Utility of genetic and non-genetic risk factors in prediction of type 2 diabetes: Whitehall II prospective cohort study

Integrating the Thrifty Genotype and Evolutionary Mismatch Hypotheses to understand variation in cardiometabolic disease risk

More than 60 years ago, James Neel proposed the Thrifty Genotype Hypothesis to explain the widespread prevalence of type 2 diabetes in Western, industrial contexts. This hypothesis posits that variants linked to conservative energy usage and increased fat deposition would have been favored throughout human evolution due to the advantages they could provide during periods of resource limitation. However, in industrial environments, these variants instead produce an increased risk of obesity, metabolic syndrome, type 2 diabetes, and related health issues. This hypothesis has been popular and impactful, with thousands of citations, many ongoing debates, and several spin-off theories in biomedicine, evolutionary biology, and anthropology. However, despite great attention, the applicability and utility of the Thrifty Genotype Hypothesis (TGH) to modern human health remains, in our opinion, unresolved. To move research in this area forward, we first discuss the original formulation of the TGH and its critiques. Second, we trace the TGH to updated hypotheses that are currently at the forefront of the evolutionary medicine literature-namely, the Evolutionary Mismatch Hypothesis. Third, we lay out empirical predictions for updated hypotheses and evaluate them against the current literature. Finally, we discuss study designs that could be fruitful for filling current knowledge gaps; here, we focus on partnerships with subsistence-level groups undergoing lifestyle transitions, and we present data from an ongoing study with the Orang Asli of Malaysia to illustrate this point. Overall, we hope this synthesis will guide new empirical research aimed at understanding how the human evolutionary past interacts with our modern environments to influence cardiometabolic health.

Comparative analysis of genetic risk scores for predicting biochemical recurrence in prostate cancer patients after radical prostatectomy

BACKGROUND

In recent years, Genome-Wide Association Studies (GWAS) has identified risk variants related to complex diseases, but most genetic variants have less impact on phenotypes. To solve the above problems, methods that can use variants with low genetic effects, such as genetic risk score (GRS), have been developed to predict disease risk.

METHODS

As the GRS model with the most incredible prediction power for complex diseases has not been determined, our study used simulation data and prostate cancer data to explore the disease prediction power of three GRS models, including the simple count genetic risk score (SC-GRS), the direct logistic regression genetic risk score (DL-GRS), and the explained variance weighted GRS based on directed logistic regression (EVDL-GRS).

RESULTS AND CONCLUSIONS

We used 26 SNPs to establish GRS models to predict the risk of biochemical recurrence (BCR) after radical prostatectomy. Combining clinical variables such as age at diagnosis, body mass index, prostate-specific antigen, Gleason score, pathologic T stage, and surgical margin and GRS models has better predictive power for BCR. The results of simulation data (statistical power = 0.707) and prostate cancer data (area under curve = 0.8462) show that DL-GRS has the best prediction performance. The rs455192 was the most relevant locus for BCR (p = 2.496 × 10-6) in our study.

AI-enhanced integration of genetic and medical imaging data for risk assessment of Type 2 diabetes

Type 2 diabetes (T2D) presents a formidable global health challenge, highlighted by its escalating prevalence, underscoring the critical need for precision health strategies and early detection initiatives. Leveraging artificial intelligence, particularly eXtreme Gradient Boosting (XGBoost), we devise robust risk assessment models for T2D. Drawing upon comprehensive genetic and medical imaging datasets from 68,911 individuals in the Taiwan Biobank, our models integrate Polygenic Risk Scores (PRS), Multi-image Risk Scores (MRS), and demographic variables, such as age, sex, and T2D family history. Here, we show that our model achieves an Area Under the Receiver Operating Curve (AUC) of 0.94, effectively identifying high-risk T2D subgroups. A streamlined model featuring eight key variables also maintains a high AUC of 0.939. This high accuracy for T2D risk assessment promises to catalyze early detection and preventive strategies. Moreover, we introduce an accessible online risk assessment tool for T2D, facilitating broader applicability and dissemination of our findings.

Risk Assessment for Gastrointestinal Diseases via Clinical Dimension and Genome-Wide Polygenic Risk Scores of Type 2 Diabetes: A Population-Based Cohort Study

OBJECTIVE

We aimed to evaluate whether individuals with type 2 diabetes (T2D) were at higher risk of developing a wide range of gastrointestinal diseases based on a population-based cohort study.

RESEARCH DESIGN AND METHODS

This study included 374,125 participants free of gastrointestinal disorders at baseline; of them, 19,719 (5.27%) with T2D were followed-up by linking to multiple medical records to record gastrointestinal disease diagnoses. Multivariable Cox models were used to estimate the hazard ratios (HRs) and CIs. Logistic models were used to examine the associations between polygenic risk scores (PRS) and clinical gastrointestinal phenotypes.

RESULTS

During a median follow-up of 12.0 years, we observed the new onset of 15 gastrointestinal diseases. Compared with nondiabetes, participants with T2D had an increased risk of gastritis and duodenitis (HR 1.58, 95% CI 1.51-1.65), peptic ulcer (HR 1.56, 95% CI 1.43-1.71), diverticular disease (HR 1.19, 95% CI 1.14-1.24), pancreatitis (HR 1.45, 95% CI 1.24-1.71), nonalcoholic fatty liver disease (HR 2.46, 95% CI 2.25-2.69), liver cirrhosis (HR 2.92, 95% CI 2.58-3.30), biliary disease (HR 1.18, 95% CI 1.10-1.26), gastrointestinal tract cancers (HR 1.28, 95% CI 1.17-1.40), and hepatobiliary and pancreatic cancer (HR 2.32, 95% CI 2.01-2.67). Positive associations of PRS of T2D with gastritis, duodenitis, and nonalcoholic fatty liver disease were also observed.

CONCLUSIONS

In this large cohort study, we found that T2D was associated with increased risks of a wide range of gastrointestinal outcomes. We suggest the importance of early detection and prevention of gastrointestinal disorders among patients with T2D.

An early prediction model for type 2 diabetes mellitus based on genetic variants and nongenetic risk factors in a Han Chinese cohort

Aims

We aimed to construct a prediction model of type 2 diabetes mellitus (T2DM) in a Han Chinese cohort using a genetic risk score (GRS) and a nongenetic risk score (NGRS).

Methods

A total of 297 Han Chinese subjects who were free from type 2 diabetes mellitus were selected from the Tianjin Medical University Chronic Disease Cohort for a prospective cohort study. Clinical characteristics were collected at baseline and subsequently tracked for a duration of 9 years. Genome-wide association studies (GWASs) were performed for T2DM-related phenotypes. The GRS was constructed using 13 T2DM-related quantitative trait single nucleotide polymorphisms (SNPs) loci derived from GWASs, and NGRS was calculated from 4 biochemical indicators of independent risk that screened by multifactorial Cox regressions.

Results

We found that HOMA-IR, uric acid, and low HDL were independent risk factors for T2DM (HR >1; P<0.05), and the NGRS model was created using these three nongenetic risk factors, with an area under the ROC curve (AUC) of 0.678; high fasting glucose (FPG >5 mmol/L) was a key risk factor for T2DM (HR = 7.174, P< 0.001), and its addition to the NGRS model caused a significant improvement in AUC (from 0.678 to 0.764). By adding 13 SNPs associated with T2DM to the GRS prediction model, the AUC increased to 0.892. The final combined prediction model was created by taking the arithmetic sum of the two models, which had an AUC of 0.908, a sensitivity of 0.845, and a specificity of 0.839.

Conclusions

We constructed a comprehensive prediction model for type 2 diabetes out of a Han Chinese cohort. Along with independent risk factors, GRS is a crucial element to predicting the risk of type 2 diabetes mellitus.

Comparisons of clinical characteristics, prognosis, epidemiological factors, and genetic susceptibility between HER2-low and HER2-zero breast cancer among Chinese females

BACKGROUND

Traditional human epidermal growth factor receptor 2 (HER2)-negative breast cancer (BC) is recommended to be divided into HER2-low and HER2-zero subtypes due to different prognosis. However, few studies investigated their differences in clinical characteristics and prognosis among Chinese HER2-negative BC and their stratified differences by hormone receptor (HR), while fewer studies investigated their differences in epidemiological factors and genetic susceptibility.

METHODS

A total of 11,911 HER2-negative BC were included to compare the clinical characteristics and prognosis between HER2-zero and HER2-low BC, and 4227 of the 11,911 HER2-negative BC were further compared to 5653 controls to investigate subtype-specific epidemiological factors and single nucleotide polymorphisms(SNPs).

RESULTS

Overall, 64.2% of HER2-negative BC were HER2-low BC, and the stratified proportions of HER2-low BC were 61.9% and 75.2% for HR-positive and HR-negative BC, respectively. Compared to HER2-zero BC, HER2-low BC among HR-positive BC showed younger age at diagnosis, later stage, poorer differentiation, and higher Ki-67, while elder age at diagnosis and lower mortality were observed for HER2-low BC among HR-negative BC (all p values <0.05). Compared to healthy controls, both HER2-low and HER2-zero BC are associated with similar epidemiological factors and SNPs. However, stronger interaction between epidemiological factors and polygenic risk scores were observed for HER2-zero BC than HER2-low BC among either HR-positive [odds ratios: 10.71 (7.55-15.17) and 8.84 (6.19-12.62) for the highest risk group compared to the lowest risk group] or HR-negative BC [7.00 (3.14-15.63) and 5.70 (3.26-9.98)].

