Nonalcoholic Fatty Liver Disease and Longitudinal Change in Imaging and Plasma Biomarkers of Alzheimer Disease and Vascular Pathology

BACKGROUND AND OBJECTIVES

Prospective measures of plasma and cerebral MRI biomarkers of Alzheimer disease (AD) and vascular neuropathology provide an opportunity to investigate possible mechanisms linking liver disease and dementia. We aimed to quantify the association of midlife nonalcoholic fatty liver disease (NAFLD) with change in plasma and brain MRI biomarkers of AD and vascular neuropathology.

METHODS

We included participants from the Atherosclerosis Risk in Communities Study with brain MRI measurements of white matter hyperintensity (WMH) volume and temporal-parietal lobe cortical thickness meta region of interest (ROI) at up to 2 different visits, in 2011-13 and 2016-19, and plasma biomarkers of β-amyloid (Aβ)42:40, phosphorylated tau at threonine 181, and neurofilament light (NfL) were measured up to 3 times in 1993-95, 2011-13, and 2016-19. NAFLD was categorized using the fatty liver index in 1990-92. Multivariate linear regression was performed for associations between midlife NAFLD and change in plasma and brain MRI biomarkers of AD and vascular neuropathology. The primary models adjusted for demographics, Apolipoprotein E, alcohol use, and kidney function.

RESULTS

Among 1,706 participants (mean age 56 years, 62% female, 28% Black), midlife NAFLD vs no NAFLD was associated with greater late-life WMH volume (difference per SD 0.19, 95% CI 0.06-0.31) and faster late-life WMH increase over 6 years (difference in annual change, SD 0.28, 95% CI 0.05-0.51), suggesting accumulating vascular pathology. Midlife NAFLD vs no NAFLD was also associated with AD biomarkers in midlife (lower Aβ42:40 [SD -0.21, 95% CI -0.39 to -0.04] measured in 1993-95) and late life (lower Aβ42:40 [SD -0.13, 95% CI -0.23 to -0.03] and lower temporal-parietal lobe cortical thickness meta ROI [SD -0.16, 95% CI -0.28 to -0.05] measured in 2011-13). Although midlife NfL was lower in individuals with vs without midlife NAFLD, those with NAFLD exhibited a faster rate of NfL increase that accelerated over time.

DISCUSSION

Midlife NAFLD shows associations with AD and accumulating vascular pathology, revealing potential pathways linking liver function to dementia. Plasma biomarkers of neuropathology and neuronal injury may serve as easily measurable and dynamic indicators for monitoring the impacts of impaired liver function on brain health.

Non-linear machine learning models incorporating SNPs and PRS improve polygenic prediction in diverse human populations

Polygenic risk scores (PRS) are commonly used to quantify the inherited susceptibility for a trait, yet they fail to account for non-linear and interaction effects between single nucleotide polymorphisms (SNPs). We address this via a machine learning approach, validated in nine complex phenotypes in a multi-ancestry population. We use an ensemble method of SNP selection followed by gradient boosted trees (XGBoost) to allow for non-linearities and interaction effects. We compare our results to the standard, linear PRS model developed using PRSice, LDpred2, and lassosum2. Combining a PRS as a feature in an XGBoost model results in a relative increase in the percentage variance explained compared to the standard linear PRS model by 22% for height, 27% for HDL cholesterol, 43% for body mass index, 50% for sleep duration, 58% for systolic blood pressure, 64% for total cholesterol, 66% for triglycerides, 77% for LDL cholesterol, and 100% for diastolic blood pressure. Multi-ancestry trained models perform similarly to specific racial/ethnic group trained models and are consistently superior to the standard linear PRS models. This work demonstrates an effective method to account for non-linearities and interaction effects in genetics-based prediction models.

Combining a standard polygenic risk score (PRS) as a feature in a machine learning model increases the percentage variance explained for those traits, helping to account for non-linearities or interaction effects in genetics-based prediction models.

Clonal Hematopoiesis Is Associated With Higher Risk of Stroke

BACKGROUND AND PURPOSE

Clonal hematopoiesis of indeterminate potential (CHIP) is a novel age-related risk factor for cardiovascular disease-related morbidity and mortality. The association of CHIP with risk of incident ischemic stroke was reported previously in an exploratory analysis including a small number of incident stroke cases without replication and lack of stroke subphenotyping. The purpose of this study was to discover whether CHIP is a risk factor for ischemic or hemorrhagic stroke.

