Genetic susceptibility testing and readiness to control weight: Results from a randomized controlled trial

Causal association between gut microbiota and hyperemesis gravidarum: a two-sample Mendelian randomization study

Background

Observational studies have reported an association between the gut microbiota (GM) and hyperemesis gravidarum (HG). However, the causal relationship is unclear. In this study, Mendelian randomization (MR) was used to infer causal relationships between GM and HG.

Methods

Inverse-variance weighted MR was performed using summary statistics for genetic variants from genome-wide association studies (GWAS). Sensitivity analyses were performed to validate the MR results and assess the robustness of the causal inference. Reverse MR analysis was performed for bacterial taxa that were causally linked to the HG risk in the forward MR analysis to evaluate reverse causality.

Results

MR analysis revealed that the genera Defluviitaleaceae UCG011, Ruminococcus1, Ruminococcus2, Turicibacter, and unknowngenus and phylum Verrucomicrobiota are positively associated with the risk of HG. Additionally, the genus Coprococcus2 was related to a decreased risk of HG. Sensitivity studies validated the strength and reliability of the link between the composition of the GM and HG. No evidence for reverse causality from HG to identified bacterial taxa was found.

Conclusion

Our MR analysis provided novel insight into the association between GM and HG. In particular, our results indicated that targeting the GM could serve as an effective therapeutic strategy for HG.

Assessment of the impact of a personalised nutrition intervention in impaired glucose regulation over 26 weeks: a randomised controlled trial

Dietary interventions can reduce progression to type 2 diabetes mellitus (T2DM) in people with non-diabetic hyperglycaemia. In this study we aimed to determine the impact of a DNA-personalised nutrition intervention in people with non-diabetic hyperglycaemia over 26 weeks. ASPIRE-DNA was a pilot study. Participants were randomised into three arms to receive either (i) Control arm: standard care (NICE guidelines) (n = 51), (ii) Intervention arm: DNA-personalised dietary advice (n = 50), or (iii) Exploratory arm: DNA-personalised dietary advice via a self-guided app and wearable device (n = 46). The primary outcome was the difference in fasting plasma glucose (FPG) between the Control and Intervention arms after 6 weeks. 180 people were recruited, of whom 148 people were randomised, mean age of 59 years (SD = 11), 69% of whom were female. There was no significant difference in the FPG change between the Control and Intervention arms at 6 weeks (- 0.13 mmol/L (95% CI [- 0.37, 0.11]), p = 0.29), however, we found that a DNA-personalised dietary intervention led to a significant reduction of FPG at 26 weeks in the Intervention arm when compared to standard care (- 0.019 (SD = 0.008), p = 0.01), as did the Exploratory arm (- 0.021 (SD = 0.008), p = 0.006). HbA1c at 26 weeks was significantly reduced in the Intervention arm when compared to standard care (- 0.038 (SD = 0.018), p = 0.04). There was some evidence suggesting prevention of progression to T2DM across the groups that received a DNA-based intervention (p = 0.06). Personalisation of dietary advice based on DNA did not result in glucose changes within the first 6 weeks but was associated with significant reduction of FPG and HbA1c at 26 weeks when compared to standard care. The DNA-based diet was effective regardless of intervention type, though results should be interpreted with caution due to the low sample size. These findings suggest that DNA-based dietary guidance is an effective intervention compared to standard care, but there is still a minimum timeframe of adherence to the intervention before changes in clinical outcomes become apparent.Trial Registration: www.clinicaltrials.gov.uk Ref: NCT03702465.

Assessing the relationship between gut microbiota and endometriosis: a bidirectional two-sample mendelian randomization analysis

BACKGROUND

An increasing body of observational studies have indicated an association between gut microbiota and endometriosis. However, the causal relationship between them is not yet clear. In this study, we employed Mendelian randomization method to investigate the causal relationship between 211 gut microbiota taxa and endometriosis.

METHODS

Independent genetic loci significantly associated with the relative abundance of 211 gut microbiota taxa, based on predefined thresholds, were extracted as instrumental variables. The primary analytical approach employed was the IVW method. Effect estimates were assessed primarily using the odds ratio and 95% confidence intervals. Supplementary analyses were conducted using MR-Egger regression, the weighted median method, the simple mode and the weighted mode method to complement the IVW results. In addition, we conducted tests for heterogeneity, horizontal pleiotropy, sensitivity analysis, and MR Steiger to assess the robustness of the results and the strength of the causal relationships.

RESULTS

Based on the IVW method, we found that the family Prevotellaceae, genus Anaerotruncus, genus Olsenella, genus Oscillospira, and order Bacillales were identified as risk factors for endometriosis, while class Melainabacteria and genus Eubacterium ruminantium group were protective factors. Additionally, no causal relationship was observed between endometriosis and gut microbiota. Heterogeneity tests, pleiotropy tests, and leave-one-out sensitivity analyses did not detect any significant heterogeneity or pleiotropic effects.

CONCLUSIONS

Our MR study has provided evidence supporting a potential causal relationship between gut microbiota and endometriosis, and it suggests the absence of bidirectional causal effects. These findings could potentially offer new insights for the development of novel strategies for the prevention and treatment of endometriosis.

Genetic support of the causal association between gut microbiota and peripheral artery disease: a bidirectional Mendelian randomization study

BACKGROUND

The causal relationship between gut microbiota and peripheral artery disease (PAD) is still not clear. In this research, we employed the Mendelian randomization (MR) technique to explore the potential causal connection between 211 gut microbiota species and PAD. We also investigated whether the causal effects operate in both directions.

