Principles for Health Information Collection, Sharing, and Use: A Policy Statement From the American Heart Association.

The evolution of the electronic health record, combined with advances in data curation and analytic technologies, increasingly enables data sharing and harmonization. Advances in the analysis of health-related and health-proxy information have already accelerated research discoveries and improved patient care. This American Heart Association policy statement discusses how broad data sharing can be an enabling driver of progress by providing data to develop, test, and benchmark innovative methods, scalable insights, and potential new paradigms for data storage and workflow. Along with these advances come concerns about the sensitive nature of some health data, equity considerations about the involvement of historically excluded communities, and the complex intersection of laws attempting to govern behavior. Data-sharing principles are therefore necessary across a wide swath of entities, including parties who collect health information, funders, researchers, patients, legislatures, commercial companies, and regulatory departments and agencies. This policy statement outlines some of the key equity and legal background relevant to health data sharing and responsible management. It then articulates principles that will guide the American Heart Association's engagement in public policy related to data collection, sharing, and use to continue to inform its work across the research enterprise, as well as specific examples of how these principles might be applied in the policy landscape. The goal of these principles is to improve policy to support the use or reuse of health information in ways that are respectful of patients and research participants, equitable in impact in terms of both risks and potential benefits, and beneficial across broad and demographically diverse communities in the United States.

An integrated approach to identify environmental modulators of genetic risk factors for complex traits.

Complex traits and diseases can be influenced by both genetics and environment. However, given the large number of environmental stimuli and power challenges for gene-by-environment testing, it remains a critical challenge to identify and prioritize specific disease-relevant environmental exposures. We propose a framework for leveraging signals from transcriptional responses to environmental perturbations to identify disease-relevant perturbations that can modulate genetic risk for complex traits and inform the functions of genetic variants associated with complex traits. We perturbed human skeletal-muscle-, fat-, and liver-relevant cell lines with 21 perturbations affecting insulin resistance, glucose homeostasis, and metabolic regulation in humans and identified thousands of environmentally responsive genes. By combining these data with GWASs from 31 distinct polygenic traits, we show that the heritability of multiple traits is enriched in regions surrounding genes responsive to specific perturbations and, further, that environmentally responsive genes are enriched for associations with specific diseases and phenotypes from the GWAS Catalog. Overall, we demonstrate the advantages of large-scale characterization of transcriptional changes in diversely stimulated and pathologically relevant cells to identify disease-relevant perturbations.

Design and Pilot Results from Million Veteran Program Return Of Actionable Genetic Results (MVP-ROAR) Study.

BACKGROUND: As a mega-biobank linked to a national healthcare system, the Million Veteran Program (MVP) can directly improve the health care of participants. To determine the feasibility and outcomes of returning medically actionable genetic results to MVP participants, the program launched the MVP Return Of Actionable Results (MVP-ROAR) Study, with familial hypercholesterolemia (FH) as an exemplar actionable condition. METHODS: The MVP-ROAR Study consists of a completed single-arm pilot phase and an ongoing randomized clinical trial (RCT), in which MVP participants are recontacted and invited to receive clinical confirmatory gene sequencing testing and a telegenetic counseling intervention. The primary outcome of the RCT is 6-month change in low-density lipoprotein cholesterol (LDL-C) between participants receiving results at baseline and those receiving results after 6 months. RESULTS: The pilot developed processes to identify and recontact participants nationally with probable pathogenic variants in low-density lipoprotein receptor (LDLR) on the MVP genotype array, invite them to clinical confirmatory gene sequencing, and deliver a telegenetic counseling intervention. Among participants in the pilot phase, 8 (100%) had active statin prescriptions after 6 months. Results were shared with 16 first-degree family members. Six-month ΔLDL-C (low-density lipoprotein cholesterol) after the genetic counseling intervention was -37 mg/dL (95% CI: -12 to -61; p=0.03). The ongoing RCT will determine between-arm differences in this primary outcome. CONCLUSION: While underscoring the importance of clinical confirmation of research results, the pilot phase of the MVP-ROAR Study marks a turning point in MVP and demonstrates the feasibility of returning genetic results to participants and their providers. The ongoing RCT will contribute to understanding how such a program might improve patient health care and outcomes.