METHODS

We utilized plasma genome sequence data of blood DNA to identify CHIP in 78 752 individuals from 8 prospective cohorts and biobanks. We then assessed the association of CHIP and commonly mutated individual CHIP driver genes (DNMT3A, TET2, and ASXL1) with any stroke, ischemic stroke, and hemorrhagic stroke.

RESULTS

CHIP was associated with an increased risk of total stroke (hazard ratio, 1.14 [95% CI, 1.03-1.27]; P=0.01) after adjustment for age, sex, and race. We observed associations with CHIP with risk of hemorrhagic stroke (hazard ratio, 1.24 [95% CI, 1.01-1.51]; P=0.04) and with small vessel ischemic stroke subtypes. In gene-specific association results, TET2 showed the strongest association with total stroke and ischemic stroke, whereas DMNT3A and TET2 were each associated with increased risk of hemorrhagic stroke.

CONCLUSIONS

CHIP is associated with an increased risk of stroke, particularly with hemorrhagic and small vessel ischemic stroke. Future studies clarifying the relationship between CHIP and subtypes of stroke are needed.

CUE: CpG impUtation ensemble for DNA methylation levels across the human methylation450 (HM450) and EPIC (HM850) BeadChip platforms

DNA methylation at CpG dinucleotides is one of the most extensively studied epigenetic marks. With technological advancements, geneticists can profile DNA methylation with multiple reliable approaches. However, profiling platforms can differ substantially in the CpGs they assess, consequently hindering integrated analysis across platforms. Here, we present CpG impUtation Ensemble (CUE), which leverages multiple classical statistical and modern machine learning methods, to impute from the Illumina HumanMethylation450 (HM450) BeadChip to the Illumina HumanMethylationEPIC (HM850) BeadChip. Data were analysed from two population cohorts with methylation measured both by HM450 and HM850: the Extremely Low Gestational Age Newborns (ELGAN) study (n = 127, placenta) and the VA Boston Posttraumatic Stress Disorder (PTSD) genetics repository (n = 144, whole blood). Cross-validation results show that CUE achieves the lowest predicted root-mean-square error (RMSE) (0.026 in PTSD) and the highest accuracy (99.97% in PTSD) compared with five individual methods tested, including k-nearest-neighbours, logistic regression, penalized functional regression, random forest, and XGBoost. Finally, among all 339,033 HM850-only CpG sites shared between ELGAN and PTSD, CUE successfully imputed 289,604 (85.4%) sites, where success was defined as RMSE < 0.05 and accuracy >95% in PTSD. In summary, CUE is a valuable tool for imputing CpG methylation from the HM450 to HM850 platform.

The trans-ancestral genomic architecture of glycemic traits

Glycemic traits are used to diagnose and monitor type 2 diabetes and cardiometabolic health. To date, most genetic studies of glycemic traits have focused on individuals of European ancestry. Here we aggregated genome-wide association studies comprising up to 281,416 individuals without diabetes (30% non-European ancestry) for whom fasting glucose, 2-h glucose after an oral glucose challenge, glycated hemoglobin and fasting insulin data were available. Trans-ancestry and single-ancestry meta-analyses identified 242 loci (99 novel; P < 5 × 10-8), 80% of which had no significant evidence of between-ancestry heterogeneity. Analyses restricted to individuals of European ancestry with equivalent sample size would have led to 24 fewer new loci. Compared with single-ancestry analyses, equivalent-sized trans-ancestry fine-mapping reduced the number of estimated variants in 99% credible sets by a median of 37.5%. Genomic-feature, gene-expression and gene-set analyses revealed distinct biological signatures for each trait, highlighting different underlying biological pathways. Our results increase our understanding of diabetes pathophysiology by using trans-ancestry studies for improved power and resolution.

Epigenome-wide association study of kidney function identifies trans-ethnic and ethnic-specific loci

BACKGROUND

DNA methylation (DNAm) is associated with gene regulation and estimated glomerular filtration rate (eGFR), a measure of kidney function. Decreased eGFR is more common among US Hispanics and African Americans. The causes for this are poorly understood. We aimed to identify trans-ethnic and ethnic-specific differentially methylated positions (DMPs) associated with eGFR using an agnostic, genome-wide approach.