CONCLUSIONS

HER2-low BC should deserve more attention than HER2-zero BC, especially in HR-negative BC, due to larger proportion, less clinical heterogeneity, better prognosis, and less susceptibility to risk factors.

Outdoor light at night in relation to glucose homoeostasis and diabetes in Chinese adults: a national and cross-sectional study of 98,658 participants from 162 study sites

AIMS/HYPOTHESIS

Exposure to artificial light at night (LAN) disrupts the circadian timing system and might be a risk factor for diabetes. Our aim was to estimate the associations of chronic exposure to outdoor LAN with glucose homoeostasis markers and diabetes prevalence based on a national and cross-sectional survey of the general population in China.

METHODS

The China Noncommunicable Disease Surveillance Study was a nationally representative study of 98,658 participants aged ≥18 years who had been living in their current residence for at least 6 months recruited from 162 study sites across mainland China in 2010. Diabetes was defined according to ADA criteria. Outdoor LAN exposure in 2010 was estimated from satellite data and the participants attending each study site were assigned the same mean radiance of the outdoor LAN at the study site. The linear regression incorporating a restricted cubic spline function was used to explore the relationships between LAN exposure and markers of glucose homoeostasis. Cox regression with a constant for the time variable assigned to all individuals and with robust variance estimates was used to assess the associations between the levels of outdoor LAN exposure and the presence of diabetes by calculating the prevalence ratios (PRs) with adjustment for age, sex, education, smoking status, drinking status, physical activity, family history of diabetes, household income, urban/rural areas, taking antihypertensive medications, taking lipid-lowering medications, and BMI.

RESULTS

The mean age of the study population was 42.7 years and 53,515 (weighted proportion 49.2%) participants were women. Outdoor LAN exposure levels were positively associated with HbA_1c, fasting and 2 h glucose concentrations and HOMA-IR and negatively associated with HOMA-B. Diabetes prevalence was significantly associated with per-quintile LAN exposure (PR 1.07 [95% CI 1.02, 1.12]). The highest quintile of LAN exposure (median 69.1 nW cm^-2 sr^-1) was significantly associated with an increased prevalence of diabetes (PR 1.28 [95% CI 1.03, 1.60]) compared with the lowest quintile of exposure (median 1.0 nW cm^-2 sr^-1).

CONCLUSIONS/INTERPRETATION

There were significant associations between chronic exposure to higher intensity of outdoor LAN with increased risk of impaired glucose homoeostasis and diabetes prevalence. Our findings contribute to the growing evidence that LAN is detrimental to health and point to outdoor LAN as a potential novel risk factor for diabetes.

The management correlation between metabolic index, cardiovascular health, and diabetes combined with cardiovascular disease

Background

Cardiovascular disease (CVD) has become a major cause of morbidity and mortality in patients with type 2 diabetes mellitus (T2DM). Although there is also evidence that multifactorial interventions to control blood glucose, blood pressure, and lipid profiles can reduce macrovascular complications and mortality in patients with T2DM, the link between these risk factors has not been established.

Methods

On 10 December 2018, 1,920 people in four cities in Anhui Province were included. Latent category analysis (LCA) was used to explore the clustering mode of HRBs (health risk behaviors). The primary exposure was HRBs and exercise and diet interventions, and the primary outcome was CVD and other variables, including zMS, triglyceride-glucose index (TyG), TyG-WC (waist circumference), TyG-BMI, TG/HDL, and cardiovascular health (CVH). A multivariable logistic regression model was used to establish the relationship between HRBs, exercise, diet interventions, and CVD. Moderate analysis and mediation moderation analysis were employed by the PROCESS method to explore the relationship between these variables. Sensitivity analysis explored the robustness of the model.

Results

The mean age was 57.10 ± 10.0 years old. Overall, CVD affects approximately 19.9% of all persons with T2DM. Macrovascular complications of T2DM include coronary heart disease, myocardial infarction (MI), cardiac insufficiency, and cerebrovascular disease. Elderly age (χ 2 = 22.70), no occupation (χ 2 = 20.97), medium and high socioeconomic status (SES) (χ 2 = 19.92), higher level of TyG-WC (χ 2 = 6.60), and higher zMS (χ 2 = 7.59) were correlated with high CVD. Many metabolic indices have shown a connection with T2DM combined with CVD, and there was a dose-response relationship between HRB co-occurrence and clustering of HRBs and zMS; there was a dose-response relationship between multifactorial intervention and CVH. In the mediation moderation analysis, there was an association between HRB, gender, TyG, TyG-BMI, and CVD. From an intervention management perspective, exercise and no diet intervention were more significant with CVD; moreover, there was an association between intervention management, gender, zMS, TyG-WC, TyG-BMI, TG/HDL, and CVD. Finally, there was an association between sex, CVH, and CVD. Sensitivity analysis demonstrated that our results were robust.

Conclusions

CVD is one of the common complications in patients with type 2 diabetes, and its long-term outcome will have more or less impact on patients. Our findings suggest the potential benefits of scaling up multifactorial and multifaceted interventions to prevent CVD in patients with T2DM.

The Correlation Between Health Risk Factors and Diabesity and Lipid Profile Indicators: The Role Mediator of TSH

Introduction

Obesity in adults is a problem, particularly when paired with other metabolic abnormalities. Previous research have linked various screening approaches to diabetes, but additional evidence points to the relevance of combining diabetes screening methods with obesity and its effects. This research examined the impact of thyroid hormones (TSHs) and health risk factors (HRFs) in screening for obesity and diabetes in Chinese populations, and whether age can modulate this association.

Methods

From March to July 2022, the Hefei Community Health Service Center connected with the First Affiliated Hospital of Anhui Medical University was chosen, and the multi-stage cluster sample approach was utilized to test adults aged 21-90 in each community. Latent category analysis (LCA) was performed to investigate the clustering patterns of HRFs. A one-way ANOVA was used to examine waist circumference (WC), biochemical markers, and general data. Furthermore, multivariate logistic regression analysis was utilized to investigate the relationship between health risk variables and WC.

Results

A total of 750 individuals without a history of major problems who had a community health physical examination were chosen, with missing data greater than 5% excluded. Finally, 708 samples were included in the study with an effective rate of 94.4%. The average WC was (90.0±10.33) cm, the prevalence in the >P₇₅, P₅₀~P₇₅, P₂₅~P₅₀, and ≤P₂₅ groups were 24.7%, 18.9%, 28.7% and 27.7%, respectively. The average TSH was (2.76±2.0) μIU/mL. Male (β=1.91), HOMA-IR (β=0.06), TyG (β=2.41), SBP (β=0.08), TG (β=0.94) and UA (β=0.03) were more likely to have a higher prevalence of WC level. The analyses revealed significant correlations between HRFs, TSH, age, other metabolic indexes and WC (P < 0.05).

Discussion

Our findings suggest that the quality of metabolic-related indicators used to successfully decrease diabetes in Chinese individuals with high HRFs levels should be prioritized. Comprehensive indicators might be a useful and practical way for measuring the metabolic evolution of diabetes level levels.

Prediction of type 2 diabetes using genome-wide polygenic risk score and metabolic profiles: A machine learning analysis of population-based 10-year prospective cohort study

BACKGROUND

Previous work on predicting type 2 diabetes by integrating clinical and genetic factors has mostly focused on the Western population. In this study, we use genome-wide polygenic risk score (gPRS) and serum metabolite data for type 2 diabetes risk prediction in the Asian population.

METHODS

Data of 1425 participants from the Korean Genome and Epidemiology Study (KoGES) Ansan-Ansung cohort were used in this study. For gPRS analysis, genotypic and clinical information from KoGES health examinee (n = 58,701) and KoGES cardiovascular disease association (n = 8105) sub-cohorts were included. Linkage disequilibrium analysis identified 239,062 genetic variants that were used to determine the gPRS, while the metabolites were selected using the Boruta algorithm. We used bootstrapped cross-validation to evaluate logistic regression and random forest (RF)-based machine learning models. Finally, associations of gPRS and selected metabolites with the values of homeostatic model assessment of beta-cell function (HOMA-B) and insulin resistance (HOMA-IR) were further estimated.

FINDINGS

During the follow-up period (8.3 ± 2.8 years), 331 participants (23.2%) were diagnosed with type 2 diabetes. The areas under the curves of the RF-based models were 0.844, 0.876, and 0.883 for the model using only demographic and clinical factors, model including the gPRS, and model with both gPRS and metabolites, respectively. Incorporation of additional parameters in the latter two models improved the classification by 11.7% and 4.2% respectively. While gPRS was significantly associated with HOMA-B value, most metabolites had a significant association with HOMA-IR value.

INTERPRETATION

Incorporating both gPRS and metabolite data led to enhanced type 2 diabetes risk prediction by capturing distinct etiologies of type 2 diabetes development. An RF-based model using clinical factors, gPRS, and metabolites predicted type 2 diabetes risk more accurately than the logistic regression-based model.

FUNDING

This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korean government (MEST) (No. 2019M3E5D1A02070863 and 2022R1C1C1005458). This work was also supported by the 2020 Research Fund (1.200098.01) of UNIST (Ulsan National Institute of Science & Technology).

