Effects of polygenic risk score of type 2 diabetes on the hippocampal topological property and episodic memory

Abstract Purpose Type 2 diabetes is associated with a higher risk of dementia. The pathogenesis is complex, partly influenced by genetic factors. The hippocampus is the most vulnerable brain region in individuals with type 2 diabetes. However, whether the genetic risk of type 2 diabetes is associated with the hippocampus and episodic memory remains unclear. This study explored the influence of polygenic risk score (PRS) of type 2 diabetes on the white matter topological properties of the hippocampus among individuals with and without type 2 diabetes and its associations with episodic memory. Methods This study included 103 individuals with type 2 diabetes and 114 well-matched individuals without type 2 diabetes. All the participants were genotyped, and a diffusion tensor imaging-based structural network was constructed. PRS was calculated based on a genome-wide association study of type 2 diabetes. The PRS-by-disease interactions on the bilateral hippocampal topological network properties were evaluated by analysis of variance (ANOVA). Results There were significant PRS-by-disease interaction effects on the nodal topological properties of the right hippocampus node. In the individuals with type 2 diabetes, the PRS was correlated with the right hippocampal nodal properties, and the nodal properties were correlated with the episodic memory. In addition, the right hippocampal nodal properties mediated the effect of PRS on the episodic memory in individuals with type 2 diabetes. Conclusion Our results suggested a gene-brain-cognition biological pathway, which might help understand the neural mechanism of the genetic risk of type 2 diabetes affects episodic memory in type 2 diabetes.

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Quality of dietary fat and genetic risk of type 2 diabetes: individual participant data meta-analysis

BMJ ◽

10.1136/bmj.l4292 ◽

2019 ◽

pp. l4292 ◽

Cited By ~ 8

Author(s):

Jordi Merino ◽

Marta Guasch-Ferré ◽

Christina Ellervik ◽

Hassan S Dashti ◽

Stephen J Sharp ◽

...

Keyword(s):

Type 2 Diabetes ◽

Cohort Studies ◽

Risk Score ◽

Dietary Fat ◽

Genetic Risk ◽

Polygenic Risk Score ◽

Polyunsaturated Fat ◽

Polygenic Risk

Abstract Objective To investigate whether the genetic burden of type 2 diabetes modifies the association between the quality of dietary fat and the incidence of type 2 diabetes. Design Individual participant data meta-analysis. Data sources Eligible prospective cohort studies were systematically sourced from studies published between January 1970 and February 2017 through electronic searches in major medical databases (Medline, Embase, and Scopus) and discussion with investigators. Review methods Data from cohort studies or multicohort consortia with available genome-wide genetic data and information about the quality of dietary fat and the incidence of type 2 diabetes in participants of European descent was sought. Prospective cohorts that had accrued five or more years of follow-up were included. The type 2 diabetes genetic risk profile was characterized by a 68-variant polygenic risk score weighted by published effect sizes. Diet was recorded by using validated cohort-specific dietary assessment tools. Outcome measures were summary adjusted hazard ratios of incident type 2 diabetes for polygenic risk score, isocaloric replacement of carbohydrate (refined starch and sugars) with types of fat, and the interaction of types of fat with polygenic risk score. Results Of 102 305 participants from 15 prospective cohort studies, 20 015 type 2 diabetes cases were documented after a median follow-up of 12 years (interquartile range 9.4-14.2). The hazard ratio of type 2 diabetes per increment of 10 risk alleles in the polygenic risk score was 1.64 (95% confidence interval 1.54 to 1.75, I 2 =7.1%, τ 2 =0.003). The increase of polyunsaturated fat and total omega 6 polyunsaturated fat intake in place of carbohydrate was associated with a lower risk of type 2 diabetes, with hazard ratios of 0.90 (0.82 to 0.98, I 2 =18.0%, τ 2 =0.006; per 5% of energy) and 0.99 (0.97 to 1.00, I 2 =58.8%, τ 2 =0.001; per increment of 1 g/d), respectively. Increasing monounsaturated fat in place of carbohydrate was associated with a higher risk of type 2 diabetes (hazard ratio 1.10, 95% confidence interval 1.01 to 1.19, I 2 =25.9%, τ 2 =0.006; per 5% of energy). Evidence of small study effects was detected for the overall association of polyunsaturated fat with the risk of type 2 diabetes, but not for the omega 6 polyunsaturated fat and monounsaturated fat associations. Significant interactions between dietary fat and polygenic risk score on the risk of type 2 diabetes (P>0.05 for interaction) were not observed. Conclusions These data indicate that genetic burden and the quality of dietary fat are each associated with the incidence of type 2 diabetes. The findings do not support tailoring recommendations on the quality of dietary fat to individual type 2 diabetes genetic risk profiles for the primary prevention of type 2 diabetes, and suggest that dietary fat is associated with the risk of type 2 diabetes across the spectrum of type 2 diabetes genetic risk.

