scholarly journals Body Mass Index and Birth Weight Improve Polygenic Risk Score for Type 2 Diabetes

2021 ◽  
Vol 11 (6) ◽  
pp. 582
Author(s):  
Avigail Moldovan ◽  
Yedael Y. Waldman ◽  
Nadav Brandes ◽  
Michal Linial
Keyword(s):  
Type 2 Diabetes ◽  
Body Mass Index ◽  
Birth Weight ◽  
Body Mass ◽  
Risk Score ◽  
Early Life ◽  
Integrated Approach ◽  
Polygenic Risk Score ◽  
Polygenic Risk

One of the major challenges in the post-genomic era is elucidating the genetic basis of human diseases. In recent years, studies have shown that polygenic risk scores (PRS), based on aggregated information from millions of variants across the human genome, can estimate individual risk for common diseases. In practice, the current medical practice still predominantly relies on physiological and clinical indicators to assess personal disease risk. For example, caregivers mark individuals with high body mass index (BMI) as having an increased risk to develop type 2 diabetes (T2D). An important question is whether combining PRS with clinical metrics can increase the power of disease prediction in particular from early life. In this work we examined this question, focusing on T2D. We present here a sex-specific integrated approach that combines PRS with additional measurements and age to define a new risk score. We show that such approach combining adult BMI and PRS achieves considerably better prediction than each of the measures on unrelated Caucasians in the UK Biobank (UKB, n = 290,584). Likewise, integrating PRS with self-reports on birth weight (n = 172,239) and comparative body size at age ten (n = 287,203) also substantially enhance prediction as compared to each of its components. While the integration of PRS with BMI achieved better results as compared to the other measurements, the latter are early-life measurements that can be integrated already at childhood, to allow preemptive intervention for those at high risk to develop T2D. Our integrated approach can be easily generalized to other diseases, with the relevant early-life measurements.

scholarly journals Body Mass Index and Birth Weight Improve Polygenic Risk Score for Type 2 Diabetes

2021 ◽  
Author(s):  
Avigail Moldovan ◽  
Yedael Y. Waldman ◽  
Nadav Brandes ◽  
Michal Linial
Keyword(s):  
Type 2 Diabetes ◽  
Body Mass Index ◽  
Birth Weight ◽  
Body Mass ◽  
Early Life ◽  
Integrated Approach ◽  
Polygenic Risk Score ◽  
Polygenic Risk ◽  
Increased Risk

One of the major challenges in the post-genomic era is elucidating the genetic basis of human diseases. In recent years, studies have shown that polygenic risk scores (PRS), based on aggregated information from millions of variants across the human genome, can estimate individual risk for common diseases. In practice, the current medical practice still predominantly relies on physiological and clinical indicators to assess personal disease risk. For example, caregivers mark individuals with high body mass index (BMI) as having an increased risk to develop type 2 diabetes (T2D). An important question is whether combining PRS with clinical metrics can increase the power of disease prediction in particular from early life. In this work we examined this question, focusing on T2D. We show that an integrated approach combining adult BMI and PRS achieves considerably better prediction than each of the measures on unrelated Caucasians in the UK Biobank (UKB, n=290,584). Likewise, integrating PRS with self-reports on birth weight (n=172,239) and comparative body size at age ten (n=287,203) also substantially enhance prediction as compared to each of its components. While the integration of PRS with BMI achieved better results as compared to the other measurements, the latter are early-life measurements that can be integrated already at childhood, to allow preemptive intervention for those at high risk to develop T2D. Our integrated approach can be easily generalized to other diseases, with the relevant early-life measurements.

scholarly journals Body mass index and birth weight improve polygenic risk score for type 2 diabetes

2021 ◽  
Author(s):  
Avigail Moldovan ◽  
Yedael Y. Waldman ◽  
Nadav Brandes ◽  
Michal Linial
Keyword(s):  
Type 2 Diabetes ◽  
Body Mass Index ◽  
Birth Weight ◽  
Body Mass ◽  
Early Life ◽  
Integrated Approach ◽  
Polygenic Risk Score ◽  
Polygenic Risk ◽  
Increased Risk