METHODS

We used Genome-wide Association Studies (GWAS) summary statistics data from the MiBioGen and FinnGen consortia to conduct a two-sample MR analysis to explore the causal link between gut microbiota and PAD. Sensitivity analysis is conducted to assess the robustness of the MR results. In addition to that, reverse MR analysis was performed to examine the inverse causal relationship.

RESULTS

The inverse variance weighted (IVW) method provided evidence supporting a causal relationship between 9 specific gut microbiota taxa and PAD. The study findings indicated that family Family XI (OR=1.11, CI 1.00-1.24, P=0.048), genus Lachnoclostridium (OR=1.24, 1.02-1.50, P=0.033), and genus Lachnospiraceae UCG001 (OR=1.17, 1.01-1.35, P=0.031) are risk factors associated with PAD. class Actinobacteria (OR=0.84, 0.72-0.99, P=0.034), family Acidaminococcaceae (OR=0.80, 0.66-0.98, P=0.029), genus Coprococcus2 (OR=0.79, 0.64-0.98, P=0.029), genus Ruminococcaceae UCG004 (OR=0.84, 0.72-0.99, P=0.032), genus Ruminococcaceae UCG010 (OR=0.74, 0.58-0.96, P=0.022), and order NB1n (OR=0.88, 0.79-0.98, P=0.02) may be associated with the risk factors of PAD. Moreover, our analysis did not uncover any evidence of a reverse causal relationship between PAD and the nine specific gut microbiota taxa investigated.

CONCLUSIONS

Our MR research has confirmed the potential causal relationship between gut microbiota and PAD while also identifying specific gut bacterial communities associated with PAD.

Genetically Predicted Causal Effects of Gut Microbiota and Gut Metabolites on Digestive Tract Cancer: A Two-Sample Mendelian Randomization Analysis

Background

Evidence from numerous observational studies and clinical trials has linked gut microbiota and metabolites to digestive tract cancer. However, the causal effect between these factors remains uncertain.

Methods

Data for this study were obtained from the MiBioGen, TwinsUK Registry, and FinnGen (version R8). Two-sample Mendelian randomization analysis with inverse variance weighting method was primarily used, and the results were validated by heterogeneity analysis, pleiotropy test, and sensitivity analysis.

Results

At P < 5 × 10-8, our analysis identified four gut microbiotas as risk factors for digestive tract cancer and six as risk factors for colorectal cancer. Conversely, one gut microbiota exhibited protection against bile duct cancer, and two showed protective effects against stomach cancer. At P < 1 × 10-5, our investigation revealed five, six, three, eight, eight, and eight gut microbiotas as risk factors for esophageal, stomach, bile duct, liver, pancreatic, and colorectal cancers, respectively. In contrast, four, two, eight, two, two, and five gut microbiotas exhibited protective effects against these cancers. Additionally, GABA, a metabolite of gut microbiota, displayed a significant protective effect against colorectal cancer.

Conclusion

In conclusion, specific gut microbiota and metabolites play roles as risk factors or protective factors for digestive tract cancer, and a causal relationship between them has been established, offering novel insights into gut microbiota-mediated cancer development.

The association between gut microbiome and PCOS: evidence from meta-analysis and two-sample mendelian randomization

BACKGROUND

Increasing evidence from observational studies and clinical experimentation has indicated a link between the gut microbiotas (GMs) and polycystic ovary syndrome (PCOS), however, the causality and direction of causality between gut microbiome and PCOS remains to be established.

METHODS

We conducted a comprehensive search of four databases-PubMed, Cochrane Library, Web of Science, and Embase up until June 1, 2023, and subjected the results to a meta-analysis. In this study, a bidirectional two-sample Mendelian randomization (MR) analysis was employed to investigate the impact of gut microbiota on polycystic ovary syndrome (PCOS). The genome-wide association study (GWAS) data for PCOS comprised 113,238 samples, while the GWAS data for gut microbiota were derived from the MiBioGen consortium, encompassing a total sample size of 18,340 individuals. As the largest dataset of its kind, this study represents the most comprehensive genome-wide meta-analysis concerning gut microbiota composition to date. Single nucleotide polymorphisms (SNPs) were selected as instrumental variables at various taxonomic levels, including Phylum, Class, Order, Family, and Genus. The causal associations between exposures and outcomes were assessed using four established MR methods. To correct for multiple testing, the false discovery rate (FDR) method was applied. The reliability and potential biases of the results were evaluated through sensitivity analysis and F-statistics.

RESULTS

The meta-analysis incorporated a total of 20 studies that met the criteria, revealing a close association between PCOS and specific gut microbiota species. As per the results from our MR analysis, we identified six causal associations between the gut microbiome and polycystic ovary syndrome (PCOS). At the genus level, Actinomyces (ORIVW = 1.369, FDR = 0.040), Streptococcus (ORIVW = 1.548, FDR = 0.027), and Ruminococcaceae UCG-005 (ORIVW = 1.488, FDR = 0.028) were identified as risk factors for PCOS. Conversely, Candidatus Soleaferrea (ORIVW = 0.723, FDR = 0.040), Dorea (ORIVW = 0.580, FDR = 0.032), and Ruminococcaceae UCG-011 (ORIVW = 0.732, FDR = 0.030) were found to be protective factors against PCOS. Furthermore, the MR-PRESSO global test and MR-Egger regression indicated that our study results were not affected by horizontal pleiotropy (p > 0.05). Finally, the leave-one-out analysis corroborated the robustness of the MR findings.

