Personalized Risk Estimates Research Articles

Optimistic bias and the consistency of hurricane track forecasts

Forecast graphics depicting a hurricane track and uncertainty cone have become pervasive in the communication of hurricane risk. This study examined whether the effect of hurricane tracks on risk perception is influenced by the consistency and optimistic bias. Specifically, it focused on the differences between forecasts that remain consistent compared to those that veer away for a forecast period. To answer the research question, this study conducted online surveys in which respondents from two coastal universities were asked risk perception questions based on a series of forecast graphics. Other variables measured included dispositional optimism, general hurricane risk perception, and hurricane information use. Optimistic bias was calculated from two of the risk perception questions. Results did not indicate strong support for an influence of optimistic bias or changing forecast track on risk perception. There was limited evidence that a veering track scenario may lead to differences in risk judgments about another location, but most measures of personal risk estimation were not influenced by the track. Dispositional optimism was not related to optimistic bias or many of the risk perception variables tested, including general hurricane risk perception. There did appear to be an interaction between track scenario and optimistic bias with more relationships being significant among those who received the consistent track scenario.

Revisiting the discriminatory accuracy of traditional risk factors in preeclampsia screening.

BackgroundPreeclampsia (PE) is associated with a high risk of perinatal morbidity and mortality. However, there is no consensus in the definition of high-risk women.AimTo question current definition of high PE risk and propose a definition that considers individual heterogeneity to improves risk classification.MethodsA stratified analysis by parity was conducted using the Swedish Birth Register between 2002–2010 including 626.600 pregnancies. The discriminatory accuracy (DA) of traditional definitions of high-risk women was compared with a new definition based on 1) specific combinations of individual variables and 2) a centile cut-off of the probability of PE predicted by a multiple logistic regression model.ResultsNone of the classical risk-factors alone reached an acceptable DA. In multiparous, any combination of a risk-factor with previous PE or HBP reached a +LR>10. The combination of obesity and multiple pregnancy reached a good DA particularly in the presence of previous preeclampsia (positive likelihood ratio (LR+) = 26.5 or chronic hypertension (HBP) LR+ = 40.5. In primiparous, a LR+>15 was observed in multiple pregnancies with the simultaneous presence of obesity and diabetes mellitus or with HBP. Predicted probabilities above 97 centile in multiparous and 99 centile in primiparous provided high (LR+ = 12.5), and moderate (LR+ = 5.85), respectively. No one risk factor alone or in combination provided a LR- sufficiently low to rule-out the disease.ConclusionsIn preeclampsia prediction the combination of specific risk factors provided a better discriminatory accuracy than traditional single risk approach. Our results contribute to a more personalized risk estimation of preeclampsia.

Abstract IA22: Using risk assessment tools to motivate behavior change

Abstract Epidemiology identifies risk factors for cancer and other diseases based on the idea that conveying such information to healthcare providers, the general public, and policy makers will result in population-wide improvements in healthy behaviors and, consequently, population-wide improvements in health outcomes. These actions assume that the audience understands and uses the information to make health-related decisions. However, the language of epidemiology, which is steeped in probabilistic thinking, is not necessarily the language of the general public. Furthermore, growing evidence suggests that the language of epidemiology is not particularly well-understood by policy makers or even, disconcertingly, by some healthcare providers. This presentation will accomplish three objectives. First, it will demonstrate how a user-friendly, Internet-based, personalized risk assessment tool that uses established principles of risk communication and is grounded in health behavior change theories can increase motivation to change behavior. It will accomplish this in the context of using Your Disease Risk to inform women about the association between physical activity and breast cancer risk. Second, it will illustrate the development of a novel personalized risk assessment tool that increases understanding of the link between lifestyle behaviors and overall health and wellness. Specifically, it translates cumulative incidence data about five diseases that cause significant morbidity and mortality (i.e., colon cancer, breast cancer (women), heart disease, diabetes, and stroke) into a tool that conveys personalized risk estimates in a comprehensible and useful way for laypeople from diverse socio-demographic backgrounds. The premise is that illustrating how a single behavior can affect the likelihood of developing several diseases could foster a more coherent and meaningful picture of the behavior's importance in reducing health risks and could increase motivation to engage in the behavior. The behavioral context for this study is also physical activity. Third, this presentation discuss the limits of tools that convey only risk information and do not help users bridge the gap between wanting to change their behavior and having the knowledge, skills, and confidence to actually initiate and maintain such changes. Citation Format: Erika A. Waters. Using risk assessment tools to motivate behavior change. [abstract]. In: Proceedings of the AACR Special Conference: Improving Cancer Risk Prediction for Prevention and Early Detection; Nov 16-19, 2016; Orlando, FL. Philadelphia (PA): AACR; Cancer Epidemiol Biomarkers Prev 2017;26(5 Suppl):Abstract nr IA22.

