Dataset Information

Estimating heritability of glycaemic response to metformin using nationwide electronic health records and population-sized pedigree.

ABSTRACT:

Background

Variability of response to medication is a well-known phenomenon, determined by both environmental and genetic factors. Understanding the heritable component of the response to medication is of great interest but challenging due to several reasons, including small study cohorts and computational limitations.

Methods

Here, we study the heritability of variation in the glycaemic response to metformin, first-line therapeutic agent for type 2 diabetes (T2D), by leveraging 18 years of electronic health records (EHR) data from Israel's largest healthcare service provider, consisting of over five million patients of diverse ethnicities and socio-economic background. Our cohort consists of 80,788 T2D patients treated with metformin, with an accumulated number of 1,611,591 HbA1C measurements and 4,581,097 metformin prescriptions. We estimate the explained variance of glycated hemoglobin (HbA1c%) reduction due to inheritance by constructing a six-generation population-size pedigree from national registries and linking it to medical health records.

Results

Using Linear Mixed Model-based framework, a common-practice method for heritability estimation, we calculate a heritability measure of h2=12.6% (95% CI, 6.1%-19.1% ) for absolute reduction of HbA1c% after metformin treatment in the entire cohort, h2=21.0% (95% CI, 7.8%-34.4% ) for males and h2=22.9% (95% CI, 10.0%-35.7% ) in females. Results remain unchanged after adjusting for pre-treatment HbA1c%, and in proportional reduction of HbA1c%.

Conclusions

To the best of our knowledge, our work is the first to estimate heritability of drug response using solely EHR data combining a pedigree-based kinship matrix. We demonstrate that while response to metformin treatment has a heritable component, most of the variation is likely due to other factors, further motivating non-genetic analyses aimed at unraveling metformin's action mechanism.

SUBMITTER: Kalka IN

PROVIDER: S-EPMC9053254 | biostudies-literature | 2021

REPOSITORIES: biostudies-literature

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Publications

Estimating heritability of glycaemic response to metformin using nationwide electronic health records and population-sized pedigree.

Kalka Iris N IN Gavrieli Amir A Shilo Smadar S Rossman Hagai H Artzi Nitzan Shalom NS Yacovzada Nancy-Sarah NS Segal Eran E

Communications medicine 20211201

<h4>Background</h4>Variability of response to medication is a well-known phenomenon, determined by both environmental and genetic factors. Understanding the heritable component of the response to medication is of great interest but challenging due to several reasons, including small study cohorts and computational limitations.<h4>Methods</h4>Here, we study the heritability of variation in the glycaemic response to metformin, first-line therapeutic agent for type 2 diabetes (T2D), by leveraging 1 ...[more]

PMID: 35602224

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Project description:BackgroundMetformin is a first-line oral agent used in the treatment of type 2 diabetes, but glycaemic response to this drug is highly variable. Understanding the genetic contribution to metformin response might increase the possibility of personalising metformin treatment. We aimed to establish the heritability of glycaemic response to metformin using the genome-wide complex trait analysis (GCTA) method.MethodsIn this GCTA study, we obtained data about HbA1c concentrations before and during metformin treatment from patients in the Genetics of Diabetes Audit and Research in Tayside Scotland (GoDARTS) study, which includes a cohort of patients with type 2 diabetes and is linked to comprehensive clinical databases and genome-wide association study data. We applied the GCTA method to estimate heritability for four definitions of glycaemic response to metformin: absolute reduction in HbA1c; proportional reduction in HbA1c; adjusted reduction in HbA1c; and whether or not the target on-treatment HbA1c of less than 7% (53 mmol/mol) was achieved, with adjustment for baseline HbA1c and known clinical covariates. Chromosome-wise heritability estimation was used to obtain further information about the genetic architecture.Findings5386 individuals were included in the final dataset, of whom 2085 had enough clinical data to define glycaemic response to metformin. The heritability of glycaemic response to metformin varied by response phenotype, with a heritability of 34% (95% CI 1-68; p=0·022) for the absolute reduction in HbA1c, adjusted for pretreatment HbA1c. Chromosome-wise heritability estimates suggest that the genetic contribution is probably from individual variants scattered across the genome, which each have a small to moderate effect, rather than from a few loci that each have a large effect.InterpretationGlycaemic response to metformin is heritable, thus glycaemic response to metformin is, in part, intrinsic to individual biological variation. Further genetic analysis might enable us to make better predictions for stratified medicine and to unravel new mechanisms of metformin action.FundingWellcome Trust.