CONCLUSION

Both our meta-analysis and MR study indicates that there is a causal relationship between the gut microbiome and PCOS, which may contribute to providing novel insights for the development of new preventive and therapeutic strategies for PCOS.

Causal relationship between gut microbiota and cancers: a two-sample Mendelian randomisation study

BACKGROUND

Evidence from observational studies and clinical trials suggests that the gut microbiota is associated with cancer. However, the causal association between gut microbiota and cancer remains to be determined.

METHODS

We first identified two sets of gut microbiota based on phylum, class, order, family, and genus level information, and cancer data were obtained from the IEU Open GWAS project. We then performed two-sample Mendelian randomisation (MR) to determine whether the gut microbiota is causally associated with eight cancer types. Furthermore, we performed a bi-directional MR analysis to examine the direction of the causal relations.

RESULTS

We identified 11 causal relationships between genetic liability in the gut microbiome and cancer, including those involving the genus Bifidobacterium. We found 17 strong associations between genetic liability in the gut microbiome and cancer. Moreover, we found 24 associations between genetic liability in the gut microbiome and cancer using multiple datasets.

CONCLUSIONS

Our MR analysis revealed that the gut microbiota was causally associated with cancers and may be useful in providing new insights for further mechanistic and clinical studies of microbiota-mediated cancer.

Components, prospects and challenges of personalized prevention

Effective preventive strategies are urgently needed to address the rising burden of non-communicable diseases such as cardiovascular disease and cancer. To date, most prevention efforts to reduce disease incidence have primarily targeted populations using "one size fits all" public health recommendations and strategies. However, the risk for complex heterogeneous diseases is based on a multitude of clinical, genetic, and environmental factors, which translate into individual sets of component causes for every person. Recent advances in genetics and multi-omics enable the use of new technologies to stratify disease risks at an individual level fostering personalized prevention. In this article, we review the main components of personalized prevention, provide examples, and discuss both emerging opportunities and remaining challenges for its implementation. We encourage physicians, health policy makers, and public health professionals to consider and apply the key elements and examples of personalized prevention laid out in this article while overcoming challenges and potential barriers to their implementation.

Acceptability of predictive testing for ischemic heart disease in those with a family history and the impact of results on behavioural intention and behaviour change: a systematic review

BACKGROUND

Tests to predict the development of chronic diseases in those with a family history of the disease are becoming increasingly available and can identify those who may benefit most from preventive interventions. It is important to understand the acceptability of these predictive approaches to inform the development of tools to support decision making. Whilst data are lacking for many diseases, data are available for ischemic heart disease (IHD). Therefore, this study investigates the willingness of those with a family history of IHD to take a predictive test, and the effect of the test results on risk-related behaviours.

METHOD

Medline, EMBASE, PsycINFO, LILACS and grey literature were searched. Primary research, including adult participants with a family history of IHD, and assessing a predictive test were included. Qualitative and quantitative outcomes measuring willingness to take a predictive test and the effect of test results on risk-related behaviours were also included. Data concerning study aims, participants, design, predictive test, intervention and findings were extracted. Study quality was assessed using the Standard Quality Assessment Criteria for Evaluating Research Papers from a Variety of Fields and a narrative synthesis undertaken.

RESULTS

Five quantitative and two qualitative studies were included. These were conducted in the Netherlands (n = 1), Australia (n = 1), USA (n = 1) and the UK (n = 4). Methodological quality ranged from moderate to good. Three studies found that most relatives were willing to take a predictive test, reporting family history (n = 2) and general practitioner (GP) recommendation (n = 1) as determinants of interest. Studies assessing the effect of test results on behavioural intentions (n = 2) found increased intentions to engage in physical activity and smoking cessation, but not healthy eating in those at increased risk of developing IHD. In studies examining actual behaviour change (n = 2) most participants reported engaging in at least one preventive behaviour, particularly medication adherence.

CONCLUSION

The results suggests that predictive approaches are acceptable to those with a family history of IHD and have a positive impact on health behaviours. Further studies are needed to provide a comprehensive understanding of predictive approaches in IHD and other chronic conditions.

Brief ‘Appetitive Trait Tailored Intervention’: Development in a Sample of Adults with Overweight and Obesity

Appetitive traits are associated with weight and could be managed using behavioural strategies. Personalised approaches to weight loss could use a person's appetitive trait profile to tailor weight management advice. This study aimed to explore participants’ experiences of a brief Appetitive Trait Tailored Intervention (ATTI) based on participants’ Adult Eating Behaviour Questionnaire (AEBQ) scores. The ATTI was developed using strategies from modified Cognitive Behavioural Therapy and behaviour change techniques. Acceptability testing of the ATTI was carried out with participants (body mass index ≥25) who completed the AEBQ online and were sent their appetitive trait profile and corresponding weight loss tips via e-mail. Participants were asked to follow the tips for 8 weeks and following the tips, perceived helpfulness, barriers, and initial and final body weight. Qualitative interviews explored their experiences. Thirty-seven participants provided feedback and reported the majority of the tips to be helpful. Thirty-two participants (92.5% female) provided their final weight; 10 reported weight loss ≥5% of initial weight. Qualitative interviews (n = 21) revealed that tailoring was seen as novel and participants felt that the ATTI increased their self-awareness and encouraged behavioural changes. The low intensity of the ATTI limited engagement for some. The ATTI is an acceptable, novel approach to weight management.