Calibration drift in regression and machine learning models for acute kidney injury.

Predictive analytics create opportunities to incorporate personalized risk estimates into clinical decision support. Models must be well calibrated to support decision-making, yet calibration deteriorates over time. This study explored the influence of modeling methods on performance drift and connected observed drift with data shifts in the patient population. Using 2003 admissions to Department of Veterans Affairs hospitals nationwide, we developed 7 parallel models for hospital-acquired acute kidney injury using common regression and machine learning methods, validating each over 9 subsequent years. Discrimination was maintained for all models. Calibration declined as all models increasingly overpredicted risk. However, the random forest and neural network models maintained calibration across ranges of probability, capturing more admissions than did the regression models. The magnitude of overprediction increased over time for the regression models while remaining stable and small for the machine learning models. Changes in the rate of acute kidney injury were strongly linked to increasing overprediction, while changes in predictor-outcome associations corresponded with diverging patterns of calibration drift across methods. Efficient and effective updating protocols will be essential for maintaining accuracy of, user confidence in, and safety of personalized risk predictions to support decision-making. Model updating protocols should be tailored to account for variations in calibration drift across methods and respond to periods of rapid performance drift rather than be limited to regularly scheduled annual or biannual intervals.

Can Communicating Personalised Disease Risk Promote Healthy Behaviour Change? A Systematic Review of Systematic Reviews

BackgroundThe assessment and communication of disease risk that is personalised to the individual is widespread in healthcare contexts. Despite several systematic reviews of RCTs, it is unclear under what circumstances that personalised risk estimates promotes change in four key health-related behaviours: smoking, physical activity, diet and alcohol consumption.PurposeThe present research aims to systematically identify, evaluate and synthesise the findings of existing systematic reviews.MethodsThis systematic review of systematic reviews followed published guidance. A search of four databases and two-stage screening procedure with good reliability identified nine eligible systematic reviews.ResultsThe nine reviews each included between three and 15 primary studies, containing 36 unique studies. Methods of personalising risk feedback included imaging/visual feedback, genetic testing, and numerical estimation from risk algorithms. The reviews were generally high quality. For a broad range of methods of estimating and communicating risk, the reviews found no evidence that risk information had strong or consistent effects on health-related behaviours. The most promising effects came from interventions using visual or imaging techniques and with smoking cessation and dietary behaviour as outcomes, but with inconsistent results. Few interventions explicitly used theory, few targeted self-efficacy or response efficacy, and a limited range of Behaviour Change Techniques were used.ConclusionsPresenting risk information on its own, even when highly personalised, does not produce strong effects on health-related behaviours or changes which are sustained. Future research in this area should build on the existing knowledge base about increasing the effects of risk communication on behaviour.

PROACT: Iterative Design of a Patient-Centered Visualization for Effective Prostate Cancer Health Risk Communication.

