Effect of communicating genetic and phenotypic risk for type 2 diabetes in combination with lifestyle advice on objectively measured physical activity: protocol of a randomised controlled trial

Social Mobile Approaches to Reducing Weight (SMART) 2.0: protocol of a randomized controlled trial among young adults in university settings

Background

Excess weight gain in young adulthood is associated with future weight gain and increased risk of chronic disease. Although multimodal, technology-based weight-loss interventions have the potential to promote weight loss among young adults, many interventions have limited personalization, and few have been deployed and evaluated for longer than a year. We aim to assess the effects of a highly personalized, 2-year intervention that uses popular mobile and social technologies to promote weight loss among young adults.

Methods

The Social Mobile Approaches to Reducing Weight (SMART) 2.0 Study is a 24-month parallel-group randomized controlled trial that will include 642 overweight or obese participants, aged 18–35 years, from universities and community colleges in San Diego, CA. All participants receive a wearable activity tracker, connected scale, and corresponding app. Participants randomized to one intervention group receive evidence-based information about weight loss and behavior change techniques via personalized daily text messaging (i.e., SMS/MMS), posts on social media platforms, and online groups. Participants in a second intervention group receive the aforementioned elements in addition to brief, technology-mediated health coaching. Participants in the control group receive a wearable activity tracker, connected scale, and corresponding app alone. The primary outcome is objectively measured weight in kilograms over 24 months. Secondary outcomes include anthropometric measurements; physiological measures; physical activity, diet, sleep, and psychosocial measures; and engagement with intervention modalities. Outcomes are assessed at baseline and 6, 12, 18, and 24 months. Differences between the randomized groups will be analyzed using a mixed model of repeated measures and will be based on the intent-to-treat principle.

Discussion

We hypothesize that both SMART 2.0 intervention groups will significantly improve weight loss compared to the control group, and the group receiving health coaching will experience the greatest improvement. We further hypothesize that differences in secondary outcomes will favor the intervention groups. There is a critical need to advance understanding of the effectiveness of multimodal, technology-based weight-loss interventions that have the potential for long-term effects and widespread dissemination among young adults. Our findings should inform the implementation of low-cost and scalable interventions for weight loss and risk-reducing health behaviors.

Trial registration

ClinicalTrials.govNCT03907462. Registered on April 9, 2019

Incorporating a brief intervention for personalised cancer risk assessment to promote behaviour change into primary care: a multi-methods pilot study

BACKGROUND

Approximately 40% of cancers could be prevented if people lived healthier lifestyles. We have developed a theory-based brief intervention to share personalised cancer risk information and promote behaviour change within primary care. This study aimed to assess the feasibility and acceptability of incorporating this intervention into primary care consultations.

METHOD

Patients eligible for an NHS Health Check or annual chronic disease review at five general practices were invited to participate in a non-randomised pilot study. In addition to the NHS Health Check or chronic disease review, those receiving the intervention were provided with their estimated risk of developing the most common preventable cancers alongside tailored behaviour change advice. Patients completed online questionnaires at baseline, immediately post-consultation and at 3-month follow-up. Consultations were audio/video recorded. Patients (n = 12) and healthcare professionals (HCPs) (n = 7) participated in post-intervention qualitative interviews that were analysed using thematic analysis.

RESULTS

62 patients took part. Thirty-four attended for an NHS Health Check plus the intervention; 7 for a standard NHS Health Check; 16 for a chronic disease review plus the intervention; and 5 for a standard chronic disease review. The mean time for delivery of the intervention was 9.6 min (SD 3) within NHS Health Checks and 9 min (SD 4) within chronic disease reviews. Fidelity of delivery of the intervention was high. Data from the questionnaires demonstrates potential improvements in health-related behaviours following the intervention. Patients receiving the intervention found the cancer risk information and lifestyle advice understandable, useful and motivating. HCPs felt that the intervention fitted well within NHS Health Checks and facilitated conversations around behaviour change. Integrating the intervention within chronic disease reviews was more challenging.

CONCLUSIONS

Incorporating a risk-based intervention to promote behaviour change for cancer prevention into primary care consultations is feasible and acceptable to both patients and HCPs. A randomised trial is now needed to assess the effect on health behaviours. When designing that trial, and other prevention activities within primary care, it is necessary to consider challenges around patient recruitment, the HCP contact time needed for delivery of interventions, and how best to integrate discussions about disease risk within routine care.