METHODS

The study included up to 5428 participants from multi-ethnic studies for discovery and 8109 participants for replication. We tested the associations between whole blood DNAm and eGFR using beta values from Illumina 450K or EPIC arrays. Ethnicity-stratified analyses were performed using linear mixed models adjusting for age, sex, smoking, and study-specific and technical variables. Summary results were meta-analyzed within and across ethnicities. Findings were assessed using integrative epigenomics methods and pathway analyses.

RESULTS

We identified 93 DMPs associated with eGFR at an FDR of 0.05 and replicated 13 and 1 DMPs across independent samples in trans-ethnic and African American meta-analyses, respectively. The study also validated 6 previously published DMPs. Identified DMPs showed significant overlap enrichment with DNase I hypersensitive sites in kidney tissue, sites associated with the expression of proximal genes, and transcription factor motifs and pathways associated with kidney tissue and kidney development.

CONCLUSIONS

We uncovered trans-ethnic and ethnic-specific DMPs associated with eGFR, including DMPs enriched in regulatory elements in kidney tissue and pathways related to kidney development. These findings shed light on epigenetic mechanisms associated with kidney function, bridging the gap between population-specific eGFR-associated DNAm and tissue-specific regulatory context.

Genetic discovery and risk characterization in type 2 diabetes across diverse populations

Genomic discovery and characterization of risk loci for type 2 diabetes (T2D) have been conducted primarily in individuals of European ancestry. We conducted a multiethnic genome-wide association study of T2D among 53,102 cases and 193,679 control subjects from African, Hispanic, Asian, Native Hawaiian, and European population groups in the Population Architecture Genomics and Epidemiology (PAGE) and Diabetes Genetics Replication and Meta-analysis (DIAGRAM) Consortia. In individuals of African ancestry, we discovered a risk variant in the TGFB1 gene (rs11466334, risk allele frequency (RAF) = 6.8%, odds ratio [OR] = 1.27, p = 2.06 × 10⁻⁸), which replicated in independent studies of African ancestry (p = 6.26 × 10⁻²³). We identified a multiethnic risk variant in the BACE2 gene (rs13052926, RAF = 14.1%, OR = 1.08, p = 5.75 × 10⁻⁹), which also replicated in independent studies (p = 3.45 × 10⁻⁴). We also observed a significant difference in the performance of a multiethnic genetic risk score (GRS) across population groups (p_{heterogeneity} = 3.85 × 10⁻²⁰). Comparing individuals in the top GRS risk category (40%–60%), the OR was highest in Asians (OR = 3.08) and European (OR = 2.94) ancestry populations, followed by Hispanic (OR = 2.39), Native Hawaiian (OR = 2.02), and African ancestry (OR = 1.57) populations. These findings underscore the importance of genetic discovery and risk characterization in diverse populations and the urgent need to further increase representation of non-European ancestry individuals in genetics research to improve genetic-based risk prediction across populations.

Genome-Wide Association Study of Apparent Treatment-Resistant Hypertension in the CHARGE Consortium: The CHARGE Pharmacogenetics Working Group

BACKGROUND

Only a handful of genetic discovery efforts in apparent treatment-resistant hypertension (aTRH) have been described.

METHODS

We conducted a case-control genome-wide association study of aTRH among persons treated for hypertension, using data from 10 cohorts of European ancestry (EA) and 5 cohorts of African ancestry (AA). Cases were treated with 3 different antihypertensive medication classes and had blood pressure (BP) above goal (systolic BP ≥ 140 mm Hg and/or diastolic BP ≥ 90 mm Hg) or 4 or more medication classes regardless of BP control (nEA = 931, nAA = 228). Both a normotensive control group and a treatment-responsive control group were considered in separate analyses. Normotensive controls were untreated (nEA = 14,210, nAA = 2,480) and had systolic BP/diastolic BP < 140/90 mm Hg. Treatment-responsive controls (nEA = 5,266, nAA = 1,817) had BP at goal (<140/90 mm Hg), while treated with one antihypertensive medication class. Individual cohorts used logistic regression with adjustment for age, sex, study site, and principal components for ancestry to examine the association of single-nucleotide polymorphisms with case-control status. Inverse variance-weighted fixed-effects meta-analyses were carried out using METAL.

