Interaction of obesity polygenic score with lifestyle risk factors in an electronic health record biobank

Abstract Background Genetic and lifestyle factors have considerable effects on obesity and related diseases, yet their effects in a clinical cohort are unknown. This study in a patient biobank examined associations of a BMI polygenic risk score (PRS), and its interactions with lifestyle risk factors, with clinically measured BMI and clinical phenotypes. Methods The Mass General Brigham (MGB) Biobank is a hospital-based cohort with electronic health record, genetic, and lifestyle data. A PRS for obesity was generated using 97 genetic variants for BMI. An obesity lifestyle risk index using survey responses to obesogenic lifestyle risk factors (alcohol, education, exercise, sleep, smoking, and shift work) was used to dichotomize the cohort into high and low obesogenic index based on the population median. Height and weight were measured at a clinical visit. Multivariable linear cross-sectional associations of the PRS with BMI and interactions with the obesity lifestyle risk index were conducted. In phenome-wide association analyses (PheWAS), similar logistic models were conducted for 675 disease outcomes derived from billing codes. Results Thirty-three thousand five hundred eleven patients were analyzed (53.1% female; age 60.0 years; BMI 28.3 kg/m2), of which 17,040 completed the lifestyle survey (57.5% female; age: 60.2; BMI: 28.1 (6.2) kg/m2). Each standard deviation increment in the PRS was associated with 0.83 kg/m2 unit increase in BMI (95% confidence interval (CI) =0.76, 0.90). There was an interaction between the obesity PRS and obesity lifestyle risk index on BMI. The difference in BMI between those with a high and low obesogenic index was 3.18 kg/m2 in patients in the highest decile of PRS, whereas that difference was only 1.55 kg/m2 in patients in the lowest decile of PRS. In PheWAS, the obesity PRS was associated with 40 diseases spanning endocrine/metabolic, circulatory, and 8 other disease groups. No interactions were evident between the PRS and the index on disease outcomes. Conclusions In this hospital-based clinical biobank, obesity risk conferred by common genetic variants was associated with elevated BMI and this risk was attenuated by a healthier patient lifestyle. Continued consideration of the role of lifestyle in the context of genetic predisposition in healthcare settings is necessary to quantify the extent to which modifiable lifestyle risk factors may moderate genetic predisposition and inform clinical action to achieve personalized medicine.

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Lifestyle and work ability in a general working population in Norway: a cross-sectional study

BMJ Open ◽

10.1136/bmjopen-2018-026215 ◽

2019 ◽

Vol 9 (4) ◽

pp. e026215 ◽

Cited By ~ 5

Author(s):

Inger M Oellingrath ◽

Marit Müller De Bortoli ◽

Martin Veel Svendsen ◽

Anne Kristin Møller Fell

Keyword(s):

Risk Factors ◽

Risk Score ◽

Work Ability ◽

Risk Index ◽

Cross Sectional Study ◽

Working Population ◽

Cross Sectional ◽

Lifestyle Risk Factors ◽

Unhealthy Diet ◽

Lifestyle Risk

ObjectivesThe aim of this study was to investigate the association between multiple lifestyle-related risk factors (unhealthy diet, low leisure-time physical activity, overweight/obesity and smoking) and self-rated work ability in a general working population.SettingPopulation-based cross-sectional study, in Telemark County, Norway, 2013.ParticipantsA random sample of 50 000 subjects was invited to answer a self-administered questionnaire and 16 099 responded. Complete data on lifestyle and work ability were obtained for 10 355 participants aged 18–50 years all engaged in paid work during the preceding 12 months.Outcome measureWork ability was assessed using the Work Ability Score (WAS)—the first question in the Work Ability Index. To study the association between multiple lifestyle risk factors and work ability, a lifestyle risk index was constructed and relationships examined using multiple logistic regression analysis.ResultsLow work ability was more likely among subjects with an unhealthy diet (ORadj1.3, 95% CI 1.02 to 1.5), inactive persons (ORadj1.4, 95% CI 1.2 to 1.6), obese respondents (ORadj1.5, 95% CI 1.3 to 1.7) and former and current smokers (ORadj1.2, 95% CI 1.1 to 1.4 and 1.3, 95% CI 1.2 to 1.5, respectively). An additive relationship was observed between the lifestyle risk index and the likelihood of decreased work ability (moderate-risk score: ORadj1.3; 95% CI 1.1 to 1.6; high-risk score: ORadj1.9; 95% CI 1.6 to 2.2; very high risk score: ORadj2.4; 95% CI 1.9 to 3.0). The overall population attributable fraction (PAF) of low work ability based on the overall risk index was 38%, while the PAFs of physical activity, smoking, body mass index and diet were 16%, 11%, 11% and 6%, respectively.ConclusionsLifestyle risk factors were associated with low work ability. An additive relationship was observed. The findings are considered relevant to occupational intervention programmes aimed at prevention and improvement of decreased work ability.

