three studies33 34 40

that assessed alcohol use (n=239) revealed no evidence of

an effect of DNA risk communication on reducing alcohol use (standardised mean difference 0.07,

95% confidence interval -0.20 to 0.35, p = 0.61, I2 = 13%). Subgroup analysis with data from a

single study40, showed no effect of communicating a high-risk genotype (standardised mean

difference 0.17, 95% confidence interval -0.42 to 0.76, p = 0.57).

Sun protection behaviours (Figure 3; Analysis 1.2.2)

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One study (n = 73) assessed self-reported sun protection behaviours35. The standardised mean

difference was 0.43, 95% confidence interval -0.03 to 0.90, p = 0.07, suggesting no effect of DNA

risk communication. Subgroup analysis was not possible.

Diet (Figure 4; Analysis 1.3.1)

Seven studies assessed self-reported dietary behaviour37 39-44

. Pooling data from these studies

(n=1784), there was no statistically significant evidence of a benefit from DNA risk

communication, although the effect was marginally non-significant (standardised mean difference

0.12, 95% confidence interval -0.00 to 0.24, p = 0.05, I2 = 17%). Pooled subgroup analysis with

data from three studies40 42 43

, showed no effect of communicating a high-risk genotype

(standardised mean difference 0.18, 95% confidence interval -0.13 to 0.50, p = 0.25).

Physical activity (Figure 4; Analysis 1.3.2)

Six studies assessed physical activity as an endpoint behaviour37 39-41 43 44

, all but one37 using self-

report measures. Pooled data from these studies (n=1704) revealed no evidence of an effect of DNA

risk communication (standardised mean difference -0.03, 95% confidence interval -0.14 to 0.07, p=

0.54, I2 = 0%). Pooled subgroup analysis with data from two studies

40 42, showed no effect of

communicating a high-risk genotype (odds ratio 1.23, 95% confidence interval 0.49 to 3.11, p =

0.65).

Attendance at screening or behavioural support programmes (Figure 4; Analysis 1.3.3)

Two studies assessed attendance at screening or behavioural support programmes36 38

. Pooled

analysis (n=891) suggested no effect of DNA risk communication (standardised mean difference

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-0.04, 95% confidence interval -0.20 to 0.11, p = 0.59, I2 = 0%). It was possible to conduct

subgroup analysis with data from both studies, which showed no effect of communicating a high-

risk genotype (standardised mean difference -0.16, 95% confidence interval -0.47 to 0.16, p = 0.33).

Secondary outcomes

The few data reported on pre-specified secondary outcomes of motivation to change behaviour,

depression and anxiety provided no evidence of any intervention impact upon these outcomes. Five

studies assessed motivation or intention to change behaviour 28 31 33 37 38

, two studies measured

depression31 41

and three studies measured anxiety 31 37 41

. In all cases, confidence intervals included

no effect.

Assessment of risk of bias (Figure 5) and quality of evidence

Only four of the 18 studies were considered to have a low summary risk of bias, having met all of

the specified criteria 32 36-38

. This reflected both a lack of clarity in reporting, and a failure or

inability to safeguard against risk of bias. In terms of GRADE assessment of the quality of the

evidence across outcomes, evidence was determined to be of low quality for all outcomes other than

attendance at screening or behavioural support, meaning limited confidence is placed in the effect

estimates. Evidence was downgraded twice for these outcomes due to both study limitations (with

all or most information for the outcome from studies at high or unclear risk of bias) and imprecision

(with sample sizes failing to meet the Optimal Information Size and/or 95% confidence intervals for

the summary effect estimate overlapping no effect and including appreciable benefit or harm). For

the outcome of attendance at screening or behavioural support, the evidence was downgraded only

once due to imprecision (and not study limitations, as information came from studies at low risk of

bias). Therefore, the evidence for this outcome was assessed to be of moderate quality.