A randomised controlled trial of the effect of providing online risk information and lifestyle advice for the most common preventable cancers

Few trial data are available concerning the impact of personalised cancer risk information on behaviour. This study assessed the short-term effects of providing personalised cancer risk information on cancer risk beliefs and self-reported behaviour. We randomised 1018 participants, recruited through the online platform Prolific, to either a control group receiving cancer-specific lifestyle advice or one of three intervention groups receiving their computed 10-year risk of developing one of the five most common preventable cancers either as a bar chart, a pictograph or a qualitative scale alongside the same lifestyle advice. The primary outcome was change from baseline in computed risk relative to an individual with a recommended lifestyle (RRI)1 at three months. Secondary outcomes included: health-related behaviours, risk perception, anxiety, worry, intention to change behaviour, and a newly defined concept, risk conviction. After three months there were no between-group differences in change in RRI (p = 0.71). At immediate follow-up, accuracy of absolute risk perception (p < 0.001), absolute and comparative risk conviction (p < 0.001) and intention to increase fruit and vegetables (p = 0.026) and decrease processed meat (p = 0.033) were higher in all intervention groups relative to the control group. The increases in accuracy and conviction were only seen in individuals with high numeracy and low baseline conviction, respectively. These findings suggest that personalised cancer risk information alongside lifestyle advice can increase short-term risk accuracy and conviction without increasing worry or anxiety but has little impact on health-related behaviour. Trial registration: ISRCTN17450583. Registered 30 January 2018.

Effect of communicating phenotypic and genetic risk of coronary heart disease alongside web-based lifestyle advice: the INFORM Randomised Controlled Trial

Objective

To determine whether provision of web-based lifestyle advice and coronary heart disease risk information either based on phenotypic characteristics or phenotypic plus genetic characteristics affects changes in objectively measured health behaviours.

Methods

A parallel-group, open randomised trial including 956 male and female blood donors with no history of cardiovascular disease (mean [SD] age=56.7 [8.8] years) randomised to four study groups: control group (no information provided); web-based lifestyle advice only (lifestyle group); lifestyle advice plus information on estimated 10-year coronary heart disease risk based on phenotypic characteristics (phenotypic risk estimate) (phenotypic group) and lifestyle advice plus information on estimated 10-year coronary heart disease risk based on phenotypic (phenotypic risk estimate) and genetic characteristics (genetic risk estimate) (genetic group). The primary outcome was change in physical activity from baseline to 12 weeks assessed by wrist-worn accelerometer.

Results

928 (97.1%) participants completed the trial. There was no evidence of intervention effects on physical activity (difference in adjusted mean change from baseline): lifestyle group vs control group 0.09 milligravity (mg) (95% CI −1.15 to 1.33); genetic group vs phenotypic group −0.33 mg (95% CI −1.55 to 0.90); phenotypic group and genetic group vs control group −0.52 mg (95% CI −1.59 to 0.55) and vs lifestyle group −0.61 mg (95% CI −1.67 to 0.46). There was no evidence of intervention effects on secondary biological, emotional and health-related behavioural outcomes except self-reported fruit and vegetable intake.

Conclusions

Provision of risk information, whether based on phenotypic or genotypic characteristics, alongside web-based lifestyle advice did not importantly affect objectively measured levels of physical activity, other health-related behaviours, biological risk factors or emotional well-being.

Trial registration number

ISRCTN17721237; Pre-results.

Protocol of a cluster randomized trial to investigate the impact of a type 2 diabetes risk prediction model on change in physical activity in primary care

BACKGROUND

Little evidence exists on the impact of diabetes risk scores, e.g. on physicians and patient's behavior, perceived risk of persons, shared-decision making and particularly on patient's health. The aim of this study is to investigate the impact of a non-invasive type 2 diabetes risk prediction model in the primary health care setting as component of routine health checks on change in physical activity.

