Lifestyle Risk Score Research Articles

Utility of the triglycerides/HDL cholesterol ratio in older adult patients in Colombia

Abstract Introduction Alterations in lipid metabolism are responsible for atherogenesis and an increased risk of atherosclerotic cardiovascular disease. The triglycerides to high-density lipoprotein cholesterol ratio (TG/HDL-C) is used to assess the risk of cardiovascular diseases; however, it has not been evaluated in older adults. The aim of the present study is to evaluate the TG/HDL-C ratio in elderly patients and analyze its correlation and discriminatory capacity with cardiovascular risk factors according to data from the SABE Colombia survey. Purpose Describe the sociodemographic characteristics, cardiovascular history, lifestyle, and cardiovascular risk score in the older adult population included in the SABE Colombia Study.Describes the distribution of triglyceride levels, cholesterol, and the TAG-HDL ratio in the older adult population included in the SABE Colombia Study. Evaluate the relationship between the TAG/HDL Cholesterol Index and cardiovascular risk factors such as hypertension, prediabetes, type 2 diabetes mellitus, cardiovascular disease, cerebrovascular disease Determine if there is a correlation and the degree of correlation between the TAG/HDL Cholesterol ratio and variables related to higher cardiovascular risk and stablish cutoff points of the triglyceride to HDL cholesterol ratio as indicators of the onset of risk factors Materials and Methods Cross-sectional study in adults aged 60 years and older included in the national SABE Colombia survey (National Survey of Health, Well-being, and Ageing conducted in Colombia in 2015), which included 244 municipalities from all departments of the country. A correlation test was performed to assess the presence and degree of correlation between the TAG/HDL index and cardiovascular risk factors, and ROC curve analysis was conducted to evaluate the performance of the TAG/HDL index in identifying risk factors. Results A total of 23,694 patients with the necessary information for the study were included. The median age was 69 years, 57.3% were women, and 34.3% were of white race. 23.5% had hypertension, and 16.4% had diabetes mellitus. 13.4% had a history of cardiovascular disease, and 2.5% had cerebrovascular disease. While the specific correlation between the index and different cardiovascular risk factors is generally low to moderate, good discriminatory capacity was found for diabetes and metabolic syndrome with the following values: diabetes, with an area under the curve (AUC) of 0.871 and a cut-off point of 9.17; and for metabolic syndrome, the AUC was 0.840, with a cut-off point of 8.81. Conclusions Our findings show that the TG/HDL ratio has intermediate correlation for different cardiovascular risk factors; however, it has excellent predictive ability for the presence of diabetes and metabolic syndrome. These findings are very useful in the clinical evaluation, screening, and detection of cardiovascular risk factors in the population aged 60 years and older in Colombia.Scatter plots of TAG/HDL ratio comparedROC curves for diabetes and metabolic s

Prevalence of unhealthy behaviors and their associations with non-suicidal self-injury, suicidal ideation and suicide attempt among Chinese adolescents

BackgroundUnhealthy lifestyle behaviors among adolescents have emerged as a significant public health concern worldwide, however, there is little investigation on the impact of unhealthy behaviors on non-suicidal self-injury (NSSI), suicidal ideation (SI) and suicide attempt (SA). This study aimed to investigate the prevalence of seven unhealthy behaviors as well as their associations with NSSI, SI and SA, and to explore whether the aforementioned associations differ across sex.MethodsA total of 74,152 adolescents were included in this study via a multi-stage, stratified cluster, random sampling method in 2021. Information about unhealthy behaviors (insufficient physical activity, current smoking, current drinking, excessive screen time, long homework time, insufficient sleep and unhealthy BMI), NSSI, SI, SA and other demographics was collected. Sampling weights were used to estimate the prevalence, and the weighted logistic regression models were performed. Stratified analyses by sex and sensitive analyses were conducted.ResultsOverview, the weighted prevalence of adolescents had more than five unhealthy behaviors were 5.2%, with boys showing a higher prevalence than girls (6.5% vs.3.8%). Current smoking, current drinking, excessive screen use, long homework time, insufficient sleep, and unhealthy BMI were significantly associated with NSSI, SI and SA. Moreover, adolescents with high lifestyle risk scores were associated with an increased risk of NSSI (5–7 vs. 0: OR 6.38, 95% CI 5.24–7.77), SI (5–7 vs. 0: OR 7.67, 95% CI 6.35–9.25), and SA (5–7 vs. 0: OR 9.57, 95% CI 6.95–13.17). Significant sex differences were found in the associations of unhealthy behaviors with NSSI, SI and SA.ConclusionUnhealthy behaviors are quite common among Chinese adolescents. Adolescents with multiple unhealthy behaviors are associated with increased risks of NSSI, SI, and SA. The implementation of school and family-based interventions to promote healthy lifestyles is recommended as a preventive measure against self-injurious behavior and suicidality in adolescents.