Is it time for a paradigm shift? Inclusion of APOE on genetic dyslipidemia panels.

APOE codes for apolipoprotein E (ApoE), which plays an important role in lipid and lipoprotein metabolism and homeostasis of tissue lipid content. Several variants in APOE have been associated with inherited dyslipidemias, and a subsequent increased risk of developing premature coronary artery disease (CAD). However, these variants and their impact on risk can be thought of on a spectrum, with some being more monogenic in nature, and others contributing in a polygenic/multifactorial manner. Despite these known associations, there is often hesitancy around ordering APOE genetic testing due to the association with Alzheimer's disease. This paper aims to catalyze discussion around APOE testing and counseling strategies, highlight the nuances around this topic, and advocate for inclusion of APOE testing on dyslipidemia panels when an inherited dyslipidemia is suspected.

A single-cell CRISPRi platform for characterizing candidate genes relevant to metabolic disorders in human adipocytes.

CROP-Seq combines gene silencing using CRISPR interference with single-cell RNA sequencing. Here, we applied CROP-Seq to study adipogenesis and adipocyte biology. Human preadipocyte SGBS cell line expressing KRAB-dCas9 was transduced with a sgRNA library. Following selection, individual cells were captured using microfluidics at different timepoints during adipogenesis. Bioinformatic analysis of transcriptomic data was used to determine the knockdown effects, the dysregulated pathways, and to predict cellular phenotypes. Single-cell transcriptomes recapitulated adipogenesis states. For all targets, over 400 differentially expressed genes were identified at least at one timepoint. As a validation of our approach, the knockdown of PPARG and CEBPB (which encode key proadipogenic transcription factors) resulted in the inhibition of adipogenesis. Gene set enrichment analysis generated hypotheses regarding the molecular function of novel genes. MAFF knockdown led to downregulation of transcriptional response to proinflammatory cytokine TNF-α in preadipocytes and to decreased CXCL-16 and IL-6 secretion. TIPARP knockdown resulted in increased expression of adipogenesis markers. In summary, this powerful, hypothesis-free tool can identify novel regulators of adipogenesis, preadipocyte, and adipocyte function associated with metabolic disease.NEW & NOTEWORTHY Genomics efforts led to the identification of many genomic loci that are associated with metabolic traits, many of which are tied to adipose tissue function. However, determination of the causal genes, and their mechanism of action in metabolism, is a time-consuming process. Here, we use an approach to determine the transcriptional outcome of candidate gene knockdown for multiple genes at the same time in a human cell model of adipogenesis.

Plasma proteomic signatures of a direct measure of insulin sensitivity in two population cohorts.