Disclosing Pharmacogenetic Feedback of Caffeine via eHealth Channels, Assessment of the Methods and Effects to Behavior Change: A Pilot Study

Background: The integration of genetic testing into eHealth applications holds great promise for the personalization of disease prevention guidelines. However, relatively little is known about the impact of eHealth applications on an individual's behavior. Aim: The aim of the pilot study was to investigate the effect of the personalized eHealth application approach to behavior change in a 1-month follow-up period on groups with previously known and unknown caffeine impacts. Method: We created a direct-to-consumer approach that includes providing relevant information and personalized reminders and goals on the digital device regarding the caffeine intake for two groups of individuals: the intervention group (IG) with the genetic raw data available and the control group (CG) to test the impact of the same content (article about caffeine metabolism) on participants without the genetic test. Study participants were all Estonians (n = 160). Results: The study suggests that eHealth applications work for short-term behavior change. Participants in the genetic IG tended to increase caffeine intake if they were informed about caffeine not being harmful. They reported feeling better physically and/or mentally after their behavioral change decision during the period of the study. Conclusions: Our pilot study revealed that eHealth applications may have a positive effect for short-term behavior change, regardless of a prior genetic test. Further studies among larger study groups are required to achieve a better understanding about behavior change of individuals in the field of personalized medicine and eHealth interventions.

Leveraging Genomic Associations in Precision Digital Care for Weight Loss: Cohort Study

BACKGROUND

The COVID-19 pandemic has highlighted the urgency of addressing an epidemic of obesity and associated inflammatory illnesses. Previous studies have demonstrated that interactions between single-nucleotide polymorphisms (SNPs) and lifestyle interventions such as food and exercise may vary metabolic outcomes, contributing to obesity. However, there is a paucity of research relating outcomes from digital therapeutics to the inclusion of genetic data in care interventions.

OBJECTIVE

This study aims to describe and model the weight loss of participants enrolled in a precision digital weight loss program informed by the machine learning analysis of their data, including genomic data. It was hypothesized that weight loss models would exhibit a better fit when incorporating genomic data versus demographic and engagement variables alone.

METHODS

A cohort of 393 participants enrolled in Digbi Health's personalized digital care program for 120 days was analyzed retrospectively. The care protocol used participant data to inform precision coaching by mobile app and personal coach. Linear regression models were fit of weight loss (pounds lost and percentage lost) as a function of demographic and behavioral engagement variables. Genomic-enhanced models were built by adding 197 SNPs from participant genomic data as predictors and refitted using Lasso regression on SNPs for variable selection. Success or failure logistic regression models were also fit with and without genomic data.

RESULTS

Overall, 72.0% (n=283) of the 393 participants in this cohort lost weight, whereas 17.3% (n=68) maintained stable weight. A total of 142 participants lost 5% bodyweight within 120 days. Models described the impact of demographic and clinical factors, behavioral engagement, and genomic risk on weight loss. Incorporating genomic predictors improved the mean squared error of weight loss models (pounds lost and percent) from 70 to 60 and 16 to 13, respectively. The logistic model improved the pseudo R2 value from 0.193 to 0.285. Gender, engagement, and specific SNPs were significantly associated with weight loss. SNPs within genes involved in metabolic pathways processing food and regulating fat storage were associated with weight loss in this cohort: rs17300539_G (insulin resistance and monounsaturated fat metabolism), rs2016520_C (BMI, waist circumference, and cholesterol metabolism), and rs4074995_A (calcium-potassium transport and serum calcium levels). The models described greater average weight loss for participants with more risk alleles. Notably, coaching for dietary modification was personalized to these genetic risks.

CONCLUSIONS

Including genomic information when modeling outcomes of a digital precision weight loss program greatly enhanced the model accuracy. Interpretable weight loss models indicated the efficacy of coaching informed by participants' genomic risk, accompanied by active engagement of participants in their own success. Although large-scale validation is needed, our study preliminarily supports precision dietary interventions for weight loss using genetic risk, with digitally delivered recommendations alongside health coaching to improve intervention efficacy.

Precision Medicine and Obesity

The highly variable response to obesity therapies justifies the search for treatment strategies that are best suited to individual patients to enhance their effectiveness and tolerability via precision medicine. Precision medicine development in recent years has been driven by the emergence of powerful methods to characterize patients ("omic" assays). Current available information has revealed that there are numerous intermediary processes that contribute to obesity and have provided a framework for partially comprehending the mechanisms behind the heterogeneity of obesity and its clinical consequences. Some of these processes have or are currently being targeted to individualize obesity therapy with some success.

Can a Lifestyle Genomics Intervention Motivate Patients to Engage in Greater Physical Activity than a Population-Based Intervention? Results from the NOW Randomized Controlled Trial

BACKGROUND

Lifestyle genomics (LGx) is a science that explores interactions between genetic variation, lifestyle components such as physical activity (PA), and subsequent health- and performance-related outcomes. The objective of this study was to determine whether an LGx intervention could motivate enhanced engagement in PA to a greater extent than a population-based intervention.

METHODS

In this pragmatic randomized controlled trial, participants received either the standard, population-based Group Lifestyle BalanceTM (GLB) program intervention or the GLB program in addition to the provision of LGx information and advice (GLB + LGx). Participants (n = 140) completed a 7-day PA recall at baseline, 3, 6, and 12 months. Data from the PA recalls were used to calculate metabolic equivalents (METs), a measure of energy expenditure. Statistical analyses included split plot analyses of covariance and binary logistic regression (generalized linear models). Differences in leisure time PA weekly METs, weekly minutes of moderate + high-intensity PA, and adherence to PA guidelines were compared between groups (GLB and GLB + LGx) across the 4 time points.

