Combining genetic markers and clinical risk factors improves the risk assessment of impaired glucose metabolism

Developing an Exposure Burden Score for Chemical Mixtures Using Item Response Theory, with Applications to PFAS Mixtures

BACKGROUND

There are few existing methods to quantify total exposure burden to chemical mixtures, independent of a health outcome. A summary metric could be advantageous for use in biomonitoring, risk assessment, health risk calculators, and mediation models.

OBJECTIVE

We developed a novel exposure burden score method for chemical mixtures, applied it to estimate exposure burden to per- and polyfluoroalkyl substances (PFAS) mixtures, and estimated associations of PFAS burden scores with cardio-metabolic outcomes in the general U.S.

POPULATION

METHODS

We applied item response theory (IRT) to biomonitoring data from 1,915 children and adults 12-80 years of age in the 2017-2018 National Health and Examination Survey to quantify a latent PFAS burden score, using serum concentrations of eight measured PFAS biomarkers, each considered an "item." The premise of IRT is that through using both information about a participant's concentration of an individual PFAS biomarker, as well as their exposure patterns for the PFAS mixture, we can estimate the participant's latent PFAS exposure burden, independent of a health outcome. We used linear regression to estimate associations of the PFAS burden score with cardio-metabolic outcomes and compared our findings to results using summed PFAS concentrations as the exposure metric.

RESULTS

PFAS burden scores and summed PFAS concentrations had moderate-high correlation (ρ=0.75). Isomers of PFOS [n-perfluorooctane sulfonic acid (n-PFOS) and perfluoromethylheptane sulfonic acid isomers (Sm-PFOS)] were the most informative to the PFAS burden scores. PFAS burden scores and summed PFAS concentrations were both significantly associated with cardio-metabolic outcomes, but associations were generally closer to the null for summed PFAS concentrations vs. the PFAS burden score. Adjusted associations (95% CIs) with total cholesterol (in milligrams per deciliter) were 8.6 (95% CI: 5.2, 11.9) and 2.4 (95% CI: 0.5, 4.2) per interquartile range increase in the PFAS burden score and summed concentrations, respectively. Sensitivity analyses showed similar associations with cardio-metabolic outcomes when only a subset of PFAS biomarkers was used to estimate PFAS burden. In a validation study, associations between PFAS burden scores and cholesterol were consistent with primary analyses but null when using summed PFAS concentrations.

DISCUSSION

IRT offers a straightforward way to include exposure biomarkers with low detection frequencies and can reduce exposure measurement error. Further, IRT enables comparisons of exposure burden to chemical mixtures across studies even if they did not measure the exact same set of chemicals, which supports harmonization across studies and consortia. We provide an accompanying PFAS burden calculator (https://pfasburden.shinyapps.io/app_pfas_burden/), enabling researchers to calculate PFAS burden scores based on U.S. population exposure reference ranges. https://doi.org/10.1289/EHP10125.

Association of gene polymorphisms with body weight changes in prediabetic patients

BACKGROUND

Recent research has demonstrated that Type 2 Diabetes (T2D) risk is influenced by a number of common polymorphisms, including MC4R rs17782313, PPARG rs1801282, and TCF7L2 rs7903146. Knowledge of the association between these single nucleotide polymorphisms (SNPs) and body weight changes in different forms of prediabetes treatment is still limited. The aim of this study was to investigate the association of polymorphisms within the MC4R, PPARG, and TCF7L2 genes on the risk of carbohydrate metabolism disorders and body composition changes in overweight or obese patients with early carbohydrate metabolism disorders.

METHODS AND RESULTS

From 327 patients, a subgroup of 81 prediabetic female patients (48.7 ± 14.8 years) of Eastern European descent participated in a 3-month study comprised of diet therapy or diet therapy accompanied with metformin treatment. Bioelectrical impedance analysis and genotyping of MC4R rs17782313, PPARG rs1801282, and TCF7L2 rs7903146 polymorphisms were performed. The MC4R CC and TCF7L2 TT genotypes were associated with increased risk of T2D (OR = 1.46, p = 0.05 and OR = 2.47, p = 0.006, respectively). PPARG CC homozygotes experienced increased weight loss; however, no additional improvements were experienced with the addition of metformin. MC4R TT homozygotes who took metformin alongside dietary intervention experienced increased weight loss and reductions in fat mass (p < 0.05).

CONCLUSIONS

We have shown that the obesity-protective alleles (MC4R T and PPARG C) were positively associated with weight loss efficiency. Furthermore, we confirmed the previous association of the MC4R C and TCF7L2 T alleles with T2D risk.