AIMS/HYPOTHESIS: The euglycaemic-hyperinsulinaemic clamp (EIC) is the reference standard for the measurement of whole-body insulin sensitivity but is laborious and expensive to perform. We aimed to assess the incremental value of high-throughput plasma proteomic profiling in developing signatures correlating with the M value derived from the EIC. METHODS: We measured 828 proteins in the fasting plasma of 966 participants from the Relationship between Insulin Sensitivity and Cardiovascular disease (RISC) study and 745 participants from the Uppsala Longitudinal Study of Adult Men (ULSAM) using a high-throughput proximity extension assay. We used the least absolute shrinkage and selection operator (LASSO) approach using clinical variables and protein measures as features. Models were tested within and across cohorts. Our primary model performance metric was the proportion of the M value variance explained (R2). RESULTS: A standard LASSO model incorporating 53 proteins in addition to routinely available clinical variables increased the M value R2 from 0.237 (95% CI 0.178, 0.303) to 0.456 (0.372, 0.536) in RISC. A similar pattern was observed in ULSAM, in which the M value R2 increased from 0.443 (0.360, 0.530) to 0.632 (0.569, 0.698) with the addition of 61 proteins. Models trained in one cohort and tested in the other also demonstrated significant improvements in R2 despite differences in baseline cohort characteristics and clamp methodology (RISC to ULSAM: 0.491 [0.433, 0.539] for 51 proteins; ULSAM to RISC: 0.369 [0.331, 0.416] for 67 proteins). A randomised LASSO and stability selection algorithm selected only two proteins per cohort (three unique proteins), which improved R2 but to a lesser degree than in standard LASSO models: 0.352 (0.266, 0.439) in RISC and 0.495 (0.404, 0.585) in ULSAM. Reductions in improvements of R2 with randomised LASSO and stability selection were less marked in cross-cohort analyses (RISC to ULSAM R2 0.444 [0.391, 0.497]; ULSAM to RISC R2 0.348 [0.300, 0.396]). Models of proteins alone were as effective as models that included both clinical variables and proteins using either standard or randomised LASSO. The single most consistently selected protein across all analyses and models was IGF-binding protein 2. CONCLUSIONS/INTERPRETATION: A plasma proteomic signature identified using a standard LASSO approach improves the cross-sectional estimation of the M value over routine clinical variables. However, a small subset of these proteins identified using a stability selection algorithm affords much of this improvement, especially when considering cross-cohort analyses. Our approach provides opportunities to improve the identification of insulin-resistant individuals at risk of insulin resistance-related adverse health consequences.

The Clinical Genome Resource (ClinGen) Familial Hypercholesterolemia Variant Curation Expert Panel consensus guidelines for LDLR variant classification.

PURPOSE: In 2015, the American College of Medical Genetics and Genomics (ACMG) and the Association for Molecular Pathology (AMP) published consensus standardized guidelines for sequence-level variant classification in Mendelian disorders. To increase accuracy and consistency, the Clinical Genome Resource Familial Hypercholesterolemia (FH) Variant Curation Expert Panel was tasked with optimizing the existing ACMG/AMP framework for disease-specific classification in FH. In this study, we provide consensus recommendations for the most common FH-associated gene, LDLR, where >2300 unique FH-associated variants have been identified. METHODS: The multidisciplinary FH Variant Curation Expert Panel met in person and through frequent emails and conference calls to develop LDLR-specific modifications of ACMG/AMP guidelines. Through iteration, pilot testing, debate, and commentary, consensus among experts was reached. RESULTS: The consensus LDLR variant modifications to existing ACMG/AMP guidelines include (1) alteration of population frequency thresholds, (2) delineation of loss-of-function variant types, (3) functional study criteria specifications, (4) cosegregation criteria specifications, and (5) specific use and thresholds for in silico prediction tools, among others. CONCLUSION: Establishment of these guidelines as the new standard in the clinical laboratory setting will result in a more evidence-based, harmonized method for LDLR variant classification worldwide, thereby improving the care of patients with FH.

Statins Are Associated With Increased Insulin Resistance and Secretion.