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DISCUSSION

Principal findings

This review provides the strongest evidence to date concerning the impact of communicating

genetic risks of disease on risk-reducing behaviour. The results suggest that communicating DNA-

based disease risk estimates has little or no effect on health-related behaviour. The evidence for

concluding an absence of effect was strongest for smoking cessation and physical activity, where

for both, six studies contributed comparably consistent effects, with pooled point estimates of effect

size close to unity, supported by relatively narrow 95% confidence intervals. The evidence

concerning attendance at screening or behavioural support shared similar characteristics and also

indicated an absence of effect, although comprised only two studies (albeit both being well-

conducted trials). For dietary behaviour, the picture is less clear. There were seven studies, and the

result was marginally non-significant, with the confidence interval suggesting that benefit may be

possible in this domain. For all other behaviours, there were considerably fewer data and further

research would add valuable information to the current insufficiency of evidence. There were also

no effects on motivation to change behaviour, and no adverse effects on depression and anxiety,

although again there were few data for these secondary outcomes. Finally, the supplementary

subgroup analyses within participants in the intervention arms only, suggest that there is no clear

effect of genetic test result. Only one of six analyses showed a statistically significant effect of

communicating presence versus absence of a risk-conferring mutation, and this was derived from a

single study.

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Strengths and weaknesses of the review

We conducted the review using rigorous Cochrane methods to minimise the risk of bias. We

included quantitative synthesis using meta-analysis, systematic assessment of risk of bias of

included studies and of quality of the evidence by outcome, and identified a substantive body of

randomised studies able to inform our specified aims. Previous reviews had identified few clinical

studies using randomised designs, did not include quantitative syntheses of effects on behaviour or

were focused on single behaviours. However, our review does have several limitations, linked to

limitations of the available evidence. Principally, a number of the studies we found were limited in

their ability to address the review question. They were often underpowered to detect plausible small

effects of risk information upon behaviour and a majority of the studies (10 of 18) were judged to

have control groups of low relevance, because their content differed from the intervention group in

more than only the absence of DNA-based disease risk information. For example, one study that

produced a medium-sized effect on behaviour had an intervention group that differed from the

control group both in the use of DNA risk communication and in the provision of telephone

counselling30. There were also few included studies determined to be at low summary risk of bias.

In particular, the failure or inability to use valid measures of behaviour may have introduced error

and bias. While we acknowledge that the use of self-report measures is sometimes necessary,

included studies typically used self-report measures even when viable objective measures are

available (for example in relation to smoking cessation45). Participants and providers are not blinded

to the intervention and it is important that outcome assessors are blinded, but this was rarely the

case (at least as reported), and, where self-report measures are used, is not possible. The potential

for selective outcome reporting was also notable, with few instances of trial registration or

published protocols. The substantive risk of bias and seemingly poor quality of many of the

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included studies, and the relative imprecision of the effect estimates, suggests caution in

interpreting the results.

Interpretation of study results

We outlined three possible competing hypotheses regarding the possible behavioural impact of

DNA based disease risk information evident in the literature - that it strongly motivates risk-

reducing behaviour change; that it demotivates risk-reducing behaviour change; and finally that, at

best, it has only a small effect on risk-reducing behaviour. Our results do not support the first two

hypotheses, but are consistent with the third, suggesting that high expectations of the potency of

such communications to change behaviour are unfounded. This is in accord with the results of a

related Cochrane Review in which the authors concluded that the current evidence does not support

the hypothesis that biomedical risk assessment increases smoking cessation14. They also agree with

the results of a recent cohort study reporting no impact on diet or physical activity of direct-to-

consumer genome-wide testing46. The theoretically-oriented literature on behaviour change also

highlights the typically small effect of risk communication upon behaviour12. While the results of

the current review are strongly suggestive of, at most, small effects on health behaviours, there is

currently insufficient high-quality research evidence to be able to be confident of this for each

individual behaviour included in the review. This would require additional, better designed and

conducted trials.

Previous reviews of the behavioural impact of genetic risk communication have included non-

randomised studies, predominantly of those with family histories of breast, ovarian and colorectal

cancer, with the dominant behaviours reported being screening or prophylactic surgery. These

indicate an increase in screening and prophylactic surgery particularly amongst those found to be

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carriers, i.e. those with an increased risk of disease16-19

. Such findings suggest that DNA-based risk

assessments are more likely to motivate clinical means of reducing risk (such as undergoing surgery

or attending screening) than behavioural means (such as altering smoking, diet or physical activity

behaviours) that are the main focus of this review47. In spite of this, the one large and well-

conducted trial included in this review36 that assessed impact of DNA risk communication on

colorectal screening found no effect on uptake.

Implications for public health and research

The available evidence does not provide support for the expectations raised by researchers as well

as direct-to-consumer testing companies that the receipt of results from DNA-based tests for gene

variants that confer increased risk of common complex diseases motivates behaviour change.