METHODS

Parallel group cluster randomized controlled trial including 30 primary care physicians (PCPs) and 300 participants in the region of Düsseldorf and surrounding urban and rural municipalities, West Germany. On cluster level, PCPs will be randomized into intervention or control group using a biased coin minimization technique. Participants in the control group are going to have a routine health check "Check-up 35" which is recommended biannually for all people ≥35 years of age in Germany. In the intervention group, the routine health check is expanded by usage of a non-invasive diabetes risk prediction model (German Diabetes Risk Score). Primary outcome is change in physical activity after 1 year. Secondary outcomes include aspects of targeted counseling, motivation of participant's to change lifestyle, perceived and objectively measured diabetes risk, acceptance of diabetes risk scores, quality of life, depression and anxiety. Patients will be followed over 12 months. Hierarchical or mixed models will be conducted, including a random intercept to adjust for cluster, the respective baseline value, and covariates to compare the groups.

DISCUSSION

This pragmatic cluster randomized controlled trial will enhance our knowledge on the clinical impact of diabetes risk scores for the first time in the real-life primary health care setting.

TRIAL REGISTRATION

ClinicalTrials.gov NCT03234322 , registered on July 28, 2017.

A randomised controlled trial of the effect of providing online risk information and lifestyle advice for the most common preventable cancers: study protocol

BACKGROUND

Cancer is a leading cause of mortality and morbidity worldwide. Prevention is recognised by many, including the World Health Organization, to offer the most cost-effective long-term strategy for the control of cancer. One approach that focuses on individuals is the provision of personalised risk information. However, whether such information motivates behaviour change and whether the effect is different with varying formats of risk presentation is unclear. We aim to assess the short-term effect of providing information about personalised risk of cancer in three different formats alongside lifestyle advice on health-related behaviours, risk perception and risk conviction.

METHODS

In a parallel group, randomised controlled trial 1000 participants will be recruited through the online platform Prolific. Participants will be allocated to either a control group receiving cancer-specific lifestyle advice alone or one of three intervention groups receiving the same lifestyle advice alongside their estimated 10-year risk of developing one of the five most common preventable cancers, calculated from self-reported modifiable behavioural risk factors, in one of three different formats (bar chart, pictograph or qualitative scale). The primary outcome is change from baseline in computed risk relative to an individual with a recommended lifestyle at three months. Secondary outcomes include: perceived risk of cancer; anxiety; cancer-related worry; intention to change behaviour; and awareness of cancer risk factors.

DISCUSSION

This study will provide evidence on the short-term effect of providing online information about personalised risk of cancer alongside lifestyle advice on risk perception and health-related behaviours and inform the development of interventions.

TRIAL REGISTRATION

ISRCTN17450583. Registered 30 January 2018.

The melanoma genomics managing your risk study: A protocol for a randomized controlled trial evaluating the impact of personal genomic risk information on skin cancer prevention behaviors

BACKGROUND

Reducing ultraviolet radiation (UV) exposure and improving early detection may reduce melanoma incidence, mortality and health system costs. This study aims to evaluate the efficacy and cost-effectiveness of providing information on personal genomic risk of melanoma in reducing UV exposure at 12 months, according to low and high traditional risk.

METHODS

In this randomized controlled trial, participants (target sample = 892) will be recruited from the general population, and randomized (1:1 ratio, intervention versus control). Intervention arm participants provide a saliva sample, receive personalized melanoma genomic risk information, a genetic counselor phone call, and an educational booklet on melanoma prevention. Control arm participants receive only the educational booklet. Eligible participants are aged 18-69 years, have European ancestry and no personal history of melanoma. All participants will complete a questionnaire and wear a UV dosimeter to objectively measure their sun exposure at baseline, 1- and 12-month time-points, except 1-month UV dosimetry will be limited to ~250 participants. The primary outcome is total daily Standard Erythemal Doses at 12 months. Secondary outcomes include objectively measured UV exposure for specific time periods (e.g. midday hours), self-reported sun protection and skin-examination behaviors, psycho-social outcomes, and ethical considerations surrounding offering genomic testing at a population level. A within-trial and modelled economic evaluation will be undertaken from an Australian health system perspective to assess the intervention costs and outcomes.

DISCUSSION

This trial will inform the clinical and personal utility of introducing genomic testing into the health system for melanoma prevention and early detection at a population-level.

TRIAL REGISTRATION

Australian New Zealand Clinical Trials Registry ACTRN12617000691347.