RESULTS

Weekly METs were significantly higher in the GLB + LGx group (1,114.7 ± 141.9; 95% CI 831.5-1,397.8) compared to the standard GLB group (621.6 ± 141.9 MET/week; 95% CI 338.4-904.8) at the 6-month follow-up (p = 0.01). All other results were non-significant.

CONCLUSIONS

The provision of an LGx intervention resulted in a greater weekly leisure time PA energy expenditure after the 6-month follow-up. Future research should determine how this could be sustained over the long-term.

CLINICAL TRIAL REGISTRATION

NCT03015012.

Enhanced long-term dietary change and adherence in a nutrigenomics-guided lifestyle intervention compared to a population-based (GLB/DPP) lifestyle intervention for weight management: results from the NOW randomised controlled trial

Background

Adherence to nutritional guidelines for chronic disease prevention and management remains a challenge in clinical practice. Innovative strategies are needed to help optimise dietary behaviour change.

Objective

The objective of this study was to determine if a nutrigenomics-guided lifestyle intervention programme could be used to motivate greater dietary adherence and change in dietary intake short-term, moderate-term and long-term compared to the gold-standard population-based weight management intervention (Group Lifestyle Balance (GLB)/Diabetes Prevention Programme (DPP)).

Design

The Nutrigenomics, Overweight/Obesity, and Weight Management (NOW) randomised controlled trial is a pragmatic, parallel-group, superiority clinical trial (n=140), which was conducted at the East Elgin Family Health Team (EEFHT). GLB weight management groups were prerandomised 1:1 to receive either the standard GLB programme or a modified GLB+nutrigenomics (GLB+NGx) programme. Three 24-hour recalls were collected at baseline, 3, 6 and 12 months using the validated multiple pass method. Research assistants collecting the three 24-hour recalls were blinded to the participants’ group assignments. Statistical analyses included split plot analyses of variance (ANOVAs), two-way ANOVAs, binary logistic regression, χ² and Fisher’s exact tests. Using the Theory of Planned Behaviour as guidance, key confounding factors of behaviour change were considered in the analyses. This study was registered with clinicaltrials.gov (NCT03015012).

Results

Only the GLB+NGx group significantly reduced their total fat intake from baseline to 12-month follow-up (from 36.0%±4.8% kcal to 30.2%±8.7% kcal, p=0.02). Long-term dietary adherence to total fat and saturated fat guidelines was also significantly (p<0.05) greater in the GLB+NGx group compared to the standard GLB group.

Conclusions

Weight management interventions guided by nutrigenomics can motivate long-term improvements in dietary fat intake above and beyond gold-standard population-based interventions.

Benefits and limitations of genome-wide association studies

Genome-wide association studies (GWAS) involve testing genetic variants across the genomes of many individuals to identify genotype-phenotype associations. GWAS have revolutionized the field of complex disease genetics over the past decade, providing numerous compelling associations for human complex traits and diseases. Despite clear successes in identifying novel disease susceptibility genes and biological pathways and in translating these findings into clinical care, GWAS have not been without controversy. Prominent criticisms include concerns that GWAS will eventually implicate the entire genome in disease predisposition and that most association signals reflect variants and genes with no direct biological relevance to disease. In this Review, we comprehensively assess the benefits and limitations of GWAS in human populations and discuss the relevance of performing more GWAS.

Learning one's genetic risk changes physiology independent of actual genetic risk

Millions of people now access personal genetic risk estimates for diseases such as Alzheimer's, cancer and obesity¹. While this information can be informative^2-4, research on placebo and nocebo effects^5-8 suggests that learning of one's genetic risk may evoke physiological changes consistent with the expected risk profile. Here we tested whether merely learning of one's genetic risk for disease alters one's actual risk by making people more likely to exhibit the expected changes in gene-related physiology, behaviour and subjective experience. Individuals were genotyped for actual genetic risk and then randomly assigned to receive either a 'high-risk' or 'protected' genetic test result for obesity via cardiorespiratory exercise capacity (experiment 1, N = 116) or physiological satiety (experiment 2, N = 107) before engaging in a task in which genetic risk was salient. Merely receiving genetic risk information changed individuals' cardiorespiratory physiology, perceived exertion and running endurance during exercise, and changed satiety physiology and perceived fullness after food consumption in a self-fulfilling manner. Effects of perceived genetic risk on outcomes were sometimes greater than the effects associated with actual genetic risk. If simply conveying genetic risk information can alter actual risk, clinicians and ethicists should wrestle with appropriate thresholds for when revealing genetic risk is warranted.

Effect of personalized nutrition on health-related behaviour change: evidence from the Food4Me European randomized controlled trial

Background

Optimal nutritional choices are linked with better health, but many current interventions to improve diet have limited effect. We tested the hypothesis that providing personalized nutrition (PN) advice based on information on individual diet and lifestyle, phenotype and/or genotype would promote larger, more appropriate, and sustained changes in dietary behaviour.

Methods

: Adults from seven European countries were recruited to an internet-delivered intervention (Food4Me) and randomized to: (i) conventional dietary advice (control) or to PN advice based on: (ii) individual baseline diet; (iii) individual baseline diet plus phenotype (anthropometry and blood biomarkers); or (iv) individual baseline diet plus phenotype plus genotype (five diet-responsive genetic variants). Outcomes were dietary intake, anthropometry and blood biomarkers measured at baseline and after 3 and 6 months' intervention.