Objective: Statin treatment reduces the risk of atherosclerotic cardiovascular disease but is associated with a modest increased risk of type 2 diabetes, especially in those with insulin resistance or prediabetes. Our objective was to determine the physiological mechanism for the increased type 2 diabetes risk. Approach and Results: We conducted an open-label clinical trial of atorvastatin 40 mg daily in adults without known atherosclerotic cardiovascular disease or type 2 diabetes at baseline. The co-primary outcomes were changes at 10 weeks versus baseline in insulin resistance as assessed by steady-state plasma glucose during the insulin suppression test and insulin secretion as assessed by insulin secretion rate area under the curve (ISRAUC) during the graded-glucose infusion test. Secondary outcomes included glucose and insulin, both fasting and during oral glucose tolerance test. Of 75 participants who enrolled, 71 completed the study (median age 61 years, 37% women, 65% non-Hispanic White, median body mass index, 27.8 kg/m2). Atorvastatin reduced LDL (low-density lipoprotein)-cholesterol (median decrease 53%, P<0.001) but did not change body weight. Compared with baseline, atorvastatin increased insulin resistance (steady-state plasma glucose) by a median of 8% (P=0.01) and insulin secretion (ISRAUC) by a median of 9% (P<0.001). There were small increases in oral glucose tolerance test glucoseAUC (median increase, 0.05%; P=0.03) and fasting insulin (median increase, 7%; P=0.01). Conclusions: In individuals without type 2 diabetes, high-intensity atorvastatin for 10 weeks increases insulin resistance and insulin secretion. Over time, the risk of new-onset diabetes with statin use may increase in individuals who become more insulin resistant but are unable to maintain compensatory increases in insulin secretion.

The need for polygenic score reporting standards in evidence-based practice: lipid genetics use case.

PURPOSE OF REVIEW: Polygenic scores (PGS) are used to quantify the genetic predisposition for heritable traits, with hypothesized utility for personalized risk assessments. Lipid PGS are primed for clinical translation, but evidence-based practice changes will require rigorous PGS standards to ensure reproducibility and generalizability. Here we review applicable reporting and technical standards for dyslipidemia PGS translation along phases of the ACCE (Analytical validity, Clinical validity, Clinical utility, Ethical considerations) framework for evaluating genetic tests. RECENT FINDINGS: New guidance suggests existing standards for study designs incorporating the ACCE framework are applicable to PGS and should be adopted. One recent example is the Clinical Genomics Resource (ClinGen) and Polygenic Score Catalog's PRS reporting standards, which define minimal requirements for describing rationale for score development, study population definitions and data parameters, risk model development and application, risk model evaluation, and translational considerations, such as generalizability beyond the target population studied. SUMMARY: Lipid PGS are likely to be integrated into clinical practice in the future. Clinicians will need to be prepared to determine if and when lipid PGS is useful and valid. This decision-making will depend on the quality of evidence for the clinical use of PGS. Establishing reporting standards for PGS will help facilitate data sharing and transparency for critical evaluation, ultimately benefiting the efficiency of evidence-based practice.

Children with Heterozygous Familial Hypercholesterolemia in the United States: Data from the Cascade Screening for Awareness and Detection-FH Registry.

OBJECTIVE: To describe enrollment characteristics of youth in the Cascade Screening for Awareness and Detection of FH Registry. STUDY DESIGN: This is a cross-sectional analysis of 493 participants aged <18 years with heterozygous familial hypercholesterolemia recruited from US lipid clinics (n = 20) between April 1, 2014, and January 12, 2018. At enrollment, some were new patients and some were already in care. Clinical characteristics are described, including lipid levels and lipid-lowering treatments. RESULTS: Mean age at diagnosis was 9.4 (4.0) years; 47% female, 68% white and 12% Hispanic. Average (SD) highest Low-density lipoprotein cholesterol (LDL-C) was 238 (61) mg/dL before treatment. Lipid-lowering therapy was used by 64% of participants; 56% were treated with statin. LDL-C declined 84 mg/dL (33%) among those treated with lipid-lowering therapy; statins produced the greatest decline, 100 mg/dL (39% reduction). At enrollment, 39% had reached an LDL-C goal, either <130 mg/dL or ≥50% decrease from pre-treatment; 20% of those on lipid-lowering therapy reached both goals. CONCLUSIONS: Among youth enrolled in the Cascade Screening for Awareness and Detection of FH Registry, diagnosis occurred relatively late, only 77% of children eligible for lipid-lowering therapy were receiving treatment, and only 39% of those treated met their LDL-C goal. Opportunities exist for earlier diagnosis, broader use of lipid-lowering therapy, and greater reduction of LDL-C levels.

Predictive network modeling in human induced pluripotent stem cells identifies key driver genes for insulin responsiveness.