Associations of Lifestyle and Genetic Risks with Obesity and Related Chronic Diseases in the UK Biobank: A Prospective Cohort Study

BackgroundInterplay between lifestyle risk scores (LRSs) and genetic risk scores (GRSs) on obesity and related chronic diseases are underinvestigated and necessary for understanding obesity causes and developing prevention strategies. ObjectivesThis study aimed to investigate independent and joint associations and interactions of LRS and GRS with obesity prevalence and risks of diabetes, cardiovascular disease (CVD), and obesity-related cancer. MethodsIn this cohort study of 444,957 UK Biobank participants [age: 56.5 ± 8.1 y; BMI (in kg/m2): 27.4 ± 4.7], LRS included physical activity, dietary score, sedentary behavior, sleep duration, and smoking (range: 0–20, each factor had 5 levels). GRS was calculated based on 941 genetic variants related to BMI. Both scores were categorized into quintiles. Obesity (n = 106,301) was defined as baseline BMI ≥30. Incident diabetes (n = 16,311), CVD (n = 18,076), and obesity-related cancer (n = 17,325) were ascertained through linkage to registries over a median of 12-y follow-up. ResultsThe LRS and GRS were independently positively associated with all outcomes. Additive interactions of LRS and GRS were observed for all outcomes (P < 0.021). Comparing the top with bottom LRS quintile, prevalence differences (95% CIs) for obesity were 17.8% (15.9%, 19.7%) in the top GRS quintile and 10.7% (8.3%, 13.1%) in the bottom GRS quintile; for diabetes, CVD, and obesity-related cancer, incidence rate differences associated with per SD increase in LRS were greater in the top than that in the bottom GRS quintile. Participants from top quintiles of both LRS and GRS had 6.16-fold, 3.81-fold, 1.56-fold, and 1.44-fold higher odds/risks of obesity, diabetes, CVD, and obesity-related cancer, respectively, than those from bottom quintiles of both scores. ConclusionsHigher LRS was associated with higher obesity prevalence and risks of related chronic diseases regardless of GRS, highlighting the broad benefits of healthy lifestyles. Additive gene–lifestyle interactions emphasize the public health importance of lifestyle interventions among people with high genetic risks.

Circulatory Metabolite Ratios as Indicators of Lifestyle Risk Factors Based on a Greek NAFLD Case-Control Study.

An ensemble of confounding factors, such as an unhealthy diet, obesity, physical inactivity, and smoking, have been linked to a lifestyle that increases one's susceptibility to chronic diseases and early mortality. The circulatory metabolome may provide a rational means of pinpointing the advent of metabolite variations that reflect an adherence to a lifestyle and are associated with the occurrence of chronic diseases. Data related to four major modifiable lifestyle factors, including adherence to the Mediterranean diet (estimated on MedDietScore), body mass index (BMI), smoking, and physical activity level (PAL), were used to create the lifestyle risk score (LS). The LS was further categorized into four groups, where a higher score group indicates a less healthy lifestyle. Drawing on this, we analyzed 223 NMR serum spectra, 89 MASLD patients and 134 controls; these were coupled to chemometrics to identify "key" features and understand the biological processes involved in specific lifestyles. The unsupervised analysis verified that lifestyle was the factor influencing the samples' differentiation, while the supervised analysis highlighted metabolic signatures. Τhe metabolic ratios of alanine/formic acid and leucine/formic acid, with AUROC > 0.8, may constitute discriminant indexes of lifestyle. On these grounds, this research contributed to understanding the impact of lifestyle on the circulatory metabolome and highlighted "prudent lifestyle" biomarkers.