Genetic risk score is associated with T2DM and diabetes complications risks

BACKGROUND

Type 2 diabetes mellitus (T2DM) is a prototypical complex disease with polygenic architecture playing an important role in determining susceptibility to develop the disease (and its complications) in subjects exposed to modifiable lifestyle factors. A current challenge is to quantify the degree of the individual's genetic risk using genetic risk scores (GRS) capturing the results of genome-wide association studies while incorporating possible ethnicity- or population-specific differences.

METHODS

This study included three groups of T2DM (T2DM-I, N=1,032; T2DM-II, N=353; and T2DM-III, N=399) patients and 2,481 diabetes-free subjects. The status of the microvascular and macrovascular diabetes complications were known for the T2DM-I patients. Overall, 21 single nucleotide polymorphisms (SNPs) were analyzed, and selected subsets were used to determine the GRS (both weighted - wGRS and unweighted - uGRS) for T2DM risk predictions (6 SNPs) and for predicting the risks of complications (7 SNPs).

RESULTS

The strongest T2DM markers (P<0.0001) were within the genes for TCF7L2 (transcription factor 7-like 2), FTO (fat mass and obesity associated protein) and ARAP1 (ankyrin repeat and PH domain 1). The T2DM-I subjects with uGRS values greater (Odds Ratio, 95% Confidence Interval) than six had at least twice (2.00, 1.72-2.32) the risk of T2DM development (P<0.0001), and these results were confirmed in the independent groups (T2DM-II 1.82, 1.45-2.27; T2DM-III 2.63, 2.11-3.27). The wGRS (>0.6) further improved (P<0.000001) the risk estimations for all three T2DM groups. The uGRS was also a significant predictor of neuropathy (P<0.0001), nephropathy (P<0.005) and leg ischemia (P<0.0005).

CONCLUSIONS

If carefully selected and specified, GRS, both weighted and unweighted, could be significant predictors of T2DM development, as well as the diabetes complications development.

Polygenic Risk Scores for Cardiovascular Disease: A Scientific Statement From the American Heart Association

Cardiovascular disease is the leading contributor to years lost due to disability or premature death among adults. Current efforts focus on risk prediction and risk factor mitigation' which have been recognized for the past half-century. However, despite advances, risk prediction remains imprecise with persistently high rates of incident cardiovascular disease. Genetic characterization has been proposed as an approach to enable earlier and potentially tailored prevention. Rare mendelian pathogenic variants predisposing to cardiometabolic conditions have long been known to contribute to disease risk in some families. However, twin and familial aggregation studies imply that diverse cardiovascular conditions are heritable in the general population. Significant technological and methodological advances since the Human Genome Project are facilitating population-based comprehensive genetic profiling at decreasing costs. Genome-wide association studies from such endeavors continue to elucidate causal mechanisms for cardiovascular diseases. Systematic cataloging for cardiovascular risk alleles also enabled the development of polygenic risk scores. Genetic profiling is becoming widespread in large-scale research, including in health care-associated biobanks, randomized controlled trials, and direct-to-consumer profiling in tens of millions of people. Thus, individuals and their physicians are increasingly presented with polygenic risk scores for cardiovascular conditions in clinical encounters. In this scientific statement, we review the contemporary science, clinical considerations, and future challenges for polygenic risk scores for cardiovascular diseases. We selected 5 cardiometabolic diseases (coronary artery disease, hypercholesterolemia, type 2 diabetes, atrial fibrillation, and venous thromboembolic disease) and response to drug therapy and offer provisional guidance to health care professionals, researchers, policymakers, and patients.

Advanced glycation end products and diabetes and other metabolic indicators

BACKGROUND

Diabetes is a global concern among adults. Previous studies have suggested an association between different screening methods and diabetes; however, increasing evidence has suggested the importance of early screening for diabetes mellitus (DM) and its influencing factors. In this study, we aimed to explore whether the non-invasive detection of advanced glycation end products (AGEs) in the early screening of DM in the Chinese community and whether body mass index (BMI) and metabolic indexes could moderate this relationship.

METHODS

Three community health service centers in Hefei that signed the medical consortium agreement with the First Affiliated Hospital of Anhui Medical University were selected to screen the population aged 30-90 years in each community using a multi-stage cluster sampling method from January 2018 to January 2019. Univariate analysis of variance was used to compare the differences in general data, biochemical indexes, skin AGEs levels, and blood glucose among groups. In addition, a multivariable logistic regression analysis was performed.

RESULTS

A total of 912 patients with a community health physical examination and no history of diabetes were selected, excluding those with missing values > 5%. Finally, 906 samples were included in the study with an effective rate of 99.3%. The prevalence in the normal, impaired glucose tolerance, and DM groups were 79.8%, 10.0%, and 10.2%, respectively. By dividing AGE by quartile, AGE accumulation was classified as ≤ P25, P25-P50, P50-P75, and > P75. Higher AGE accumulation (χ2 = 37.95), BMI (χ2 = 12.20), systolic blood pressure (SBP) (χ2 = 8.46), triglyceride (TG) (χ2 = 6.23), and older age (χ2 = 20.11) were more likely to have a higher prevalence of fasting blood glucose (FBG). The analyses revealed significant correlations between AGE accumulation, BMI, TG, total cholesterol (TC), and FBG (P < 0.05).

CONCLUSION

As the findings indicate, priority should be given to the quality of metabolic-related indicators, such as BMI, TG, and TC, employed to effectively reduce the FBG of Chinese participants with high AGE accumulation. Skin autofluorescence may prove to be a rapid and non-invasive method for assessing the metabolic progression of all glucose level layers.

Post-translational Modifications: The Signals at the Intersection of Exercise, Glucose Uptake, and Insulin Sensitivity

Diabetes is a global epidemic, of which type 2 diabetes makes up the majority of cases. Nonetheless, for some individuals, type 2 diabetes is eminently preventable and treatable via lifestyle interventions. Glucose uptake into skeletal muscle increases during and in recovery from exercise, with exercise effective at controlling glucose homeostasis in individuals with type 2 diabetes. Furthermore, acute and chronic exercise sensitizes skeletal muscle to insulin. A complex network of signals converge and interact to regulate glucose metabolism and insulin sensitivity in response to exercise. Numerous forms of post-translational modifications (eg, phosphorylation, ubiquitination, acetylation, ribosylation, and more) are regulated by exercise. Here we review the current state of the art of the role of post-translational modifications in transducing exercise-induced signals to modulate glucose uptake and insulin sensitivity within skeletal muscle. Furthermore, we consider emerging evidence for noncanonical signaling in the control of glucose homeostasis and the potential for regulation by exercise. While exercise is clearly an effective intervention to reduce glycemia and improve insulin sensitivity, the insulin- and exercise-sensitive signaling networks orchestrating this biology are not fully clarified. Elucidation of the complex proteome-wide interactions between post-translational modifications and the associated functional implications will identify mechanisms by which exercise regulates glucose homeostasis and insulin sensitivity. In doing so, this knowledge should illuminate novel therapeutic targets to enhance insulin sensitivity for the clinical management of type 2 diabetes.

Genetics and Epigenetics in Personalized Nutrition: Evidence, Expectations and Experiences

With the presentation of the blueprint of the first human genome in 2001 and the advent of technologies for high-throughput genetic analysis, personalized nutrition (PN) became a new scientific field and the first commercial offerings of genotype-based nutrition advice emerged at the same time. Here, we summarize the state of evidence for the effect of genetic and epigenetic factors in the development of obesity, the metabolic syndrome and resulting illnesses such as non-insulin-dependent diabetes mellitus and cardiovascular diseases. We also critically value the concepts of PN that were built around the new genetic avenue from both the academic and a commercial perspective and their effectiveness in causing sustained changes in diet, lifestyle and for improving health. Despite almost 20 years of research and commercial direct-to-consumer offerings, evidence for the success of gene-based dietary recommendations is still generally lacking. This calls for new concepts of future PN solutions that incorporate more phenotypic measures and provide a panel of instruments (e.g., self- and bio-monitoring tools, feedback systems, algorithms based on artificial intelligence) that increases compliance based on the individual´s physical and social environment and value system. This article is protected by copyright. All rights reserved.

Group-specific component exon 11 haplotypes (D432E and T436K) and risk of albuminuria in type 2 diabetes mellitus patients

BACKGROUND

Emerging evidence indicates group-specific component (GC) variants are associated with ethnicity. We aimed to investigate the association of GC variants and protein expression level with T2DM and diabetic nephropathy (DN) in Saudi patients.

SUBJECTS AND METHODS

A total of 200 participants (120 T2DM/80 controls) were genotyped for GC-rs7041/GC-rs4588 by real-time polymerase chain reaction. Serum GC was assessed by ELISA and in silico analysis was executed.

RESULTS

GC-rs7041 frequency distribution showed no difference between the study groups, while GC-rs4588 showed association with T2DM under all genetic models. rs4588*AA variant was correlated with higher serum GC globulin, albuminuria, and poor glycaemic control. A higher frequency of rs7041*TT and rs4588*AA was evident in macroalbuminuria vs. normoalbuminuria group. Carrying GC-2 haplotype was 2.5 more likely to develop diabetes and correlated with the levels of albuminuria.

CONCLUSIONS

GC variants could have independent effects on the risk of T2DM and DN in the study population.

Using the optimal method-explained variance weighted genetic risk score to predict the efficacy of folic acid therapy to hyperhomocysteinemia

BACKGROUND

Genetic risk score (GRS) is a useful way to explore genetic architectures and the relationships of complex diseases. Several studies had revealed many single nucleotide polymorphisms (SNPs) associated with the efficacy of folic acid treatment to hyperhomocysteinemia (HHcy).