Concerns that communicating DNA based disease risk estimates may demotivate behaviour change

are also unsupported by the results of this review. Where such tests are being used, be it in public or

private sector domains, their use warrants the collection of evidence regarding behaviour change as

part of research protocols, thereby contributing to the limited existing evidence base. At present

there is little evidence to suggest that simply communicating DNA tests has a role in strategies

aimed at improving population health by motivating risk-reducing behaviour change 48. Such tests

may, however, have a role in such strategies if supplemented by the offer of effective behaviour

change interventions. DNA testing, alone or in combination with other assessments of disease risk,

may have a role in stratifying populations by risk, to enable clinical and behavioural interventions –

such as screening tests, surgery and drug treatments - to be targeted at those at increased risk49.

Given that the evidence available for this review was limited both in quantity and quality, the

principal implication for research is that data from better quality randomised controlled trials are

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needed. Given the continued high expectations for the communication of DNA-based disease risk

estimates to motivate risk reducing behaviour change, it is timely and important for the next

generation of research to use methodologically more robust designs. Such designs would be

significantly improved by being powered to detect possible small effects on behaviour (that might

have important population consequences), and by conducting and reporting trials cognisant of the

risks of bias, for example, incorporating pre-specified outcomes, valid measures of behaviour, and

the blinding of outcome assessors.

Conclusion

The results of this review suggest that communicating DNA-based disease risk estimates has little

or no effect on risk-reducing health behaviour. Expectations that such interventions could play a

significant role in motivating behaviour change to improve population health are not supported by

the existing evidence.

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Contributors: GJH, SK and TMM searched for, screened and selected studies. All authors extracted

data. GJH, ATP and TMM conducted the analysis. All authors interpreted the analysis, drafted the

final manuscript and read and approved the final version. Guarantor and transparency: TMM is the

guarantor. She has had full access to all of the data in the review and takes responsibility for the

integrity of the data and accuracy of the data analysis. She accepts full responsibility for the conduct

of the review and has controlled the decision to publish. She affirms that the manuscript is an

honest, accurate, and transparent account of the study being reported; that no important aspects of

the study have been omitted; and that any discrepancies from the study as planned (and, if relevant,

registered) have been explained.

Funding: A previous version of this review was funded as part of a grant from the Medical Research

Council, UK (Risk communication in preventive medicine: optimising the impact of DNA risk

information; G0500274). The funder had no role in the design and conduct of the study; collection,

management, analysis, and interpretation of the data; and preparation, review, or approval of the

manuscript.

Competing interests: GJH, SJG, ATP, SS and TMM were authors on at least one of the included

studies. These authors were not involved in decisions regarding the inclusion of these studies nor in

the extraction of data from these studies. All authors have completed the ICMJE uniform disclosure

form at www.icmje.org/coi_disclosure.pdf (available on request from the corresponding author) and

declare: no support from any organisation for the submitted work; no financial relationships with

any organisations that might have an interest in the submitted work in the previous three years; and

no other relationships or activities that could appear to have influenced the submitted work.

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31. Sanderson SC, Humphries SE, Hubbart C, et al. Psychological and behavioural impact of

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32. Hollands GJ, Whitwell SCL, Parker RA, et al. Effect of communicating DNA based risk

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35. Glanz K, Volpicelli K, Kanetsky PA, et al. Melanoma Genetic Testing, Counseling, and

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38. Grant RW, O’Brien KE, Waxler JL, et al. Personalized Genetic Risk Counseling to Motivate

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39. Voils C, Coffman C, Grubber J, et al. Does Type 2 Diabetes Genetic Testing and Counseling

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apoE Genotype-Based Dietary and Physical-Activity Advice: Impact on Health Behavior.

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41. Marteau T, Senior V, Humphries SE. Psychological impact of genetic testing for familial

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42. Nielsen DE, El-Sohemy A. Disclosure of Genetic Information and Change in Dietary Intake: A

Randomized Controlled Trial. PLoS ONE 2014;9(11):e112665.

43. Chao S, Scott Roberts J, Marteau TM, et al. Health behavior changes after genetic risk

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44. Meisel SF, Beeken RJ, van Jaarsveld CHM, et al. Genetic susceptibility testing and readiness to

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49. Academy of Medical Sciences. Realising the potential of stratified medicine. London, UK:

Academy of Medical Sciences, 2013.