Prostate cancer is the most common cancer among men in the US, and yet most cases represent localized cancer for which the optimal treatment is unclear. Accumulating evidence suggests that the available treatment options, including surgery and conservative treatment, result in a similar prognosis for most men with localized prostate cancer. However, approximately 90% of patients choose surgery over conservative treatment, despite the risk of severe side effects like erectile dysfunction and incontinence. Recent medical research suggests that a key reason is the lack of patient-centered tools that can effectively communicate personalized risk information and enable them to make better health decisions. In this paper, we report the iterative design process and results of developing the PROgnosis Assessment for Conservative Treatment (PROACT) tool, a personalized health risk communication tool for localized prostate cancer patients. PROACT utilizes two published clinical prediction models to communicate the patients' personalized risk estimates and compare treatment options. In collaboration with the Maine Medical Center, we conducted two rounds of evaluations with prostate cancer survivors and urologists to identify the design elements and narrative structure that effectively facilitate patient comprehension under emotional distress. Our results indicate that visualization can be an effective means to communicate complex risk information to patients with low numeracy and visual literacy. However, the visualizations need to be carefully chosen to balance readability with ease of comprehension. In addition, due to patients' charged emotional state, an intuitive narrative structure that considers the patients' information need is critical to aid the patients' comprehension of their risk information.

Alzheimer's biomarkers in daily practice (ABIDE) project: Rationale and design

IntroductionThe Alzheimer's biomarkers in daily practice (ABIDE) project is designed to translate knowledge on diagnostic tests (magnetic resonance imaging [MRI], cerebrospinal fluid [CSF], and amyloid positron emission tomography [PET]) to daily clinical practice with a focus on mild cognitive impairment (MCI) MethodsABIDE is a 3-year project with a multifaceted design and is structured into interconnected substudies using both quantitative and qualitative research methods. ResultsBased on retrospective data, we develop personalized risk estimates for MCI patients. Prospectively, we collect MRI and CSF data from 200 patients from local memory clinics and amyloid PET from 500 patients in a tertiary setting, to optimize application of these tests in daily practice. Furthermore, ABIDE will develop strategies for optimal patient-clinician conversations. DiscussionUltimately, this will result in a set of practical tools for clinicians to support the choice of diagnostic tests and facilitate the interpretation and communication of their results.

Predictors of Breast Cancer Worry in a Hispanic and Predominantly Immigrant Mammography Screening Population

ObjectiveWorry about developing breast cancer (BC) has been associated with participation in screening and genetic testing and with follow-up of abnormal screening results. Little is known about the scope and predictors of BC worry in Hispanic and immigrant populations. MethodsWe collected in-person interview data from 250 self-identified Hispanic women recruited from an urban mammography facility (average age 50.4 years; 82% foreign-born). Women reported whether they worried about developing breast cancer rarely/never (low worry), sometimes (moderate worry), or often/all the time (high worry). We examined whether sociocultural and psychological factors (e.g., acculturation, education, perceived risk), and risk factors and objective risk for BC (e.g., family history, Gail model 5-year risk estimates, parity) predicted BC worry using multinomial and logistic regression. ResultsIn multivariable models, women who perceived higher absolute BC risk (odds ratio, 1.66 [95% confidence interval, 1.28–2.14] for a one-unit increase in perceived lifetime risk) and comparative BC risk (e.g., odds ratio, 2.73, 95% confidence interval, 1.23–6.06) were more likely to report high BC worry than moderate or low BC worry. There were no associations between BC worry and indicators of objective risk or acculturation. ConclusionsIn Hispanic women undergoing screening mammography, higher perceptions of BC risk, in both absolute and comparative terms, were associated independently with high BC worry, and were stronger predictors of BC worry than indicators of objective BC risk, including family history, mammographic density, and personal BC risk estimates.

Factors Associated with a Positive Baseline Screening Exam Result in the National Lung Screening Trial.