Insulin resistance (IR) precedes the development of type 2 diabetes (T2D) and increases cardiovascular disease risk. Although genome wide association studies (GWAS) have uncovered new loci associated with T2D, their contribution to explain the mechanisms leading to decreased insulin sensitivity has been very limited. Thus, new approaches are necessary to explore the genetic architecture of insulin resistance. To that end, we generated an iPSC library across the spectrum of insulin sensitivity in humans. RNA-seq based analysis of 310 induced pluripotent stem cell (iPSC) clones derived from 100 individuals allowed us to identify differentially expressed genes between insulin resistant and sensitive iPSC lines. Analysis of the co-expression architecture uncovered several insulin sensitivity-relevant gene sub-networks, and predictive network modeling identified a set of key driver genes that regulate these co-expression modules. Functional validation in human adipocytes and skeletal muscle cells (SKMCs) confirmed the relevance of the key driver candidate genes for insulin responsiveness.

Increasing Mortality Among Patients With Diabetes and Chronic Liver Disease From 2007 to 2017.

The age-standardized prevalence of diabetes increased from 9.8% in 1988-1994, to 10.8% in 2001-2002, to 12.4% in 2011-2012 in the United States.1 According to the National Vital Statistics System data, diabetes-related mortality has remained stable as the seventh-leading cause of death nationally since 2006.2 However, the age-standardized diabetes-related mortality decreased from 112.2 per 100,000 individuals in 2007 to 104.3 per 100,000 individuals in 2017.3 In terms of cause-specific mortality, the age-standardized mortality for cardiovascular disease, complications of diabetes, and cancer among individuals with diabetes declined annually by approximately 1%.3 In contrast, chronic liver disease-related mortality has been reported to be increasing in individuals with diabetes.4,5 However, the trends in mortality due to chronic liver disease in the setting of diabetes remain unknown. In this study, we estimated the trends in chronic liver disease-related mortality among individuals with diabetes from 2007 to 2017 in the United States.

Association of insulin resistance, from mid-life to late-life, with aortic stiffness in late-life: the Atherosclerosis Risk in Communities Study.

BACKGROUND: Insulin resistance may contribute to aortic stiffening that leads to end-organ damage. We examined the cross-sectional association and prospective association of insulin resistance and aortic stiffness in older adults without diabetes. METHODS: We analyzed 2571 men and women at Visit 5 (in 2011-2013), and 2350 men and women at repeat examinations from baseline at Visit 1 (in 1987-1989) to Visit 5 (in 2011-2013). Linear regression was used to estimate the difference in aortic stiffness per standard unit of HOMA-IR, TG/HDL-C, and TyG at Visit 5. Linear mixed effects were used to assess if high, as opposed to non-high, aortic stiffness (> 75th percentile) was preceded by a faster annual rate of change in log-HOMA-IR, log-TG/HDL-C, and log-TyG from Visit 1 to Visit 5. RESULTS: The mean age of participants was 75 years, 37% (n = 957) were men, and 17% (n = 433) were African American. At Visit 5, higher HOMA-IR, higher TG/HDL-C, and higher TyG were associated with higher aortic stiffness (16 cm/s per SD (95% CI 6, 27), 29 cm/s per SD (95% CI 18, 40), and 32 cm/s per SD (95% CI 22, 42), respectively). From Visit 1 to Visit 5, high aortic stiffness, compared to non-high aortic stiffness, was not preceded by a faster annual rate of change in log-HOMA-IR from baseline to 9 years (0.030 (95% CI 0.024, 0.035) vs. 0.025 (95% CI 0.021, 0.028); p = 0.15) or 9 years onward (0.011 (95% CI 0.007, 0.015) vs. 0.011 (95% CI 0.009, 0.013); p = 0.31); in log-TG/HDL-C from baseline to 9 years (0.019 (95% CI 0.015, 0.024) vs. 0.024 (95% CI 0.022, 0.026); p = 0.06) or 9 years onward (- 0.007 (95% CI - 0.010, - 0.005) vs. - 0.009 (95% CI - 0.010, - 0.007); p = 0.08); or in log-TyG from baseline to 9 years (0.002 (95% CI 0.002, 0.003) vs. 0.003 (95% CI 0.003, 0.003); p = 0.03) or 9 years onward (0 (95% CI 0, 0) vs. 0 (95% CI 0, 0); p = 0.08). CONCLUSIONS: Among older adults without diabetes, insulin resistance was associated with aortic stiffness, but the putative role of insulin resistance in aortic stiffness over the life course requires further study.