Project description:BackgroundElectronic health records (EHRs) provide rich data on many domains not routinely available in other data, as such, they are a promising source to study changes in health outcomes using longitudinal study designs (eg, cohort studies, natural experiments, etc.). Yet, patient attrition rates in these data are unknown.ObjectiveThe objective of this study was to estimate overall and among adults with diabetes or hypertension: (1) patient attrition over a 3-year period at community health centers; and (2) the likelihood that patients with Medicaid permanently switched their source of primary care.Research designA retrospective cohort study of 2012-2017 data from the Accelerating Data Value Across a National Community Health Center Network (ADVANCE) Clinical Data Research Network of community health centers were used to assess EHR data attrition. Oregon Medicaid enrollment and claims data were used to estimate the likelihood of changing the source of primary care.SubjectsA total of 827,657 patients aged 19-64 with ≥1 ambulatory visit from 76 community health center systems across 20 states. In all, 232,891 Oregon Medicaid enrollees (aged 19-64) with a gap of ≥6 months following a claim for a visit billed to a primary care source.MeasuresPercentage of patients not returning within 3 years of their qualifying visit (attrition). The probability that a patient with Medicaid permanently changed their primary care source.ResultsAttrition over the 3 years averaged 33.5%; attrition rates were lower (<25%) among patients with diabetes and/or hypertension. Among Medicaid enrollees, the percentage of provider change after a 6-month gap between visits was 12% for community health center patients compared with 39% for single-provider practice patients. Over 3 years, the likelihood of a patient changing to a new provider increased with length of time since their last visit but remained lowest among community health center patients.ConclusionThis study demonstrates the use of the EHR dataset is a reliable source of data to support longitudinal studies while highlighting variability in attrition by primary care source and chronic conditions.

Project description:BACKGROUND:Administrative health records (AHRs) and electronic medical records (EMRs) are two key sources of population-based data for disease surveillance, but misclassification errors in the data can bias disease estimates. Methods that combine information from error-prone data sources can build on the strengths of AHRs and EMRs. We compared bias and error for four data-combining methods and applied them to estimate hypertension prevalence. METHODS:Our study included rule-based OR and AND methods that identify disease cases from either or both data sources, respectively, rule-based sensitivity-specificity adjusted (RSSA) method that corrects for inaccuracies using a deterministic rule, and probabilistic-based sensitivity-specificity adjusted (PSSA) method that corrects for error using a statistical model. Computer simulation was used to estimate relative bias (RB) and mean square error (MSE) under varying conditions of population disease prevalence, correlation amongst data sources, and amount of misclassification error. AHRs and EMRs for Manitoba, Canada were used to estimate hypertension prevalence using validated case definitions and multiple disease markers. RESULTS:The OR method had the lowest RB and MSE when population disease prevalence was 10%, and the RSSA method had the lowest RB and MSE when population prevalence increased to 20%. As the correlation between data sources increased, the OR method resulted in the lowest RB and MSE. Estimates of hypertension prevalence for AHRs and EMRs alone were 30.9% (95% CI: 30.6-31.2) and 24.9% (95% CI: 24.6-25.2), respectively. The estimates were 21.4% (95% CI: 21.1-21.7), for the AND method, 34.4% (95% CI: 34.1-34.8) for the OR method, 32.2% (95% CI: 31.8-32.6) for the RSSA method, and ranged from 34.3% (95% CI: 34.1-34.5) to 35.9% (95% CI, 35.7-36.1) for the PSSA method, depending on the statistical model. CONCLUSIONS:The OR and AND methods are influenced by correlation amongst the data sources, while the RSSA method is dependent on the accuracy of prior sensitivity and specificity estimates. The PSSA method performed well when population prevalence was high and average correlations amongst disease markers was low. This study will guide researchers to select a data-combining method that best suits their data characteristics.

Dataset Information

Estimating heritability of glycaemic response to metformin using nationwide electronic health records and population-sized pedigree.

Background

Methods

Results

Conclusions

Publications

Estimating heritability of glycaemic response to metformin using nationwide electronic health records and population-sized pedigree.

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