Lung cancer screening with low-dose computed tomography (LDCT) has been shown to decrease mortality in eligible high-risk patients. However, this mortality benefit comes with a high rate of false-positive findings, which require further evaluation. To identify patient- and center-specific factors associated with having a pulmonary nodule on baseline LDCT, and to develop a prediction rule to help in shared decision making. We identified individuals who underwent baseline LDCT screening as part of the National Lung Screening Trial. A positive screen was defined as a nodule 4 mm or greater in largest dimension. Using multiple logistic regression, we identified variables independently associated with having a positive screen. Among the 26,004 patients with complete data who underwent baseline LDCT, 7,123 patients (27%) had a positive screen. In a multivariate analysis, older age (odds ratio [OR] = 1.03 per 1-year increase, 95% confidence interval [CI] = 1.03-1.04), female sex (OR = 1.08, 95% CI = 1.01-1.14), white race (OR = 1.39, 95% CI = 1.25-1.55), heavier smoking history (OR = 1.02 per 5 pack-years smoked over 30, 95% CI = 1.00-1.04), history of chronic obstructive pulmonary disease (OR = 1.08, 95% CI = 1.01-1.17), being married (OR = 1.08, 95% CI = 1.02-1.15), hard rock mining (OR = 1.40, 95% CI = 1.04-1.89), and farm work (OR = 1.13, 95% CI = 1.03-1.23) were independently associated with having a positive screen, whereas having a college degree (OR = 0.94, 95% CI = 0.86-1.00) and abstinence from smoking (OR = 0.98 per year, 95% CI = 0.98-0.99) were associated with not having a positive screen. Patients enrolled at a site in an area highly endemic for histoplasma were 30% more likely to have a positive baseline LDCT screen (OR = 1.30, 95% CI = 1.21-1.40). The area under the receiver operator characteristic curve for the full model was 0.57 (0.56-0.58); including enrollment center as a random effect increased the area under the receiver operator characteristic curve to 0.65. In the National Lung Screening Trial, both patient- and center-specific factors were associated with having a positive baseline screen. Although the model does not have sufficient accuracy to provide personalized risk estimates to guide shared decision making on an individual basis, it can nonetheless inform screening centers of the likelihood of further follow-up testing for their populations at large when allocating resources. Data collected from centers as broad-based screening is implemented can be used to improve model accuracy further.

A pessimistic view of optimistic belief updating

Received academic wisdom holds that human judgment is characterized by unrealistic optimism, the tendency to underestimate the likelihood of negative events and overestimate the likelihood of positive events. With recent questions being raised over the degree to which the majority of this research genuinely demonstrates optimism, attention to possible mechanisms generating such a bias becomes ever more important. New studies have now claimed that unrealistic optimism emerges as a result of biased belief updating with distinctive neural correlates in the brain. On a behavioral level, these studies suggest that, for negative events, desirable information is incorporated into personal risk estimates to a greater degree than undesirable information (resulting in a more optimistic outlook). However, using task analyses, simulations, and experiments we demonstrate that this pattern of results is a statistical artifact. In contrast with previous work, we examined participants’ use of new information with reference to the normative, Bayesian standard. Simulations reveal the fundamental difficulties that would need to be overcome by any robust test of optimistic updating. No such test presently exists, so that the best one can presently do is perform analyses with a number of techniques, all of which have important weaknesses. Applying these analyses to five experiments shows no evidence of optimistic updating. These results clarify the difficulties involved in studying human ‘bias’ and cast additional doubt over the status of optimism as a fundamental characteristic of healthy cognition.