Determining the optimal screening interval for type 2 diabetes mellitus using a risk prediction model

Background

Progression to diabetes mellitus (DM) is variable and the screening time interval not well defined. The American Diabetes Association and US Preventive Services Task Force suggest screening every 3 years, but evidence is limited.

The objective of the study was to develop a model to predict the probability of developing DM and suggest a risk-based screening interval.

Methods

We included non-diabetic adult patients screened for DM in the Cleveland Clinic Health System if they had at least two measurements of glycated hemoglobin (HbA1c), an initial one less than 6.5% (48 mmol/mol) in 2008, and another between January, 2009 and December, 2013. Cox proportional hazards models were created.

The primary outcome was DM defined as HbA1C greater than 6.4% (46 mmol/mol). The optimal rescreening interval was chosen based on the predicted probability of developing DM.

Results

Of 5084 participants, 100 (4.4%) of the 2281 patients with normal HbA1c and 772 (27.5%) of the 2803 patients with prediabetes developed DM within 5 years. Factors associated with developing DM included HbA1c (HR per 0.1 units increase 1.20; 95%CI, 1.13–1.27), family history (HR 1.31; 95%CI, 1.13–1.51), smoking (HR 1.18; 95%CI, 1.03–1.35), triglycerides (HR 1.01; 95%CI, 1.00–1.03), alanine aminotransferase (HR 1.07; 95%CI, 1.03–1.11), body mass index (HR 1.06; 95%CI, 1.01–1.11), age (HR 0.95; 95%CI, 0.91–0.99) and high-density lipoproteins (HR 0.93; 95% CI, 0.90–0.95). Five percent of patients in the highest risk tertile developed DM within 8 months, while it took 35 months for 5% of the middle tertile to develop DM. Only 2.4% percent of the patients in the lowest tertile developed DM within 5 years.

Conclusion

A risk prediction model employing commonly available data can be used to guide screening intervals. Based on equal intervals for equal risk, patients in the highest risk category could be rescreened after 8 months, while those in the intermediate and lowest risk categories could be rescreened after 3 and 5 years respectively.

Genetic markers of type 2 diabetes: Progress in genome-wide association studies and clinical application for risk prediction

Type 2 diabetes (T2D) has become a leading public health challenge worldwide. To date, a total of 83 susceptibility loci for T2D have been identified by genome-wide association studies (GWAS). Application of meta-analysis and modern genotype imputation approaches to GWAS data from diverse ethnic populations has been key in the effort to discover T2D loci. Genetic information is expected to play a vital role in the prediction of T2D, and many efforts have been made to develop T2D risk models that include both conventional and genetic risk factors. Yet, because most T2D genetic variants identified have small effect size individually (10%-20% increased risk of T2D per risk allele), their clinical utility remains unclear. Most studies report that a genetic risk score combining multiple T2D genetic variants does not substantially improve T2D risk prediction beyond conventional risk factors. In this article, we summarize the recent progress of T2D GWAS and further review the incremental predictive performance of genetic markers for T2D.

An explained variance-based genetic risk score associated with gestational diabetes antecedent and with progression to pre-diabetes and type 2 diabetes: a cohort study

OBJECTIVE

To determine whether an explained-variance genetic risk score (GRS), with 36 single nucleotide polymorphisms (SNPs) previously associated with type 2 diabetes (T2D), is also associated with gestational diabetes mellitus (GDM), and with the progression to pre-diabetes and T2D among women with prior GDM.

DESIGN

A cohort study.

SETTING

Clinical investigation unit of Laval University, Quebec, Canada.

POPULATION

A cohort of 214 women with prior GDM and 82 controls recruited between 2009 and 2012.

METHODS

Associations between the GRS and GDM.

MAIN OUTCOMES MEASURES

GDM and prevalence of pre-diabetes and T2D.

RESULTS

Women with prior GDM had a higher GRS compared with controls (38.6 ± 3.9, 95% CI 38.1-39.1, versus 37.4 ± 3.2, 95% CI 36.7-38.1; P < 0.0001). In women with prior GDM, the explained-variance GRS was higher for pre-diabetic women compared with women who remained normoglucotolerant at testing (1.21 ± 0.18, 95% CI 1.18-1.23, versus 1.17 ± 0.15, 95% CI 1.13-1.20; P < 0.0001). Similarly, women with T2D had a higher explained-variance GRS compared with women with prior GDM who remained normoglucotolerant (1.20 ± 0.18, 95% CI 1.14-1.25, versus 1.17 ± 0.17, 95% CI 1.13-1.20; P < 0.0001). The predictive effects of the explained-variance GRS, age, and body mass index (BMI), or the additive effects of the three variables, were tested for pre-diabetes and T2D. We observed an area under the curve of 0.6269 (95% CI 0.5638-0.6901) for age and BMI, and adding the explained-variance GRS into the model increased the area to 0.6672 (95% CI 0.6064-0.7281) for the prediction of pre-diabetes.