Risk accuracy of type 2 diabetes in middle aged adults: Associations with sociodemographic, clinical, psychological and behavioural factors

OBJECTIVE

To identify the proportion of individuals with an accurate perception of their risk of type 2 diabetes (T2D) prior to, immediately after and eight weeks after receiving a personalised risk estimate. Additionally, we aimed to explore what factors are associated with underestimation and overestimation immediately post-intervention.

METHODS

Cohort study based on the data collected in the Diabetes Risk Communication Trial. We included 379 participants (mean age 48.9 (SD 7.4) years; 55.1% women) who received a genotypic or phenotypic risk estimate for T2D.

RESULTS

While only 1.3% of participants perceived their risk accurately at baseline, this increased to 24.7% immediately after receiving a risk estimate and then dropped to 7.3% at eight weeks. Those who overestimated their risk at baseline continued to overestimate it, whereas those who underestimated their risk at baseline improved their risk accuracy. We did not identify any other characteristics associated with underestimation or overestimation immediately after receiving a risk estimate.

CONCLUSION

Understanding a received risk estimate is challenging for most participants with many continuing to have inaccurate risk perception after receiving the estimate.

PRACTICE IMPLICATIONS

Individuals who overestimate or underestimate their T2D risk before receiving risk information might require different approaches for altering their risk perception.

Lifestyle Advice Combined with Personalized Estimates of Genetic or Phenotypic Risk of Type 2 Diabetes, and Objectively Measured Physical Activity: A Randomized Controlled Trial

BACKGROUND

Information about genetic and phenotypic risk of type 2 diabetes is now widely available and is being incorporated into disease prevention programs. Whether such information motivates behavior change or has adverse effects is uncertain. We examined the effect of communicating an estimate of genetic or phenotypic risk of type 2 diabetes in a parallel group, open, randomized controlled trial.

METHODS AND FINDINGS

We recruited 569 healthy middle-aged adults from the Fenland Study, an ongoing population-based, observational study in the east of England (Cambridgeshire, UK). We used a computer-generated random list to assign participants in blocks of six to receive either standard lifestyle advice alone (control group, n = 190) or in combination with a genetic (n = 189) or a phenotypic (n = 190) risk estimate for type 2 diabetes (intervention groups). After 8 wk, we measured the primary outcome, objectively measured physical activity (kJ/kg/day), and also measured several secondary outcomes (including self-reported diet, self-reported weight, worry, anxiety, and perceived risk). The study was powered to detect a between-group difference of 4.1 kJ/kg/d at follow-up. 557 (98%) participants completed the trial. There were no significant intervention effects on physical activity (difference in adjusted mean change from baseline: genetic risk group versus control group 0.85 kJ/kg/d (95% CI -2.07 to 3.77, p = 0.57); phenotypic risk group versus control group 1.32 (95% CI -1.61 to 4.25, p = 0.38); and genetic risk group versus phenotypic risk group -0.47 (95% CI -3.40 to 2.46, p = 0.75). No significant differences in self-reported diet, self-reported weight, worry, and anxiety were observed between trial groups. Estimates of perceived risk were significantly more accurate among those who received risk information than among those who did not. Key limitations include the recruitment of a sample that may not be representative of the UK population, use of self-reported secondary outcome measures, and a short follow-up period.

CONCLUSIONS

In this study, we did not observe short-term changes in behavior associated with the communication of an estimate of genetic or phenotypic risk of type 2 diabetes. We also did not observe changes in worry or anxiety in the study population. Additional research is needed to investigate the conditions under which risk information might enhance preventive strategies. (Current Controlled Trials ISRCTN09650496; Date applied: April 4, 2011; Date assigned: June 10, 2011).

TRIAL REGISTRATION

The trial is registered with Current Controlled Trials, ISRCTN09650496.

The effect of communicating the genetic risk of cardiometabolic disorders on motivation and actual engagement in preventative lifestyle modification and clinical outcome: a systematic review and meta-analysis of randomised controlled trials