Abstract 2598: Predicting breast and ovarian cancer risks for BRCA1 and BRCA2 mutation carriers using polygenic risk scores

Abstract Women who carry a pathogenic mutation in the BRCA1 or BRCA2 gene are at high risk of breast (BC) and ovarian cancer (OC). Their clinical management usually includes invasive risk-reducing interventions with substantial side effects. Improved personalized cancer risk estimates may help to identify women at particularly high risk or with high risk of disease at early ages who may benefit from early intervention as well as women at lower risk who may opt to delay surgery or chemoprevention. Genome-wide association studies have identified >100 common genetic variants that are associated with BC or OC risks. Several of these variants are also individually associated with risk of BC or OC for BRCA1 and BRCA2 mutation carriers. However, no study has evaluated the combined effects of all the known common genetic variants on BC or OC risk for BRCA1/2 mutation carriers. We constructed polygenic risk scores (PRS) based on results of genetic association studies conducted in the general population. Each PRS was formed by the sum of the number of risk alleles across the variants weighted by their log-Odds Ratio estimate from population-based studies of BC or OC. We investigated 3 PRS for BC (overall, estrogen receptor (ER) positive, and ER-negative) and one PRS for OC. We used data for 15,252 BRCA1 and 8,211 BRCA2 female carriers. The association of each PRS with BC or OC risk was evaluated using a weighted cohort analysis with time to diagnosis as the outcome and estimated the Hazard Ratios (HR) per standard deviation increase in the PRS. All PRS were significantly associated with cancer risks for BRCA1/2 carriers. The PRS for ER-negative BC displayed the strongest association with BC risk in BRCA1 carriers (HR = 1.29 [1.25-1.33], p = 8×10−64). In BRCA2 carriers, the strongest association was seen for the overall BC PRS (HR = 1.26 [1.21-1.31], p = 3×10−27). The OC PRS was strongly associated with OC risk for both BRCA1 and BRCA2 carriers. These relative risks translate to large differences in absolute risks for carriers: e.g., the OC risk was 6% by age 80 for BRCA2 carriers at the 10th percentile of the OC PRS compared with 19% risk for those at the 90th percentile of PRS. Our findings demonstrate that BC and OC PRS derived from studies in the general population are predictive of cancer risks in BRCA1 and BRCA2 carriers. Incorporation of the PRS into risk prediction models would improve risk prediction and hence inform decisions on cancer risk management. Citation Format: Karoline Kuchenbaecker, Jacques Simard, Kenneth Offit, Fergus J. Couch, Douglas F. Easton, Georgia Chenevix-Trench, Antonis C. Antoniou, Consortium of Investigators of Modifiers of BRCA1/2. Predicting breast and ovarian cancer risks for BRCA1 and BRCA2 mutation carriers using polygenic risk scores. [abstract]. In: Proceedings of the 107th Annual Meeting of the American Association for Cancer Research; 2016 Apr 16-20; New Orleans, LA. Philadelphia (PA): AACR; Cancer Res 2016;76(14 Suppl):Abstract nr 2598.

Counterpoint: Low-Density Lipoprotein Cholesterol Targets Are Not Needed in Lipid Treatment Guidelines.

On the basis of accumulating evidence, low-density lipoprotein cholesterol (LDL-C) treat-to-goal approaches no longer seem to be the best way to optimize lipid-modifying therapy to prevent atherosclerotic cardiovascular disease (ASCVD). The potential for a net ASCVD risk reduction benefit is a more individualized approach to clinical decision making and may better inform patient preferences. However, risk estimation tools will need to be developed to facilitate more personalized CVD risk estimation in statin-treated patients. In the meantime, LDL-C thresholds rather than targets may aid in determining which patients might benefit from additional LDL-C-lowering therapy beyond statins.

The readability of online breast cancer risk assessment tools.