Results

At baseline, mean age of participants was 39.8 years (range 18-79), 59% of participants were female and mean body mass index (BMI) was 25.5 kg/m 2 . From the enrolled participants, 1269 completed the study. Following a 6-month intervention, participants randomized to PN consumed less red meat [-5.48 g, (95% confidence interval:-10.8,-0.09), P  = 0.046], salt [-0.65 g, (-1.1,-0.25), P  = 0.002] and saturated fat [-1.14 % of energy, (-1.6,-0.67), P  < 0.0001], increased folate [29.6 µg, (0.21,59.0), P  = 0.048] intake and had higher Healthy Eating Index scores [1.27, (0.30, 2.25), P  = 0.010) than those randomized to the control arm. There was no evidence that including phenotypic and phenotypic plus genotypic information enhanced the effectiveness of the PN advice.

Conclusions

Among European adults, PN advice via internet-delivered intervention produced larger and more appropriate changes in dietary behaviour than a conventional approach.

A Systematic Review of Genetic Testing and Lifestyle Behaviour Change: Are We Using High-Quality Genetic Interventions and Considering Behaviour Change Theory?

BACKGROUND

Studying the impact of genetic testing interventions on lifestyle behaviour change has been a priority area of research in recent years. Substantial heterogeneity exists in the results and conclusions of this literature, which has yet to be explained using validated behaviour change theory and an assessment of the quality of genetic interventions. The theory of planned behaviour (TPB) helps to explain key contributors to behaviour change. It has been hypothesized that personalization could be added to this theory to help predict changes in health behaviours.

PURPOSE

This systematic review provides a detailed, comprehensive identification, assessment, and summary of primary research articles pertaining to lifestyle behaviour change (nutrition, physical activity, sleep, and smoking) resulting from genetic testing interventions. The present review further aims to provide in-depth analyses of studies conducted to date within the context of the TPB and the quality of genetic interventions provided to participants while aiming to determine whether or not genetic testing facilitates changes in lifestyle habits. This review is timely in light of a recently published "call-to-action" paper, highlighting the need to incorporate the TPB into personalized healthcare behaviour change research.

METHODS

Three bibliographic databases, one key website, and article reference lists were searched for relevant primary research articles. The PRISMA Flow Diagram and PRISMA Checklist were used to guide the search strategy and manuscript preparation. Out of 32,783 titles retrieved, 26 studies met the inclusion criteria. Three quality assessments were conducted and included: (1) risk of bias, (2) quality of genetic interventions, and (3) consideration of theoretical underpinnings - primarily the TPB.

RESULTS

Risk of bias in studies was overall rated to be "fair." Consideration of the TPB was "poor," with no study making reference to this validated theory. While some studies (n = 11; 42%) made reference to other behaviour change theories, these theories were generally mentioned briefly, and were not thoroughly incorporated into the study design or analyses. The genetic interventions provided to participants were overall of "poor" quality. However, a separate analysis of studies using controlled intervention research methods demonstrated the use of higher-quality genetic interventions (overall rated to be "fair"). The provision of actionable recommendations informed by genetic testing was more likely to facilitate behaviour change than the provision of genetic information without actionable lifestyle recommendations. Several studies of good quality demonstrated changes in lifestyle habits arising from the provision of genetic interventions. The most promising lifestyle changes were changes in nutrition.

CONCLUSIONS

It is possible to facilitate behaviour change using genetic testing as the catalyst. Future research should ensure that high-quality genetic interventions are provided to participants, and should consider validated theories such as the TPB in their study design and analyses. Further recommendations for future research are provided.

Genetics of obesity: what genetic association studies have taught us about the biology of obesity and its complications

Genome-wide association studies (GWAS) for BMI, waist-to-hip ratio, and other adiposity traits have identified more than 300 single-nucleotide polymorphisms (SNPs). Although there is reason to hope that these discoveries will eventually lead to new preventive and therapeutic agents for obesity, this will take time because such developments require detailed mechanistic understanding of how an SNP influences phenotype (and this information is largely unavailable). Fortunately, absence of functional information has not prevented GWAS findings from providing insights into the biology of obesity. Genes near loci regulating total body mass are enriched for expression in the CNS, whereas genes for fat distribution are enriched in adipose tissue itself. Gene by environment and lifestyle interaction analyses have revealed that our increasingly obesogenic environment might be amplifying genetic risk for obesity, yet those at highest risk could mitigate this risk by increasing physical activity and possibly by avoiding specific dietary components. GWAS findings have also been used in mendelian randomisation analyses probing the causal association between obesity and its many putative complications. In supporting a causal association of obesity with diabetes, coronary heart disease, specific cancers, and other conditions, these analyses have clinical relevance in identifying which outcomes could be preventable through weight loss interventions.

Can genetic-based advice help you lose weight? Findings from the Food4Me European randomized controlled trial