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

10.2196/preprints.25132 ◽

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.

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

International Journal of Environmental Research and Public Health ◽

10.3390/ijerph18030909 ◽

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).

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A genome-first approach to aggregating rare genetic variants in LMNA for association with electronic health record phenotypes

Genetics in Medicine ◽

10.1038/s41436-019-0625-8 ◽

2019 ◽

Vol 22 (1) ◽

pp. 102-111 ◽

Cited By ~ 7

Author(s):

Joseph Park ◽

◽

Michael G. Levin ◽

Christopher M. Haggerty ◽

Dustin N. Hartzel ◽

...

Keyword(s):

Electronic Health Record ◽

Genetic Variants ◽

Health Record ◽

A Genome ◽

Rare Genetic Variants ◽

Electronic Health

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Smoking, drinking, diet and physical activity—modifiable lifestyle risk factors and their associations with age to first chronic disease

International Journal of Epidemiology ◽

10.1093/ije/dyz078 ◽

2019 ◽

Vol 49 (1) ◽

pp. 113-130 ◽

Cited By ~ 7

Author(s):

Ryan Ng ◽

Rinku Sutradhar ◽

Zhan Yao ◽

Walter P Wodchis ◽

Laura C Rosella

Keyword(s):

Risk Factors ◽

Chronic Disease ◽

Chronic Diseases ◽

Lifestyle Factors ◽

Competing Risk ◽

Chronic Obstructive ◽

Common Disease ◽

Heavy Smoking ◽

Lifestyle Risk Factors ◽

Lifestyle Risk

AbstractBackgroundThis study examined the incidence of a person’s first diagnosis of a selected chronic disease, and the relationships between modifiable lifestyle risk factors and age to first of six chronic diseases.MethodsOntario respondents from 2001 to 2010 of the Canadian Community Health Survey were followed up with administrative data until 2014 for congestive heart failure, chronic obstructive respiratory disease, diabetes, lung cancer, myocardial infarction and stroke. By sex, the cumulative incidence function of age to first chronic disease was calculated for the six chronic diseases individually and compositely. The associations between modifiable lifestyle risk factors (alcohol, body mass index, smoking, diet, physical inactivity) and age to first chronic disease were estimated using cause-specific Cox proportional hazards models and Fine-Gray competing risk models.ResultsDiabetes was the most common disease. By age 70.5 years (2015 world life expectancy), 50.9% of females and 58.1% of males had at least one disease and few had a death free of the selected diseases (3.4% females; 5.4% males). Of the lifestyle factors, heavy smoking had the strongest association with the risk of experiencing at least one chronic disease (cause-specific hazard ratio = 3.86; 95% confidence interval = 3.46, 4.31). The lifestyle factors were modelled for each disease separately, and the associations varied by chronic disease and sex.ConclusionsWe found that most individuals will have at least one of the six chronic diseases before dying. This study provides a novel approach using competing risk methods to examine the incidence of chronic diseases relative to the life course and how their incidences are associated with lifestyle behaviours.