Numerous breast cancer risk assessment tools that allow users to input personal risk information and obtain a personalized breast cancer risk estimate are available on the Internet. The goal of these tools is to increase screening awareness and identify modifiable health behaviors; however, the utility of this risk information is limited by the readability of the material. We undertook this study to assess the overall readability of breast cancer risk assessment tools and accompanying information, as well as to identify areas of suggested improvement. We searched for breast cancer risk assessment tools, using five search terms, on three search engines. All searches were performed on June 12, 2014. Sites that met inclusion criteria were then assessed for readability using the suitability assessment of materials (SAM) and the SMOG readability formula (July 1, 2014–January 31, 2015). The primary outcomes are the frequency distribution of overall SAM readability category (superior, adequate, or not suitable) and mean SMOG reading grade level. The search returned 42 sites were eligible for assessment, only 9 (21.4 %) of which achieved an overall SAM superior rating, and 27 (64.3 %) were deemed adequate. The average SMOG reading grade level was grade 12.1 (SD 1.6, range 9–15). The readability of breast cancer risk assessment tools and the sites that host them is an important barrier to risk communication. This study demonstrates that most breast cancer risk assessment tools are not accessible to individuals with limited health literacy skills. More importantly, this study identifies potential areas of improvement and has the potential to heighten a physician’s awareness of the Internet resources a patient might navigate in their quest for breast cancer risk information.Electronic supplementary materialThe online version of this article (doi:10.1007/s10549-015-3601-2) contains supplementary material, which is available to authorized users.

Thinking Concretely Increases the Perceived Likelihood of Risks: The Effect of Construal Level on Risk Estimation.

Recent findings on construal level theory (CLT) suggest that abstract thinking leads to a lower estimated probability of an event occurring compared to concrete thinking. We applied this idea to the risk context and explored the influence of construal level (CL) on the overestimation of small and underestimation of large probabilities for risk estimates concerning a vague target person (Study 1 and Study 3) and personal risk estimates (Study 2). We were specifically interested in whether the often-found overestimation of small probabilities could be reduced with abstract thinking, and the often-found underestimation of large probabilities was reduced with concrete thinking. The results showed that CL influenced risk estimates. In particular, a concrete mindset led to higher risk estimates compared to an abstract mindset for several adverse events, including events with small and large probabilities. This suggests that CL manipulation can indeed be used for improving the accuracy of lay people's estimates of small and large probabilities. Moreover, the results suggest that professional risk managers' risk estimates of common events (thus with a relatively high probability) could be improved by adopting a concrete mindset. However, the abstract manipulation did not lead managers to estimate extremely unlikely events more accurately. Potential reasons for different CL manipulation effects on risk estimates' accuracy between lay people and risk managers are discussed.

Bayesian Assessment of Genetic Risk in Families with a Balanced Translocation.

An important problem from the field of genetics involves the calculation of a personalized risk estimate on behalf of a heterozygous carrier of a balanced translocation. Though phenotypically normal, the carrier may be at increased risk of having a child who is mentally and physically abnormal due to an unbalanced translocation of chromosomal segments. An accurate estimate of the probability of this event is understandably desirable. Unfortunately, translocations are almost always family-specific so there is very little data that are perfectly relevant and one has to rely heavily on general risk figures derived from studies of families with similar translocations. This makes the problem particularly well suited to Bayesian analysis, which coherently combines family-specific data and a priori knowledge. However, much of the genetics counseling literature recommends an either/or approach: if the family is large enough, use family data; else, use pooled population data. In this article, we describe how uncertainty can be significantly reduced by incorporating all available information in the context of deriving a risk estimate for a hypothetical familial translocation.

Association Between ESR1 PvuII, XbaI, and P325P Polymorphisms and Breast Cancer Susceptibility: A Meta-Analysis.