RESULTS

The known hypertension locus, CASZ1, was a top finding among EAs (P = 1.1 × 10-8) and in the race-combined analysis (P = 1.5 × 10-9) using the normotensive control group (rs12046278, odds ratio = 0.71 (95% confidence interval: 0.6-0.8)). Single-nucleotide polymorphisms in this locus were robustly replicated in the Million Veterans Program (MVP) study in consideration of a treatment-responsive control group. There were no statistically significant findings for the discovery analyses including treatment-responsive controls.

CONCLUSION

This genomic discovery effort for aTRH identified CASZ1 as an aTRH risk locus.

A fully adjusted two-stage procedure for rank-normalization in genetic association studies

When testing genotype-phenotype associations using linear regression, departure of the trait distribution from normality can impact both Type I error rate control and statistical power, with worse consequences for rarer variants. Because genotypes are expected to have small effects (if any) investigators now routinely use a two-stage method, in which they first regress the trait on covariates, obtain residuals, rank-normalize them, and then use the rank-normalized residuals in association analysis with the genotypes. Potential confounding signals are assumed to be removed at the first stage, so in practice, no further adjustment is done in the second stage. Here, we show that this widely used approach can lead to tests with undesirable statistical properties, due to both combination of a mis-specified mean-variance relationship and remaining covariate associations between the rank-normalized residuals and genotypes. We demonstrate these properties theoretically, and also in applications to genome-wide and whole-genome sequencing association studies. We further propose and evaluate an alternative fully adjusted two-stage approach that adjusts for covariates both when residuals are obtained and in the subsequent association test. This method can reduce excess Type I errors and improve statistical power.

A robust and powerful two-step testing procedure for local ancestry adjusted allelic association analysis in admixed populations

Genetic association studies in admixed populations allow us to gain deeper understanding of the genetic architecture of human diseases and traits. However, population stratification, complicated linkage disequilibrium (LD) patterns, and the complex interplay of allelic and ancestry effects on phenotypic traits pose challenges in such analyses. These issues may lead to detecting spurious associations and/or result in reduced statistical power. Fortunately, if handled appropriately, these same challenges provide unique opportunities for gene mapping. To address these challenges and to take these opportunities, we propose a robust and powerful two-step testing procedure Local Ancestry Adjusted Allelic (LAAA) association. In the first step, LAAA robustly captures associations due to allelic effect, ancestry effect, and interaction effect, allowing detection of effect heterogeneity across ancestral populations. In the second step, LAAA identifies the source of association, namely allelic, ancestry, or the combination. By jointly modeling allele, local ancestry, and ancestry-specific allelic effects, LAAA is highly powerful in capturing the presence of interaction between ancestry and allele effect. We evaluated the validity and statistical power of LAAA through simulations over a broad spectrum of scenarios. We further illustrated its usefulness by application to the Candidate Gene Association Resource (CARe) African American participants for association with hemoglobin levels. We were able to replicate independent groups' previously identified loci that would have been missed in CARe without joint testing. Moreover, the loci, for which LAAA detected potential effect heterogeneity, were replicated among African Americans from the Women's Health Initiative study. LAAA is freely available at https://yunliweb.its.unc.edu/LAAA.

Genome-wide meta-analysis associates HLA-DQA1/DRB1 and LPA and lifestyle factors with human longevity

Genomic analysis of longevity offers the potential to illuminate the biology of human aging. Here, using genome-wide association meta-analysis of 606,059 parents’ survival, we discover two regions associated with longevity (HLA-DQA1/DRB1 and LPA). We also validate previous suggestions that APOE, CHRNA3/5, CDKN2A/B, SH2B3 and FOXO3A influence longevity. Next we show that giving up smoking, educational attainment, openness to new experience and high-density lipoprotein (HDL) cholesterol levels are most positively genetically correlated with lifespan while susceptibility to coronary artery disease (CAD), cigarettes smoked per day, lung cancer, insulin resistance and body fat are most negatively correlated. We suggest that the effect of education on lifespan is principally mediated through smoking while the effect of obesity appears to act via CAD. Using instrumental variables, we suggest that an increase of one body mass index unit reduces lifespan by 7 months while 1 year of education adds 11 months to expected lifespan.