METHODS

We aimed to construct and screen out the optimal predictive model based on four GRSs and traditional risk factors. Four GRSs enrolled four SNPs (MTHFR rs1801131, MTHFR rs1801133, MTRR rs1801394, BHMT rs3733890) were presented as follows: (a) simple count genetic risk score (SC-GRS), (b) direct logistic regression genetic risk score (DL-GRS), (c) polygenic genetic risk score (PG-GRS), and (d) explained variance weighted genetic risk score (EV-GRS). We performed a prospective cohort study including 638 HHcy patients. Then we evaluated the associations of four GRSs with folic acid's efficacy and the performance of four GRSs.

RESULTS

Four GRSs were independently associated with efficacy of treatment (p < 0.05). When combining GRSs with traditional risk factors, the AUC of the four models were all above 0.900 in the training set (Tradition + SC-GRS: 0.909, Tradition + DL-GRS: 0.909, Tradition + PG-GRS: 0.904, Tradition + EV-GRS: 0.910). And EV-GRS got the highest AUC. When evaluating the models in the testing set, we got the same conclusion that EV-GRS was optimal among four GRSs with the highest AUC (0.878) and the highest increase of AUC (0.008).

CONCLUSION

A more precise predictive model combing the optimal GRS with traditional risk factors was constructed to predict the efficacy of folic acid therapy to HHcy.

Combining genetic risk score with artificial neural network to predict the efficacy of folic acid therapy to hyperhomocysteinemia

Artificial neural network (ANN) is the main tool to dig data and was inspired by the human brain and nervous system. Several studies clarified its application in medicine. However, none has applied ANN to predict the efficacy of folic acid treatment to Hyperhomocysteinemia (HHcy). The efficacy has been proved to associate with both genetic and environmental factors while previous studies just focused on the latter one. The explained variance genetic risk score (EV-GRS) had better power and could represent the effect of genetic architectures. Our aim was to add EV-GRS into environmental factors to establish ANN to predict the efficacy of folic acid therapy to HHcy. We performed the prospective cohort research enrolling 638 HHcy patients. The multilayer perception algorithm was applied to construct ANN. To evaluate the effect of ANN, we also established logistic regression (LR) model to compare with ANN. According to our results, EV-GRS was statistically associated with the efficacy no matter analyzed as a continuous variable (OR = 3.301, 95%CI 1.954-5.576, P < 0.001) or category variable (OR = 3.870, 95%CI 2.092-7.159, P < 0.001). In our ANN model, the accuracy was 84.78%, the Youden's index was 0.7073 and the AUC was 0.938. These indexes above indicated higher power. When compared with LR, the AUC, accuracy, and Youden's index of the ANN model (84.78%, 0.938, 0.7073) were all slightly higher than the LR model (83.33% 0.910, 0.6687). Therefore, clinical application of the ANN model may be able to better predict the folic acid efficacy to HHcy than the traditional LR model. When testing two models in the validation set, we got the same conclusion. This study appears to be the first one to establish the ANN model which added EV-GRS into environmental factors to predict the efficacy of folic acid to HHcy. This model would be able to offer clinicians a new method to make decisions and individual therapeutic plans.

ATP2B1 genotypes rs2070759 and rs2681472 polymorphisms and risk of hypertension in Saudi population

This study examined an association of ATP2B1 gene polymorphism and hypertension in the Saudi population. The 246 hypertensive cases and 300 healthy human controls were genotyped. The results showed that genotypes rs.207075 (CA + AA) [p = 0.05; OR: 95% CI, 1.5:(1.0 to 2.4) and p = 0.001, OR: 95% CI, 2.4: (1.5 to 4.0) and rs2681472 (CT + TT) [p = 0.05; OR: 95% CI, 1.5 (1.0 to 2.4) and p = 0.006 OR: 95% CI, 2.0 (1.2 to 3.1) respectively] associated with the risk of hypertension. Cases carrying the recessive models: [(CA + AA)/(CT + TT)] and [(AA)/(TT)] genotypes confer a strong susceptibility risk of hypertension [p = 0.002; OR: (95%CI) 1.8 (1.2 to 2.6) and p = 0.001; OR: (95%CI) 2.6 (1.5 to 4.7) respectively]. However, cases with body-mass-index (BMI)<25, carrying homozygous mutant genotypes [AA, rs2070759, p = 0.007; OR: (95%CI) 2.75(1.37 to 5.5) and (TT, rs2681472, p = 0.05; OR: (95%CI) 1.96 (1.03 to 3.72)] as well as A allele of rs2070759 [p = 0.006; OR: (95%CI) 1.62 (1.16 to 2.25)] and T allele of rs2681472, p = 0.04, 1.43(1.03 to 1.98)] showed a significant association with high risk of hypertension. In short, a significant association between ATP2B1 gene polymorphism and risk of hypertension was noticed. In addition, individuals carrying recessive genotypes have greater risk in developing hypertension than those carrying dominant genotypes. Moreover, cases with high-risk BMI associated with ATP2B1 variants may play a critical role in developing hypertension.Supplemental data for this article is available online at https://doi.org/10.1080/15257770.2021.1973034 .

Do we measure or compute polygenic risk scores? Why language matters

Here, we argue that polygenic risk scores (PRSs) are different epistemic objects as compared to other biomarkers such as blood pressure or sodium level. While the latter two may be subject to variation, measured inaccurately or interpreted in various ways, blood flow has pressure and sodium is available in a concentration that can be quantified and visualised. In stark contrast, PRSs are calculated, compiled or constructed through the statistical assemblage of genetic variants. How researchers frame and name PRSs has consequences for how we interpret and value their results. We distinguish between the tangible and inferential understanding of PRS and the corresponding languages of measurement and computation, respectively. The conflation of these frames obscures important questions we need to ask: what PRS seeks to represent, whether current ways of 'doing PRS' are optimal and responsible, and upon what we base the credibility of PRS-based knowledge claims.

Relevance of Physical Activities, Sedentary Behaviors, and Genetic Predisposition in Body Fatness: Population-Based Study on Chinese Adults

INTRODUCTION

Little attention has been paid to the interacting effect of specific intensities of physical activities (PAs) and sedentary lifestyle, like television watching, and genetic predisposition on body composition indices among Chinese adults. Herein, we aimed to examine whether specific types of PAs and sedentary behaviors (SBs) were associated with body composition indices among Chinese adults and to further explore whether these associations interacted with the genetic predisposition to high BMI.

METHODS

Cross-sectional data regarding PAs and time spent on SBs and dietary intake of 3,976 Chinese adults (54.9% women) aged 25-65 years in Southwest China were obtained via questionnaires in 2013-2015. Weight, height, and waist circumference (WC) were measured, and BMI, percentage of body fat (%BF), fat mass index (FMI), and fat-free mass index (FFMI) of the participants were calculated. Genetic risk score (GRS) was calculated on 9 established BMI-associated SNPs among Chinese adults.

RESULTS

When the participants were stratified by GRS for BMI, significant associations were only found for adults with high GRS for BMI: moderate-to-vigorous physical activity (MVPA) was negatively associated with WC and %BF and positively related to FFMI. The adjusted positive relationship of time spent watching television with BMI, WC, %BF, and FMI were also just found between adults with high weighted GRS for high BMI: for every 1 h increment in television watching, the BMI, WC, %BF, and FMI of the participants increased by 0.2 kg/m2, 0.9 cm, 0.3%, and 0.1 kg/m2, respectively (p < 0.02).

CONCLUSION

MVPA may be a protective factor against obesity, and prolonged television watching may accentuate adiposity. These putative effects may be more pronounced among individuals with a high genetic risk of a high BMI.

Family history of diabetes determines the association of HOMA-IR with fasting and postprandial triglycerides in individuals with normal glucose tolerance

BACKGROUND

Individuals with family history of diabetes carry nearly double the risk of diabetes than those without. However, the mechanism for this increased risk of diabetes in them is not fully understood.

OBJECTIVE

To study fasting and postprandial triglyceride levels in individuals with normal glucose tolerance (NGT) who had family history of diabetes and to ascertain their association with insulin resistance.

METHODS

Fasting triglyceride levels and HOMA-IR were compared in 671 NGT individuals with and without a family history of diabetes. A standardized fat challenge test was also done in one tenth of individuals of each group and postprandial triglyceride responses were compared between them. Association of HOMA-IR with fasting and postprandial triglyceride levels was ascertained through pearson's coefficient of correlation.

RESULTS

Individuals with family history of diabetes had significantly higher HOMA-IR (P < 0.001) and significantly higher postprandial triglyceride AUC (P = 0.04) after standardized fat meal despite having similar fasting triglyceride levels (P = 0.51) as those without family history of diabetes. Fasting as well as postprandial triglyceride levels significantly correlated with HOMA-IR (r = 0.35, P < 0.001 and r = 0.39, P = 0.04) only in those with a positive family history of diabetes but not in those without. Triglyceride levels mediated the associations of BMI (Δ β = -0.053) and waist circumference (Δ β = -0.075) with HOMA-IR.

CONCLUSION

Triglyceride levels, both in the fasting and the postprandial state are associated with insulin resistance in NGT individuals with a family history of diabetes but not in those without.

Effectiveness of Early Advanced Glycation End Product Accumulation Testing in the Diagnosis of Diabetes: A Health Risk Factor Analysis Using the Body Mass Index as a Moderator

OBJECTIVE

To evaluate the value of non-invasive detection of advanced glycation end products (AGEs) in the early screening of type 2 diabetes mellitus (T2DM) in the community of China.