BackgroundBreast cancer is one of the leading causes of cancer-related deaths for women. Numerous studies have shown that single-nucleotide polymorphisms (SNPs) on the ESR1 gene are associated to this disease. However, data and conclusions are inconsistent and controversial.Material/MethodsTo investigate the association between PvuII (rs2234693), XbaI (rs9340799) and P325P (rs1801132) polymorphisms of ESR1 gene with the risk of breast cancer under different population categorizations, we searched multiple databases for data collection, and performed the meta-analysis on a total of 25 case-control studies. Three different comparison models – dominant model, recessive model, and homozygote comparison model – were applied to evaluate the association.ResultsOur results indicated that people with TT+TC or TT genotype were at a greater risk of developing breast cancer than those with CC genotype in the PvuII polymorphism. While for XbaI and P325P polymorphisms, no significance was found using any of the 3 models. Furthermore, the data were also stratified into different subgroups according to the ethnicity (white or Asian) and source of controls (hospital-based or population-based), and separate analyses were conducted to assess the association. The ethnicity subgroup assessment showed that the higher risk of breast cancer for TT genotype of PvuII polymorphism than CC genotype only occurred in Asian people, but not in white populations. For the source-stratified subgroup analysis, significant association suggested that people with TT + TC genotype were at a greater risk of developing breast cancer than those with CC genotype in the hospital-based subgroup.ConclusionsThus, this meta-analysis clarified the inconsistent conclusions from previous studies, conducted analyses for the entire population as well as for different subgroups using diverse population categorization strategies, and has the potential to help provide a personalized risk estimate for breast cancer susceptibility.

Polymorphisms of genes involved in lipid metabolism and risk of chronic kidney disease in Japanese - cross-sectional data from the J-MICC study.

BackgroundChronic kidney disease (CKD) is known to be one of the causes of cardiovascular disease and end-stage renal disease. Among the several treatable risk factors of CKD, that of dyslipidemia is relatively controversial. To clarify the association of polymorphisms in genes involved in lipid metabolism with the risk of CKD in the Japanese population, we used cross-sectional data from the Japan Multi-Institutional Collaborative Cohort (J-MICC) Study.MethodsA total of 3,268 men and women, aged 35–69 years, were selected from J-MICC Study participants for inclusion in this study. Twenty-eight candidate single nucleotide polymorphisms (SNPs) were selected in 17 genes associated with the risk of lipid metabolism disorders, and genotyping of the subjects was conducted using the multiplex PCR-based invader assay. The prevalence of CKD was determined for stages 3–5 (defined as estimated glomerular filtration rate <60 ml/min/1.73 m2).ResultsLogistic regression analysis revealed that SNPs APOA5 T − 1131C (rs662799), APOA5 T1259C (rs2266788), TOMM40 A/G (rs157580), and CETP TaqIB (rs708272) were significantly associated with CKD risk in those individuals genotyped, with age- and sex-adjusted odds ratios (ORs) per minor allele (and 95% confidence intervals (CIs)) of OR 1.22 (95% CI: 1.06–1.39), 1.19 (1.03–1.37), 1.27 (1.12–1.45), and 0.81 (0.71–0.92), respectively. Analysis of the gene–environment interaction revealed that body mass index (BMI) was a significant effect modifier for APOA5 T − 1131C (rs662799) and a marginally significant effect modifier for APOA5 T/C (rs2266788), with the interaction between BMI ≥30 and individuals with at least one minor allele of each genotype of OR 10.43 (95% CI: 1.29–84.19) and 3.36 (0.87–13.01), respectively.ConclusionsFour polymorphisms in APOA5, TOMM40, and CETP were shown to be significantly associated with CKD risk, and a significant interaction between the two APOA5 SNPs and BMI on CKD risk was also demonstrated. This suggests the future possibility of personalized risk estimation for this life-limiting disease.Electronic supplementary materialThe online version of this article (doi:10.1186/1476-511X-13-162) contains supplementary material, which is available to authorized users.

From 'Image Gently' to image intelligently: a personalized perspective on diagnostic radiation risk.