50. Lerman C, Boyd NR, Orleans CT, et al. Incorporating biomarkers of exposure and genetic

susceptibility into smoking cessation treatment: effects on smoking-related cognitions,

emotions and behaviour change. Health Psychology 1997;16(1):87-9.

51. Marteau T, Munafo M, Aveyard P, et al. Trial Protocol: Using genotype to tailor prescribing of

nicotine replacement therapy: a randomised controlled trial assessing impact of

communication upon adherence. BMC Public Health 2010;10(1):680.

52. Meisel S, Beeken R, van Jaarsveld C, et al. Genetic test feedback with weight control advice:

study protocol for a randomized controlled trial. Trials 2012;13(1):235.

53. Voils C, Coffman C, Edelman D, et al. Examining the impact of genetic testing for type 2

diabetes on health behaviors: study protocol for a randomized controlled trial. Trials

2012;13(1):1-10.

54. Myers RE, Manne SL, Wilfond B, et al. A Randomized Trial of Genetic and Environmental

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27

TABLES AND FIGURES

Table 1: Characteristics of included studies

Study Country Setting and

participants

Study

design

Intervention Comparison Outcome(s)

selected for

review

Timing of

outcome

assessment

Audrain et al,

199727 50

USA Smokers in stop-

smoking clinic

Randomised

controlled

trial

DNA-based + non-DNA

based disease risk estimates

(CYP2D6 genotype status +

carbon monoxide level

readings + smoking

cessation consultation)

Disease risk: lung cancer

Non-DNA-based disease

risk estimates (carbon

monoxide level readings +

smoking cessation

consultation)

Self-reported

smoking

cessation

(abstinence

over previous

30 days)

2 months,

12 months

Chao et al,

200843

USA Individuals with

family history of

Alzheimer’s Disease

recruited from

community

Randomised

controlled

trial

DNA-based + non-DNA


(education session + APOE

genotype (e4+ or e4-) +

individualised lifetime risk

estimate)

Disease risk: Alzheimer’s

disease


risk estimates (education

session + individualised

numerical risk estimate

based on family history and

gender)

Self-reported

health

behaviour

change

(dietary,

exercise,

medication

/vitamin use)

12 months

Glanz et al, USA Individuals with

family history of

Cluster-

randomised

DNA-based disease risk

estimates (CDKN2A and

No disease risk estimates

(usual care of skin cancer

Self-reported

sun

4 months

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201335 melanoma recruited

from outpatient

clinic

controlled

trial*

MC1R genotyping, genetic

counselling on associated

risks, and skin cancer

prevention brochure)

Disease risk: melanoma

prevention brochure) protection

behaviours

(sun

protection

habits index)

Godino et al,

unpublished37

UK General population

recruited from

ongoing population-

based study

Randomised

controlled

trial


estimates (standard lifestyle

advice plus genetic risk

estimate for type 2 diabetes,

including residual lifetime

risk estimate by age and

sex)

Disease risk: type 2 diabetes


risk estimates (standard

lifestyle advice plus

phenotypic risk estimate for

type 2 diabetes (based on

e.g. family history,

anthropometric measures))

Physical

activity

assessed

objectively

with monitor

Self-reported

fruit and

vegetable

consumption.

8 weeks

Grant et al,

201338

USA Overweight

individuals at

increased diabetes

risk, primary care

setting

Randomised

controlled

trial


estimates (genetic risk

feedback (summing 36

SNPs associated with type 2

diabetes) placing genetic

risk within context of

overall diabetes risk, 12-

week diabetes prevention

programme)


(untested controls also

participated in 12-week

diabetes prevention

programme)

Behavioural

support

programme

attendance

12 weeks

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Hendershot et

al, 201033

USA Individuals

participating in

study of drinking

behaviour

Randomised

controlled

trial


estimates (web-based

genetic feedback of

genotype associated with

alcohol-related cancer risk)

Disease risk: alcohol-related

cancers


(web-based attention-

control feedback of generic

information)

Self-reported

frequency of

alcohol use

30 days

Hieteranta-

Luoma et al,

201440

Finland Healthy individuals

from general

population

Randomised

controlled

trial


estimates (apoE genotype

status with personalised

feedback plus additional

voluntary medical

consultations)

Disease risk: cardiovascular

disease


(no feedback of apoE

genotyping, general health

information on lifestyle and

CVD risk)