Genetic risk prediction of chronic conditions including obesity, diabetes and CVD currently has limited predictive power but its potential to engage healthy behaviour change has been of immense research interest. We aimed to understand whether the latter is indeed true by conducting a systematic review and meta-analysis investigating whether genetic risk communication affects motivation and actual behaviour change towards preventative lifestyle modification. We included all randomised controlled trials (RCT) since 2003 investigating the impact of genetic risk communication on health behaviour to prevent cardiometabolic disease, without restrictions on age, duration of intervention or language. We conducted random-effects meta-analyses for perceived motivation for behaviour change and clinical changes (weight loss) and a narrative analysis for other outcomes. Within the thirteen studies reviewed, five were vignette studies (hypothetical RCT) and seven were clinical RCT. There was no consistent effect of genetic risk on actual motivation for weight loss, perceived motivation for dietary change (control v. genetic risk group standardised mean difference (smd) -0·15; 95 % CI -1·03, 0·73, P=0·74) or actual change in dietary behaviour. Similar results were observed for actual weight loss (control v. high genetic risk SMD 0·29 kg; 95 % CI -0·74, 1·31, P=0·58). This review found no clear or consistent evidence that genetic risk communication alone either raises motivation or translates into actual change in dietary intake or physical activity to reduce the risk of cardiometabolic disorders in adults. Of thirteen studies, eight were at high or unclear risk of bias. Additional larger-scale, high-quality clinical RCT are warranted.

The impact of communicating genetic risks of disease on risk-reducing health behaviour: systematic review with meta-analysis

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.

DATA SOURCES

Medline, Embase, PsycINFO, CINAHL, and the Cochrane Central Register of Controlled Trials up to 25 February 2015. Backward and forward citation searches were also conducted.

STUDY SELECTION

Randomised and quasi-randomised controlled trials involving adults in which one group received personalised DNA based estimates of disease risk for conditions where 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 that reported on seven behavioural outcomes, including smoking cessation (six studies; n=2663), diet (seven studies; n=1784), and physical activity (six studies; n=1704). Meta-analysis revealed no significant effects of communicating DNA based risk estimates on smoking cessation (odds ratio 0.92, 95% confidence interval 0.63 to 1.35, P=0.67), diet (standardised mean difference 0.12, 95% confidence interval -0.00 to 0.24, P=0.05), 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) or on motivation to change behaviour, and no adverse effects, such as depression and anxiety. Subgroup analyses provided no clear evidence that communication of a risk-conferring genotype affected behaviour more than 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 is 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, adding 11 studies to the seven previously identified.

Information and Risk Modification Trial (INFORM): design of a randomised controlled trial of communicating different types of information about coronary heart disease risk, alongside lifestyle advice, to achieve change in health-related behaviour

BACKGROUND

Cardiovascular disease (CVD) remains the leading cause of death globally. Primary prevention of CVD requires cost-effective strategies to identify individuals at high risk in order to help target preventive interventions. An integral part of this approach is the use of CVD risk scores. Limitations in previous studies have prevented reliable inference about the potential advantages and the potential harms of using CVD risk scores as part of preventive strategies. We aim to evaluate short-term effects of providing different types of information about coronary heart disease (CHD) risk, alongside lifestyle advice, on health-related behaviours.

METHODS/DESIGN

In a parallel-group, open randomised trial, we are allocating 932 male and female blood donors with no previous history of CVD aged 40-84 years in England to either no intervention (control group) or to one of three active intervention groups: i) lifestyle advice only; ii) lifestyle advice plus information on estimated 10-year CHD risk based on phenotypic characteristics; and iii) lifestyle advice plus information on estimated 10-year CHD risk based on phenotypic and genetic characteristics. The primary outcome is change in objectively measured physical activity. Secondary outcomes include: objectively measured dietary behaviours; cardiovascular risk factors; current medication and healthcare usage; perceived risk; cognitive evaluation of provision of CHD risk scores; and psychological outcomes. The follow-up assessment takes place 12 weeks after randomisation. The experiences, attitudes and concerns of a subset of participants will be also studied using individual interviews and focus groups.

DISCUSSION

The INFORM study has been designed to provide robust findings about the short-term effects of providing different types of information on estimated 10-year CHD risk and lifestyle advice on health-related behaviours.

TRIAL REGISTRATION

Current Controlled Trials ISRCTN17721237 . Registered 12 January 2015.

Is the intention-behaviour gap greater amongst the more deprived? A meta-analysis of five studies on physical activity, diet, and medication adherence in smoking cessation

Objectives

Unhealthy behaviour is more common amongst the deprived, thereby contributing to health inequalities. The evidence that the gap between intention and behaviour is greater amongst the more deprived is limited and inconsistent. We tested this hypothesis using objective and self‐report measures of three behaviours, both individual‐ and area‐level indices of socio‐economic status, and pooling data from five studies.