The risk of ionizing radiation from diagnostic imaging has been a popular topic in the radiology literature and lay press. Communicating the magnitude of risk to patients and caregivers is problematic because of the uncertainty in estimates derived principally from epidemiological studies of large populations, and alternative approaches are needed to provide a scientific basis for personalized risk estimates. The underlying patient disease and life expectancy greatly influence risk projections. Research into the biological mechanisms of radiation-induced DNA damage and repair challenges the linear no-threshold dose-response assumption and reveals that individuals vary in sensitivity to radiation. Studies of decision-making psychology show that individuals are highly susceptible to irrational biases when judging risks. Truly informed medical decision-making that respects patient autonomy requires appropriate framing of radiation risks in perspective with other risks and with the benefits of imaging. To follow the principles of personalized medicine and treat patients according to their specific phenotypic and personality profiles, diagnostic imaging should optimally be tailored not only to patient size, body region and clinical indication, but also to underlying disease conditions, radio-sensitivity and risk perception and preferences that vary among individuals.

Common variants modify the age of onset for basal cell carcinomas in Gorlin syndrome.

Gorlin syndrome is an autosomal dominant disorder, characterized by multiple early-onset basal cell carcinomas (BCCs) and jaw keratocysts. Through association studies in cohorts of sporadic BCC, nine genetic variants have previously been identified to increase the risk of BCC. The nine SNPs were genotyped by Taqman allelic discrimination in 125 individuals with Gorlin syndrome. Kaplan-Meier survival curves and Cox proportional-Hazard regression analysis were applied to determine the association between genotypes and age of first BCC in individuals with Gorlin syndrome. The p.(Arg151Cys) variant in MC1R (rs1805007) was associated with an earlier median age of onset of BCC of 27 years (95% CI: 20-34) compared with 34 years (95% CI: 30-40) for wild-type individuals (hazard ratio (HR)=1.64, 95% CI: 1.04-2.58, P=0.034). The risk allele of the variant at the chromosome 5p15 locus encompassing TERT-CLPTM1L (rs401681) was also associated with an earlier median onset of BCC, 31 years (95% CI: 28-37) compared with 41 years (95% CI: 32-48, HR=1.44, 95% CI: 1.08-1.93, P=0.014). In individuals with a risk allele at either rs1805007 or rs401681 the median time to BCC was 31 years of age (95% CI: 28-34) compared with 44 years of age (95% CI: 38-53) in wild-type individuals (HR=2.48, 95% CI: 1.47-4.17, P=0.0002). Our findings may have implications for future personalized risk estimates and BCC screening strategies in individuals with Gorlin syndrome.

Implementation and utilization of genetic testing in personalized medicine.

Clinical genetic testing began over 30 years ago with the availability of mutation detection for sickle cell disease diagnosis. Since then, the field has dramatically transformed to include gene sequencing, high-throughput targeted genotyping, prenatal mutation detection, preimplantation genetic diagnosis, population-based carrier screening, and now genome-wide analyses using microarrays and next-generation sequencing. Despite these significant advances in molecular technologies and testing capabilities, clinical genetics laboratories historically have been centered on mutation detection for Mendelian disorders. However, the ongoing identification of deoxyribonucleic acid (DNA) sequence variants associated with common diseases prompted the availability of testing for personal disease risk estimation, and created commercial opportunities for direct-to-consumer genetic testing companies that assay these variants. This germline genetic risk, in conjunction with other clinical, family, and demographic variables, are the key components of the personalized medicine paradigm, which aims to apply personal genomic and other relevant data into a patient’s clinical assessment to more precisely guide medical management. However, genetic testing for disease risk estimation is an ongoing topic of debate, largely due to inconsistencies in the results, concerns over clinical validity and utility, and the variable mode of delivery when returning genetic results to patients in the absence of traditional counseling. A related class of genetic testing with analogous issues of clinical utility and acceptance is pharmacogenetic testing, which interrogates sequence variants implicated in interindividual drug response variability. Although clinical pharmacogenetic testing has not previously been widely adopted, advances in rapid turnaround time genetic testing technology and the recent implementation of preemptive genotyping programs at selected medical centers suggest that personalized medicine through pharmacogenetics is now a reality. This review aims to summarize the current state of implementing genetic testing for personalized medicine, with an emphasis on clinical pharmacogenetic testing.