Self-reported

consumption

of fruit and

vegetables,

alcohol

consumption

and physical

activity

10 weeks, 6

months, 12

months

Hishida et al,

201028

Japan Smokers in

workplace

Quasi-

randomised

controlled

trial


estimates (L-myc EcoRI

polymorphism status)

Disease risk: lung or

oesophageal cancer


(no intervention)

Self-reported

smoking

status (quit

smoking)

12 months

Hollands et al, UK First-degree relatives

of individuals with

Cluster-

randomised


estimates (genetic risk


risk estimates (phenotypic

Biochemicall

y validated

6 months

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201232 51

Crohn’s Disease controlled

trial*

estimate for developing

Crohn’s Disease (also based

on familial risk and smoking

status) + risk assessment

booklet by mail and brief

smoking cessation advice by

telephone)

Disease risk: Crohn’s

Disease

risk estimate for developing

Crohn’s disease + risk

assessment booklet by mail

and brief smoking cessation

advice by telephone)

smoking

cessation

Ito et al, 200629 Japan Outpatient smokers

in cancer centre

hospital

Quasi-

randomised

controlled

trial


estimates (information

session + L-myc EcoRI

polymorphism status +

follow-up mailed checklist)

Disease risk: lung or

oesophageal cancer


(no intervention)

Self-reported

smoking

status

3 months, 9

months

Komiya et al,

200634

Japan Employees of a

manufacturing

factory

Randomised

controlled

trial


estimates (ALDH2 genotype

plus information on

associated disease risk from

alcohol)

Disease risk: cancers


(intervention received at a

later date)

Self-reported

weekly

alcohol

intake

18 months

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Marteau et al,

200441

UK Adults attending

lipid clinics for

assessment

Randomised

controlled

trial

DNA-based + non-DNA


(routine clinical diagnosis of

familial

hypercholesterolaemia +

cholesterol results + LDLR

mutation status feedback +

lifestyle advice)

Disease risk: familial

hypercholesterolaemia


risk estimates (routine

clinical diagnosis of familial

hypercholesterolemia +

cholesterol results +

lifestyle advice)

Self-reported

risk-reducing

behaviour

change (low

fat diet,

increased

physical

activity)

6 months

McBride et al,

200230

USA Smokers attending

community health

clinic

Randomised

controlled

trial


estimates (feedback of

GSTM1 status in booklet

with advice on smoking

risks + 4 telephone

counselling sessions over

the follow-up period)



(quit advice + referral to

smoking specialist + quit

guide and nicotine patches

where required)

Self-reported

smoking

abstinence in

last 7 days

6 months,

12 months

Meisel et al,

201544 52

UK Students recruited

from a university

Randomised

controlled

trial


estimates (obesity gene

(FTO) feedback, plus

weight control advice

leaflet)


(Weight control advice

leaflet, genetic feedback

given at later date)

Self-reported

risk-reducing

diet and

physical

activity

1 month

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Disease risk: obesity behaviours

Nielsen et al,

201442

Canada Online recruitment

of healthy

individuals

Randomised

controlled

trial


estimates (genetic tests for

caffeine metabolism,

vitamin C utilisation, sweet

taste perception, and

sodium-sensitivity (ACE)

linked to disease risk, with

personalised results and

dietary recommendations)

Disease risk: sodium-

sensitive hypertension


(dietary recommendations

based on current guidelines;

genetic feedback was given

at later date)

Self-reported

sodium

intake

3 months,

12 months

Sanderson et

al, 200831

UK Smokers in stop-

smoking clinic

Randomised

controlled

trial


estimates (leaflet + 20

minute quit smoking

intervention + GSTM1

status feedback)



(leaflet + 20 minute quit

smoking intervention)

Self-reported

number of

cigarettes

smoked per

day, quit

rates

1 week, 2

months

Voils et al,

201539 53

USA Overweight/obese

veteran outpatients,

primary care setting

Randomised

controlled

trial


estimates + non-DNA based

disease risk estimates

(Genetic testing for diabetes

related genes with

Non-DNA based disease

risk estimates (Education on

age-related macular

degeneration, cataracts,

glaucoma + conventional

Self-reported

dietary

energy intake

and physical

activity

3 months, 6

months

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33

personalised feedback +

conventional diabetes risk

counselling and brief

lifestyle counselling)


diabetes risk counselling

and brief lifestyle

counselling)