One of the major challenges in the post-genomic era is elucidating the genetic basis of human diseases. In recent years, studies have shown that polygenic risk scores (PRS), based on aggregated information from millions of variants across the human genome, can estimate individual risk for common diseases. In practice, the current medical practice still predominantly relies on physiological and clinical indicators to assess personal disease risk. For example, caregivers mark individuals with high body mass index (BMI) as having an increased risk to develop type 2 diabetes (T2D). An important question is whether combining PRS with clinical metrics can increase the power of disease prediction in particular from early life. In this work we examined this question, focusing on T2D. We show that an integrated approach combining adult BMI and PRS achieves considerably better prediction than each of the measures on unrelated Caucasians in the UK Biobank (UKB, n=290,584). Likewise, integrating PRS with self-reports on birth weight (n=172,239) and comparative body size at age ten (n=287,203) also substantially enhance prediction as compared to each of its components. While the integration of PRS with BMI achieved better results as compared to the other measurements, the latter are early-life measurements that can be integrated already at childhood, to allow preemptive intervention for those at high risk to develop T2D. Our integrated approach can be easily generalized to other diseases, with the relevant early-life measurements.

1645-P: Polygenic Risk Score for Prediction of Complications in Men and Women with Type 2 Diabetes

Diabetes ◽  
2020 ◽  
Vol 69 (Supplement 1) ◽  
pp. 1645-P
Author(s):  
JOHANNE TREMBLAY ◽  
REDHA ATTAOUA ◽  
MOUNSIF HALOUI ◽  
RAMZAN TAHIR ◽  
CAROLE LONG ◽  
...  
Keyword(s):  
Type 2 Diabetes ◽  
Risk Score ◽  
Polygenic Risk Score ◽  
Men And Women ◽  
Polygenic Risk ◽  
Prediction Of Complications

304-OR: Polygenic Risk Score Predicts Type 2 Diabetes Susceptibility in a Diverse Consumer Genetic Database

Diabetes ◽  
2019 ◽  
Vol 68 (Supplement 1) ◽  
pp. 304-OR
Author(s):  
MICHAEL L. MULTHAUP ◽  
RYOSUKE KITA ◽  
NICHOLAS ERIKSSON ◽  
STELLA ASLIBEKYAN ◽  
JANIE SHELTON ◽  
...  
Keyword(s):  
Type 2 Diabetes ◽  
Risk Score ◽  
Polygenic Risk Score ◽  
Diabetes Susceptibility ◽  
Genetic Database ◽  
Polygenic Risk

1134-P: Polygenic Risk Score of Type 2 Diabetes as a Predictive Factor for Macrovascular Complications: A Prospective Population-Based UK Biobank Study

Diabetes ◽  
2021 ◽  
Vol 70 (Supplement 1) ◽  
pp. 1134-P
Author(s):  
SANGHYUK JUNG ◽  
DOKYOON KIM ◽  
MANU SHIVAKUMAR ◽  
HONG-HEE WON ◽  
JAE-SEUNG YUN
Keyword(s):  
Type 2 Diabetes ◽  
Risk Score ◽  
Predictive Factor ◽  
Population Based ◽  
Macrovascular Complications ◽  
Polygenic Risk Score ◽  
Uk Biobank ◽  
Polygenic Risk

scholarly journals Haplotypes in theCRPGene Associated with Increased BMI and Levels of CRP in Subjects with Type 2 Diabetes or Obesity from Southwestern Mexico

2012 ◽  
Vol 2012 ◽  
pp. 1-7 ◽  
Author(s):  
América Martínez-Calleja ◽  
Irma Quiróz-Vargas ◽  
Isela Parra-Rojas ◽  
José Francisco Muñoz-Valle ◽  
Marco A. Leyva-Vázquez ◽  
...  
Keyword(s):  
Type 2 Diabetes ◽  
Body Mass Index ◽  
Coronary Heart Disease ◽  
Heart Disease ◽  
Body Mass ◽  
Risk Score ◽  
Blood Levels ◽  
Reactive Protein ◽  
Pcr Rflp