Women Living with Familial Hypercholesterolemia: Challenges and Considerations Surrounding Their Care.

PURPOSE OF REVIEW: To highlight the gender-based differences in presentation and disparities in care for women with familial hypercholesterolemia (FH). RECENT FINDINGS: Women with FH experience specific barriers to care including underrepresentation in research, significant underappreciation of risk, and interrupted therapy during childbearing. National and international registry and clinical trial data show significant healthcare disparities for women with FH. Women with FH are less likely to be on guideline-recommended high-intensity statin medications and those placed on statins are more likely to discontinue them within their first year. Women with FH are also less likely to be on regimens including non-statin agents such as PCSK9 inhibitors. As a result, women with FH are less likely to achieve target low-density lipoprotein cholesterol (LDL-C) targets, even those with prior atherosclerotic cardiovascular disease (ASCVD). FH is common, under-diagnosed, and under-treated. Disparities of care are more pronounced in women than men. Additionally, FH weighs differently on women throughout the course of their lives starting from choosing contraceptives as young girls along with lipid-lowering therapy, timing pregnancy, choosing breastfeeding or resumption of therapy, and finally deciding goals of care during menopause. Early identification and appropriate treatment prior to interruptions of therapy for childbearing can lead to marked reduction in morbidity and mortality. Women access care differently than men and increasing awareness among all providers, especially cardio-obstetricians, may improve diagnostic rates. Understanding the unique challenges women with FH face is crucial to close the gaps in care they experience.

Body composition and atrial fibrillation: a Mendelian randomization study.

AIMS: Increases in fat-free mass and fat mass have been associated with higher risk of atrial fibrillation (AF) in observational studies. It is not known whether these associations reflect independent causal processes. Our aim was to evaluate independent causal roles of fat-free mass and fat mass on AF. METHODS AND RESULTS: We conducted a large observational study to estimate the associations between fat-free mass and fat mass on incident AF in the UK Biobank (N = 487 404, N events = 10 365). Genome-wide association analysis was performed to obtain genetic instruments for Mendelian randomization (MR). We evaluated the causal effects of fat-free mass and fat mass on AF with two-sample method by using genetic associations from AFGen consortium as outcome. Finally, we evaluated independent causal effects of fat-free mass and fat mass with multivariate MR. Both fat-free mass and fat mass had observational associations with incident AF [hazard ratio (HR) = 1.77, 95% confidence interval (CI) 1.72-1.83; HR = 1.40, 95% CI 1.37-1.43 per standard deviation increase in fat-free and fat mass, respectively]. The causal effects using the inverse-variance weighted method were 1.55 (95% CI 1.38-1.75) for fat-free mass and 1.30 (95% CI 1.17-1.45) for fat mass. Weighted median, Egger regression, and penalized methods showed similar estimates. The multivariate MR analysis suggested that the causal effects of fat-free and fat mass were independent of each other (causal risk ratios: 1.37, 95% CI 1.06-1.75; 1.28, 95% CI 1.03-1.58). CONCLUSION: Genetically programmed increases in fat-free mass and fat mass independently cause an increased risk of AF.

No evidence of a causal association of type 2 diabetes and glucose metabolism with atrial fibrillation.

