scholarly journals Epigenome-wide analyses identify DNA methylation signatures of dementia risk

2020 ◽  
Author(s):  
Rosie M. Walker ◽  
Mairead L. Bermingham ◽  
Kadi Vaher ◽  
Stewart W. Morris ◽  
Toni-Kim Clarke ◽  
...  
Keyword(s):  
Risk Factors ◽  
Dna Methylation ◽  
Genetic Risk ◽  
Risk Measures ◽  
Genetic Risk Score ◽  
Genomic Region ◽  
Risk Scores ◽  
Lifestyle Risk Factors ◽  
Dementia Risk ◽  
Lifestyle Risk

AbstractINTRODUCTIONDementia pathogenesis begins years before clinical symptom onset, necessitating the understanding of premorbid risk mechanisms. Here, we investigated potential pathogenic mechanisms by assessing DNA methylation associations with dementia risk factors in Alzheimer’s disease (AD)-free participants.METHODSAssociations between dementia risk measures (family history, genetic risk score (GRS), and dementia risk scores (combining lifestyle, demographic and genetic factors) and whole-blood DNA methylation were assessed in discovery and replication samples (n=∼400 – ∼5,000) from Generation Scotland.RESULTSAD genetic risk and two risk scores were associated with differential methylation. The GRS predominantly associated with methylation differences in cis but also identified a genomic region implicated in Parkinson’s disease. Loci associated with the risk scores were enriched for those previously associated with body mass index and alcohol consumption.DISCUSSIONDementia risk measures show widespread association with blood-based methylation, which indicates differences in the processes affected by genetic and demographic/lifestyle risk factors.

Abstract P259: Genetic Risk Score, Lifestyle Cardiovascular Risk Score, and Myocardial Infarction in Hispanics

Circulation ◽  
2016 ◽  
Vol 133 (suppl_1) ◽  
Author(s):  
Mercedes Sotos-Prieto ◽  
Ana Baylin ◽  
Hannia Campos ◽  
Lu Qi ◽  
Josiemer Mattei
Keyword(s):  
Physical Activity ◽  
Risk Factors ◽  
Myocardial Infarction ◽  
Socioeconomic Status ◽  
Risk Score ◽  
Genetic Risk ◽  
Genetic Risk Score ◽  
Lifestyle Risk Factors ◽  
Cardiovascular Risk Score ◽  
Lifestyle Risk

Background: A genetic risk score (GRS) and a lifestyle cardiovascular risk score (LCRS) have been independently associated with myocardial infarction (MI) in Hispanics. However, it is unknown if there is an interaction or a joint association between these scores. Objectives: To assess the interactive and joint associations between a GRS and a LCRS, as well as each individual lifestyle risk factor on the likelihood of MI. Methods: Data included 1534 Costa Rican adults with nonfatal acute MI and 1534 without MI participating in a case-control study. The GRS was calculated by summing the number of the top three MI-associated risk alleles. The LCRS was calculated using the estimated coefficients as weights for each lifestyle risk factors (diet, physical activity, smoking, waist:hip ratio, low or high alcohol intake, and low socioeconomic status). Conditional logistic regression was used to calculate odds ratios (OR), adjusting for age, sex, and area of residence (matching condition), and to test for interaction and joint association. Results: The multivariable OR for MI was 1.14 (95% CI 1.07, 1.22) per GRS unit and 2.72 (2.33, 3.91) per LCRS unit. Participants in the highest tertile of the GRS and highest tertile of the LCRS had higher odds of MI (5.43 [3.80, 7.76]) compared to those in the lowest category. A significant joint association was detected (p <0.0001), while the interaction term was non-significant (p=0.44). Similar results were found for the joint association between GRS and each individual lifestyle component: joint odds for highest risk category vs. lowest was 2.16 (1.53, 3.04) for diet, 1.85 (1.33, 2.59) for physical activity, 3.31 (2.45, 4.48) for smoking, 1.32 (0.92, 1.89) for alcohol, 2.84 (1.82, 4.42) for waist:hip ratio, and 1.86 (1.29, 2.69) for socioeconomic status. Conclusion: Although lifestyle risk factors and genetics contribute independently and in combination to the odds of MI, lifestyle risk factors were stronger among Costa Ricans. Efforts to improve lifestyle behaviors in this population, regardless of genetic susceptibility, may help prevent MI and related heart conditions.