Objective. We evaluated the association between four polymorphisms in theCRPgene with circulating levels of C-reactive protein (CRP), type 2 diabetes (T2D), obesity, and risk score of coronary heart disease.Methods. We studied 402 individuals and classified them into four groups: healthy, obese, T2D obese, and T2D without obesity, from Guerrero, Southwestern Mexico. Blood levels of CRP, glucose, cholesterol, triglycerides, and leukocytes were measured. Genotyping was performed by PCR/RFLP, and the risk score for coronary heart disease was determined by the Framingham's methodology.Results. The TT genotype of SNP rs1130864 was associated with increased body mass index and T2D patients with obesity. We found that the haplotype 2 (TGAG) was associated with increased levels of CRP (β=0.3; 95%CI: 0.1, 0.5;P=0.005) and haplotype 7 (TGGG) with higher body mass index (BMI) (β=0.2; 95%CI: 0.1, 0.3;P<0.001). The risk score for coronary heart disease was associated with increased levels of CRP, but not with any polymorphism or haplotype.Conclusions. The association between the TT genotype of SNP rs1130864 with obesity and the haplotype 7 with BMI may explain how obesity and genetic predisposition increase the risk of diseases such as T2D in the population of Southwestern Mexico.

scholarly journals Joint association of mammographic density adjusted for age and body mass index and polygenic risk score with breast cancer risk

2019 ◽  
Vol 21 (1) ◽  
Author(s):  
Celine M. Vachon ◽  
Christopher G. Scott ◽  
Rulla M. Tamimi ◽  
Deborah J. Thompson ◽  
Peter A. Fasching ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Body Mass Index ◽  
Breast Cancer Risk ◽  
Cancer Risk ◽  
Body Mass ◽  
Mammographic Density ◽  
Risk Score ◽  
Polygenic Risk Score ◽  
Polygenic Risk

scholarly journals Non-linear interaction between physical activity and polygenic risk score of body mass index in Danish and Russian populations

PLoS ONE ◽  
2021 ◽  
Vol 16 (10) ◽  
pp. e0258748
Author(s):  
Dmitrii Borisevich ◽  
Theresia M. Schnurr ◽  
Line Engelbrechtsen ◽  
Alexander Rakitko ◽  
Lars Ängquist ◽  
...  
Keyword(s):  
Physical Activity ◽  
Body Mass Index ◽  
Environmental Factors ◽  
Body Mass ◽  
Risk Score ◽  
Polygenic Risk Score ◽  
Environment Interaction ◽  
Linear Interaction ◽  
Polygenic Risk ◽  
Non Linear

Body mass index (BMI) is a highly heritable polygenic trait. It is also affected by various environmental and behavioral risk factors. We used a BMI polygenic risk score (PRS) to study the interplay between the genetic and environmental factors defining BMI. First, we generated a BMI PRS that explained more variance than a BMI genetic risk score (GRS), which was using only genome-wide significant BMI-associated variants (R2 = 13.1% compared to 6.1%). Second, we analyzed interactions between BMI PRS and seven environmental factors. We found a significant interaction between physical activity and BMI PRS, even when the well-known effect of the FTO region was excluded from the PRS, using a small dataset of 6,179 samples. Third, we stratified the study population into two risk groups using BMI PRS. The top 22% of the studied populations were included in a high PRS risk group. Engagement in self-reported physical activity was associated with a 1.66 kg/m2 decrease in BMI in this group, compared to a 0.84 kg/m2 decrease in BMI in the rest of the population. Our results (i) confirm that genetic background strongly affects adult BMI in the general population, (ii) show a non-linear interaction between BMI genetics and physical activity, and (iii) provide a standardized framework for future gene-environment interaction analyses.

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