APOE Genotypes, Lipid Profiles, and Associated Clinical Markers in a Finnish Population with Cardiovascular Disease Risk Factors

Introduction: The APOE ε4 allele predisposes to high cholesterol and increases the risk for lifestyle-related diseases such as Alzheimer’s disease and cardiovascular diseases (CVDs). The aim of this study was to analyse interrelationships of APOE genotypes with lipid metabolism and lifestyle factors in middle-aged Finns among whom the CVD risk factors are common. Methods: Participants (n = 211) were analysed for APOE ε genotypes, physiological parameters, and health- and diet-related plasma markers. Lifestyle choices were determined by a questionnaire. Results: APOE genotypes ε3/ε4 and ε4/ε4 (ε4 group) represented 34.1% of the participants. Genotype ε3/ε3 (ε3 group) frequency was 54.5%. Carriers of ε2 (ε2 group; ε2/ε2, ε2/ε3 and ε2/ε4) represented 11.4%; 1.9% were of the genotype ε2/ε4. LDL and total cholesterol levels were lower (p < 0.05) in the ε2 carriers than in the ε3 or ε4 groups, while the ε3 and ε4 groups did not differ. Proportions of plasma saturated fatty acids (SFAs) were higher (p < 0.01), and omega-6 fatty acids lower (p = 0.01) in the ε2 carriers compared with the ε4 group. The ε2 carriers had a higher (p < 0.05) percentage of 22:4n-6 and 22:5n-6 and a lower (p < 0.05) percentage of 24:5n-3 and 24:6n-3 than individuals without the ε2 allele. Conclusions: The plasma fatty-acid profiles in the ε2 group were characterized by higher SFA and lower omega-6 fatty-acid proportions. Their lower cholesterol values indicated a lower risk for CVD compared with the ε4 group. A novel finding was that the ε2 carriers had different proportions of 22:4n-6, 22:5n-6, 24:5n-3, and 24:6n-3 than individuals without the ε2 allele. The significance of the differences in fatty-acid composition remains to be studied.Peer reviewe

High prevalence of obesity, central obesity and abnormal glucose tolerance in the middle-aged Finnish population

<p>Abstract</p> <p>Background</p> <p>There is a worldwide increase in the prevalence of obesity and disturbances in glucose metabolism. The aim of this study was to assess the current prevalence of obesity, central obesity and abnormal glucose tolerance in Finnish population, and to investigate the associations between body mass index (BMI), waist circumference and abnormal glucose tolerance.</p> <p>Methods</p> <p>A cross-sectional population-based survey was conducted in Finland during October 2004 and January 2005. A total of 4500 randomly selected individuals aged 45–74 years were invited to a health examination that included an oral glucose tolerance test. The participation rate was 62% in men and 67% in women.</p> <p>Results</p> <p>The prevalence of obesity was 23.5% (95% Confidence Interval (CI) 21.1–25.9) in men, and 28.0% (95% CI 25.5–30.5) in women. The overall prevalence of abnormal glucose tolerance (including type 2 diabetes, impaired glucose tolerance, or impaired fasting glucose) was 42.0% (95% CI 39.2–44.8) in men and 33.4% (95% CI 30.9–36.0) in women. The prevalence of previously unknown, screen-detected type 2 diabetes was 9.3% (95% CI 7.7–11.0) in men and 7.3% (95% CI 5.9–8.7) in women. Central obesity was associated with abnormal glucose tolerance within each of the three BMI categories normal (< 25 kg/m²), overweight (25–29 kg/m²), and obese (≥ 30 kg/m²).</p> <p>Conclusion</p> <p>In a population-based random sample of Finnish population, prevalences of obesity, central obesity and abnormal glucose tolerance were found to be high. A remarkably high number of previously undetected cases of type 2 diabetes was detected. Waist circumference is a predictor of abnormal glucose tolerance in all categories of obesity.</p

Genome-wide physical activity interactions in adiposity. A meta-analysis of 200,452 adults