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

Diabetes ◽

10.2337/db20-1645-p ◽

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

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

Diabetes ◽

10.2337/db19-304-or ◽

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

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

Diabetes ◽

10.2337/db21-1134-p ◽

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

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Interaction between physical activity and polygenic risk score for type-2 diabetes in older Americans

European Journal of Public Health ◽

10.1093/eurpub/ckaa165.888 ◽

2020 ◽

Vol 30 (Supplement_5) ◽

Author(s):

Y Y Wu ◽

M D Thompson ◽

F Youkhana ◽

C M Pirkle

Keyword(s):

Physical Activity ◽

Type 2 Diabetes ◽

Genetic Risk ◽

Significant Interaction ◽

The United States ◽

Lifestyle Factors ◽

Polygenic Risk Score ◽

Environment Interaction ◽

Polygenic Risk

Abstract Background Genetics plays an important role in the development of type-2 diabetes (T2D). Polygenic risk scores (PRS) are increasingly used to quantify genetic risk of T2D in epidemiological studies. These scores, when integrated into analyses of modifiable lifestyle factors, may improve understanding of T2D etiology, as the strength of association with T2D and some lifestyle or demographic factors may vary according to genetic predisposition. Methods We examined PRS-lifestyle factor interactions on T2D with data from the United States Health and Retirement Study (HRS), a prospective longitudinal cohort of older adults (≥50 at baseline). HRS contains nationally representative samples of Black and White Americans with pre-calculated PRS for T2D (N = 14,001). Covariates included sex, education, BMI, smoking, alcohol, and physical activity. Predicted prevalence and incidence of T2D were calculated with logistic regression models. Nonparametric bootstrap method was performed to calculate differences in T2D prevalence and incidence by PRS percentiles and interaction variables. Results Significant interaction (p_interaction=0.0096) was detected between PRS and physical activity among Whites only. In those with the lowest decile of PRS, T2D prevalence was similar (∼10%) for those reporting no physical activity compared to low or moderate activity. In those with the top decile of PRS, lower T2D prevalence (17%, 95%CI:14.8,19.6) was observed among those with moderate compared to no activity (24%, 95%CI:20.4,27.5). Incident T2D in Whites followed a similar pattern (p_interaction=0.0194). Among Black participants, no significant interaction with any lifestyle variables was detected. Conclusions Interaction of different genetic risk profiles with lifestyle factors may inform understanding of why certain inventions are more or less effective in different groups of people, potentially improving clinical and prevention interventions. Key messages Protection conferred by physical activity on T2D varied by underlying genetic risk. Gene-environment interaction studies provide insights on why lifestyle factors vary in their associations with T2D.

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Genome-wide polygenic risk score for retinopathy of type 2 diabetes

Human Molecular Genetics ◽

10.1093/hmg/ddab067 ◽

2021 ◽

Author(s):

Iain S Forrest ◽

Kumardeep Chaudhary ◽

Ishan Paranjpe ◽

Ha My T Vy ◽

Carla Marquez-Luna ◽

...

Keyword(s):

Type 2 Diabetes ◽

Risk Score ◽

Large Scale ◽

European Ancestry ◽

Polygenic Risk Score ◽

Standard Deviation Increase ◽

Polygenic Risk ◽

Genome Wide ◽

A Genome

Abstract Diabetic retinopathy (DR) is a common consequence in type 2 diabetes (T2D) and a leading cause of blindness in working-age adults. Yet, its genetic predisposition is largely unknown. Here, we examined the polygenic architecture underlying DR by deriving and assessing a genome-wide polygenic risk score (PRS) for DR. We evaluated the PRS in 6079 individuals with T2D of European, Hispanic, African and other ancestries from a large-scale multi-ethnic biobank. Main outcomes were PRS association with DR diagnosis, symptoms and complications, and time to diagnosis, and transferability to non-European ancestries. We observed that PRS was significantly associated with DR. A standard deviation increase in PRS was accompanied by an adjusted odds ratio (OR) of 1.12 [95% confidence interval (CI) 1.04–1.20; P = 0.001] for DR diagnosis. When stratified by ancestry, PRS was associated with the highest OR in European ancestry (OR = 1.22, 95% CI 1.02–1.41; P = 0.049), followed by African (OR = 1.15, 95% CI 1.03–1.28; P = 0.028) and Hispanic ancestries (OR = 1.10, 95% CI 1.00–1.10; P = 0.050). Individuals in the top PRS decile had a 1.8-fold elevated risk for DR versus the bottom decile (P = 0.002). Among individuals without DR diagnosis, the top PRS decile had more DR symptoms than the bottom decile (P = 0.008). The PRS was associated with retinal hemorrhage (OR = 1.44, 95% CI 1.03–2.02; P = 0.03) and earlier DR presentation (10% probability of DR by 4 years in the top PRS decile versus 8 years in the bottom decile). These results establish the significant polygenic underpinnings of DR and indicate the need for more diverse ancestries in biobanks to develop multi-ancestral PRS.

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Economic Evaluation of a New Polygenic Risk Score to Predict Nephropathy in Adult Patients With Type 2 Diabetes

Canadian Journal of Diabetes ◽

10.1016/j.jcjd.2020.06.010 ◽

2020 ◽

Author(s):

Kimberly Guinan ◽

Catherine Beauchemin ◽

Johanne Tremblay ◽

John Chalmers ◽

Mark Woodward ◽

...

Keyword(s):

Type 2 Diabetes ◽

Economic Evaluation ◽

Risk Score ◽

Adult Patients ◽

Polygenic Risk Score ◽

Polygenic Risk

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

Journal of Personalized Medicine ◽

10.3390/jpm11060582 ◽

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.

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