CONCLUSIONS

An explained-variance GRS is associated with both GDM and progression to pre-diabetes and T2D in women with prior GDM.

Predicting risk of type 2 diabetes mellitus with genetic risk models on the basis of established genome-wide association markers: a systematic review

This study aimed to evaluate the predictive performance of genetic risk models based on risk loci identified and/or confirmed in genome-wide association studies for type 2 diabetes mellitus. A systematic literature search was conducted in the PubMed/MEDLINE and EMBASE databases through April 13, 2012, and published data relevant to the prediction of type 2 diabetes based on genome-wide association marker-based risk models (GRMs) were included. Of the 1,234 potentially relevant articles, 21 articles representing 23 studies were eligible for inclusion. The median area under the receiver operating characteristic curve (AUC) among eligible studies was 0.60 (range, 0.55-0.68), which did not differ appreciably by study design, sample size, participants' race/ethnicity, or the number of genetic markers included in the GRMs. In addition, the AUCs for type 2 diabetes did not improve appreciably with the addition of genetic markers into conventional risk factor-based models (median AUC, 0.79 (range, 0.63-0.91) vs. median AUC, 0.78 (range, 0.63-0.90), respectively). A limited number of included studies used reclassification measures and yielded inconsistent results. In conclusion, GRMs showed a low predictive performance for risk of type 2 diabetes, irrespective of study design, participants' race/ethnicity, and the number of genetic markers included. Moreover, the addition of genome-wide association markers into conventional risk models produced little improvement in predictive performance.

Added value of novel circulating and genetic biomarkers in type 2 diabetes prediction: a systematic review

AIMS

To provide a systematic overview of the added value of novel circulating and genetic biomarkers in predicting type 2 diabetes (T2DM).

METHODS

We searched MEDLINE and EMBASE (January 2000 to September 2012) for studies that reported a measure of improvement in the performance of T2DM risk prediction models subsequent to adding novel biomarkers to traditional risk factors. We extracted data on study methods and metrics of incremental predictive value of novel biomarkers.

RESULTS

We included 34 publications from 30 studies. All studies reported a change in the area under the receiver-operating characteristic curve, which was modest, ranging from -0.004 to 0.1, with claims of statistically significant improvements in eleven studies. The net reclassification index was evaluated in 11 studies, and ranged from -2.2% to 10.2% after inclusion of genetic markers in six studies (statistically significant in two cases), and from -0.5% to 27.5% after inclusion of non-genetic markers in five studies (non-significant in two studies). The integrated discrimination index (0-2.04) was reported in eight studies, being statistically significant in five of these.

CONCLUSIONS

Currently known novel circulating and genetic biomarkers do not substantially improve T2DM risk prediction above and beyond the ability of traditional risk factors.

Hyperuricemia in young adults and risk of insulin resistance, prediabetes, and diabetes: a 15-year follow-up study

The objective of this study was to assess the utility of hyperuricemia as a marker for diabetes and prediabetes (impaired fasting glucose) and insulin resistance in young adults. Using Cox proportional hazards regression models, the authors analyzed 15-year follow-up data on 5,012 persons in 4 US cities who were aged 18-30 years and diabetes-free at the time of enrollment. At baseline (1986), 88% of participants had a body mass index (weight (kg)/height (m)(2)) less than 30. During the follow-up period (through 2001), the incidence rates of diabetes and prediabetes (insulin resistance and impaired fasting glucose) were higher among persons with greater serum urate concentrations. In multivariable Cox regression analyses that adjusted for age, gender, race, body mass index, family history of diabetes, diastolic blood pressure, total cholesterol, smoking, and alcohol use, the hazard ratios for diabetes, insulin resistance, and prediabetes among persons with hyperuricemia (serum urate level >7 mg/dL vs. ≤7.0 mg/dL) were 1.87 (95% confidence interval (CI): 1.33, 2.62), 1.36 (95% CI: 1.23, 1.51), and 1.25 (95% CI: 1.04, 1.52), respectively. This observation was generally consistent across subgroups. The authors conclude that hyperuricemia in the midtwenties is an independent marker for predicting diabetes and prediabetes among young adults in the subsequent 15 years.

Genetic risk profiling for prediction of type 2 diabetes

Type 2 diabetes (T2D) is a common disease caused by a complex interplay between many genetic and environmental factors. Candidate gene studies and recent collaborative genome-wide association efforts revealed at least 38 common single nucleotide polymorphisms (SNPs) associated with increased risk of T2D. Genetic testing of multiple SNPs is considered a potentially useful tool for early detection of individuals at high diabetes risk leading to improved targeting of preventive interventions.