METHODS

From January 2018 to January 2019, a total of 912 patients with community health physical examination and no history of T2DM were selected, excluding the results of missing value > 5%. Finally, 906 samples were included in the study, with a response rate of 99.3%. Non-invasive diabetic detection technology was used to detect AGEs in the upper arm skin of all participants, AGE accumulations were classified as ≤P25, P25∼P50, P50∼P75, and >P75; HbA1c, insulin, C-peptide, total cholesterol (TC), triglyceride (TG), high-density lipoprotein cholesterol (HDL-C), creatinine, urea, and other indicators were measured at the same time. Univariate analysis of variance was used to compare the differences in general data, biochemical indexes, skin AGE levels, and blood glucose among groups, and logistic regression analysis and latent category analysis were performed.

RESULTS

In univariate analysis, SBP, FBG, HbA1c, and age were correlated with higher AGE (p < 0.01); TG, TC, HDL, UA, and gender were not positively correlated with AGE (p < 0.01). After controlling for covariates (waist circumference, hip circumference), AGE accumulation was interacted with other variables. The results of latent category analysis (LCA) showed that the health risk factors (HRFs), including age, systolic blood pressure, HbA1c, FBG, triglyceride, total cholesterol, HDL-C, and uric acid, were divided as three groups, and AGE is divided into four categories according to the quartile method, which were low risk (≤P25), low to medium risk (P25∼P50), medium to high (P50∼P75), and high risk (>P75), respectively. The association between the quartile AGE and risk factors of the OR values was 1.09 (95% CI: 1.42, 2.86), 2.61 (95% CI: 1.11, 6.14), and 5.41 (95% CI: 2.42, 12.07), respectively. The moderation analysis using the PROCESS program was used to analyze whether BMI moderated the link between risk factors and AGE accumulation. There was also a significant three-way interaction among HRFs, BMI, and gender for AGE accumulation in the total sample (β = -0.30).

CONCLUSION

Non-invasive skin detection of AGEs has a certain application value for the assessment of T2DM risk and is related to a variety of risk factors.

Use of Genomic Information in Health Impact Assessment is Yet to Come: A Systematic Review

Information generated by genetic epidemiology and genomics studies has been accumulating at fast pace, and this knowledge opens new vistas in public health, allowing for the understanding of gene-environment interactions. However, the translation of genome-based knowledge and technologies to the practice of healthcare, and especially of public health, is challenging. Because health impact assessment (HIA) proved to be an effective tool to assist consideration of health issues is sectoral policymaking, this study aimed at exploring its role in the translational process by a systematic literature review on the use of genetic information provided by genetic epidemiology and genomics studies in HIA. PubMed, Scopus, and Web of Science electronic databases were searched and the findings systematically reviewed and reported by the PRISMA guidelines. The review found eight studies that met the inclusion criteria, most of them theoretically discussing the use of HIA for introducing genome-based technologies in healthcare practice, and only two articles considered, in short, the possibility for a generic application of genomic information in HIA. The findings indicate that HIA should be more extensively utilized in the translation of genome-based knowledge to public health practice, and the use of genomic information should be facilitated in the HIA process.

Cardioinformatics: the nexus of bioinformatics and precision cardiology

Cardiovascular disease (CVD) is the leading cause of death worldwide, causing over 17 million deaths per year, which outpaces global cancer mortality rates. Despite these sobering statistics, most bioinformatics and computational biology research and funding to date has been concentrated predominantly on cancer research, with a relatively modest footprint in CVD. In this paper, we review the existing literary landscape and critically assess the unmet need to further develop an emerging field at the multidisciplinary interface of bioinformatics and precision cardiovascular medicine, which we refer to as 'cardioinformatics'.

High genetic burden of type 2 diabetes can promote the high prevalence of disease: a longitudinal cohort study in Iran

Type 2 diabetes (T2D) is emerging as one of the serious public health issues in both developed and developing counties. Here, we surveyed the worldwide population differentiation in T2D-associated variants and assessed the genetic burden of the disease in an ongoing Tehran Cardio-Metabolic Genetic Study (TCGS) cohort represented the Iranian population. We found multiple SNPs that were significantly depleted or enriched in at least one of the five populations of 1,000 Genome Project (African, American, East Asian, European, and South Asian) as well as the Iranian population. Interestingly, TCF7L2, a well-known associated gene with T2D, harbors the highest number of enriched risk alleles almost in all populations except for East Asian, where this gene embraces the largest number of significantly depleted risk alleles. The polygenic risk score (PRS) of the enriched risk alleles was calculated for 1,867 diabetic and 2,855 non-diabetic participants in the TCGS cohort, interestingly demonstrating that the risk of developing T2D was almost two times higher in top PRS quintile compared with the lowest quintile after adjusting for other known risk factors.

Gender and age differences in the association between sleep characteristics and fasting glucose levels in Chinese adults

AIM

The present study examined the associations between night-time sleep duration, midday napping duration and bedtime, and fasting glucose levels, and whether or not such associations are dependent on gender and age.

METHODS

This study was a cross-sectional analysis of 172,901 adults aged≥40 years living in mainland China. Sleep duration was obtained by self-reports of bedtime at night, waking-up time the next morning and average napping duration at midday. Fasting plasma glucose (FPG)≥7.0mmol/L was defined as hyperglycaemia. Independent associations between night-time sleep duration, midday naptime duration and bedtime with hyperglycaemia were evaluated using regression models.

RESULTS

Compared with night-time sleep durations of 6-7.9h, both short (<6h) and long (≥8h) night-time sleep durations were significantly associated with an increased risk of hyperglycaemia in women [odds ratio (OR): 1.12, 95% confidence interval (CI): 1.01-1.29 and OR: 1.14, 95% CI: 1.08-1.21, respectively], and revealed a U-shaped distribution of risk in women and no significant association in men. Long midday nap durations (≥1h) were significantly but weakly associated with hyperglycaemia (OR: 1.04, 95% CI: 1.01-1.09) compared with no napping without interactions from gender or age, whereas the association between bedtime and fasting glucose levels did vary according to gender and age.

CONCLUSION

Night-time sleep duration, midday napping duration and bedtime were all independently associated with the risk of hyperglycaemia, and some of the associations between these sleep characteristics and hyperglycaemia were gender- and age-dependent.

The genetic risk for hypertension is lower among the Hungarian Roma population compared to the general population

Estimating the prevalence of cardiovascular diseases (CVDs) and risk factors among the Roma population, the largest minority in Europe, and investigating the role of genetic or environmental/behavioral risk factors in CVD development are important issues in countries where they are significant minority. This study was designed to estimate the genetic susceptibility of the Hungarian Roma (HR) population to essential hypertension (EH) and compare it to that of the general (HG) population. Twenty EH associated SNPs (in AGT, FMO3, MTHFR-NPPB, NPPA, NPPA-AS1, AGTR1, ADD1, NPR3-C5orf23, NOS3, CACNB2, PLCE1, ATP2B1, GNB3, CYP1A1-ULK3, UMOD and GNAS-EDN3) were genotyped using DNA samples obtained from HR (N = 1176) and HG population (N = 1178) subjects assembled by cross-sectional studies. Allele frequencies and genetic risk scores (unweighted and weighted genetic risk scores (GRS and wGRS, respectively) were calculated for the study groups and compared to examine the joint effects of the SNPs. The susceptibility alleles were more frequent in the HG population, and both GRS and wGRS were found to be higher in the HG population than in the HR population (GRS: 18.98 ± 3.05 vs. 18.25 ± 2.97, p<0.001; wGRS: 1.52 [IQR: 0.99–2.00] vs. 1.4 [IQR: 0.93–1.89], p<0.01). Twenty-seven percent of subjects in the HR population were in the bottom fifth (GRS ≤ 16) of the risk allele count compared with 21% of those in the HG population. Thirteen percent of people in the HR group were in the top fifth (GRS ≥ 22) of the GRS compared with 21% of those in the HG population (p<0.001), i.e., the distribution of GRS was found to be left-shifted in the HR population compared to the HG population. The Roma population seems to be genetically less susceptible to EH than the general one. These results support preventive efforts to lower the risk of developing hypertension by encouraging a healthy lifestyle.

Genetic Risk, a Healthy Lifestyle, and Type 2 Diabetes: the Dongfeng-Tongji Cohort Study

OBJECTIVE

The objective of this study is to examine whether healthy lifestyle could reduce diabetes risk among individuals with different genetic profiles.

DESIGN

A prospective cohort study with a median follow-up of 4.6 years from the Dongfeng-Tongji cohort was performed.

PARTICIPANTS

A total of 19 005 individuals without diabetes at baseline participated in the study.

MAIN VARIABLE MEASURE

A healthy lifestyle was determined based on 6 factors: nonsmoker, nondrinker, healthy diet, body mass index of 18.5 to 23.9 kg/m2, waist circumference less than 85 cm for men and less than 80 cm for women, and higher level of physical activity. Associations of combined lifestyle factors and incident diabetes were estimated using Cox proportional hazard regression. A polygenic risk score of 88 single-nucleotide polymorphisms previously associated with diabetes was constructed to test for association with diabetes risk among 7344 individuals, using logistic regression.