Variability in human longevity is genetically influenced. Using genetic data of parental lifespan, the authors identify associations at HLA-DQA/DRB1 and LPA and find that genetic variants that increase educational attainment have a positive effect on lifespan whereas increasing BMI negatively affects lifespan.

Novel Genetic Variants Associated With Increased Vertebral Volumetric BMD, Reduced Vertebral Fracture Risk, and Increased Expression of SLC1A3 and EPHB2

Genome-wide association studies (GWASs) have revealed numerous loci for areal bone mineral density (aBMD). We completed the first GWAS meta-analysis (n = 15,275) of lumbar spine volumetric BMD (vBMD) measured by quantitative computed tomography (QCT), allowing for examination of the trabecular bone compartment. SNPs that were significantly associated with vBMD were also examined in two GWAS meta-analyses to determine associations with morphometric vertebral fracture (n = 21,701) and clinical vertebral fracture (n = 5893). Expression quantitative trait locus (eQTL) analyses of iliac crest biopsies were performed in 84 postmenopausal women, and murine osteoblast expression of genes implicated by eQTL or by proximity to vBMD-associated SNPs was examined. We identified significant vBMD associations with five loci, including: 1p36.12, containing WNT4 and ZBTB40; 8q24, containing TNFRSF11B; and 13q14, containing AKAP11 and TNFSF11. Two loci (5p13 and 1p36.12) also contained associations with radiographic and clinical vertebral fracture, respectively. In 5p13, rs2468531 (minor allele frequency [MAF] = 3%) was associated with higher vBMD (β = 0.22, p = 1.9 × 10^-8 ) and decreased risk of radiographic vertebral fracture (odds ratio [OR] = 0.75; false discovery rate [FDR] p = 0.01). In 1p36.12, rs12742784 (MAF = 21%) was associated with higher vBMD (β = 0.09, p = 1.2 × 10^-10 ) and decreased risk of clinical vertebral fracture (OR = 0.82; FDR p = 7.4 × 10^-4 ). Both SNPs are noncoding and were associated with increased mRNA expression levels in human bone biopsies: rs2468531 with SLC1A3 (β = 0.28, FDR p = 0.01, involved in glutamate signaling and osteogenic response to mechanical loading) and rs12742784 with EPHB2 (β = 0.12, FDR p = 1.7 × 10^-3 , functions in bone-related ephrin signaling). Both genes are expressed in murine osteoblasts. This is the first study to link SLC1A3 and EPHB2 to clinically relevant vertebral osteoporosis phenotypes. These results may help elucidate vertebral bone biology and novel approaches to reducing vertebral fracture incidence. © 2016 American Society for Bone and Mineral Research.

Criteria for level 1 and level 2 trauma codes: Are pelvic ring injuries undertriaged?

AIM: To determine the association of unstable pelvic ring injuries with trauma code status.

METHODS: A retrospective review of all pelvic ring injuries at a single academic center from July 2010 to June 2013 was performed. The trauma registry was used to identify level 1 and level 2 trauma codes for each injury. The computed tomography scans in all patients were classified as stable or unstable using the Abbreviated Injury Scale. Pelvic injury classifications in level 1 and level 2 groups were compared. Patient disposition at discharge in level 1 and level 2 groups were also compared.

RESULTS: There were 108 level 1 and 130 level 2 blunt trauma admissions. In the level 1 group, 67% of pelvic injuries were classified as stable fracture patterns and 33% were classified as unstable. In the level 2 group, 62% of pelvic injuries were classified as stable fracture patterns and 38% were classified as unstable. level 1 trauma code was not associated with odds of having an unstable fracture pattern (OR = 0.83, 95%CI: 0.48-1.41, P = 0.485). In the level 1 group with unstable pelvic injuries, 33% were discharged to home, 36% to a rehabilitation facility, and 32% died. In the level 2 group with unstable pelvic injuries, 65% were discharged to home, 31% to a rehabilitation facility, and 4% died. For those with unstable pelvic fractures (n = 85), assignment of a level 2 trauma code was associated with reduced odds of death (OR = 0.07, 95%CI: 0.01-0.35, P = 0.001) as compared to being discharged to home.

CONCLUSION: Trauma code level assignment is not correlated with severity of pelvic injury. Because an unstable pelvis can lead to hemodynamic instability, these injuries may be undertriaged.