Design

Secondary data analysis.

Methods

Multiple linear regressions and meta‐analyses of data on physical activity, diet, and medication adherence in smoking cessation from 2,511 participants.

Results

Across five studies, we found no evidence for an interaction between deprivation and intention in predicting objective or self‐report measures of behaviour. Using objectively measured behaviour and area‐level deprivation, meta‐analyses suggested that the gap between self‐efficacy and behaviour was greater amongst the more deprived (B = .17 [95% CI = 0.02, 0.31]).

Conclusions

We find no compelling evidence to support the hypothesis that the intention–behaviour gap is greater amongst the more deprived.

Genomic-based tools for the risk assessment, management, and prevention of type 2 diabetes

Type 2 diabetes (T2D) is a common and serious disorder and is a significant risk factor for the development of cardiovascular disease, neuropathy, nephropathy, retinopathy, periodontal disease, and foot ulcers and amputations. The burden of disease associated with T2D has led to an emphasis on early identification of the millions of individuals at high risk so that management and intervention strategies can be effectively implemented before disease progression begins. With increasing knowledge about the genetic basis of T2D, several genomic-based strategies have been tested for their ability to improve risk assessment, management and prevention. Genetic risk scores have been developed with the intent to more accurately identify those at risk for T2D and to potentially improve motivation and adherence to lifestyle modification programs. In addition, evidence is building that oral antihyperglycemic medications are subject to pharmacogenomic variation in a substantial number of patients, suggesting genomics may soon play a role in determining the most effective therapies. T2D is a complex disease that affects individuals differently, and risk prediction and treatment may be challenging for health care providers. Genomic approaches hold promise for their potential to improve risk prediction and tailor management for individual patients and to contribute to better health outcomes for those with T2D.

Understanding perceived risk of type 2 diabetes in healthy middle-aged adults: a cross-sectional study of associations with modelled risk, clinical risk factors, and psychological factors

AIMS

To determine the perceived risk of type 2 diabetes in a sample of healthy middle-aged adults and examine the association between perceived risk and modelled risk, clinical risk factors, and psychological factors theorised to be antecedents of behaviour change.

METHODS

An exploratory, cross-sectional analysis of perceived risk of type 2 diabetes (framed according to time and in comparison with peers) was conducted using baseline data collected from 569 participants of the Diabetes Risk Communication Trial (Cambridgeshire, UK). Type 2 diabetes risk factors were measured during a health assessment and the Framingham Offspring Diabetes Risk Score was used to model risk. Questionnaires assessed psychological factors including anxiety, diabetes-related worry, behavioural intentions, and other theory-based antecedents of behaviour change. Multivariable regression analyses were used to examine associations between perceived risk and potential correlates.

RESULTS

Participants with a high perceived risk were at higher risk according to the Framingham Offspring Diabetes Risk Score (p<0.001). Higher perceived risk was observed in those with a higher body fat percentage, lower self-rated health, higher diabetes-related worry, and lower self-efficacy for adhering to governmental recommendations for physical activity (all p<0.001). The framing of perceived risk according to time and in comparison with peers did not influence these results.

CONCLUSIONS

High perceived risk of type 2 diabetes is associated with higher risk of developing the disease, and a decreased likelihood of engagement in risk-reducing health behaviours. Risk communication interventions should target high-risk individuals with messages about the effectiveness of prevention strategies.

A low-frequency variant in MAPK14 provides mechanistic evidence of a link with myeloperoxidase: a prognostic cardiovascular risk marker

BACKGROUND

Genetics can be used to predict drug effects and generate hypotheses around alternative indications. To support Losmapimod, a p38 mitogen-activated protein kinase inhibitor in development for acute coronary syndrome, we characterized gene variation in MAPK11/14 genes by exome sequencing and follow-up genotyping or imputation in participants well-phenotyped for cardiovascular and metabolic traits.