Weinberg et al,

201436 54

USA Individuals with

average-risk status

for colorectal cancer

who did not adhere

to screening

recommendations,

primary care setting

Randomised

controlled

trial


estimates (Feedback on

combination of MTHFR

polymorphisms and serum

folate levels, and risk

counselling)

Disease risk: colorectal

cancer


(Usual care with no risk

counselling)

Colorectal

screening

assessed via

manual and

electronic

medical chart

review

3 weeks, 6

months

* Study was cluster-randomised with family clusters but treated as individual randomisation because reported that clustering did not impact on

results

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34

FIGURES

Figure 1: PRISMA Flow Diagram

Figure 2: Primary outcome analysis: Smoking cessation; Medication use

Figure 3: Primary outcome analysis: Reduced alcohol use; Sun protection behaviours

Figure 4: Primary outcome analysis: Diet; Physical activity; Attendance at screening or behavioural support programmes

Figure 5: Assessment of risk of bias

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nlyFigure 1: PRISMA Flow Diagram

Records identified through electronic

database searching

(n = 10503)

Additional records identified through

other sources

(n = 12)

Title and abstract records

screened

(n = 10515)

Records excluded

(n = 10463)

Full-text articles assessed for

eligibility

(n = 52)

Full-text articles excluded

Ineligible study design (n = 3)

Ineligible intervention (n = 11)

Ineligible outcome (n = 9)

Ineligible comparison (n = 3)

Ongoing study (n = 8)

Studies included in

qualitative synthesis

(n = 18)

Studies included in

quantitative analysis (meta-

analysis)

(n = 18)

Identification

Screening

Eligibility

Included

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Figure 2: Primary outcome analysis: Smoking cessation; Medication use

87x43mm (300 x 300 DPI)

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Figure 3: Primary outcome analysis: Reduced alcohol use; Sun protection behaviours

85x31mm (300 x 300 DPI)

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Figure 4: Primary outcome analysis: Diet; Physical activity; Attendance at screening or behavioural support

programmes

85x50mm (300 x 300 DPI)

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Figure 5: Assessment of risk of bias 40x86mm (300 x 300 DPI)

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nlyAppendix – MEDLINE search

MEDLINE

1. genetic services/

2. genetic testing/

3. genetic counseling/

4. genetic predisposition to disease/

5. or/1-4

6. ((gene or genes or genetic* or genotype*) adj3 (test* or assess* or risk* or susceptib* or

predispos* or disease* or screen* or prognos* or predict* or servic*)).ti,ab,kw.

7. counseling/ or directive counseling/

8. health communication/

9. (consult* or assess* or support* or inform* or advis* or advice or counsel* or educat* or

shar* or communicat* or teach* or discuss* or decid* or decision*).ti,ab,kw.

10. patient education as topic/

11. or/7-10

12. 6 and 11

13. 5 or 12

14. (adher* or nonadher* or complian* or noncomplian*).ti,ab,kw.

15. patient compliance/

16. exp health behavior/

17. risk reduction behavior/

18. (diet* or smok* or tobacco or nicotine or alcohol or weight or activ* or behavio* or attitud*

or motivat* or intention* or perceiv* or perception* or decid* or decision*).ti,ab,kw.

19. or/14-18

20. 13 and 19

21. randomized controlled trial.pt.

22. controlled clinical trial.pt.

23. randomized.ab.

24. placebo.ab.

25. drug therapy.fs.

26. randomly.ab.

27. trial.ab.

28. groups.ab.

29. or/21-28

30. exp animals/ not humans.sh.

31. 29 not 30

32. 20 and 31

33. 32 and (2010* or 2011* or 2012* or 2013* or 2014* or 2015*).ed,ep,dc.

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nlyWhat is already known:

1. Genetic testing is being increasingly used in a growing number of healthcare settings

and in direct-to-consumer testing for a range of common complex disorders

2. There is an expectation that communicating DNA-based disease risk estimates will

motivate changes in key health behaviours including smoking, diet and physical

activity

3. There is a need for a rigorous systematic review to examine whether communicating

genetic risks does indeed motivate risk-reducing behaviour change

What this adds:

4. We have conducted a systematic review and meta-analysis, using Cochrane methods,

that includes the most up-to-date and highest quality evidence available

5. Results suggest that communicating DNA-based disease risk estimates has little or no

impact on risk-reducing health behaviour

6. Expectations that such interventions could play a significant role in motivating

behaviour change to improve population health are not supported by the existing

evidence.

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Confidential: For Review Only - bmj.com

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