Clustering of lifestyle risk factors in relation to suicidal thoughts and behaviors in young adolescents: a cross-national study of 45 low- and middle-income countries

BackgroundPrior research has reaffirmed lifestyle risk behaviors to cluster among adolescents. However, the lifestyle cluster effect on suicidal thoughts and behaviors (STBs) was unclear among adolescents in low- and middle-income countries (LMICs). No comparison of such associations was conducted across nations.MethodsData from 45 LMICs were obtained from the Global School-based Student Health Survey (GSHS) between 2009 and 2019. Lifestyle behavior factors were collected through a structured questionnaire. Suicidal ideation, plan, and attempt were ascertained by three single-item questions. Lifestyle risk scores were calculated via a sufficient dimension reduction technique, and lifestyle risk clusters were constructed using a latent class analysis. Generalized linear mixed models with odds ratio (OR) and 95% confidence interval (CI) were used to estimate the lifestyle-STB associations.ResultsA total of 229,041 adolescents were included in the final analysis. The weighted prevalence of suicidal ideation, plan, and attempt was 7.37%, 5.81%, and 4.59%, respectively. Compared with the favorable lifestyle group, the unfavorable group had 1.48-, 1.53-, and 3.11-fold greater odds of suicidal ideation (OR = 1.48, 95%CI: 1.30–1.69), plan (OR = 1.53, 95%CI 1.34–1.75), and attempt (OR = 3.11, 95%CI 2.64–3.65). Four clusters of lifestyle risk behaviors were identified, namely healthy lifestyles (H–L), insufficient intake of vegetables and fruit (V-F), frequent consumption of soft drinks and fast food (D-F), and tobacco smoking and alcohol drinking (S-A) clusters. Compared with H–L cluster, V-F cluster was associated with 43% and 42% higher odds of suicidal ideation and plan, followed by S-A cluster (26% for ideation and 20% for plan), but not significant in D-F cluster (P > 0.05). D-F cluster was associated with 2.85-fold increased odds of suicidal attempt, followed by V-F cluster (2.43-fold) and S-A cluster (1.18-fold).ConclusionsClustering of lifestyle risk behaviors is informative for risk stratification of STBs in resource-poor settings. Lifestyle-oriented suicide prevention efforts should be initiated among school-attending adolescents in LMICs.

Metabolites, Healthy Lifestyle, and Polygenic Risk Score Associated with Upper Gastrointestinal Cancer: Findings from the UK Biobank Study.

Previous metabolomics studies have highlighted the predictive value of metabolites on upper gastrointestinal (UGI) cancer, while most of them ignored the potential effects of lifestyle and genetic risk on plasma metabolites. This study aimed to evaluate the role of lifestyle and genetic risk in the metabolic mechanism of UGI cancer. Differential metabolites of UGI cancer were identified using partial least-squares discriminant analysis and the Wilcoxon test. Then, we calculated the healthy lifestyle index (HLI) score and polygenic risk score (PRS) and divided them into three groups, respectively. A total of 15 metabolites were identified as UGI-cancer-related differential metabolites. The metabolite model (AUC = 0.699) exhibited superior discrimination ability compared to those of the HLI model (AUC = 0.615) and the PRS model (AUC = 0.593). Moreover, subgroup analysis revealed that the metabolite model showed higher discrimination ability for individuals with unhealthy lifestyles compared to that with healthy individuals (AUC = 0.783 vs 0.684). Furthermore, in the genetic risk subgroup analysis, individuals with a genetic predisposition to UGI cancer exhibited the best discriminative performance in the metabolite model (AUC = 0.770). These findings demonstrated the clinical significance of metabolic biomarkers in UGI cancer discrimination, especially in individuals with unhealthy lifestyles and a high genetic risk.

The usability and reliability of a smartphone application for monitoring future dementia risk in ageing UK adults.

The rising number of dementia diagnoses and imminent adoption of disease-modifying treatments necessitate innovative approaches to identify individuals at risk, monitor disease course and intervene non-pharmacologically earlier in the disease course. Digital assessments of dementia risk and cognitive function have the potential to outperform traditional in-person assessments in terms of their affordability, accuracy and longitudinal tracking abilities. However, their accessibility and reliability in older adults is unclear. To evaluate the usability and reliability of a smartphone assessment of lifestyle and cognitive factors relevant to dementia risk in a group of UK-based older adults. Cognitively healthy adults (n = 756) recruited through the Dementias Platform UK Great Minds volunteer register completed three assessments of cognitive function and dementia risk over a 3-month period and provided usability feedback on the Five Lives smartphone application (app). We evaluated cognitive test scores for age, gender and higher education effects, normality distributions, test-retest reliability and their relationship with participants' lifestyle dementia risk factors. Participants found the app 'easy to use', 'quick to complete' and 'enjoyable'. The cognitive tests showed normal or near-to-normal distributions, variable test-retest reliabilities and age-related effects. Only tests of verbal ability showed gender and education effects. The cognitive tests did not correlate with lifestyle dementia risk scores. The Five Lives assessment demonstrates high usability and reliability among older adults. These findings highlight the potential of digital assessments in dementia research and clinical practice, enabling improved accessibility and better monitoring of cognitive health on a larger scale than traditional in-person assessments.