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Lifestyle Risk Factors and Cardiovascular Disease in Cubans and Cuban Americans

Cardiology Research and Practice ◽

10.1155/2012/470705 ◽

2012 ◽

Vol 2012 ◽

pp. 1-6 ◽

Cited By ~ 6

Author(s):

Melissa S. Burroughs Peña ◽

Dhaval Patel ◽

Delfin Rodríguez Leyva ◽

Bobby V. Khan ◽

Laurence Sperling

Keyword(s):

Diabetes Mellitus ◽

Risk Factors ◽

Type 2 Diabetes ◽

Cardiovascular Disease ◽

Type 2 Diabetes Mellitus ◽

Cardiovascular Prevention ◽

Lifestyle Risk Factors ◽

Lifestyle Risk ◽

Cuban Americans

Cardiovascular disease is the leading cause of mortality in Cuba. Lifestyle risk factors for coronary heart disease (CHD) in Cubans have not been compared to risk factors in Cuban Americans. Articles spanning the last 20 years were reviewed. The data on Cuban Americans are largely based on the Hispanic Health and Nutrition Examination Survey (HHANES), 1982–1984, while more recent data on epidemiological trends in Cuba are available. The prevalence of obesity and type 2 diabetes mellitus remains greater in Cuban Americans than in Cubans. However, dietary preferences, low physical activity, and tobacco use are contributing to the rising rates of obesity, type 2 diabetes mellitus, and CHD in Cuba, putting Cubans at increased cardiovascular risk. Comprehensive national strategies for cardiovascular prevention that address these modifiable lifestyle risk factors are necessary to address the increasing threat to public health in Cuba.

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Lifestyle risk factors for lower limb venous reflux in the general population: Edinburgh Vein Study

International Journal of Epidemiology ◽

10.1093/ije/30.4.846 ◽

2001 ◽

Vol 30 (4) ◽

pp. 846-852 ◽

Cited By ~ 73

Author(s):

FGR Fowkes ◽

AJ Lee ◽

CJ Evans ◽

PL Allan ◽

AW Bradbury ◽

...

Keyword(s):

Risk Factors ◽

General Population ◽

Lower Limb ◽

Venous Reflux ◽

Lifestyle Risk Factors ◽

Lifestyle Risk

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Influence of lifestyle risk factors on work ability and sick leave in a general working population in Norway: a 5-year longitudinal study

BMJ Open ◽

10.1136/bmjopen-2020-045678 ◽

2021 ◽

Vol 11 (2) ◽

pp. e045678

Author(s):

Marit Müller De Bortoli ◽

Inger M. Oellingrath ◽

Anne Kristin Moeller Fell ◽

Alex Burdorf ◽

Suzan J. W. Robroek

Keyword(s):

Risk Factors ◽

Longitudinal Study ◽

Sick Leave ◽

Work Ability ◽

Working Population ◽

Short Term ◽

Lifestyle Risk Factors ◽

Lifestyle Risk

ObjectivesThe aim of this study is to assess (1) whether lifestyle risk factors are related to work ability and sick leave in a general working population over time, and (2) these associations within specific disease groups (ie, respiratory diseases, cardiovascular disease and diabetes, and mental illness).SettingTelemark county, in the south-eastern part of Norway.DesignLongitudinal study with 5 years follow-up.ParticipantsThe Telemark study is a longitudinal study of the general working population in Telemark county, Norway, aged 16 to 50 years at baseline in 2013 (n=7952) and after 5-year follow-up.Outcome measureSelf-reported information on work ability (moderate and poor) and sick leave (short-term and long-term) was assessed at baseline, and during a 5-year follow-up.ResultsObesity (OR=1.64, 95% CI: 1.32 to 2.05) and smoking (OR=1.62, 95% CI: 1.35 to 1.96) were associated with long-term sick leave and, less strongly, with short-term sick leave. An unhealthy diet (OR=1.57, 95% CI: 1.01 to 2.43), and smoking (OR=1.67, 95% CI: 1.24 to 2.25) were associated with poor work ability and, to a smaller extent, with moderate work ability. A higher lifestyle risk score was associated with both sick leave and reduced work ability. Only few associations were found between unhealthy lifestyle factors and sick leave or reduced work ability within disease groups.ConclusionLifestyle risk factors were associated with sick leave and reduced work ability. To evaluate these associations further, studies assessing the effect of lifestyle interventions on sick leave and work ability are needed.

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