scholarly journals Individual and joint performance of DNA methylation profiles, genetic risk score and environmental risk scores for predicting breast cancer risk

2019 ◽  
Vol 14 (1) ◽  
pp. 42-53
Author(s):  
Zhong Guan ◽  
Janhavi R. Raut ◽  
Korbinian Weigl ◽  
Ben Schöttker ◽  
Bernd Holleczek ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Dna Methylation ◽  
Breast Cancer Risk ◽  
Cancer Risk ◽  
Risk Score ◽  
Environmental Risk ◽  
Genetic Risk ◽  
Genetic Risk Score ◽  
Risk Scores ◽  
Joint Performance

scholarly journals Education and Lifestyle Factors Are Associated with DNA Methylation Clocks in Older African Americans

2019 ◽  
Vol 16 (17) ◽  
pp. 3141 ◽  
Author(s):  
Wei Zhao ◽  
Farah Ammous ◽  
Scott Ratliff ◽  
Jiaxuan Liu ◽  
Miao Yu ◽  
...  
Keyword(s):  
Risk Factors ◽  
Dna Methylation ◽  
Cellular Level ◽  
Cellular Aging ◽  
Longitudinal Analyses ◽  
Cross Sectional ◽  
Lifestyle Risk Factors ◽  
Age Related ◽  
Epigenetic Age Acceleration ◽  
Lifestyle Risk

DNA methylation (DNAm) clocks are important biomarkers of cellular aging and are associated with a variety of age-related chronic diseases and all-cause mortality. Examining the relationship between education and lifestyle risk factors for age-related diseases and multiple DNAm clocks can increase the understanding of how risk factors contribute to aging at the cellular level. This study explored the association between education or lifestyle risk factors for age-related diseases and the acceleration of four DNAm clocks, including intrinsic (IEAA) and extrinsic epigenetic age acceleration (EEAA), PhenoAge acceleration (PhenoAA), and GrimAge acceleration (GrimAA) in the African American participants of the Genetic Epidemiology Network of Arteriopathy. We performed both cross-sectional and longitudinal analyses. In cross-sectional analyses, gender, education, BMI, smoking, and alcohol consumption were all independently associated with GrimAA, whereas only some of them were associated with other clocks. The effect of smoking and education on GrimAA varied by gender. Longitudinal analyses suggest that age and BMI continued to increase GrimAA, and that age and current smoking continued to increase PhenoAA after controlling DNAm clocks at baseline. In conclusion, education and common lifestyle risk factors were associated with multiple DNAm clocks. However, the association with each risk factor varied by clock, which suggests that different clocks may capture adverse effects from different environmental stimuli.

Abstract MP070: Genetic Predisposition to High Blood Pressure Associates With Cardiovascular Complications Among Patients With Type 2 Diabetes: Two Independent Studies

Circulation ◽  
2012 ◽  
Vol 125 (suppl_10) ◽  
Author(s):  
Qibin Qi ◽  
Frank B Hu ◽  
Lu Qi
Keyword(s):  
Blood Pressure ◽  
High Blood Pressure ◽  
Risk Score ◽  
Genetic Predisposition ◽  
Genetic Risk ◽  
Genetic Risk Score ◽  
Cardiovascular Complications ◽  
Lifestyle Risk Factors ◽  
Increased Risk ◽  
Lifestyle Risk