Interaction between parental myopia and children lifestyle on the incidence of myopia among children aged 6–18 years: a cross-sectional study in Tianjin, China

ObjectivesThis study aimed to explore the influence of the interaction between parental myopia and lifestyle on myopia among school-age children.DesignCross-sectional study.SettingThis study used data from the Tianjin Child and Adolescent...

Healthy lifestyle, lipoprotein (a) levels and the risk of coronary artery disease.

Lipoprotein (a) [Lp(a)] is associated with coronary artery disease (CAD). However, the role of healthy lifestyle against the risk of CAD with consideration of high Lp(a) levels remains unclear. This study examined 4512 participants who underwent serum Lp(a) level assessment at Kanazawa University Hospital from 2008 to March 2016. Their lifestyle habits were examined based on four questionnaires regarding dietary pattern, exercise habits, smoking status and body weight. Logistic regression analyses were performed to identify the association between healthy lifestyle and CAD independent of Lp(a) levels. The Lp(a) levels were significantly associated with CAD (odds ratio [OR]: 1.12, 95% confidence interval [CI]: 1.08-1.17, p = 1.3 × 10-7 per 10 mg/dL). Under these circumstances, the lifestyle risk score was also significantly associated with CAD (OR: 1.24, 95% CI: 1.12-1.36, p = 2.4 × 10-8 ). Compared with patients with a favourable lifestyle who have Lp(a) levels of <30 mg/dL, those with an intermediate or unfavourable lifestyle were at higher risk for CAD (OR: 1.11, 95% CI: 1.02-1.20, p = 0.003 and OR: 1.40, 95% CI: 1.16-1.54, p = 3.6 × 10-5 , respectively). Further, patients with a favourable, intermediate or unfavourable lifestyle who have Lp(a) levels of ≥30 mg/dL were at high risk for CAD (OR: 1.21, 95% CI: 1.08-1.34, p = 0.0014; OR: 1.31, 95% CI: 1.14-1.48, p = 1.2 × 10-4 ; and OR: 1.81, 95% CI: 1.44-2.18, p = 2.2 × 10-7 , respectively). Healthy lifestyle was associated with a lower risk of CAD regardless of Lp(a) levels.

Implementation of risk stratification within bowel cancer screening: a community jury study exploring public acceptability and communication needs

BackgroundPopulation-based cancer screening programmes are shifting away from age and/or sex-based screening criteria towards a risk-stratified approach. Any such changes must be acceptable to the public and communicated effectively. We aimed to explore the social and ethical considerations of implementing risk stratification at three different stages of the bowel cancer screening programme and to understand public requirements for communication.MethodsWe conducted two pairs of community juries, addressing risk stratification for screening eligibility or thresholds for referral to colonoscopy and screening interval. Using screening test results (where applicable), and lifestyle and genetic risk scores were suggested as potential stratification strategies. After being informed about the topic through a series of presentations and discussions including screening principles, ethical considerations and how risk stratification could be incorporated, participants deliberated over the research questions. They then reported their final verdicts on the acceptability of risk-stratified screening and what information should be shared about their preferred screening strategy. Transcripts were analysed using codebook thematic analysis.ResultsRisk stratification of bowel cancer screening was acceptable to the informed public. Using data within the current system (age, sex and screening results) was considered an obvious next step and collecting additional data for lifestyle and/or genetic risk assessment was also preferable to age-based screening. Participants acknowledged benefits to individuals and health services, as well as articulating concerns for people with low cancer risk, potential public misconceptions and additional complexity for the system. The need for clear and effective communication about changes to the screening programme and individual risk feedback was highlighted, including making a distinction between information that should be shared with everyone by default and additional details that are available elsewhere.ConclusionsFrom the perspective of public acceptability, risk stratification using current data could be implemented immediately, ahead of more complex strategies. Collecting additional data for lifestyle and/or genetic risk assessment was also considered acceptable but the practicalities of collecting such data and how the programme would be communicated require careful consideration.