RESULTS

A total of 1555 incident diabetes were ascertained. Per SD increment of simple and weighted genetic risk score was associated with a 1.39- and 1.34-fold higher diabetes risk, respectively. Compared with poor lifestyle, intermediate and ideal lifestyle were reduced to a 23% and 46% risk of incident diabetes, respectively. Association of lifestyle with diabetes risk was independent of genetic risk. Even among individuals with high genetic risk, intermediate and ideal lifestyle were separately associated with a 29% and 49% lower risk of diabetes.

CONCLUSION

Genetic and combined lifestyle factors were independently associated with diabetes risk. A healthy lifestyle could lower diabetes risk across different genetic risk categories, emphasizing the benefit of entire populations adhering to a healthy lifestyle.

Benzene Exposure Induces Insulin Resistance in Mice

Benzene is a ubiquitous pollutant associated with hematotoxicity but its metabolic effects are unknown. We sought to determine if and how exposure to volatile benzene impacted glucose handling. We exposed wild type C57BL/6 mice to volatile benzene (50 ppm × 6 h/day) or HEPA-filtered air for 2 or 6 weeks and measured indices of oxidative stress, inflammation, and insulin signaling. Compared with air controls, we found that mice inhaling benzene demonstrated increased plasma glucose (p = .05), insulin (p = .03), and HOMA-IR (p = .05), establishing a state of insulin and glucose intolerance. Moreover, insulin-stimulated Akt phosphorylation was diminished in the liver (p = .001) and skeletal muscle (p = .001) of benzene-exposed mice, accompanied by increases in oxidative stress and Nf-κb phosphorylation (p = .025). Benzene-exposed mice also demonstrated elevated levels of Mip1-α transcripts and Socs1 (p = .001), but lower levels of Irs-2 tyrosine phosphorylation (p = .0001). Treatment with the superoxide dismutase mimetic, TEMPOL, reversed benzene-induced effects on oxidative stress, Nf-κb phosphorylation, Socs1 expression, Irs-2 tyrosine phosphorylation, and systemic glucose intolerance. These findings suggest that exposure to benzene induces insulin resistance and that this may be a sensitive indicator of inhaled benzene toxicity. Persistent ambient benzene exposure may be a heretofore unrecognized contributor to the global human epidemics of diabetes and cardiovascular disease.

SNP-Based Genetic Risk Score Modeling Suggests No Increased Genetic Susceptibility of the Roma Population to Type 2 Diabetes Mellitus

BACKGROUND

In a previous survey, an elevated fasting glucose level (FG) and/or known type 2 diabetes mellitus (T2DM) were significantly more frequent in the Roma population than in the Hungarian general population. We assessed whether the distribution of 16 single nucleotide polymorphisms (SNPs) with unequivocal effects on the development of T2DM contributes to this higher prevalence.

METHODS

Genetic risk scores, unweighted (GRS) and weighted (wGRS), were computed and compared between the study populations. Associations between GRSs and FG levels and T2DM status were investigated in separate and combined study populations.

RESULTS

The Hungarian general population carried a greater genetic risk for the development of T2DM (GRS_General = 15.38 ± 2.70 vs. GRS_Roma = 14.80 ± 2.68, p < 0.001; wGRS_General = 1.41 ± 0.32 vs. wGRS_Roma = 1.36 ± 0.31, p < 0.001). In the combined population models, GRSs and wGRSs showed significant associations with elevated FG (p < 0.001) and T2DM (p < 0.001) after adjusting for ethnicity, age, sex, body mass index (BMI), high-density Lipoprotein Cholesterol (HDL-C), and triglyceride (TG). In these models, the effect of ethnicity was relatively strong on both outcomes (FG levels: βethnicity = 0.918, p < 0.001; T2DM status: OR_ethnicity = 2.484, p < 0.001).

CONCLUSIONS

The higher prevalence of elevated FG and/or T2DM among Roma does not seem to be directly linked to their increased genetic load but rather to their environmental/cultural attributes. Interventions targeting T2DM prevention among Roma should focus on harmful environmental exposures related to their unhealthy lifestyle.

Prediction model for the efficacy of folic acid therapy on hyperhomocysteinaemia based on genetic risk score methods

No risk assessment tools for the efficacy of folic acid treatment for hyperhomocysteinaemia (HHcy) have been developed. We aimed to use two common genetic risk score (GRS) methods to construct prediction models for the efficacy of folic acid therapy on HHcy, and the best gene-environment prediction model was screened out. A prospective cohort study enrolling 638 HHcy patients was performed. We used a logistic regression model to estimate the associations of two GRS methods with the efficacy. Performances were compared using area under the receiver operating characteristic curve (AUC). The simple count genetic risk score (SC-GRS) and weighted genetic risk score (wGRS) were found to be independently associated with the efficacy of folic acid treatment for HHcy. Using the SC-GRS, per risk allele increased with a 1·46-fold increased failure risk (P < 0·001) after adjustment for traditional risk factors, including age, sex, BMI, smoking, alcohol consumption, history of diabetes, history of hypertension, history of hyperlipidaemia, history of stroke and history of CHD. When used the wGRS, the association was strengthened (OR = 2·08, P < 0·001). Addition of the SC-GRS and wGRS to the traditional risk model significantly improved the predictive ability by AUC (0·859). A precise gene-environment predictive model with good performance was developed for predicting the treatment failure rate of folic acid therapy for HHcy.

Statistical learning approaches in the genetic epidemiology of complex diseases

In this paper, we give an overview of methodological issues related to the use of statistical learning approaches when analyzing high-dimensional genetic data. The focus is set on regression models and machine learning algorithms taking genetic variables as input and returning a classification or a prediction for the target variable of interest; for example, the present or future disease status, or the future course of a disease. After briefly explaining the basic motivation and principle of these methods, we review different procedures that can be used to evaluate the accuracy of the obtained models and discuss common flaws that may lead to over-optimistic conclusions with respect to their prediction performance and usefulness.

Genetic profiling revealed an increased risk of venous thrombosis in the Hungarian Roma population

BACKGROUND

Besides modifiable risk factors, genetic susceptibility may also explain the high cardiovascular disease burden of the Roma population.

OBJECTIVES

Aim of this study was to define the genetic susceptibility of Hungarian Roma to venous thrombosis (VT) and comparing it to that of the general population.

METHODS

Fifty-two SNPs associated with VT (in F2, F5, F9, F11, F15, FGA, FGB, FGG, CYP4V2, KLKB1 and vWF) were selected and analyzed in the group of Roma (N = 962) and general (N = 1492) subjects collected by cross-sectional studies. Allele frequencies and genetic risk scores (GRS, unweighted and weighted) were computed for the study groups and compared to estimate the joint effects of SNPs.

RESULTS

The majority of the susceptible alleles were more prevalent in the Roma population, and both GRS and wGRS were found to be significantly higher in Roma than in the general population (GRS: 41.83 ± 5.78 vs. 41.04 ± 6.04; wGRS: 7.78 ± 1.28 vs. 7.46 ± 1.33, p = .001). Only 2.39% of subjects in the Roma population were in the bottom fifth of the wGRS (wGRS≤0.19) compared with 3.62% of those in the general population (p = .080); 2.88% of the general subjects were in the top fifth of the wGRS (wGRS≥10.02), while 4.26% of the Roma population were (p = .066).

CONCLUSION

In conclusion, the Roma population seems to have increased genetic susceptibility to VT. This might have important implications in the future in identifying possible new opportunities for targeted prevention and treatment for those subgroups in the populations who are at greater risk for VT development.

The Interaction of a Diabetes Gene Risk Score With 3 Different Antihypertensive Medications for Incident Glucose-level Elevation

BACKGROUND

Elevations of fasting glucose (FG) levels are frequently encountered in people treated with thiazide diuretics. The risk is lower in people treated with ACE inhibitors (ACEi). To determine if genetic factors play a role in FG elevation, we examined the interaction of a diabetes gene risk score (GRS) with the use of 3 different antihypertensive medications.

METHODS

We examined 376 nondiabetic hypertensive individuals with baseline FG <100 mg/dl who were genotyped for 24 genes associated with risk of elevated glucose levels. All participants had ≥1 follow-up FG level over 6 years of follow-up. Participants were randomized to treatment with a thiazide-like diuretic (chlorthalidone), a calcium channel blocker (CCB; amlodipine), or an ACEi (lisinopril). Outcomes were an FG increase of ≥13 or ≥27 mg/dl, the upper 75% and 90% FG increase in the parent cohort from which the present cohort was obtained. Odds ratios were adjusted for factors that increase FG levels.

RESULTS

For every 1 allele increase in GRS, the adjusted odds ratios (ORs) were 1.06 (95% confidence interval (CI): 0.99, 1.14; P = 0.06) and 1.09 (95% CI: 0.99, 1.20; P = 0.08). When results were examined by randomized medications, participants randomized to amlodipine had statistically significant odds for either outcome (OR: 1.23; 95% CI: 1.03, 1.48; P = 0.01 and OR: 1.31; 95% CI: 1.06, 1.62; P = 0.01). No such risk increase was found in participants randomized to the other 2 medications.

CONCLUSIONS

A diabetes GRS predicts FG elevation in people treated with a CCB, but not with an ACEi or diuretic. These findings require confirmation.

CLINICAL TRIALS REGISTRATION

Trial number NCT00000542.