Background Hypertension has been associated with an increased risk of cardiovascular disease (CVD).We examined the effect of the genetic predisposition to high blood pressure and its potential interactions with diet and lifestyle factors on risk of cardiovascular complications among diabetic patients. Methods The present study included 1005 men and 1299 women with type 2 diabetes from the Health Professionals Follow-up Study and Nurses’ Health Study: 732 (380 men and 352 women) CVD cases (coronary heart disease [CHD] or stroke) and 1572 non-CVD controls. Genetic predisposition to high blood pressure was estimated by a genetic risk score on basis of 29 blood pressure-predisposing variants identified by a recent GWAS meta-analysis in ∼200,000 Europeans. Results The genetic risk score was associated with prevalent hypertension in both men and women, with a combined OR of 1.07 (95% CI 1.04-1.09) for each additional risk allele. Restricted cubic spline regression analysis indicated a linear relationship between the genetic risk score and increased CVD risk in both men ( P =0.002) and women. ( P =0.028) ( Figure ). The results from the 2 cohorts were combined since the genetic risk score showed consistent associations in men and women. After adjustment for age, BMI, and diet and lifestyle risk factors, each additional risk allele in the genetic risk score was associated with a 5% increased risk of developing CVD (OR 1.05 [95% CI 1.02-1.09]). The OR was 1.49 (1.08-2.07) for CVD when comparing the extreme quartiles of the genetic risk score ( P for trend =0.007). We did not observe significant interactions of the genetic risk score with Dietary Approach to Stop Hypertension diet score, and other lifestyle risk factors such as BMI, physical activity, alcohol intake, use of nonnarcotic analgesics and supplemental folic acid intake. Conclusions Genetic predisposition to high blood pressure is associated with an increased risk of cardiovascular complications in diabetic patients, independent of diet and lifestyle risk factors for hypertension.

scholarly journals Genetic and lifestyle risk factors for MRI-defined brain infarcts in a population-based setting

Neurology ◽  
2019 ◽  
Vol 92 (5) ◽  
pp. e486-e503 ◽  
Author(s):  
Ganesh Chauhan ◽  
Hieab H.H. Adams ◽  
Claudia L. Satizabal ◽  
Joshua C. Bis ◽  
Alexander Teumer ◽  
...  
Keyword(s):  
Risk Factors ◽  
Genetic Risk ◽  
Association Studies ◽  
Large Population ◽  
Population Based ◽  
Genome Wide Association Studies ◽  
Lifestyle Risk Factors ◽  
Brain Infarcts ◽  
Genome Wide ◽  
Lifestyle Risk

ObjectiveTo explore genetic and lifestyle risk factors of MRI-defined brain infarcts (BI) in large population-based cohorts.MethodsWe performed meta-analyses of genome-wide association studies (GWAS) and examined associations of vascular risk factors and their genetic risk scores (GRS) with MRI-defined BI and a subset of BI, namely, small subcortical BI (SSBI), in 18 population-based cohorts (n = 20,949) from 5 ethnicities (3,726 with BI, 2,021 with SSBI). Top loci were followed up in 7 population-based cohorts (n = 6,862; 1,483 with BI, 630 with SBBI), and we tested associations with related phenotypes including ischemic stroke and pathologically defined BI.ResultsThe mean prevalence was 17.7% for BI and 10.5% for SSBI, steeply rising after age 65. Two loci showed genome-wide significant association with BI: FBN2, p = 1.77 × 10−8; and LINC00539/ZDHHC20, p = 5.82 × 10−9. Both have been associated with blood pressure (BP)–related phenotypes, but did not replicate in the smaller follow-up sample or show associations with related phenotypes. Age- and sex-adjusted associations with BI and SSBI were observed for BP traits (p value for BI, p[BI] = 9.38 × 10−25; p[SSBI] = 5.23 × 10−14 for hypertension), smoking (p[BI] = 4.4 × 10−10; p[SSBI] = 1.2 × 10−4), diabetes (p[BI] = 1.7 × 10−8; p[SSBI] = 2.8 × 10−3), previous cardiovascular disease (p[BI] = 1.0 × 10−18; p[SSBI] = 2.3 × 10−7), stroke (p[BI] = 3.9 × 10−69; p[SSBI] = 3.2 × 10−24), and MRI-defined white matter hyperintensity burden (p[BI] = 1.43 × 10−157; p[SSBI] = 3.16 × 10−106), but not with body mass index or cholesterol. GRS of BP traits were associated with BI and SSBI (p ≤ 0.0022), without indication of directional pleiotropy.ConclusionIn this multiethnic GWAS meta-analysis, including over 20,000 population-based participants, we identified genetic risk loci for BI requiring validation once additional large datasets become available. High BP, including genetically determined, was the most significant modifiable, causal risk factor for BI.