Socioeconomic position, perceived weight, lifestyle risk, and multimorbidity in young adults aged 18 to 35 years: a Multi-country Study

BackgroundMultimorbidity-risk is established early in life, therefore reducing modifiable risk factors such as overweight or obesity may, in part, tackle the burden of multimorbidity in later life.MethodsWe made use of a cross-sectional online survey that included young adults (18-35yrs old) from three countries – Kenya, South Africa, and the United Kingdom (n = 3000). Information pertaining to socio-demographic, health, lifestyle, and perceived weight was collected. Additionally, the sum of affirmed morbidities was used to determine a morbidity score. Likewise, a lifestyle risk score was calculated based on information obtained from questions surrounding four unhealthy lifestyle behaviours, namely current smoking, alcohol consumption, physical inactivity, and overweight/obese weight status as a confirmed clinic condition. We further explored differences in socioeconomic position, and the prevalence of perceived weight, multimorbidity, and lifestyle risk factors between the three countries. We also determined the odds ratio of multimorbidity with perceived weight as a main predictor variable. We furthermore performed a generalised structural equation model to determine whether the association between socioeconomic position and multimorbidity was mediated via perceived weight and/or lifestyle risk.ResultsSocioeconomic position, weight perceptions, lifestyle risk, and multimorbidity varied significantly across the different economic countries. Higher morbidity (by > 11.9%) and lifestyle risk (by > 20.7%) scores were observed in those who reported an overweight weight perception when compared to those with an underweight or normal weight perception. In pooled analyses, the odds ratio in developing 2 or more morbidities increased multiple times in those who perceived themselves as overweight (all models: OR ≥ 2.241 [95% CI ≥ 1.693; ≥ 2.966] p < 0.001), showing a larger odds ratio with high significance in those who reported 3 or more morbidities (all models: OR ≥ 3.656 [95% CI ≥ 2.528; ≥ 5.286] p < 0.001). Furthermore, this study showed that an overweight weight perception partially mediated (p ≤ 0.001) the association between socioeconomic position and multimorbidity.ConclusionsThis study confirmed poorer health outcomes in those who perceived themselves as overweight. The findings from this study further emphasise the importance of targeted intervention strategies directed at raising weight-related awareness and potentiating risk factors, specifically in those who reside in lower economic developed countries.

New-onset atrial fibrillation prediction: the HARMS2-AF risk score.

Lifestyle risk factors are a modifiable target in atrial fibrillation (AF) management. The relative contribution of individual lifestyle risk factors to AF development has not been described. Development and validation of an AF lifestyle risk score to identify individuals at risk of AF in the general population are the aims of the study. The UK Biobank (UKB) and Framingham Heart Study (FHS) are large prospective cohorts with outcomes measured >10 years. Incident AF was based on International Classification of Diseases version 10 coding. Prior AF was excluded. Cox proportional hazards regression identified independent AF predictors, which were evaluated in a multivariable model. A weighted score was developed in the UKB and externally validated in the FHS. Kaplan-Meier estimates ascertained the risk of AF development. Among 314 280 UKB participants, AF incidence was 5.7%, with median time to AF 7.6 years (interquartile range 4.5-10.2). Hypertension, age, body mass index, male sex, sleep apnoea, smoking, and alcohol were predictive variables (all P < 0.001); physical inactivity [hazard ratio (HR) 1.01, 95% confidence interval (CI) 0.96-1.05, P = 0.80] and diabetes (HR 1.03, 95% CI 0.97-1.09, P = 0·38) were not significant. The HARMS2-AF score had similar predictive performance [area under the curve (AUC) 0.782] to the unweighted model (AUC 0.802) in the UKB. External validation in the FHS (AF incidence 6.0% of 7171 participants) demonstrated an AUC of 0.757 (95% CI 0.735-0.779). A higher HARMS2-AF score (≥5 points) was associated with a heightened AF risk (score 5-9: HR 12.79; score 10-14: HR 38.70). The HARMS2-AF risk model outperformed the Framingham-AF (AUC 0.568) and ARIC (AUC 0.713) risk models (both P < 0.001) and was comparable to the CHARGE-AF risk score (AUC 0.754, P = 0.73). The HARMS2-AF score is a novel lifestyle risk score which may help identify individuals at risk of AF in the general community and assist population screening.

Systemic Metabolomics in a Framework of Genetics and Lifestyle in Age-Related Macular Degeneration.