Background: There has been limited evidence about whether genotype-tailored advice provides extra benefits in reducing obesity-related traits compared with the benefits of conventional one-size-fits-all advice.Objective: We determined whether the disclosure of information on fat-mass and obesity-associated (FTO) genotype risk had a greater effect on a reduction of obesity-related traits in risk carriers than in nonrisk carriers across different levels of personalized nutrition.Design: A total of 683 participants (women: 51%; age range: 18-73 y) from the Food4Me randomized controlled trial were included in this analysis. Participants were randomly assigned to 4 intervention arms as follows: level 0, control group; level 1, dietary group; level 2, phenotype group; and level 3, genetic group. FTO (single nucleotide polymorphism rs9939609) was genotyped at baseline in all participants, but only subjects who were randomly assigned to level 3 were informed about their genotypes. Level 3 participants were stratified into risk carriers (AA/AT) and nonrisk carriers (TT) of the FTO gene for analyses. Height, weight, and waist circumference (WC) were self-measured and reported at baseline and months 3 and 6.Results: Changes in adiposity markers were greater in participants who were informed that they carried the FTO risk allele (level 3 AT/AA carriers) than in the nonpersonalized group (level 0) but not in the other personalized groups (level 1 and 2). Mean reductions in weight and WC at month 6 were greater for FTO risk carriers than for noncarriers in the level 3 group [-2.28 kg (95% CI: -3.06, -1.48 kg) compared with -1.99 kg (-2.19, -0.19 kg), respectively (P = 0.037); and -4.34 cm (-5.63, -3.08 cm) compared with -1.99 cm (-4.04, -0.05 cm), respectively, (P = 0.048)].Conclusions: There are greater body weight and WC reductions in risk carriers than in nonrisk carriers of the FTO gene. This trial was registered at clinicaltrials.gov as NCT01530139.

Predicting Polygenic Obesity Using Genetic Information

Except in rare cases, obesity tends to be a consequence of both an unhealthy lifestyle and a genetic susceptibility to gain weight. With more than 200 common genetic variants identified, there is a growing interest in developing personalized preventive and treatment strategies to predict an individual's obesity risk. We review the literature on the prediction of obesity and show that models based on the established genetic variants have poorer predictive ability than traditional predictors, such as family history of obesity and childhood obesity. Current findings suggest that opportunities for precision medicine in common obesity may be limited.

Genetic Testing for Obesity: Implications and Challenges

PURPOSE OF REVIEW

This review summarizes the experimental literature on behavioral outcomes and psychological effects of genetic testing for obesity. Such tests, although of dubious value, are increasingly marketed to the public, and there has been concern that while results may encourage some consumers to increase the healthfulness of their lifestyles, others may interpret feedback in maladaptive ways.

RECENT FINDINGS

Hypothetical vignettes have been used in artificial settings; few studies have investigated outcomes of actual test results. At present, the effects of genetic testing for obesity seem limited to improving consumers' weight control intentions and motivation rather than actual dietary behavior. Evidence for negative psychological consequences is scarce and seems of greater concern for normal weight persons than for those who are overweight. Better research designs carried out in the field rather than the laboratory with more diverse samples are needed.

Applied Interventions in the Prevention and Treatment of Obesity Through the Research of Professor Jane Wardle

PURPOSE OF REVIEW

Obesity presents a challenge for practitioners, policy makers, researchers and for those with obesity themselves. This review focuses on psychological approaches to its management and prevention in children and adults.

RECENT FINDINGS

Through exploring the work of the late Professor Jane Wardle, we look at the earliest behavioural treatment approaches and how psychological theory has been used to develop more contemporary approaches, for example incorporating genetic feedback and habit formation theory into interventions. We also explore how Jane has challenged thinking about the causal pathways of obesity in relation to eating behaviour. Beyond academic work, Jane was an advocate of developing interventions which had real-world applications. Therefore, we discuss how she not only developed new interventions but also made these widely available and the charity that she established.

Psychological and behavioural impact of returning personal results from whole-genome sequencing: the HealthSeq project

Providing ostensibly healthy individuals with personal results from whole-genome sequencing could lead to improved health and well-being via enhanced disease risk prediction, prevention, and diagnosis, but also poses practical and ethical challenges. Understanding how individuals react psychologically and behaviourally will be key in assessing the potential utility of personal whole-genome sequencing. We conducted an exploratory longitudinal cohort study in which quantitative surveys and in-depth qualitative interviews were conducted before and after personal results were returned to individuals who underwent whole-genome sequencing. The participants were offered a range of interpreted results, including Alzheimer's disease, type 2 diabetes, pharmacogenomics, rare disease-associated variants, and ancestry. They were also offered their raw data. Of the 35 participants at baseline, 29 (82.9%) completed the 6-month follow-up. In the quantitative surveys, test-related distress was low, although it was higher at 1-week than 6-month follow-up (Z=2.68, P=0.007). In the 6-month qualitative interviews, most participants felt happy or relieved about their results. A few were concerned, particularly about rare disease-associated variants and Alzheimer's disease results. Two of the 29 participants had sought clinical follow-up as a direct or indirect consequence of rare disease-associated variants results. Several had mentioned their results to their doctors. Some participants felt having their raw data might be medically useful to them in the future. The majority reported positive reactions to having their genomes sequenced, but there were notable exceptions to this. The impact and value of returning personal results from whole-genome sequencing when implemented on a larger scale remains to be seen.

A randomized trial Examining The Impact Of Communicating Genetic And Lifestyle Risks For Obesity

OBJECTIVE

Genetic testing for obesity is available directly to consumers, yet little is understood about its behavioral impact and its added value to nongenetic risk communication efforts based on lifestyle factors.

METHODS

A randomized trial examined the short-term impact of providing personalized obesity risk information, using a 2 × 2 factorial design. Participants were recruited from the Coriell Personalized Medicine Collaborative (CPMC) and randomized to receive (1) no risk information (control), (2) genetic risk, (3) lifestyle risk, or (4) combined genetic/lifestyle risks. Baseline and 3-month follow-up survey data were collected. Analyses examined the impact of risk feedback on intentions to lose weight and self-reported weight.

RESULTS

A total of 696 participants completed the study. A significant interaction effect was observed for genetic and lifestyle information on intent to lose weight (P = 0.0150). Those who received genetic risk alone had greater intentions at follow-up, compared with controls (P = 0.0034). The impact of receiving elevated risk information on intentions varied by source and combination of risks presented. Non-elevated genetic risk did not lower intentions. No group differences were observed for self-reported weight.