Physical activity (PA) may modify the genetic effects that give rise to increased risk of obesity. To identify adiposity loci whose effects are modified by PA, we performed genome-wide interaction meta-analyses of BMI and BMI-adjusted waist circumference and waist-hip ratio from up to 200,452 adults of European (n = 180,423) or other ancestry (n = 20,029). We standardized PA by categorizing it into a dichotomous variable where, on average, 23% of participants were categorized as inactive and 77% as physically active. While we replicate the interaction with PA for the strongest known obesity-risk locus in the FTO gene, of which the effect is attenuated by similar to 30% in physically active individuals compared to inactive individuals, we do not identify additional loci that are sensitive to PA. In additional genome-wide meta-analyses adjusting for PA and interaction with PA, we identify 11 novel adiposity loci, suggesting that accounting for PA or other environmental factors that contribute to variation in adiposity may facilitate gene discovery.Peer reviewe

A principal component meta-analysis on multiple anthropometric traits identifies novel loci for body shape

Large consortia have revealed hundreds of genetic loci associated with anthropometric traits, one trait at a time. We examined whether genetic variants affect body shape as a composite phenotype that is represented by a combination of anthropometric traits. We developed an approach that calculates averaged PCs (AvPCs) representing body shape derived from six anthropometric traits (body mass index, height, weight, waist and hip circumference, waist-to-hip ratio). The first four AvPCs explain >99% of the variability, are heritable, and associate with cardiometabolic outcomes. We performed genome-wide association analyses for each body shape composite phenotype across 65 studies and meta-analysed summary statistics. We identify six novel loci: LEMD2 and CD47 for AvPC1, RPS6KA5/C14orf159 and GANAB for AvPC3, and ARL15 and ANP32 for AvPC4. Our findings highlight the value of using multiple traits to define complex phenotypes for discovery, which are not captured by single-trait analyses, and may shed light onto new pathways

Genome-wide physical activity interactions in adiposity. A meta-analysis of 200,452 adults

Physical activity (PA) may modify the genetic effects that give rise to increased risk of obesity. To identify adiposity loci whose effects are modified by PA, we performed genome-wide interaction meta-analyses of BMI and BMI-adjusted waist circumference and waist-hip ratio from up to 200,452 adults of European (n = 180,423) or other ancestry (n = 20,029). We standardized PA by categorizing it into a dichotomous variable where, on average, 23% of participants were categorized as inactive and 77% as physically active. While we replicate the interaction with PA for the strongest known obesity-risk locus in the FTO gene, of which the effect is attenuated by similar to 30% in physically active individuals compared to inactive individuals, we do not identify additional loci that are sensitive to PA. In additional genome-wide meta-analyses adjusting for PA and interaction with PA, we identify 11 novel adiposity loci, suggesting that accounting for PA or other environmental factors that contribute to variation in adiposity may facilitate gene discovery

A principal component meta-analysis on multiple anthropometric traits identifies novel loci for body shape

Large consortia have revealed hundreds of genetic loci associated with anthropometric traits, one trait at a time. We examined whether genetic variants affect body shape as a composite phenotype that is represented by a combination of anthropometric traits. We developed an approach that calculates averaged PCs (AvPCs) representing body shape derived from six anthropometric traits (body mass index, height, weight, waist and hip circumference, waist-to-hip ratio). The first four AvPCs explain >99% of the variability, are heritable, and associate with cardiometabolic outcomes. We performed genome-wide association analyses for each body shape composite phenotype across 65 studies and meta-analysed summary statistics. We identify six novel loci: LEMD2 and CD47 for AvPC1, RPS6KA5/C14orf159 and GANAB for AvPC3, and ARL15 and ANP32 for AvPC4. Our findings highlight the value of using multiple traits to define complex phenotypes for discovery, which are not captured by single-trait analyses, and may shed light onto new pathways

A principal component meta-analysis on multiple anthropometric traits identifies novel loci for body shape

Large consortia have revealed hundreds of genetic loci associated with anthropometric traits, one trait at a time. We examined whether genetic variants affect body shape as a composite phenotype that is represented by a combination of anthropometric traits. We developed an approach that calculates averaged PCs (AvPCs) representing body shape derived from six anthropometric traits (body mass index, height, weight, waist and hip circumference, waist-to-hip ratio). The first four AvPCs explain >99% of the variability, are heritable, and associate with cardiometabolic outcomes. We performed genome-wide association analyses for each body shape composite phenotype across 65 studies and meta-analysed summary statistics. We identify six novel loci: LEMD2 and CD47 for AvPC1, RPS6KA5/C14orf159 and GANAB for AvPC3, and ARL15 and ANP32 for AvPC4. Our findings highlight the value of using multiple traits to define complex phenotypes for discovery, which are not captured by single-trait analyses, and may shed light onto new pathways.Peer reviewe