Novel Machine-Learned Approach for COVID-19 Resource Allocation: A Tool for Evaluating Community Susceptibility (Preprint)

2020 ◽  
Author(s):  
Neil Kale
Keyword(s):  
Machine Learning ◽  
Risk Factors ◽  
Insurance Coverage ◽  
Premature Mortality ◽  
Health Insurance Coverage ◽  
Heart Attacks ◽  
Lifestyle Risk Factors ◽  
The People ◽  
Potential Risk Factors ◽  
Lifestyle Risk

BACKGROUND Despite worldwide efforts to develop an effective COVID vaccine, it is quite evident that initial supplies will be limited. Therefore, it is important to develop methods that will ensure that the COVID vaccine is allocated to the people who are at major risk until there is a sufficient global supply. OBJECTIVE The purpose of this study was to develop a machine-learning tool that could be applied to assess the risk in Massachusetts towns based on community-wide social, medical, and lifestyle risk factors. METHODS I compiled Massachusetts town data for 29 potential risk factors, such as the prevalence of preexisting comorbid conditions like COPD and social factors such as racial composition, and implemented logistic regression to predict the amount of COVID cases in each town. RESULTS Of the 29 factors, 14 were found to be significant (p < 0.1) indicators: poverty, food insecurity, lack of high school education, lack of health insurance coverage, premature mortality, population, population density, recent population growth, Asian percentage, high-occupancy housing, and preexisting prevalence of cancer, COPD, overweightness, and heart attacks. The machine-learning approach is 80% accurate in the state of Massachusetts and finds the 9 highest risk communities: Lynn, Brockton, Revere, Randolph, Lowell, New Bedford, Everett, Waltham, and Fitchburg. The 5 most at-risk counties are Suffolk, Middlesex, Bristol, Norfolk, and Plymouth. CONCLUSIONS With appropriate data, the tool could evaluate risk in other communities, or even enumerate individual patient susceptibility. A ranking of communities by risk may help policymakers ensure equitable allocation of limited doses of the COVID vaccine.

Has the time come to integrate genetic risk scores into clinical practice?

2020 ◽  
Vol 41 (Supplement_2) ◽  
Author(s):  
F.V Moniz Mendonca ◽  
M.I Mendonca ◽  
A Pereira ◽  
J Monteiro ◽  
J Sousa ◽  
...  
Keyword(s):  
Risk Factors ◽  
Clinical Practice ◽  
Genetic Variants ◽  
Genetic Risk ◽  
Odds Ratio ◽  
Risk Scores ◽  
Cumulative Number ◽  
Genetic Risk Scores ◽  
Risk Alleles ◽  
Cad Risk

Abstract Background The risk for Coronary Artery Disease (CAD) is determined by both genetic and environmental factors, as well as by the interaction between them. It is estimated that genetic factors could account for 40% to 55% of the existing variability among the population (inheritability). Therefore, some authors have advised that it is time we integrated genetic risk scores into clinical practice. Aim The aim of this study was to evaluate the magnitude of the association between an additive genetic risk score (aGRS) and CAD based on the cumulative number of risk alleles in these variants, and to estimate whether their use is valuable in clinical practice. Methods A case-control study was performed in a Portuguese population. We enrolled 3120 participants, of whom 1687 were CAD patients and 1433 were normal controls. Controls were paired to cases with respect to gender and age. 33 genetic variants known to be associated with CAD were selected, and an aGRS was calculated for each individual. The aGRS was further subdivided into deciles groups, in order to estimate the CAD risk in each decile, defined by the number of risk alleles. The magnitude of the risk (odds ratio) was calculated for each group by multiple logistic regression using the 5th decile as the reference group (median). In order to evaluate the ability of the aGRS to discriminate susceptibility to CAD, two genetic models were performed, the first with traditional risk factors (TRF) and second with TRF plus aGRS. The AUC of the two ROC curves was calculated. Results A higher prevalence of cases over controls became apparent from the 6th decile of the aGRS, reflecting the higher number of risk alleles present (see figure). The difference in CAD risk was only significant from the 6th decile, increasing gradually until the 10th decile. The odds ratio (OR) for the last decile related to 5th decile (median) was 1.87 (95% CI:1.36–2.56; p&lt;0.0001). The first model yielded an AUC=0.738 (95% CI:0.720–0.755) and the second model was slightly more discriminative for CAD risk (AUC=0.748; 95% CI:0.730–0.765). The DeLong test was significant (p=0.0002). Conclusion Adding an aGRS to the non-genetic risk factors resulted in a modest improvement in the ability to discriminate the risk of CAD. Such improvement, even if statistically significant, does not appear to be of real value in clinical practice yet. We anticipate that with the development of further knowledge about different SNPs and their complex interactions, and with the inclusion of rare genetic variants, genetic risk scores will be better suited for use in a clinical setting. Funding Acknowledgement Type of funding source: None