METHODS AND RESULTS

Investigation of genetic variation in MAPK11 and MAPK14 genes using additive genetic models in linear or logistic regression with cardiovascular, metabolic, and biomarker phenotypes highlighted an association of RS2859144 in MAPK14 with myeloperoxidase in a dyslipidemic population (Genetic Epidemiology of Metabolic Syndrome Study), P=2.3×10(-6)). This variant (or proxy) was consistently associated with myeloperoxidase in the Framingham Heart Study and Cardiovascular Health Study studies (replication meta-P=0.003), leading to a meta-P value of 9.96×10(-7) in the 3 dyslipidemic groups. The variant or its proxy was then profiled in additional population-based cohorts (up to a total of 58 930 subjects) including Cohorte Lausannoise, Ely, Fenland, European Prospective Investigation of Cancer, London Life Sciences Prospective Population Study, and the Genetics of Obesity Associations study obesity case-control for up to 40 cardiovascular and metabolic traits. Overall analysis identified the same single nucleotide polymorphisms to be nominally associated consistently with glomerular filtration rate (P=0.002) and risk of obesity (body mass index ≥30 kg/m(2), P=0.004).

CONCLUSIONS

As myeloperoxidase is a prognostic marker of coronary events, the MAPK14 variant may provide a mechanistic link between p38 map kinase and these events, providing information consistent with current indication of Losmapimod for acute coronary syndrome. If replicated, the association with glomerular filtration rate, along with previous biological findings, also provides support for kidney diseases as alternative indications.

Reliability and validity of a domain-specific last 7-d sedentary time questionnaire

PURPOSE

The objective of this study is to examine test-retest reliability, criterion validity, and absolute agreement of a self-report, last 7-d sedentary behavior questionnaire (SIT-Q-7d), which assesses total daily sedentary time as an aggregate of sitting/lying down in five domains (meals, transportation, occupation, nonoccupational screen time, and other sedentary time). Dutch (DQ) and English (EQ) versions of the questionnaire were examined.

METHODS

Fifty-one Flemish adults (ages 39.4 ± 11.1 yr) wore a thigh accelerometer (activPAL3™) and simultaneously kept a domain log for 7 d. The DQ was subsequently completed twice (median test-retest interval: 3.3 wk). Thigh-acceleration sedentary time was log annotated to create comparable domain-specific and total sedentary time variables. Four hundred two English adults (ages 49.6 ± 7.3 yr) wore a combined accelerometer and HR monitor (Actiheart) for 6 d to objectively measure total sedentary time. The EQ was subsequently completed twice (median test-retest interval: 3.4 wk). In both samples, the questionnaire reference frame overlapped with the criterion measure administration period. All participants had five or more valid days of criterion data, including one or more weekend day.

RESULTS

Test-retest reliability (intraclass correlation coefficient (95% CI)) was fair to good for total sedentary time (DQ: 0.68 (0.50-0.81); EQ: 0.53 (0.44-0.62)) and poor to excellent for domain-specific sedentary time (DQ: from 0.36 (0.10-0.57) (meals) to 0.66 (0.46-0.79) (occupation); EQ: from 0.45 (0.35-0.54) (other sedentary time) to 0.76 (0.71-0.81) (meals)). For criterion validity (Spearman rho), significant correlations were found for total sedentary time (DQ: 0.52; EQ: 0.22; all P <0.001). Compared with domain-specific criterion variables (DQ), modest-to-strong correlations were found for domain-specific sedentary time (from 0.21 (meals) to 0.76 (P < 0.001) (screen time)). The questionnaire generally overestimated sedentary time compared with criterion measures.

CONCLUSION

The SIT-Q-7d appears to be a useful tool for ranking individuals in large-scale observational studies examining total and domain-specific sitting.

Clinical utility of genetic risk testing in primary care: the example of Type 2 diabetes

Genetic advances in Type 2 diabetes (T2D) have led to the discovery and validation of multiple markers for this complex disease. Despite low predictive value of current T2D markers beyond clinical risk factors and family history, researchers are exploring the clinical utility and outcomes of implementation in practice, and testing is available via direct-to-consumer markets. Clinical utility research demonstrates high hypothetical utility to patients for motivating behavior change and potentially reducing risk. However, trials to date have not demonstrated improvements in behavioral and clinical outcomes over and above counseling based on traditional risk factors. Ongoing research in T2D genetics and associated risk-prediction models is necessary to refine genetic risk pathways, algorithms for risk prediction and use of this information in clinical care. Further research is also needed to explore care models and support interventions that address the needs of personalized risk information and sustainable preventive behaviors to reduce the rising prevalence of T2D.