Insights into the pathogenesis of age-related macular degeneration (AMD), a leading cause of blindness, point towards a complex interplay of genetic and lifestyle factors triggering various systemic pathways. This study aimed to characterize metabolomic profiles for AMD and to evaluate their position in the trias with genetics and lifestyle. This study included 5923 individuals from five European studies. Blood metabolomics were assessed using a nuclear magnetic resonance platform of 146 metabolites. Associations were studied using regression analyses. A genetic risk score (GRS) was calculated using β-values of 49 AMD variants, a lifestyle risk score (LRS) using smoking and diet data, and a metabolite risk score (MRS) using metabolite values. We identified 61 metabolites associated with early-intermediate AMD, of which 94% were lipid-related, with higher levels of HDL-subparticles and apolipoprotein-A1, and lower levels of VLDL-subparticles, triglycerides, and fatty acids (false discovery rate (FDR) p-value < 1.4 × 10-2). Late AMD was associated with lower levels of the amino acids histidine, leucine, valine, tyrosine, and phenylalanine, and higher levels of the ketone bodies acetoacetate and 3-hydroxybutyrate (FDR p-value < 1.5 × 10-3). A favorable lifestyle characterized by a healthy diet was associated with higher levels of amino acids and lower levels of ketone bodies, while an unfavorable lifestyle, including smoking, showed opposite effects (FDR p-value < 2.7 × 10-2). The MRS mediated 5% of the effect of the GRS and 20% of that of the LRS on late AMD. Our findings show that metabolomic profiles differ between AMD stages and show that blood metabolites mostly reflect lifestyle. The severity-specific profiles spur further interest into the systemic effects related to disease conversion.

Lifestyle risks associated with brain functional connectivity and structure.

Some lifestyle factors are related to health and brain function and structure, but the brain systems involved are incompletely understood. A general linear model was used to test the associations of the combined and separate lifestyle risk measures of alcohol use, smoking, diet, amounts of physical activity, leisure activity, and mobile phone use, with brain functional connectivity with the high resolution Human Connectome Project (HCP) atlas in 19,415 participants aged 45-78 from the UK Biobank, with replication with HCP data. Higher combined lifestyle risk scores were associated with lower functional connectivity across the whole brain, but especially of three brain systems. Low physical, and leisure and social, activity were associated with low connectivities of the somatosensory/motor cortical regions and of hippocampal memory-related regions. Low mobile phone use, perhaps indicative of poor social communication channels, was associated with low functional connectivity of brain regions in and related to the superior temporal sulcus that are involved in social behavior and face processing. Smoking was associated with lower functional connectivity of especially frontal regions involved in attention. Lower cortical thickness in some of these regions, and also lower subcortical volume of the hippocampus, amygdala, and globus pallidus, were also associated with the sum of the poor lifestyle scores. This very large scale analysis emphasizes how the lifestyle of humans relates to their brain structure and function, and provides a foundation for understanding the causalities that relate to the differences found here in the brains of different individuals.

Public Preferences for Determining Eligibility for Screening in Risk-Stratified Cancer Screening Programs: A Discrete Choice Experiment.

Risk stratification has been proposed to improve the efficiency of population-level cancer screening. We aimed to describe and quantify the relative importance of different attributes of potential screening programs among the public, focusing on stratifying eligibility. We conducted a discrete choice experiment in which respondents selected between 2 hypothetical screening programs in a series of 9 questions. We presented the risk factors used to determine eligibility (age, sex, or lifestyle or genetic risk scores) and anticipated outcomes based on eligibility criteria with different sensitivity and specificity levels. We performed conditional logit regression models and used the results to estimate preferences for different approaches. We also analyzed free-text comments on respondents' views on the programs. A total of 1,172 respondents completed the survey. Sensitivity was the most important attribute (7 and 11 times more important than specificity and risk factors, respectively). Eligibility criteria based on age and sex or genetics were preferred over age alone and lifestyle risk scores. Phenotypic and polygenic risk prediction models would be more acceptable than screening everyone aged 55 to 70 y if they had high discrimination (area under the receiver-operating characteristic curve ≥0.75 and 0.80, respectively). Although our sample was representative with respect to age, sex, and ethnicity, it may not be representative of the UK population regarding other important characteristics. Also, some respondents may have not understood all the information provided to inform decision making. The public prioritized lives saved from cancer over reductions in numbers screened or experiencing unnecessary follow-up. Incorporating personal-level risk factors into screening eligibility criteria is acceptable to the public if it increases sensitivity; therefore, maximizing sensitivity in model development and communication could increase uptake. The public prioritized lives saved when considering changing from age-based eligibility criteria to risk-stratified cancer screening over reductions in numbers of people being screened or experiencing unnecessary follow-up.The risk stratification strategy used to do this was the least important component, although age plus sex or genetics were relatively preferable to using age alone and lifestyle risk scores.Communication strategies that emphasize improvements in the numbers of cancers detected or not missed across the population are more likely to be salient than reductions in unnecessary investigations or follow-up among some groups.Future research should focus on developing implementation strategies that maximize gains in sensitivity within the context of resource constraints and how to present attributes relating to specificity to facilitate understanding and informed decision making.