Driving Type 2 Diabetes Risk Scores into Clinical Practice: Performance Analysis in Hospital Settings

Electronic health records and computational modelling have paved the way for the development of Type 2 Diabetes risk scores to identify subjects at high risk. Unfortunately, few risk scores have been externally validated, and their performance can be compromised when routine clinical data is used. The aim of this study was to assess the performance of well-established risk scores for Type 2 Diabetes using routinely collected clinical data and to quantify their impact on the decision making process of endocrinologists. We tested six risk models that have been validated in external cohorts, as opposed to model development, on electronic health records collected from 2008-2015 from a population of 10,730 subjects. Unavailable or missing data in electronic health records was imputed using an existing validated Bayesian Network. Risk scores were assessed on the basis of statistical performance to differentiate between subjects who developed diabetes and those who did not. Eight endocrinologists provided clinical recommendations based on the risk score output. Due to inaccuracies and discrepancies regarding the exact date of Type 2 Diabetes onset, 76 subjects from the initial population were eligible for the study. Risk scores were useful for identifying subjects who developed diabetes (Framingham risk score yielded a c-statistic of 85%), however, our findings suggest that electronic health records are not prepared to massively use this type of risk scores. Use of a Bayesian Network was key for completion of the risk estimation and did not affect the risk score calculation (p > 0.05). Risk score estimation did not have a significant effect on the clinical recommendation except for starting pharmacological treatment (p = 0.004) and dietary counselling (p = 0.039). Despite their potential use, electronic health records should be carefully analyzed before the massive use of Type 2 Diabetes risk scores for the identification of high-risk subjects, and subsequent targeting of preventive actions.

THE GENETIC INFORMATION NONDISCRIMINATION ACT AT AGE 10: GINA'S CONTROVERSIAL ASSERTION THAT DATA TRANSPARENCY PROTECTS PRIVACY AND CIVIL RIGHTS

The genomic testing industry is an edifice built on data transparency: transparent and often unconsented sharing of our genetic information with researchers to fuel scientific discovery, transparent sharing of our test results to help regulators infer whether the tests are safe and effective, and transparent sharing of our health information to help treat other patients on the premise that we gain reciprocity of advantage when each person's health care is informed by the best available data about all of us. Transparency undeniably confers many social benefits but creates risks to the civil rights of the people whose genetic information is shared. Touted as a major civil rights law at the time of its passage, the Genetic Information Nondiscrimination Act of 2008 (GINA) has endured ten years of criticism that its protections are ineffectual, insufficient, or even unethical and overtly unsafe for the people it aims to protect. At the center of this controversy are provisions of GINA that expand people's access to genetic information that others store about them-a heavily contested assertion that data transparency implies sharing data not just with third parties, but with the people whose data are being shared. This Article traces the decades-long roots of this assertion and explores pathways to resolve the controversy that engulfs it. It is important to resolve this controversy. As GINA enters its second decade, genomics is finally starting to gain sufficient predictive power to support discriminatory and other nefarious uses that GINA was designed to prevent. We are entering a positive feedback loop in which the genomic research that exposes us to risk of unwanted data disclosures simultaneously fuels discoveries that make such disclosures potentially more damaging.

Genetic Determinants of Telomere Length in African American Youth

Telomere length (TL) is associated with numerous disease states and is affected by genetic and environmental factors. However, TL has been mostly studied in adult populations of European or Asian ancestry. These studies have identified 34 TL-associated genetic variants recently used as genetic proxies for TL. The generalizability of these associations to pediatric populations and racially diverse populations, specifically of African ancestry, remains unclear. Furthermore, six novel variants associated with TL in a population of European children have been identified but not validated. We measured TL from whole blood samples of 492 healthy African American youth (children and adolescents between 8 and 20 years old) and performed the first genome-wide association study of TL in this population. We were unable to replicate neither the 34 reported genetic associations found in adults nor the six genetic associations found in European children. However, we discovered a novel genome-wide significant association between TL and rs1483898 on chromosome 14. Our results underscore the importance of examining genetic associations with TL in diverse pediatric populations such as African Americans.

Association of genetic risk score and chronic kidney disease in a Japanese population

Chronic kidney disease (CKD) is a public health problem worldwide including Japan. Recent genome-wide association studies have discovered CKD susceptibility variants. We developed a genetic risk score (GRS) based on CKD-associated variants and assessed a possibility that the GRS can improve the discrimination capability for the prevalence of CKD in a Japanese population. The present study consists of 11 283 participants randomly selected from 12 Japan Multi-Institutional Collaborative Cohort Study sites. Individual GRS was constructed combining 18 single-nucleotide polymorphisms identified in a Japanese population. Participants with eGFR <60 mL/min per 1.73 m² was defined as case (stage 3 CKD or higher) in this study. Logistic regression analysis was used to examine the association between the GRS and CKD risk with adjustment for sex, age, hypertension and type 2 diabetes mellitus. The frequency of individuals with CKD was 8.3%, which was relatively low compared with those previously reported in a Japanese population. The odds ratio of having CKD was 1.120 (95% confidence interval: 1.042-1.203) per 10 GRS increment in the fully adjusted model (P = 0.002). The C-statistic was significantly increased in the model with the GRS, comparing with the model without the GRS (0.720 vs 0.719, P_difference  = 0.008). Increment of the GRS was associated with increased risk of CKD. Additionally, the GRS significantly improved the discriminatory ability of CKD prevalence in a Japanese population; however, the improvement of discriminatory ability brought about by the GRS seemed to be small compared with that of non-genetic CKD risk factors.

Statistical methods to detect pleiotropy in human complex traits

In recent years pleiotropy, the phenomenon of one genetic locus influencing several traits, has become a widely researched field in human genetics. With the increasing availability of genome-wide association study summary statistics, as well as the establishment of deeply phenotyped sample collections, it is now possible to systematically assess the genetic overlap between multiple traits and diseases. In addition to increasing power to detect associated variants, multi-trait methods can also aid our understanding of how different disorders are aetiologically linked by highlighting relevant biological pathways. A plethora of available tools to perform such analyses exists, each with their own advantages and limitations. In this review, we outline some of the currently available methods to conduct multi-trait analyses. First, we briefly introduce the concept of pleiotropy and outline the current landscape of pleiotropy research in human genetics; second, we describe analytical considerations and analysis methods; finally, we discuss future directions for the field.

Evaluating the effect of multiple genetic risk score models on colorectal cancer risk prediction

Currently, genetic risk score (GRS) model has been a widely used method to evaluate the genetic effect of cancer risk prediction, but seldom studies investigated their discriminatory power, especially for colorectal cancer (CRC) risk prediction. In this study, we applied both simulation and real data to comprehensively compare the discriminability of different GRS models. The GRS models were fitted by logistic regression with three scenarios, including simple count GRS (SC-GRS), logistic regression weighted GRS (LR-GRS, including DL-GRS and OR-GRS) and explained variance weighted GRS (EV-GRS, including EV_DL-GRS and EV_OR-GRS) models. The model performance was evaluated by receiver operating characteristic (ROC) curves and area under curves (AUC) metric, net reclassification improvement (NRI) and integrated discrimination improvement (IDI). In real data analysis, as DL-GRS and EV_DL-GRS models were carried with serious over-fitting, the other three models were kept for further comparison. Compared to unweighted SC-GRS model, reclassification was significantly decreased in OR-GRS model (NRI = -0.082, IDI = -0.002, P < 0.05), while EV_OR-GRS model showed negative NRI and IDI (NRI = -0.077, IDI = -5.54E-04, P < 0.05) compared to OR-GRS model. Besides, traditional model with smoking status (AUC = 0.523) performed lower discriminability compared to the combined model (AUC = 0.607) including genetic (i.e., SC-GRS) and smoking factors. Similarly, the findings from simulation were all consistent to real data results. It is plausible that SC-GRS model could be optimal for predicting genetic risk of CRC. Moreover, the addition of more significant genetic variants to traditional model could further improve predictive power on CRC risk prediction.

Predictive performance of a genetic risk score using 11 susceptibility alleles for the incidence of Type 2 diabetes in a general Japanese population: a nested case-control study

AIMS

To assess the predictive ability of a genetic risk score for the incidence of Type 2 diabetes in a general Japanese population.

METHODS

This prospective case-control study, nested within a Japan Public Health Centre-based prospective study, included 466 participants with incident Type 2 diabetes over a 5-year period (cases) and 1361 control participants, as well as 1463 participants with existing diabetes and 1463 control participants. Eleven susceptibility single nucleotide polymorphisms, identified through genome-wide association studies and replicated in Japanese populations, were analysed.

RESULTS

Most single nucleotide polymorphism loci showed directionally consistent associations with diabetes. From the combined samples, one single nucleotide polymorphism (rs2206734 at CDKAL1) reached a genome-wide significance level (odds ratio 1.28, 95% CI 1.18-1.40; P = 1.8 × 10^-8 ). Three single nucleotide polymorphisms (rs2206734 in CDKAL1, rs2383208 in CDKN2A/B, and rs2237892 in KCNQ1) were nominally significantly associated with incident diabetes. Compared with the lowest quintile of the total number of risk alleles, the highest quintile had a higher odds of incident diabetes (odds ratio 2.34, 95% CI 1.59-3.46) after adjusting for conventional risk factors such as age, sex and BMI. The addition to the conventional risk factor-based model of a genetic risk score using the 11 single nucleotide polymorphisms significantly improved predictive performance; the c-statistic increased by 0.021, net reclassification improved by 6.2%, and integrated discrimination improved by 0.003.

CONCLUSIONS

Our prospective findings suggest that the addition of a genetic risk score may provide modest but significant incremental predictive performance beyond that of the conventional risk factor-based model without biochemical markers.