CONCLUSIONS

Genetic risk information for obesity may add value to lifestyle risk information depending on the context in which it is presented.

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.

Psychological and behavioral effects of genetic risk testing for obesity: a systematic review

BACKGROUND

Interest is growing in the use of genetic risk testing for lifestyle-related chronic diseases, including obesity, to promote health behavior change.

OBJECTIVE

A systematic review of the literature was conducted to determine the effects that genetic risk feedback for obesity may have on psychological and behavioral factors influencing weight.

METHODS

The MEDLINE/PubMed online database was searched using predefined search terms.

RESULTS

The studies revealed that risk feedback may increase motivation to improve health behaviors, especially among individuals at higher genetic risk. Overweight and obese individuals seemed to experience additional psychological benefits when provided an external explanation for their weight status.

CONCLUSION

While the psychological benefits are promising, the clinical utility of genetic risk testing for obesity remains uncertain.

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.

Changes in Physical Activity Following a Genetic-Based Internet-Delivered Personalized Intervention: Randomized Controlled Trial (Food4Me)

Background

There is evidence that physical activity (PA) can attenuate the influence of the fat mass- and obesity-associated (FTO) genotype on the risk to develop obesity. However, whether providing personalized information on FTO genotype leads to changes in PA is unknown.

Objective

The purpose of this study was to determine if disclosing FTO risk had an impact on change in PA following a 6-month intervention.

Methods

The single nucleotide polymorphism (SNP) rs9939609 in the FTO gene was genotyped in 1279 participants of the Food4Me study, a four-arm, Web-based randomized controlled trial (RCT) in 7 European countries on the effects of personalized advice on nutrition and PA. PA was measured objectively using a TracmorD accelerometer and was self-reported using the Baecke questionnaire at baseline and 6 months. Differences in baseline PA variables between risk (AA and AT genotypes) and nonrisk (TT genotype) carriers were tested using multiple linear regression. Impact of FTO risk disclosure on PA change at 6 months was assessed among participants with inadequate PA, by including an interaction term in the model: disclosure (yes/no) × FTO risk (yes/no).

Results

At baseline, data on PA were available for 874 and 405 participants with the risk and nonrisk FTO genotypes, respectively. There were no significant differences in objectively measured or self-reported baseline PA between risk and nonrisk carriers. A total of 807 (72.05%) of the participants out of 1120 in the personalized groups were encouraged to increase PA at baseline. Knowledge of FTO risk had no impact on PA in either risk or nonrisk carriers after the 6-month intervention. Attrition was higher in nonrisk participants for whom genotype was disclosed (P=.01) compared with their at-risk counterparts.

Conclusions

No association between baseline PA and FTO risk genotype was observed. There was no added benefit of disclosing FTO risk on changes in PA in this personalized intervention. Further RCT studies are warranted to confirm whether disclosure of nonrisk genetic test results has adverse effects on engagement in behavior change.

Trial Registration

ClinicalTrials.gov NCT01530139; http://clinicaltrials.gov/show/NCT01530139 (Archived by WebCite at: http://www.webcitation.org/6XII1QwHz)

NIH working group report-using genomic information to guide weight management: From universal to precision treatment

OBJECTIVE

Precision medicine utilizes genomic and other data to optimize and personalize treatment. Although more than 2,500 genetic tests are currently available, largely for extreme and/or rare phenotypes, the question remains whether this approach can be used for the treatment of common, complex conditions like obesity, inflammation, and insulin resistance, which underlie a host of metabolic diseases.

METHODS

This review, developed from a Trans-NIH Conference titled "Genes, Behaviors, and Response to Weight Loss Interventions," provides an overview of the state of genetic and genomic research in the area of weight change and identifies key areas for future research.

RESULTS

Although many loci have been identified that are associated with cross-sectional measures of obesity/body size, relatively little is known regarding the genes/loci that influence dynamic measures of weight change over time. Although successful short-term weight loss has been achieved using many different strategies, sustainable weight loss has proven elusive for many, and there are important gaps in our understanding of energy balance regulation.

CONCLUSIONS

Elucidating the molecular basis of variability in weight change has the potential to improve treatment outcomes and inform innovative approaches that can simultaneously take into account information from genomic and other sources in devising individualized treatment plans.

Appetitive traits as behavioural pathways in genetic susceptibility to obesity: a population-based cross-sectional study

The mechanisms through which genes influence body weight are not well understood, but appetite has been implicated as one mediating pathway. Here we use data from two independent population-based Finnish cohorts (4632 adults aged 25–74 years from the DILGOM study and 1231 twin individuals aged 21–26 years from the FinnTwin12 study) to investigate whether two appetitive traits mediate the associations between known obesity-related genetic variants and adiposity. The results from structural equation modelling indicate that the effects of a polygenic risk score (90 obesity-related loci) on measured body mass index and waist circumference are partly mediated through higher levels of uncontrolled eating (β_indirect= 0.030–0.032, P < 0.001 in DILGOM) and emotional eating (β_indirect= 0.020–0.022, P < 0.001 in DILGOM and β_indirect= 0.013–0.015, P = 0.043–0.044 in FinnTwin12). Our findings suggest that genetic predispositions to obesity may partly exert their effects through appetitive traits reflecting lack of control over eating or eating in response to negative emotions. Obesity prevention and treatment studies should examine the impact of targeting these eating behaviours, especially among individuals having a high genetic predisposition to obesity.