AIMS/HYPOTHESIS: Several epidemiological studies have shown an increased risk of atrial fibrillation in individuals with type 2 diabetes or milder forms of dysglycaemia. We aimed to assess whether this relation is causal using a Mendelian randomisation approach. METHODS: Two-sample Mendelian randomisation was used to obtain estimates of the influence of type 2 diabetes, fasting blood glucose (FBG), and HbA1c on the risk of atrial fibrillation. Instrumental variables were constructed using available summary statistics from meta-analyses of genome-wide association studies (GWAS) for type 2 diabetes and associated phenotypes. Pleiotropic SNPs were excluded from the analyses. The most recent GWAS meta-analysis summary statistics for atrial fibrillation, which included over 1 million individuals (approximately 60,000 individuals with atrial fibrillation) was used for outcome analysis. RESULTS: Neither type 2 diabetes (OR 1.01 [95% CI 0.98, 1.03]; p = 0.37), nor FBG (OR 0.95 [95% CI 0.82, 1.09] per mmol/l; p = 0.49) or HbA1c (OR 1.01 [95% CI, 0.85, 1.17] per mmol/mol [%]; p = 0.88) were associated with atrial fibrillation in Mendelian randomisation analyses. We had >80% statistical power to detect ORs of 1.08, 1.06 and 1.09 or larger for type 2 diabetes, FBG and HbA1c, respectively, for associations with atrial fibrillation. CONCLUSIONS/INTERPRETATION: This Mendelian randomisation analysis does not support a causal role of clinical significance between genetically programmed type 2 diabetes, FBG or HbA1c and development of atrial fibrillation. These data suggest that drug treatment to reduce dysglycaemia is unlikely to be an effective strategy for atrial fibrillation prevention. DATA AVAILABILITY: The datasets analysed during the current study are available from the following repository: Nielsen JB, Thorolfsdottir RB, Fritsche LG, et al (2018) GWAS summary statistics for AF (N=60,620 AF cases and 970,216 controls). Center for Statistical Genetics: http://csg.sph.umich.edu/willer/public/afib2018/nielsen-thorolfsdottir-willer-NG2018-AFib-gwas-summary-statistics.tbl.gz.

Trends in overall, cardiovascular and cancer-related mortality among individuals with diabetes reported on death certificates in the United States between 2007 and 2017.

AIMS/HYPOTHESIS: The determination of diabetes as underlying cause of death by using the death certificate may result in inaccurate estimation of national mortality attributed to diabetes, because individuals who die with diabetes generally have other conditions that may contribute to their death. We investigated the trends in age-standardised mortality due to diabetes as underlying or contributing cause of death and cause-specific mortality from cardiovascular disease (CVD), complications of diabetes and cancer among individuals with diabetes listed on death certificates in the USA from 2007 to 2017. METHODS: Using the US Census and national mortality database, we calculated age-standardised mortality due to diabetes as underlying or contributing cause of death and cause-specific mortality rates among adults over 20 years with diabetes listed on death certificates. A total of 2,686,590 deaths where diabetes was underlying or contributing cause of death were analysed. We determined temporal mortality rate patterns by joinpoint regression analysis with estimates of annual percentage change (APC). RESULTS: Age-standardised diabetes mortality rates compared among underlying cause of death, contributing cause of death and all-cause mortality were 32.2 vs 75.7 vs 105.1 per 100,000 individuals during the study period. The age-standardised mortality rates due to diabetes as underlying or contributing cause of death declined from 112.2 per 100,000 individuals in 2007 to 104.3 per 100,000 individuals in 2017 with the most pronounced decline noted from 2007 to 2014 (APC -1.4%; 95% CI -1.9%, -1.0%) and stabilisation in decline from 2014 to 2017 (APC 1.1%; 95% CI -0.6%, 2.8%). In terms of cause-specific mortality among individuals with diabetes listed on death certificates, the age-standardised mortality rates for CVD declined at an annual rate of 1.2% with a marked decline of 2.3% between 2007 and 2014. Age-standardised diabetes-specific mortality rates as underlying cause of death decreased from 2007 to 2009 (APC -4.5%) and remained stable from 2009 to 2017. Age-standardised mortality rates for cancer steadily decreased with an average APC of -1.4% (95% CI -1.8%, -1.0%) during the 11-year period. Mortality in the subcategory of CVD demonstrated significant differences. CONCLUSIONS/INTERPRETATION: Current national estimates capture about 30% of all-cause mortality among individuals with diabetes listed as underlying or contributing cause of death on death certificates. The age-standardised mortality due to diabetes as underlying or contributing cause of death and cause-specific mortality from CVD in individuals with diabetes listed as underlying or contributing cause of death plateaued from 2014 onwards except for hypertensive heart disease and heart failure.