scholarly journals The Prevalence of Insulin Resistance and the Associated Risk Factors in a Sample of 14–18-Year-Old Slovak Adolescents

2021 ◽  
Vol 18 (3) ◽  
pp. 909
Author(s):  
Jana Jurkovičová ◽  
Katarína Hirošová ◽  
Diana Vondrová ◽  
Martin Samohýl ◽  
Zuzana Štefániková ◽  
...  
Keyword(s):  
Physical Activity ◽  
Risk Factors ◽  
Insulin Resistance ◽  
Venous Blood ◽  
Significant Risk ◽  
Lifestyle Changes ◽  
Skewed Distribution ◽  
Model Assessment ◽  
Lifestyle Risk Factors ◽  
Lifestyle Risk

The prevalence of cardiometabolic risk factors has increased in Slovakian adolescents as a result of serious lifestyle changes. This cross-sectional study aimed to assess the prevalence of insulin resistance (IR) and the associations with cardiometabolic and selected lifestyle risk factors in a sample of Slovak adolescents. In total, 2629 adolescents (45.8% males) aged between 14 and 18 years were examined in the study. Anthropometric parameters, blood pressure (BP), and resting heart rate were measured; fasting venous blood samples were analyzed; and homeostasis model assessment (HOMA)-insulin resistance (IR) was calculated. For statistical data processing, the methods of descriptive and analytical statistics for normal and skewed distribution of variables were used. The mean HOMA-IR was 2.45 ± 1.91, without a significant sex differences. IR (cut-off point for HOMA-IR = 3.16) was detected in 18.6% of adolescents (19.8% males, 17.6% females). IR was strongly associated with overweight/obesity (especially central) and with almost all monitored cardiometabolic factors, except for total cholesterol (TC) and systolic BP in females. The multivariate model selected variables such as low level of physical fitness, insufficient physical activity, breakfast skipping, a small number of daily meals, frequent consumption of sweetened beverages, and low educational level of fathers as significant risk factors of IR in adolescents. Recognizing the main lifestyle risk factors and early IR identification is important in terms of the performance of preventive strategies. Weight reduction, regular physical activity, and healthy eating habits can improve insulin sensitivity and decrease the incidence of metabolic syndrome, type 2 diabetes, and cardiovascular disease (CVD).

scholarly journals Identifying individuals with high risk of Alzheimer’s disease using polygenic risk scores

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Ganna Leonenko ◽  
Emily Baker ◽  
Joshua Stevenson-Hoare ◽  
Annerieke Sierksma ◽  
Mark Fiers ◽  
...  
Keyword(s):  
High Risk ◽  
Risk Score ◽  
Genetic Risk ◽  
Prediction Accuracy ◽  
Genetic Risk Score ◽  
Low Risk ◽  
Risk Scores ◽  
Sample Mean ◽  
Polygenic Risk ◽  
Reliable Identification

AbstractPolygenic Risk Scores (PRS) for AD offer unique possibilities for reliable identification of individuals at high and low risk of AD. However, there is little agreement in the field as to what approach should be used for genetic risk score calculations, how to model the effect of APOE, what the optimal p-value threshold (pT) for SNP selection is and how to compare scores between studies and methods. We show that the best prediction accuracy is achieved with a model with two predictors (APOE and PRS excluding APOE region) with pT<0.1 for SNP selection. Prediction accuracy in a sample across different PRS approaches is similar, but individuals’ scores and their associated ranking differ. We show that standardising PRS against the population mean, as opposed to the sample mean, makes the individuals’ scores comparable between studies. Our work highlights the best strategies for polygenic profiling when assessing individuals for AD risk.

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