Association of outdoor air pollution, lifestyle, genetic factors with the risk of lung cancer: A prospective cohort study

ObjectivesThe effect of air pollution exposure on incident lung cancer remains uncertain, and the modifying role of lifestyle and genetic susceptibility in association between air pollution and lung cancer is ambiguous. MethodsA total of 367,623 participants from UK biobank cohort were enrolled in the analysis. The concentrations of particle matter (PM2.5, PM10), nitrogen dioxide (NO2), and nitrogen oxides (NOx), were evaluated by land-use regression model. Cox proportional hazard model was applied to assess the associations between air pollution and incident lung cancer. A lifestyle risk score and a polygenic risk score were established to investigate whether lifestyle and heritable risk could modify the effect of air pollution on lung cancer risk. ResultsPer interquartile range (IQR) increment in annual concentrations of PM2.5 (HR = 1.22, 95% CI, 1.15∼1.30), NO2 (HR = 1.19, 95% CI, 1.10∼1.27), and NOx (HR = 1.14, 95% CI, 1.09∼1.20) were associated with increased risk of lung cancer. We observed an additive interaction between air pollution including PM2.5 and NOx and lifestyle or genetic risk. Individuals with high air pollution exposure, poor lifestyle and high genetic risk had the highest risk of incident lung cancer. ConclusionLong-term exposures to air pollution is associated with increased risk of lung cancer, and this effect was modified by lifestyle or genetic risk. Integrated interventions for environmental pollution by government and adherence to healthy lifestyle by individuals are advocated for lung cancer prevention.

The power of a healthy lifestyle for cancer prevention: the example of colorectal cancer

We aimed to directly compare the estimated effects of adherence to a healthy lifestyle with those of risk predisposition according to known genetic variants affecting colorectal cancer (CRC) risk, to support effective risk communication for cancer prevention. A healthy lifestyle score (HLS) was derived from 5 lifestyle factors: smoking, alcohol consumption, diet, physical activity, and body adiposity. The association of lifestyle and polygenic risk score (PRS) (based on 140 CRC-associated risk loci) with CRC risk was assessed with multiple logistic regression and compared through the genetic risk equivalent (GRE), a novel approach providing an estimate of the effects of adherence to a healthy lifestyle in terms of percentile differences in PRS. A higher HLS was associated with lower CRC risk (4,844 cases, 3,964 controls). Those adhering to all 5 healthy lifestyle factors had a 62% (95% CI 54%-68%) lower CRC risk than those adhering to ≤ 2 healthy lifestyle factors. The estimated effect of adherence to all 5 compared with ≤ 2 healthy lifestyle factors was as strong as the effect of having a 79 percentile (GRE 79, 95% CI 61-97) lower PRS. The association between a healthy lifestyle and CRC risk was independent of PRS level but was particularly pronounced among those with a family history of CRC in ≥ 1 first-degree relative (P-interaction = 0.0013). A healthy lifestyle was strongly inversely associated with CRC risk. The large GRE indicated that CRC risk determined by polygenic risk may be offset to a substantial extent by adherence to a healthy lifestyle.

Multifactor mid‐life risk for Alzheimer’s disease associated with alterations in navigation but not episodic memory: the PREVENT‐Dementia study