Application of Single-Nucleotide Polymorphism-Related Risk Estimates in Identification of Increased Genetic Susceptibility to Cardiovascular Diseases: A Literature Review

Background

Although largely preventable, cardiovascular diseases (CVDs) are the biggest cause of death worldwide. Common complex cardiovascular disorders (e.g., coronary heart disease, hypertonia, or thrombophilia) result from a combination of genetic alterations and environmental factors. Recent advances in the genomics of CVDs have fostered huge expectations about future use of susceptibility variants for prevention, diagnosis, and treatment. Our aim was to summarize the latest developments in the field from a public health perspective focusing on the applicability of data on single-nucleotide polymorphisms (SNPs), through a systematic review of studies from the last decade on genetic risk estimating for common CVDs.

Methods

Several keywords were used for searching the PubMed, Embase, CINAHL, and Web of Science databases. Recent advances were summarized and structured according to the main public health domains (prevention, early detection, and treatment) using a framework suggested recently for translational research. This framework includes four recommended phases: “T1. From gene discovery to candidate health applications; T2. From health application to evidence-based practice guidelines; T3. From evidence-based practice guidelines to health practice; and T4. From practice to population health impacts.”

Results

The majority of translation research belongs to the T1 phase “translation of basic genetic/genomic research into health application”; there are only a few population-based impacts estimated. The studies suggest that an SNP is a poor estimator of individual risk, whereas an individual’s genetic profile combined with non-genetic risk factors may better predict CVD risk among certain patient subgroups. Further research is needed to validate whether these genomic profiles can prospectively identify individuals at risk to develop CVDs. Several research gaps were identified: little information is available on studies suggesting “Health application to evidence-based practice guidelines”; no study is available on “Guidelines to health practice.” It was not possible to identify studies that incorporate environmental or lifestyle factors in the risk estimation.

Conclusion

Currently, identifying populations having a larger risk of developing common CVDs may result in personalized prevention programs by reducing people’s risk of onset or disease progression. However, limited evidence is available on the application of genomic results in health and public health practice.

Integration of Distributed Services and Hybrid Models Based on Process Choreography to Predict and Detect Type 2 Diabetes

Life expectancy is increasing and, so, the years that patients have to live with chronic diseases and co-morbidities. Type 2 diabetes is one of the most prevalent chronic diseases, specifically linked to being overweight and ages over sixty. Recent studies have demonstrated the effectiveness of new strategies to delay and even prevent the onset of type 2 diabetes by a combination of active and healthy lifestyle on cohorts of mid to high risk subjects. Prospective research has been driven on large groups of the population to build risk scores that aim to obtain a rule for the classification of patients according to the odds for developing the disease. Currently, there are more than two hundred models and risk scores for doing this, but a few have been properly evaluated in external groups and integrated into a clinical application for decision support. In this paper, we present a novel system architecture based on service choreography and hybrid modeling, which enables a distributed integration of clinical databases, statistical and mathematical engines and web interfaces to be deployed in a clinical setting. The system was assessed during an eight-week continuous period with eight endocrinologists of a hospital who evaluated up to 8080 patients with seven different type 2 diabetes risk models implemented in two mathematical engines. Throughput was assessed as a matter of technical key performance indicators, confirming the reliability and efficiency of the proposed architecture to integrate hybrid artificial intelligence tools into daily clinical routine to identify high risk subjects.

Enrichment of minor allele of SNPs and genetic prediction of type 2 diabetes risk in British population

Type 2 diabetes (T2D) is a complex disorder characterized by high blood sugar, insulin resistance, and relative lack of insulin. The collective effects of genome wide minor alleles of common SNPs, or the minor allele content (MAC) in an individual, have been linked with quantitative variations of complex traits and diseases. Here we studied MAC in T2D using previously published SNP datasets and found higher MAC in cases relative to matched controls. A set of 357 SNPs was found to have the best predictive accuracy in a British population. A weighted risk score calculated by using this set produced an area under the curve (AUC) score of 0.86, which is comparable to risk models built by phenotypic markers. These results identify a novel genetic risk element in T2D susceptibility and provide a potentially useful genetic method to identify individuals with high risk of T2D.

Genetic risk scores in the prediction of plasma glucose, impaired insulin secretion, insulin resistance and incident type 2 diabetes in the METSIM study

AIMS/HYPOTHESIS

Many SNPs have been associated with glycaemic traits and type 2 diabetes, but their joint effects on glycaemic traits and the underlying mechanisms leading to hyperglycaemia over time are largely unknown. We aimed to investigate the association of six genetic risk scores (GRSs) with changes in plasma glucose, insulin sensitivity, insulin secretion and incident type 2 diabetes in the prospective METabolic Syndrome In Men (METSIM) study.

METHODS

We generated weighted GRSs for fasting plasma glucose ([FPG] GRS_FPG, 35 SNPs), 2 h plasma glucose ([2hPG] GRS_2hPG, 9 SNPs), insulin secretion (GRS_IS, 17 SNPs), insulin resistance (GRS_IR, 9 SNPs) and BMI (GRS_BMI, 95 SNPs) and a non-weighted GRS for type 2 diabetes (GRS_T2D, 76 SNPs) in up to 8749 non-diabetic Finnish men. Linear regression was used to test associations of the GRSs with changes in glycaemic traits over time.

RESULTS

GRS_T2D, GRS_FPG and GRS_IS were associated with an increase in FPG, GRS_T2D with an increase in glucose AUC and a decrease in insulin secretion, and GRS_2hPG with an increase in 2hPG during the follow-up (p < 0.0017 for all models). GRS_T2D, GRS_FPG and GRS_IS were associated with incident type 2 diabetes (p < 0.008 for all models). GRS_BMI and GRS_IR were not significantly associated with any changes in glycaemic traits.

CONCLUSIONS/INTERPRETATION

In the METSIM follow-up study, GRS_T2D, GRS_FPG and GRS_IS were associated with the worsening of FPG and an increase in incident type 2 diabetes. GRS_T2D was additionally associated with a decrease in insulin secretion, and GRS_2hPG with an increase in 2hPG.

Genetic Association and Risk Scores in a Chronic Obstructive Pulmonary Disease Meta-analysis of 16,707 Subjects

The heritability of chronic obstructive pulmonary disease (COPD) cannot be fully explained by recognized genetic risk factors identified as achieving genome-wide significance. In addition, the combined contribution of genetic variation to COPD risk has not been fully explored. We sought to determine: (1) whether studies of variants from previous studies of COPD or lung function in a larger sample could identify additional associated variants, particularly for severe COPD; and (2) the impact of genetic risk scores on COPD. We genotyped 3,346 single-nucleotide polymorphisms (SNPs) in 2,588 cases (1,803 severe COPD) and 1,782 control subjects from four cohorts, and performed association testing with COPD, combining these results with existing genotyping data from 6,633 cases (3,497 severe COPD) and 5,704 control subjects. In addition, we developed genetic risk scores from SNPs associated with lung function and COPD and tested their discriminatory power for COPD-related measures. We identified significant associations between SNPs near PPIC (P = 1.28 × 10^-8) and PPP4R4/SERPINA1 (P = 1.01 × 10^-8) and severe COPD; the latter association may be driven by recognized variants in SERPINA1. Genetic risk scores based on SNPs previously associated with COPD and lung function had a modest ability to discriminate COPD (area under the curve, ∼0.6), and accounted for a mean 0.9-1.9% lower forced expiratory volume in 1 second percent predicted for each additional risk allele. In a large genetic association analysis, we identified associations with severe COPD near PPIC and SERPINA1. A risk score based on combining genetic variants had modest, but significant, effects on risk of COPD and lung function.

Genetic factors exist behind the high prevalence of reduced high-density lipoprotein cholesterol levels in the Roma population

BACKGROUND AND AIMS

Previous findings showed that reduced plasma high-density lipoprotein cholesterol (HDL-C) levels are more frequent in all age groups of the Hungarian Roma compared to the general population. It suggests that genetic factors may exist behind this phenomenon. Our present study was designed to test this hypothesis, i.e., to define whether genetic factors contribute to the higher prevalence of reduced HDL-C among Roma. Single nucleotide polymorphisms (N = 21) contributing to the variation in plasma HDL-C concentrations were analysed in the Hungarian Roma (N = 646) and general (N = 1542) populations.

METHODS

Genetic risk scores, unweighted (GRS) and weighted (wGRS), were computed and compared. Associations between the GRSs and the prevalence of reduced HDL-C levels were analysed.

RESULTS

The GRS and wGRS were significantly higher in the Roma compared to the general population (GRS: 22.2 ± 3.2 vs. 21.5 ± 3.3; wGRS: 0.57 ± 0.1 vs. 0.53 ± 0.1; p<0.001). One half per cent of Roma subjects were in the bottom fifth of the wGRS (wGRS≤ 0.3) compared with 1.8% of those in the general population (p=0.025), while 5% of the Roma subjects were in the top fifth of the wGRS (wGRS≥ 0.75) compared with 2.6% of those in the general population (p=0.004). The GRS showed similar correlation with reduced plasma HDL-C levels in the two populations, whilst the wGRS showed stronger correlation with the trait among Roma after controlling for confounders.

CONCLUSIONS

These results strongly suggest that genetic factors contribute to the higher prevalence of reduced HDL-C levels among Roma, so interventions aiming to improve Roma health status need to consider their increased genetic susceptibility.