Polygenic risk scores in coronary artery disease.

PURPOSE OF REVIEW: Large genome-wide association studies (GWAS) have identified variants accounting for a substantial portion of the heritable risk for coronary artery disease (CAD). These studies have catalyzed drug discovery and generated the possibility of improved risk prediction and stratification. Here, we review the current state-of-the art in polygenic risk scores (PRSs) and look to the future, as these scores move towards clinical application. RECENT FINDINGS: Over the last decade, multilocus PRSs for CAD have expanded to include millions of variants and demonstrated strong association with CAD outcomes, even when adjusted for traditional risk factors. Recently, PRSs have shown better prediction of CAD outcomes than any single traditional risk factor alone. Advances in statistical methods used to generate PRSs have improved their predictive ability and transferability between populations with varied ancestries. Initial clinical studies have also demonstrated the potential of genetic information to impact shared decision-making between patients and providers, leading to improved outcomes. SUMMARY: PRSs can improve risk stratification for CAD especially in white/European populations and have the potential to alter routine clinical care. However, unlocking this potential will require additional research in PRSs in nonwhite populations and substantial investment in clinical implementation studies.

CRISPR-Cas9-mediated knockout of SPRY2 in human hepatocytes leads to increased glucose uptake and lipid droplet accumulation.

BACKGROUND: The prevalence of obesity and its comorbidities, including type 2 diabetes mellitus (T2DM), is dramatically increasing throughout the world; however, the underlying aetiology is incompletely understood. Genome-wide association studies (GWAS) have identified hundreds of genec susceptibility loci for obesity and T2DM, although the causal genes and mechanisms are largely unknown. SPRY2 is a candidate gene identified in GWAS of body fat percentage and T2DM, and has recently been linked to insulin production in pancreatic ß-cells. In the present study, we aimed to further understand SPRY2 via functional characterisation in HepG2 cells, an in vitro model of human hepatocytes widely used to investigate T2DM and insulin resistance. METHODS: CRISPR-Cas9 genome editing was used to target SPRY2 in HepG2 cells, and the functional consequences of SPRY2 knockout (KO) and overexpression subsequently assessed using glucose uptake and lipid droplet assays, measurement of protein kinase phosphorylation and RNA sequencing. RESULTS: The major functional consequence of SPRY2 KO was a significant increase in glucose uptake, along with elevated lipid droplet accumulation. These changes were attenuated, but not reversed, in cells overexpressing SPRY2. Phosphorylation of protein kinases across key signalling pathways (including Akt and mitogen activated protein kinases) was not altered after SPRY2 KO. Transcriptome profiling in SPRY2 KO and mock (control) cells revealed a number of differentially expressed genes related to cholesterol biosynthesis, cell cycle regulation and cellular signalling pathways. Phospholipase A2 group IIA (PLA2G2A) mRNA level was subsequently validated as significantly upregulated following SPRY2 KO, highlighting this as a potential mediator downstream of SPRY2. CONCLUSION: These findings suggest a role for SPRY2 in glucose and lipid metabolism in hepatocytes and contribute to clarifying the function of this gene in the context of metabolic diseases.