AbstractBackgroundThe entorhinal cortex (EC) is the first cortical brain region to exhibit neurodegeneration in Alzheimer’s disease (AD). Given that the EC contains unique grid cells underpinning path integration, tests probing this aspect of navigation may have added value in detecting AD in its earliest preclinical stages. Building on our past work showing that a VR path integration task was highly sensitive and specific for prodromal AD (Howett et al., 2019 Brain), we tested the hypothesis that path integration may be affected in asymptomatic middle‐aged individuals at increased risk of AD.Method100 cognitively healthy adults (aged 43–66) from the PREVENT Dementia cohort performed an immersive VR path integration task in a simulated environment with distal boundary cues to generate allocentric spatial representations. We manipulated EC‐dependence by pseudo‐randomly removing supportive spatial cues from the virtual environment (Figure 1). Participants additionally performed a battery of cognitive tasks including the ACE‐R and COGNITO episodic memory. Linear mixed effects models and Tukey‐corrected post‐hoc contrasts were used to explore the contributions to task performance of parental family history, APOE‐e4 status, global amyloid PET uptake value (N = 46) and CAIDE lifestyle dementia risk score, adjusted for age, sex and education.ResultIndividuals with a positive family history, APOE‐e4 allele or higher CAIDE dementia risk score showed a selective impairment in path integration only when supportive boundary cues were removed. Significant sex interactions indicated that the family history effect (t = 2.31, pTukey = 0.022, Figure 2) and APOE‐e4 effect (t = 3.18, pTukey = 0.023) was specific to males. The effect of CAIDE risk score was specific to individuals with a family history (t = 2.45, pTukey = 0.038) (Figure 3). Conversely, these AD risk factors showed no effect on performance for the other cognitive tasks. Global amyloid PET uptake values showed no association to any task performance.ConclusionThese results suggest that navigation tests based on EC grid cell function may identify the earliest cognitive changes in individuals at greater risk of AD. The additional moderation by sex indicates a future need to consider male/female differences when using family history or APOE status to inform detection of initial stages of AD.

Do lifestyle risk behaviors affect anxiety, depressive symptoms, and suicidal ideation among adolescents?

Cultivating and maintaining good mental health and a healthy lifestyle during adolescence—the period when physical and psychological development occurs—is important. Further, an unhealthy lifestyle can have long-term effects on health. Few studies have examined the multiple effects of lifestyle risk behavior on mental health, and the behaviors that interact with this relationship remain unclear. This study aimed to explore the associations between anxiety, depressive symptoms, and suicidal ideation and a cumulative unhealthy lifestyle. The study included 54,948 participants from the 2020 Korea Youth Risk Behavior Web-Based Survey. The generalized anxiety disorder-7 scale was used to define the disorder. Depressive symptoms and suicidal ideation were evaluated using a self-reported questionnaire. The relationship between mental health and lifestyle risk scores was examined using multiple logistic regression analysis. There was a significantly positive relationship of a high lifestyle risk score with the risk of generalized anxiety disorder, depressive symptoms, and suicidal ideation. There were interaction effects between sex and academic achievement. Insufficient/prolonged sleep was strongly associated with poor mental health among Korean adolescents. Our findings showed an association between poor mental health and high lifestyle risk scores. Longitudinal studies to identify potential risk behaviors and to reveal causal relationships are warranted.

Development and validation of the HARMS2-AF lifestyle risk score to predict incident AF

Abstract Background Lifestyle risk factors (RFs) are a modifiable target in atrial fibrillation (AF) management. However, the relative contribution of individual lifestyle RFs to AF incidence has not been described. Purpose Development and validation of a novel AF-lifestyle risk score to determine AF risk in the general population. Methods The UK Biobank (UKB) is a large prospective cohort with outcomes measured &amp;gt;10 years. In the UKB, we performed regression analysis of AF lifestyle RFs which were then evaluated in a multivariable model and a weighted score was developed. Next, the risk score was externally validated in the Framingham Heart Study (FHS) population. Kaplan-Meier estimates ascertained the 10-year risk of AF development. Results In the UKB, AF incidence was 5.3% among 302,926 participants, with a median time to AF 7.3 years (IQR 4.3–9.8). Hypertension, sleep apnoea, male sex, age, obesity (BMI&amp;gt;30 kg/m2), alcohol and smoking were predictive variables (all p&amp;lt;0.001); physical inactivity (OR 1.02,95% CI 0.97–1.10, p=0.3), diabetes (OR 0.98,95% CI 0.91–1.06, p=0.2) and BMI 27–30 kg/m2 (OR 1.02, 95% CI 0.97–1.07, p=0.424) were not significant. The HARMS2-AF score (Figure 1) had similar predictive performance (AUC=0.782, LogLoss 0.178, Brier Score 0.046) to the unweighted regression model (AUC 0.808) in the UKB. Validation in the FHS (AF incidence 6.7% of 7206 participants) maintained excellent predictive performance with an AUC of 0.747 (95% CI 0.724–0.769, Figure 2). A higher HARMS2-AF score (&amp;gt;5 points) was associated with a heightened 10-year AF risk (score 5–9: OR 9.35, score 10–14: OR 33.34). Conclusions The HARMS2-AF score is a novel lifestyle risk score which may help identify individuals at risk of AF and assists in general population screening. Funding Acknowledgement Type of funding sources: None.